Cited by 510Air Quality Monitoring and ForecastingAir Quality and Health ImpactsAtmospheric chemistry and aerosols

Assessing PM 2.5 Exposures with High Spatiotemporal Resolution across the Continental United States

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Di, Qian, Kloog, Itai, Koutrakis, Petros, Lyapustin, Alexei, Wang, Yujie, Schwartz, Joel·2016·Environmental Science & Technology

Abstract

A number of models have been developed to estimate PM2.5 exposure, including satellite-based aerosol optical depth (AOD) models, land-use regression, or chemical transport model simulation, all with both strengths and weaknesses. Variables like normalized difference vegetation index (NDVI), surface reflectance, absorbing aerosol index, and meteoroidal fields are also informative about PM2.5 concentrations. Our objective is to establish a hybrid model which incorporates multiple approaches and input variables to improve model performance. To account for complex atmospheric mechanisms, we used a neural network for its capacity to model nonlinearity and interactions. We used convolutional layers, which aggregate neighboring information, into a neural network to account for spatial and temporal autocorrelation. We trained the neural network for the continental United States from 2000 to 2012 and tested it with left out monitors. Ten-fold cross-validation revealed a good model performance with a total R(2) of 0.84 on the left out monitors. Regional R(2) could be even higher for the Eastern and Central United States. Model performance was still good at low PM2.5 concentrations. Then, we used the trained neural network to make daily predictions of PM2.5 at 1 km × 1 km grid cells. This model allows epidemiologists to access PM2.5 exposure in both the short-term and the long-term.

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  • [179] use deep neural networks to estimate the effects of aerosols on human health, while [148] use them to estimate the effects of solar geoengineering on agriculture.

    “For example, [179] use deep neural networks to estimate the effects of aerosols on human health, while [148] use them to estimate the effects of solar geoengineering on agriculture.”

    From Tackling climate change with machine learning (2023) · cites this paper as [179]

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Research works printed but not linked: 2

  1. [52]Chudnovsky A Lyapustin A Wang Y Schwartz J Koutrakis P Analyses of high resolution aerosol data from MODIS satellite: a MAIAC retrieval, southern New England, US First International Conference on Remote Sensing and Geoinformation of the Environment 2013 8795 Aug 5th
  2. [56]Di Q Schwartz J Using Chemical Transport Model to Fill in the Missingness of Satellite-Based AOD Atmos Environ In review

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Authors
Di, Q., Kloog, I., Koutrakis, P., Lyapustin, A., Wang, Y., & Schwartz, J.
Affiliations
  • Harvard University (US)
  • Goddard Space Flight Center (US)
  • University of Maryland, Baltimore County, Baltimore, Maryland 21250, United States
Corresponding author
Di, Qian
Venue
Environmental Science & Technology
Published
2016-04-22
Volume
50
Issue
9
Pages
4712-4721
ISSN
  • 0013-936X (ISSN-L)
  • 1520-5851
OpenAlex
W2323483937
Cited by
510
References
72
Citations by year
2026: 35 · 2025: 48 · 2024: 55 · 2023: 47 · 2022: 81 · 2021: 59 · 2020: 55 · 2019: 70 · 2018: 34 · 2017: 22 · 2016: 3 · 2013: 1
FWCI
26.81
Type
journal article
Language
en
Open access
Locations
  • Environmental Science & Technology (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • NASA STI Repository (National Aeronautics and Space Administration) (submittedVersion)
Funders
  • National Institute of Environmental Health Sciences (ES000002, R01 ES024332-01A1)
  • U.S. Environmental Protection Agency (RD83479801)
Topics
  • Physical Sciences › Environmental Science › Environmental Engineering › Air Quality Monitoring and Forecasting
  • Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Air Quality and Health Impacts
  • Physical Sciences › Earth and Planetary Sciences › Atmospheric Science › Atmospheric chemistry and aerosols
Keywords
artificial neural networks, convolutional neural networks, aerosol optical depth, land use regression, NDVI, surface reflectance, absorbing aerosol index, spatiotemporal autocorrelation, spatiotemporal resolution, continental United States, exposure mapping
MeSH terms
Aerosols, Humans, Models, Chemical, Models, Theoretical, United States, Particulate Matter

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