ISGlobal: Developed the first model to predict exposure to air pollution in low-income countries

ISGlobal: Developed the first model to predict exposure to air pollution in low-income countries

News from ISGlobal

For the first time the air pollution levels in a rural area in South India were predicted using a specific model developed taking into account the conditions of a low-income country. This model was designed by an international team led by the ISGlobal. In this study, encompassed in the CHAI project and published in the journal Science of the Total Environment, researchers used a method often used in urban areas in high-income countries to predict levels of fine particulate matter and black carbon. Then, taking into account factors such as road length, density of the built-environment, population density, and the presence of industrial sites and green spaces, they predicted air pollution in a rural area close to the city of Hyderabad in India.

Although 87% of the more than three million premature deaths attributed to air pollution every year occur in low- and middle-income countries, most scientific studies on the health effects of air pollution are carried out in urban areas in high-income countries. As Cathryn Tonne, ISGlobal researcher and coordinator of the CHAI Project, emphasizes “there is a huge gap in the epidemiological data on exposure to air pollution in low- and middle-income countries. To address this problem, it is essential to develop and apply prediction models for these zones so that we can measure the impact of air pollution on the health of these populations”. - Carlos Sierra/PRBB

Reference article:
Margaux Sanchez, Albert Ambros, Carles Milà, Maëlle Salmon, Kalpana Balakrishnan, Sankar Sambandam, V. Sreekanth, Julian D. Marshall, Cathryn Tonne. Development of land-use regression models for fine particles and black carbon in peri-urban South India. Science of The Total Environment. April 2018.

More information:
ISGlobal website