Science of the Total Environment 408 (2010) 5086–5096

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Science of the Total Environment

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Topographic and spatial impacts of temperature inversions on air quality usingmobile air pollution surveys

Julie Wallace a,⁎, Denis Corr b,c, Pavlos Kanaroglou a

a Centre for Spatial Analysis, School of Geography and Earth Sciences, McMaster University, 1280 Main Street West, Hamilton, Ontario L8S 4L8, Canadab Department of Engineering Physics, McMaster University, 1280 Main Street West, Hamilton, Ontario L8S 4L8, Canadac Rotek Environmental Inc, 43 Keefer Court, Unit 104, Hamilton, Ontario, Canada

⁎ Corresponding author. School of Geography anUniversity, 1280 Main Street West, Hamilton, Ontario525 9140x28613.

E-mail address: wallaju@mcmaster.ca (J. Wallace).

0048-9697/$ – see front matter © 2010 Elsevier B.V. Adoi:10.1016/j.scitotenv.2010.06.020

a b s t r a c t

a r t i c l e i n f o

Article history:Received 8 February 2010Received in revised form 4 June 2010Accepted 16 June 2010Available online 12 August 2010

Keywords:Mobile monitoringTemperature inversionsAir qualityTopography

We investigated the spatial and topographic effects of temperature inversions on air quality in theindustrial city of Hamilton, located at the western tip of Lake Ontario, Canada. The city is divided by a90-m high topographic scarp, the Niagara Escarpment, and dissected by valleys which open towardsLake Ontario. Temperature inversions occur frequently in the cooler seasons, exacerbating the impact ofemissions from industry and traffic. This study used pollution data gathered from mobile monitoringsurveys conducted over a 3-year period, to investigate whether the effects of the inversions variedacross the city. Temperature inversions were identified with vertical temperature data from ameteorological tower located within the study area. We divided the study area into an upper andlower zone separated by the Escarpment and further into six zones, based on location with respect tothe Escarpment and industrial and residential areas, to explore variations across the city. The resultsidentified clear differences in the responses of nitrogen dioxide (NO2) and fine particulate matter(PM2.5) to temperature inversions, based on the topographic and spatial criteria. We found thatpollution levels increased as the inversion strengthened, in the lower city. However, the results alsosuggested that temperature inversions identified in the lower city were not necessarily experienced inthe upper city with the same intensity. Further, pollution levels in the upper city appeared to decreaseas the inversion deepened in the lower city, probably because of an associated change in prevailingwind direction and lower wind speeds, leading to decreased long-range transport of pollutants.

d Earth Science, McMasterL8S 4L8, Canada. Tel.: +1 905

ll rights reserved.

© 2010 Elsevier B.V. All rights reserved.

1. Introduction

Dispersion and dilution of air pollutants through vertical mixingand horizontal transport are essential to maintain acceptable airquality near emission sources. However, temperature inversions inthe lower troposphere create stable atmospheric conditions whichconstrain vertical airflow, trapping pollutants near the surface. Thisleads to poor air quality which can have serious health consequencesranging from exacerbation of respiratory diseases (Wallace et al.,2010) to premature death (Laskin, 2006).

Various topographic landscapes and meteorological factors areconducive to the development and strengthening of temperatureinversions. Radiation inversions occur near the surface and arecommon on cold, clear nights, when the Earth's surface loses heatrapidly, warming the air above. If this occurs in a valley or basin, coldair on the sides of a valley will flow down the slopes and settle under

the warm air, enhancing the inversion. Advective inversions occurwith a horizontal flow of cool air, such as from a lake or ocean, underwarmer air from the land. Subsidence inversions occur when airassociated with high pressure systems descends to lower elevations,warming as it is compressed. If there is cooler air at the surface, suchas from an onshore ocean or lake breeze, an inversion configuration iscreated. These types of inversions all occur in the study area.

We investigate the impact of topography on concentrations ofnitrogen dioxide (NO2) and particulatematter of aerodynamic diameter2.5 μm or less (PM2.5) during temperature inversions, and assess thespatial variation of these pollutants across the City of Hamilton, Ontario,Canada. This urban area has varied topography,which, coupledwith theproximity to Lake Ontario, affects local meteorology.

Pollution data were acquired from mobile air pollution surveyswhich have been ongoing since 2005. The rationale for mobilemonitoring lies in its flexibility to measure air pollution throughoutthe city. While fixed monitors are invaluable to record pollutioncontinuously and over the long term, they do not capture spatialvariability across the entire city, since they are fixed in a singlelocation. Mobile monitoring complements fixed monitors withcitywide surveys which capture the spatio-temporal variability

5087J. Wallace et al. / Science of the Total Environment 408 (2010) 5086–5096

and can also target locations of particular concern. The measuredvalues represent instantaneous quantities, often close to the pollutionsources, and hence have higher spectral variability when compared tofixed air quality monitors that average, and therefore smooth, theconcentrations over an hour or day. The advantages and versatility ofmobile monitoring have been discussed in other studies in Beijing(Wang et al., 2009), Helsinki (Pirjola et al., 2004), Zurich (Bukowieckiet al., 2002) and Las Vegas (Etyemezian et al., 2003). The pollutantsNO2 and PM2.5 have been chosen as the foci of this study as both havebeen shown to have adverse health impacts (Pope et al., 2009; Latza etal., 2009).

