Time series study on air pollution and mortality in Indian cities

R Uma, Kaplana Balakrishnan, Rajesh kumar

Background

• Air quality issues are of major concern for many cities in Asia and other developing countries

• Increasing attention from policy makers, legal body, NGOs, research, academic institutions and funding agencies

• Many initiatives, but gaps in research still exist

PAPA study: Indian cities• Selection based on geographical representation,

population, data availability, Research team capability

• Delhi: National capital with 13.8 million population

• Ludhiana: Industrial town in Punjab, North India with population of about 1.5 million

• Chennai: Located in coastal area of Southern India with population of 4.5 million

Aim and Objectives of the study• Aim: To generate site specific database on effect

of air pollution on mortality for Indian cities Delhi, India

• Specific objectives:– To assess the time series data on air quality parameters

and mortality to study the relationship between air pollution and mortality due to respiratory diseases in Indian cities

– To assess the daily change in mortality in relation with change in air quality after controlling for the exogenous parameters

Salient features of the study

• Multidisciplinary team• Meeting ICMR guidelines on ethical

aspects• Review and guidance from ISOC• QA/QC audit• Capacity building

– Training on developing exposure series– R Package– Core model for time series analysis

Study Locations

Methodology

• Collection of retrospective time series data (2002, 2003 & 2004) on– Ambient air quality

– Mortality data

– Meteorological data (Temperature, humidity, visiblity)

• Statistical analysis of data to study the association of age specific death (all cause mortality) with exposure to air pollution

Station nameStation code

No of observations Mean

Std.Deviation Min Max

IHC IHC 366 244 131 35 988

ITO ITO 360 230 96 57 896

Sarojini Nagar San 94 151 94 34 582

Town Hall Toh 95 167 108 9 550

Mayapuri Map 96 213 134 31 689

Nizamudin Nmd 75 135 61 50 416

Srifort Srf 71 128 62 35 382

Janakpuri Jkp 61 140 69 36 321

Shazadabagh Shb 64 139 58 36 303

Shadara Sad 75 133 64 32 318

Ashok Vihar Asv 69 137 98 24 482

Descriptive Statistics of RSPM in Delhi

Distribution of RSPM (PM 10) levels in Chennai

Descriptive Statistics of Air Pollutants - Ludhiana

Statistics RSPM SO2 NOx

2002 2003 2004 Overall 2002 2003 2004 Overall 2002 2003 2004 Overall

N 271 239 255 765 274 238 255 767 274 238 255 767

Mean 208.7 239.5 266.3 237.5 11.7 11.4 20.6 14.6 31.7 35.4 46.8 37.9

S.D. 74.6 88.2 97.2 90.1 1.7 2.4 6.7 5.6 3.1 8.3 15.3 12.0

Minimum 52.0 70.0 84.0 52.0 7.0 7.0 11.0 7.0 15.0 22.0 23.0 15.0

Maximum 542 572 754 754 17.0 21.0 36.0 36.0 44.0 70.0 89.0 89.0

Descriptive Statistics of Mortality-Ludhiana (2004)

• Total deaths : 9322

• Number of male deaths : 6103

• Number of female deaths : 3219

Mean SD Min Max 25.5 6.17 9.0 48

Mortality data descriptives in Chennai

Core model

• Generalized Additive Model (GAM) with penalized and natural spline smoothers in R.

• Quasi-Poisson function with all natural causes as the dependent variables.

• Smoothers for time, temp,RH• Day of week terms (i.e, dichotomous variables

for each day of the week from Monday through Saturday).

• Exposure at single-day lags of 0 to 3 days & a cumulative two-day average of lags considered.

Summary results of model -DelhiExposure variable

N of observations

P % per Δ µg/m3 in Pm10

IHC 364 0.004 0.15

ITO 361 0.0000019 0.28

AV 365 0.00005 0.26

IHC<400 345 0.0006 0.28

AV<400 360 0.0004 0.31

CEN 365 0.06 0.09

CEN<400 345 0.0004 0.25

Lag1 365 0.05 0.13

Lag1<400 326 0.015 0.27

Lag2 364 0.0004 0.16

Lag2<400 325 0.0017 0.29

Model result-Chennai

GAM output from all single monitors

GAM output from the best single monitors

Gam Model Results – Ludhiana (After Selecting Base Model, Natural Spline (6,4,4))

Lag Effects % per Δ µg/m3 in Pm10

P - value

Lag 0 0.0001622 0.32

Lag 1 0.0001793 0.28

Lag 2 0.0003445 0.041*

Lag 3 0.0003927 0.015*

* RSPM Effects are significant with lag2 and lag3

Work in progress

• Sensitivity analyses for different exposure series

• Multi-pollutant models

• Population (Exposure) weighted assignment for individual monitors using spatial models

• Gender specific analysis

• Cause specific impact estimations

Conclusions

• The data processing steps for the pollutant and mortality data could be completed as envisaged as a result of co-operation and access to raw data provided by the local bodies in-charge of routine data collection.

• The results of models developed thus far show that the estimates for impacts of PM10 (an approximately 0.2% to 0.6% increase in all-cause mortality for every 10 g/m3 of exposure) are in the range reported by other on-going PAPA studies and earlier studies reported in North America and Europe (using similar statistical methods).

• More sophisticated analyses including use of spatial models to estimate population-weighted exposures from individual monitors and use of EM algorithms to address missing data and Poisson auto-correlation are being undertaken to refine these initial estimates.

• Data quality issues may limit development of multi-pollutant models and differential cause specific estimates.

• Consistency of results obtained using similar methodological approaches between Indian cities and other Asian cities indicate that routinely collected pollutant and mortality data may be reliably used in time-series analyses of air pollution related health impacts. However, significant challenges remain in making clean data readily accessible to environmental health researchers.

Thank you