Fine particle mass monitoring with low-cost sensors: Corrections and long-term performance evaluation
Low-cost sensors for the measurement of fine particulate matter mass (PM 2.5 ) enable dense networks to increase the spatial resolution of air quality monitoring. However, these sensors are affected by environmental factors such as temperature and humidity and their effects on ambient aerosol, which...
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| Veröffentlicht in: | Aerosol science and technology 2020-02, Vol.54 (2), p.160-174 |
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| creator | Malings, Carl Tanzer, Rebecca Hauryliuk, Aliaksei Saha, Provat K. Robinson, Allen L. Presto, Albert A. Subramanian, R |
| description | Low-cost sensors for the measurement of fine particulate matter mass (PM
2.5
) enable dense networks to increase the spatial resolution of air quality monitoring. However, these sensors are affected by environmental factors such as temperature and humidity and their effects on ambient aerosol, which must be accounted for to improve the in-field accuracy of these sensors. We conducted long-term tests of two low-cost PM
2.5
sensors: Met-One NPM and PurpleAir PA-II units. We found a high level of self-consistency within each sensor type after testing 25 NPM and 9 PurpleAir units. We developed two types of corrections for the low-cost sensor measurements to better match regulatory-grade data. The first correction accounts for aerosol hygroscopic growth using particle composition and corrects for particle mass below the optical sensor size cut-point by collocation with reference Beta Attenuation Monitors (BAM). A second, fully-empirical correction uses linear or quadratic functions of environmental variables based on the same collocation dataset. The two models yielded comparable improvements over raw measurements. Sensor performance was assessed for two use cases: improving community awareness of air quality with short-term semi-quantitative comparisons of sites and providing long-term reasonably quantitative information for health impact studies. For the short-term case, both sensors provided reasonably accurate concentration information (mean absolute error of ∼4 µg/m
3
) in near-real time. For the long-term case, tested using year-long collocations at one urban background and one near-source site, error in the annual average was reduced below 1 µg/m
3
. Hence, these sensors can supplement sparse networks of regulatory-grade instruments, perform high-density neighborhood-scale monitoring, and be used to better understand spatial patterns and temporal air quality trends across urban areas.
Copyright © 2019 American Association for Aerosol Research |
| doi_str_mv | 10.1080/02786826.2019.1623863 |
| format | Article |
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2.5
) enable dense networks to increase the spatial resolution of air quality monitoring. However, these sensors are affected by environmental factors such as temperature and humidity and their effects on ambient aerosol, which must be accounted for to improve the in-field accuracy of these sensors. We conducted long-term tests of two low-cost PM
2.5
sensors: Met-One NPM and PurpleAir PA-II units. We found a high level of self-consistency within each sensor type after testing 25 NPM and 9 PurpleAir units. We developed two types of corrections for the low-cost sensor measurements to better match regulatory-grade data. The first correction accounts for aerosol hygroscopic growth using particle composition and corrects for particle mass below the optical sensor size cut-point by collocation with reference Beta Attenuation Monitors (BAM). A second, fully-empirical correction uses linear or quadratic functions of environmental variables based on the same collocation dataset. The two models yielded comparable improvements over raw measurements. Sensor performance was assessed for two use cases: improving community awareness of air quality with short-term semi-quantitative comparisons of sites and providing long-term reasonably quantitative information for health impact studies. For the short-term case, both sensors provided reasonably accurate concentration information (mean absolute error of ∼4 µg/m
3
) in near-real time. For the long-term case, tested using year-long collocations at one urban background and one near-source site, error in the annual average was reduced below 1 µg/m
3
. Hence, these sensors can supplement sparse networks of regulatory-grade instruments, perform high-density neighborhood-scale monitoring, and be used to better understand spatial patterns and temporal air quality trends across urban areas.