While Hamilton is our study area, there are many urban areasglobally that bear similar characteristics of moderate to highpollution, topographically constrained wind flows and frequenttemperature inversions. A few examples are Los Angeles, California(Lu and Turco, 1995), the Highveld Plateau industrial region in SouthAfrica (Ross et al., 2003; Jury and Tosen, 2004) and Perth, Australia(Pitts and Lyons, 1988).

2. Study area

The study area is centred on the city of Hamilton, Ontario andextends into the neighbouring city of Burlington (Fig. 1). Hamilton is amid-sized industrial city with a population of approximately 500,000.Major trans-Ontario and trans-USA highways are routed through thecity and emissions from transportation augment industrial emissions.The Census Metropolitan Area shown in Fig. 1 covers approximately1300 km2.

The topography has significant impact on local meteorology. Thestudy area is divided topographically by the Niagara Escarpment, a 90-m high limestone cuesta which divides the area into a lower city alongthe shoreline of Lake Ontario, and an upper city (Fig. 2). The upper andlower sections are primarily smooth landscapes, dissected by two

Fig. 1. Location of study area

major valleys, the Dundas Valley and the Red Hill Valley (Fig. 2).Elevation in the study area ranges from 74 m at lake level to 240 m onthe plateau. The population is divided approximately equally betweenthe upper and lower areas, and the major industrial zone is located inthe lower city (Fig. 1).

Temperature inversions occur frequently and air qualityexceedances are often associated with these inversions (MOE,2007). The number of smog advisory days in the city averaged 30per year between 2002 and 2007 (MOE, 2009). The inversionsdevelop under typical meteorological conditions (Oke, 1987) butare also strongly influenced by topography. Radiation inversionsare most common and occur primarily in the winter, spring andfall seasons. They often dissipate in the morning as the sun warmsthe surface, but may persist during the daytime. The NiagaraEscarpment facilitates advective inversions which occur duringdaytime when cool lake breezes flow onto the lower city, underthe warmer land air from the upper city. Both daytime andnighttime inversions are enhanced in the valleys which channelwind flows and trap pollutants. At higher tropospheric heights,subsidence inversions also occur.

Very few published studies on temperature inversions in the cityexist. Wallace and Kanaroglou (2009) have used atmosphericsatellite data to identify inversions in the lowest 1 km of thetroposphere over Hamilton and surrounding areas. These inversionswere regional, rather than localized, and were most prevalent in thespring and winter. An earlier study by Rouse et al. (1973) identifiedlocalized elevated inversions over the industrial sector in Hamilton,at altitudes of 1070 m–1980 m. That study was conducted using athermopile transducer mounted on the wing of a Cessna 172 aircraft.The authors surmised that the pollution and heating effect of theindustrial zone contributed to the temperature inversions whichwere not as well developed or frequent in other areas of the city,suggesting a localized phenomenon.

and mobile survey zones.

Fig. 2. Hillshade model of Hamilton showing the Niagara Escarpment, fixed air quality monitoring stations and meteorological stations with wind rose diagrams, for inversion days,classified by wind speed.

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3. Methodology

3.1. Mobile monitoring

The mobile monitoring methods are fully described in Wallace etal. (2009) so we present only a synopsis of the methodology here. Themobile unit is a large, enclosed van equipped with gas and particulatemonitors, a Global Positioning System (GPS) unit and computerlaptop, all powered by a battery pack. Among the monitors are aTECO™ Model 42C NOx Analyzer and a Grimm™ Model 1.107 DustMonitor, which record data for NO2 and PM2.5, respectively. Themobile unit traverses highways and arterial routes throughout thecity. Pollution data are collected each minute as the mobile unittravels as slowly as permissible along the roads. Locations aremappedwith the on-board GPS.

In the city of Hamilton, road traffic is a very important source ofpollution.While the industrial areas are confined to specific zones in thecity, heavily traversed roads are located throughout the extent of city.The surveysweredesigned to access asmanymajor roads andhighwaysas possible, and to include minor neighbourhood roads as well.Numerous studies have pointed to the detrimental effects of trafficpollution on commuters and residents who live close to major roads(e.g. Peters et al., 2004; Gauderman et al., 2007; Jerrett et al., 2009;Wallace et al., in press). Some studies have shown that this effectdiminishes exponentially with distance but linger within 200–500

meters of the roads (Zhou and Levy, 2007). A study by Buonocore et al.(2009) shows that ultra-fine particles in a neighbourhood diminishedby just 50% at a distanceof 400 m frommajor roads. The city ofHamiltonhas many neighbourhoods that are located in close proximity to majorroads,withmanyof themboundedbymajor roads on twoormore sides.Traffic emissions contribute significantly to the air quality in Hamiltonand it is therefore important that emissions from the entire network,including major and minor roads, be monitored.

