Examining spatiotemporal variability of urban particulate matter and application of high-time resolution data from a network of low-cost air pollution sensors

Feinberg, Stephen N; Williams, Ron; Hagler, Gayle; Low, Judy; Smith, Larry; Brown, Ryan; Garver, Daniel; Davis, Michael F.; Morton, Michael; Schaefer, Joe; Campbell, J. Elliott

doi:10.1016/j.atmosenv.2019.06.026

Cited by 44 publications

(34 citation statements)

References 20 publications

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“…As a result of low unit costs and compact size, sensors can be deployed to the field in much higher quantities than before, thus enabling higher-resolution spatiotemporal data. Few studies have demonstrated applications of sensor networks (Caubel et al, 2019;Feinberg et al, 2019;Gao et al, 2015;Jiao et al, 2016;Popoola et al, 2018;Yuval et al, 2019). Distributed sensing of air quality can be seen as an important progression towards a more comprehensive understanding of city-scale air quality dynamics as air pollution, and particulate matter (PM) in particular, may have highly localized concentration "hot spots" in urban areas.…”

Section: Introductionmentioning

confidence: 99%

Laboratory evaluation of particle-size selectivity of optical low-cost particulate matter sensors

et al. 2020

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Abstract. Low-cost particulate matter (PM) sensors have been under investigation as it has been hypothesized that the use of low-cost and easy-to-use sensors could allow cost-efficient extension of the currently sparse measurement coverage. While the majority of the existing literature highlights that low-cost sensors can indeed be a valuable addition to the list of commonly used measurement tools, it often reiterates that the risk of sensor misuse is still high and that the data obtained from the sensors are only representative of the specific site and its ambient conditions. This implies that there are underlying reasons for inaccuracies in sensor measurements that have yet to be characterized. The objective of this study is to investigate the particle-size selectivity of low-cost sensors. Evaluated sensors were Plantower PMS5003, Nova SDS011, Sensirion SPS30, Sharp GP2Y1010AU0F, Shinyei PPD42NS, and Omron B5W-LD0101. The investigation of size selectivity was carried out in the laboratory using a novel reference aerosol generation system capable of steadily producing monodisperse particles of different sizes (from ∼0.55 to 8.4 µm) on-line. The results of the study show that none of the low-cost sensors adhered to the detection ranges declared by the manufacturers; moreover, cursory comparison to a mid-cost aerosol size spectrometer (Grimm 1.108, 2020) indicates that the sensors can only achieve independent responses for one or two size bins, whereas the spectrometer can sufficiently characterize particles with 15 different size bins. These observations provide insight into and evidence of the notion that particle-size selectivity has an essential role in the analysis of the sources of errors in sensors.

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Section: Introductionmentioning

confidence: 99%

Laboratory evaluation of particle-size selectivity of optical low-cost particulate matter sensors

et al. 2020

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“…Other technologies include tapered element oscillating microbalances (TEOM), beta attenuation monitors (BAM) and light scattering monitors. The last of these is currently the simplest and cheapest to manufacture [16,17].…”

Section: Measurementsmentioning

confidence: 99%

Traffic exhaust to wildfires: PM2.5 measurements with fixed and portable, low-cost LoRaWAN-connected sensors

et al. 2020

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Air pollution with PM 2.5 (particulate matter smaller than 2.5 micro-metres in diameter) is a major health hazard in many cities worldwide, but since measuring instruments have traditionally been expensive, monitoring sites are rare and generally show only background concentrations. With the advent of low-cost, wirelessly connected sensors, air quality measurements are increasingly being made in places where many people spend time and pollution is much worse: on streets near traffic. In the interests of enabling members of the public to measure the air that they breathe, we took an open-source approach to designing a device for measuring PM 2.5. Parts are relatively cheap, but of good quality and can be easily found in electronics or hardware stores, or on-line. Software is open source and the free LoRaWAN-based "The Things Network" the platform. A number of low-cost sensors we tested had problems, but those selected performed well when co-located with referencequality instruments. A network of the devices was deployed in an urban centre, yielding valuable data for an extended time. Concentrations of PM 2.5 at street level were often ten times worse than at air quality stations. The devices and network offer the opportunity for measurements in locations that concern the public.

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“…If deployed in dense networks, low-cost sensors have the potential to provide near real-time measurements of pollutants at a spatial resolution representative of the neighbourhood scale. They can offer insights into the influence of local pollution sources at different temporal and spatial scales that may not be detected by the usually sparsely distributed regulatory monitoring networks (Feinberg et al, 2019;Li et al, 2019b;Popoola et al, 2018;Weissert et al, 2019a). Hence, the increasingly available data from lowcost sensor networks has led to new research aimed at combining continuous measurements obtained from a low-cost sensor network with land use data to get spatially and temporally dense air pollution information (Deville Cavellin et al, 2016;Lim et al, 2019;Masiol et al, 2019;Miskell et al, 2018b;Schneider et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Low-cost sensor networks and land-use regression: Interpolating nitrogen dioxide concentration at high temporal and spatial resolution in Southern California

Weissert

Alberti²,

Miles³

et al. 2020

Atmospheric Environment

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The development of low-cost sensors and novel calibration algorithms offer new opportunities to supplement existing regulatory networks to measure air pollutants at a high spatial resolution and at hourly and sub-hourly timescales. We use a random forest model on data from a network of low-cost sensors to describe the effect of land use features on local-scale air quality, extend this model to describe the hourly-scale variation of air quality at high spatial resolution, and show that deviations from the model can be used to identify particular conditions and locations

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Examining spatiotemporal variability of urban particulate matter and application of high-time resolution data from a network of low-cost air pollution sensors

Cited by 44 publications

References 20 publications

Laboratory evaluation of particle-size selectivity of optical low-cost particulate matter sensors

Laboratory evaluation of particle-size selectivity of optical low-cost particulate matter sensors

Traffic exhaust to wildfires: PM2.5 measurements with fixed and portable, low-cost LoRaWAN-connected sensors

Low-cost sensor networks and land-use regression: Interpolating nitrogen dioxide concentration at high temporal and spatial resolution in Southern California

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