Abstract
ABSTRACT Low-cost particulate matter (PM) sensors can be widely deployed to measure aerosol concentrations at higher spatial and temporal resolutions than traditional instruments, but they need to be carefully calibrated under ambient conditions. In this study, a long-term field experiment was conducted from December 2015 to May 2017 at a site in Nanjing to evaluate the capabilities of in-house built low-cost PM monitors using the Shinyei PPD42NS sensor for ambient PM2.5 monitoring. A BAM-1020 particulate monitor was co-located with the low-cost sensors to provide reference readings. Least-square regressions with linear and power-law functions, and an artificial neural network (ANN) technique were used to convert electrical instrument readings to ambient aerosol concentrations. Applying the ANN technique resulted in the best estimation of the hourly PM2.5 (R2 = 0.84; mean normalized bias = 12.7% and mean normalized error (MNE) = 29.7%). The low-cost sensors displayed relatively good performance with high aerosol concentrations but larger errors with concentrations below 35 µg m–3. High humidity (RH > 75%) can cause a larger MNE for these sensors, but the impact of temperature was negligible in this study. A clear sensor deterioration trend was observed during the 18-month field calibration. High correlations were found between the data from a single low-cost sensor and the data from the BAM-1020 when the low-cost sensor was individually calibrated, but the correlations between measurements taken by different low-cost sensor units were only moderate, possibly due to internal sensor variations. The results suggest that these low-cost sensors can measure ambient PM2.5 concentrations with an acceptable level of accuracy, which can and should be improved by calibrating each sensor individually. Special attention should be paid to the accuracy of these sensors after long-term application and in highly humid environments.
Highlights
Fine particulate matter (PM2.5) is a major air pollutant in China (Chan and Yao, 2008; Wang et al, 2014; Zhang and Cao, 2015)
The monitors were calibrated with a co-located beta attenuation monitor (BAM)-1020 particulate monitor for long-term measurements from December 2015 to May 2017 on the campus of Nanjing University of Information Science & Technology
The artificial neural network (ANN) technique yielded the highest correlation between the low-cost sensor estimates and the BAM-1020 PM2.5 measurements (R2 = 0.84), and the lowest MNB (12.66%) and mean normalized error (MNE) (29.71%)
Summary
Fine particulate matter (PM2.5) is a major air pollutant in China (Chan and Yao, 2008; Wang et al, 2014; Zhang and Cao, 2015). Low-cost PM sensors have been developed in recent years in order to fill the data gaps in the existing regulatory monitoring networks (Li and Biswas, 2017) to provide particle concentrations at much higher spatial and temporal resolutions due to their relatively low cost. These aerosol monitors can be used to locate pollution hotspots and generate three-dimensional maps of PM concentrations (Rajasegarar et al, 2014). Careful calibration/ evaluation of these low-cost PM sensors is needed for data quality assurance
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