Our data were collected over a 3-year period. The same roads weresurveyed on both normal and inversion days, so that the type of roaddoes not contribute to the differences observed in each scenario. Thesurveys were conducted during the daytime and in all seasons, withmultiple visits to each area over the 3-year period. The mobile datawere preprocessed to merge the GPS and pollution data. The prepareddata included attributes of date and time and concentrations of NO2

and PM2.5, with associated GPS locations. Metadata includedconsiderable notes to record observations relevant to the survey,such as anomalously high values. For example, NO2 values in excess of200 ppb may have an annotation such as “surrounded by passingtrucks” or a PM2.5 value of 732 μg/m3 may be noted in an area of highroad dust. PM2.5 values ranged from 0 to 732 μg/m3, with median16 μg/m3 and interquartile range 13–20 μg/m3. The highest extremevalues ranged from 267 to 732 μg/m3. We placed the cutoff at the 90thpercentile value of 62 μg/m3. This would allow the anomalously highvalues to be omitted while retaining 90% of the data points. NO2

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values ranged from 0 to 278 ppb, with median of 19 ppb andinterquartile range of 11–29 ppb. Highest extremes ranged from 174to 278 ppb. We placed the cutoff at the 90% percentile value of 89 ppb.The data points were mapped in ArcGIS (ESRI, 2009).

3.2. Vertical temperature data

Vertical temperature data were obtained from archived recordsfrom a 91-m high meteorological tower (MT), located on WoodwardAvenue in the lower city (Fig. 2). This station is maintained by theOntario Ministry of the Environment (MOE) in conjunction with alocal environmental company, Rotek Environmental Inc. Tempera-tures are measured at heights of 10, 30 and 91 m. Temperaturesensors are R. M. Young platinum resistance devices with aspiratedradiation shields. The instruments are calibrated every 2 years as permanufacturer's specifications. The station is monitored electronically,and temperatures are recorded each minute. In this study, temper-ature differences between the 91 m and 10 m heights were used todetermine normal and inversion scenarios. Data for 2005–2008 wereretrieved and hourly averages were computed. The strength of theMT-measured inversions averaged 1.05 °C during daytime mobilemonitoring hours.

Positive temperature differences (Δt) between the 91 m and 10 mheights were designated an inversion configuration while negativevalues were considered normal scenarios. A Δt of 0 C is indicative ofstable air and was also designated an inversion. A relational join wasused to link hourly Δt values to the corresponding hours and dates ofthe mobile surveys, as well as the NO2 and PM2.5 values at that time.In order to characterise the pollution levels in each of the six zones on

Fig. 3. Wind rose diagrams showing the frequency of wind directions classified by wind spinversion, c: Lower City normal, d: Lower City inversion. Tick marks represent 1% frequenc

normal and inversion days, NO2 and PM2.5 in each zone wereaveraged for each Δt value. In total, this study compiled NO2 andPM2.5 for 46 days of mobile air pollution surveys carried out from2005 to 2008. Data consisted of over 115,000 data points (Fig. 1)collected over 210 h of surveys, and composed of 142 normal verticaltemperature profiles and 68 inversion profiles.

3.3. Wind data

Other meteorological data (wind speed and direction) wereacquired from two stations representing the lower city and uppercity. Data for the lower city were obtained from the Woodward MTand for the upper city, from the Environment Canada meteorologicalstation located at the Hamilton International Airport (Fig. 2). Data forthe hours from 8:00 am to 5:00 pm local time were extracted tocoincidewith the hourswithinwhichmobile surveyswere conducted.Hourly data for 2005–2008 were joined by date and hour to Δt datafrom the Woodward MT, in a relational database. The wind data foreach station, for the 4 years, were therefore associated with inversionand normal hours based on the Δt values from the MT. In total therewere 2703 inversion hours and 11,906 normal hours linked to thewind data.

Rose diagrams depicting wind direction and wind speed wereconstructed for both inversion and normal hours (Fig. 3). Winddirections were binned in 10-degree sectors, with the length of eachsector representative of the percentage of data values included in thatbin. Each sector was further classified by wind speed as designated bya colour legend. The length of each coloured section indicates thepercentage of wind speed values within that bin.

eed, during the hours from 8:00 am to 5:00 pm: a: Upper City normal, b: Upper Cityy.

