To effectively manage air pollution, we need to measure it accurately and at high spatial resolution.
We use molecular simulation to study the electric double-layer (EDLC) to understand this behaviour.
Air pollution monitoring is an important aspect of air quality management.
Low-cost air quality sensors are the talk of the town.
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.
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.