4.6 Article

Laboratory Comparison of Low-Cost Particulate Matter Sensors to Measure Transient Events of Pollution

期刊

SENSORS
卷 20, 期 8, 页码 -

出版社

MDPI
DOI: 10.3390/s20082219

关键词

air pollution; low-cost sensor; laboratory study; particulate matter

资金

  1. Next Generation of Unmanned Systems Centre for Doctoral Training - Natural Environmental Research Council [NE/L002531/1]
  2. Leverhulme Trust through the Southampton Marine and Maritime Institute
  3. BBSRC Future Leader Fellowship [BB/P011365/1]
  4. NIHR Southampton Biomedical Research Centre Senior Fellowship
  5. Airlabs
  6. Aarhus University Graduate School of Science and Technology (GSST)
  7. BERTHA-the Danish Big Data Centre for Environment and Health - Novo Nordisk Foundation Challenge Programme [NNF17OC0027864]
  8. Natural Environment Research Council
  9. BBSRC [BB/P011365/1] Funding Source: UKRI
  10. NERC [noc010013] Funding Source: UKRI

向作者/读者索取更多资源

Airborne particulate matter (PM) exposure has been identified as a key environmental risk factor, associated especially with diseases of the respiratory and cardiovascular system and with almost 9 million premature deaths per year. Low-cost optical sensors for PM measurement are desirable for monitoring exposure closer to the personal level and particularly suited for developing spatiotemporally dense city sensor networks. However, questions remain over the accuracy and reliability of the data they produce, particularly regarding the influence of environmental parameters such as humidity and temperature, and with varying PM sources and concentration profiles. In this study, eight units each of five different models of commercially available low-cost optical PM sensors (40 individual sensors in total) were tested under controlled laboratory conditions, against higher-grade instruments for: lower limit of detection, response time, responses to sharp pollution spikes lasting <1 min, and the impact of differing humidity and PM source. All sensors detected the spikes generated with a varied range of performances depending on the model and presenting different sensitivity mainly to sources of pollution and to size distributions with a lesser impact of humidity. The sensitivity to particle size distribution indicates that the sensors may provide additional information to PM mass concentrations. It is concluded that improved performance in field monitoring campaigns, including tracking sources of pollution, could be achieved by using a combination of some of the different models to take advantage of the additional information made available by their differential response.

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