Do Machine Learning Methods Improve Prediction of Ambient Air Pollutants with High Spatial Contrast? A Systematic Review

Author:

Vachon JulienORCID,Kerckhoffs JulesORCID,Buteau Stéphane,Smargiassi Audrey

Funder

Canadian Institutes of Health Research

Publisher

Elsevier BV

Reference101 articles.

1. McDuffie, E., Martin, R., Yin, H., & Brauer, M. (2021). Global Burden of Disease from Major Air Pollution Sources (GBD MAPS): A Global Approach (210; p. 62). Health Effects Institute (HEI).

2. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019;Murray;The Lancet,2020

3. Assessing the recent estimates of the global burden of disease for ambient air pollution: Methodological changes and implications for low- and middle-income countries;Ostro;Environmental Research,2018

4. US EPA. (2019). Integrated Science Assessment for Particulate Matter (EPA/600/R-19/188; p. 1967). Center for Public Health and Environmental Assessment, Office of Research and Development, U.S. Environmental Protection Agency.

5. Diaz, E., Mariën, K., Manahan, L., & Fox, J. (2019). Summary of Health Research on Ultrafine Particles (DOH 334-454; p. 27). Office of Environmental Public Health Sciences, Washington State Department.

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