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Investigation of Fine Particulate Matter Characteristics and Sources in Edmonton, Alberta
Final Report
Warren B. Kindzierski, Ph.D., P.Eng.
Md. Aynul Bari, Dr.-Ing.
19 November 2015
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Executive Summary This study investigated characteristics of air quality and various source contributions to ambient fine particulate matter (PM2.5) in the Edmonton Capital Region. The study used historical data measured at National Air Pollution Surveillance (NAPS) air monitoring stations in Edmonton, including a chemical speciation monitoring station (Edmonton McIntyre station). Three objectives were sought:
1. Investigate the characteristics and trends of individual air pollutants, including fine particulate matter (PM2.5), in order to understand how bad or good air quality is in Edmonton.
2. Investigate the sources of PM2.5 at an Environment Canada National Air Pollution Surveillance (NAPS) chemical speciation monitoring site in Edmonton, including identifying the contribution from coal combustion sources.
3. Investigate origins and causes of PM2.5 concentration differences in Edmonton during 2010 relative to other years.
Objective 1 – Investigate characteristics and trends of individual air pollutants in Edmonton Trends in Environmental Canada’s National Pollutant Release Inventory reported industrial emissions in the Edmonton Capital Region for PM2.5, oxides of nitrogen (NOX), sulfur dioxide (SO2), ammonia (NH3), volatile organic compounds (VOCs) and major trace elements were investigated over the last decade (2003–2014):
Statistically significant decreasing trends were observed for the industrial combustion pollutants NOX (p ≤ 0.01) and SO2 (p ≤ 0.05) over the last decade.
A statistically significant decrease (p ≤ 0.05) was observed for arsenic (As).
Statistically significant downward trends were observed for other industrial elements – e.g., vanadium (V), manganese (Mn), chromium (Cr), copper (Cu) and cobalt (Co).
Surrogate data (~20,600 additional motor vehicle registrations annually) suggest an increasingly important role of transportation sector emissions in Edmonton Capital Region over the past decade. This is opposite to reported industrial emissions trends described above. Statistically significant trends (p ≤ 0.05) for hourly average percentile concentrations (50th, 65th, 80th, 90th, 95th and 98th percentiles (%iles)) of air pollutants measured using continuous monitors were observed at Edmonton central and east stations (17-year period of record: 1998–2014) and at Edmonton south and McIntyre stations (9-year period: 2006–2014) (Table ES1):
Decreasing trends were observed for hourly average percentile concentrations of nitrogen dioxide (NO2), SO2, total hydrocarbon (THC), and carbon monoxide (CO). Air quality in Edmonton has improved for these air pollutants over the past 17 years.
No change was observed for PM2.5 at any of the monitoring stations. During 2009 equipment at many of the original continuous PM2.5 monitoring stations in Alberta was upgraded to improve capture of some components of fine particulate matter (i.e., semi-volatile material) which were lost under the previous method. Thus despite absolute PM2.5 levels at air monitoring stations being ‘bumped’ higher in 2010 and subsequent years relative to previous years as a result of equipment upgrades in 2009, no statistically significant trends were observed at any of the stations.
For ground-level ozone (O3), a small increasing trend was found at lower hourly percentile concentrations (50th to 80th %iles) at Edmonton central, a small decreasing trend was detected at higher percentile concentrations (at 80th to 98th %iles) at Edmonton east and no trend was observed at Edmonton south at all percentile concentrations. These characteristics indicate evidence of spatial variation of O3 precursor concentrations – e.g., NOX, VOCs – across Edmonton rather than any type of consistent trend in O3 concentrations in Edmonton over the past 17 years.
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Table ES1. Summary of linear trends for hourly average percentile concentrations of air pollutants at Edmonton air monitoring stations.
Concentration Edmonton
Central (1998–2014)
Edmonton
East (1998–2014)
Edmonton
South (2006–2014)
Edmonton McIntyre
(2006–2014)
(percentile) Trend Change/year R2 Trend Change/year R2 Trend Change/year R2 Trend Change/year R2 NO2 50th ▼ 0.71 ppb 0.94 ▼ 0.33 ppb 0.76 ▼ 0.22 ppb 0.74
65th ▼ 0.77 ppb 0.92 ▼ 0.51 ppb 0.84 ▼ 0.30 ppb 0.74 80th ▼ 0.75 ppb 0.89 ▼ 0.59 ppb 0.86 ▼ 0.46 ppb 0.74 90th ▼ 0.70 ppb 0.83 ▼ 0.50 ppb 0.82 ▼ 0.32 ppb 0.29 95th ▼ 0.68 ppb 0.73 ▼ 0.45 ppb 0.75 ▬ n/a n/a 98th ▼ 0.72 ppb 0.68 ▼ 0.45 ppb 0.62 ▬ n/a n/a
SO2* 50th ▼ 0.11 ppb 0.61 ▬ n/a n/a 65th ▼ 0.17 ppb 0.85 ▬ n/a n/a 80th ▼ 0.15 ppb 0.65 ▬ n/a n/a 90th ▼ 0.15 ppb 0.70 ▼ 0.18 ppb 0.71 95th ▼ 0.14 ppb 0.40 ▼ 0.19 ppb 0.76 98th ▼ 0.17 ppb 0.31 ▬ n/a n/a
PM2.5 50th ▬ n/a n/a ▬ n/a n/a ▬ n/a n/a ▬ n/a n/a 65th ▬ n/a n/a ▬ n/a n/a ▬ n/a n/a ▬ n/a n/a 80th ▬ n/a n/a ▬ n/a n/a ▬ n/a n/a ▬ n/a n/a 90th ▬ n/a n/a ▬ n/a n/a ▬ n/a n/a ▬ n/a n/a 95th ▬ n/a n/a ▬ n/a n/a ▬ n/a n/a ▬ n/a n/a 98th ▬ n/a n/a ▬ n/a n/a ▬ n/a n/a ▬ n/a n/a
O3 50th ▲ 0.38 ppb 0.75 ▬ n/a n/a ▬ n/a n/a 65th ▲ 0.33 ppb 0.68 ▬ n/a n/a ▬ n/a n/a 80th ▲ 0.26 ppb 0.43 ▼ 0.26 ppb 0.47 ▬ n/a n/a 90th ▬ n/a n/a ▼ 0.34 ppb 0.55 ▬ n/a n/a 95th ▬ n/a n/a ▼ 0.44 ppb 0.57 ▬ n/a n/a
98th ▬ n/a n/a ▼ 0.55 ppb 0.54 ▬ n/a n/a THC 50th ▬ n/a n/a ▼ <0.1 ppm 0.35 ▬ n/a n/a
65th ▼ <0.1 ppm 0.35 ▼ <0.1 ppm 0.52 ▲ <0.1 ppm 0.60 80th ▼ <0.1 ppm 0.28 ▼ <0.1 ppm 0.53 ▬ n/a n/a 90th ▼ <0.1 ppm 0.40 ▼ <0.1 ppm 0.56 ▲ <0.1 ppm 0.49 95th ▼ <0.1 ppm 0.35 ▼ <0.1 ppm 0.38 ▬ n/a n/a
98th ▼ <0.1 ppm 0.25 ▼ <0.1 ppm 0.35 ▬ n/a n/a CO 50th ▼ <0.1 ppm 0.93 ▼ <0.1 ppm 0.69 ▬ n/a n/a
65th ▼ <0.1 ppm 0.93 ▼ <0.1 ppm 0.68 ▬ n/a n/a 80th ▼ <0.1 ppm 0.92 ▼ <0.1 ppm 0.84 ▬ n/a n/a 90th ▼ <0.1 ppm 0.93 ▼ <0.1 ppm 0.66 ▼ <0.1 ppm 0.53 95th ▼ 0.10 ppm 0.91 ▼ <0.1 ppm 0.78 ▼ <0.1 ppm 0.50
98th ▼ <0.1 ppm 0.92 ▼ <0.1 ppm 0.92 ▼ <0.1 ppm 0.92
*not measured at Edmonton central; Direction of trend (p = 0.05): ▬ no change; ▲ increasing; ▼ decreasing; n/a: not applicable
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Eight-year trends in chemical species present in 24 h integrated PM2.5 samples collected at Edmonton McIntyre station were investigated (Table ES2):
Statistically significant decreasing trends for 24 h concentrations of OC (organic carbon), EC (elemental carbon), oxalate, barium (Ba) and lead (Pb) (p ≤ 0.05) and cadmium (Cd) (p ≤ 0.10) were observed.
A statistically significant increase (p ≤ 0.05) was observed for NaCl (a component of road-salt).
No statistically significant changes were observed for all other chemical species examined. Concentrations of K+ and Zn exhibited strong and significant seasonal variability with higher concentrations in winter than in summer. Seasonal patterns with high winter levels of these tracer elements likely reflect wood smoke origins more than other potential sources in the Edmonton Capital Region. Table ES2. Temporal trends (8-year) in ambient concentrations of PM2.5 chemical species at Edmonton McIntyre air monitoring station.
Chemical species Unit 2007
gmean 2014
gmean
2007-2014 Slope
unit/year
% Change per year
Significance level
p-value
OC μg/m3 1.53 0.93 –0.14 –7.3 95% 0.05 EC μg/m3 0.51 0.08 –0.15 –12.4 95% 0.05
SO42– μg/m3 0.59 0.59 –0.01 –1.5 n.s. 0.268
NO3– μg/m3 0.29 0.50 0.004 1.2 n.s. 0.548
NH4+ μg/m3 0.34 0.33 –0.005 –1.5 n.s. 0.268
NaCl μg/m3 0.05 0.09 0.005 10.7 95% 0.05 K+ μg/m3 0.04 0.03 0.001 1.8 n.s 0.548
Oxalate μg/m3 0.05 0.03 –0.005 –7.9 95% 0.05 Al ng/m3 2.77 1.75 –0.16 –4.1 n.s. 0.355 As ng/m3 0.16 0.14 –0.002 –1.3 n.s. 0.268 Ba ng/m3 1.83 1.41 –0.07 –3.8 99% 0.01 Cd ng/m3 0.06 0.04 –0.004 –5.5 90% 0.054 Co ng/m3 0.05 0.04 –0.003 –5.2 n.s. 0.089 Cr ng/m3 0.43 0.36 –0.01 –1.3 n.s. 0.451 Cu ng/m3 1.30 1.52 –0.02 –1.3 n.s. 0.355 Fe ng/m3 10.99 10.11 –0.07 –0.5 n.s. 0.452 Mn ng/m3 2.24 2.34 0.05 2.2 n.s. 0.193 Mo ng/m3 0.26 0.20 0.002 0.9 n.s. 0.451 Ni ng/m3 0.31 0.31 0.01 2.1 n.s. 0.193 Pb ng/m3 0.45 0.24 –0.03 –6.1 95% 0.05 Sb ng/m3 0.18 0.16 –0.004 –2.1 n.s. 0.054 Se ng/m3 0.14 0.06 –0.01 –6.9 n.s. 0.113 Sn ng/m3 0.07 0.08 –0.001 –1.1 n.s. 0.451 Sr ng/m3 0.19 0.24 0.01 3.1 n.s. 0.193 Ti ng/m3 0.26 0.22 –0.01 –4.0 n.s. 0.268 V ng/m3 0.07 0.08 –0.001 –0.8 n.s. 0.548 Zn ng/m3 6.30 4.51 –0.43 –5.6 n.s. 0.054
gmean = geometric mean
Objective 2 – Investigate fine particulate matter sources in Edmonton The U.S. Environmental Protection Agency Positive Matrix Factorization (PMF) model version 5.0 was run under two scenarios (a base and a constrained run) using data from the Edmonton McIntyre station for the time period of 2010–2014. The plausibility and interpretability of solutions with six to eleven factors (sources) were examined and a 9-factor solution best represented the makeup of ambient PM2.5 sources at Edmonton McIntyre station (Table ES3 and Figure ES1).
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Table ES3. Predicted sources and their contributions to PM2.5 at Edmonton McIntyre station for 2010–2014.
Possible sources Key chemical species Base run Constrained run
μg/m3 % μg/m3 % Factor 1 SOA OC, EC, arabitol, oxalate 2.43 29.8 2.37 29.1 Factor 2 Secondary sulfate SO4
2–, NH4+ 1.78 21.9 1.75 21.5
Factor 3 Secondary nitrate NO3–, NH4
+ 1.32 16.2 1.34 16.4 Factor 4 Soil Ca+2, Mg+2, Al, Fe, Sr, Ti 0.94 11.5 0.80 9.9 Factor 5 Traffic Ba, As, Cu, Sb, Co, EC, OC 0.53 6.6 0.71 8.7 Factor 6 Biomass burning Levoglucosan, mannosan, K+, Cd, OC 0.56 6.9 0.59 7.3 Factor 7 Road-salt Na+, Cl– 0.20 2.4 0.21 2.5 Factor 8 Refinery V, Mo 0.09 1.1 0.11 1.3 Factor 9 Mixed industrial Cr, Cu, Mn, Fe, Mo, Co, Ni, Sn, Ti, Zn 0.30 3.7 0.26 3.3
Figure ES1. Average contributions of constrained PMF-derived sources at Edmonton McIntyre station for 2010–2014 (a. seasonal, b. winter).
a b
Winter
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The major PM2.5 sources identified in Edmonton were made up of secondary particulates (i.e., those that form in the atmosphere from other gaseous pollutants). These included secondary organic aerosol (SOA), secondary sulfate and secondary nitrate and together they contributed to two-thirds of the PM2.5 mass concentrations on average (5.5 µg/m3). Other PM2.5 sources identified in Edmonton were made up of primary particulates (i.e., those that are directly released into the atmosphere). For these particles, soil, traffic and biomass burning emissions contributed to one-quarter of PM2.5 mass concentrations on average (2.0 µg/m3). Minor primary particle sources (road-salt, refinery and mixed industrial emissions) contributed to less than one-tenth of PM2.5 mass concentrations on average (0.6 µg/m3). Coal combustion emissions are associated with secondary particles. This is discussed further below: Secondary Organic Aerosol (SOA) – The potential for SOA formation in coal combustion plumes is
considered to be small or unimportant based on in-plume versus out-of-plume measurement studies published elsewhere. Consequently coal combustion emissions are not considered an important source of SOA identified in this study. Backward trajectory analysis was performed using the National Oceanic and Atmospheric Administration (NOAA) Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. The trajectory analysis supports these published studies as it identified other plausible local and long range SOA sources. These include local sources such as vehicle exhaust and industrial activities, and distant sources such as the Yellowhead transportation corridor west of Edmonton, biogenic (rural) emissions, biomass burning (wildfire smoke), and residential wood fireplace burning.
Secondary sulfate – Secondary sulfate was interpreted to be related to background regional sulfate
that is found in high abundance due to oil and gas extraction and production activities throughout Alberta. Based on backward trajectory analysis, only a small contribution to secondary sulfate was observed from the region immediately west of Edmonton where coal combustion sources are located. The backward trajectory analysis indicated that air parcels traveling over the region immediately west of Edmonton would be, on average, associated with lower concentrations of PM2.5 for secondary sulfate at Edmonton McIntyre station compared to air parcels traveling over numerous other locations.
Possible presence of local industrial sources and backward trajectory (long-range) analysis support that coal combustion sources west of Edmonton do not dominate the contribution to PM2.5 for secondary sulfate at Edmonton McIntyre station. While the analysis undertaken here is insufficient to accurately quantify the contribution to secondary sulfate from coal combustion sources, their contribution is projected to be in the range of less than one-tenth to less than one-fifth of the secondary sulfate mass. This is consistent with a small contribution of tracer elements typically associated with coal combustion – such as Se, As, Cd, Pb, and Sn – observed with this factor.
Secondary nitrate – Secondary nitrate showed strong seasonality with the highest concentrations in
winter. Correlations of secondary nitrate with NO2, CO, THC and some VOCs such as benzene, toluene, ethylbenzene, xylene and other aromatic hydrocarbons (e.g., ethyltoluene isomers, trimethylbenzene isomers) as well as with alkanes suggest a strong influence of local sources such as vehicle exhaust and industrial activities. Backward trajectory analysis indicated that the region immediately west of Edmonton where coal combustion sources are located is not the only trajectory path associated with elevated levels of PM2.5 for secondary nitrate at Edmonton McIntyre station. Other important regional precursor sources of secondary nitrate influencing Edmonton McIntyre station are located in Alberta south of Edmonton, northwestern British Columbia and southern Saskatchewan. Plausible explanations for these regional sources include oil and gas extraction and production activities (NOX emissions) and
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animal feeding operations located south of Edmonton thru to the Alberta-Montana border and elsewhere (ammonia (NH3) emissions). Levels of both secondary nitrate and sulfate particles tend to be simultaneously enhanced within plumes from coal combustion emissions relative to background. Again, while the analysis undertaken here is insufficient to accurately quantify the contribution to secondary nitrate from coal combustion sources west of Edmonton, their contribution is projected to be in the range of less than one-tenth to less than one-fifth of the secondary nitrate mass.
Objective 3 – Investigate origins and causes of PM2.5 concentration differences in Edmonton
during 2010 relative to other years A newspaper article circulated by the National Post newspaper in April 2015 reported that “…Edmonton had higher levels of a harmful air pollutant compared to Toronto, a city with five times the population and more industry.” The article also stated that “…particulate matter (i.e., PM2.5) exceeded legal limits of 30 µg/m3 at two city monitoring stations on several winter days in 2010 through 2012.” A limitation of PM2.5 monitoring data presented for Edmonton in the National Post article is that it did not acknowledge changes in the operation of continuous PM2.5 monitoring equipment during 2009. Equipment at many of the original continuous PM2.5 monitoring stations in Alberta was upgraded to better capture some components of fine particulate matter (i.e., semi-volatile material) which were lost under the previous equipment operation methods. As a result of this change, PM2.5 levels observed at air monitoring stations are higher in 2010 and subsequent years compared to previous years. For example, data comparing the newer and older 24 h PM2.5 measurement method for 2010 showed good agreement during summer (slope = 1.06, R2 = 0.99). While during winter the newer method measured 1.5 times (50%) higher concentrations than the older method. Analysis showed that a combination of changes in operation of continuous PM2.5 monitoring equipment made during 2009 and a single major wildfire smoke event in 2010 (August 19–22) explain exceedances of 3-year averages of annual 98th percentile 24 h average concentrations at the Edmonton central station. Results of the PMF model were examined further to identify how the contribution of identified sources (factors) for PM2.5 at Edmonton McIntyre station differed in 2010 compared to other years in the analysis (2011–2014) – refer to Figure ES2. Several observations are made based on this examination:
In order of relative importance, the secondary organic aerosol, secondary nitrate and biomass burning factors showed increased contributions during 2010 compared to 2011–2014.
The secondary sulfate factor showed no year-to-year variation over the 5-year period indicating that secondary sulfate precursor (i.e., SO2) emissions influencing Edmonton McIntyre station were unchanged over the period.
Other factors identified in the PMF analysis showed unimportant differences during 2010 compared to 2011–2014.
Multiple factors (i.e., secondary organic aerosol, secondary nitrate and biomass burning) other than secondary sulfate precursor emission sources showed increased contributions during 2010 compared to 2011–2014 at Edmonton McIntyre station. Levels of both secondary nitrate and sulfate particles tend to be simultaneously enhanced within plumes from coal combustion emissions relative to background. Thus the observation of secondary sulfate displaying no year-to-year variation over the 5 year period provides
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evidence that coal combustion emission sources would have played an unimportant role in explaining the year 2010 having a greater frequency of high PM2.5 concentration events. On the other hand, increased contributions from secondary organic aerosol, secondary nitrate and biomass burning emission sources best explains the year 2010 having a greater frequency of high PM2.5 concentration events.
Figure ES2. Yearly average contributions of constrained PMF-derived sources at Edmonton McIntyre station for 2010–2014.
Increased factor contribution during 2010 relative to other years
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Table of Contents Executive Summary ..................................................................................................................................... i
Table of Contents ...................................................................................................................................... viii
Introduction and Objectives ....................................................................................................................... 1
Study Objectives ....................................................................................................................................... 1
Report Sections ........................................................................................................................................ 1
References ................................................................................................................................................ 2
Part I Air Pollutant Data Characteristics and Trends ............................................................................. 3
Methods .................................................................................................................................................... 3
Local meteorology ................................................................................................................................ 3
Air quality trend analysis ...................................................................................................................... 3
Results and Discussion ............................................................................................................................ 5
Local meteorology ................................................................................................................................ 5
Trends in criteria air pollutants from continuous sampling ................................................................... 7
Emission trends of PM2.5 chemical components ................................................................................ 10
Trends in PM2.5 chemical components from intermittent sampling .................................................... 11
References .............................................................................................................................................. 23
Supplemental Material Part I ................................................................................................................. 26
Part II Identification of Fine Particulate Matter Sources ...................................................................... 33
Methods .................................................................................................................................................. 33
Source apportionment method ........................................................................................................... 33
Verification of source assignments from PMF Model ......................................................................... 35
Results and Discussion .......................................................................................................................... 38
Ambient levels of PM2.5 and chemical composition ............................................................................ 38
Identification and apportionment of PM2.5 sources: PMF analysis ..................................................... 41
Correlation between sources .............................................................................................................. 64
Comparison with other source apportionment studies ....................................................................... 64
Performance of the PMF model ......................................................................................................... 66
References .............................................................................................................................................. 68
Supplemental Material Part II ................................................................................................................ 74
Part III Origins and Causes of PM2.5 Concentration Differences in Edmonton during 2010 Relative to Other Years ............................................................................................................................. 83
Methodology ........................................................................................................................................... 86
Results and Discussion .......................................................................................................................... 86
References .............................................................................................................................................. 88
Findings ..................................................................................................................................................... 90
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Introduction and Objectives A recent investigation of elevated hourly concentrations of ambient fine particulate matter (PM2.5) was undertaken in the Capital Region of Alberta (AESRD, 2014). It was reported that elevated hourly concentrations of fine particulate matter concentrations near roadways do not co-vary with traffic volumes and concentrations of nitrogen dioxides on ‘high-hourly concentration’ event days. It was stated these findings suggest that air quality monitoring stations are not impacted by primary fine particulate matter from vehicle traffic. It was also suggested that fine particulate matter on event days may be the result of formation of secondary fine particulate matter from regional precursor sources that may include, but are not limited to, vehicle emissions. It was concluded that the fine particulate matter issue in the Capital Region is complex. A more recent investigation of sources of ambient submicron particles (PM1) was undertaken in Capital Region (Bari et al., 2015). This investigation involved the collection of seven consecutive 24 h PM1 samples during winter and summer 2010 from the backyards of 74 non-smoking homes in the Capital Region using Harvard Coarse Impactors (HCI, Harvard School of Public Health, Boston, MA, USA), which function well in comparison to dichotomous samplers (Case et al., 2008). A source receptor model – positive matrix factorization (PMF) – was applied to identify and apportion outdoor sources of elements in PM1 mass. Nine sources were identified contributing to outdoor PM1 concentrations, including secondary sulfate, soil, biomass smoke, traffic, settled and mixed dust, coal combustion, road salt/road dust and an urban mixture source (Bari et al., 2015). Then biggest source of PM1 identified was secondary sulfate. Based on elemental characteristics and backward trajectory analysis, this source was interpreted to be related to Alberta’s background regional sulfate that is found in high abundance due to oil and gas extraction and production activities and other industrial processes and non-specific industrial activities. Bari et al. (2015) stated that more work was needed to further resolve the various source contributions to ambient air quality in the Capital Region in order to inform policy makers about sources of fine particulate matter.
Study Objectives This study further investigated characteristics of air quality and various source contributions to ambient fine particulate matter (PM2.5) in the Edmonton Capital Region using historical data measured at National Air Pollution Surveillance (NAPS) air monitoring stations in Edmonton, including a chemical speciation monitoring station (Edmonton McIntyre station) (refer to Figure 0.1). Three objectives were sought:
1. Investigate the characteristics and trends of individual air pollutants, including fine particulate matter (PM2.5), in order to understand how bad or good air quality is in Edmonton.
2. Investigate the sources of PM2.5 at an Environment Canada National Air Pollution Surveillance (NAPS) chemical speciation monitoring site in Edmonton, including identifying the contribution from coal combustion sources.
3. Investigate origins and causes of PM2.5 concentration differences in Edmonton during 2010 relative to other years.
Report Sections Part I of this report presents methods, results and discussion of characteristics and trends of individual air pollutants in Edmonton. Part II presents methods, results and discussion of sources of PM2.5 at the Environment Canada National Air Pollution Surveillance (NAPS) chemical speciation monitoring site in Edmonton. Part III presents methods, results and discussion of the origins and causes of PM2.5
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concentration differences in Edmonton during 2010 relative to other years. Finally, the key findings of the study are summarized in the Findings section.
Figure 0.1. Location of Edmonton McIntyre air monitoring station (Edm McIntyre) and industries in Edmonton reporting to Environment Canada’s National Pollution Release Inventory (NPRI) during 2013 using Google Earth (Image © 2015 Digital Globe). References Alberta Environment and Sustainable Development (AERSD), 2014. Capital Region Fine Particulate
Matter Science Report. http://aep.alberta.ca/focus/cumulative-effects/capital-region-industrial-heartland/documents/CapitalRegion-PM-ScienceReport-Dec2014.pdf.
