Insights from application of a hierarchical spatio-temporal model to an intensive urban black carbon monitoring dataset
Abstract
Existing regulatory pollutant monitoring networks rely on a small number of centrally located measurement sites that are purposefully sited away from major emission sources. While informative of general air quality trends regionally, these networks often do not fully capture the local variability of air pollution exposure within a community. Recent technological advancements have reduced the cost of sensors, allowing air quality monitoring campaigns with high spatial resolution. The 100×100 black carbon (BC) monitoring network deployed 100 low-cost BC sensors across the 15 km2 West Oakland, CA community for 100 days in the summer of 2017, producing a nearly continuous site-specific time series of BC concentrations which we aggregated to one-hour averages. Leveraging this dataset, we employed a hierarchical spatio-temporal model to accurately predict local spatio-temporal concentration patterns throughout West Oakland, at locations without monitors (average cross-validated hourly temporal R 2=0.60). Using our model, we identified spatially varying temporal pollution patterns associated with small-scale geographic features and proximity to local sources. In a sub-sampling analysis, here we demonstrated that fine scale predictions of nearly comparable accuracy can be obtained with our modeling approach by using ~30% of the 100×100 BC network supplemented by a shorter-term high-density campaign.
- Authors:
-
[1];
[4];
[5]
- Department of Veterans Affairs, Seattle, WA (United States)
- University of California, Berkeley, CA (United States)
- Environmental Defense Fund, Washington, DC (United States)
- University of California, Berkeley, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- University of Washington, Seattle, WA (United States)
- Publication Date:
- Research Org.:
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC); National Institutes of Health (NIH); Signe Ostby and Scott Cook, Valhalla Foundation
- OSTI Identifier:
- 1969648
- Grant/Contract Number:
- AC02-05CH11231; 5R01ES026246
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Atmospheric Environment (1994)
- Additional Journal Information:
- Journal Name: Atmospheric Environment (1994); Journal Volume: 277; Journal ID: ISSN 1352-2310
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; black carbon; spatiotemporal modeling; exposure Assessment; fine scale prediction
Citation Formats
Wai, Travis Hee, Apte, Joshua S., Harris, Maria H., Kirchstetter, Thomas W., Portier, Christopher J., Preble, Chelsea V., Roy, Ananya, and Szpiro, Adam A. Insights from application of a hierarchical spatio-temporal model to an intensive urban black carbon monitoring dataset. United States: N. p., 2023.
Web. doi:10.1016/j.atmosenv.2022.119069.
Wai, Travis Hee, Apte, Joshua S., Harris, Maria H., Kirchstetter, Thomas W., Portier, Christopher J., Preble, Chelsea V., Roy, Ananya, & Szpiro, Adam A. Insights from application of a hierarchical spatio-temporal model to an intensive urban black carbon monitoring dataset. United States. https://doi.org/10.1016/j.atmosenv.2022.119069
Wai, Travis Hee, Apte, Joshua S., Harris, Maria H., Kirchstetter, Thomas W., Portier, Christopher J., Preble, Chelsea V., Roy, Ananya, and Szpiro, Adam A. Thu .
"Insights from application of a hierarchical spatio-temporal model to an intensive urban black carbon monitoring dataset". United States. https://doi.org/10.1016/j.atmosenv.2022.119069. https://www.osti.gov/servlets/purl/1969648.
@article{osti_1969648,
title = {Insights from application of a hierarchical spatio-temporal model to an intensive urban black carbon monitoring dataset},
author = {Wai, Travis Hee and Apte, Joshua S. and Harris, Maria H. and Kirchstetter, Thomas W. and Portier, Christopher J. and Preble, Chelsea V. and Roy, Ananya and Szpiro, Adam A.},
abstractNote = {Existing regulatory pollutant monitoring networks rely on a small number of centrally located measurement sites that are purposefully sited away from major emission sources. While informative of general air quality trends regionally, these networks often do not fully capture the local variability of air pollution exposure within a community. Recent technological advancements have reduced the cost of sensors, allowing air quality monitoring campaigns with high spatial resolution. The 100×100 black carbon (BC) monitoring network deployed 100 low-cost BC sensors across the 15 km2 West Oakland, CA community for 100 days in the summer of 2017, producing a nearly continuous site-specific time series of BC concentrations which we aggregated to one-hour averages. Leveraging this dataset, we employed a hierarchical spatio-temporal model to accurately predict local spatio-temporal concentration patterns throughout West Oakland, at locations without monitors (average cross-validated hourly temporal R 2=0.60). Using our model, we identified spatially varying temporal pollution patterns associated with small-scale geographic features and proximity to local sources. In a sub-sampling analysis, here we demonstrated that fine scale predictions of nearly comparable accuracy can be obtained with our modeling approach by using ~30% of the 100×100 BC network supplemented by a shorter-term high-density campaign.},
doi = {10.1016/j.atmosenv.2022.119069},
journal = {Atmospheric Environment (1994)},
number = ,
volume = 277,
place = {United States},
year = {Thu Mar 23 00:00:00 EDT 2023},
month = {Thu Mar 23 00:00:00 EDT 2023}
}
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