using analysis over multiple time scales to assess air
TRANSCRIPT
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Using Analysis over Multiple time Scales to Assess Air Quality Over
Delhi
Milind KandlikarArvind Saraswat
UBC
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This Talk
• Overview of Air Quality in Delhi• Detection of Trends, Seasonal Cycles and
Oscillations using multiple approaches– Daily Data at a hotspot (ITO)
• Spectral Analysis (FFT, Singular Spectrum)• Weekend-Weekday differences
– Monthly Data at Multiple Locations• Trend detection
– Hourly Data at hotspot (ITO)
• Insights
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Criteria Pollutants
?Lung DamageMortality?
0.08 ppm (8 hour)Ozone
40% Transport60% Industry
PM, O3, ToxicsPrecursor
0.053 ppm (100 µg/ m3)-ANOx
20% Transport
80% IndustryRespiratoryPM precursor
0.03 ppm (80 µg/ m3)-A0.14 ppm (364 µg/ m3)-D
SOx
65% Transport
10% Biomass
25% Industry
Acute effectsOzone formation
9 ppm/10 mg/ m3 (8 hr)35 ppm/40 mg/m3 (1 hr)
CO
20% Biomass
30% Transport fuel
50% Rest
Mortality (Heart disease, Lung Function, Stroke)
50 µg/ m3 (A)150 µg/ m3 (D)
PM-10
A- Annual; D- Daily
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Delhi Air Quality
HEI (2001-2004)
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Delhi Data at Multiple Time Scales
• Daily average data (2000-2006) from CPCB for PM-10, CO, NOx, SOx at a hotspot (ITO Crossing)
• Monthly Data at 7 locations (2000-2007)
• Hourly data at hotspot (2006-2007)
• Data at different scales provide us with different insights
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Analysis of Daily Data
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PM-10µ = 234 µg/ m3
σσσσ = 125 µg/ m3
Daily limit exceeded on 80% of days!
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µ = 3.45 mg/ m3
σσσσ = 2.62 mg/ m3
CO
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µ = 10.9 µg/ m3
σσσσ = 4.5 µg/ m3
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µ = 80 µg/ m3
σσσσ = 23 µg/ m3
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Spectral Analysis
• Trends and oscillations in the Data • Trends correspond to low frequency
components• Three Methods
– Power Spectral Density• Fourier Transform of the Auto-Correlation function of a
time series
– Singular Spectrum Analysis• Preserves phase information• Non-linear trends and oscillations
– LOWESS Regression
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Power Spectral DensityCO and NOx
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Power Spectral Density PM10 and SOx
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FFT findings
• Low frequency variation, i.e., trends, dominate for CO, NOx, and SOx, but not PM-10.
• Annual/seasonal cycles for all pollutants• Filter noisy data and decompose into
trends and seasonal cycles
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Trend and Seasonal Cycle Detection
• The Model
– X(t) = T(t) + ΣΣΣΣOi(t) + εεεε– T(t) = Trend
– Oi(t) = ith Oscillation /Seasonal Cycle
– & εεεε is zero mean noise
• Find Trend and Oscillations in the face of “Red Noise”
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Singular Spectrum Analysis (Overview)
• A Method to Detect Oscillations – including long term ones (“trends”)
• Suited for environmental variables (climatology) because it allows for red noise
• An “optimal” spectral approach – unlike FFT, orthogonal functions emerge from data.
• Allows for non-linear modulations
• Very similar to Principal Components Analysis
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PM10 with Component overlay
T +O1+O2 (47%)T +O1 (31%)T (7%)
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Reconstructed Components
16%22%
11% 9%
F=1/yr
F=2/yr F=3/yr
25%
16%
11%
F=1/yr
F=2/yr
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SOx Reconstructed Components
8%
F=1,0.4/yr
8% F=2/yr
32%
Net 48%
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Daily data – Weekday/Weekend EffectsParameter Nox CO RSPM SOx
Intercept 27.88*1943.49*53.88* 7.35*Lagged-value 0.64* 0.75* 0.70* 0.63*Temperature -0.10*-8.70* 0.02 -0.01*Windspeed -1.56*-123.24*-2.60* -0.23*Max. Windspeed 0 0.35 0.02* 0Precipitation -1.30* 1.18 -3.96 0.08Fog -1.64 -111.429.38* -0.56*Weekday 6.80*348.72* 3.87 1.01*2001 2.89* 137.89 1.06 -1.21*2002 4.93*-466.78*21.71* -58292003 11.66*-603.01*18.58* -3.19*2004 9.87*-616.31* 9.38 -3.62*2005 8.33*-599.52*22.56* -3.34*2006 6.13*-652.43* 10.57 -2.06*
R-squared 0.65 0.72 0.58 0.67
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Daily Data Findings
• Strong downward trends (50% reduction) in Sox (’01-’03) and CO (’01-'02)
• Strong upward trend in NOx (50% rise)• ~ 15% PM dip in (’04)• Strong annual cycles for all pollutants with
winter peaks (weather related)• Roughly 50% of variation in trends and
oscillations• No PM traffic signal in weekend-weekday
differences
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Monthly Data
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Hourly Data
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Chowdhury, Z., M. Zheng, J. J. Schauer, R. J. Sheesley, L. G. Salmon, G. R. Cass, and A. G. Russell (2007), Speciation ofambient fine organic carbon particles and source apportionment of PM2.5 in Indian cities, J. Geophys. Res., 112, D15303, doi:10.1029/2007JD008386.
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Wrapping up
• Increasing trends for NOx, decreasing trends for SOx and CO, PM10 ambiguous
• CNG - contributed to 10% drop in PM10….– but only at a very traffic sensitive location ?
• Need better characterization of in-use emissions
• Weather-air pollution interaction• Other sources contribute to poor air quality in
Delhi – Need more focus on those• Very little known about AQ in other cities