Low-cost air quality sensors are the talk of the town.
To effectively manage air pollution, we need to measure it accurately and at high spatial resolution.
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.
Low-cost sensors (LCSs) that measure PM2.
Lower-cost air pollution sensors can fill critical air quality data gaps in India, which experiences very high fine particulate matter (PM2.
Across India, the lack of adequate data on air pollution is a challenge to addressing air pollution.
Air pollution monitoring is an important aspect of air quality management.
Strategically placed sensors can monitor air pollution and provide a detailed picture of air quality and its variability within a region.
Low-cost sensors (LCSs) have revolutionized the air pollution monitoring landscape.
The study conducted by the Center for Study of Science, Technology and Policy (CSTEP) employed a hybrid measurement approach to generate high-resolution air pollution maps.
