Air quality intervention in developing countries is a new phenomenon. Delhi, the Capital of India, has witnessed a series of air quality interventions in recent years, which provides a natural experimental setting to evaluate the health effects of change in air quality in response to these interventions. Two overarching goals of this application are: (a) to evaluate the feasibility of satellite remote sensing to estimate daily air quality at different geographic scales, particularly for developing countries, which lack adequate coverage of air pollution monitoring, and (b) to assess the burden of mortality alleviated in response to these interventions in Delhi by differencing the cause-specific mortality with reference to air pollution in pre- (2000-02) and post-regulation (2004-06) periods. The spatially detailed air pollution data are unavailable for the study area. Nevertheless, an air pollution field campaign data collected during July-December 2003 at 113 sites in Delhi and its surroundings will be used to develop an empirical relation between ground measurements at these sites and satellite based aerosol optical depth (AOD). Exploiting this empirical relationship, the daily AOD will be used to predict daily estimates of airborne particles =2.5?m, = 10?m in aerodynamic diameter (PM2.5 and PM10, respectively) and PM10-2.5, after controlling for meteorological conditions at a spatial resolution = 5km. Although AOD can be computed using the data from various sensors, we will use data from Moderate Resolution Radiospectrometer (MODIS), onboard Terra and Aqua satellites, because these two satellites together capture both peak and off-peak estimates of air quality. Satellite data missing due to cloud cover will be filled using multiple imputation techniques within a Bayesian framework. An empirical relationship between the personal exposure of 4000 subjects (interviewed from January-April 2004) and their ambient exposure will be established. This relationship, in turn, will be used to predict the personal exposure for all cases of mortality for the entire study period. A Bayesian hierarchical model will be employed to examine cause- specific morality with reference to estimated personal exposure and potential confounders at different spatial-temporal scales. To evaluate the impact of air quality interventions on morality we propose differencing mortality and personal exposure association for infants, age cohorts with relatively stable migration pattern (30 to 40 years) and different age-sex groups across time, i.e. pre and post regulation time windows, and across Delhi and Kanpur in both pre- and post-regulation periods.
The findings of this research will advance our understanding of the health benefits of air quality interventions in developing countries, which in turn will be useful for enforcing the similar regulations in other cities of developing countries to protect human health. ? ? ? ?
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