Bari, M.A., Kindzierski, W.B., Wallace, L.A., Wheeler, A.J., MacNeill, M., Heroux, M.-E. 2015. Indoor and outdoor levels and sources of submicron particles (PM1) at homes in Edmonton, Canada. Environmental Science & Technology, 49, 6419–6429.
Case, M., Williams, R., Yeatts, K., Chen, F.–L, Scott, J., Svendsen, E., Devlin, R., 2008. Evaluation of a direct personal coarse particulate matter monitor. Atmospheric Environment, 42(19), 4446–4452.
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Part I Air Pollutant Data Characteristics and Trends Background information for several factors should be collected and evaluated in order to understand the state of air quality of a geographical region (U.S. EPA, 1977, 1999). To better understand the state of air quality in Edmonton, local meteorology and trends in criteria air pollutants – including major and trace components of fine particulate matter (PM2.5) – were reviewed and analyzed. Methods Local meteorology Ambient air quality depends on a number of factors, including meteorological conditions (e.g., inversions with reduced vertical mixing), and other atmospheric factors affecting removal of pollutants from the atmosphere. With respect to ambient air quality within an area surrounding emission sources, prevailing meteorology may play a major role in influencing ground level concentrations of air pollutants across the area. Among meteorological parameters, wind speed and wind direction have been reported as important factors influencing dispersion processes and chemical formation of airborne pollutant concentrations (Keary et al., 1998, U.S. EPA, 1999, Kukkonen et al., 2005, Barmpadimos et al., 2011). Wind rose plots were generated to show historical 17-year (1998–2014) and 9-year (2006–2014) average prevailing wind directions at existing air monitoring stations in Edmonton using Windrose Pro software (Enviroware, Italy). Typically, a wind rose plot consists of 16 wind sectors each of which 22.5 degrees of the horizon and five wind speed classes (in km/h) distinguished by color. The rose segments extend in the direction that the wind is originating, with the length indicating the relative frequency of wind originating from that direction (i.e., the percent of the time that the wind is blowing from that direction). Air quality trend analysis Trends in criteria air pollutants from continuous sampling To characterize the state of air quality in the Capital Region of Alberta, trend analysis of air pollutants was undertaken. Alberta Environment and Parks has been routinely monitoring criteria air pollutants in Edmonton as part of Environment Canada’s National Air Pollution Surveillance Network (NAPS). Hourly concentration data for criteria air pollutants, i.e., nitrogen dioxide (NO2), sulfur dioxide (SO2), fine particulate matter (PM2.5), ground-level ozone (O3), total hydrocarbon (THC) and carbon monoxide (CO) were accessed for a 17-year period of record (1998–2014) at Edmonton central and east stations and a 9-year period (2006–2014) at Edmonton McIntyre station via the Clean Air Strategic Alliance data warehouse (CASA, 2015). SO2 was not measured at Edmonton central station and PM2.5 was only monitored at Edmonton McIntyre station. Data were obtained in temporal order of year, month, day, and hour. A cut-off criterion of 80% completeness will be used as an initial screening step to establish whether to include an annual dataset in trend analysis. This criterion represents ~7,000 hourly values for an annual dataset and is judged more than adequate for purposes of this study; in addition it is a criterion similar to that used by others (Blanchard, 1999). The general approach for detecting air quality trends over time is to begin with valid datasets, and then select response variable (metrics) – such as means, medians, maxima, minima, selected percentiles, etc., select appropriate time periods to investigate (e.g., season, episode, annual, etc.), apply statistical methods for detecting trends and then evaluate the trends for direction, rate of change, statistical significance, etc. A parametric method was chosen in this study after Bari and Kindzierski (2015a), which tend to be more powerful than non-parametric tests and has potential to quantify the magnitude of a trend (McLeod et al., 1991).The method consists of time series linear regression using various percentiles of a distribution of 1-hr concentrations for a pollutant during a year. Zero values and values below the limit of detection (LOD) were replaced by half of the LOD and missing values were excluded from the dataset.
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Hourly concentration values for a year were first sorted in ascending order and the whole range of the dataset was divided into sub-ranges. The concentration values falling in each sub-range was identified and transformed into a percentile of the total number of concentration values (i.e., frequency expressed as percentiles of the annual dataset). Benchmarks representing 50th, 65th, 80th, 90th, 95th and 98th percentile concentrations for a year were identified as response variables from these frequency distributions and used for parametric trend analysis using simple linear regression and hypothesis testing (p < 0.05) to establish the significance of a trend over the period tested. Further details of trend analysis method are provided elsewhere (Bari and Kindzierski, 2015a). Trends in PM2.5 components from intermittent sampling PM2.5 sampling was performed at Edmonton McIntyre station in every 3 days for 24 h sampling times from May 2006 as per the NAPS PM2.5 speciation program. In general, the distributions of PM2.5 components are right skewed and data are usually not normally distributed. A Shapiro-Wilks test of normality revealed that data were log-normally distributed and therefore they were presented by geometric mean concentrations. A non-parametric method was chosen in this study to investigate temporal trends in PM2.5 components. The Mann-Kendall test was used to determine the presence of a monotonic increasing or decreasing trend (i.e., overall change in one direction) over time and Theil-Sen approach was applied to estimate the magnitude of the rate of change per year (i.e., slope). The MAKESENS (Mann-Kendall test for trend and Sen’s slope estimate) template application was used to determine the statistical significance levels (p = 0.001, 0.01, 0.05, and 0.1) of the trend (Salmi et al., 2002) and verified using software ProUCL version 5.0 from the United States Environmental Protection Agency (U.S. EPA, 2013). The Mann-Kendall test is limited to annual mean concentrations and appears to be free from autocorrelation and seasonal variation. To evaluate year to year variation in PM2.5 components, a non-parametric Kruskal-Wallis test (McBean and Rovers, 1998) was performed to determine whether daily concentrations of PM2.5 and its components were significantly different during at least one year. Monthly and seasonal variations were also evaluated.
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Results and Discussion Local meteorology Figure 1.1 shows a wind rose plot using historical hourly meteorological observations of wind speeds and wind directions from 2006 to 2014 at Edmonton McIntyre station. Available hourly data were accessed for the 9-year via the Clean Air Strategic Alliance (CASA, 2015) data warehouse. The wind rose plot indicates a general tendency for winds to be from southerly and west-northwesterly directions at Edmonton McIntyre station. Prevailing winds blew from the west-northwest direction 40% of the time and from the south 23% of the time over the 8-year period. To understand the strength and directions of the prevailing winds contributing to Edmonton air quality all the year round, seasonal wind roses were generated in Figure 1.2. The prevailing wind directions observed in different seasons help to better characterize the contribution of local emission sources in respective seasons. During winter prevailing wind directions were from south and south-southwest directions blowing 48% of the time with minor contribution from the west-northwest representing 18% of the time. In spring months dominant contributions were found when winds originated from south and south-east direction 41% of the time with some contributions from the west-northwest direction 19% of the time. Similar wind patterns were also observed during fall. Summer months showed an opposite trend where prevailing wind directions were from west and west-northwest directions representing 39% of the time and some contributions from the south and south-east directions (30% of the time). Figure 1.1. Wind rose plot at Edmonton McIntyre for 2006–2014.
6
Figure 1.2. Seasonal wind roses at Edmonton McIntyre for 2006–2014.
Winter Spring
Summer Fall
7
Trends in criteria air pollutants from continuous sampling A summary of statistically significant trends (p ≤ 0.05) for hourly average percentile concentrations of air pollutants is shown in Table 1.1 for Edmonton central and east stations (17-year period of record: 1998–2014) and Edmonton south and McIntyre stations (9-year period: 2006–2014). Trends for hourly NO2, SO2, O3, PM2.5, THC, and CO percentile concentrations are depicted in Figure 1.3 for selected stations. Trends for all criteria pollutants at each station are shown in the supplemental material in Figures S1.1 through S1.3. A statistically significant downward trend was observed for NO2 at all percentile concentrations at Edmonton central and east sites and at lower percentile concentrations at Edmonton south site with annual decreases of 0.68 to 0.77 ppb, 0.33 to 0.59 ppb, and 0.22 to 0.46 ppb at Edmonton central, east and south sites, respectively. SO2 represents the best marker for emissions of industrial activities involving fossil fuel combustion. However, a small decreasing trend (0.11 to 0.17 ppb change per year) was observed for SO2 at all percentile concentrations at the industrial site (Edmonton east). No change was observed for PM2.5 at any of the monitoring stations and a clear spike was found in 2010 indicating the influence of forest fire smoke on particulate air quality at Edmonton. This represents an interesting situation because during 2009 equipment at many of the original continuous PM2.5 monitoring stations in Alberta was upgraded to better capture some components of fine particulate matter (i.e., semi-volatile fraction) which were lost under the previous equipment operation methods (AEP, 2015). As a result of this change, PM2.5 levels observed at air monitoring stations are higher in 2010 and subsequent years compared to what they had been in the past. Thus despite absolute PM2.5 levels at air monitoring stations being ‘bumped’ higher in 2010 and subsequent years relative to previous years as a result of equipment upgrades in 2009, no statistically significant trends were observed at any of the stations. For O3 a small increasing trend was found at lower percentile concentrations (50th to 80th percentiles) at Edmonton central, a small decreasing trend was detected at higher percentile concentrations (at 80th to 98th percentiles) at Edmonton east site and no trend was observed at Edmonton south at all percentiles. These characteristics indicate evidence of spatial variation of O3 precursor concentrations – e.g., oxides of nitrogen (NOX), volatile organic compounds (VOCs) – across Edmonton rather than any type of consistent trend in O3 concentrations in Edmonton over the period examined. A small decreasing trend (i.e., ≤0.1 ppm per year) was observed for THC at almost all percentile concentrations at Edmonton central and east sites, with an exception at Edmonton south site where a small increasing trend was detected only at the 65th and 90th percentiles. A small decreasing trend (i.e., ≤0.1 ppm per year) was observed for CO for all percentile concentrations at both Edmonton central and east sites (similar to NO2), and at higher percentile concentrations at Edmonton south site, suggesting a decline of traffic emission contributions to NO2 and CO over the time period at these locations.
8
Table 1.1. Summary of linear trends for hourly average percentile concentrations of air pollutants at Edmonton stations.
Concentration Edmonton
Central (1998–2014)
Edmonton
East (1998–2014)
Edmonton
South (2006–2014)
Edmonton McIntyre
(2006–2014)
(percentile) Trend Change/year R2 Trend Change/year R2 Trend Change/year R2 Trend Change/year R2 NO2 50th ▼ 0.71 ppb 0.94 ▼ 0.33 ppb 0.76 ▼ 0.22 ppb 0.74
65th ▼ 0.77 ppb 0.92 ▼ 0.51 ppb 0.84 ▼ 0.30 ppb 0.74 80th ▼ 0.75 ppb 0.89 ▼ 0.59 ppb 0.86 ▼ 0.46 ppb 0.74 90th ▼ 0.70 ppb 0.83 ▼ 0.50 ppb 0.82 ▼ 0.32 ppb 0.29 95th ▼ 0.68 ppb 0.73 ▼ 0.45 ppb 0.75 ▬ n/a n/a 98th ▼ 0.72 ppb 0.68 ▼ 0.45 ppb 0.62 ▬ n/a n/a
SO2* 50th ▼ 0.11 ppb 0.61 ▬ n/a n/a 65th ▼ 0.17 ppb 0.85 ▬ n/a n/a 80th ▼ 0.15 ppb 0.65 ▬ n/a n/a 90th ▼ 0.15 ppb 0.70 ▼ 0.18 ppb 0.71 95th ▼ 0.14 ppb 0.40 ▼ 0.19 ppb 0.76 98th ▼ 0.17 ppb 0.31 ▬ n/a n/a
PM2.5 50th ▬ n/a n/a ▬ n/a n/a ▬ n/a n/a ▬ n/a n/a 65th ▬ n/a n/a ▬ n/a n/a ▬ n/a n/a ▬ n/a n/a 80th ▬ n/a n/a ▬ n/a n/a ▬ n/a n/a ▬ n/a n/a 90th ▬ n/a n/a ▬ n/a n/a ▬ n/a n/a ▬ n/a n/a 95th ▬ n/a n/a ▬ n/a n/a ▬ n/a n/a ▬ n/a n/a 98th ▬ n/a n/a ▬ n/a n/a ▬ n/a n/a ▬ n/a n/a
O3 50th ▲ 0.38 ppb 0.75 ▬ n/a n/a ▬ n/a n/a 65th ▲ 0.33 ppb 0.68 ▬ n/a n/a ▬ n/a n/a 80th ▲ 0.26 ppb 0.43 ▼ 0.26 ppb 0.47 ▬ n/a n/a 90th ▬ n/a n/a ▼ 0.34 ppb 0.55 ▬ n/a n/a 95th ▬ n/a n/a ▼ 0.44 ppb 0.57 ▬ n/a n/a
98th ▬ n/a n/a ▼ 0.55 ppb 0.54 ▬ n/a n/a THC 50th ▬ n/a n/a ▼ <0.1 ppm 0.35 ▬ n/a n/a
65th ▼ <0.1 ppm 0.35 ▼ <0.1 ppm 0.52 ▲ <0.1 ppm 0.60 80th ▼ <0.1 ppm 0.28 ▼ <0.1 ppm 0.53 ▬ n/a n/a 90th ▼ <0.1 ppm 0.40 ▼ <0.1 ppm 0.56 ▲ <0.1 ppm 0.49 95th ▼ <0.1 ppm 0.35 ▼ <0.1 ppm 0.38 ▬ n/a n/a
98th ▼ <0.1 ppm 0.25 ▼ <0.1 ppm 0.35 ▬ n/a n/a CO 50th ▼ <0.1 ppm 0.93 ▼ <0.1 ppm 0.69 ▬ n/a n/a
65th ▼ <0.1 ppm 0.93 ▼ <0.1 ppm 0.68 ▬ n/a n/a 80th ▼ <0.1 ppm 0.92 ▼ <0.1 ppm 0.84 ▬ n/a n/a 90th ▼ <0.1 ppm 0.93 ▼ <0.1 ppm 0.66 ▼ <0.1 ppm 0.53 95th ▼ 0.10 ppm 0.91 ▼ <0.1 ppm 0.78 ▼ <0.1 ppm 0.50
98th ▼ <0.1 ppm 0.92 ▼ <0.1 ppm 0.92 ▼ <0.1 ppm 0.92
*not measured at Edmonton central; Direction of trend (p = 0.05): ▬ no change; ▲ increasing; ▼ decreasing; n/a: not applicable
9
Figure 1.3. Trends for hourly percentile concentrations of criteria air pollutants at Edmonton stations.
0
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pb)
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50%ile 65%ile 80%ile 90%ile 95%ile 98%ile
Edmonton central
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Mic
rogr
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10
Emission trends of PM2.5 chemical components The National Pollutant Release Inventory (NPRI) reports annual releases of pollutants to air from Canadian industrial facilities/operations (Environment Canada, 2015). PM2.5, NOX, SO2, ammonia (NH3), VOCs and major trace elements releases to air from industrial facilities/operations in the Edmonton Capital Region for last one decade (2003–2014) and are summarized in Table 1.2. NPRI quantities do not account for small emission sources, such as small compressors and generators, and emissions from private/commercial vehicles. Table 1.2. Reported NPRI emissions for PM2.5, NOX, SO2, NH3, VOCs and and major trace elements in the Edmonton Capital Region over the period of 2003–2014.
Unit 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 PM2.5 tonnes 3047 3176 2487 2578 2880 2980 2848 2683 2476 2381 2496 3290 NOX tonnes 75443 76918 75463 73689 72598 72888 72912 73414 64174 59966 61064 71234 SO2 tonnes 86489 85698 86968 82277 80453 83455 82588 84343 75503 69131 72594 84702 NH3 tonnes 3155 3349 3605 3548 3352 3553 3158 3151 3198 3319 3840 3722
VOCs tonnes 8344 8189 8198 7619 7374 8005 7262 7399 8198 8577 9083 9145 As kg 376 401 388 556 430 527 477 250 270 215 200 196 Pb kg 973 1395 1252 2718 2091 2128 2022 1672 2537 2264 990 1057 Cd kg 157 170 167 122 226 304 299 125 557 383 316 81 Ni tonnes 0.95 2.1 2.2 2.1 3 2.7 2.8 1.9 2.8 1.9 1.5 0.919 V tonnes 1.2 1.2 0.892 1.2 1.1 2.1 1.3 1.1 1 0.276 0.267 0.043
Mn tonnes 8.9 8.1 7.5 7.4 5.9 6.3 5.7 5.1 12 8.1 7.1 2.1 Cr tonnes 2 2.4 2.4 2.8 2.5 2.7 3 0.948 1.2 0.444 0.377 0.253 Zn tonnes 7.1 7.9 7.4 8.7 6.6 7.1 9.2 7.4 10 5.3 4.8 4.6 Cu tonnes 1.2 1.3 1.3 1.3 1.1 1.2 1.4 1.3 0.776 0.636 0.59 0.386 Co tonnes 0.36 0.442 0.842 0.7 0.248 0.242 0.222 0.909 0.24 0.174 0.099 0.095
Table 1.3 shows trends in NPRI reported emissions for PM2.5 and major trace elements in the Edmonton Capital Region using the non-parametric (Mann-Kendall and Theil-Sen) approach. Emission trend plots are shown in Figure S1.4. A downward trend with an annual decrease of –37 tonnes/year (–1.3% per year) was observed for industrial PM2.5 emissions over the 2003–2014 period, however the trend was not statistically significant (p = 0.152). Statistically significant downward trends for industrial emissions were observed for NOX (p = 0.01) and SO2 (p = 0.05) with annual decreases of –933 and –1,204 tonnes/year (–1.2% and –1.4% per year), respectively. Industrial emissions for NH3 showed a non-significant upward trend (p = 0.225) with a small annual increase of 19 tonnes/year (0.6% per year). A small non-significant increasing trend in VOCs emissions was also observed (69 tonnes/year, 0.9% per year) (p = 0.136). Table 1.3. Reported NPRI emission trends for PM2.5 and major trace elements in the Edmonton Capital Region over the period of 2003–2014.
Unit 2003 2014 2003–2014
slope unit/year % change per year
Significance level p-value
PM2.5 tonnes 3047 3290 –37 –1.3 n.s. 0.152 NOX tonnes 75443 71234 –933 –1.2 99% 0.01 SO2 tonnes 86489 84702 –1204 –1.4 95% 0.05 NH3 tonnes 3155 3722 19 0.6 n.s. 0.225
VOCs tonnes 8344 9145 69 0.9 n.s. 0.136 As kg 376 196 –21 –4.9 95% 0.05 Pb kg 973 1057 21 1.2 n.s. 0.41 Cd kg 157 81 17 10.8 n.s. 0.15 Ni tonnes 0.953 0.919 –0.04 –1.7 n.s. 0.25 V tonnes 1.2 0.043 –0.1 –7.1 95% 0.05
Mn tonnes 8.9 2.1 –0.5 –5.6 90% 0.10 Cr tonnes 2 0.253 –0.2 –7.6 90% 0.10 Zn tonnes 7.1 4.6 –0.3 –3.1 n.s. 0.121 Cu tonnes 1.2 0.386 –0.08 –5.7 95% 0.05 Co tonnes 0.357 0.095 –0.03 –6.7 99% 0.01
n.s. = not significant
11
With respect to industrial emissions of elements that are considered potentially toxic by regulatory agencies (e.g., Health Canada), arsenic (As) showed a statistically significant downward trend (p = 0.05) with an annual decrease of –21 kg/year (–4.9% per year). Other potentially toxic elements e.g., nickel (Ni) also showed a decreasing trend (–0.04 tonnes/year), while lead (Pb) and cadmium (Cd) showed non-significant upward trends with annual increases of 21 and 17 kg/year, respectively (1.2% and 10.8% per year, respectively). Vanadium (V) – a typical marker element for petroleum-related sources (Khalaf et al., 1982; Duce and Hoffman, 1976) – also showed a statistically significant downward trend (p = 0.05) with –0.1 tonnes decrease per year. Manganese (Mn) and chromium (Cr), which are considered as hazardous elements by the United States Environmental Protection Agency (U.S. EPA) also showed statistically significant downward trends (p = 0.1) with annual decreases of –0.5 and –0.2 tonnes/year (–5.6% and –7.6% per year), respectively. Downward trends (at 95% and 99% significance levels, respectively) were also found for copper (Cu) and cobalt (Co) with annual decrease of –5.7% and –6.7%, respectively. While NPRI-reported industrial emissions trends for PM2.5 and major trace elements have decreased or unchanged (Table 1.3), the same type of information is absent for transportation sector emissions in Edmonton Capital Region. The transportation sector is acknowledged to account for an important fraction of key air pollutant emissions – including PM2.5, secondary nitrate precursor (NOX) and secondary organic aerosol precursor (NOX and VOC) emissions – to urban areas (Fine et al., 2004; Zhang et al., 2004; Wang et al., 2009; Gordon et a., 2014). Over a 13-year period the population of the City of Edmonton alone increased from 657,350 (2001) to 877,926 (2014) – or ~17,000 more people each year over the period (City of Edmonton, 2015). In addition, over a 10-year period the number of motor vehicles registered in Edmonton and St Albert increased from 502,200 registrations (2004) to 708,500 registrations (2014) – or ~20,600 more vehicles using Edmonton and St Albert roadways each year over this period (Alberta Transportation, 2008, 2012, 2014). These surrogate data suggest an increasing importance of the role of transportation sector emissions in Edmonton Capital Region over the past decade. This is opposite to NPRI-reported industrial emissions trends for PM2.5 and major trace elements in Edmonton Capital Region which are decreasing or unchanged over the past decade. Trends in PM2.5 chemical components from intermittent sampling Ambient levels of PM2.5 components Ambient PM2.5 speciation data quality was assessed for validity and species selection for trend analysis. Data quality for all measured PM2.5 components (n = 50) during the period 2007–2014 is shown in Table S1.1. Table 1.4 shows descriptive statistics for only 34 components, including ions and trace elements, that were selected for trend investigation based on their higher data completeness – i.e., at least 50% of the samples above the detection limit (DL) and source-specific tracers (e.g., levoglucosan and mannosan for biomass smoke). Data below the minimum detection limit were replaced by half of the DL and non-detect or missing values were excluded from the dataset. The geometric mean concentration of PM2.5 in Edmonton McIntyre was 7.22 µg/m3 (median = 6.90 µg/m3, interquartile range, IQR = 4.95–10.39 µg/m3, range = 0.14–62.54 µg/m3). Concentrations measured in this station were comparable to other monitoring stations in Edmonton (median levels: central: 7.0 µg/m3, east 7.4 µg/m3, south 6.9 µg/m3), but lower than Calgary central (median: 10.1 µg/m3) for the same time period (CASA, 2015). The most dominant component of PM2.5 at Edmonton was organic carbon (OC), contributing to 28% (2.40 µg/m3) of the measured PM2.5 mass on average. Concentrations of OC ranged from 0.19 to 29.45 µg/m3 with a geometric mean of 1.68 µg/m3 (median 1.81 µg/m3). Elemental carbon (EC), a primary pollutant formed in the combustion processes ranged from 0.02 to 10.20 µg/m3 with a geometric mean of 0.65 µg/m3 (median 0.71 µg/m3) and contributed to 13% of the PM2.5 mass on average.
