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A platform for research: civil engineering, architecture and urbanism
Low-cost sensors and Machine Learning aid in identifying environmental factors affecting particulate matter emitted by household heating
https://orcid.org/0000-0002-6470-0490
https://orcid.org/0000-0001-8192-5432
https://orcid.org/0000-0001-5686-8683
https://orcid.org/0000-0003-4262-4770
Abstract Poland continues to rely heavily on coal and fossil fuels for household heating, despite efforts to reduce Particulate Matter (PM) levels. The availability of reliable air quality data is essential for policymakers, environmentalists, and citizens to advocate for cleaner energy sources. However, Polish air quality monitoring is challenging due to the limited coverage of reference stations and outdated equipment. Here, we report the results of a study on the spatio-temporal variability of Particulate Matter in Legionowo, Poland, using residents’ network of low-cost sensors. Along with identifying the hotspots of household-emitted PM, (1) we propose a data quality assurance scheme for PM sensors, (2) suggest an approach for estimating the Relative Humidity-induced uncertainty in the sensors without co-location with reference instruments, and (3) develop an interpretable Machine Learning (ML) model, a Generalized Additive Model (RMSE = 6.16 μg m−3, and R 2 = 0.88), for unveiling the underlying relations between PM2.5 levels and other environmental parameters. The results in Legionowo suggest that as air temperature and wind speed increase by 1 °C and 1 km h−1, PM2.5 would respectively decrease by 0.26 μg m−3 and 0.14 μg m−3 while PM2.5 increases by 0.03 μg m−3 as RH increases by 1%.
Highlights PM variability is analyzed using residents' network of low-cost sensors. A data quality assurance scheme for PM sensors is proposed. Relative Humidity-induced uncertainty of PM sensors is estimated using a new approach. PM2.5 and environmental settings relations are explored with an explainable ML model. Sensors identify fossil-fuel-induced air pollution hotspots.
Low-cost sensors and Machine Learning aid in identifying environmental factors affecting particulate matter emitted by household heating
https://orcid.org/0000-0002-6470-0490
https://orcid.org/0000-0001-8192-5432
https://orcid.org/0000-0001-5686-8683
https://orcid.org/0000-0003-4262-4770
Abstract Poland continues to rely heavily on coal and fossil fuels for household heating, despite efforts to reduce Particulate Matter (PM) levels. The availability of reliable air quality data is essential for policymakers, environmentalists, and citizens to advocate for cleaner energy sources. However, Polish air quality monitoring is challenging due to the limited coverage of reference stations and outdated equipment. Here, we report the results of a study on the spatio-temporal variability of Particulate Matter in Legionowo, Poland, using residents’ network of low-cost sensors. Along with identifying the hotspots of household-emitted PM, (1) we propose a data quality assurance scheme for PM sensors, (2) suggest an approach for estimating the Relative Humidity-induced uncertainty in the sensors without co-location with reference instruments, and (3) develop an interpretable Machine Learning (ML) model, a Generalized Additive Model (RMSE = 6.16 μg m−3, and R 2 = 0.88), for unveiling the underlying relations between PM2.5 levels and other environmental parameters. The results in Legionowo suggest that as air temperature and wind speed increase by 1 °C and 1 km h−1, PM2.5 would respectively decrease by 0.26 μg m−3 and 0.14 μg m−3 while PM2.5 increases by 0.03 μg m−3 as RH increases by 1%.
Highlights PM variability is analyzed using residents' network of low-cost sensors. A data quality assurance scheme for PM sensors is proposed. Relative Humidity-induced uncertainty of PM sensors is estimated using a new approach. PM2.5 and environmental settings relations are explored with an explainable ML model. Sensors identify fossil-fuel-induced air pollution hotspots.
Low-cost sensors and Machine Learning aid in identifying environmental factors affecting particulate matter emitted by household heating
https://orcid.org/0000-0002-6470-0490
https://orcid.org/0000-0001-8192-5432
https://orcid.org/0000-0001-5686-8683
https://orcid.org/0000-0003-4262-4770
Abstract Poland continues to rely heavily on coal and fossil fuels for household heating, despite efforts to reduce Particulate Matter (PM) levels. The availability of reliable air quality data is essential for policymakers, environmentalists, and citizens to advocate for cleaner energy sources. However, Polish air quality monitoring is challenging due to the limited coverage of reference stations and outdated equipment. Here, we report the results of a study on the spatio-temporal variability of Particulate Matter in Legionowo, Poland, using residents’ network of low-cost sensors. Along with identifying the hotspots of household-emitted PM, (1) we propose a data quality assurance scheme for PM sensors, (2) suggest an approach for estimating the Relative Humidity-induced uncertainty in the sensors without co-location with reference instruments, and (3) develop an interpretable Machine Learning (ML) model, a Generalized Additive Model (RMSE = 6.16 μg m−3, and R 2 = 0.88), for unveiling the underlying relations between PM2.5 levels and other environmental parameters. The results in Legionowo suggest that as air temperature and wind speed increase by 1 °C and 1 km h−1, PM2.5 would respectively decrease by 0.26 μg m−3 and 0.14 μg m−3 while PM2.5 increases by 0.03 μg m−3 as RH increases by 1%.
Highlights PM variability is analyzed using residents' network of low-cost sensors. A data quality assurance scheme for PM sensors is proposed. Relative Humidity-induced uncertainty of PM sensors is estimated using a new approach. PM2.5 and environmental settings relations are explored with an explainable ML model. Sensors identify fossil-fuel-induced air pollution hotspots.
Low-cost sensors and Machine Learning aid in identifying environmental factors affecting particulate matter emitted by household heating
Hassani, Amirhossein (author) / Bykuć, Sebastian (author) / Schneider, Philipp (author) / Zawadzki, Paweł (author) / Chaja, Patryk (author) / Castell, Núria (author)
Atmospheric Environment ; 314
2023-09-21
Article (Journal)
Electronic Resource
English
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