Air Pollution in India: Bridging the Gap between Science and Policy

Abstract

Air pollution is an emerging public health concern as there are increasing evidences that the quality of air adversely affects human health due to the presence of various toxic pollutants. Linking air pollution from its source to adverse human health effects is a complicated phenomenon that requires a multidisciplinary approach for better understanding. Decision-makers need relevant, comprehensive estimates of the disease burden attributable to different risk factors. Many statistical models have become very relevant for estimating atmospheric concentrations by analysis of complex datasets to produce inferences and predictions that can lead to better management of air pollution. This paper focuses on the Indian scenario as a case study and presents the current status of air quality in India with special reference to particulate matter. The study suggest that air-quality networks need to be developed that can depict and forecast pollution levels with health advisories for public and for pollution emergencies measures. Development of statistical models, and methods for Big Data Analytics, can yield a wide array of actionable insights to facilitate policy decisions. Models may also be used to predict the cost of the air-pollution control measures as well as the benefits in terms of the control of acute and chronic diseases caused by air pollution. This study concludes that the application of statistical models and algorithms can act as an important tool to bridge the gap between science and policy.