@article{35930,
  keywords = {Exposure assessment, Black carbon, Spatiotemporal Modeling, Fine Scale Prediction},
  author = {Travis Hee Wai and Joshua S Apte and Maria H Harris and Thomas W Kirchstetter and Christopher J Portier and Chelsea V Preble and Ananya Roy and Adam A Szpiro},
  title = {Insights from application of a hierarchical spatio-temporal model to an intensive urban black carbon monitoring dataset},
  abstract = {<p>Existing regulatory&nbsp;<a class="topic-link" title="Learn more about pollutant monitoring from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/pollutant-monitoring">pollutant monitoring</a>&nbsp;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&nbsp;<a class="topic-link" title="Learn more about air quality monitoring from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/air-quality-monitoring">air quality monitoring</a>&nbsp;campaigns with&nbsp;<a class="topic-link" title="Learn more about high spatial resolution from ScienceDirect's AI-generated Topic Pages" href="https://www.sciencedirect.com/topics/earth-and-planetary-sciences/high-spatial-resolution">high spatial resolution</a>. The 100&nbsp;×&nbsp;100 black carbon (BC) monitoring network deployed 100 low-cost BC sensors across the 15&nbsp;km2&nbsp;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 1-h 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&nbsp;R2" role="presentation" style="box-sizing: border-box; display: inline-block; line-height: normal; font-size: 14.4px; word-spacing: normal; overflow-wrap: normal; text-wrap: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; position: relative;"&gt;�2&nbsp;=&nbsp;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, we demonstrated that fine scale predictions of nearly comparable accuracy can be obtained with our modeling approach by using ∼30% of the 100&nbsp;×&nbsp;100 BC network supplemented by a shorter-term high-density campaign.</p>},
  year = {2022},
  journal = {Atmospheric Environment},
  volume = {277},
  pages = {119069},
  month = {05/2022},
  issn = {13522310},
  url = {https://linkinghub.elsevier.com/retrieve/pii/S1352231022001340},
  doi = {10.1016/j.atmosenv.2022.119069},
  language = {eng},
}