A novel spatiotemporal prediction approach to fill air pollution data gaps using mobile sensors, machine learning and citizen science techniques

npj Climate and Atmospheric Science, Journal Year: 2024, Volume and Issue: 7(1)

Published: Dec. 19, 2024

Abstract Particulate Matter (PM) air pollution poses significant threats to public health. We introduce a novel machine learning methodology predict PM 2.5 levels at 30 m long segments along the roads and temporal scale of 10 seconds. A hybrid dataset was curated from an intensive campaign in Selly Oak, Birmingham, UK, utilizing citizen scientists low-cost instruments strategically placed static mobile settings. Spatially resolved proxy variables, meteorological parameters, properties were integrated, enabling fine-grained analysis . Calibration involved three approaches: Standard Random Forest Regression, Sensor Transferability Road Evaluations. This significantly increased spatial resolution beyond what is possible with regulatory monitoring, thereby improving exposure assessments. The findings underscore importance approaches science advancing our understanding pollution, small number participants enhancing local quality assessment for thousands residents.

Language: Английский

Evaluating pedestrian exposure to traffic-related airborne particles: Insights for sustainable and healthier urban environments

Atmospheric Environment, Journal Year: 2025, Volume and Issue: 354, P. 121255 - 121255

Published: April 18, 2025

Language: Английский

Web service framework to identify multiple pollutions in potential contaminated sites

Expert Systems with Applications, Journal Year: 2024, Volume and Issue: 263, P. 125800 - 125800

Published: Nov. 20, 2024

Language: Английский