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Generating High Spatial Resolution Exposure Estimates from Sparse Regulatory Monitoring Data.

Publication ,  Journal Article
Ge, Y; Yang, Z; Lin, Y; Hopke, PK; Presto, AA; Wang, M; Rich, DQ; Zhang, J
Published in: Atmospheric environment (Oxford, England : 1994)
November 2023

Random Forest algorithms have extensively been used to estimate ambient air pollutant concentrations. However, the accuracy of model-predicted estimates can suffer from extrapolation problems associated with limited measurement data to train the machine learning algorithms. In this study, we developed and evaluated two approaches, incorporating low-cost sensor data, that enhanced the extrapolating ability of random-forest models in areas with sparse monitoring data. Rochester, NY is the area of a pregnancy-cohort study. Daily PM2.5 concentrations from the NAMS/SLAMS sites were obtained and used as the response variable in the model, with satellite data, meteorological, and land-use variables included as predictors. To improve the base random-forest models, we used PM2.5 measurements from a pre-existing low-cost sensors network, and then conducted a two-step backward selection to gradually eliminate variables with potential emission heterogeneity from the base models. We then introduced the regression-enhanced random forest method into the model development. Finally, contemporaneous urinary 1-hydroxypyrene was used to evaluate the PM2.5 predictions generated from the two approaches. The two-step approach increased the average external validation R2 from 0.49 to 0.65, and decreased the RMSE from 3.56 μg/m3 to 2.96 μg/m3. For the regression-enhanced random forest models, the average R2 of the external validation was 0.54, and the RMSE was 3.40 μg/m3. We also observed significant and comparable relationships between urinary 1-hydroxypyrene levels and PM2.5 predictions from both improved models. This PM2.5 model estimation strategy could improve the extrapolating ability of random forest models in areas with sparse monitoring data.

Duke Scholars

Published In

Atmospheric environment (Oxford, England : 1994)

DOI

ISSN

1352-2310

Publication Date

November 2023

Volume

313

Start / End Page

120076

Related Subject Headings

  • Meteorology & Atmospheric Sciences
  • 4011 Environmental engineering
  • 3702 Climate change science
  • 3701 Atmospheric sciences
  • 0907 Environmental Engineering
  • 0401 Atmospheric Sciences
  • 0104 Statistics
 

Citation

APA
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ICMJE
MLA
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Ge, Y., Yang, Z., Lin, Y., Hopke, P. K., Presto, A. A., Wang, M., … Zhang, J. (2023). Generating High Spatial Resolution Exposure Estimates from Sparse Regulatory Monitoring Data. Atmospheric Environment (Oxford, England : 1994), 313, 120076. https://doi.org/10.1016/j.atmosenv.2023.120076
Ge, Yihui, Zhenchun Yang, Yan Lin, Philip K. Hopke, Albert A. Presto, Meng Wang, David Q. Rich, and Junfeng Zhang. “Generating High Spatial Resolution Exposure Estimates from Sparse Regulatory Monitoring Data.Atmospheric Environment (Oxford, England : 1994) 313 (November 2023): 120076. https://doi.org/10.1016/j.atmosenv.2023.120076.
Ge Y, Yang Z, Lin Y, Hopke PK, Presto AA, Wang M, et al. Generating High Spatial Resolution Exposure Estimates from Sparse Regulatory Monitoring Data. Atmospheric environment (Oxford, England : 1994). 2023 Nov;313:120076.
Ge, Yihui, et al. “Generating High Spatial Resolution Exposure Estimates from Sparse Regulatory Monitoring Data.Atmospheric Environment (Oxford, England : 1994), vol. 313, Nov. 2023, p. 120076. Epmc, doi:10.1016/j.atmosenv.2023.120076.
Ge Y, Yang Z, Lin Y, Hopke PK, Presto AA, Wang M, Rich DQ, Zhang J. Generating High Spatial Resolution Exposure Estimates from Sparse Regulatory Monitoring Data. Atmospheric environment (Oxford, England : 1994). 2023 Nov;313:120076.
Journal cover image

Published In

Atmospheric environment (Oxford, England : 1994)

DOI

ISSN

1352-2310

Publication Date

November 2023

Volume

313

Start / End Page

120076

Related Subject Headings

  • Meteorology & Atmospheric Sciences
  • 4011 Environmental engineering
  • 3702 Climate change science
  • 3701 Atmospheric sciences
  • 0907 Environmental Engineering
  • 0401 Atmospheric Sciences
  • 0104 Statistics