Copyright © 2019 American Association for Aerosol Research</description><identifier>ISSN: 0278-6826</identifier><identifier>EISSN: 1521-7388</identifier><identifier>DOI: 10.1080/02786826.2019.1623863</identifier><language>eng</language><publisher>New York: Taylor & Francis</publisher><subject>Aerosol effects ; Aerosol research ; Aerosols ; Air monitoring ; Air quality ; Air quality assessments ; Attenuation ; Collocation ; Environmental factors ; Environmental monitoring ; Low cost ; Monitoring instruments ; Neighborhoods ; Optical measuring instruments ; Particle composition ; Particle mass ; Particulate matter ; Performance evaluation ; Quadratic equations ; Residential density ; Sensors ; Spatial resolution ; Temperature effects ; Urban air quality ; Urban areas</subject><ispartof>Aerosol science and technology, 2020-02, Vol.54 (2), p.160-174</ispartof><rights>2019 American Association for Aerosol Research 2019</rights><rights>2019 American Association for Aerosol Research</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c338t-86d86ac0f12d5a0ff0e56a9d3f79d9f43713e44668e955409456a769597637b83</citedby><cites>FETCH-LOGICAL-c338t-86d86ac0f12d5a0ff0e56a9d3f79d9f43713e44668e955409456a769597637b83</cites><orcidid>0000-0002-2242-4328 ; 0000-0002-5553-5913 ; 0000-0002-1819-083X ; 0000-0002-9156-1094</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,777,781,27905,27906</link.rule.ids></links><search><creatorcontrib>Malings, Carl</creatorcontrib><creatorcontrib>Tanzer, Rebecca</creatorcontrib><creatorcontrib>Hauryliuk, Aliaksei</creatorcontrib><creatorcontrib>Saha, Provat K.</creatorcontrib><creatorcontrib>Robinson, Allen L.</creatorcontrib><creatorcontrib>Presto, Albert A.</creatorcontrib><creatorcontrib>Subramanian, R</creatorcontrib><title>Fine particle mass monitoring with low-cost sensors: Corrections and long-term performance evaluation</title><title>Aerosol science and technology</title><description>Low-cost sensors for the measurement of fine particulate matter mass (PM
2.5
) enable dense networks to increase the spatial resolution of air quality monitoring. However, these sensors are affected by environmental factors such as temperature and humidity and their effects on ambient aerosol, which must be accounted for to improve the in-field accuracy of these sensors. We conducted long-term tests of two low-cost PM
2.5
sensors: Met-One NPM and PurpleAir PA-II units. We found a high level of self-consistency within each sensor type after testing 25 NPM and 9 PurpleAir units. We developed two types of corrections for the low-cost sensor measurements to better match regulatory-grade data. The first correction accounts for aerosol hygroscopic growth using particle composition and corrects for particle mass below the optical sensor size cut-point by collocation with reference Beta Attenuation Monitors (BAM). A second, fully-empirical correction uses linear or quadratic functions of environmental variables based on the same collocation dataset. The two models yielded comparable improvements over raw measurements. Sensor performance was assessed for two use cases: improving community awareness of air quality with short-term semi-quantitative comparisons of sites and providing long-term reasonably quantitative information for health impact studies. For the short-term case, both sensors provided reasonably accurate concentration information (mean absolute error of ∼4 µg/m
3
) in near-real time. For the long-term case, tested using year-long collocations at one urban background and one near-source site, error in the annual average was reduced below 1 µg/m
3
. Hence, these sensors can supplement sparse networks of regulatory-grade instruments, perform high-density neighborhood-scale monitoring, and be used to better understand spatial patterns and temporal air quality trends across urban areas.
Copyright © 2019 American Association for Aerosol Research</description><subject>Aerosol effects</subject><subject>Aerosol research</subject><subject>Aerosols</subject><subject>Air monitoring</subject><subject>Air quality</subject><subject>Air quality assessments</subject><subject>Attenuation</subject><subject>Collocation</subject><subject>Environmental factors</subject><subject>Environmental monitoring</subject><subject>Low cost</subject><subject>Monitoring instruments</subject><subject>Neighborhoods</subject><subject>Optical measuring instruments</subject><subject>Particle composition</subject><subject>Particle mass</subject><subject>Particulate matter</subject><subject>Performance evaluation</subject><subject>Quadratic equations</subject><subject>Residential density</subject><subject>Sensors</subject><subject>Spatial resolution</subject><subject>Temperature effects</subject><subject>Urban air quality</subject><subject>Urban areas</subject><issn>0278-6826</issn><issn>1521-7388</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9kE1LAzEQhoMoWKs_QQh43ppsdvPhSSlWhYIXPYe4m9SU3aROUkv_vbu2Xj3NYZ73HeZB6JqSGSWS3JJSSC5LPisJVTPKSyY5O0ETWpe0EEzKUzQZmWKEztFFSmtCCBUlnSC78MHijYHsm87i3qSE-xh8juDDCu98_sRd3BVNTBknG1KEdIfnEcA22ceQsAntQIRVkS30eGPBRehNaCy236bbmpG6RGfOdMleHecUvS8e3-bPxfL16WX-sCwaxmQuJG8lNw1xtGxrQ5wjtuZGtcwJ1SpXMUGZrSrOpVV1XRFVDWvBVa0EZ-JDsim6OfRuIH5tbcp6HbcQhpO6ZIwrToUUA1UfqAZiSmCd3oDvDew1JXo0qv-M6tGoPhodcveHnA-_P-4idK3OZt9FcDC87JNm_1f8AF2Qfaw</recordid><startdate>20200201</startdate><enddate>20200201</enddate><creator>Malings, Carl</creator><creator>Tanzer, Rebecca</creator><creator>Hauryliuk, Aliaksei</creator><creator>Saha, Provat K.</creator><creator>Robinson, Allen L.</creator><creator>Presto, Albert A.</creator><creator>Subramanian, R</creator><general>Taylor & Francis</general><general>Taylor & Francis Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>7TG</scope><scope>8FD</scope><scope>FR3</scope><scope>KL.