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Breathing in fine particulate matter of diameter less than 2.5 µm (PM2.5) greatly increases an individual’s risk of cardiovascular and respiratory diseases. As climate change progresses, extreme weather events, including wildfires, are expected to increase, exacerbating air pollution. However, models often struggle to capture extreme pollution events due to the rarity of high PM2.5 levels in training datasets. To address this, we implemented cluster-based undersampling and trained Transformer models to improve extreme event prediction using various cutoff thresholds (12.1 µg/m3 and 35.5 µg/m3) and partial sampling ratios (10/90, 20/80, 30/70, 40/60, 50/50). Our results demonstrate that the 35.5 µg/m3 threshold, paired with a 20/80 partial sampling ratio, achieved the best performance, with an RMSE of 2.080, MAE of 1.386, and R2 of 0.914, particularly excelling in forecasting high PM2.5 events. Overall, models trained on augmented data significantly outperformed those trained on original data, highlighting the importance of resampling techniques in improving air quality forecasting accuracy, especially for high-pollution scenarios. These findings provide critical insights into optimizing air quality forecasting models, enabling more reliable predictions of extreme pollution events. By advancing the ability to forecast high PM2.5 levels, this study contributes to the development of more informed public health and environmental policies to mitigate the impacts of air pollution, and advanced the technology for building better air quality digital twins.
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Performance evaluation of the Alphasense OPC-N3 and Plantower PMS5003 sensor in measuring dust events in the Salt Lake Valley, Utah
Abstract. As the changing climate expands the extent of arid andsemi-arid lands, the number of, severity of, and health effects associated with dust events are likely to increase. However, regulatory measurements capable of capturing dust (PM10, particulate matter smaller than10 µm in diameter) are sparse, sparser than measurements of PM2.5 (PM smaller than 2.5 µm in diameter). Although low-cost sensors couldsupplement regulatory monitors, as numerous studies have shown forPM2.5 concentrations, most of these sensors are not effective atmeasuring PM10 despite claims by sensor manufacturers. This studyfocuses on the Salt Lake Valley, adjacent to the Great Salt Lake, whichrecently reached historic lows exposing 1865 km2 of dry lake bed. Itevaluated the field performance of the Plantower PMS5003, a common low-costPM sensor, and the Alphasense OPC-N3, a promising candidate for low-costmeasurement of PM10, against a federal equivalent method (FEM, betaattenuation) and research measurements (GRIMM aerosol spectrometer model1.109) at three different locations. During a month-long field study thatincluded five dust events in the Salt Lake Valley with PM10 concentrations reaching 311 µg m−3, the OPC-N3 exhibited strong correlation with FEM PM10 measurements (R2 = 0.865, RMSE = 12.4 µg m−3) and GRIMM (R2 = 0.937, RMSE = 17.7 µg m−3). The PMS exhibited poor to moderate correlations(R2 < 0.49, RMSE = 33–45 µg m−3) withreference or research monitors and severely underestimated the PM10concentrations (slope < 0.099) for PM10. We also evaluated aPM-ratio-based correction method to improve the estimated PM10concentration from PMSs. After applying this method, PMS PM10concentrations correlated reasonably well with FEM measurements (R2 > 0.63) and GRIMM measurements (R2 > 0.76), andthe RMSE decreased to 15–25 µg m−3. Our results suggest that itmay be possible to obtain better resolved spatial estimates of PM10concentration using a combination of PMSs (often publicly availablein communities) and measurements of PM2.5 and PM10, such as thoseprovided by FEMs, research-grade instrumentation, or the OPC-N3.
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- PAR ID:
- 10422410
- Date Published:
- Journal Name:
- Atmospheric Measurement Techniques
- Volume:
- 16
- Issue:
- 10
- ISSN:
- 1867-8548
- Page Range / eLocation ID:
- 2455 to 2470
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.5194/amt-16-2455-2023