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3.4. Zones

To assess the topographic and spatial effects of temperatureinversions, the city was first divided into two zones, one representingthe lower city (LC) (Fig. 1, zones 1, 2, 3, 6 combined) and the otherrepresenting the upper city (UC) (Fig. 1, zones 4 and 5 combined). TheNiagara Escarpment defined the division between the UC and LC. Thecity was then divided into six zones as shown in Fig. 1, to determinewhether a more detailed spatial effect was evident. The zones weredefined first, by topography and second, based on proximity toindustrial, residential or commercial areas. The zones were: 1—LC_West, which is mixed residential and commercial, and close toindustry; 2—LC_East, which is mixed residential and commercial andclose to industry; 3—Dundas Valley, which is west of industry,primarily residential and a major topographic feature, 4—UC_Centralwhich is mixed residential and commercial and lies south of industry,5—UC_South which is less dense residential and furthest south ofindustry, and 6—Burlington which is mixed residential and commer-cial, and lies north of industry (Fig. 1). Table 1 displays the breakdownof survey hours and data points for each zone.

3.5. Data validation

Our best data for validation of the mobile monitored NO2 andPM2.5 data were obtained from fixed air quality monitoring stationsin the city—the Hamilton Mountain station in the upper city, and theHamilton Downtown station in the lower city (Fig. 4). We comparedmobile data from the zones within which these stations are located, tothe station data. Because the fixed stations measure pollution at apoint location, smoothed over time, while the mobile survey data areaveraged over the zone, we do not necessarily expect an accuratematch in actual values, but perhaps a similarity in the normal-inversion day spatio-temporal patterns. Hourly data from the fixedmonitors for 2006–2008 provided approximately 11,000 values fordaytime hours from 8:00 am to 5:00 pm. These were joined in arelational database to the vertical temperature profiles from the MT.Values for PM2.5 and NO2 were averaged for unique Δt values, asapplied to the mobile data.

4. Results and discussion

4.1. Wind direction and wind speed

We first discuss the distribution patterns revealed by the windrose diagrams, as these have significant bearing on the effects of thetemperature inversions across the city. The results of the mobilesurveys follow, and an account of the data evaluation with fixedmonitoring stations is discussed.

The wind rose diagrams (Fig. 3) identified pronounced differencesin the wind direction and wind speed between the upper and lowercity, as well as between the inversion and normal scenarios. During

Table 1Number of days and hours spent and number of data points in each mobile monitoringzone.

Zone Location Days Hours Data points

1 Lower City West 45 159 45,0252 Lower City East 46 111 41,2683 Dundas Valley 16 30 40854 Upper City 20 34 11,9825 Upper City South 11 19 41460 City Totals 46 210* 115,814

*142 normal and 68 inversion hours.

normal configurations, the predominant winds over the upper citywere the prevailing SW winds (48%) with only 26% from the NE(Fig. 3a). The resultant wind direction was 265°. During inversionconfigurations, this proportion was virtually unchanged, with 49%from the SW and 26% from the NE (Fig. 3b) but the resultant wasmoresoutherly, at 226°. Wind speeds over the upper city averaged 19 km/h during normal scenarios and 17 km/h during inversions, with 1.3%recorded wind speeds of 0 km/h during inversions.

In the lower city on normal days, SW winds also prevailed with45% frequency, complemented by 34% from the NE (Fig. 3c). Themeanresultant wind direction was 266°. During inversions however, NEwinds accounted for 51%, with 31% from the SW (Fig. 3d) and a meanresultant direction of 066°. The lower city was therefore primarilyinfluenced by NE winds during inversions measured by the MTlocated in the lower city. This is particularly important point, since NEwinds transport pollution from the industrial areas and majorhighways over the lower city, thereby compounding poor air quality(Fig. 1). Wind speeds were lower than those in the upper city,averaging 13 km/h during normal configurations and 11 km/h duringinversions, with 0.5% recorded wind speeds of 0 km/h duringinversions.

Windroses superimposed on the hillshade model at the appro-priate meteorological station location (Fig. 2) highlight the differ-ences in wind flows over the city. The logical implication is that theNiagara Escarpment strongly influences the observed differencesbetween the upper and lower city, for an inversion measured in thelower city.

4.2. Mobile data

Fig. 5 shows scatter plots of mobile monitored NO2 and PM2. 5versus Δt for the city divided into two groups – one for the entire UCand the other for the LC. Both pollutants increase withΔt in the LC, butdecrease with Δt in the UC. Regression coefficients are higher for NO2

(0.19 and 0.14) compared to PM2.5 (0.04 and 0.01) in the LC and UC,respectively. Slopes of the regression lines are also higher for NO2 (1.9and -1.5) than for PM2.5 (1.28 and -0.38) in the LC and UC,respectively. Both results suggest that NO2 is more sensitive to thestrengthening of the inversion, in general, and that the effect of theinversion is stronger in the lower city. We define the stability value ofthe pollutant as the intercept value at Δt=0. For both pollutants, thisvalue was higher in the LC, indicating higher levels of pollution. Toexplorewhether the differences observed in these two large zones canbe identified in smaller spatial divisions, we examine the results forthe six zones.

