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

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Laboratory Comparison of Low-Cost Particulate Matter Sensors to Measure Transient Events of Pollution
Creator	Morris, Richard Bulot, Jacques Cox, Simon Easton, Celeste Hazel, Natasha James Basford, Philip James, Steven Johnson, Matthew Kevin, Andrew Lee Foster, Gavin Loxham, Matthew Michel, Florentin Ossont, Johnston Rezaei, Mohsen Russell, Hugo
source	Medline; PMC
abstract	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 [Formula: see text] , 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.
has issue date	2020-04-15(xsd:dateTime)
bibo:doi	10.3390/s20082219
bibo:pmid	32326452
has license	cc-by
sha1sum (hex)	d7f5d1d70cc1112f188a443ad82fe5014e8c21b2
schema:url	https://doi.org/10.3390/s20082219
resource representing a document's title	Laboratory Comparison of Low-Cost Particulate Matter Sensors to Measure Transient Events of Pollution
has PubMed Central identifier	PMC7218914
has PubMed identifier	32326452
schema:publication	Sensors (Basel)
resource representing a document's body	covid:d7f5d1d70cc1112f188a443ad82fe5014e8c21b2#body_text
is schema:about of	named entity 'impact' named entity 'suited' named entity 'premature' named entity 'detection' named entity 'pollution' named entity 'MEASUREMENT' named entity 'LEVEL' named entity 'GRADE' named entity 'SHARP' named entity 'NETWORKS' named entity 'PERFORMANCES' named entity 'detected' named entity 'sensors' named entity 'performance' named entity 'study' named entity 'sensors' named entity 'advantage' named entity 'individual' named entity 'respiratory' named entity 'combination' named entity 'cardiovascular system' named entity 'Laboratory' named entity 'Airborne particulate matter' named entity 'mass concentrations' named entity 'Particulate Matter' named entity 'ultrafine particles' named entity 'sensor' named entity 'orders of magnitude' named entity 'sensor' named entity 'PM 10' named entity 'standard deviation' named entity 'refractive index' named entity 'A15' named entity 'PM 2.5' named entity 'relative humidity' named entity 'gravity' named entity 'environmental conditions' named entity 'linear model' named entity 'long-term' named entity 'coefficient of variation' named entity 'cloud-condensation nuclei' named entity 'Honeywell' named entity 'sensor' named entity 'sensor' named entity 'mass concentrations' named entity 'aerosol' named entity 'PM 2.5' named entity 'coefficients of variation' named entity 'standard deviation' named entity 'diabetes' named entity 'Pearson coefficient' named entity 'ischaemic heart disease' named entity 'light-scattering' named entity 'accuracy and precision' named entity 'PM 10' named entity 'specific density' named entity 'data analysis' named entity 'sensor' named entity 'weather patterns' named entity 'PM 2.5' named entity 'size distribution' named entity 'standard deviation' named entity 'air quality' named entity '300 nm' named entity 'ultrafine particles' named entity 'Air Quality' named entity 'standard deviation'

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