Use of Citizen Science-Derived Data for Spatial and Temporal Modeling of Particulate Matter near the US/Mexico Border
This paper describes the use of citizen science-derived data for the creation of a land-use regression (LUR) model for particulate matter (PM<sub>2.5</sub> and PM<sub>coarse</sub>) for a vulnerable community in Imperial County, California (CA), near the United States (US)/Mex...
| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-08-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/10/9/495 |
| _version_ | 1818037536239386624 |
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| author | Graeme N. Carvlin Humberto Lugo Luis Olmedo Ester Bejarano Alexa Wilkie Dan Meltzer Michelle Wong Galatea King Amanda Northcross Michael Jerrett Paul B. English Jeff Shirai Michael Yost Timothy Larson Edmund Seto |
| author_facet | Graeme N. Carvlin Humberto Lugo Luis Olmedo Ester Bejarano Alexa Wilkie Dan Meltzer Michelle Wong Galatea King Amanda Northcross Michael Jerrett Paul B. English Jeff Shirai Michael Yost Timothy Larson Edmund Seto |
| author_sort | Graeme N. Carvlin |
| collection | DOAJ |
| description | This paper describes the use of citizen science-derived data for the creation of a land-use regression (LUR) model for particulate matter (PM<sub>2.5</sub> and PM<sub>coarse</sub>) for a vulnerable community in Imperial County, California (CA), near the United States (US)/Mexico border. Data from the Imperial County Community Air Monitoring Network community monitors were calibrated and added to a LUR, along with meteorology and land use. PM<sub>2.5</sub> and PM<sub>coarse</sub> were predicted across the county at the monthly timescale. Model types were compared by cross-validated (CV) <i>R</i><sup>2</sup> and root-mean-square error (RMSE). The Bayesian additive regression trees model (BART) performed the best for both PM<sub>2.5</sub> (CV <i>R</i><sup>2</sup> = 0.47, RMSE = 1.5 µg/m<sup>3</sup>) and PM<sub>coarse</sub> (CV <i>R</i><sup>2</sup> = 0.65, RMSE = 8.07 µg/m<sup>3</sup>). Model predictions were also compared to measurements from the regulatory monitors. RMSE for the monthly models was 3.6 µg/m<sup>3</sup> for PM<sub>2.5</sub> and 17.7 µg/m<sup>3</sup> for PM<sub>coarse</sub>. Variable importance measures pointed to seasonality and length of roads as drivers of PM<sub>2.5</sub>, and seasonality, type of farmland, and length of roads as drivers of PM<sub>coarse</sub>. Predicted PM<sub>2.5</sub> was elevated near the US/Mexico border and predicted PM<sub>coarse</sub> was elevated in the center of Imperial Valley. Both sizes of PM were high near the western edge of the Salton Sea. This analysis provides some of the initial evidence for the utility of citizen science-derived pollution measurements to develop spatial and temporal models which can make estimates of pollution levels throughout vulnerable communities. |
| first_indexed | 2024-12-10T07:28:24Z |
| format | Article |
| id | doaj.art-422aca21b62f45bc9a5ccfdf30f678f3 |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-12-10T07:28:24Z |
| publishDate | 2019-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-422aca21b62f45bc9a5ccfdf30f678f32022-12-22T01:57:39ZengMDPI AGAtmosphere2073-44332019-08-0110949510.3390/atmos10090495atmos10090495Use of Citizen Science-Derived Data for Spatial and Temporal Modeling of Particulate Matter near the US/Mexico BorderGraeme N. Carvlin0Humberto Lugo1Luis Olmedo2Ester Bejarano3Alexa Wilkie4Dan Meltzer5Michelle Wong6Galatea King7Amanda Northcross8Michael Jerrett9Paul B. English10Jeff Shirai11Michael Yost12Timothy Larson13Edmund Seto14Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, USAComite Civico del Valle, Brawley, CA 92227, USAComite Civico del Valle, Brawley, CA 92227, USAComite Civico del Valle, Brawley, CA 92227, USATracking California, Public Health Institute, Richmond, CA 94804, USATracking California, Public Health Institute, Richmond, CA 94804, USATracking California, Public Health Institute, Richmond, CA 94804, USATracking California, Public Health Institute, Richmond, CA 