4.2.1. Mobile monitored NO2

Scatterplots of NO2 versus Δt for each of the mobile survey zonesshown in Fig. 1 are presented in Fig. 6. The scatterplot for Zone 6, thecity of Burlington, has not been presented as there were only twopositive Δt points, and therefore cannot be interpreted reliably.

Regression lines were plotted for the inversion configurationsonly, that is forΔt values 0 °C or greater, and the associated equationsand regression coefficients are related to these values only.Regression parameters were determined at the 5% level of signifi-cance. The results for Zone 1, LC_West, show an increase in NO2 withincreasing Δt, as the inversion strengthens. The R2 coefficient is 0.24with a slope of 3.7 (ppb per 1 °C), and a stability value of 19.0 ppb.The relationship is similar in Zone 2, LC_East, but with a more gentleslope of 1.6 and R2 coefficient of 0.14, but a similar stability value of19.3 ppb. This zone lies east of the main industrial zone and includesthe Red Hill Valley, a potential pollution trap during inversions.During an inversion, gentle NE winds place both zones downwind ofindustry and major highway traffic, thus compounding the effect ofthe inversion.

Fig. 4. Air quality monitoring station located in the Upper City (top) and Lower City (bottom).Source: maps.google.com

5091J. Wallace et al. / Science of the Total Environment 408 (2010) 5086–5096

Zone 3, Dundas Valley, exhibits a pattern that is not very clearlydefined for NO2. The inversion values show a tendency to decreasebut exhibit a cluster of values at Δt=+1. The slope is −1.98 with anR2 coefficient of 0.17 and a stability value of 24.6 ppb. This stabilityvalue is comparatively high, exceeded only by Zone 5, the UC_Centralzone. Zone 4, which demarcates the UC_Central zone, exhibits a

distinct decrease in NO2 with increasing Δt. The decrease occurs at arate of 5.5 ppb per 1 °C with an R2 coefficient of 0.35. This is alsoobserved in Zone 5, the UC_South zone, which displays a decline inNO2, at a rate of 6.3 ppb per 1 °C, with high R2 coefficient of 0.76. Itmust be noted that while the declining trend is clearly evident, the R2

values must be interpreted with caution, as the regression point

Fig. 5. Mobile monitored NO2 and PM2.5 versus vertical temperature difference (Δt). 1: NO2 Lower City, 2: NO2 Upper City, 3: PM2.5 Lower City, 4: PM2.5 Upper City.

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scatter is relatively sparse but nonetheless, persistent. Zone 0combines all the zones to represent the entire city, and displaysincreasing NO2 with strengthening of the inversion. The regressioncoefficient is 0.12 with a slope of 2.5 ppb per 1 °C, and a stabilityvalue of 18.8 ppb. This plot is most similar to those of the lower city.

4.2.2. Mobile monitored PM2.5PM2.5 plots are displayed in Fig. 7. Zone 1 shows a positive slope of

3.96 (μg/m3 per 1 °C), and R2 coefficient of 0.27. In Zone 2, theregression slope is 2.7, with an R2 coefficient of 0.15. The DundasValley (Zone 3) shows the strongest increase of 7.8 μg/m3 per 1 °C and

Fig. 6.Mobile monitored NO2 versus vertical temperature difference (Δt). 1: Lower City_WeCitywide.

R2 of 0.83. Mindful of the fact that the town of Dundas does not haveindustries that are likely to generate high particulate concentrations,the sharp rise in PM2.5 during inversions suggests that the DundasValley is strongly impacted on inversions days, and the effect is likelyenhanced by particulates transported from traffic and industrysources, by the associated NE winds.

Zone 4 in the upper city shows a decline in PM2.5 withincreasing Δt. The relationship appears to be well defined, with anR2 value of 0.8 and a decline of 8.2 μg/m3 per 1 °C. While thedeclining trend is clear, the high R2 value itself should not beinterpreted strictly because of the sparse point scatter. This is also

st; 2: Lower City _East; 3: Dundas Valley, 4: Upper City_Central; 5: Upper City_South; 0:

Fig. 7.Mobile monitored PM2.5 versus vertical temperature difference (Δt). 1: Lower City_West; 2: Lower City _East; 3: Dundas Valley, 4: Upper City_Central; 5: Upper City_South;0: Citywide.

5093J. Wallace et al. / Science of the Total Environment 408 (2010) 5086–5096

true for Zone 5, the UC_South, which also shows a marked declinein PM2.5 with increasing Δt. The plot for the overall city (Zone 0)defines a positive relationship, with a slope of 5.0 and regressioncoefficient of 0.45.

