Abstract
Many low-cost sensors (LCSs) are distributed for air monitoring without any rigorous calibrations. This work applies machine learning with PM2.5 from Taiwan monitoring stations to conduct in-field corrections on a network of 39 PM2.5 LCSs from July 2017 to December 2018. Three candidate models were evaluated: Multiple linear regression (MLR), support vector regression (SVR), and random forest regression (RFR). The model-corrected PM2.5 levels were compared with those of GRIMM-calibrated PM2.5. RFR was superior to MLR and SVR in its correction accuracy and computing efficiency. Compared to SVR, the root mean square errors (RMSEs) of RFR were 35% and 85% lower for the training and validation sets, respectively, and the computational speed was 35 times faster. An RFR with 300 decision trees was chosen as the optimal setting considering both the correction performance and the modeling time. An RFR with a nighttime pattern was established as the optimal correction model, and the RMSEs were 5.9 ± 2.0 μg/m3, reduced from 18.4 ± 6.5 μg/m3 before correction. This is the first work to correct LCSs at locations without monitoring stations, validated using laboratory-calibrated data. Similar models could be established in other countries to greatly enhance the usefulness of their PM2.5 sensor networks.
Highlights
Millions of premature deaths worldwide can be attributed to particulate matter with an aerodynamic diameter less than or equal to 2.5 μm (PM2.5 ) [1,2], which is one of the human carcinogens classified by the International Agency for Research on Cancer [3]
The low-cost sensors (LCSs) network corrected by Taiwan Environmental Protection Administrations (EPAs) data in this work consists of LCS devices designed for research purposes, namely, AS-LUNG-O
The indicators used for evaluating model performance are root mean square error (RMSE), Pearson correlation coefficient (r), and coefficient of determination (R2 )
Summary
Millions of premature deaths worldwide can be attributed to particulate matter with an aerodynamic diameter less than or equal to 2.5 μm (PM2.5 ) [1,2], which is one of the human carcinogens classified by the International Agency for Research on Cancer [3]. In eastern Asia during 1998–2000, 51% of the population lived in areas with annual mean PM2.5 levels above the recommended guideline of the World Health Organization (35 μg/m3 ). This percentage increased to 70% during 2010–2012 [6], showing the deterioration of the air quality in this region. The purpose of the PM2.5 monitoring stations of Environmental Protection Administrations (EPAs) worldwide is to assess the well-mixed ambient pollutant levels. Such monitors are situated at a height of 10–15 m above the ground.
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