12
The second most abundant component in PM2.5 mass was nitrate (NO3–), accounting for 14% (1.25
µg/m3) of the PM2.5 mass on average. It was assumed that all nitrates are present as ammonium nitrate (Malm et al., 1994) particularly in western Canada, where ammonium ion (NH4
+) is abundant (Dabek-Zlotorzynska et al., 2011). NO3
– and NH4+ concentrations ranged from 0.01 to 20.15 µg/m3 and 0.002 to
10.21 µg/m3 with geometric means of 0.32 µg/m3 (median 0.19 µg/m3) and 0.32 µg/m3 (0.31 µg/m3), respectively. Comparatively lower nitrate levels were found in the 1985–1995 study in Edmonton and Calgary with median of 0.20 µg/m3 and maximum of 15.6 µg/m3, making up almost 5% of PM2.5 mass in both cities (Cheng et al., 1998). In the Edmonton Capital Region, contribution of secondary nitrate to PM2.5 was reported by Jeong et al. (2011) to come from emissions of ammonia and oxides of nitrogen (NOX) from agricultural activities and primary emissions (vehicle exhaust or industry and local oil refinery sources). The third most predominant component in PM2.5 mass was sulfate (SO4
2–) contributing to 11% (0.91 µg/m3) of the PM2.5 mass on average. SO4
2– can be present in the atmosphere in the particulate form as secondary sulphate consisting of sulfuric acid, ammonium bisulfate and ammonium sulfate (Ansari and Pandis, 1998) and can remain airborne for hundreds of kilometres (Tuncel et al., 1985). SO4
2–
concentrations ranged from 0.02 to 14.75 µg/m3 with geometric means of 0.61 µg/m3 (median 0.63 µg/m3). In Alberta, background regional sulphate is found in high abundance due to oil and gas production (e.g., natural and sour gas extraction, flaring and processing), other industrial emissions like coal- and gas-fired industrial boilers for power generation and other non-specific industrial sources (Schulz and Kindzierski, 2001). SO4
2– was also found as the abundant mass fraction of ambient fine particles (~11% of PM2.5 mass) in Edmonton (median 1.0 µg/m3, range 0.01 – 11.14 µg/m3) and Calgary (median 1.0 µg/m3, range 0.1–16.20 µg/m3) during the study period of 1985–1995 (Cheng et al., 1998). Other components such as NaCl contributed 1.2% (0.11 µg/m3) to the PM2.5 mass on average likely associated with road salt applications during winter months in Edmonton. Median concentrations of K+, levoglucosan and mannosan, typical markers for biomass burning, were 0.03 µg/m3 (range 0.003–0.25 µg/m3), 39.74 ng/m3 (range 1.75–1492 ng/m3) and 8.20 ng/m3 (range 0.94–347 ng/m3), respectively and are likely associated with biomass smoke-related sources including winter fireplace burning, open pit camp fires and barbeques as well as wild fires in Alberta and nearby provinces. Concentrations of potential carcinogens – e.g., As (median 0.16 ng/m3) and Ni (median 0.34 ng/m3) were far below Alberta Ambient Air Quality Objectives and Guidelines (10 ng/m3 for As and 50 ng/m3 for Ni, AESRD, 2013).
13
Table 1.4. Descriptive statistics of PM2.5 and detected 34 chemical components (more than 50% of the samples) for 2007–2014.
Analytical method
% of samples >MDL
% of samples <MDL
Non-detect or missing
nd
N Units Gmean Min P10 P25 Median P75 P98 Max
PM2.5 Gravimetric 100 0 0 867 μg/m3 7.22 0.14 3.63 4.95 6.90 10.39 29.42 62.54 Organic carbon (OC) TOR 90 10 0 867 μg/m3 1.68 0.19 0.46 0.99 1.81 3.31 7.57 29.45
Elemental carbon (EC)) TOR 94 6 0 867 μg/m3 0.65 0.018 0.18 0.38 0.71 1.31 4.55 10.20 Sulfate (SO4
2–) IC 100 0 0 869 μg/m3 0.61 0.024 0.21 0.33 0.63 1.10 3.82 14.75 Nitrate (NO3
–) IC 92 7 1 869 μg/m3 0.32 0.010 0.06 0.10 0.19 1.03 11.57 20.15 Ammonium (NH4
+) IC 100 0 0 869 μg/m3 0.32 0.002 0.08 0.14 0.31 0.68 4.17 10.21 Sodium (Na+) IC 67 33 0 869 μg/m3 0.03 0.002 0.01 0.02 0.03 0.06 0.31 2.15 Chloride (Cl–)* IC 47 53 0 869 μg/m3 0.02 0.005 0.01 0.01 0.02 0.05 0.32 2.85 Potassium (K+) IC 85 15 0 869 μg/m3 0.03 0.003 0.01 0.02 0.03 0.05 0.12 0.25 Calcium (Ca+2) IC 88 12 0 869 μg/m3 0.01 0.001 0.00 0.01 0.01 0.01 0.03 0.08
Magnesium (Mg+2) IC 92 7 0 869 μg/m3 0.04 0.001 0.01 0.02 0.05 0.07 0.20 0.92 Floride (F–) IC 51 16 32 869 μg/m3 0.00 0.001 0.00 0.00 0.00 0.01 0.02 0.02 Formate* IC 45 43 13 869 μg/m3 0.02 0.005 0.01 0.01 0.02 0.02 0.06 0.20 Oxalate IC 82 12 6 869 μg/m3 0.04 0.005 0.02 0.03 0.05 0.07 0.16 0.61
Levoglucosan GC-MS 96 0 3 561 ng/m3 40.95 1.75 9.99 18.17 39.74 84.71 416.06 1492 Mannosan GC-MS 60 11 29 561 ng/m3 8.75 0.94 2.84 3.71 8.20 16.81 84.98 347
Aluminium (Al) ICP-MS 73 27 0 833 ng/m3 3.24 0.52 1.04 1.56 3.50 6.74 28.00 488 Arsenic (As) ICP-MS 98 2 0 833 ng/m3 0.16 0.01 0.06 0.10 0.16 0.26 0.98 12.52 Barium (Ba) ICP-MS 100 0 0 833 ng/m3 1.56 0.05 0.65 1.01 1.65 2.51 5.16 14.61
Cadmium (Cd) ICP-MS 81 18 0 833 ng/m3 0.05 0.007 0.02 0.03 0.05 0.09 0.31 1.15 Cobalt (Co) ICP-MS 62 37 0 833 ng/m3 0.05 0.005 0.01 0.02 0.04 0.09 1.16 4.80
Chromium (Cr) ICP-MS 71 28 1 833 ng/m3 0.42 0.026 0.10 0.21 0.43 0.81 2.30 8.97 Cupper (Cu) ICP-MS 90 10 0 833 ng/m3 1.49 0.21 0.52 0.99 1.57 2.40 6.47 56
Iron (Fe) ICP-MS 99 1 0 833 ng/m3 12.73 0.52 4.59 7.74 13.57 22.43 49.73 130 Manganese (Mn) ICP-MS 100 0 0 833 ng/m3 2.43 0.05 0.62 1.25 2.93 5.01 11.06 21 Molybdenum (Mo) ICP-MS 74 26 0 833 ng/m3 0.18 0.01 0.04 0.10 0.18 0.34 1.77 10.44
Nickel (Ni) ICP-MS 81 18 1 833 ng/m3 0.34 0.04 0.07 0.16 0.34 0.74 2.84 11.02 Lead (Pb) ICP-MS 89 11 0 833 ng/m3 0.38 0.05 0.10 0.22 0.37 0.67 2.82 10.44
Antimony (Sb) ICP-MS 99 1 0 833 ng/m3 0.17 0.01 0.08 0.11 0.17 0.25 0.69 1.46 Selenium (Se)* ICP-MS 19 75 6 833 ng/m3 0.08 0.021 0.05 0.05 0.07 0.14 0.32 0.53
Tin (Sn) ICP-MS 61 32 8 833 ng/m3 0.11 0.026 0.03 0.05 0.10 0.18 0.96 3.34 Strontium (Sr) ICP-MS 96 4 0 833 ng/m3 0.26 0.021 0.12 0.18 0.26 0.39 0.85 2.51 Titanium (Ti) ICP-MS 55 45 0 833 ng/m3 0.32 0.10 0.16 0.16 0.34 0.52 1.15 4.28 Vanadium (V) ICP-MS 66 33 1 833 ng/m3 0.08 0.01 0.02 0.03 0.06 0.16 2.61 8.66
Zinc (Zn) ICP-MS 93 7 0 833 ng/m3 5.80 0.52 1.47 3.25 6.31 11.37 39.64 226 *Se, Cl– and formate selected due to source specific tracers, P: percentile, MDL: method detection limit.
14
Temporal and seasonal variation Temporal profiles for 24 h concentrations of PM2.5, OC, EC and secondary pollutants (i.e., SO4
2–, NO3–,
and NH4+) over the period of 2007–2014 are shown in Figure 1.4 using box-whisker plots. To evaluate
year to year variation in concentrations, a non-parametric Kruskal-Wallis test was performed to determine whether daily concentrations of PM2.5 and major components were significantly different during at least one year (Table 1.5). We found no significant variation in year to year concentrations for PM2.5 and ammonium concentrations (p > 0.05). While statistically significant differences in year to year concentrations were observed for OC, EC, SO4
2– and NO3– (p < 0.05).
Month to month variation in PM2.5, OC, EC and secondary pollutants over the time period of 2007–2014 is shown in Figure 1.5 using box-whisker plots (other components are shown in Figure S1.5). We found statistically significant differences in month to month concentrations for PM2.5 and all components except for Ni (p < 0.05). Significantly higher PM2.5 concentrations were observed during winter than summer months (p < 0.0001), which can possibly be explained by a combination of atmospheric conditions and anthropogenic emissions (e.g., vehicle and industrial emissions, road de-icing salt). During winter, high atmospheric stability, low wind speed and morning ground-based temperature inversions with low mixing layer heights can favor accumulation of pollutants resulting in higher particle concentrations than in summer (Myrick et al., 1994). Similar winter patters for PM2.5 have been found in Edmonton in the earlier study of 1985–1995 (Cheng et al., 1998) and during 2004–2006 in Golden, British Columbia (Jeong et al., 2008), where poor atmospheric dispersion conditions due to frequent temperature inversions contributed to elevated PM2.5 concentrations in winter months. PM2.5 concentration maxima were also observed during summer in July/August. Several events occurred when maximum 24 h concentrations exceeded Canada-Wide Standard of 30 µg/m3 (e.g., 62 µg/m3 on August 21, 2010) suggesting an influence of long-range transport of wildfire smoke episodes occurring every year in Alberta and nearby provinces such as British Columbia, Saskatchewan, and Manitoba. Table 1.5. Yearly and monthly variation (p-value, Kruskal-Wallis test) in PM2.5 and selected tracer components in Edmonton over the period 2007–2014.
Annual variation p-value
Monthly variation p-value
Seasonal variation (winter vs summer)
p-value PM2.5 0.270 <0.0001 <0.0001 OC <0.0001 <0.0001 0.485 EC <0.0001 <0.0001 0.012 SO4
2– 0.037 <0.0001 0.069 NO3
– <0.0001 <0.0001 <0.0001 NH4
+ 0.148 <0.0001 <0.0001 Oxalate <0.0001 <0.0001 <0.0001K+ <0.0001 <0.0001 <0.0001As 0.047 0.025 0.004 Ni 0.710 0.418 0.967 Pb <0.0001 <0.0001 0.007 Se <0.0001 <0.0001 <0.0001 V <0.0001 <0.0001 0.660 Zn 0.013 <0.0001 <0.0001
No significant variation was found in OC concentrations between summer and winter months (p = 0.485). The highest OC concentration of 30 µg/m3 was found in August 2010, which was due the influence of out-of-region wildfire smoke. A significant variation was found in EC concentrations (p = 0.012) with comparatively higher concentrations in winter (geomean 0.90 µg/m3) than in summer (geomean 0.77 µg/m3).
15
An apparent seasonal variation was found for SO42– with higher concentrations in winter months. In
general, a summer high pattern would be expected for SO42– concentrations as observed in eastern
Canadian cities (Dabek-Zlotorzynska et al., 2011) possibly due to enhanced photochemical reactions associated with higher temperature and intense solar radiation. In Edmonton, geometric mean SO4
–2 concentrations were 0.66 µg/m3 (median 0.69 µg/m3) in winter and 0.57 µg/m3 (median 0.61 µg/m3) in summer, however the difference was not statistically significant (p = 0.069). The observed apparent higher levels in winter months are presumably due to dominant heterogeneous oxidation of SO2 from local sources and regional transport of oil and gas activities coupled with stable weather and inversion conditions during winter. NO3
– levels showed a strong and significant seasonal variability (p < 0.0001) with higher concentrations during colder months in winter (geomean 1.39 µg/m3) than in summer (geomean 0.07 µg/m3). This is probably due to the low thermal stability of ammonium nitrate in warmer months (Adams et al., 1999; Querol et al., 2001).The influence of local vehicle exhaust and industrial sources are likely associated with higher winter levels of NO3
– in Edmonton (Jeong et al., 2011). Like NO3–, NH4
+ levels also exhibited a similar seasonal pattern with elevated concentrations in winter (geomean 0.59 µg/m3) than in summer (geomean 0.21 µg/m3). Oxalate exhibited strong seasonal variability (p < 0.0001) with 2-fold higher concentrations during warmer months in summer (geomean 0.06 µg/m3) than in winter (geomean 0.03 µg/m3). Oxalate can be emitted from several primary and secondary emission sources such as fuel oil combustion, biomass burning and biogenic sources (Chebbi and Carlier, 1996). Elevated levels in summer months suggest the formation of secondary organic aerosol (SOA) due to photochemical activity. Concentrations of K+ exhibited a strong and significant seasonal variability (p < 0.0001) with higher concentrations in winter (geomean 0.04 µg/m3) particularly November through February than in summer (geomean 0.02 µg/m3). The City of Edmonton transportation department does not use potassium in the sand/salt mixture that is applied to Edmonton roadways for winter maintenance (Carnegie, 2015). Seasonal patterns with high winter levels likely reflect wood smoke origins more than other potential sources (Ming-Yi et al., 2015). This suggests an influence of emissions from residential wood stove/fireplace heating and/or agricultural slash burning during winter months in the Capital Region. Some peak K+ concentrations were also evident during summer months (e.g., July, August), indicating the contribution of long range transport of smoke from wildfires episodes in Alberta and nearby provinces such as British Columbia, Saskatchewan and Manitoba during the study period. Similar to K+, concentrations of Zn exhibited strong and significant seasonal variability (p < 0.0001) with higher concentrations in winter (geomean 10.39 µg/m3) than in summer (geomean 4.89 µg/m3). Zn is a major trace element of wood (Sippula et al., 2009; Chalot et al., 2012) and others have reported high Zn emissions from waste wood combustion (Wellinger et al., 2012) and that increased concentrations of Zn occur in ambient air affected by wood combustion (Echalar et al., 1995). This also supports a winter time influence of emissions from wood burning in the Capital Region and is an issue worthy of further investigation. Significant seasonal variations were also found for trace elements e.g., As, Pb, Se and Zn. While Ni and V displayed no significant seasonal variation suggesting an influence from sources such as local refineries and regional oil and gas activities throughout the year.
16
Figure 1.4. Annual profiles of 24 h concentrations of PM2.5 and secondary pollutants over the period 2007–2014 using box-whisker plots. Boxes represent 25th (lower quartile) and 75th (upper quartile) percentile values, with median values as lines across the boxes, geometric mean values as round black ball and minimum and maximum concentrations as whiskers.
17
Figure 1.5. Monthly profiles of 24-h concentrations of PM2.5, OC, EC and secondary pollutants over the period 2007–2014 using box-whisker plots. Boxes represent 25th (lower quartile) and 75th (upper quartile) percentile values, with median values as lines across the boxes, geometric mean values as round black ball and minimum and maximum concentrations as whiskers.
0.1
1
10
100Ja
n
Fe
b
Ma
r
Ap
r
Ma
y
Jun
Jul
Au
g
Se
p
Oct
No
v
De
c
μg
/m3
PM2.5
0.001
0.01
0.1
1
10
100
μg
/m3
OC
0.001
0.01
0.1
1
10
100
r r v c
μg
/m3
EC
0.01
0.1
1
10
100
Jan
Fe
b
Ma
r
Ap
r
Ma
y
Jun
Jul
Au
g
Se
p
Oct
No
v
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c
μg
/m3
SO42–
0.0001
0.001
0.01
0.1
1
10
100
Jan
Fe
b
Ma
r
Ap
r
Ma
y
Jun
Jul
Au
g
Se
p
Oct
No
v
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c
μg
/m3
NO3–
0.001
0.01
0.1
1
10
100
Jan
Fe
b
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r
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r
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Jul
Au
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p
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/m3
NH4+
18
Trends in ambient PM2.5 concentrations Figure 1.6 shows temporal trends in ambient PM2.5 concentrations at Edmonton McIntyre station using both parametric (linear regression) and non-parametric (Mann-Kendal and Thiel-Sen) approaches. As reported earlier (Table 1.1), no change was observed for PM2.5 using parametric approach over the 8-year period (2007–2014). This is consistent with the results obtained for a longer time period (2001–2014) at Edmonton central and east stations, where no change was also observed for PM2.5. Similar findings are also found for other cities like Calgary and Fort McMurray (Bari and Kindzierski 2015b). In contrast, a non-parametric approach suggested a slight downward trend for geometric mean PM2.5 concentrations with an annual decrease of –0.05 µg/m3 (0.7% per year) over the 2007–2014 period, however the trend was not statistically significant (p = 0.268). Non-parametric tests are alternatives to parametric tests when assumptions for parametric tests cannot be met. Although these methods make fewer assumptions regarding the distribution of data, they tend to be less powerful than parametric tests, and are not robust to homogeneity of variance of data (Day and Quinn, 1989; McLeod et al., 1991). It is noted that if a true temporal trend for any environmental parameter is strong enough, it will be detected regardless of the statistical approach (parametric or non-parametric) used. The observed inconsistency among the trends in PM2.5 using both methods suggests that PM2.5 concentrations are largely unchanged over the last decade in the Capital Region of Alberta.
Figure 1.6. Temporal trends in PM2.5 concentrations at Edmonton McIntyre using (a) parametric and (b) non-parametric approaches. Trends in PM2.5 components – A summary of trends in annual geometric mean concentrations of higher detected PM2.5 components (i.e., species detect in >50% of samples) is shown in Table 1.6 for the 2007–2014 period. Trend plots for OC, EC, secondary pollutants, NaCl, K+ and oxalate are shown in Figure 1.7 and selected trace elements are shown in Figure 1.8. Concentrations of OC and EC showed downward trends at 95% significance level with annual decreases of –0.14 and –0.15 µg/m3 per year, (–7.3% and –12.4%), respectively. As reported previously, statistically significant variation (p < 0.05) in year to year concentrations were observed for OC, EC. Maximum values of OC and EC were recorded in 2008 (4.6 and 3.3 µg/m3, respectively) and minimum were recorded in 2014 (0.93 and 0.08 µg/m3, respectively). The observed decrease in OC and EC concentrations is likely due to reduction OC/EC emissions or precursors from primary emission sources such as vehicle exhaust and consistent with observed decline of traffic-related gaseous pollutants like NO2 and CO over the last 17 years at other Edmonton stations (Table 1.1). As stated previously, OC and EC accounted for 28% and 13% of the total PM2.5 mass on average. It is also likely that the observed reduction of OC, EC may partly be attributed to the reduction in PM2.5 mass.
PM2.5 b)
PM2.5 a)
19
Table 1.6. Temporal trends (8-year) in ambient concentrations of PM2.5 components at Edmonton McIntyre (Theil-Sen trend test).
Unit
2007 gmean
2014 gmean
2007-2014 Slope
unit/year
% Change per year
Significance level
p-value
OC μg/m3 1.53 0.93 –0.14 –7.3 95% 0.05 EC μg/m3 0.51 0.08 –0.15 –12.4 95% 0.05
SO42– μg/m3 0.59 0.59 –0.01 –1.5 n.s. 0.268
NO3– μg/m3 0.29 0.50 0.004 1.2 n.s. 0.548
NH4+ μg/m3 0.34 0.33 –0.005 –1.5 n.s. 0.268
NaCl μg/m3 0.05 0.09 0.005 10.7 95% 0.05 K+ μg/m3 0.04 0.03 0.001 1.8 n.s 0.548
Oxalate μg/m3 0.05 0.03 –0.005 –7.9 95% 0.05 Al ng/m3 2.77 1.75 –0.16 –4.1 n.s. 0.355 As ng/m3 0.16 0.14 –0.002 –1.3 n.s. 0.268 Ba ng/m3 1.83 1.41 –0.07 –3.8 99% 0.01 Cd ng/m3 0.06 0.04 –0.004 –5.5 90% 0.054 Co ng/m3 0.05 0.04 –0.003 –5.2 n.s. 0.089 Cr ng/m3 0.43 0.36 –0.01 –1.3 n.s. 0.451 Cu ng/m3 1.30 1.52 –0.02 –1.3 n.s. 0.355 Fe ng/m3 10.99 10.11 –0.07 –0.5 n.s. 0.452 Mn ng/m3 2.24 2.34 0.05 2.2 n.s. 0.193 Mo ng/m3 0.26 0.20 0.002 0.9 n.s. 0.451 Ni ng/m3 0.31 0.31 0.01 2.1 n.s. 0.193 Pb ng/m3 0.45 0.24 –0.03 –6.1 95% 0.05 Sb ng/m3 0.18 0.16 –0.004 –2.1 n.s. 0.054 Se ng/m3 0.14 0.06 –0.01 –6.9 n.s. 0.113 Sn ng/m3 0.07 0.08 –0.001 –1.1 n.s. 0.451 Sr ng/m3 0.19 0.24 0.01 3.1 n.s. 0.193 Ti ng/m3 0.26 0.22 –0.01 –4.0 n.s. 0.268 V ng/m3 0.07 0.08 –0.001 –0.8 n.s. 0.548 Zn ng/m3 6.30 4.51 –0.43 –5.6 n.s. 0.054
At Edmonton McIntyre, secondary pollutants e.g., SO4
2– and NH4+ showed non-significant downward
trends with small annual decreases of –0.01 and –0.005 µg/m3 per year, (each –1.5% per year), respectively, while NO3
– showed a non-significant upward trend with a small increase of 0.004 µg/m3 (1.2%) per year. The trend plot (Figure 1.7) for SO4
2–exhibited a relatively similar trend compared to NH4+,
with concentrations increasing from 2007 to 2009, followed by a sharp drop in 2011, maintaining an increasing trend thereafter until 2013 and then dropped again in 2014. As stated previously, an annual decreasing trend of –1,204 tonnes/year or –1.4% per year (Table 1.3) was observed for Edmonton Capital Region SO2 releases from Environmental Canada’s NPRI reported emissions, which likely contributes to the reduction in ambient concentrations of secondary sulfate. The observed decreasing trend in SO4
2– could be due to reduced combustion emissions as a result from the implementation of emission abatement strategies employed by the industries in recent years. The use of road salt in winter as de-icing or anti-icing chemicals is common practice in Edmonton for winter road maintenance. A statistically significant upward trend (p = 0.05) was observed for NaCl with an annual increase of 0.005 µg/m3 per year (10.7% per year). Concentrations of a biomass burning tracer – K+ showed a small non-significant increasing trend of 0.001 µg/m3 (1.8%) per year. The influence of long-range transport of wildfires smoke frequently occurring in recent years in Alberta and nearby provinces e.g., British Columbia, Saskatchewan is likely associated with this. Oxalate exhibited a statistically significant (p = 0.05) downward trend with an annual decrease of –0.005 µg/m3 (–7.9%) per year. Crustal elements – e.g., Al, Fe, Ti – showed non-significant downward trends with annual decreases ranging from –0.16 to –0.01 ng/m3/year (–4.1 to –0.5% per year). Ba showed a downward trend with statistical significance (p = 0.01) with annual decrease of –0.07 ng/m3 (–3.8%) per year. Ba can be emitted from exhaust and non-exhaust emissions of vehicles (Lee and Hopke, 2006; Lough et al., 2005; Sternbeck et al.,2002).
20
For trace elements considered potentially toxic by regulatory agencies, As displayed a non-significant downward trend with small annual decrease –0.002 ng/m3 (–1.3%) per year, consistent with the NPRI emission trend for As observed in Edmonton (Table 1.3). As stated previously, NPRI emission trends for other potentially toxic elements – e.g., Pb and Cd – showed non-significant upward trends with annual increases of 21 and 17 kg/year (1.2% and 10.8%) per year, respectively. In contrast, ambient concentrations of Pb and Cd exhibited significant downward trends (p values of 0.05 and 0.054, respectively) with small annual decreases of –0.03 and –0.004 ng/m3 (–6.1% and –5.5%) per year, respectively. A non-significant upward trend was observed for Ni with small increase of 0.01 ng/m3 (2.1%) per year, contrary to the observed emission trend in Edmonton. NPRI emission trends for Mn and V (Table 1.3) showed statistically significant downward trends with annual decreases of –0.5 and –0.1 tonnes/year, respectively. While for ambient concentrations (Table 1.6), concentrations of Mn displayed a non-significant opposite trend (0.05 ng/m3 increases per year) and V exhibited a non-significant downward trend (–0.001 ng/m3 decrease per year). Other trace elements – e.g., Se, Sb, V, Co, Cu and Zn – exhibited non-significant downward trends with annual decreases ranging from –0.43 to –0.004 ng/m3 (–6.9% to –1.3%) per year. In this study, some limitations exist for PM2.5 trend investigation. As discussed previously, different monitoring methods were employed for the continuous measurement of PM2.5 concentrations for the study period 2007–2014. During 2009 monitoring equipment at many of the original continuous PM2.5 monitoring stations in Alberta were upgraded to better capture some components of fine particulate matter (i.e., semi-volatile and water-bound fraction) which were lost under the previous equipment operation methods (Alberta Environment and Park, 2015; Alberta Environment, 2013). Notwithstanding this limitation and absolute PM2.5 levels at air monitoring stations being ‘bumped’ higher in 2010 and subsequent years relative to previous years as a result of equipment upgrades, no statistically significant trends were observed at any of the stations for the continuous data. It is not surprising that most of the observed trends for PM2.5 and its components were not statistically significant. Indications of statistically significant trends for datasets that are limited in duration (years) or where only small changes are occurring from year-to-year (i.e., less a couple of percent) are unlikely to be truly representative of the trends that are actually occurring. This would only be captured with a much longer time period – e.g., two to three decades as suggested by Weatherhead et al. (1998). However, the data presented here provides reasonably strong baseline information and key insights about current levels and observed short-term trends of ambient PM2.5 and its components in the Capital Region of Alberta.
