Abstract
This paper presents a field evaluation of IoT-enabled Plantower PMS5003 particulate matter sensors in Birmingham, United Kingdom. Commercial, off the shelf, sensors were adapted to utilise Low Power Wide Area Network (LPWAN) IoT technology enabling batteries to be used as a power source. The devices are capable of measuring and communicating data to an online platform with a battery life of ∼2 months, at a measurement interval of 15 min, allowing for automated air quality monitoring for extended periods at high density. The sensors demonstrate success at being integrated into a wireless sensor network, with a high presence of readings. The average correlation coefficients (r2) between raw PMS device data and reference instrumentation are 0.718, 0.703, and 0.543 for PM1, PM2.5, and PM10, respectively. The devices also demonstrate good intersensor consistency, with Pearson’s r values between pairs ranging from 0.92 to 0.99 across all size ranges. Relative humidity (RH) clearly influences the response of the sensors, especially for RH >85%, in keeping with previous laboratory evaluations and evaluations of similar devices. The development of a multi-linear correction factor that accounts for humidity effects on the performance of the sensors is described; using this model, Pearson’s r values range from 0.81 to 0.91 compared to 0.73–0.85 from uncorrected values. There is also some evidence of drift at high humidity over an 8-week period, suggesting that such sensors will (at least currently) need recalibration approximately bimonthly. The limit of detection (LoD) (1.60–4.75 μg m−3) calculated from this study also demonstrates that the sensors are suitable for capturing concentrations typical of a moderately polluted United Kingdom urban environment—LoDs of PM2.5 in this study would have allowed for capture of 94.7% of the concentrations recorded at a typical United Kingdom urban roadside monitoring site between 2017 and 2020.
Highlights
There are estimated to be 4.2 million premature global deaths as a result of exposure to ambient air pollution, particulate matter (PM)
Clean air or low emission zones are becoming increasingly popular as an air quality management technique, with cities across the United Kingdom and further afield introducing charges and restrictions for combustion engine vehicles driving within city centers (Pisoni et al, 2019; Quarmby, Santos, and Mathias 2019)
Frequently mandated by exceedances of NO2 air quality standards, may reduce gaseous and aerosol exhaust emissions, constraints by vehicle age/EURO classification may have limited the impacts on non-exhaust particulates despite research demonstrating that non-exhaust emissions contributed 60 and 73% of primary road traffic emissions in the United Kingdom for PM2.5 and PM10, respectively (Air Quality Expert Group, 2019)
Summary
There are estimated to be 4.2 million premature global deaths as a result of exposure to ambient air pollution, particulate matter (PM). With 80% of the population of Europe living within urban areas, urban air quality policy and management is key to reducing human exposure to pollutants (Dogeanu et al, 2019) Motor vehicles are both a prominent primary source of particulate matter from exhaust emission and mechanical wear and a contributor to secondary particulate sources within photochemical smog, as the nitrous oxides and VOCs released can contribute to ozone and secondary aerosol formation in locations of favorable meteorology (Environment Protection Authority, 2004). Road vehicles are not the only anthropogenic source of PM, with combustion in homes, industry, and energy production, agriculture, metalworks, quarries, and waste burning being just some of the other activities contributing to particulate concentrations (Air Quality Expert Group, 2012). All of these sources will be contributing regional and wider PM concentrations, highlighting the need for wider PM monitoring
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