Laboratory comparison of low-cost particulate matter sensors to measure transient events of pollution

Bulot, Florentin Michel Jacques; Russell, Hugo Savill; Rezaei, Mohsen; Johnson, Matthew Stanley; Ossont, Steven James Johnston; Morris, Andrew Kevin Richard ORCID:; Basford, Philip James; Easton, Natasha Hazel Celeste; Foster, Gavin Lee; Loxham, Matthew; Cox, Simon James. 2020 Laboratory comparison of low-cost particulate matter sensors to measure transient events of pollution. Sensors, 20 (8), 2219.

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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.

Item Type: Publication - Article
Digital Object Identifier (DOI):
ISSN: 1424-8220
Date made live: 11 Jun 2020 14:00 +0 (UTC)

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