Measurement variability in residential PM2.5: an evaluation of a low-cost sensor in the Netherlands

Abstract

Accurate residential air quality assessment is crucial for studying health risks, evaluating local mitigation measures, and empowering citizens. Low-cost-sensors (LCS) have gained popularity for enhancing monitoring coverage and providing individuals with air quality measurement tools. This study examines the added value of a low-cost sensor in estimating residential fine particulate matter (PM2.5) concentrations in the Netherlands. We employed a real-time Sensirion SPS30 dust sensor to monitor residential PM2.5 concentrations. 73 sensors were deployed outdoors at participants’ residences with an average measurement time of 131 days. Data from LCS were compared with that of regulatory stations, using hourly and daily averages for comparison. Spatial variability of sensor measurements was assessed, and determinants were explored that explain potential differences between PM2.5 concentrations from regulatory stations and LCS measurements. After data cleaning, 95.7% of measurements were retained. Meteorological factors did not impact sensor performance. The mean Pearson temporal correlation between the LCS and regulatory network was 0.75 for hourly and 0.88 for daily PM2.5 averages. The average difference ranged from − 0.17 to 0.63 µg/m3, and the average absolute difference ranged from 2.42 to 4.50 µg/m3. Spatial variability of LCS-based average concentrations at similar locations was larger than that of regulatory stations. LCS measuring direction, traffic intensity, humidity, LCS readings, and distance to nearest background station had a significant effect on the difference between sensor and regulatory station concentrations. This study demonstrates that PM2.5 can be accurately measured over extended periods using LCS, offering a dynamic, high-quality perspective on air quality, recording variations that regulatory stations and predictive air quality models may overlook.