Table 2 displays the average concentrations of PM2.5 and NO2 fornormal and inversion scenarios, for each zone. Increases in averageNO2 in the lower city zones were observed on inversion days.Analysis of variance (ANOVA) indicates that the difference for theLC_West zone is statistically significant at the 5% significance leveland marginal (p=0.06) for the LC_East zone. A modest increase wasalso observed in the UC_Central zone but is not statisticallysignificant. This increase does not in any way detract from thedecreasing trend observed on during inversions (Fig. 6), since thescatter of non-inversion values includes a range of low values as well.Inversion NO2 in the UC_South and Dundas Valley was also lower butnot statistically significant. While PM2.5 averages were higherduring inversions, in all cases except for the upper city overall,none of the differences were statistically significant. Again, the

Table 2NO2 and PM2.5 averages during normal and inversion scenarios from mobile monitoring su

Mobile monitoring

NO2 ppb

Location Normal Inversion AN

Mean Std Dev Mean Std Dev F

Lower city 17.2 6.4 22.7 6.6 6.Upper city 19.7 5.3 16.1 4.9 2.Lower city west 18.0 6.3 24.1 7.3 6.Lower city east 16.8 7.8 21.4 5.1 3.Dundas Valley 22.4 7.5 20.8 7.6 0.Upper city central 20.0 5.2 21.8 7.0 0.Upper city south 18.9 8.1 10.6 6.3 2.City 17.1 6.4 22.1 6.0 6.

Fixed air quality monitorsDowntown (lower city station) 11.8 4.9 22.3 5.8 61.Mountain (upper city station) 7.7 3.4 11.4 2.7 9.

higher inversion values do not in any way diminish the increasing ordecreasing trends identified during inversions (Fig. 7), as the range ofvalues in both normal and inversion situations masks the patternsdisplayed with the strengthening of the inversion.

4.3. Data validation—fixed air quality stations

Fig. 4 shows the location of the two air quality stations used forevaluation of the mobile survey data. The Hamilton Downtownstation is located in an industrial–commercial zone, and issurrounded by a mix of buildings and paved or unpaved openspaces. A few large trees are in close proximity. The HamiltonMountain station is located in a residential area, at one end of apark. It is located near a minor residential road, and approximately250 m from a major road. It is shielded from prevailing winds bylarge buildings to the west and south. Both stations capturepollution from their respective immediate vicinity, which aremarkedly different. It can be argued that measurements at neither

rveys and fixed air quality monitoring stations.

PM2.5

OVA Normal Inversion ANOVA

p Mean Std Dev Mean Std Dev F p

6 0/01 19.2 11.6 20.7 8.5 0.2 0.672 0.16 16.7 5.4 15.9 7.2 0.1 0.803 0.02 19.7 12.5 21.1 8.0 0.1 0.719 0.06 18.4 10.6 19.8 6.8 0.2 0.662 0.69 20.3 5.8 23.1 13.2 0.3 0.574 0.54 16.3 5.9 16.7 7.0 0.0 0.913 0.16 16.3 3.2 17.4 7.6 0.1 0.763 0.02 19.3 11.6 20.4 8.4 0.1 0.75

0 b0.01 8.5 2.5 16.3 5.9 42.9 b0.010 b0.01 8.4 2.3 11.6 3.1 21.8 b0.01

5094 J. Wallace et al. / Science of the Total Environment 408 (2010) 5086–5096

of these stations are truly representative of the levels of pollutionover the entire city.

We compared values from the fixed Hamilton Mountain station tothe average of the mobile survey data for the UC_Central zone whichincludes this station location, and the Hamilton Downtown station tothe mobile data for the LC_West zone which includes this stationlocation. Four characteristics were evident in these results, shown inTable 2. First, in the lower city, both mobile and fixed data showedincreases in NO2 and PM2.5 on inversion days. Second, the lower cityvalues were generally higher than upper city values for both fixedstation and mobile survey data. Third, mobile data values were higherthan fixed station data and the inversion day increases were lowerthan those of the fixed station data. These results are not surprisingsince the lower city is more polluted because of industry and traffic,and experiences greater restriction of air flow because of theEscarpment. The mobile data values were higher because theyrepresent instantaneous values from on-road and other sources,with higher variability in the data.

The fourth characteristic was the lower inversion day increases inthe upper city, compared to the lower city, for both fixed station andmobile survey data. This lends support to the stipulation that thestrength and effect of the inversion weremore subtle or non-existentin the upper city. The characteristics of the fixed station data becomeclearer when we examine graphs of average PM2.5 and NO2 for bothstations (Fig. 8). These graphs display the average pollution level foreach Δt. We note that the Hamilton Downtown station showed thatNO2 and PM2.5 increasedwithΔt. The HamiltonMountain station, onthe other hand, showed that for values higher than Δt=−0.5, bothPM2.5 and NO2 exhibit very little change with Δt. For the highest Δtvalues, there was an increase in the scatter of pollutant values, butthe slopes of the graphs remained very shallow. Hence, while theaverages in Table 2 indicate that pollution on inversion days wassignificantly higher than on normal days, the graphs portray a trendof values increasing to the point of stability, but with little variationonce stability was attained. On the other hand, the graphs for the

Fig. 8. NO2 and PM2.5 measured at the fixed air quality monitoring stations, versus verticalMountain station, 3: PM2.5 at Hamilton Downtown station, 4: PM2.5 at Hamilton Mountai

lower city indicate a clear trend of increasing pollutant level with thestrengthening of the inversion.