94804, USADepartment of Environmental and Occupational Health, George Washington University, Washington, DC 98824, USADepartment of Environmental Health Sciences, University of California, Los Angeles, CA 90095, USATracking California, Public Health Institute, Richmond, CA 94804, USADepartment of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, USADepartment of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, USADepartment of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, USADepartment of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA 98195, USAThis paper describes the use of citizen science-derived data for the creation of a land-use regression (LUR) model for particulate matter (PM<sub>2.5</sub> and PM<sub>coarse</sub>) for a vulnerable community in Imperial County, California (CA), near the United States (US)/Mexico border. Data from the Imperial County Community Air Monitoring Network community monitors were calibrated and added to a LUR, along with meteorology and land use. PM<sub>2.5</sub> and PM<sub>coarse</sub> were predicted across the county at the monthly timescale. Model types were compared by cross-validated (CV) <i>R</i><sup>2</sup> and root-mean-square error (RMSE). The Bayesian additive regression trees model (BART) performed the best for both PM<sub>2.5</sub> (CV <i>R</i><sup>2</sup> = 0.47, RMSE = 1.5 µg/m<sup>3</sup>) and PM<sub>coarse</sub> (CV <i>R</i><sup>2</sup> = 0.65, RMSE = 8.07 µg/m<sup>3</sup>). Model predictions were also compared to measurements from the regulatory monitors. RMSE for the monthly models was 3.6 µg/m<sup>3</sup> for PM<sub>2.5</sub> and 17.7 µg/m<sup>3</sup> for PM<sub>coarse</sub>. Variable importance measures pointed to seasonality and length of roads as drivers of PM<sub>2.5</sub>, and seasonality, type of farmland, and length of roads as drivers of PM<sub>coarse</sub>. Predicted PM<sub>2.5</sub> was elevated near the US/Mexico border and predicted PM<sub>coarse</sub> was elevated in the center of Imperial Valley. Both sizes of PM were high near the western edge of the Salton Sea. This analysis provides some of the initial evidence for the utility of citizen science-derived pollution measurements to develop spatial and temporal models which can make estimates of pollution levels throughout vulnerable communities.https://www.mdpi.com/2073-4433/10/9/495PM<sub>2.5</sub>PM<sub>coarse</sub>land-use regressioncommunity-based participatory researchcitizen scienceair sensorscommunity air monitoring |
| spellingShingle | Graeme N. Carvlin Humberto Lugo Luis Olmedo Ester Bejarano Alexa Wilkie Dan Meltzer Michelle Wong Galatea King Amanda Northcross Michael Jerrett Paul B. English Jeff Shirai Michael Yost Timothy Larson Edmund Seto Use of Citizen Science-Derived Data for Spatial and Temporal Modeling of Particulate Matter near the US/Mexico Border Atmosphere PM<sub>2.5</sub> PM<sub>coarse</sub> land-use regression community-based participatory research citizen science air sensors community air monitoring |
| title | Use of Citizen Science-Derived Data for Spatial and Temporal Modeling of Particulate Matter near the US/Mexico Border |
| title_full | Use of Citizen Science-Derived Data for Spatial and Temporal Modeling of Particulate Matter near the US/Mexico Border |
| title_fullStr | Use of Citizen Science-Derived Data for Spatial and Temporal Modeling of Particulate Matter near the US/Mexico Border |
| title_full_unstemmed | Use of Citizen Science-Derived Data for Spatial and Temporal Modeling of Particulate Matter near the US/Mexico Border |
| title_short | Use of Citizen Science-Derived Data for Spatial and Temporal Modeling of Particulate Matter near the US/Mexico Border |
| title_sort | use of citizen science derived data for spatial and temporal modeling of particulate matter near the us mexico border |
| topic | PM<sub>2.5</sub> PM<sub>coarse</sub> land-use regression community-based participatory research citizen science air sensors community air monitoring |
| url | https://www.mdpi.com/2073-4433/10/9/495 |
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