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, 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 PM_2.5 (PM smaller than 2.5 µm in diameter). Although low-cost sensors could supplement regulatory monitors, as numerous studies have shown for PM_2.5 concentration, 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 lakebed. It evaluated the field performance of the Plantower PMS 5003, 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 spectrophotometer) 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/m³, the OPC-N3 exhibited strong correlation with FEM PM₁₀ measurements (R² = 0.865, RMSE = 12.4 µg/m³) and GRIMM (R²= 0.937, RMSE = 17.7 µg/m³). The PMS sensor exhibited poor to moderate correlations (R²<0.49, RMSE = 33–45 µg/m³) with reference/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 PMS sensors. 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/m³. Our results suggest that it may be possible to obtain better resolved spatial estimates of PM₁₀ concentration using a combination of PMS sensors (often publicly available in communities) and measurements of PM_2.5 and PM₁₀, such as those provided by FEMs, research-grade instrumentation, or the OPC-N3.