5. Discussion

There results indicated clear differences between upper and lowercity responses to temperature inversions recorded in the lower city.This was seen in the wind data (Fig. 3) as well as the pollution data(Figs. 5–7). During inversions, winds in the upper city were primarilySW prevailing winds while in the lower city, they were NE winds. Wenoted that both pollutants increased with the strengthening of theinversion in the lower city. However, a declining trend was observedin mobile survey data in the upper city, during inversions. This is aninteresting and previously unknown phenomenon. When we com-pare this to the data from the fixed stations (Fig. 8), we note that,while PM2.5 and NO2 did not decrease on inversion days at the fixedstations, there was in fact, very little change in the pollution levels.This contrasts with the marked increases observed in the lower city.This in itself is significant because one would expect to see an increasein concentrations of pollution on inversion days at any location in thecity. It seems evident that there is some other explanation for thisphenomenon. The obvious inference is that the inversions measuredand experienced in the lower city are not similarly experienced in theupper city, likely because of the presence of the Escarpment. First, theEscarpment promotes the development of near-surface, advectiveinversions in the lower city, as cool lake breezes flow onshore over thelower city, under warm air from plateau. The strength of the inversionis enhanced by heating from industrial smoke stacks in the lower city,and the urban heat island effect as discussed by Rouse et al. (1973). Inaddition, the Escarpment acts as a barrier to the free movement of airover the city, confining the lake breezes to the lower city.

While the conclusion that the upper city does not similarlyexperience the inversion accounts for the absence of an increase inconcentrations of PM2.5 and NO2 in the upper city, it does not explainthe decline in pollution values observed in mobile survey data. The

temperature difference Δt. 1: NO2 at Hamilton Downtown station, 2: NO2 at Hamiltonn station.

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question that is immediately posed is whether or not this is a realphenomenon, or a flaw in the data. In support, we note that the fixedstation data also indicate a difference in the upper city response. Thephenomenon persists with both pollutants and in both the UC_Centraland UC_South zones, and therefore is unlikely to be accidental. Wealso have many hours of monitoring data over all seasons, over3 years, to suggest that we have sufficient data points.

One possible explanation is that themeteorological conditions thatare conducive to the formation of inversions in the lower city are alsoassociated with lower levels of air pollution in the upper city. This isevident in the wind rose diagrams in Fig. 3. Under normal verticaltemperature configurations, SWwinds prevail in the upper and lowercity, with mean resultant wind directions of 265° and 266°,respectively (Fig. 3a, c). During inversions, the upper city winds aremore southerly (mean resultant 226°) with lower speeds. Thesewinds are not experienced in the lower city (Fig. 3d), but instead, lightNE winds dominate. We know that the regional scale prevailing SWwinds are typically associated with high levels of transportedpollutants from industries in Ohio, Michigan and southwest Ontario(Yap et al., 2005; Wallace and Kanaroglou, 2009). Less long-rangepollution comes directly from the south, so the lighter SSW winds oninversion days would be consistent with declining pollution levels inthe upper city, as the localized inversion deepens in the lower city.

It is also worthwhile to note that during inversions, the light NEwindsmay cause pollution from the lower city to drift over the edge ofthe Escarpment and into the northern extent of the UC_Central zone.This may account, in part, for the wider scatter and lower rates ofpollution decrease in this zone, when compared to the more distantUC_South zone (Figs. 6 and 7). The stronger decline in NO2 in theUC_South zone is indicative of the diminished effect of the inversionmeasured in the lower city, less pollution from long-range transportand less pollution from the lower city industrial sector. TheUC_Central zone contains the Hamilton Mountain fixed station, andthis spill over effect may also explain the very small increases inpollution recorded on inversion days by this station.

The localized nature of the inversions measured in the lower city issupported by an earlier study byWallace and Kanaroglou (2009), usingvertical temperature profiles from the Atmospheric Infrared Sounder(AIRS) (NASA, 2009). The satellite sensors record regional data, overtens and hundreds of kilometers. The authors observed that only 30% ofdaytime inversionsmeasured by theWoodwardMT coincided with theinversions recorded by AIRS. This adds to the evidence that the daytimeinversionsmeasured by theMT are local, low-level inversions which donot necessarily apply to the upper city or regionally. Rouse et al. (1973)also discussed the localized nature of inversions over the industrial area.The Escarpment effect is observed in other cities, such as Perth, Australiawhich sits at the base of the Darling Scarp, an escarpment which rises300 m above the city. The Darling Scarp lies about 20 km from the coastand this scenario is therefore a larger scale version of the Hamiltonlandscape. Pitts and Lyons (1988) determined that radiosondemeasurements taken approximately 7 km inland, west of the Scarp,were not representative of the coastal plains below the Scarp.

