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Part 1: Document Description
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Citation |
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Title: |
Daily census tract-level wildfire fine particulate matter concentrations for California, 2006-2020 |
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Identification Number: |
doi:10.7910/DVN/CICODO |
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Distributor: |
Harvard Dataverse |
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Date of Distribution: |
2024-01-09 |
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Version: |
1 |
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Bibliographic Citation: |
Casey, Joan; Benmarhnia, Tarik; Aguilera, Rosana, 2024, "Daily census tract-level wildfire fine particulate matter concentrations for California, 2006-2020", https://doi.org/10.7910/DVN/CICODO, Harvard Dataverse, V1 |
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Citation |
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Title: |
Daily census tract-level wildfire fine particulate matter concentrations for California, 2006-2020 |
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Identification Number: |
doi:10.7910/DVN/CICODO |
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Authoring Entity: |
Casey, Joan (University of Washington School of Public Health) |
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Benmarhnia, Tarik (Scripps Institution of Oceanography, University of California San Diego) |
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Aguilera, Rosana (Scripps Institution of Oceanography, University of California San Diego) |
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Distributor: |
Harvard Dataverse |
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Access Authority: |
Aguilera, Rosana |
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Depositor: |
Casey, Joan |
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Date of Deposit: |
2023-12-12 |
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Holdings Information: |
https://doi.org/10.7910/DVN/CICODO |
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Study Scope |
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Keywords: |
Earth and Environmental Sciences, Smoke, Wildfires, Models, Statistical, Air Pollution |
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Abstract: |
This dataset contains aggregated measurements of daily wildfire-specific fine particulate matter (PM2.5) concentrations at the census tract level in California from 2006 to 2020. Similar data at the zip code level was first described in a study by Aguilera et al. (2023): "A novel ensemble-based statistical approach to estimate daily wildfire-specific PM2.5 in California (2006-2020)," published in Environment International. We used monitoring data and statistical techniques to estimate daily wildfire PM2.5 concentrations at each census tract population-weighted centroid across California. Input data included monitored PM2.5 concentrations and a wide range of predictors for PM2.5, such as aerosol optical depth, land cover, and meteorological conditions, to estimate daily concentrations of PM2.5. We also used the National Oceanic and Atmospheric Administration Hazard Mapping System and fire perimeter data from CalFIRE to isolate daily wildfire smoke PM2.5 from total PM2.5. |
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Country: |
United States |
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Geographic Coverage: |
California |
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Geographic Unit(s): |
Census tract |
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Methodology and Processing |
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Sources Statement |
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Notes: |
Dear depositor, as we collect information about your dataset, it's important for us to know what ontology supported your deposits so we can build a better system. please drop ontology URL here: |
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Data Access |
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Notes: |
<a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0</a> |
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Other Study Description Materials |
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Related Publications |
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Citation |
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Title: |
Aguilera R, Luo N, Basu R, Wu J, Clemesha R, Gershunov A, Benmarhnia T. A novel ensemble-based statistical approach to estimate daily wildfire-specific PM2. 5 in California (2006–2020). Environment International. 2023 Jan 1;171:107719. |
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Identification Number: |
36592523 |
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Bibliographic Citation: |
Aguilera R, Luo N, Basu R, Wu J, Clemesha R, Gershunov A, Benmarhnia T. A novel ensemble-based statistical approach to estimate daily wildfire-specific PM2. 5 in California (2006–2020). Environment International. 2023 Jan 1;171:107719. |
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Label: |
wfpm25_CT_2006to2020_updated_Aug2023.csv |
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Text: |
This dataset contains four columns: geoid--the census tract identifier date--the year, month, and day of the estimated PM2.5 concentration ml_pm25_aug2023--estimated non-wildfire PM2.5 concentration in ug/m3 wf_pm25_aug2023--estimated wildfire PM2.5 concentration in ug/m3 |
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Notes: |
text/csv |