</scope><orcidid>https://orcid.org/0000-0002-2242-4328</orcidid><orcidid>https://orcid.org/0000-0002-5553-5913</orcidid><orcidid>https://orcid.org/0000-0002-1819-083X</orcidid><orcidid>https://orcid.org/0000-0002-9156-1094</orcidid></search><sort><creationdate>20200201</creationdate><title>Fine particle mass monitoring with low-cost sensors: Corrections and long-term performance evaluation</title><author>Malings, Carl ; Tanzer, Rebecca ; Hauryliuk, Aliaksei ; Saha, Provat K. ; Robinson, Allen L. ; Presto, Albert A. ; Subramanian, R</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c338t-86d86ac0f12d5a0ff0e56a9d3f79d9f43713e44668e955409456a769597637b83</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Aerosol effects</topic><topic>Aerosol research</topic><topic>Aerosols</topic><topic>Air monitoring</topic><topic>Air quality</topic><topic>Air quality assessments</topic><topic>Attenuation</topic><topic>Collocation</topic><topic>Environmental factors</topic><topic>Environmental monitoring</topic><topic>Low cost</topic><topic>Monitoring instruments</topic><topic>Neighborhoods</topic><topic>Optical measuring instruments</topic><topic>Particle composition</topic><topic>Particle mass</topic><topic>Particulate matter</topic><topic>Performance evaluation</topic><topic>Quadratic equations</topic><topic>Residential density</topic><topic>Sensors</topic><topic>Spatial resolution</topic><topic>Temperature effects</topic><topic>Urban air quality</topic><topic>Urban areas</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Malings, Carl</creatorcontrib><creatorcontrib>Tanzer, Rebecca</creatorcontrib><creatorcontrib>Hauryliuk, Aliaksei</creatorcontrib><creatorcontrib>Saha, Provat K.</creatorcontrib><creatorcontrib>Robinson, Allen L.</creatorcontrib><creatorcontrib>Presto, Albert A.</creatorcontrib><creatorcontrib>Subramanian, R</creatorcontrib><collection>CrossRef</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Meteorological & Geoastrophysical Abstracts - Academic</collection><jtitle>Aerosol science and technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Malings, Carl</au><au>Tanzer, Rebecca</au><au>Hauryliuk, Aliaksei</au><au>Saha, Provat K.</au><au>Robinson, Allen L.</au><au>Presto, Albert A.</au><au>Subramanian, R</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Fine particle mass monitoring with low-cost sensors: Corrections and long-term performance evaluation</atitle><jtitle>Aerosol science and technology</jtitle><date>2020-02-01</date><risdate>2020</risdate><volume>54</volume><issue>2</issue><spage>160</spage><epage>174</epage><pages>160-174</pages><issn>0278-6826</issn><eissn>1521-7388</eissn><abstract>Low-cost sensors for the measurement of fine particulate matter mass (PM
2.5
) enable dense networks to increase the spatial resolution of air quality monitoring. However, these sensors are affected by environmental factors such as temperature and humidity and their effects on ambient aerosol, which must be accounted for to improve the in-field accuracy of these sensors. We conducted long-term tests of two low-cost PM
2.5
sensors: Met-One NPM and PurpleAir PA-II units. We found a high level of self-consistency within each sensor type after testing 25 NPM and 9 PurpleAir units. We developed two types of corrections for the low-cost sensor measurements to better match regulatory-grade data. The first correction accounts for aerosol hygroscopic growth using particle composition and corrects for particle mass below the optical sensor size cut-point by collocation with reference Beta Attenuation Monitors (BAM). A second, fully-empirical correction uses linear or quadratic functions of environmental variables based on the same collocation dataset. The two models yielded comparable improvements over raw measurements. Sensor performance was assessed for two use cases: improving community awareness of air quality with short-term semi-quantitative comparisons of sites and providing long-term reasonably quantitative information for health impact studies. For the short-term case, both sensors provided reasonably accurate concentration information (mean absolute error of ∼4 µg/m
3
) in near-real time. For the long-term case, tested using year-long collocations at one urban background and one near-source site, error in the annual average was reduced below 1 µg/m
3
. Hence, these sensors can supplement sparse networks of regulatory-grade instruments, perform high-density neighborhood-scale monitoring, and be used to better understand spatial patterns and temporal air quality trends across urban areas.
Copyright © 2019 American Association for Aerosol Research</abstract><cop>New York</cop><pub>Taylor & Francis</pub><doi>10.1080/02786826.2019.1623863</doi><tpages>15</tpages><orcidid>https://orcid.org/0000-0002-2242-4328</orcidid><orcidid>https://orcid.org/0000-0002-5553-5913</orcidid><orcidid>https://orcid.org/0000-0002-1819-083X</orcidid><orcidid>https://orcid.org/0000-0002-9156-1094</orcidid></addata></record> |
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| source | Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Free Full-Text Journals in Chemistry |
| subjects | Aerosol effects Aerosol research Aerosols Air monitoring Air quality Air quality assessments Attenuation Collocation Environmental factors Environmental monitoring Low cost Monitoring instruments Neighborhoods Optical measuring instruments Particle composition Particle mass Particulate matter Performance evaluation Quadratic equations Residential density Sensors Spatial resolution Temperature effects Urban air quality Urban areas |
| title | Fine particle mass monitoring with low-cost sensors: Corrections and long-term performance evaluation |
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