21
Figure 1.7. Temporal trends in annual geometric mean concentrations of PM2.5, OC, EC, secondary pollutants, NaCl, K+ and oxalate for 8-year period (2007–2014) at Edmonton McIntyre.
OC EC
NaCl
SO4-2 NO3
-
NH4+
Sen’s estimate
K+ Oxalate
22
Figure 1.8. Temporal trends in annual geometric mean concentrations of selected trace components for 2007–2014 at Edmonton McIntyre.
As
Ba
Cd Pb
V
Ni Sen’s estimate
Cu Sb
23
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Supplemental Material Part I Figure S1.1. Trends for hourly percentile concentrations of NO2 and O3 at Edmonton stations.
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Figure S1.2. Trends for hourly percentile concentrations of PM2.5 and SO2 at Edmonton stations.
28
Figure S1.3. Trends for hourly percentile concentrations of CO and THC at Edmonton stations.
29
Figure S1.4. Trends in reported NPRI emissions for PM2.5, NOX, NO2, NH3, VOCs and major trace elements in the Edmonton Capital Region over last one decade (2003–2014).
Pb
PM2.5
As Cd
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NOX SO2 NH3
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30
Figure S1.5. Box-whisker plots of monthly concentrations of oxalate, K+, Zn, As, Pb, Se, Ni and V over the period 2007–2014. Boxes represent 25th (lower quartile) and 75th (upper quartile) percentile values, with median values as lines across the boxes, geometric mean values as round black ball and minimum and maximum concentrations as whiskers.
31
Figure S1.6. Temporal trends in annual geometric mean concentrations of trace elements for 2007–2014 at Edmonton McIntyre.
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32
Table S1.1. Data quality of PM2.5 and its chemical components (n = 50) for an 8-year period (2007–2014).
Analytical method
No. of samples
>DL
No. of samples
<DL
No. of non-
detect or missing
(nd)
Total no. of
samples N
% of samples
>DL
% of samples
<DL
% of non-detect or missing
(nd)
PM2.5 Gravimetric 870 1 0 871 100 0 0 Organic carbon (OC) TOR 783 83 1 866 90 10 0 Elemental carbon (EC) TOR 815 49 3 866 94 6 0
Sulfate (SO42–) IC 868 1 0 869 100 0 0
Nitrate (NO3–) IC 799 57 13 869 92 7 1
Ammonium (NH4+) IC 867 2 0 869 100 0 0
Sodium (Na+) IC 584 285 0 869 67 33 0 Chloride (Cl–) IC 412 457 0 869 47 53 0
Potassium (K+) IC 740 128 1 869 85 15 0 Oxalate IC 712 104 53 869 82 12 6 Formate IC 387 372 110 869 45 43 13
Magnasium (Mg2+) IC 802 65 2 869 92 7 0 Calcium (Ca2+) IC 764 105 0 869 88 12 0
Floride (F–) IC 446 143 280 869 51 16 32 Levoglucosan GC-MS 540 2 19 561 96 0 3
Mannosan GC-MS 339 61 161 561 60 11 29 Aluminium (Al) ICP-MS 604 229 0 833 73 27 0 Arsenic (As) ICP-MS 814 19 0 833 98 2 0 Barium (Ba) ICP-MS 830 3 0 833 100 0 0
Cadmium (Cd) ICP-MS 675 154 4 833 81 18 0 Cobalt (Co) ICP-MS 517 312 4 833 62 37 0
Chromium (Cr) ICP-MS 593 233 7 833 71 28 1 Cupper (Cu) ICP-MS 746 83 4 833 90 10 0
Iron (Fe) ICP-MS 823 10 0 833 99 1 0 Manganese (Mn) ICP-MS 829 4 0 833 100 0 0 Molybdenum (Mo) ICP-MS 618 215 0 833 74 26 0
Nickel (Ni) ICP-MS 673 154 6 833 81 18 1 Lead (Pb) ICP-MS 739 93 1 833 89 11 0
Antimony (Sb) ICP-MS 826 6 1 833 99 1 0 Selenium (Se) ICP-MS 159 623 51 833 19 75 6
Tin (Sn) ICP-MS 504 264 65 833 61 32 8 Stontium (Sr) ICP-MS 800 33 0 833 96 4 0 Titanium (Ti) ICP-MS 458 371 4 833 55 45 0 Vanadium (V) ICP-MS 553 273 7 833 66 33 1
Zinc (Zn) ICP-MS 771 62 0 833 93 7 0 Acetate IC 246 318 305 869 28 37 35
MSA IC 160 123 586 869 18 14 67 Nitrite (NO2
–) IC 95 114 660 869 11 13 76 Barium ion (Ba2+) IC 58 182 629 869 7 21 72
Lithium (Li2+) IC 43 186 640 869 5 21 74 Bromide (Br–) IC 11 38 820 869 1 4 94
Strontium ion (Sr+) IC 7 58 804 869 1 7 93 Phosphate (PO4
3–) IC 5 56 808 869 1 6 93 Propionate IC 2 54 813 869 0 6 94
Arabitol GC-MS 225 49 287 561 40 9 51 Mannitol GC-MS 136 21 404 561 24 4 72
Galactosan GC-MS 93 35 433 561 17 6 77 Silver (Ag) ICP-MS 35 711 87 833 4 85 10
Beryllium (Be) ICP-MS 0 559 274 833 0 67 33 Thallium (Tl) ICP-MS 49 744 40 833 6 89 5 Uranium (U) ICP-MS 2 378 453 833 0 45 54
DL: detection limit
33
Part II Identification of Fine Particulate Matter Sources Characterization of fine particulate matter (PM2.5) and chemical composition of particles are important parameters for identifying sources and their contributions. Fine particles formed from combustion sources and photochemical reactions followed by gas to particle conversion mainly consist of carbonaceous compounds, sulfate, nitrate, and other trace metals. Most locations, including the Capital Region, receive PM2.5 from local (near-field) and regional (far-field) sources. Sources of ambient PM2.5 can be derived by statistical techniques from source oriented air quality models such as the U.S. Environmental Protection Agency’s positive matrix factorization (PMF) model. Methods Source apportionment method Receptor-oriented or receptor models can be used to identify emission sources and apportion the observed pollutant concentration to those sources (Hopke, 1991). These models are based on the assumptions that composition of the emission sources is constant over the period of sampling at the receptors and the chemical profiles are linearly independent of each other. The method is based on the analysis of the correlation between measured concentrations of chemical species in a number of samples, assuming that highly correlated compounds come from the same source. The method can thus be used to detect the hidden source information from ambient measurement datasets. The multivariate receptor model positive matrix factorization (PMF) was used to identify and apportion possible emission sources of PM2.5 using historical datasets at an Environment Canada National Air Pollution Surveillance (NAPS) chemical speciation air monitoring station in Edmonton (Edmonton McIntyre). A 5-year dataset (2010–2014) was used and it included results for 35 chemical components and 526 daily (24 h) samples for PMF analysis (four 24 h samples were excluded due to outliers). PMF is a multivariate technique based on a constrained, weighted least squares fit, where the weights are derived from analytical uncertainties (Paatero and Tapper; 1994, Paatero, 1997). In comparison to principal component analysis (PCA), PMF produces a better fit to the data and provides non-negative factors, error estimates and better data treatment including handling or adaptation for missing values and values below the detection limit (Paatero and Tapper, 1994). PMF has been widely used for receptor modeling of ambient fine particulate matter. The objective of a PMF model applied to an airborne PM data matrix, X of dimensions n by m (n is the number of samples and m is chemical species to be measured) is to resolve the number of source factors p, the species profile F (p x m) of each source, and the amount of mass G (n x p) contributed by each factor to each individual sample, which is based on the mass conservation principle as follows (Hopke, 1991):
ij
p
k kjikij efgx 1 Where: xij is the concentration at a receptor for the jth species measured in the ith sample gik is the contribution of the kth factor to the ith sample fkj is the mass fraction of the jth species from the kth factor eij is the residual for the jth species in the ith sample It is assumed that the contributions and mass fractions are all non-negative, i.e., results are constrained to physically possible solutions (Eatough et al., 2008). Moreover, each data point can be weighted individually e.g., by adjusting the uncertainty of measured values below the detection limit so that they have less influence on the solution than measurements above the detection limit. Based upon the uncertainties of each observation, PMF provides a solution that minimizes an object function, Q(E), as follows (Polissar et al., 1998):
34
21 1
1/)()(
n
i
m
jij
p
k kjikij ufgxEQ
Where: uij is the overall uncertainty for the jth species measured in the ith sample. In this study, EPA PMF program version 5.0 was used for PM2.5 source apportionment. EPA PMF operates in a robust mode, meaning that ‘outliers’ are not allowed to influence the fitting of the contributions and profiles. Uncertainty estimation is an important step in PMF, where each of the data values is assigned an estimated uncertainty including both measurement uncertainty and source profile variability. In this study, the approaches of Polissar et al. (1998) and U.S. EPA (2014) were adopted to estimate concentration values and their associated uncertainties including missing data and below detection level values. At the Edmonton McIntyre station, analytical uncertainty values of each PM2.5 component in each sampling day were not available. Therefore, uncertainty of each chemical component was assigned by estimating analytical uncertainty by multiplying the method detection limit (MDL) by 0.5 and adding sampling uncertainty for each component (set at 10% of the measured concentration) as follows:
0.5 The concentration values were used for the measured values and the sum of the analytical and sampling uncertainty was used as the overall uncertainty assigned to each measured value. It is general practice in PMF analysis that below detection level data values are replaced by ½ of the detection limit. However this censoring practice can (Brown et al., 2015): prevent error estimation features of the EPA PMF version 5.0 model, introduce hard-to-estimate bias, and occasionally give rise to ghost factors. Therefore, in this study no censoring was done for data below detection limit and original measured values were used as measured concentrations of each component. Uncertainties of below detection level data values were set to 5/6 of the MDL. Missing data values were replaced by the median of all measured concentrations of the given species, and their associated uncertainties were set at four times the median concentration after U.S. EPA (2014). Measured components with more than 80% of samples below MDL were not included for the PMF analysis. Chemical species included or excluded were selected using the signal to noise ratio (S/N) as described by Paatero and Hopke (2003). The model was run 20 times and a seed value of 30 was taken in order to replicate the results. To consider possible temporal changes in source profiles and other sources of variability, 10% extra modeling uncertainty was applied. All runs were converged and a global minimum was found. The optimal number of factors was chosen after analyzing several model performance criteria after Lee et al. (1999) – e.g., goodness-of-fit Q-values for the entire run, scaled residual matrices, scatter plots between species, agreement between predicted and measured mass, and physical meaningfulness of the source profiles and contributions. The plausibility and interpretability of solutions with six to eleven factors were checked and a 9 factor-solution was chosen. All runs converged and a global minimum was found. Qrobust equalled Qtrue, indicating no outliers impacting the results. Qexpected (number of non-weak data values in X) – (number of element in G and F, taken together) was calculated. If all species are perfectly accounted for by the computed factors and if all input uncertainties fully account for all true uncertainty, then the computed Qrobust should be equal or at least within a factor of 2 of Qexpected (Eberly, 2005).
35
In this study, the ratios of Qrobust and Qexpected were 1.57 indicating an overall acceptable fit of the model. FPEAK solutions were checked and the base run yielded the optimum solution. In this study, a constrained model was also run to better resolve some factors. Imposing constraints in PMF analysis relies on a priori knowledge about source composition and contribution. Known source composition such as levoglucosan and mannosan are typical tracers for biomass burning and these can be used to reduce the rotational space of the PMF solution (Paatero et al., 2002; Paatero and Hopke, 2009). Constraints were imposed for the factors using information about well-known tracers and their associated sources. Specifically, constraints were set for the following factors in the constrained model run: (1) levoglucosan and mannosan were set to zero in sources other than biomass burning; (2) arabitol was set to zero in all sources except for secondary organic aerosol (SOA) and biomass burning; and (3) EC and OC were pulled up maximally. To quantify precision of the PMF results (i.e., reproducibility of the solution), a bootstrapping analysis was implemented. A total of 100 bootstrap runs were performed with a minimum r2-value of 0.8. Results were generally stable with all 9 factors mapped to a base factor in 100% of runs except for the refinery (mapped on 85% of runs) and traffic (mapped on 91% of runs) factors. To understand uncertainty of the PMF solution including effects of random errors and rotational ambiguity, two additional error estimation methods were implemented – displacement (DISP) analysis and bootstrapping with displacement (BS-DISP). Details of these methods are described elsewhere (U.S. EPA, 2014). Verification of source assignments from PMF Model In environmental applications the goal of receptor modeling is to estimate number and composition of sources (i.e., the factors that explains the data variability), but also to point out any trend and/or correlation among observations and identify potential markers for pollutant sources. To verify different local and regional emission sources of PM2.5, a number of different statistical approaches were undertaken using methods described after Bari et al. (2015a). These included investigating relationship between sources using linear regression analysis, conditional probability function (CPF) analysis and backward trajectory analysis. Relationship between sources Linear regression analysis was carried out to investigate relationships between identified sources and combustion (NO2, SO2, O3, CO) and volatile organic (e.g., volatile organic compound) pollutants measured at Edmonton NAPS stations as well as meteorological parameters at Edmonton McIntyre station. This assisted in the interpretation of source profiles (i.e., chemical composition of the emissions). Correlation among identified sources was also evaluated. Conditional probability function (CPF) analysis To understand probable ‘local’ point source impacts, conditional probability function (CPF) was calculated using source contribution estimates from PMF coupled with wind direction values measured at the Edmonton McIntyre station (Ashbaugh et al., 1985; Kim and Hopke 2004). The daily fractional mass contribution from each source relative to the total of all sources was used rather than using the absolute source contributions. CPF is a common tool used to show wind directions that dominate a (specified) high concentration of a pollutant; showing the probability of such concentrations occurring by wind direction and it is defined as:
n
mCPF
Where m∆θ indicates wind frequency blowing from the direction of ∆θ in days with the concentration higher than a threshold criterion, and n∆θ is the total number of data from the same wind sector.
36
In this study, calm wind conditions with a wind speed less than 1 m/s (i.e. 3.6 km/h) were excluded from the calculation due to isotropic behavior of wind vanes under calm winds. ‘High’ concentrations were defined as those greater than the 75th percentile concentrations of all the observations in the study period. Therefore, the threshold criterion was set at the highest 25% of the concentrations to define the directionality of the sources, as commonly used in other studies (Bari et al., 2015a; Amato and Hopke, 2012; Jeong et al., 2011; Kim et al., 2004). An accepted assumption is that local sources are likely to be located in the directions that have high conditional probability values. Similarly to rose plots, CPF plots are usually shown as a graph in polar coordinates with the radial distance defined by the magnitude of the CPF value and the angle derived from the wind direction associated with ‘high’ concentrations of the pollutants. Backward trajectory analysis To better understand long-range transport from hypothesized distant upwind source regions, backward trajectory analysis was conducted using the National Oceanic and Atmospheric Administration (NOAA) Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model (Draxler and Rolph, 2003). Source data was the Global Data Assimilation System (GDAS) model accessed through the HYSPLIT web archive (http://ready.arl.noaa.gov/archives.php). The method used for trajectory analysis was based on the GIS-based software TrajStat developed by Wang et al (2009). Potential Source Contribution Function (PSCF) To study the most probable upwind distant source regions, analysis of potential source contribution function (PSCF) was performed. PSCF is a widely used common tool (Hopke et al., 1995; Begum et al., 2010; Pekney et al., 2006) to identify regional sources based on the HYSPLIT model. For PMF-derived source contributions, PSCF was calculated for every sample day using 72-hr backward trajectories starting at noon local time at a height of 500 m above the ground level. The geographic regions covered by the air trajectories are divided into an array of 0.5° x 0.5° latitude and longitude. PSCF values for the grid cells in the study domain are calculated by counting the trajectory segment endpoints that terminate within each cell (Ashbaugh et al., 1985). The PSCF value for the ijth cell is defined as:
where the number of endpoints that fall in the ijth cell is designated by , and the number of endpoints for the same cell when the corresponding samples show concentrations higher than an arbitrarily set criterion value is defined to be . Cells related to the high values of PSCF are indicative of areas of ‘high potential’ contribution for the source. In this study, the criterion value was set at the 75th percentiles of each factor, which means only the higher 25% of the contributions were used for defining upwind potential source regions. Polissar et al. (1999) stated it is likely that small values of may produce high PSCF values with high uncertainties. To reduce this effect, PSCF values were multiplied by an arbitrary weight function to better reflect uncertainty in the values for these cells (Polissar et al., 1999). The weighting function reduced PSCF values when the total number of the endpoints in a particular cell was less than about three times the average value of the end points per cell (PPC). In this case, was defined as below (after Polissar et al., 1999):
0.7 30.42 0.5
0.2 0.5
37
Concentration-weighted trajectory (CWT) method A limitation of the PSCF method is that grid cells can have the same PSCF value when sample concentrations are either only slightly higher or much higher than the criterion value. As a result, it can be difficult to distinguish moderate sources from strong ones. To overcome this limitation of PSCF, Concentration-weighted trajectory (CWT) method was used, where each grid cell was assigned a weighted concentration by averaging the sample concentrations that have associated trajectories crossing the grid cell as follows (after Seibert et al., 1994; Hsu et al., 2003):
1
τ
where is the average weighted concentration in the ijth cell, l is the index of the trajectory, M is the total number of trajectories, is the concentration observed on arrival of trajectory l, and is the time spent in the ijth cell by trajectory l. A high value for implies that air parcels traveling over the ijth cell would be, on average, associated with high concentrations at the receptor. In this study, CWT is a function of PMF-derived source contributions of different factors and the residence time of a trajectory arriving at the Edmonton McIntyre station in each grid cell. To reduce the effect of small values of , the arbitrary weight function as described above in PSCF was also used in the CWT analysis.
38
Results and Discussion Ambient levels of PM2.5 and chemical composition Ambient PM2.5 speciation data quality was assessed for validity and species selection for source apportionment analysis. Data status for all measured PM2.5 components (n=50) during the period of 2010–2014 is shown in the Supplemental Information, SI (Table S2.1). Only 35 components including ions and trace elements were selected for source apportionment analysis based on their higher data completeness i.e., at least 50% of the samples above the detection limit (DL) and source-specific tracers (e.g., levoglucosan and mannosan for biomass smoke). Table 2.1 shows descriptive statistics of PM2.5 and selected 35 components over the 5-year period (2010–2014). For descriptive statistics data below the minimum detection limit were replaced by half of the DL and non-detect or missing values were excluded from the dataset. The geometric mean concentration of PM2.5 in Edmonton McIntyre was 7.23 µg/m3 (median = 6.87 µg/m3, interquartile range, IQR = 4.93–10.35 µg/m3, range = 1.04–62.54 µg/m3). Concentrations measured in this station were comparable to other monitoring stations in Edmonton (median levels: central: 8.0 µg/m3, east 9.0 µg/m3, south 8.0 µg/m3), but lower than Calgary central (median: 10.0 µg/m3) for the same time period (CASA, 2015). The reconstructed mass calculated as the sum of major PM2.5 components has been used as a quality assurance tool for gravimetrically measured mass (Malm et al., 1994; Frank, 2006). PM2.5 components were grouped as ammonium sulfate-(NH4)2SO4, ammonium nitrate (NH4NO3), organic matter (OM), elemental carbon (EC), crustal matter, trace element oxides (TEO), sodium chloride (NaCl) and particle-bound water (PBW) and the reconstructed mass was calculated after Malm et al. (1994) as reported in Dabek-Zlotorzynska et al. (2011) (Table S2.2). To calculate OM from measured OC, several multiplying factor values (1.2–2.1) have been used (Malm et al., 1994; Turpin and Lim, 2001). In this study, OM was calculated using seasonal multiplying factors adopted from Dabek-Zlotorzynska et al. (2011). Figure 2.1 shows the relative proportions of major groups of PM2.5 components for the period of 2010–2014. The sum of major groups accounted for 90% of the gravimetrically measured PM2.5 mass and 10% of the mass was unaccounted. The most dominant component of PM2.5 at Edmonton was OM, contributing to 36% (3.16 µg/m3) of the PM2.5 mass. Concentrations of organic carbon (OC) ranged from 0.20 to 29.45 µg/m3 with a geometric mean of 1.18 µg/m3 (median 1.32 µg/m3). OM is a complex mixture of organic compounds originating from primary sources and secondary formation processes (Seinfeld and Pandis, 1998). Sources of OM can be attributed to secondary organic aerosols (SOA) formed from the emissions of fossil fuel combustion, traffic, biomass burning including forest fires and agricultural burning as well as biogenic volatile organic compounds (VOCs) (Hoyle et al., 2011). EC, a primary pollutant formed in combustion processes, ranged from 0.02 to 3.86 µg/m3 with a geometric mean of 0.43 µg/m3 (median 0.56 µg/m3) and contributed to 7.3% (0.64 µg/m3) of PM2.5 mass. The second most predominant component in PM2.5 mass was ammonium nitrate, which contributed 19.2% (1.69 µg/m3) to the PM2.5 mass. It was assumed that all nitrates are present as ammonium nitrate in western Canada. Nitrate (NO3
–) and ammonium (NH4+) concentrations ranged from 0.01 to 20.14 µg/m3
and 0.006 to 10.21 µg/m3 with geometric means of 0.31 µg/m3 (median 0.18 µg/m3) and 0.30 µg/m3 (0.29 µg/m3), respectively. Comparatively lower nitrate levels were found in a 1985–1995 study in Edmonton and Calgary with a median of 0.2 µg/m3 and maximum of 15.6 µg/m3, making up almost 5% of PM2.5 mass in both cities (Cheng et al. 1998). In the Edmonton Capital Region, contribution of ammonium nitrate to PM2.5 appears to come from emissions of ammonia and oxides of nitrogen (NOX) from agricultural activities and primary emissions such as vehicle exhaust or industry as well as influences from local oil refinery sources (AENV, 2008; Jeong et al., 2011).
39
Table 2.1. Descriptive statistics of PM2.5 and detected 34 chemical components (more than 50% of the samples) for 2010–2014.
