Abstract
Integration of low-cost air quality sensors with the internet of things (IoT) has become a feasible approach towards the development of smart cities. Several studies have assessed the performance of low-cost air quality sensors by comparing their measurements with reference instruments. We examined the performance of a low-cost IoT particulate matter (PM10 and PM2.5) sensor in the urban environment of Santiago, Chile. The prototype was assembled from a PM10–PM2.5 sensor (SDS011), a temperature and relative humidity sensor (BME280) and an IoT board (ESP8266/Node MCU). Field tests were conducted at three regulatory monitoring stations during the 2018 austral winter and spring seasons. The sensors at each site were operated in parallel with continuous reference air quality monitors (BAM 1020 and TEOM 1400) and a filter-based sampler (Partisol 2000i). Variability between sensor units (n = 7) and the correlation between the sensor and reference instruments were examined. Moderate inter-unit variability was observed between sensors for PM2.5 (normalized root-mean-square error 9–24%) and PM10 (10–37%). The correlations between the 1-h average concentrations reported by the sensors and continuous monitors were higher for PM2.5 (R2 0.47–0.86) than PM10 (0.24–0.56). The correlations (R2) between the 24-h PM2.5 averages from the sensors and reference instruments were 0.63–0.87 for continuous monitoring and 0.69–0.93 for filter-based samplers. Correlation analysis revealed that sensors tended to overestimate PM concentrations in high relative humidity (RH > 75%) and underestimate when RH was below 50%. Overall, the prototype evaluated exhibited adequate performance and may be potentially suitable for monitoring daily PM2.5 averages after correcting for RH.
Highlights
Particulate matter (PM) air pollution is currently the leading environmental risk factor for premature death (Cohen et al 2017; Gakidou et al 2017)
Reproducibility was calculated for the data reported by 7 units of the SDS011, the low-cost PM sensor integrated into the internet of things (IoT) prototype
The frequency distribution histogram revealed an incline in the distribution towards lower values, indicating that most of the hourly PM10 average concentrations recorded by the sensors were below 40 μg m−3
Summary
Particulate matter (PM) air pollution is currently the leading environmental risk factor for premature death (Cohen et al 2017; Gakidou et al 2017). Given the health risk of fine particles, the ambient air concentrations of PM are widely monitored by public agencies at so-called regulatory air quality stations. These sites are equipped with instrumentation that performs standard reference methods, namely beta attenuation monitors (BAM), tapered element oscillating microbalances (TEOM), and filterbased gravimetric samplers. These scientific-grade devices are characterized as being large and expensive, among other features that hinder the expansion of air quality monitoring networks (Borrego et al 2015)
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