Low-cost sensors (LCSs) have revolutionized the air pollution monitoring landscape.
In this study, we combined state-of-the-art data modelling techniques (machine learning [ML] methods) and data from state-of-the-art low-cost particulate matter (PM) sensors (LCSs) to improve the accuracy of LCS-measured PM2.
Lower-cost air pollution sensors can fill critical air quality data gaps in India, which experiences very high fine particulate matter (PM2.
The Tamil Nadu Air Pollution and Health Effects study (TAPHE-2) aims to evaluate the relationship between air pollution and birth outcome in a rural-urban cohort of 300 pregnant women.
Optical PM2.
Low-cost, long-term measures of air pollution concentrations are often needed for epidemiological studies and policy analyses of household air pollution.