Analytical method
% of samples >MDL
% of samples <MDL
Non-detect or missing
nd
N Units Gmean Min P10 P25 Median P75 P98 Max
PM2.5 Gravimetric 100 0 0 529 μg/m3 7.23 1.04 3.61 4.93 6.87 10.35 29.4 62.54 Organic carbon (OC) TOR 87 13 0 526 μg/m3 1.18 0.20 0.33 0.77 1.32 2.01 5.4 29.45 Elemental carbon (EC) TOR 92 8 0 526 μg/m3 0.43 0.02 0.11 0.30 0.56 0.89 2.0 3.86
Sulfate (SO42–) IC 100 0 0 529 μg/m3 0.59 0.02 0.20 0.31 0.63 1.07 3.7 14.75
Nitrate (NO3–) IC 89 10 1 529 μg/m3 0.31 0.01 0.04 0.09 0.18 1.03 12.2 20.14
Ammonium (NH4+) IC 100 0 0 529 μg/m3 0.30 0.006 0.07 0.13 0.29 0.68 4.3 10.21
Sodium (Na+) IC 56 44 0 529 μg/m3 0.03 0.002 0.006 0.02 0.03 0.06 0.25 0.36 Chloride (Cl–) IC 44 56 0 529 μg/m3 0.03 0.005 0.006 0.02 0.03 0.05 0.30 1.03
Potassium (K+) IC 82 18 0 529 μg/m3 0.03 0.003 0.01 0.01 0.03 0.05 0.13 0.25 Calcium (Ca+2) IC 82 18 0 529 μg/m3 0.04 0.001 0.01 0.02 0.05 0.08 0.21 0.92
Magnasium (Mg+2) IC 92 8 0 529 μg/m3 0.01 0.001 0.004 0.008 0.01 0.02 0.03 0.08 Floride (F–) IC 51 22 28 529 μg/m3 0.004 0.001 0.002 0.003 0.004 0.006 0.02 0.02
Formate IC 25 64 11 529 μg/m3 0.01 0.005 0.005 0.01 0.01 0.02 0.07 0.20 Oxalate IC 75 19 6 529 μg/m3 0.04 0.007 0.017 0.02 0.04 0.06 0.16 0.61
Levoglucosan GC-MS 96 0 4 529 ng/m3 40.38 1.75 9.95 17.93 39.74 84.19 404 1492 Mannosan GC-MS 59 11 30 529 ng/m3 8.89 0.94 2.84 3.84 8.31 16.84 79.83 347
Arabitol GC-MS 38 9 53 529 ng/m3 3.04 0.33 0.99 1.80 3.45 5.25 11.69 35.22 Aluminium (Al) ICP-MS 65 35 0 529 ng/m3 3.30 0.52 1.04 1.18 3.46 6.81 29.26 488 Arsenic (As) ICP-MS 98 2 0 529 ng/m3 0.17 0.01 0.06 0.10 0.16 0.27 0.91 12.21 Barium (Ba) ICP-MS 100 0 0 529 ng/m3 1.48 0.05 0.57 0.94 1.57 2.43 4.90 8.36
Cadmium (Cd) ICP-MS 73 27 1 529 ng/m3 0.05 0.007 0.02 0.02 0.05 0.09 0.29 1.15 Cobalt (Co) ICP-MS 61 38 1 529 ng/m3 0.04 0.005 0.01 0.02 0.03 0.08 1.17 4.49
Chromium (Cr) ICP-MS 65 34 1 529 ng/m3 0.43 0.10 0.16 0.21 0.44 0.80 2.57 8.97 Cupper (Cu) ICP-MS 87 13 0 529 ng/m3 1.44 0.26 0.52 0.96 1.53 2.30 5.18 26.84
Iron (Fe) ICP-MS 98 2 0 529 ng/m3 13.47 1.04 4.80 8.78 14.0 23.83 54.59 130 Manganes (Mn) ICP-MS 99 1 0 529 ng/m3 2.57 0.05 0.62 1.38 3.24 5.37 11.87 20.87
Molybdenum (Mo) ICP-MS 83 17 0 529 ng/m3 0.18 0.02 0.04 0.09 0.20 0.37 1.48 3.65 Nickel (Ni) ICP-MS 81 19 0 529 ng/m3 0.35 0.04 0.10 0.16 0.35 0.71 2.67 11.02 Lead (Pb) ICP-MS 84 16 0 529 ng/m3 0.35 0.05 0.10 0.20 0.34 0.58 2.86 10.44
Antimony (Sb) ICP-MS 99 1 0 529 ng/m3 0.17 0.01 0.08 0.12 0.16 0.25 0.66 1.46 Selenium (Se)* ICP-MS 23 72 5 529 ng/m3 0.07 0.02 0.05 0.05 0.05 0.07 0.34 0.46
Tin (Sn) ICP-MS 54 34 12 529 ng/m3 0.10 0.03 0.05 0.05 0.09 0.18 0.99 3.34 Stontium (Sr) ICP-MS 99 1 0 529 ng/m3 0.28 0.02 0.13 0.19 0.28 0.41 0.88 2.51 Titanium (Ti) ICP-MS 52 48 0 529 ng/m3 0.32 0.10 0.16 0.16 0.31 0.51 1.19 4.28 Vanadium (V) ICP-MS 63 37 0 529 ng/m3 0.08 0.02 0.02 0.04 0.06 0.17 2.37 5.86
Zinc (Zn) ICP-MS 90 10 0 529 ng/m3 5.26 0.52 1.09 2.75 5.61 11.07 39.71 103 *Se, Cl- and formate selected due to source specific tracers, P: percentile, MDL: method detection limit
40
Figure 2.1. Mean PM2.5 composition (μg/m3) at Edmonton McIntyre for 2010–2014. Another important contributor to PM2.5 mass was ammonium sulfates (1.18 µg/m3, 13.4%), which can be present in the atmosphere in the particulate form as secondary sulphate consisting of sulfuric acid, ammonium bisulfate and ammonium sulfate (Ansari and Pandis, 1998) and can remain airborne for hundreds of kilometres (Tuncel et al., 1985). The geometric mean concentration of sulfate (SO4
2–) was 0.59 µg/m3 (median 0.63 µg/m3, range = 0.02–14.75 µg/m3). In Alberta, background regional sulphate is found in high abundance due to oil and gas production (e.g., natural and sour gas extraction, flaring and processing), other industrial emissions like coal- and gas-fired industrial boilers for power generation and other non-specific industrial sources (Schulz and Kindzierski, 2001). SO4
2– was also found as the abundant mass fraction of ambient fine particles (~11% of PM2.5 mass) in Edmonton (median 1.0 µg/m3, range 0.01–11.14 µg/m3) and Calgary (median 1.0 µg/m3, range 0.1–16.20 µg/m3) during the study period of 1985–1995 (Cheng et al., 1998). Crustal matter (i.e. soil) contributed only 2.3% (0.20 µg/m3) of the PM2.5 mass. Soil components are presumably emitted from windblown dust sources (soil and road dust resuspension, construction and agricultural activities) in spring and summer months. A low inferred contribution of windblown dust would be expected in winter due to a greater predominance of light winds, and presence of snow cover and/or wet/frozen ground conditions. The contribution of NaCl was 1.1% (0.10 µg/m3) of the PM2.5 mass and road salt represents a likely source during winter months. Concentrations of TEO that normally occur at very low levels in the environment were found in less abundance accounting for 0.2 % (0.02 µg/m3) of the PM2.5 mass. Concentrations of potential carcinogenic elements – e.g., As (geomean 0.17 ng/m3) and Ni (geomean 0.35 ng/m3) were far below Alberta Ambient Air Quality Objectives and Guidelines (10 ng/m3 for As and 50 ng/m3 for Ni, AERSD, 2013). On average, PBW accounted for 10.8% (0.95 µg/m3) of the PM2.5 mass in Edmonton, which was higher than values found in the earlier study by Cheng et al. (1998) (~6% of PM2.5 mass).
Organic matter (OM)
Elemental carbon (EC)
Ammonium sulfate
Ammonium nitrate
Crustal matter0.20 μg/m3, 2.3%
NaCl 0.10 μg/m3, 1.1%
Trace element oxides (TEO)
0.02 μg/m3, 0.2%
Particle-boud water (PBW)
Unaccounted
3.16 μg/m3, 36%
41
Median concentrations of K+, levoglucosan and mannosan (typical markers for biomass burning) were 0.03 µg/m3 (range 0.003–0.25 µg/m3), 39.74 ng/m3 (range 1.75–1,492 ng/m3 and 8.31 ng/m3 (range 0.94–347 ng/m3), respectively and are likely associated with biomass smoke-related sources including winter time wood burning fireplaces, open pit camp fires and barbeque as well as wild fires in Alberta and nearby provinces. Identification and apportionment of PM2.5 sources: PMF analysis A 9-factor solution was chosen to represent ambient PM2.5 sources at Edmonton McIntyre station using EPA PMF modeling for the time period of 2010–2014. Model input data statistics, performance criteria and error estimates are shown in the SI (Tables S2.3 to S2.5). Figure 2.2 shows source profiles – i.e., chemical composition of emission sources (concentration and percentage of species apportioned to each factor) of each of the identified source factors from the base run and the bootstrap runs. Time series plots of daily contributions (in µg/m3) of individual PMF-derived source factors in the constrained PMF run are displayed in Figure 2.3. The average percent mass contribution of each source factor to measured PM2.5 mass concentrations from the base model run is shown in Table 2.2. Results of a constrained model run are also presented in Table 2.2. Constraints were set for the following factors in the constrained model run: (1) levoglucosan and mannosan were set to zero in sources other than biomass burning; (2) arabitol was set to zero in all sources except for secondary organic aerosol (SOA) and biomass burning; and (3) EC and OC were pulled up maximally, which increased EC from 0.29% to 7.8% and OC from 0% to 4.0% of their total mass, respectively in the traffic factor. Figure 2.4 and 2.5 display seasonal and annual source contributions from constrained PMF analysis. Pearson correlations of PMF-derived sources with other criteria pollutants and meteorological parameters, and with volatile organic compounds (VOCs) are shown in Table 2.3 and Table 2.4. Conditional probability function (CPF) plots of all identified sources, and spatial distribution (by season) of values of potential source contribution function (PSCF) and concentration-weighted trajectory (CWT) for the highest 25% of the contributions of long-range sources are shown in Figure 2.6 to Figure 2.14. Factor 1: Secondary organic aerosol (SOA) – Factor 1 was characterized by high abundances of EC, OC, oxalate, and arabitol accounting for 98%, 57%, 48% and 47% of their total mass concentrations, respectively. This factor was interpreted as secondary organic aerosol (SOA) and it was the largest source at Edmonton McIntyre station, contributing 29.8% (2.43 µg/m3) to the PM2.5 mass concentration on average. Approximately similar contribution (29.1%, 2.37 µg/m3) was found in the constrained model run. Oxalate can be emitted from several primary and secondary emission sources such as traffic exhaust, fuel oil combustion, biomass burning and biogenic sources (Chebbi and Carlier, 1996), while arabitol is considered as a tracer for biogenic aerosols (Burshtein et al., 2011). In this factor, presence of OC, EC, dicarboxylic acids (i.e., oxalate) and sugar (i.e., arabitol) are suggestive of the formation of SOA. SOA formation is largely enhanced during summer months due to more favourable conditions for gas/particle conversion of VOCs as a result of photochemical activity (Turpin and Huntzicker, 1995). SOA can be linked to various local and long-range transport of anthropogenic and natural emissions such as traffic exhaust, fossil fuel combustion primarily due to evaporation during fossil fuel extraction, processing and transportation, biomass burning and biogenic emissions (Hoyle et al., 2011; Skyllakou et al., 2014). Studies indicate that primary emission sources contain large amounts of semi-volatile organic aerosols, which may rapidly oxidize into SOA in the atmosphere (Robinson et al., 2007; de Gouw et al., 2011). Notable correlations of this factor were found with NO2, CO, THC, and some VOCs such as isoprene and monoterpenes (p-cymene, α-pinene, d-limonene) as well as with anthropogenic VOCs like BTEX (benzene, toluene, ethylbenzene, xylene) and other aromatic hydrocarbons, and alkanes (Tables 2.3, 2.4). This suggests likely influences of biogenic emissions (distant-rural) and local sources such as vehicular exhaust and industrial activities. A positive correlation of was also found with temperature.
42
Time series of daily contributions and seasonal variation plots (Figures 2.3, 2.4a, S2.4) showed higher contributions in summer (3.0 µg/m3) followed by in fall (2.49 µg/m3) and winter (2.44 µg/m3). Several peak concentrations were found in summer, indicating the influence of biogenic emissions and contribution of biomass burning from wildfire smoke. In winter lower emissions of biogenic SOA precursors (e.g., isoprene, monoterpenes) are expected due to lower photochemistry compared to summer. However, the observed SOA contribution during winter may be associated with residential biomass-fired heating (wood fireplace burning) as well as vehicular traffic and industrial emissions. Annual variation of this factor revealed that the highest contribution occurred in 2010, which tended to be associated with extreme biomass burning activities including wildfires in August 2010 and winter heating as well as traffic and industrial activities (Figure 2.5). A CPF plot of this factor showed high contributions at Edmonton McIntrye station when winds originated from south, west and east directions (Figure 2.6). Seasonal PSCF plots (Figure 2.7) and CWT plots (Figure 2.8) indicated that the contribution of SOA was associated with regional sources located in northern and southern Alberta, British Columbia and Saskatchewan. These results agree well with a previous source apportionment study conducted in Edmonton for a shorter time period – May 2006 to Jan 2008 (Jeong et al., 2011), where the SOA factor was attributed to potential biogenic sources in boreal forest regions. Another notable observation made is the presence of a preferred trajectory path aligned with the Yellowhead transportation corridor west of Edmonton and an area southwest of Edmonton during winter (possible residential biomass-fired heating (wood fireplace burning)) that is associated with elevated levels of PM2.5 for the SOA factor (winter PSCF plot, Figure 2.7 and winter CWT plot, Figure 2.8). In this study, contributions of traffic exhaust, biomass burning and industrial emissions to SOA in PM2.5 were not able to be quantified further. Additional source apportionment study would be needed to better identify and apportion sources of SOA in Edmonton, particularly during winter using molecular markers. Role of coal combustion – SOA was the largest factor contributing to PM2.5 at Edmonton McIntyre station. Atmospheric oxidation of precursor chemical (VOCs) from a variety of sources and/or emissions from other activities have been shown to lead to formation of SOA in controlled experimental chamber studies. For example, chamber studies simulating biomass burning leads to significant amounts of SOA formation (Robinson et al., 2007). In addition, chamber studies simulating oxidation of: biogenic VOCs (e.g., isoprene, monoterpenes, and sesquiterpenes (C5H24 terpenes)) (Hoffmann et al., 1997), precursor VOCs emitted from anthropogenic sources including alkanes (Lim and Ziemann, 2005), light aromatics (e.g., m-xylene, toluene, benzene (Ng et al., 2007), naphthalene (Chan et al., 2009) and low-volatility compounds emitted from diesel combustion (Robinson et al., 2007) have also been shown to lead to SOA formation. Coal combustion plumes contain higher amounts of reactive nitrogen (e.g., NOX and NO3) relative to background (Peltier et al., 2007; Zaveri et al., 2010). A reactive nitrogen–VOC oxidation pathway has been proposed by others based on controlled experimental chamber studies (e.g., Fry et al., 2009, 2011; Rollins et al., 2009) and computer simulation models (e.g., Pye et al., 2010) that leads to enhanced SOA production from potential interaction of anthropogenic plume components with SOA precursors (VOCs) in the atmosphere. Consequently, we further investigated the potential for SOA production downwind of anthropogenic, including coal combustion, plumes in published literature. Zaveri et al. (2010) analyzed O3, NOX and other reactive nitrogen species measurements in four segments of a single coal combustion plume during night time in New England during 2002. They observed that concentrations of particulate organics, nitrate and sulfate aerosol were about 25%, 100% and 100% higher, respectively relative to respective background values at plume peak and dissipated beyond peak values. In−plume particles were inferred to be acidic based on NH4
+/SO42– molar ratios of
less than two. Further computer modeling predicted enhanced equilibrium nitric acid (HNO3) concentrations over these particles and, as a result, Zaveri et al. (2010) suggested that the observed particulate nitrate was likely in the form of organic nitrate as opposed to an inorganic nitrate ion form.
43
Particulate organic and nitrate aerosols in the plume were further suggested by Zaveri et al. (2010) to be SOA based on their model results. Tanner et al. (2009) examining hourly speciated fine particle data at three air monitoring stations in southeastern U.S. for evidence of ambient aerosol acidity–catalyzed SOA formation indicated by larger increases in concentrations of organic aerosol mass occurring on days and in locations where more acidic aerosol (lower NH4
+/SO42- molar ratios) existed. Datasets over the period 2003–2004 were
examined for which hourly acidity of PM2.5 aerosols could be estimated, and for which hourly organic carbon (OC) content had been measured simultaneously. Tanner et al. (2009) was unable to observe consistent evidence of an ambient aerosol acidity–catalyzed SOA formation pathway. No positive change in OC concentration as a function of acidity was found at one site; whereas at another site no positive change in OC and total carbon concentration as a function of acidity was found, even though daytime increases in acidity (i.e., decreases in NH4
+/SO42- molar ratios) were observed. At the third site, except for
a subset of daytime summer data, only small changes in OC concentration over 6 h were observed as a function of acidity, averaging –6.6%, 1.6%, and –2.2% for day, night, and mixed 6-h periods, respectively, for an average OC concentration of 3.6 µg/m3. Measurements of the water-soluble fraction of fine PM organic carbon (WSOC) have been used to estimate SOA from aerosol samples collected in different environments (Snyder et al., 2009). Peltier et al. (2007) made aircraft-based measurements of WSOC and inorganic salt composition in five different coal combustion plumes in metropolitan Atlanta and the surrounding region in the summer of 2004. Biogenic and anthropogenic VOCs were spatially variable (i.e., large standard deviations for each VOC). For example, the median concentration of toluene, a representative of anthropogenic VOCs that has the potential for SOA formation, was 71 pptv within plumes and 62 pptv outside of plumes. Median biogenic VOCs (isoprene + methacrolein + methyl vinyl ketone) were ~1.1 ppbv within plumes and 0.94 ppbv outside of plumes. VOCs both in and outside the plumes were of sufficient concentration that there should be secondary semi-volatile organic carbon (SVOC) available for SOA formation. However, lack of WSOC enhancement inside the plumes relative to outside the plumes indicated that formation of SOA via the reactive nitrogen–VOC oxidation pathway was insignificant. They observed that more acidic conditions in freshly emitted coal combustion plumes with NH4
+/SO42– low molar ratios in the range of ~0.9– 1.4
(compared to molar ratios near 2 outside of the plumes) did not lead to substantial increases in the water-soluble organic component (i.e., SOA) of ambient particles. Sulfate aerosol concentrations increased from a regional background of 5–8 μg/m3 to as high as 19.5 μg/m3 within plumes. Their measurements suggest that SOA formation via the reactive nitrogen–VOC oxidation pathway within coal combustion plumes are not likely a significant contributor to ambient aerosol (SOA) mass loading. Because the region is rich in both biogenic and anthropogenic VOCs, Peltier et al. (2007) indicated that their results may be widely applicable elsewhere. Similar to Zaveri et al. (2010), Brown et al. (2011) used an aircraft to take nighttime measurements off multiple plumes downwind of several source regions in Texas, with a focus on Houston as part of the 2006 Texas Air Quality Study (TexAQS) to quantify loss rates and budgets for NO3 and highly reactive VOCs. The aircraft measurements complemented those from surface sites and their measurement data provided a regional view of transport and nighttime transformation of reactive NOX and VOC emissions from urban (Houston) and adjacent rural areas. Brown et al. (2011) observed that downwind aircraft measurements had elevated aromatic VOC and ethene levels throughout the broader plumes, but no significant enhancements associated with the largest NO3 reactivity. They also observed no significant enhancements in fine particle volume (mass) within these plumes, indicating that the increased NO3 reactivity was not attributable to heterogeneous reactions of N2O5, and that the reactive NO3–VOC oxidation pathway suggested from experimental chamber studies did not lead to large SOA production. Finally, Kim et al. (2014) applied a plume-in-grid Eulerian model to power plant and refinery source emissions in the Paris region during July 2009 to study potential secondary aerosol formation. Model results indicated that the formation potential of SOA was small, consistent with that observed by Peltier et al. (2007) and Brown et al. (2011), and was mostly from the effect of different oxidant concentrations on biogenic VOC (monoterpenes).
44
While controlled experimental chamber studies suggest a potential reactive nitrogen–VOC oxidation pathway for enhanced SOA production from the interaction of anthropogenic plumes with SOA precursor chemicals (VOCs) in the atmosphere, in-plume measurements (Peltier et al., 2007; Brown et al., 2011) have not observed this. Further, Peltier et al. (2007) stated that their results may be widely applicable elsewhere in regions rich in both biogenic and anthropogenic VOCs. It is plausible to apply the findings of Peltier et al. (2007) and Brown et al. (2011), i.e., low potential for SOA formation in coal combustion plumes, to the Edmonton and surrounding region as background biogenic and anthropogenic VOCs are known to exist throughout rural Alberta and in the Edmonton urban area in winter and summer. For example, You et al. (2008) reported on the characteristics of atmospheric VOCs measured at rural fixed locations throughout Western Canada, including rural Alberta, northeastern British Columbia and central and southern Saskatchewan between April 2001 and December 2002. Multivariate analysis (PCA) was used to group VOCs into three source factors. One factor was a group of biogenic-related monoterpenes and dichlorobenzenes and linear mixed effects model analysis showed seasonal variation with minima in winter and maxima in summer for these compounds, consistent with vegetation emission behavior. A second factor was characterized by high levels of aromatics, e.g., BTEX (benzene, toluene, ethylbenzene, xylenes) and hexane, showing seasonal variation with maxima in winter and minima in summer and was inferred to originate from oil and gas extraction and production activities throughout the monitored areas. A third factor was a group of chlorinated VOC and did not show a clear seasonal pattern. Further, anthropogenic VOCs are common in Edmonton’s air historically (Cheng et al., 1997) and recently in 24-h outdoor air samples measured in the back yards of 50 non-smoking homes within Edmonton in both winter and summer of 2010 (Bari et al., 2015b). Ten outdoor factors were identified using PMF model with oil and gas industry activities, traffic emissions, background and biogenic emissions found as major VOC sources in Edmonton. The potential for SOA formation in coal combustion plumes is considered to be small or unimportant based on our review of published literature. Consequently coal combustion emissions are not considered an important source of SOA identified in this study. Backward trajectory analysis supports these published studies as it identified other plausible local and long range SOA sources. These include local sources such as vehicle exhaust and industrial activities, and distant sources such as the Yellowhead transportation corridor west of Edmonton, biogenic (rural) emissions, biomass burning (wildfire smoke), and residential wood fireplace burning. Factor 2: Secondary sulfate – Factor 2 was identified by high concentrations of SO4
2–, NH4+ and oxalate
representing 80%, 44% and 36% of their total mass, respectively with some amounts of OC, formate, and K+ (7% to 20% of their total mass). A small contribution of tracer elements typically associated with coal combustion – such as Se, As, Cd, Pb, and Sn (13% to 42% of their total mass) – were also present in this factor. This factor was assigned to secondary sulfate and it was the second largest source at Edmonton McIntyre station, accounting for 21.9% (1.78 µg/m3) and 21.5% (1.75 µg/m3) of the PM2.5 mass concentration in base and constrained runs, respectively. This factor is interpreted to be related to Alberta’s background regional sulfate that is found in high abundance due to oil and gas extraction and production activities (Figures S2.1, S2.2), other industrial processes like gas- and coal-fired industrial boilers, power plants and other non-specific industrial sources (Schulz and Kindzierski, 2001; Environment Canada, 2014). In general, the secondary sulfate contribution tends to be enhanced in summer months due to increased photochemical oxidation of SO2 from local and regional sources as found in eastern Canadian cities (Jeong et al., 2011). However, in this study daily contributions and seasonal variation plots (Figures 2.3, 2.4a) of this factor showed higher contributions in spring followed by winter months rather than in summer. This can be explained by differences in winter time conditions. Unlike other major Canadian cities (e.g., Toronto, Montreal), Edmonton experiences longer winters with stable weather conditions coupled with early morning ground-based temperature inversions and low mixing layer heights (Myrick et al., 1994) that can lead to accumulation of pollutants resulting in higher ground levels in winter than in summer. Also use of natural gas for residential/commercial space heating in Edmonton is more common and also contributes to elevated secondary sulfate contributions during winter and spring months.
