Abstract
To tackle the challenge of the data accuracy issues of low-cost sensors (LCSs), the objective of this work was to obtain robust correction equations to convert LCS signals into data comparable to that of research-grade instruments using side-by-side comparisons. Limited sets of seed LCS devices, after laboratory evaluations, can be installed strategically in areas of interest without official monitoring stations to enable reading adjustments of other uncalibrated LCS devices to enhance the data quality of sensor networks. The robustness of these equations for LCS devices (AS-LUNG with PMS3003 sensor) under a hood and a chamber with two different burnt materials and before and after 1.5 years of field campaigns were evaluated. Correction equations with incense or mosquito coils burning inside a chamber with segmented regressions had a high R2 of 0.999, less than 6.0% variability in the slopes, and a mean RMSE of 1.18 µg/m3 for 0.1–200 µg/m3 of PM2.5, with a slightly higher RMSE for 0.1–400 µg/m3 compared to EDM-180. Similar results were obtained for PM1, with an upper limit of 200 µg/m3. Sensor signals drifted 19–24% after 1.5 years in the field. Practical recommendations are given to obtain equations for Federal-Equivalent-Method-comparable measurements considering variability and cost.
Highlights
Particulate matter with an aerodynamic diameter less than or equal to 2.5 μm (PM2.5 ) is a classified human carcinogen [1]
The current work focuses on the first part of this process to obtain reliable and robust correction equations to convert the readings of low-cost sensors (LCSs) devices to research-grade measurements via side-by-side comparisons with research-grade instruments in the laboratory
In conjunction with machine learning methods, traditional laboratory evaluations could be applied for limited sets of seed LCS devices installed in areas of interest without official monitoring stations for the adjustment of other uncalibrated LCS devices in the sensor networks and enhance the data quality and potential applications of the sensor networks
Summary
Particulate matter with an aerodynamic diameter less than or equal to 2.5 μm (PM2.5 ) is a classified human carcinogen [1]. Many areas worldwide experience annual mean levels of PM2.5 reaching 100 μg/m3 [4,5], much higher than 10 μg/m3 , the value recommended by the World Health Organization [6]. Since monitoring stations equipped with expensive instruments established by environmental regulatory agencies are only situated in limited areas, the development of low-cost sensors (LCSs) provides opportunities to measure pollutant levels at much higher spatial densities than ever before [7,8,9]. Most LCSs for air pollutants face the data accuracy challenges [9,10], as they are typically not calibrated by the manufacturers due to cost considerations. Inaccurate underestimated pollutant levels may give false impressions of acceptable air quality, while inaccurate overestimated pollutant levels (2–3 fold, [10]) may mislead residents and result in unnecessary societal costs.
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