Using Analysis over Multiple time Scales to Assess Air Quality Over

Delhi

Milind KandlikarArvind Saraswat

UBC

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

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

Delhi Air Quality

HEI (2001-2004)

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

Analysis of Daily Data

PM-10µ = 234 µg/ m3

σσσσ = 125 µg/ m3

Daily limit exceeded on 80% of days!

µ = 3.45 mg/ m3

σσσσ = 2.62 mg/ m3

CO

µ = 10.9 µg/ m3

σσσσ = 4.5 µg/ m3

µ = 80 µg/ m3

σσσσ = 23 µg/ m3

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

Power Spectral DensityCO and NOx

Power Spectral Density PM10 and SOx

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

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”

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

PM10 with Component overlay

T +O1+O2 (47%)T +O1 (31%)T (7%)

Reconstructed Components

16%22%

11% 9%

F=1/yr

F=2/yr F=3/yr

25%

16%

11%

F=1/yr

F=2/yr

SOx Reconstructed Components

8%

F=1,0.4/yr

8% F=2/yr

32%

Net 48%

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

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

Monthly Data

Hourly Data

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.

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