45
This factor showed positive correlations with relative humidity and ozone and negative correlation with temperature suggesting that dominant heterogeneous oxidation of SO2 from local sources and regional transport of emissions from oil and gas activities may be apparent during winter months. No year-to-year variation of this factor was observed (Figure 2.5). A similar high ‘spring pattern’ of the secondary sulfate factor was found in Golden, British Columbia during a 2004–2006 study by Jeong et al., (2008). It is noteworthy that the secondary sulfate contribution to PM2.5 was much lower in Edmonton (less than 22%) compared to levels found in other urban areas in eastern Canada (33%–47%) (Lee et al., 2003; Jeong et al., 2011; Gibson et al., 2013). In the previous study conducted in May 2006 to Jan 2008 (Jeong et al., 2011), an approximately similar contribution of secondary sulfate was found in Edmonton (1.5 µg/m3, 19% of the PM2.5 mass). A CPF plot of this factor showed high local contributions at Edmonton McIntyre station when winds originated from east and southeast directions (Figure 2.6). The presence of Strathcona industries and Alberta’s industrial heartland located east and north/northeast of Edmonton McIntyre station where numerous petrochemical plants, upgraders and refineries exist. The presence of local secondary sulfate sources to the southeast of Edmonton McIntyre station within 1-hr travel time is not apparent. A minor local contribution of this factor could be also found when winds blew from northwest (Figure 2.6) indicating the possible influence from a coal-fired cement kiln located in northwest Edmonton. A positive correlation of the sulfate factor with VOCs such as alkanes (Table 2.4) also suggest an influence of local contributions from oil and gas-related industries in the greater Edmonton area. Backward trajectory analysis (seasonal PSCF and CWT plots, Figures 2.9 and 2.10) for this factor indicated potential source regions to the south in Alberta, and to a lesser extent from southern Saskatchewan, and also northern Alberta in the Athabasca Oil Sands Region, as well as from U.S. regions e.g., eastern Washington, Ohio, Indiana, West Virginia and North Dakota. For Alberta locations, background regional sulfate that is found in high abundance due to oil and gas extraction and production activities (Figures S2.1, S2.2) is a plausible source. The other potential source areas are also consistent with long range source regions reported by Jeong et al. (2011) for the Edmonton McIntyre station. Role of coal combustion – Only a small contribution to secondary sulfate from the immediate west Edmonton region where coal combustion sources are located can be observed from seasonal PSCF and CWT plots. Figure 2.9 (seasonal PSCF plots) indicates that the Wabamun Lake area is not a trajectory path associated with elevated levels of PM2.5 for the secondary sulfate factor. In addition, Figure 2.10 (seasonal CWT plots) only shows low values for grid cells immediately west of Edmonton indicating that air parcels traveling over these grid cells would be, on average, associated with lower concentrations of PM2.5 for the secondary sulfate factor at Edmonton McIntyre station compared to air parcels traveling over numerous other grid cell areas indicated in the plots. Presence of local industrial sources and backward trajectory (long-range) analysis suggest other potential source locations are important for the secondary sulfate contribution to PM2.5 at Edmonton McIntyre station. While the analysis undertaken in this study is insufficient to accurately quantify the contribution from coal combustion sources, their contribution is projected to be in the range of less than one-tenth to less than one-fifth of the secondary sulfate mass. This is consistent with a small contribution of tracer elements typically associated with coal combustion – such as Se, As, Cd, Pb, and Sn – observed with this factor. Factor 3: Secondary nitrate – Factor 3 was represented by high concentrations of NO3
–, NH4+ and Cl–
explaining 85%, 53% and 15% of the variation, respectively. This factor was interpreted as secondary nitrate and it was the third largest source in Edmonton, contributing 16.2% (1.32 µg/m3) to the PM2.5 mass concentration on average. The constrained model run indicated similar contribution (16.4%, 1.34 µg/m3) to the PM2.5 mass concentration. Typically, secondary nitrate formation in the atmosphere depends on emissions of its precursor gases (i.e., oxides of nitrogen (NOX) and ammonia (NH3)), temperature, relative humidity, hydroxyl radical
46
(OH-)/radiation and nighttime chemistry reactions via NO3 (Seinfeld and Pandis, 2006). Semi-volatile ammonium nitrate (NH4NO3) is formed from oxidation of NOX to nitric acid (HNO3) and by reversible phase equilibrium with NH3 and HNO3 (Pio and Harrison, 1987). In this study, the secondary nitrate factor showed strong seasonality with highest concentrations in winter which would primarily be due to thermal instability and increased partitioning of ammonium nitrate into particle phases favored by lower wintertime temperatures and higher relative humidity (Seinfeld and Pandis, 1998; Mozurkewich, 1993). Correlation analysis supported this finding with positive associations of this factor with relative humidity and negative correlation with ambient temperature (Table 2.3). Notable correlations of secondary nitrate factor with NO2, CO, THC and some VOCs such as BTEX (benzene, toluene, ethylbenzene, xylene) and other aromatic hydrocarbons (e.g., ethyltoluene isomers, trimethylbenzene isomers) as well as with alkanes were observed (Table 2.3, 2.4), suggesting the strong influence of local sources such as vehicular exhaust and industrial activities. Figure 2.6 (CFP plot) revealed northeast, south and southeast as dominant directions for local sources, indicating contribution of vehicular exhaust from local roads in the south and petroleum related sources (e.g., refineries, petrochemical plants) in the northeast of Edmonton. Seasonal PSCF plots (Figure 2.11) and CWT plots (Figure 2.12) implied long-range contribution from the west Yellowhead transportation corridor and from sources located to the south in Alberta; and from western Washington and southern Saskatchewan locations again consistent with previous findings reported by Jeong et al. (2011). Role of coal combustion – Coal combustion can be a large emitter of NOX (precursor of secondary nitrate). Figure 2.11 (seasonal PSCF plots) indicates that the Wabamun Lake area is not the only trajectory path associated with elevated levels of PM2.5 for secondary nitrate. Figure 2.12 (seasonal CWT plots) show high values for the grid cells immediately west and also south of Edmonton. Both of these figures imply a contribution from the area where coal combustion sources are located. However, both of these figures also indicate other important regional precursor sources of secondary nitrate are located in Alberta south of Edmonton, northwestern British Columbia and southern Saskatchewan influencing Edmonton McIntyre station. Plausible explanations for some of these regional sources include oil and gas extraction and production activities (NOX emissions) (Figures S2.1, S2.2) and animal production operations located to the northwest and south of Edmonton thru to the Alberta-Montana border and elsewhere (Figure S2.3) (NH3 emissions) (Chai et al., 2014; AENV, 2008). The possible presence of local sources based on CPF analysis and the backward trajectory (long-range) analysis support that coal combustion sources west of Edmonton do not dominate the contribution to PM2.5 for the secondary nitrate factor at Edmonton McIntyre station. Levels of both secondary nitrate and sulfate particles tend to be simultaneously enhanced within plumes from coal combustion emissions relative to background (Zaveri et al., 2010). Again, while the analysis undertaken here is insufficient to accurately quantify the contribution to secondary nitrate from coal combustion sources west of Edmonton, their contribution is projected to be in the range of less than one-tenth to less than one-fifth of the secondary nitrate mass. Factor 4: Soil – Factor 4 was distinguished by high levels of typical soil components – i.e., Ca2+, Sr, Mg2+ and Al – representing 80%, 54%, 51% and 44% of the explained variation, respectively. Some mass fractions of Ti, Fe, Fl–, Ba, Pb, Se, Ni, and Sb (10% to 26% of their total mass) were also contained in this factor. This factor was interpreted as soil and it contributed 11.5% (0.94 µg/m3) to the total measured PM2.5 mass concentration. After adjusting levoglucosan, mannoson and arabitol (set to zero) in the constrained PMF analysis, a lower contribution of this soil factor to PM2.5 (9.9%, 0.80 µg/m3) was found. A spring-high pattern was observed. A positive correlation of this factor with ambient temperature and wind speed was found. Soil elements are assumed to be emitted from windblown dust sources (e.g., soil and road dust resuspension) in late spring, summer and fall. Normally a low inferred contribution of windblown dust would be expected in winter due to a greater predominance of light winds, and presence of snow cover and/or wet/frozen ground conditions. However, the observed abundance of soil contribution during March to May is hypothesized to be attributed to spring dust episodes during the sampling days.
47
As Alberta is a prairie province, agricultural operations are common around the greater Edmonton. Ba can be linked to fertilizers and soil amendments (Raven and Loeppert 1997), while Se is typically associated with surface coal mining activities in west of Edmonton (Goodarzi and Sanei 2002). Ti, Fe, Ba, and Sb can be emitted from resuspended road dust from traffic-related emissions (Lough et al., 2005), while Pb and Ni are known markers related to industrial emissions. It is likely that this factor is associated with mixed soil dust or fugitive dust sources derived from windblown resuspension of traffic-related dust on paved and unpaved roads, industrial materials handling, construction and demolition as well as agricultural activities (Watson et al., 2000; Chow et al., 1994). This is plausible as the city of Edmonton is influenced by different construction activities and is surrounded by a number of industries and agricultural farming lands (Cheng et al., 1998). However, correlation analysis indicated that this factor was negatively correlated with traffic-related gaseous pollutants like NO2, CO and positively correlated with O3, while no association was found with any VOC species. The CPF plot of this factor showed dominant wind directions of west, northwest and southeast where the highest concentrations were observed at Edmonton. The presence of a cement kiln and fugitive dust from agricultural activities in the northwest and coal-strip mining operations in the west of Edmonton are likely associated with this soil factor. Factor 5: Traffic – Factor 5 was characterized by high concentrations of Ba, Sb, Cu and As explaining 56%, 48%, 30% and 29% of the variation, respectively. Some fractions of EC, OC, Ti, Zn, Co, Mo, Pb, Sn, Fl–, formate and oxalate (10% to 26% of the explained variation) were also present in this factor. These components are associated with traffic-related emissions (Thurston and Spengler, 1985; Sternbeck et al., 2002; Lough et al., 2005; Bari et al., 2009; Jeong et al., 2011). Ba, Sb, Cu are linked to non-exhaust emissions such as brake and tyre wear (Lough et al., 2005; Sternbeck et al.,2002; Dietl et al., 1997), while Ba can be also emitted from diesel vehicles (Lee and Hopke, 2006). Significant correlations were found between Factor 5 and traffic-related gaseous pollutants – NO2, CO, and THC. Factor 5 was also well correlated with some VOCs, particularly species associated with diesel vehicle emissions such, ethyltoluene isomers, xylene and trimethylbenzene isomers, higher alkanes (e.g, nonane, decane) as well as with species related to gasoline vehicles emissions such as 1,3-butadiene, benzene, toluene and alkenes (Schauer et al., 1999; Watson et al., 2001; Bari et al., 2015b). Therefore, Factor 5 was interpreted as exhaust and non-exhaust emissions of traffic and the contribution of this factor was 6.6% (0.53 µg/m3) to total measured PM2.5 mass concentration. A higher contribution (8.7%, 0.71 µg/m3) was found in the constrained model run compared to the base run. Contributions of the traffic factor showed notable seasonality with fall and winter-high patterns, which may be related to more-frequent occurrence of stable atmospheric conditions (low temperature and wind speed, surface inversions with low mixing layer heights), cold-start emissions and increased car idling during winter. A positive correlation of this factor with ambient temperature and negative correlations with wind speed and relative humidity were also observed. It is noteworthy that the presence of minor fractions of oxalate and formate and positive association of this factor with temperature might suggest that traffic factor was influenced by formation of SOA. The CFP plot for this traffic factor indicated the west and southwest as the dominant directions, suggesting the influence of local highways (Figure 2.6). This is consistent with results found in previous source apportionment studies conducted in Edmonton for PM2.5 and VOCs (Jeong et al., 2011; McCarthy et al., 2013; Bari et al., 2015b). Factor 6: Biomass burning – Factor 6 was identified as biomass burning and was represented by high concentrations of tracer compounds for biomass smoke – i.e., levoglucosan and mannosan (Hornig et al., 1985; Jordan et al., 2006; Bari et al., 2009) – which explained 74% and 78% of the variation, respectively. Other smoke tracers such as K+, OC, formate, Cd and Zn (representing from 14% to 24% of their total mass) were also contained in this factor. These species are typically emitted from biomass smoke-related
48
sources, for example wood burning for winter heating, brush burning and biomass smoke from forest fires (Khalil and Rasmussen, 2003; Boman et al., 2004; Hays et al. 2005). On average this factor contributed 6.9% (0.56 µg/m3) to measured PM2.5 mass concentration. Approximately similar contribution (7.3%, 0.59 µg/m3) was identified in the constrained model run. Contributions of this factor exhibited significant positive correlation with NO2, THC, CO, negative correlation with temperature and moderate associations with some VOCs such as BTEX, ethane, propane, acetylene, 1-butene and 1,3-butadiene, consistent with previous findings by Jeong et al. (2011). A high winter-trend of this factor was observed (Figure 2.4) suggesting an influence of agricultural slash burning and/or residential heating using wood stoves/fire places during winter months. For example, agricultural slash burning by farmers (cutting, clearing and burning of trees and plants in forests or woodlands) occurs in the Parkland and Leduc County year round. Many urban and semi-urban areas of Canada and northern United States have households increasingly using wood burning as an alternate method for domestic heating because of energy costs (Zelikoff et al., 2002; Xue and Wakelin, 2006). Wood is burned regularly in United States in ~30 million homes and residential wood combustion is responsible for an estimated 9% of national space heating energy requirements (Houck et al., 1998). It is estimated that ~400,000 homes in Canada use wood as a primary heating fuel, and many others use fireplaces and wood stoves as supplementary sources of heat or for esthetics (Xue and Wakelin, 2006). In many areas of North America, including major cities, residential wood burning is a noteworthy wintertime source of ambient PM2.5 (Polissar et al., 2001; Fine et al., 2002; Maykut et al., 2003; Kim et al., 2003). Wood-burning fireplaces are in common use among residences in rural and small communities in the Edmonton Capital Region. For example, the Paul Band First Nations (population ~1,700) is situated on the southeastern edge of Lake Wabamun and many Paul Band First Nations residents heavily rely on wood burning for home heating purposes during winter (Swain 2014, personal communication). The CPF plot of this factor showed dominant wind directions from north (e.g. Legal, Morinville), west/northwest (e.g., Parkland Country) and south (e.g., Devon, Leduc County), where the highest concentrations were observed at Edmonton. From the time series plot of this factor (Figure 2.3), several peak concentrations were clearly observed during summer months, suggesting the influence of long range transport of smoke from wildfires. Several wildfires episodes occurred in Alberta and nearby provinces such as British Columbia, Saskatchewan and Manitoba during the period 2010–2014 (e.g., August 18-20, 2010, May 15-31, 2011). The seasonal PSCF and CWT analysis indicated the influence of long-range transport of smoke particles coming from British Columbia, northern Alberta and Saskatchewan. For example, high CWT values (Figure 2.14) were found in the northwest during spring and in the west during summer reflecting major wildfire smoke intrusion in Edmonton from Slave Lake, Alberta on May 15-31, 2011 and from British Columbia on August 18–20, 2010, respectively. Factor 7: Road-salt – Factor 7 was identified by high concentrations of Na+ and Cl– representing 86% and 73% of the explained variation. The time series plot of daily contributions for this factor (Figure 2.3) showed a strong seasonality with a winter-high (winter/summer ratio = 7.6) trend suggesting the influence from the use of de-icing agents such as, salts or sands in winter. A spring-high (spring/summer ration = 4.3) pattern was also observed. Some contributions of Ba, Cu, Zn, Co. Mo and Ni were present in this factor explaining from 6% to 15% of the variation. As mentioned previously, these elements are released from traffic-related exhaust and non-exhaust emissions including brake/tire wear and road abrasion (Dietl et al., 1997; Sternbeck et al., 2002; Lough et al., 2005). This factor was negatively correlated with ambient temperature and positively correlated with NO2, CO, THC and some traffic-related VOCs. The CPF plot pointed to southwest and northwest as dominant directions of local sources. These results suggest that use of road salt in winter as de-icing or anti-icing
49
chemicals for winter road maintenance and resuspension (vehicle or wind induced) of dried road salt particles in spring are likely associated with this factor. Therefore, factor 7 was assigned to road salt and it contributed 2.4% (0.20 µg/m3) to total measured PM2.5 mass on average (2.5%, 0.21 µg/m3 in constrained PMF analysis). Factor 8: Refinery – The most abundant element found in factor 8 was V (82% of the explained variation) with some amounts of Mo (21%), Pb (15%), Zn (15%), Cd (10%) and Ni (5%). V is typically emitted from oil and petrochemical refining and natural gas extraction and processing (Khalaf et al., 1982; Duce and Hoffman, 1976). This factor showed positive correlations with SO2, THC and petroleum-related VOCs such as alkanes and benzene. In Edmonton, two major oil refineries – Imperial Oil and Suncor Energy reported to have crude oil processing capacities of 187,200 and 142,000 barrels per day, respectively (Alberta Energy, 2013) – are located north/northeast of Edmonton in the Alberta’s industrial heartland. CPF analysis (Figure 2.6) clearly indicated north and northeast directions for this source. Thus, the factor 8 was assigned as a refinery source and it accounted for 1.1% (0.09 µg/m3) of the PM2.5 mass on average (1.3%, 0.11 µg/m3 in constrained PMF analysis). It is worth noting here that choosing a 10-factor solution instead of a 9 factors split factor 8 into ‘refinery’ and ‘metallurgy’ factors. However, in the 10-factor solution, the metallurgy factor was mapped with bootstrapping (BS) in only 54% of the runs, and 30% of bootstrapping-displacement (BS-DISP) runs were rejected due to factor swaps, indicating greater uncertainty with a 10-factor solution compared to 9-factor solution (Table S2.5). Therefore, the additional ‘metallurgy’ factor appeared as a less stable solution than factors identified in the 9-factor solution and the ‘metallurgy’ factor was not incorporated in this study. In Alberta’s industrial heartland – along with refineries and upgrader facilities – several other industries are present including chemical, petrochemical, agriculture, and manufacturing. It is acknowledged that the presence of Pb, Zn, Cd in factor 8 may possibly suggest influences from other industries like manufacturing and/or metallurgy-related sources. Factor 9: Mixed industrial – The predominant elements found in factor 9 were Mn, Fe, Ni, Cr, Cu, Co, Mo, explaining from 40% to 74% of the variation with some association of Zn, Cl–, Sn and Ti (representing from 20% to 37% of their total mass). These elements are typically emitted from a mixture of metal-industry related sources. Notable correlations of this factor with combustion pollutants like SO2, NO2, THC, CO and some VOCs such as aromatic hydrocarbons and alkanes were found suggesting the influence of local industrial sources. The CFP plot (Figure 2.6) revealed west and northwest as dominant directions for industrial sources (e.g., refer to Figure 1.1), along with some contributions from industrial sources in the northeast direction. As indicated previously the coal-fired cement kiln located in northwest Edmonton and industrial sources in the Alberta industrial heartland in the north/northeast can be attributed to this factor. Therefore, factor 9 was interpreted as mixed industrial and the average contribution of this factor was 3.7% (0.30 µg/m3) to total measured PM2.5 mass concentration (3.3%, 0.26 µg/m3 in the constrained run).
50
Figure 2.2. PMF-derived source profiles (concentration and percentage of species apportioned to factor) of ambient PM2.5 at Edmonton McIntyre station for 2010–2014.
0
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Concentration of species_base runConcentration of species_bootstrap run% of species_base run% of species_bootstrap run
Factor 1: Secondary organic aerosol (SOA)
Factor 2: Secondary sulfate
Factor 3: Secondary nitrate
Factor 4: Soil
Factor 6: Biomass burning
Factor 5: Traffic
Factor 8: Refinery
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Factor 7: Road-salt
Factor 9: Mixed industrial
51
Figure 2.3. Time series plots of daily contributions (in µg/m3) of PMF-derived sources at Edmonton McIntyre station (no measurement data available from May 6 to September 22, 2014).
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Factor 1: Secondary organic aerosol (SOA)
Factor 2: Secondary sulfate
Factor 3: Secondary nitrate
Factor 4: Soil
Factor 6: Biomass burning
Factor 5: Traffic
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Factor 8: Refinery
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Factor 9: Mixed industrial
52
Table 2.2. Predicted sources and their contributions to PM2.5 at Edmonton McIntyre station for 2010–2014.
Possible sources Key chemical species Base run Constrained run
μg/m3 % μg/m3 % Factor 1 SOA OC, EC, arabitol, oxalate 2.43 29.8 2.37 29.1 Factor 2 Secondary sulfate SO4
2–, NH4+ 1.78 21.9 1.75 21.5
Factor 3 Secondary nitrate NO3–, NH4
+ 1.32 16.2 1.34 16.4 Factor 4 Soil Ca+2, Mg+2, Al, Fe, Sr, Ti 0.94 11.5 0.80 9.9 Factor 5 Traffic Ba, As, Cu, Sb, Co, EC, OC 0.53 6.6 0.71 8.7 Factor 6 Biomass burning Levoglucosan, mannosan, K+, Cd, OC 0.56 6.9 0.59 7.3 Factor 7 Road-salt Na+, Cl– 0.20 2.4 0.21 2.5 Factor 8 Refinery V, Mo 0.09 1.1 0.11 1.3 Factor 9 Mixed industrial Cr, Cu, Mn, Fe, Mo, Co, Ni, Sn, Ti, Zn 0.30 3.7 0.26 3.3
Figure 2.4. Average contributions of constrained PMF-derived sources at Edmonton McIntyre station for 2010–2014 (a. seasonal, b. winter).
a b
Winter
53
Figure 2.5. Annual average contributions of constrained PMF-derived sources at Edmonton McIntyre station for 2010–2014. Table 2.3. Correlation (r = Pearson correlation coefficient) of PMF-derived sources with other pollutants measured at Edmonton south station and meteorological parameters measured at Edmonton McIntyre station for 2010–2014.
NO2 O3 SO2 THC CO Wind speed
Temperature Relative humidity
SOA 0.38** -0.23** 0.03 0.30** 0.38** -0.47** 0.14** -0.01
Secondary sulfate -0.04 0.12** -0.03 0.14** 0.04 -0.09* -0.23** 0.30**
Secondary nitrate 0.56** -0.34** -0.08 0.38** 0.41** -0.30** -0.42** 0.34**
Soil -0.18** 0.35** 0.05 -0.08 -0.16** 0.16** 0.36** -0.59**
Traffic 0.51** -0.38** 0.04 0.18** 0.27** -0.40** 0.15** -0.20**
Biomass burning 0.36** -0.32** -0.10* 0.20** 0.36** -0.27** -0.23** 0.22**
Road-salt 0.47** -0.22** -0.10** 0.13** 0.25** -0.15* -0.42** 0.10*
Refinery 0.02 -0.06 0.18** 0.16** -0.03 -0.07 -0.15** 0.19**
Mixed industrial 0.28** -0.19** 0.13** 0.31** 0.19** -0.24** 0.11* 0.02
** Correlation is significant at p = 0.01, * Correlation is significant at p = 0.05
54
Table 2.4. Correlation (r = Pearson correlation coefficient) of PMF-derived sources with volatile organic compounds (VOCs) measured at Edmonton east station for 2010–2014.
SOA Secondary sulfate
Secondary nitrate Soil Traffic
Biomass burning Road-salt Refinery
Mixed industrial
Benzene 0.43** 0.12 0.30** -0.08 0.28** 0.32** 0.25** 0.28** 0.41** Toluene 0.38** -0.03 0.26** -0.04 0.57** 0.22** 0.25** 0.08 0.37** Ethylbenzene 0.31** -0.17** 0.35** -0.08 0.59** 0.17** 0.29** -0.02 0.22** m,p-Xylene 0.28** -0.16* 0.33** -0.08 0.59** 0.15* 0.29** -0.02 0.22** Styrene 0.12 -0.16* 0.07 -0.01 0.47** 0.01 0.19** -0.06 0.13 o-Xylene 0.27** -0.19** 0.31** -0.06 0.59** 0.16* 0.29** -0.04 0.20** n-Propylbenzene 0.26** -0.15* 0.30** -0.04 0.52** 0.14* 0.27** 0.02 0.25** n-Butylbenzene 0.24** -0.13* 0.35** -0.04 0.46** 0.12 0.30** -0.01 0.22** 2-Ethyltoluene 0.25** -0.16* 0.31** -0.03 0.51** 0.12 0.26** 0.00 0.25** 3-Ethyltoluene 0.24** -0.18** 0.29** -0.04 0.54** 0.12 0.26** -0.02 0.23** 4-Ethyltoluene 0.25** -0.18** 0.30** -0.03 0.54** 0.13 0.27** -0.02 0.23** 1,3,5-Trimethylbenzene 0.22** -0.18** 0.27** -0.04 0.52** 0.11 0.28** -0.03 0.21** 1,2,4-Trimethylbenzene 0.23** -0.19** 0.27** -0.03 0.53** 0.11 0.25** -0.04 0.22** Ethane 0.29** 0.22** 0.56** -0.156* 0.26** 0.25** 0.34** 0.26** 0.42** Propane 0.26** 0.23** 0.37** -0.08 0.15* 0.17** 0.17* 0.31** 0.50** Butane 0.23** 0.17** 0.13 0.07 0.27** 0.06 -0.04 0.33** 0.45** Isobutane 0.19** 0.19** 0.20** 0.05 0.22** 0.05 0.01 0.35** 0.47** Pentane 0.29** 0.20** 0.07 0.04 0.09 0.07 -0.08 0.38** 0.49** Isopentane 0.34** 0.18** 0.08 0.06 0.17** 0.09 -0.10 0.35** 0.48** Hexane 0.27** 0.20** 0.16* 0.01 0.13 0.09 0.03 0.41** 0.49** Octane 0.17** 0.08 0.21** -0.07 0.17** 0.08 0.20** 0.25** 0.28** Heptane 0.23** 0.11 0.23** -0.06 0.19** 0.10 0.17* 0.32** 0.40** Nonane 0.20** -0.01 0.29** -0.07 0.31** 0.10 0.29** 0.14* 0.24** Decane 0.20** -0.10 0.35** -0.06 0.40** 0.11 0.35** 0.03 0.21** Undecane 0.20** -0.11 0.33** -0.04 0.43** 0.10 0.27** -0.02 0.20** Dodecane 0.18** -0.07 0.29** -0.01 0.32** 0.10 0.19** -0.03 0.18** 2,2-Dimethylbutane 0.25** 0.18** 0.07 0.02 0.10 0.06 0.01 0.35** 0.43** 2,3-Dimethylbutane 0.31** 0.13* 0.11 0.02 0.25** 0.08 0.00 0.32** 0.46** 2-Methylpentane 0.31** 0.18** 0.10 0.03 0.19** 0.09 -0.02 0.37** 0.48** 3-Methylpentane 0.32** 0.18** 0.12 0.02 0.22** 0.10 0.00 0.37** 0.49** 2,2-Dimethylpentane 0.25** 0.12 0.12 0.00 0.20** 0.08 0.10 0.32** 0.42** 2,4-Dimethylpentane 0.26** -0.05 0.12 0.00 0.36** 0.07 0.05 0.14* 0.32** Cyclopentane 0.24** -0.06 0.11 -0.04 0.40** 0.05 0.20** 0.13 0.39** Methylcyclopentane 0.30** 0.18** 0.15* 0.02 0.21** 0.09 0.03 0.36** 0.49** Cyclohexane 0.22** 0.25** 0.18** -0.02 0.05 0.07 0.09 0.41** 0.46** Methylcyclohexane 0.20** 0.20** 0.19** -0.06 0.10 0.08 0.16* 0.36** 0.39** 2-Methylhexane 0.30** 0.11 0.21** -0.04 0.30** 0.12 0.15* 0.33** 0.45** 3-Methylhexane 0.30** 0.07 0.22** -0.04 0.33** 0.12 0.16* 0.30** 0.43** Ethylene 0.09 -0.14* 0.07 -0.01 0.17* 0.07 0.15* -0.04 0.12 Acetylene 0.13* -0.15* 0.48** -0.18** 0.34** 0.17* 0.34** -0.05 0.11 1-Butene 0.25** -0.02 0.27** -0.13* 0.40** 0.19** 0.06 0.08 0.16* cis-2-Butene 0.14* -0.08 0.00 -0.02 0.38** 0.05 -0.06 -0.04 0.05 trans-2-Butene 0.16* -0.05 0.04 -0.06 0.37** 0.07 -0.06 0.00 0.05 2-Methyl-1-butene 0.17** -0.16* -0.08 0.06 0.46** 0.03 -0.08 -0.11 0.09 2-Methyl-2-butene 0.14* -0.17** -0.08 0.05 0.42** 0.02 -0.07 -0.12 0.08 3-Methyl-1-butene 0.19** -0.11 -0.03 0.06 0.44** 0.05 -0.05 -0.06 0.12 1-Pentene 0.19** -0.17* -0.07 0.02 0.46** 0.07 -0.07 -0.11 0.09 cis-2-Pentene 0.15* -0.19** -0.10 0.07 0.45** 0.02 -0.06 -0.14* 0.07 trans-2-Pentene 0.16* -0.19** -0.10 0.06 0.44** 0.02 -0.07 -0.13* 0.08 3-Methyl-1-pentene 0.20** -0.20** 0.00 0.05 0.45** 0.07 0.01 -0.12 0.12 4-Methyl-1-pentene 0.13* -0.18** -0.02 0.00 0.37** 0.04 -0.02 -0.05 0.16* 1,3-Butadiene 0.34** -0.13* 0.52** -0.16* 0.54** 0.30** 0.41** 0.05 0.26** 1-Hexene 0.21** -0.21** 0.01 -0.05 0.49** 0.10 0.02 -0.03 0.17* cis-2-Hexene 0.12 -0.21** 0.02 -0.01 0.44** 0.03 0.07 -0.11 0.09 trans-2-Hexene 0.20** -0.23** -0.02 0.00 0.51** 0.09 0.06 -0.09 0.09 Isoprene 0.18** -0.12 -0.22** 0.00 -0.01 -0.02 -0.27** -0.08 0.04 p-Cymene 0.43** -0.23** 0.20** 0.00 0.53** 0.34** 0.18** -0.08 0.16* a-Pinene 0.18** -0.25** 0.12 0.02 0.54** 0.06 0.21** -0.15* 0.12 b-Pinene 0.17* -0.26** -0.01 -0.03 0.33** 0.03 0.02 -0.14* 0.11 d-Limonene 0.23** -0.14* 0.28** -0.07 0.39** 0.18** 0.25** -0.10 0.08 Naphthalene 0.33** -0.13* 0.20** 0.05 0.50** 0.15* 0.05 -0.08 0.26**
** Correlation is significant at p = 0.01, * Correlation is significant at p = 0.05
55
Figure 2.6. Conditional probability function (CPF) plots of PMF-derived source factors at Edmonton McIntyre station.