The study also demonstrated an increase in PM2.5 in the DundasValley on inversion days, coincident with a decline in NO2. Thesmog layers caused by inversions are very visible in the DundasValley, and the decline in NO2 seems counter-intuitive. It is possiblethat there is a complex effect because of the valley itself. Pisano etal. (1997) demonstrated that in the complex topography of theLower Fraser Valley in British Columbia, Canada, NO2 concentra-tions during inversions were 10 times higher 300 m above thesurface than at ground-level. This indicated that on inversion days,NO2 in the valley may become entrained at higher elevations abovethe valley floor. When the inversion dissipated and there wasthorough mixing of the layers, the layering of pollution was notevident. McKendry et al. (1997) and Lehner and Gohm (2010)discussed the phenomenon of pollutants being vented along the

heated sidewalls of valleys to elevations above the inversions. Wealso noted that the stability value for NO2 in the Dundas Valley wasrelatively high, at 24.6 ppb, compared to 19.1 in the LC_West zone,and was exceeded only by the UC_Central zone. It may be thatsimilar phenomena associated with the topography of the valley,combined with the complex chemical reactions with NO, NO2,ozone and other pollutants (Sillman and He, 2002) are responsiblefor the apparent decline in NO2 at surface, in this valley. Thephenomenon was probably not observed in PM2.5 because the solidparticles settle more easily to the lowest layers near surface. TheDundas Valley is a small geographic area with two major accessroads and large tracts of designated conservation lands. We wouldlike to increase the survey hours in this zone to confirm theseresults.

While this valley does not house high emission sources, theinversion increase in PM2.5 can be explained by transport of PM2.5from roadways and industry, by NE winds. The pollutant becomestrapped in the valley and the effects are exacerbated by theinversions. An extreme case of the inversion effect in valleys occurredin a pollution episode in the Meuse Valley, Belgium in 1930 (Nemeryet al., 2001), when 60 deaths were attributed to air pollution build-up. The Muese Valley is 1–2 km wide, surrounded by hills 90–120 m,and contained industries within the valley. A similar disasteroccurred in the horseshoe-shaped valley of Donora, Pennsylvania in1948, when 20 people died as a result of industrial pollution trappedby an inversion in the valley (Snyder, 2003). While in both cases, thedevastation occurred in the early to mid 20th century prior topollution controls, and the offending industries were located in thevalleys, the scenarios highlight the amplified effect of restrictedpollution dispersion within the valleys, particularly during temper-ature inversions. Although the pollution levels now experienced aresignificantly lower than these historical situations, it remains truethat currently there are significant health impacts from poor ambientair quality. These inversion-related health impacts are significantbecause much greater numbers of people are exposed to the harmfullevels of pollution.

6. Conclusions

The study sought to investigate the spatial and topographic effectsof temperature inversions on NO2 and PM2.5 in an urban area withdistinct topographic features of moderate relief. Mobile pollutionmonitoring revealed that the effect of inversions varies across the cityand with topography. In particular, the Niagara Escarpment hassignificant impact, and the intensity of inversions measured in thelower city do not necessarily apply to the upper city. These low-level,localized inversions were associated with light NE winds in the lowercity while SSWwinds prevailed in the upper city. Both NO2 and PM2.5exhibited an increase in concentration with deepening of theinversion in the lower city. Northeast winds associated with theinversions place the lower city and Dundas Valley downwind ofindustry and major highway traffic, thus compounding the effect ofthe inversion. We observed the opposite effect in the upper city, withdecreasing NO2 and PM2.5 levels during inversions measured in thelower city. We propose that this is partially the result of less long-range transported pollutants on inversion days which are associatedwith a shift in wind direction, from SW to SSW in the upper city, andwith lower wind speeds.

The results of this study highlight the impact of varied topographyon air quality in urban environments, during temperature inversions.The inversions, coupledwith light NEwinds, will not only increase thelevels of pollution but also expose up to 250,000 citizenswho reside orwork in the lower city. The overall health impact is high in terms ofthe number of persons affected. This is added to the fact that personswho live closer to the industry, highways and other sources arealready exposed to higher levels of pollution than those who live

5096 J. Wallace et al. / Science of the Total Environment 408 (2010) 5086–5096

further away. As a result, these data have immediate policyimplications for pollution control. Mobile monitoring is ongoing andthis study will be strengthened as additional data are added to thecurrent inventory.

Acknowledgments

We would like to thank Bill Branch, Paul Daszko and Ralph Frisinafor technical support, Anna Corr and John Corr for assistance with datapre-processing and Clean Air Hamilton, the Ontario Ministry of theEnvironment and GeoConnections Canada for financial support.

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