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Long-term field comparison of multiple low-cost particulate matter sensors in an outdoor urban environment.

Florentin Michel Jacques BulotSteven J JohnstonPhilip J BasfordNatasha H C EastonMihaela Apetroaie-CristeaGavin L FosterAndrew K R MorrisSimon J CoxMatthew Loxham
Published in: Scientific reports (2019)
Exposure to ambient particulate matter (PM) air pollution is a leading risk factor for morbidity and mortality, associated with up to 8.9 million deaths/year worldwide. Measurement of personal exposure to PM is hindered by poor spatial resolution of monitoring networks. Low-cost PM sensors may improve monitoring resolution in a cost-effective manner but there are doubts regarding data reliability. PM sensor boxes were constructed using four low-cost PM micro-sensor models. Three boxes were deployed at each of two schools in Southampton, UK, for around one year and sensor performance was analysed. Comparison of sensor readings with a nearby background station showed moderate to good correlation (0.61 < r < 0.88, p < 0.0001), but indicated that low-cost sensor performance varies with different PM sources and background concentrations, and to a lesser extent relative humidity and temperature. This may have implications for their potential use in different locations. Data also indicates that these sensors can track short-lived events of pollution, especially in conjunction with wind data. We conclude that, with appropriate consideration of potential confounding factors, low-cost PM sensors may be suitable for PM monitoring where reference-standard equipment is not available or feasible, and that they may be useful in studying spatially localised airborne PM concentrations.
Keyphrases
  • low cost
  • particulate matter
  • air pollution
  • lung function
  • big data
  • machine learning
  • cystic fibrosis
  • climate change
  • chronic obstructive pulmonary disease
  • wastewater treatment
  • cross sectional
  • high intensity