Mortality and Air Quality Regulation in Two Indian Cities

描述（申请人提供）：发展中国家的空气质量干预是一种新现象。近年来，印度首都德里见证了一系列空气质量干预措施，这为评估空气质量变化对健康的影响提供了一个自然的实验环境。该应用程序的两个首要目标是：评价卫星遥感在不同地理尺度上估计每日空气质量的可行性，特别是对空气污染监测覆盖面不足的发展中国家而言，和（B）通过比较前-（2000- 2002年）和监管后（2004- 2006年）时期。研究区没有详细的空气污染数据。尽管如此，空气污染领域的运动收集的数据在2003年7月至12月期间在113个站点在德里及其周边地区将被用来开发在这些网站和基于卫星的气溶胶光学厚度（AOD）的地面测量之间的经验关系。利用这一经验关系，在空间分辨率= 5公里的气象条件控制后，将使用每日AOD预测空气动力学直径=2.5 <$m和= 10 <$m的空气悬浮颗粒物（分别为PM2.5和PM10）和PM10-2.5的每日估计值。虽然可以使用来自各种传感器的数据来计算AOD，但我们将使用来自Terra和Aqua卫星上的中分辨率无线电光谱仪（MODIS）的数据，因为这两颗卫星一起捕获了空气质量的峰值和非峰值估计。将在贝叶斯框架内使用多重插补技术填补因云层覆盖而缺失的卫星数据。 将建立4000名受试者（2004年1月至4月接受采访）的个人暴露与其环境暴露之间的经验关系。反过来，这种关系将用于预测整个研究期间所有死亡病例的个人暴露量。将采用贝叶斯分层模型，参照不同时空尺度下估计的个人暴露和潜在混杂因素，检查特定原因的道德。为了评估空气质量干预措施对道德的影响，我们提出了不同的死亡率和个人暴露协会的婴儿，年龄组相对稳定的迁移模式（30至40岁）和不同的年龄性别组跨时间，即前和后的监管时间窗口，并在德里和坎普尔在前和后的监管期间。公共卫生相关性：这项研究的结果将促进我们对发展中国家空气质量干预措施的健康益处的理解，这反过来将有助于在发展中国家的其他城市执行类似的法规，以保护人类健康。

DESCRIPTION (provided by applicant): 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. PUBLIC HEALTH RELEVANCE: 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.