Air pollution is a critical concern for both human health and ecosystems and has become a high-priority environmental issue. Concentrations of air pollutants, such as particulate matter (PM), ozone, and toxic chemicals (mercury, persistent organic pollutants, and lead), are contributing to increased rates of asthma, lung and cardiovascular disease, and cancer. The World Health Organization estimatesthat in 2004, slightly less than one million disability-adjusted life years were lost due to outdoor air pollution.
Policy interventions, such as the Clean Air Act in the United States and the Clean Air Directive in Europe, have helped, but in other parts of the world (Asia in particular), air pollution is becoming an increasingly severe problem due to rapid industrial and urban growth.
Policymakers and governments need timely, accurate information to develop and implement air pollution abatement and control policies, but existing datasets for air pollutant emissions are either incomplete or incomparable among countries and in a global context. This lack of comparability is largely due to the variation in air quality monitoring systems between countries, which often produce fundamentally dissimilar data. Some countries do not have adequate monitoring stations or networks to produce representative data samples. In other cases, countries may lack the technical capacity to measure some critical air pollutants, which results in data gaps and leaves policymakers unable to develop relevant global indicators and indices.
One way scientists have tried to address such shortcomings is by using models to estimate emission concentrations, predict future growth, and simulate transport of pollutants across national boundaries. At the most fundamental level these models are based on algorithms -- not ambient empirical data – which results in some inherent degree of uncertainty. Previous editions of the Environmental Performance Index (EPI)have relied on a combination of reported air quality statistics from international organizations, such as the World Bank, and some modeled data for outdoor air pollution indicators, but the 2012 EPI abandoned both sources and opted instead to use a estimation of fine particulate matter concentrations (PM 2.5)derived from the MODISsatellite. While not perfect, these country-level PM 2.5 estimations were consistently calculated for each country, providing a basis for comparing long-term average exposures to a pollutant that is known to have acute human health effects.
To address these persistent data challenges in global air quality, the Yale Center for Environmental Law and Policy (YCELP) and the Center for International Earth Science Information Network (CIESIN) at Columbia University, are teaming up with the Asian Institute for Energy and Environmental Sustainability (AIEES) to launch a new initiative, “Towards a next generation of air quality monitoring.”
The resulting report will include a series of background papers that will each focus on a critical pollutant (i.e. ozone) or group of pollutants (i.e. persistent organic pollutants or POPs), as well as a policy blueprint with recommendations for policymakers on investments and improvements in air quality monitoring, data, and indicators. The report also aims to bring the scientific and policy communities together to provide clear direction for both groups. First, for scientists, it will provide guidance on short-term actions related to monitoring and modeling as well as longer-term challenges. Secondly, for decisionmakers, the report will provide targeted activities at different levels – regional, national, global – also divided into short- and longer-term categories.
The project launched in May, and AIEES will host a workshop in Seoul, South Korea, in October that will convene scientists and policymakers to review the draft report, which will be released in early 2013.