It is universally recognized that air pollution is a pressing environmental challenge that has increased considerably in recent years, leading to a rise in premature deaths, threatening livelihoods, and the sustainable development of the region. In particular, many cities in Asia and the Pacific where air pollution is a major public health hazard to an increasing urban population, especially it’s most vulnerable demographics.
However, many rapidly urbanizing cities in developing countries lack the resources and data to clearly identify the causes of air pollution impacting local conditions. In order to address the pervasive problems of low data quality and availability, the “Methodology To Develop City-Level, Science-Based, Air-Pollution Action Plans” working paper describes methodologies using machine learning to enable better decision making. Through the use of these innovative techniques, decision makers and stakeholders can have greater data-based analysis to create city level air pollution action plans.