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 and semi-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 than 10 µm in diameter) are sparse, sparser than measurements of PM2.5 (PM smaller than 2.5 µm in diameter). Although low-cost sensors could supplement regulatory monitors, as numerous studies have shown for PM2.5 concentrations, most of these sensors are not effective at measuring PM10 despite claims by sensor manufacturers. This study focuses on the Salt Lake Valley, adjacent to the Great Salt Lake, which recently reached historic lows exposing 1865 km2 of dry lake bed. It evaluated the field performance of the Plantower PMS5003, a common low-cost PM sensor, and the Alphasense OPC-N3, a promising candidate for low-cost measurement of PM10, against a federal equivalent method (FEM, beta attenuation) and research measurements (GRIMM aerosol spectrometer model 1.109) at three different locations. During a month-long field study that included 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) with reference or research monitors and severely underestimated the PM10 concentrations (slope < 0.099) for PM10. We also evaluated a PM-ratio-based correction method to improve the estimated PM10 concentration from PMSs. After applying this method, PMS PM10 concentrations correlated reasonably well with FEM measurements (R2 > 0.63) and GRIMM measurements (R2 > 0.76), and the RMSE decreased to 15–25 µg m−3. Our results suggest that it may be possible to obtain better resolved spatial estimates of PM10 concentration using a combination of PMSs (often publicly available in communities) and measurements of PM2.5 and PM10, such as those provided by FEMs, research-grade instrumentation, or the OPC-N3.