SOA Secondary sulfate
Secondary nitrate
Soil Biomass burning
Road-salt
Traffic
Refinery Mixed industrial
56
Figure 2.7. Spatial distribution of PSCF values for the highest 25% of the contributions of SOA factor.
Winter
Fall Spring
Summer Secondary organic aerosol (SOA)
57
Figure 2.8. Spatial distribution of CWT values (μg/m3) for the highest 25% of the contributions of SOA factor.
Winter
Fall Spring
Summer Secondary organic aerosol (SOA)
(µg/m3) (µg/m3)
(µg/m3) (µg/m3)
58
Figure 2.9. Potential source contribution function (PSCF) values for the highest 25% of the contributions of secondary sulfate factor.
Winter
Fall Spring
Summer Secondary sulfate
59
Figure 2.10. Concentration weighted trajectory (CWT) values (μg/m3) for the highest 25% of the contributions of secondary sulfate factor.
Winter
Fall Spring
Summer Secondary sulfate
(µg/m3) (µg/m3)
(µg/m3) (µg/m3)
60
Figure 2.11. Spatial distribution of PSCF values for the highest 25% of the contributions of secondary nitrate factor.
Winter
Fall Spring
Summer Secondary nitrate
61
Figure 2.12. Spatial distribution of CWT values (μg/m3) for the highest 25% of the contributions of secondary nitrate factor.
Winter
Fall Spring
Summer Secondary nitrate
(µg/m3)
(µg/m3)
(µg/m3)
(µg/m3)
62
Figure 2.13. Spatial distribution of PSCF values for the highest 25% of the contributions of biomass burning factor.
Winter
Fall Spring
Summer Biomass burning
63
Figure 2.14. Spatial distribution of CWT values (μg/m3) for the highest 25% of the contributions of biomass burning factor.
Winter
Fall Spring
Summer Biomass burning
(µg/m3)
(µg/m3)
(µg/m3)
(µg/m3)
64
Correlation between sources One of the important features of the PMF model is the non-orthogonality (statistical independence) of resolved factors to each other (Paatero, 1997). Table 2.5 shows the correlation among PMF-derived sources at Edmonton. In this study, a good correlation was found between SOA and biomass burning (r = 0.61), suggesting that biomass burning tended to be a dominate contributor to SOA formation. SOA was also correlated with vehicle-related sources (traffic, road-salt, secondary nitrate) and mixed industrial sources. Secondary sulfate factor showed notable correlations with refinery, secondary nitrate and mixed industrial – indicating a relationship with industrial emissions. Correlation analysis also indicated positive association of secondary nitrate with refinery, biomass burning, road-salt and mixed industrial factors. These types of correlation results are consistent with the context of real-world emissions, where sources are not orthogonal and can be temporally correlated to each other partly due to meteorological influences (Lewis et al., 2003). Table 2.5. Correlation (r = Pearson correlation coefficient) between PMF-derived sources at Edmonton McIntyre station for 2010–2014.
SOA
Secondary sulfate
Secondary nitrate
Soil Traffic Biomass burning
Road-salt
Refinery Mixed
industrial SOA 1 0.07 0.25** .02 0.36** 0.61** 0.09* 0.02 0.35**
Secondary sulfate 1 0.34** -0.12** -0.31** 0.11* 0.0 0.31** 0.17** Secondary nitrate 1 -0.26** 0.07 0.33** 0.39** 0.12** 0.22**
Soil 1 0.05 -0.17** -0.06 -0.10* 0.24** Traffic 1 0.14** 0.12** -0.14** 0.20**
Biomass burning 1 0.19** -0.01 0.05 Road-salt 1 0.02 0.06 Refinery 1 0.31**
Mixed industrial 1 ** Correlation is significant at p = 0.01, * Correlation is significant at p = 0.05 Comparison with other source apportionment studies PM2.5 sources identified and apportioned in this study were compared with other PMF source apportionment studies conducted in major urban areas in Canada (Table 2.6). From our 5-year study period (2010–2014), we found about two times higher SOA contribution (2.4 µg/m3, 29.1%) in Edmonton compared to a previous study (1.4 µg/m3, 17.7%) performed for a shorter time period (Jeong et al., 2011). The SOA contribution in Edmonton was also higher than Toronto (0.55 µg/m3, 7.0%), Ottawa (1.3 µg/m3, 19.9%) and in similar magnitude with Windsor for PM2.5 mass (2.1 µg/m3, 20.1%) (Sofowote et al., 2015). This higher contribution of SOA in Edmonton is likely associated with higher inputs from biomass burning events in recent years (wildfires) and winter heating, traffic emissions and fossil fuel combustion sources (refineries, petrochemical plants). Although Edmonton is influenced by background regional sulfate due to oil and gas extraction and production activities in and around Alberta, the magnitude of secondary sulfate contribution (1.8 µg/m3) was comparable to Toronto (1.8 µg/m3), and Ottawa (1.5 µg/m3), but lower than Montreal (3.5 µg/m3), Windsor (2.5 µg/m3), and Halifax (2.6 µg/m3) (Jeong et al., 2011; Sofowote et al., 2015). The traffic contribution was lower in Edmonton (0.71 µg/m3) than found in the previous study (1.0 µg/m3) by Jeong et al. (2011) and also lower compared to other cities like Toronto (1.2 µg/m3), Montreal (1.4 µg/m3), and Windsor (2.0 µg/m3). These findings are consistent with observed declining trends in traffic-related pollutants e.g., NO2, CO in recent years in Edmonton (Table 1.1, Part 1). Like Halifax, a refinery source was only identified in Edmonton confirming the presence of conventional oil and gas activities in the Alberta Industrial heartland located in the north and northeast of Edmonton. Other common sources included soil, biomass burning and road-salt which are found in almost all Canadian cities.
65
Table 2.6. Comparison of PMF-derived predicted average source contributions to PM2.5 (µg/m3, %) in major urban areas in Canada.
Predicted sources Edmonton Edmontona Torontoa Torontob Montreala Ottawab Windsora Windsorb Halifaxa Halifaxc
This study 2010–2014 (n = 522)
May 2006–Jan 2008 (n = 154)
Mar 2004–Sep 2007 (n = 341)
Jan 2005–Dec 2010 (n = 676)
Jan 2003–Nov 2007 (n = 403)
May 2007–Dec 2010 (n = 432)
Jun 2004–Mar2008 (n = 190)
Jan 2005–Nov2010 (n = 350)
Apr 2006–Jan 2008 (n = 114)
Jul–Aug 2011 (n = 45)
Secondary organic aerosol (SOA)
2.4 (29.1) 1.4 (17.7)
0.55 (7.0)
1.3 (19.9)
2.1 (20.1)
Secondary sulfate 1.8 (21.5) 1.5 (19.0) 4.1 (33.4) 1.8 (23.4) 3.5 (33.7) 1.5 (24.2) 5.2 (37.1) 2.5 (24.3) 2.6 (37.3) 1.8 (47%) Seconary nitrate 1.3 (16.4) 1.8 (21.9) 3.2 (26.4) 0.84 (10.7) 1.4 (13.5) 0.94 (14.8) 3.4 (24.0) 2.2 (22.1) 0.7 (9.3) 1.0 (27.9)
Soil 0.80 (9.9) 0.2 (1.9) 0.08 (1.0) 0.4 (3.8) 0.44 (6.9) 0.7 (5.3) (<1) 0.3 (3.8) 0.23 (6.3) Traffic 0.71 (8.7) 1.0 (12.4) 1.2 (10.4) 1.4 (13.9) 2.0 (13.9) 1.0 (14.2) 0.49 (13.2)
Biomass burning 0.59 (7.3) 1.0 (12.0) 0.1 (1.1) 1.1 (13.8) 0.7 (6.4) 1.0 (16.2) 1.3 (12.9) Road-salt 0.21 (2.5) 0.2 (2.5) 0.2 (1.8) (<1) 0.5 (4.4) 0.31 (4.8) 0.3 (2.3) (<1) 1.3 (18.3)* Refinery 0.11 (1.3) 0.3 (3.5) 0.08 (2.2)
Mixed industrial 0.26 (3.3) Road dust 0.6 (7.0) 0.4 (3.2) 0.4 (3.9) Metallurgy 0.4 (4.9) 0.7 (6.2) 1.5 (19.6) 1.1 (7.6) 0.55 (5.4) Cemet Kiln 0.2 (2.6)
EC-rich 1.9 (15.6) 1.5 (14.9) 0.9 (6.1) Oil combustion 0.6 (5.5) 0.5 (3.7) 0.22 (2.1) 0.3 (4.5) Ship emission 0.6 (9.1) 0.13 (3.4)
As-Pb 1.8 (22.8) 0.49 (7.7) 1.0 (9.9) Al-rich 0.35 (5.6)
aJeong et al. (2011) bSofowote et al. (2015) aGibson et al. (2013)
66
A metallurgy factor was identified in Edmonton in the previous study (Jeong et al., 2011) and also in Toronto, and Windsor. It is worthwhile to mention that in this study the metallurgy factor was not separated from the refinery factor due to more uncertainty found in PMF error estimation. However, it is acknowledged that the influence a metallurgy source in Edmonton is more likely due to the presence of numerous chemical, petrochemical, manufacturing or metallurgy-related sources in the Alberta Industrial Heartland. Performance of the PMF model Performance of the PMF model was evaluated using several variable metrics – e.g., coefficient of determination (R2), mean absolute error (MAE), relative error, and root mean square error (RMSE). A comparison of model-predicted PM2.5 mass (sum of the contributions of all identified sources) with gravimetrically measured PM2.5 mass is shown in Figure 2.15. The linear regression fit between modeled and measured PM2.5 concentrations showed good agreement and yielded a slope of 0.80, intercept of 1.13 and square of the Pearson correlation coefficient (R) of 0.85, with modeled resolved source factors explaining 85% (statistical significance, p = 0.03) of the variance in measured PM2.5 mass concentrations. This suggests that the PMF-derived source factors effectively reproduced the measured PM2.5 concentrations at Edmonton McIntyre.
Figure 2.15. Scatter plot of measured versus model predicted PM2.5 concentrations at Edmonton McIntyre The other performance variable metrics are defined as follows:
1| |
1 | |
100
y = 0.80x + 1.13R² = 0.85
0
25
50
75
0 25 50 75
Mod
eled
PM
2.5
(μg/
m3 )
Measured PM2.5 (μg/m3)
Edmonton McIntyre (2010–2014)
67
1
where, is the model predicted PM2.5 concentration and is the observed or measured PM2.5 concentration and n is the number of model-measured pairs. Table 2.7 shows summary statistics of metrics values for the model performance including median, interquartile range and 90th percentile values. The mean (MAE) and median absolute values were 1.54 and 0.97 ug/m3, while relative errors were 20% and 14%, respectively. The RMSE was 2.68 ug/m3. These metrics support overall acceptable performance of the PMF model results. Table 2.7 Summary statistics of performance of the PMF model.
Modeled
PM2.5 Measured
PM2.5 Absolute
error Relative
error µg/m3 µg/m3 µg/m3 %
Mean (MAE) 8.14 8.80 1.54 20 Median 6.53 6.91 0.97 14 25th percentile 4.69 4.95 0.49 6 75th percentile 9.61 10.37 1.88 26 90th percentile 14.53 15.65 3.15 44
Number of paired data = 522
68
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Supplemental Material Part II Table S2.1. Data quality of PM2.5 and its chemical components (n = 50) for a 5-year period (2010–2014).
Analytical method
No. of samples
>DL
No. of samples
<DL
No. of non-
detect or missing
(nd)
Total no. of
samples N
% of samples
>DL
% of samples
<DL
% of non-detect or missing
(nd)
PM2.5 Gravimetric 529 0 0 529 100 0 0 Organic carbon (OC) TOR 455 71 0 526 87 13 0 Elemental carbon (EC) TOR 483 42 1 526 92 8 0
Sulfate (SO42–) IC 528 1 0 529 100 0 0
Nitrate (NO3–) IC 472 52 5 529 89 10 1
Ammonium (NH4+) IC 528 1 0 529 100 0 0
Sodium (Na+) IC 295 234 0 529 56 44 0 Chloride (Cl–) IC 234 295 0 529 44 56 0
Potassium (K+) IC 432 96 1 529 82 18 0 Calcium (Ca2+) IC 436 93 0 529 82 18 0
Magnesium (Mg2+) IC 488 40 1 529 92 8 0 Floride (F–) IC 268 115 146 529 51 22 28
Formate IC 134 336 59 529 25 64 11 Oxalate IC 395 102 32 529 75 19 6
Levoglucosan GC-MS 508 2 19 529 96 0 4 Mannosan GC-MS 312 58 159 529 59 11 30
Arabitol GC-MS 203 48 278 529 38 9 53 Aluminium (Al) ICP-MS 346 183 0 529 65 35 0 Arsenic (As) ICP-MS 521 8 0 529 98 2 0 Barium (Ba) ICP-MS 527 2 0 529 100 0 0
Cadmium (Cd) ICP-MS 385 141 3 529 73 27 1 Cobalt (Co) ICP-MS 323 203 3 529 61 38 1
Chromium (Cr) ICP-MS 346 179 4 529 65 34 1 Cupper (Cu) ICP-MS 459 69 1 529 87 13 0
Iron (Fe) ICP-MS 520 9 0 529 98 2 0 Manganese (Mn) ICP-MS 526 3 0 529 99 1 0 Molybdenum (Mo) ICP-MS 438 91 0 529 83 17 0
Nickel (Ni) ICP-MS 430 99 0 529 81 19 0 Lead (Pb) ICP-MS 444 85 0 529 84 16 0
Antimony (Sb) ICP-MS 525 3 1 529 99 1 0 Selenium (Se) ICP-MS 120 382 27 529 23 72 5
Tin (Sn) ICP-MS 288 178 63 529 54 34 12 Strontium (Sr) ICP-MS 525 4 0 529 99 1 0 Titanium (Ti) ICP-MS 274 255 0 529 52 48 0 Vanadium (V) ICP-MS 331 198 0 529 63 37 0
Acetate IC 476 53 0 529 90 10 0 MSA IC 114 255 160 529 22 48 30
Nitrite (NO2–) IC 119 121 289 529 22 23 55
Barium ion (Ba2+) IC 63 105 361 529 12 20 68 Lithium ion (Li2+) IC 39 182 308 529 7 34 58
Bromide (Br–) IC 39 163 327 529 7 31 62 Strontium ion (Sr+) IC 10 38 481 529 2 7 91 Phosphate (PO4
3–) IC 6 58 465 529 1 11 88 Propionate IC 4 55 470 529 1 10 89 Mannitol GC-MS 2 54 473 529 0 10 89
Galactosan GC-MS 128 21 380 529 24 4 72 Silver (Ag) ICP-MS 80 35 414 529 15 7 78
Beryllium (Be) ICP-MS 34 464 31 529 6 88 6 Thallium (Tl) ICP-MS 0 338 191 529 0 64 36 Uranium (U) ICP-MS 28 486 15 529 5 92 3
DL: detection limit
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Table S2.2. Chemical components used in PM2.5 mass reconstruction (Dabek-Zlotorzynska et al., 2011)
Component Abbreviation Formula
Ammonium Nitrate (NH4NO3) [ANO3] [ANO3] = 1.29[NO3–]
Ammonium sulfates (e.g., (NH4)2SO4, (NH4)3H(SO4)2, NH4HSO4)
[ASO4] [ASO4] = [SO42–] + [NH4
+] – 0.29[NO3–]
Organic matter [OM]a [OM] = k [OC]
Elemental carbon [EC] [EC] = [EC]
Crustal matter [Soil] [Soil] = 3.48[Si] + 1.63[Ca] +2.42[Fe] + 1.41[K] + 1.94[Ti]
Trace element oxides [TEO] [TEO] = 1.47[V] + 1.29[Mn] + 1.27[Ni] + 1.25[Cu] + 1.24[Zn] + 1.32[As]
Sodium chloride [NaCl] [NaCl] = [Na] + [Cl]
Particle-bound water [PBW]b [PBW] = 0.32([SO42–] + [NH4
+])
Reconstructed mass [RCM] [RCM] = [ANO3] + [ASO4] + [OM] + [EC] + [Soil] + [TEO] + [NaCl] + [PBW] aOM/OC correction factor k was calculated using the SANDWICH method (Frank, 2006) bPBW was calculated using the Aerosol Inorganic Model (Clegg et al., 1998a)
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Table S2.3. PMF Input data statistics for Edmonton.
Species Category S/N Min 25th Median 75th Max % Modeled Samples
% Raw Samples
PM2.5 Weak 4.85 1.04 4.95 6.98 10.41 62.54 99.24 % 100.00 % OC Strong 2.60 -0.35 0.77 1.32 2.01 29.45 99.24 % 100.00 % EC Strong 5.39 -0.42 0.30 0.57 0.89 3.86 99.24 % 100.00 %
SO42– Strong 6.70 -0.04 0.31 0.63 1.07 14.75 99.24 % 100.00 %
NO3– Strong 4.59 0.00 0.09 0.18 1.02 20.14 99.24 % 100.00 %
NH4+ Strong 7.38 0.00 0.13 0.28 0.69 10.21 99.24 % 100.00 %
Na+ Strong 2.19 -0.04 0.01 0.02 0.06 0.36 99.24 % 100.00 % Cl– Strong 1.37 -0.03 0.00 0.02 0.05 1.03 99.24 % 100.00 % K+ Strong 2.83 -0.01 0.02 0.03 0.05 0.25 99.24 % 100.00 %
Ca2+ Strong 3.78 -0.02 0.02 0.05 0.08 0.92 99.24 % 100.00 % Mg2+ Strong 2.85 0.00 0.01 0.01 0.02 0.08 99.24 % 100.00 %
F– Weak 1.66 0.00 0.00 0.01 0.01 0.02 99.24 % 100.00 % Formate Weak 0.42 0.00 0.01 0.02 0.02 0.20 99.24 % 100.00 % Oxalate Strong 2.57 0.00 0.03 0.04 0.06 0.61 99.24 % 100.00 %
Levoglucosan Strong 5.73 0.00 0.02 0.04 0.08 1.49 99.24 % 100.00 % Mannosan Strong 2.06 0.00 0.01 0.01 0.01 0.35 99.24 % 100.00 %
Arabitol Weak 0.98 0.00 0.00 0.00 0.00 0.04 99.24 % 100.00 % Al Weak 2.09 0.00 0.00 0.00 0.01 0.05 99.24 % 100.00 % As Weak 4.28 0.00 0.00 0.00 0.00 0.00 99.24 % 100.00 % Ba Strong 6.42 0.00 0.00 0.00 0.00 0.01 99.24 % 100.00 % Cd Weak 2.05 0.00 0.00 0.00 0.00 0.00 99.24 % 100.00 % Co Weak 1.89 0.00 0.00 0.00 0.00 0.00 99.24 % 100.00 % Cr Weak 1.60 0.00 0.00 0.00 0.00 0.01 99.24 % 100.00 % Cu Weak 2.23 0.00 0.00 0.00 0.00 0.03 99.24 % 100.00 % Fe Strong 5.08 0.00 0.01 0.01 0.02 0.13 99.24 % 100.00 % Mn Strong 7.03 0.00 0.00 0.00 0.01 0.02 99.24 % 100.00 % Mo Weak 3.01 0.00 0.00 0.00 0.00 0.00 99.24 % 100.00 % Ni Weak 2.79 0.00 0.00 0.00 0.00 0.01 99.24 % 100.00 % Pb Weak 2.46 0.00 0.00 0.00 0.00 0.01 99.24 % 100.00 % Sb Strong 4.20 0.00 0.00 0.00 0.00 0.00 99.24 % 100.00 % Se Weak 0.37 0.00 0.00 0.00 0.00 0.00 99.24 % 100.00 % Sn Weak 1.27 0.00 0.00 0.00 0.00 0.00 99.24 % 100.00 % Sr Strong 4.01 0.00 0.00 0.00 0.00 0.00 99.24 % 100.00 % Ti Weak 0.78 0.00 0.00 0.00 0.00 0.00 99.24 % 100.00 % V Strong 2.19 0.00 0.00 0.00 0.00 0.01 99.24 % 100.00 % Zn Weak 3.78 0.00 0.00 0.01 0.01 0.10 99.24 % 100.00 %
Excluded Samples: 01/20/10, 08/27/10, 10/06/11, 10/05/14
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Table S2.4. Regression diagnostics of a 9-factor solution for Edmonton dataset (2010–2014).
Species Intercept Slope SE r2 KS test
Stat KS test p-value
PM2.5 1.13 0.80 2.24 0.849 0.063 0.031 OC 0.52 0.61 0.53 0.808 0.045 0.245 EC 0.04 0.92 0.16 0.895 0.081 0.002
SO42– 0.24 0.66 0.28 0.869 0.083 0.002
NO3– 0.00 1.01 0.35 0.985 0.154 0.000
NH4+ 0.02 0.94 0.10 0.989 0.048 0.182
Na+ 0.01 0.88 0.02 0.871 0.103 0.000 Cl– 0.02 0.48 0.02 0.778 0.078 0.004 K+ 0.01 0.59 0.02 0.564 0.047 0.200
Ca2+ 0.03 0.54 0.02 0.749 0.066 0.020 Mg2+ 0.00 0.61 0.00 0.575 0.058 0.057
F– 0.00 0.21 0.00 0.096 0.182 0.000 Formate 0.01 0.20 0.01 0.165 0.129 0.000 Oxalate 0.02 0.38 0.02 0.501 0.063 0.030
Levoglucosan 0.00 0.94 0.02 0.967 0.107 0.000 Mannosan 0.00 0.67 0.01 0.817 0.177 0.000
Arabitol 0.00 0.16 0.00 0.098 0.256 0.000 Al 0.00 0.18 0.00 0.335 0.083 0.002 As 0.00 0.22 0.00 0.288 0.029 0.755 Ba 0.00 0.86 0.00 0.915 0.041 0.337 Cd 0.00 0.17 0.00 0.182 0.113 0.000 Co 0.00 0.01 0.00 0.044 0.161 0.000 Cr 0.00 0.31 0.00 0.417 0.097 0.000 Cu 0.00 0.32 0.00 0.328 0.089 0.001 Fe 0.00 0.70 0.00 0.798 0.028 0.817 Mn 0.00 0.94 0.00 0.945 0.102 0.000 Mo 0.00 0.35 0.00 0.417 0.104 0.000 Ni 0.00 0.19 0.00 0.254 0.093 0.000 Pb 0.00 0.17 0.00 0.296 0.113 0.000 Sb 0.00 0.49 0.00 0.524 0.079 0.003 Se 0.00 0.27 0.00 0.199 0.129 0.000 Sn 0.00 0.10 0.00 0.140 0.167 0.000 Sr 0.00 0.71 0.00 0.718 0.097 0.000 Ti 0.00 0.25 0.00 0.221 0.106 0.000 V 0.00 0.96 0.00 0.981 0.172 0.000 Zn 0.00 0.30 0.00 0.438 0.036 0.504
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Table S2.5. Summary of PMF analysis and diagnostics of error estimation by run for Edmonton data.
