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, 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 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 PM2.5 and PM10, such as those provided by FEMs, research-grade instrumentation, or the OPC-N3.

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.…”

Short-Term PV Power Forecasting Based on Sky Imagery. A Case Study at the West University of Timisoara

“…Low-cost detectors, have been shown to be quite unreliable in recording particles with diameters between 2.5µm and 10µm, making their PM10 readings questionable (Jaffe 2023; Karu and Kelly 2023; Kuula et al 2020;Ouimette et al 2022). In particular, both the Plantower and Sensirion sensors used in this study, have been shown to have poor performance during dust-dominated conditions (Kaur and Kelly 2022). The aerosol loading at the locations of the present study is, generally, dominated by fine aerosols, with occasional episodes of wind-blown dust from the Sahara desert.…”

Emotion on the Edge: Air Quality Sensors Decoded as a Real-World Emotion Indicator

“…The Raspberry Pi 3 B+ is powered by a quad-core ARM Cortex-A53 CPU, providing adequate processing power for a variety PM sensors work by having a small fan that draws air through the device and past a laser which detects both the concentration number and size of the particles in the surrounding air. The sensor within the handheld device used is PMS5003 PM sensor which has previously been correlated for use in the real-world [23]. Previous studies have shown that the addition of particulate matter supports the classification of emotions [11].…”