Cargando…

Next-Generation Community Air Quality Sensors for Identifying Air Pollution Episodes

Conventional regulatory air quality monitoring sites tend to be sparsely located. The availability of lower-cost air pollution sensors, however, allows for their use in spatially dense community monitoring networks, which can be operated by various stakeholders, including concerned residents, organi...

Descripción completa

Detalles Bibliográficos
Autores principales: Seto, Edmund, Carvlin, Graeme, Austin, Elena, Shirai, Jeffry, Bejarano, Esther, Lugo, Humberto, Olmedo, Luis, Calderas, Astrid, Jerrett, Michael, King, Galatea, Meltzer, Dan, Wilkie, Alexa, Wong, Michelle, English, Paul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6774374/
https://www.ncbi.nlm.nih.gov/pubmed/31492020
http://dx.doi.org/10.3390/ijerph16183268
_version_ 1783456069385715712
author Seto, Edmund
Carvlin, Graeme
Austin, Elena
Shirai, Jeffry
Bejarano, Esther
Lugo, Humberto
Olmedo, Luis
Calderas, Astrid
Jerrett, Michael
King, Galatea
Meltzer, Dan
Wilkie, Alexa
Wong, Michelle
English, Paul
author_facet Seto, Edmund
Carvlin, Graeme
Austin, Elena
Shirai, Jeffry
Bejarano, Esther
Lugo, Humberto
Olmedo, Luis
Calderas, Astrid
Jerrett, Michael
King, Galatea
Meltzer, Dan
Wilkie, Alexa
Wong, Michelle
English, Paul
author_sort Seto, Edmund
collection PubMed
description Conventional regulatory air quality monitoring sites tend to be sparsely located. The availability of lower-cost air pollution sensors, however, allows for their use in spatially dense community monitoring networks, which can be operated by various stakeholders, including concerned residents, organizations, academics, or government agencies. Networks of many community monitors have the potential to fill the spatial gaps between existing government-operated monitoring sites. One potential benefit of finer scale monitoring might be the ability to discern elevated air pollution episodes in locations that have not been identified by government-operated monitoring sites, which might improve public health warnings for populations sensitive to high levels of air pollution. In the Imperial Air study, a large network of low-cost particle monitors was deployed in the Imperial Valley in Southeastern California. Data from the new monitors is validated against regulatory air monitoring. Neighborhood-level air pollution episodes, which are defined as periods in which the PM(2.5) (airborne particles with sizes less than 2.5 μm in diameter) hourly average concentration is equal to or greater than 35 μg m(−3), are identified and corroborate with other sites in the network and against the small number of government monitors in the region. During the period from October 2016 to February 2017, a total of 116 episodes were identified among six government monitors in the study region; however, more than 10 times as many episodes are identified among the 38 community air monitors. Of the 1426 episodes identified by the community sensors, 723 (51%) were not observed by the government monitors. These findings suggest that the dense network of community air monitors could be useful for addressing current limitations in the spatial coverage of government air monitoring to provide real-time warnings of high pollution episodes to vulnerable populations.
format Online
Article
Text
id pubmed-6774374
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-67743742019-10-03 Next-Generation Community Air Quality Sensors for Identifying Air Pollution Episodes Seto, Edmund Carvlin, Graeme Austin, Elena Shirai, Jeffry Bejarano, Esther Lugo, Humberto Olmedo, Luis Calderas, Astrid Jerrett, Michael King, Galatea Meltzer, Dan Wilkie, Alexa Wong, Michelle English, Paul Int J Environ Res Public Health Article Conventional regulatory air quality monitoring sites tend to be sparsely located. The availability of lower-cost air pollution sensors, however, allows for their use in spatially dense community monitoring networks, which can be operated by various stakeholders, including concerned residents, organizations, academics, or government agencies. Networks of many community monitors have the potential to fill the spatial gaps between existing government-operated monitoring sites. One potential benefit of finer scale monitoring might be the ability to discern elevated air pollution episodes in locations that have not been identified by government-operated monitoring sites, which might improve public health warnings for populations sensitive to high levels of air pollution. In the Imperial Air study, a large network of low-cost particle monitors was deployed in the Imperial Valley in Southeastern California. Data from the new monitors is validated against regulatory air monitoring. Neighborhood-level air pollution episodes, which are defined as periods in which the PM(2.5) (airborne particles with sizes less than 2.5 μm in diameter) hourly average concentration is equal to or greater than 35 μg m(−3), are identified and corroborate with other sites in the network and against the small number of government monitors in the region. During the period from October 2016 to February 2017, a total of 116 episodes were identified among six government monitors in the study region; however, more than 10 times as many episodes are identified among the 38 community air monitors. Of the 1426 episodes identified by the community sensors, 723 (51%) were not observed by the government monitors. These findings suggest that the dense network of community air monitors could be useful for addressing current limitations in the spatial coverage of government air monitoring to provide real-time warnings of high pollution episodes to vulnerable populations. MDPI 2019-09-05 2019-09 /pmc/articles/PMC6774374/ /pubmed/31492020 http://dx.doi.org/10.3390/ijerph16183268 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Seto, Edmund
Carvlin, Graeme
Austin, Elena
Shirai, Jeffry
Bejarano, Esther
Lugo, Humberto
Olmedo, Luis
Calderas, Astrid
Jerrett, Michael
King, Galatea
Meltzer, Dan
Wilkie, Alexa
Wong, Michelle
English, Paul
Next-Generation Community Air Quality Sensors for Identifying Air Pollution Episodes
title Next-Generation Community Air Quality Sensors for Identifying Air Pollution Episodes
title_full Next-Generation Community Air Quality Sensors for Identifying Air Pollution Episodes
title_fullStr Next-Generation Community Air Quality Sensors for Identifying Air Pollution Episodes
title_full_unstemmed Next-Generation Community Air Quality Sensors for Identifying Air Pollution Episodes
title_short Next-Generation Community Air Quality Sensors for Identifying Air Pollution Episodes
title_sort next-generation community air quality sensors for identifying air pollution episodes
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6774374/
https://www.ncbi.nlm.nih.gov/pubmed/31492020
http://dx.doi.org/10.3390/ijerph16183268
work_keys_str_mv AT setoedmund nextgenerationcommunityairqualitysensorsforidentifyingairpollutionepisodes
AT carvlingraeme nextgenerationcommunityairqualitysensorsforidentifyingairpollutionepisodes
AT austinelena nextgenerationcommunityairqualitysensorsforidentifyingairpollutionepisodes
AT shiraijeffry nextgenerationcommunityairqualitysensorsforidentifyingairpollutionepisodes
AT bejaranoesther nextgenerationcommunityairqualitysensorsforidentifyingairpollutionepisodes
AT lugohumberto nextgenerationcommunityairqualitysensorsforidentifyingairpollutionepisodes
AT olmedoluis nextgenerationcommunityairqualitysensorsforidentifyingairpollutionepisodes
AT calderasastrid nextgenerationcommunityairqualitysensorsforidentifyingairpollutionepisodes
AT jerrettmichael nextgenerationcommunityairqualitysensorsforidentifyingairpollutionepisodes
AT kinggalatea nextgenerationcommunityairqualitysensorsforidentifyingairpollutionepisodes
AT meltzerdan nextgenerationcommunityairqualitysensorsforidentifyingairpollutionepisodes
AT wilkiealexa nextgenerationcommunityairqualitysensorsforidentifyingairpollutionepisodes
AT wongmichelle nextgenerationcommunityairqualitysensorsforidentifyingairpollutionepisodes
AT englishpaul nextgenerationcommunityairqualitysensorsforidentifyingairpollutionepisodes