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2016
2022
Low-Processing Data Enrichment and Calibration for PM2.5 Low-Cost Sensors
Particulate matter (PM) in air has been proven to be hazardous to human health. Here we focused on analysis of PM data we obtained from the same campaign which was presented in our previous study. Multivariate linear and random forest models were used for the calibration and analysis. In our linear regression model the inputs were PM, temperature and humidity measured with low-cost sensors, and the target was the reference PM measurements obtained from SEPA in the same timeframe.
2023
2011
The increase of the commercial availability of low-cost sensor technology to monitor atmospheric composition is contributing to the rapid adoption of such technology by both public authorities and self-organized initiatives (e.g. grass root movements, citizen science, etc.). Low-cost sensors (LCS) can provide real time measurements, in principle at lower cost than traditional monitoring reference stations, allowing higher spatial coverage than the current reference methods. However, data quality from LCS is lower than the one provided by reference methods. Also, the total cost of deploying a dense sensor network needs to consider the costs associated not only to the sensor platforms but also the costs associated for instance with deployment, maintenance and data transmission.
This report aims to give an overview of the current status of LCS technology in relation to commercialization, measuring capabilities and data quality, with especial emphasis on the challenges associated to the use of this novel technology, and the opportunities they open when correctly used.
NILU
2021
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 However, Polish air quality monitoring is challenging due to the limited coverage of reference stations and outdated equipment.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 R2 = 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%.
Elsevier
2023