Performance evaluation of the Alphasense OPC-N3 and Plantower PMS5003 sensor in measuring dust events in the Salt Lake Valley, Utah

Abstract

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.

Development of a Unified IoT Platform for Assessing Meteorological and Air Quality Data in a Tropical Environment

“…A relatively poor fit was achieved for the PM 10 concentrations (R 2 = 0.66). This low coefficient can be explained by the overestimation of the PM 10 concentrations by OPC-N3 and its predecessor model, OPC-N2 [69].…”

“…A relatively poor fit was achieved for the PM10 concentration (R 2 = 0.66). This low coefficient can be explained by the overestimation of the PM10 co centrations by OPC-N3 and its predecessor model, OPC-N2 [69]. For particulate matter, PM 2.5 showed a high fit (R 2 = 0.85) between the Nova SDS011 sensor (Nova Fitness, Jinan, China) used for HZS-GARP-AQ-04 and the Alphasense OPC-N3 in the Libelium system.…”

Dust Under the Radar: Rethinking How to Evaluate the Impacts of Dust Events on Air Quality in the United States

“…In recent years, commercially available, low‐cost air quality sensors have become common, and regulatory agencies started using them to obtain more granular information on air quality spatial and temporal distribution (Jaffe et al., 2023). However, the accuracy and precision of these sensors need to be characterized (Zheng et al., 2018), the sensors require calibrations (Ardon‐Dryer et al., 2020) and recent work suggests they are unable to accurately characterize coarse particles (>2.5 μm) (Jaffe et al., 2023; Kaur & Kelly, 2023; Rueda et al., 2023) and they still contain spatial gaps. These spatial gaps limit our ability to fully quantify the number and nature of dust events and their subsequent impacts.…”

Determination of Hygroscopic Aerosol Growth Based on the OPC-N3 Counter

“…As stated in [16], previous studies report that Alphasense OPCs demonstrate strong correlations (R2 = 0.93-0.99) with PM10 in controlled laboratory investigations [17][18][19][20]. However, field-based studies have indicated relatively lower correlations (R2: 0.53-0.8) [21][22][23], primarily due to the variability in ambient meteorological conditions and fluctuations in PM compositions.…”