From low-cost sensors to high-quality data: A summary of challenges and best practices for effectively calibrating low-cost particulate matter mass sensors
<p>Low-cost sensors for particulate matter mass (PM) enable spatially dense, high temporal resolution measurements of air quality that traditional reference monitoring cannot. Low-cost PM sensors are especially beneficial in low and middle-income countries where few, if any, reference grade me...
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| مؤلفون آخرون: | , , , , , , |
| منشور في: |
2021
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| _version_ | 1864513505665744896 |
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| author | Michael R. Giordano (9976173) |
| author2 | Carl Malings (4717005) Spyros N. Pandis (1917946) Albert A. Presto (1288032) V.F. McNeill (19646314) Daniel M. Westervelt (11331878) Matthias Beekmann (19646317) R. Subramanian (537564) |
| author2_role | author author author author author author author |
| author_facet | Michael R. Giordano (9976173) Carl Malings (4717005) Spyros N. Pandis (1917946) Albert A. Presto (1288032) V.F. McNeill (19646314) Daniel M. Westervelt (11331878) Matthias Beekmann (19646317) R. Subramanian (537564) |
| author_role | author |
| dc.creator.none.fl_str_mv | Michael R. Giordano (9976173) Carl Malings (4717005) Spyros N. Pandis (1917946) Albert A. Presto (1288032) V.F. McNeill (19646314) Daniel M. Westervelt (11331878) Matthias Beekmann (19646317) R. Subramanian (537564) |
| dc.date.none.fl_str_mv | 2021-11-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.jaerosci.2021.105833 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/From_low-cost_sensors_to_high-quality_data_A_summary_of_challenges_and_best_practices_for_effectively_calibrating_low-cost_particulate_matter_mass_sensors/26984341 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Environmental sciences Environmental management Low-cost sensors Particulate matter Air quality Air pollution |
| dc.title.none.fl_str_mv | From low-cost sensors to high-quality data: A summary of challenges and best practices for effectively calibrating low-cost particulate matter mass sensors |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Low-cost sensors for particulate matter mass (PM) enable spatially dense, high temporal resolution measurements of air quality that traditional reference monitoring cannot. Low-cost PM sensors are especially beneficial in low and middle-income countries where few, if any, reference grade measurements exist and in areas where the concentration fields of air pollutants have significant spatial gradients. Unfortunately, low-cost PM sensors also come with a number of challenges that must be addressed if their data products are to be used for anything more than a qualitative characterization of air quality. The various PM sensors used in low-cost monitors are all subject to biases and calibration dependencies, corrections for which range from relatively straightforward (e.g. meteorology, age of sensor) to complex (e.g. aerosol source, composition, refractive index). The methods for correcting and calibrating these biases and dependencies that have been used in the literature likewise range from simple linear and quadratic models to complex machine learning algorithms. Here we review the needs and challenges when trying to get high-quality data from low-cost sensors. We also present a set of best practices to follow to obtain high-quality data from these low-cost sensors.</p><h2>Other Information</h2> <p> Published in: Journal of Aerosol Science<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.jaerosci.2021.105833" target="_blank">https://dx.doi.org/10.1016/j.jaerosci.2021.105833</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_76e723bc0484b8ac8a35a1069b781663 |
| identifier_str_mv | 10.1016/j.jaerosci.2021.105833 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26984341 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | From low-cost sensors to high-quality data: A summary of challenges and best practices for effectively calibrating low-cost particulate matter mass sensorsMichael R. Giordano (9976173)Carl Malings (4717005)Spyros N. Pandis (1917946)Albert A. Presto (1288032)V.F. McNeill (19646314)Daniel M. Westervelt (11331878)Matthias Beekmann (19646317)R. Subramanian (537564)Environmental sciencesEnvironmental managementLow-cost sensorsParticulate matterAir qualityAir pollution<p>Low-cost sensors for particulate matter mass (PM) enable spatially dense, high temporal resolution measurements of air quality that traditional reference monitoring cannot. Low-cost PM sensors are especially beneficial in low and middle-income countries where few, if any, reference grade measurements exist and in areas where the concentration fields of air pollutants have significant spatial gradients. Unfortunately, low-cost PM sensors also come with a number of challenges that must be addressed if their data products are to be used for anything more than a qualitative characterization of air quality. The various PM sensors used in low-cost monitors are all subject to biases and calibration dependencies, corrections for which range from relatively straightforward (e.g. meteorology, age of sensor) to complex (e.g. aerosol source, composition, refractive index). The methods for correcting and calibrating these biases and dependencies that have been used in the literature likewise range from simple linear and quadratic models to complex machine learning algorithms. Here we review the needs and challenges when trying to get high-quality data from low-cost sensors. We also present a set of best practices to follow to obtain high-quality data from these low-cost sensors.</p><h2>Other Information</h2> <p> Published in: Journal of Aerosol Science<br> License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.jaerosci.2021.105833" target="_blank">https://dx.doi.org/10.1016/j.jaerosci.2021.105833</a></p>2021-11-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.jaerosci.2021.105833https://figshare.com/articles/journal_contribution/From_low-cost_sensors_to_high-quality_data_A_summary_of_challenges_and_best_practices_for_effectively_calibrating_low-cost_particulate_matter_mass_sensors/26984341CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/269843412021-11-01T00:00:00Z |
| spellingShingle | From low-cost sensors to high-quality data: A summary of challenges and best practices for effectively calibrating low-cost particulate matter mass sensors Michael R. Giordano (9976173) Environmental sciences Environmental management Low-cost sensors Particulate matter Air quality Air pollution |
| status_str | publishedVersion |
| title | From low-cost sensors to high-quality data: A summary of challenges and best practices for effectively calibrating low-cost particulate matter mass sensors |
| title_full | From low-cost sensors to high-quality data: A summary of challenges and best practices for effectively calibrating low-cost particulate matter mass sensors |
| title_fullStr | From low-cost sensors to high-quality data: A summary of challenges and best practices for effectively calibrating low-cost particulate matter mass sensors |
| title_full_unstemmed | From low-cost sensors to high-quality data: A summary of challenges and best practices for effectively calibrating low-cost particulate matter mass sensors |
| title_short | From low-cost sensors to high-quality data: A summary of challenges and best practices for effectively calibrating low-cost particulate matter mass sensors |
| title_sort | From low-cost sensors to high-quality data: A summary of challenges and best practices for effectively calibrating low-cost particulate matter mass sensors |
| topic | Environmental sciences Environmental management Low-cost sensors Particulate matter Air quality Air pollution |