Diagnostic 9-factor 10-factor Qespected 4896 5916
Qtrue 7682 8539 Qrobust 7677 8527
Qrobust/Qespected 1.57 1.44 Displacement (DISP) %dQ <0.1% <0.1%
DISP swaps 0 0
Bootstrapping (BS) mapping Refinery 85%,
traffic 91% Metallurgy 54%
Bootstrapping with displacement (BS-DISP) % cases with swaps
17% with swaps 30% with swaps
Computer run-time (hours) >12 >12
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Figure S2.1. The density of ‘conventional oil and gas extraction’ sources in Alberta from Environment Canada’s National Pollutant Release Inventory for the year 2012 using Google Earth (Image IBCAO © 2014 Google). The image shows the heavy density of conventional oil and gas extraction sources throughout Alberta that, for many years, Alberta Environment and Sustainable Resource Development has attributed to the presence of a sulfur background in the atmosphere in Alberta.
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Figure S2.2. Reported Environment Canada’s National Pollution Release Inventory (NPRI) during 2013 using Google Earth (Image Landsat © 2015 Google) for Edmonton and Surrounding Region.
Coal combustion sources related to power generation
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Figure S2.3. Manure production index map for the agricultural area of Alberta (Alberta Agriculture and Forestry, 2005).
Note: This map describes the relative amount of manure production in Alberta. The classes shown on the map are ranked between 0 (lowest amount of production, dark green color) and 1 (highest amount of production, red color). The map gives an indication of where livestock production is most concentrated in the province.
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Figure S2.4. Seasonal average contributions (µg/m3, %) of constrained PMF-derived sources at Edmonton McIntyre for 2010–2014.
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Part III Origins and Causes of PM2.5 Concentration Differences in Edmonton during 2010 Relative to Other Years
A newspaper article circulated by National Post (2015) in April 2015 reported that “…Edmonton had higher levels of a harmful air pollutant compared to Toronto, a city with five times the population and more industry.” The article presented a figure prepared by the Canadian Association of Physicians for the Environment comparing 3 year averages of annual 98th percentile 24-hr average concentration for Edmonton and Toronto (reproduced below in Figure 3.1 after National Post, 2015). The article also stated that “…particulate matter (i.e., PM2.5) exceeded legal limits of 30 µg/m3 at two city monitoring stations on several winter days in 2010 through 2012.” No information was provided in the article about whether the right analytical procedure (CCME, 2007) was used to calculate the 3-year average values depicted in Figure 3.1 for proper determination of whether ‘legal limits’ were exceeded for a city in a given 3-year period.
Figure 3.1. Three-year averages of annual 98th percentile 24 h average concentration for Edmonton and Toronto reported in National Post, 2015 (after Vipond, 2015). A limitation of PM2.5 monitoring data over the period shown in Figure 3.1 is that it did not acknowledge changes in the operation of continuous PM2.5 monitoring equipment during 2009. Equipment at many of the original continuous PM2.5 monitoring stations in Alberta was changed (upgraded) to better capture some components of fine particulate matter (i.e., semi-volatile material) which was lost under the previous equipment operation methods (AEP, 2015). As a result of this change, PM2.5 levels observed at air monitoring stations are higher in 2010 and subsequent years compared to what they had been in the past. This can be observed in Figure 3.2a, which shows that the ‘Newer’ PM2.5 monitoring method (Tapered Element Oscillating Microbalances-Filter Dynamics Measurement Systems, TEOM-FDMS) results in higher measured values than the ‘Older’ PM2.5 monitoring method (TEOM) at the Edmonton McIntrye station during 2010 using data obtained from CASA (2015).
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Figure 3.2. Comparison of 24 h PM2.5 concentration at Edmonton McIntrye station for 2010 based on Newer (TEOM-FDMS) and Older (TEOM) monitoring methods (data after CASA, 2015).
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The 24 h average PM2.5 mass concentrations measured by the new TEOM-FDMS and old TEOM methods were 17.7 and 8.0 μg/m3 during winter (December 2009–February 2010) and 13.5 and 9.9 μg/m3 during summer (June–August 2010), respectively. The difference between the FDMS-TEOM mass concentration and the TEOM mass concentration provides a good estimate of the amount of semi-volatile material present in PM2.5 mass (Eatough et al., 2003; Grover et al., 2005). The discrepancy between 24-hr PM2.5 TEOM-FDMS and TEOM concentrations fluctuates with time, which appears to be due to variation of meteorological conditions (e.g., temperature, relative humidity) and varying strength of precursor sources. A scatter plot between the new TEOM-FDMS and old TEOM 24 h PM2.5 methods for 2010 are shown in Figure 3.2b. The scatter plot shows good agreement during the summer months June thru August (slope = 1.06, R2 = 0.99). While during winter months (December 2009–February 2010) the new TEOM-FDMS method (slope = 1.5, R2 = 0.85) measured 50% higher concentrations than the older TEOM method. Therefore, an increase in the 3-year average of annual 98th percentile 24 h average concentrations is not unexpected for 2008–2010 and subsequent 3-year periods compared to the 2007–2009 period because of changes in operation of continuous PM2.5 monitoring equipment. In addition, Alberta Government (2014) stated that the year 2010 had a greater frequency of high PM2.5 concentration events (e.g., from the influence of long-range transport of wildfire smoke and other possible factors) at monitoring stations in Edmonton compared to other years. This is better observed in Figure 3.3 showing 3-year averages of annual 98th percentile 24 h average concentration for Downtown Edmonton and Toronto stations without the year 2010 data. We reproduced Figure 3.1 in Figure 3.3 by obtaining 98th percentile 24 h concentration data for Edmonton and Toronto downtown from NAPS annual summaries (Environment Canada, 2015) and then simply omitted the year 2010 data. Excluding data for the year 2010 results in no exceedances of the 30 µg/m3 Canada-Wide standard (Figure 3.3).
Figure 3.3. Three-year averages of annual 98th percentile 24 h average concentration for Downtown Edmonton and Toronto stations based on Figure 3.1 after excluding data for year 2010 (data after Environment Canada, 2015).
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To better understand the potential role that changes in operation of continuous PM2.5 monitoring equipment played, we reproduced Figure 3.1 again using data from CASA (2015). Only this time we applied a 50% correction factor to the winter months of December 2009 and the months of January, February and December 2010. This scenario is plotted in Figure 3.4 and it shows only one slight exceedance of the 30 µg/m3 Canada-Wide standard for the 3-year periods (i.e., 2010–2012 period). Finally, to understand the potential cumulative role that a single major wildfire smoke event occurring August 19–22, 2010 played, we again reproduced Figure 3.1 using data from CASA (2015). This time we applied a 50% correction factor to the winter months of December 2009 and January, February and December 2010; and we also omitted data for August 19–22, 2010. This scenario is plotted in Figure 3.5 and it shows much lower 3-year averages of annual 98th percentile 24 h average concentration for Downtown Edmonton. Keep in mind that is based on omitting data for a single wildfire smoke event. It is readily apparent from comparison of Figure 3.5 with Figure 3.1 that a combination of: i) changes (upgrades) in operation of continuous PM2.5 monitoring equipment made during 2009, and ii) a single major wildfire smoke event in 2010 (August 19–22) explain exceedances of 3-year averages of annual 98th percentile 24 h average concentrations compared to the 30 µg/m3 Canada-Wide standard at the Downtown Edmonton station. Methodology Notwithstanding the circumstances described above, there was still interest in better understanding the Alberta Government (2014) observation that the year 2010 had a greater frequency of high PM2.5 concentration events. Constrained PMF model output from Part II was further examined to identify how the contribution of identified sources (factors) for PM2.5 at Edmonton McIntyre station differed in 2010 compared to other years in the analysis (2011–2014). Results and Discussion Figure 3.6 summarizes yearly average contributions of constrained PMF-derived sources at Edmonton McIntyre station for the 2010–2014 period. Several observations are made of Figure 3.6:
In order of relative importance, the ‘secondary organic aerosol,’ secondary nitrate’ and biomass burning factors showed increased contributions during 2010 compared to 2011–2014.
The ‘secondary sulfate’ factor showed no year-to-year variation over the 5-year period indicating that secondary sulfate precursor (i.e., SO2) emissions influencing Edmonton McIntyre station were unchanged over the period.
Other factors identified in the PMF analysis showed unimportant differences during 2010 compared to 2011–2014.
Figure 3.6 shows that multiple factors other than secondary sulfate precursor sources showed increased contributions during 2010 compared to 2011–2014 at Edmonton McIntyre station. In addition, it shows that secondary sulfate displayed no year-to-year variation over the 5-year period. Levels of both secondary nitrate and sulfate particles tend to be simultaneously enhanced within plumes from coal combustion emissions relative to background (Zaveri et al., 2010). Thus the observation of secondary sulfate displaying no year-to-year variation over the 5-year period provides evidence that coal combustion emission sources would have played an unimportant role in explaining the year 2010 having a greater frequency of high PM2.5 concentration events. This is consistent with backward trajectory analysis that showed air parcels traveling over grid cells immediately west of Edmonton would be, on average, associated with lower concentrations of PM2.5 for the secondary sulfate factor at Edmonton McIntyre station compared to air parcels traveling over numerous other areas. On the other hand, increased contributions from secondary organic aerosol, secondary nitrate and biomass burning emission sources best explains the year 2010 having a greater frequency of high PM2.5 concentration events.
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Figure 3.4. Three-year averages of annual 98th percentile 24 h average concentration for Downtown Edmonton and Toronto stations based on Figure 3.1 after applying a 50% correction factor to winter months of December 2009 and January, February and December 2010 (data after CASA, 2015).
Figure 3.5. Three-year averages of annual 98th percentile 24 h average concentration for Downtown Edmonton and Toronto stations based on Figure 3.1 after applying a 50% correction factor to winter months of December 2009 and January, February and December 2010 and omitting data for a single August 19–22, 2010 major wildfire smoke event (data after CASA, 2015).
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Figure 3.6. Yearly average contributions of constrained PMF-derived sources at Edmonton McIntyre station. References Alberta Environment and Parks (AEP), 2015. Fine Particulate Levels (PM2.5). AEP, Edmonton, AB.
http://esrd.alberta.ca/focus/state-of-the-environment/air/condition-indicators/fine-particulate-levels.aspx.
Alberta Government, 2014. Capital Region Fine Particulate Matter Science Report. December 2014. http://esrd.alberta.ca/focus/cumulative-effects/capital-region-industrial-heartland/documents/CapitalRegion-PM-ScienceReport-Dec2014.pdf.
Canadian Council of Ministers of the Environment (CCME), 2007. Guidance Document on Achievement Determination Canada-wide Standards for Particulate Matter and Ozone. PN 1391, 978-1-896997-74-2 PDF. CCME, Winnipeg, MB. http://www.ccme.ca/files/Resources/air/pm_ozone/1391_gdad_e.pdf.
Clean Air Strategic Alliance (CASA), 2015. CASA Data Warehouse. http://www.casadata.org.
Eatough, D.J., Long, R.W., Modey, W.K., Eatough, N.L., 2003. Semi-volatile secondary organic aerosol in urban atmospheres: meeting a measurement challenge. Atmospheric Environment, 37, 1277–1292.
Environment Canada, 2015. NAPS data products. http://maps-cartes.ec.gc.ca/rnspa-naps/data.aspx.
Grover, B.D., Kleinman, M.A., Eatough, N.L., Eatough, D.J., Hopke, P.K., Long, R.W., Wilson, W.E., Meyer, M.B., Ambs, J.L., 2005. Measurement of total PM2.5 mass (nonvolatile plus semi-volatile) with the FDMS TEOM monitor. Journal of Geophysical Research, 110, D07S03.
Increased factor contribution during 2010 relative to other years
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National Post, 2015. Edmonton’s air quality is often worse than Toronto’s, which has five times more people. National Post, Toronto, ON. http://news.nationalpost.com/news/canada/edmontons-air-quality-is-often-worse-than-torontos-which-has-five-times-more-people.
Vipond, J., 2015. Email communication with W. Kindzierski, University of Alberta, 16 April 2015.
Zaveri, R.A., Berkowitz, C.M., Brechtel, F.J., Gilles, M.K., Hubbe, J.M., Jayne, J.T., Kleinman, L.I., Laskin, A., Madronich, S., Onasch, T.B., Pekour, M.S., Springston, S.R., Thornton, J.A., Tivanski, A.V., Worsnop, D.R., 2010. Nighttime chemical evolution of aerosol and trace gases in a power plant plume: Implications for secondary organic nitrate and organosulfate aerosol formation, NO3 radical chemistry, and N2O5 heterogeneous hydrolysis. Journal of Geophysical Research D. (Atmospheres), 115, D12304, doi:10.1029/2009JD013250.
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Findings This study investigated characteristics of air quality and various source contributions to ambient fine particulate matter (PM2.5) in the Edmonton Capital Region using historical data measured at National Air Pollution Surveillance (NAPS) air monitoring stations in Edmonton, including a chemical speciation monitoring station (Edmonton McIntyre station). The findings for three objectives are presented: Objective 1 – Investigate characteristics and trends of individual air pollutants in Edmonton Trends in Environmental Canada’s National Pollutant Release Inventory (NPRI) reported industrial emissions for PM2.5, oxides of nitrogen (NOX), SO2, ammonia (NH3), VOCs and major trace elements in the Edmonton Capital Region were investigated over the last decade (2003–2014):
Statistically significant decreasing trends were observed for the industrial combustion pollutants NOX (p ≤ 0.01) and SO2 (p ≤ 0.05) over the last decade.
A statistically significant decrease (p ≤ 0.05) was observed for arsenic (As).
Statistically significant downward trends were observed for other industrial elements – e.g., V, Mn, Cr, Cu and Co.
Surrogate data (~20,600 additional motor vehicle registrations annually) suggest an increasingly important role of transportation sector emissions in Edmonton Capital Region over the past decade. This is opposite to NPRI-reported industrial emissions trends described above. Statistically significant trends (p ≤ 0.05) for hourly average percentile concentrations (50th, 65th, 80th, 90th, 95th and 98th percentiles) of air pollutants measured using continuous monitors were observed at Edmonton central and east stations (17-year period of record: 1998–2014) and Edmonton south and McIntyre stations (9-year period: 2006–2014):
Decreasing trends were observed for hourly average percentile concentrations of NO2, SO2, THC, and CO. Thus air quality in the City of Edmonton has improved with respect to these air pollutants over the past 17 years.
No statistically significant change was observed for PM2.5 at any of the monitoring stations and a clear spike was found in 2010. This lack of trend represents an interesting situation because during 2009 equipment at many of the original continuous PM2.5 monitoring stations in Alberta was upgraded to improve capture of some components of fine particulate matter (i.e., semi-volatile material) which were lost under the previous operation method. As a result of this change, PM2.5 levels observed at air monitoring stations are higher in 2010 and subsequent years compared to what they had been in the past. Thus despite absolute PM2.5 levels at air monitoring stations being ‘bumped’ higher in 2010 and subsequent years relative to previous years as a result of equipment upgrades in 2009, no statistically significant trends were observed at any of the stations.
For O3, a small increasing trend was found at lower hourly percentile concentrations (50th to 80th percentiles) at Edmonton central, a small decreasing trend was detected at higher percentile concentrations (at 80th to 98th percentiles) at Edmonton east site and no trend was observed at Edmonton south at all percentile concentrations. These characteristics indicate evidence of spatial variation of O3 precursor concentrations – e.g., NOX, VOCs – across Edmonton rather than any type of consistent trend in O3 concentrations in Edmonton over the period examined.
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Eight-year trends (2007–2014) in chemical species present in 24 h integrated PM2.5 samples collected at Edmonton McIntyre station were investigated:
Statistically significant decreasing trends for 24 h concentrations of OC, EC, oxalate, Ba and Pb (p ≤ 0.05) and Cd (p ≤ 0.10) were observed.
A statistically significant increase (p ≤ 0.05) was observed for NaCl (a component of road-salt).
No statistically significant changes were observed for all other chemical species examined. Concentrations of K+ and Zn exhibited strong and significant seasonal variability with higher concentrations in winter than in summer. Seasonal patterns with high winter levels of these trace elements likely reflect wood smoke origins more than other potential sources in the Edmonton Capital Region. Objective 2 – Investigate fine particulate matter sources in Edmonton The plausibility and interpretability of solutions with six to eleven factors (sources) were examined and a 9-factor solution best represented the makeup of ambient PM2.5 sources at Edmonton McIntyre station using the U.S. Environmental Protection Agency PMF model (version 5.0) under two scenarios (a base and a constrained run) for the time period of 2010–2014:
Possible source Key chemical species Base run Constrained run
μg/m3 % μg/m3 % Factor 1 SOA OC, EC, arabitol, oxalate 2.43 29.8 2.37 29.1 Factor 2 Secondary sulfate SO4
2–, NH4+ 1.78 21.9 1.75 21.5
Factor 3 Secondary nitrate NO3–, NH4
+ 1.32 16.2 1.34 16.4 Factor 4 Soil Ca2+, Mg2+, Al, Fe, Sr, Ti 0.94 11.5 0.80 9.9 Factor 5 Traffic Ba, As, Cu, Sb, Co, EC, OC 0.53 6.6 0.71 8.7 Factor 6 Biomass burning Levoglucosan, mannosan, K+, Cd, OC 0.56 6.9 0.59 7.3 Factor 7 Road-salt Na+, Cl– 0.20 2.4 0.21 2.5 Factor 8 Refinery V, Mo 0.09 1.1 0.11 1.3 Factor 9 Mixed industrial Cr, Cu, Mn, Fe, Mo, Co, Ni, Sn, Ti, Zn 0.30 3.7 0.26 3.3
The major PM2.5 sources identified in Edmonton were made up of secondary particulates (i.e., those that form in the atmosphere from other gaseous pollutants). These included secondary organic aerosol (SOA), secondary sulfate and secondary nitrate and they contributed to two-thirds of PM2.5 mass concentrations on average (5.5 µg/m3). Other PM2.5 sources identified in Edmonton were made up of primary particulates (i.e., those that are directly released into the atmosphere). For these particles, soil, traffic and biomass burning emissions contributed to one-quarter of PM2.5 mass concentrations on average (2.0 µg/m3). Minor primary particle sources (road-salt, refinery and mixed industrial emissions) contributed to less than one-tenth of PM2.5 mass concentrations on average (0.6 µg/m3). Coal combustion emissions are associated with secondary particles. This is discussed further below: Secondary Organic Aerosol (SOA) – The potential for SOA formation in coal combustion plumes is
considered to be small or unimportant based on in-plume versus out-of-plume measurement studies published elsewhere. Consequently coal combustion emissions are not considered an important source of SOA identified in this study. Backward trajectory analysis was performed using the National Oceanic and Atmospheric Administration (NOAA) Hybrid Single Particle Lagrangian
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Integrated Trajectory (HYSPLIT) model. The trajectory analysis supports these published studies as it identified other plausible local and long range SOA sources. These include local sources such as vehicle exhaust and industrial activities, and distant sources such as the Yellowhead transportation corridor west of Edmonton, biogenic (rural) emissions, biomass burning (wildfire smoke), and residential wood fireplace burning.
Secondary sulfate – Secondary sulfate was interpreted to be related to background regional sulfate
that is found in high abundance due to oil and gas extraction and production activities throughout Alberta. Based on backward trajectory analysis, only a small contribution to secondary sulfate was observed from the region immediately west of Edmonton where coal combustion sources are located. The backward trajectory analysis indicated that air parcels traveling over the region immediately west of Edmonton would be, on average, associated with lower concentrations of PM2.5 for secondary sulfate at Edmonton McIntyre station compared to air parcels traveling over numerous other locations.
Possible presence of local industrial sources and backward trajectory (long-range) analysis support that coal combustion sources west of Edmonton do not dominate the contribution to PM2.5 for secondary sulfate at Edmonton McIntyre station. While the analysis undertaken here is insufficient to accurately quantify the contribution to secondary sulfate from coal combustion sources, their contribution is projected to be in the range of less than one-tenth to less than one-fifth of the secondary sulfate mass. This is consistent with a small contribution of tracer elements typically associated with coal combustion – such as Se, As, Cd, Pb, and Sn – observed with this factor.
Secondary nitrate – Secondary nitrate showed strong seasonality with the highest concentrations in
winter. Correlations of secondary nitrate with NO2, CO, THC and some VOCs such as benzene, toluene, ethylbenzene, xylene and other aromatic hydrocarbons (e.g., ethyltoluene isomers, trimethylbenzene isomers) as well as with alkanes suggest a strong influence of local sources such as vehicle exhaust and industrial activities. Backward trajectory analysis indicated that the region immediately west of Edmonton where coal combustion sources are located is not the only trajectory path associated with elevated levels of PM2.5 for secondary nitrate at Edmonton McIntyre station. Other important regional precursor sources of secondary nitrate influencing Edmonton McIntyre station are located in Alberta south of Edmonton, northwestern British Columbia and southern Saskatchewan. Plausible explanations for these regional sources include oil and gas extraction and production activities (NOX emissions) and animal feeding operations located south of Edmonton thru to the Alberta-Montana border and elsewhere (NH3 emissions). Levels of both secondary nitrate and sulfate particles tend to be simultaneously enhanced within plumes from coal combustion emissions relative to background. Again, while the analysis undertaken here is insufficient to accurately quantify the contribution to secondary nitrate from coal combustion sources west of Edmonton, their contribution is projected to be in the range of less than one-tenth to less than one-fifth of the secondary nitrate mass.
Objective 3 – Investigate origins and causes of PM2.5 concentration differences in Edmonton
during 2010 relative to other years A newspaper article circulated by the National Post newspaper in April 2015 reported that “…Edmonton had higher levels of a harmful air pollutant compared to Toronto, a city with five times the population and more industry.” The article also stated that “…particulate matter (i.e., PM2.5) exceeded legal limits of 30 µg/m3 at two city monitoring stations on several winter days in 2010 through 2012.”
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A limitation of PM2.5 monitoring data presented for Edmonton in the National Post article is that it did not acknowledge changes in the operation of continuous PM2.5 monitoring equipment during 2009. Equipment at many of the original continuous PM2.5 monitoring stations in Alberta was upgraded to better capture some components of fine particulate matter (i.e., semi-volatile material) which were lost under the previous equipment operation methods. As a result of this change, PM2.5 levels observed at air monitoring stations are higher in 2010 and subsequent years compared to previous years. For example, a scatter plot comparing the newer and older 24 h PM2.5 measurement method for 2010 showed good agreement during summer (slope = 1.06, R2 = 0.99). While during winter months (December 2009–February 2010) the newer method (slope = 1.5, R2 = 0.85) measured 50% higher concentrations than the older method. Analysis showed that a combination of: i) changes (upgrades) in operation of continuous PM2.5 monitoring equipment made during 2009, and ii) a single major wildfire smoke event in 2010 (August 19–22) explain exceedances of 3-year averages of annual 98th percentile 24 h average concentrations at the Downtown Edmonton station. Results of the PMF model were also examined further to identify how the contribution of identified sources (factors) for PM2.5 at Edmonton McIntyre station differed in 2010 compared to other years (2011–2014). Several observations are made based on this examination:
In order of relative importance, the secondary organic aerosol, secondary nitrate and biomass burning factors showed increased contributions during 2010 compared to 2011–2014.
The secondary sulfate factor showed no year-to-year variation over the 5-year period indicating that secondary sulfate precursor (i.e., SO2) emissions influencing Edmonton McIntyre station were unchanged over the period.
Other factors identified in the PMF analysis showed unimportant differences during 2010 compared to 2011–2014.
Multiple factors (i.e., secondary organic aerosol, secondary nitrate and biomass burning) other than secondary sulfate precursor emission sources showed increased contributions during 2010 compared to 2011–2014 at Edmonton McIntyre station. Levels of both secondary nitrate and sulfate particles tend to be simultaneously enhanced within plumes from coal combustion emissions relative to background. Thus the observation of secondary sulfate displaying no year-to-year variation over the 5-year period provides evidence that coal combustion emission sources would have played an unimportant role in explaining the year 2010 having a greater frequency of high PM2.5 concentration events. On the other hand, increased contributions from secondary organic aerosol, secondary nitrate and biomass burning emission sources best explains the year 2010 having a greater frequency of high PM2.5 concentration events.