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Within-City Variation in Ambient Carbon Monoxide Concentrations: Leveraging Low-Cost Monitors in a Spatiotemporal Modeling Framework.

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Title: Within-City Variation in Ambient Carbon Monoxide Concentrations: Leveraging Low-Cost Monitors in a Spatiotemporal Modeling Framework.
Authors: Bi, Jianzhao1 jbi6@uw.edu, Zuidema, Christopher1, Clausen, David2, Kirwa, Kipruto1, Young, Michael T.1, Gassett, Amanda J.1, Seto, Edmund Y. W.1, Sampson, Paul D.3, Larson, Timothy V.4, Szpiro, Adam A.2, Sheppard, Lianne1,2, Kaufman, Joel D.1,5,6
Source: Environmental Health Perspectives. Sep2022, Vol. 130 Issue 9, p097008-1-097008-11. 11p. 2 Charts, 2 Graphs, 2 Maps.
Subject Terms: *Air pollution, *Carbon monoxide, Risk factors of environmental exposure, Relative medical risk, Population geography, Risk assessment, Theory, Descriptive statistics, Research funding, Prediction models, Data analysis software, Algorithms, Baroclinicity
Geographic Terms: Maryland
Abstract: BACKGROUND: Based on human and animal experimental studies, exposure to ambient carbon monoxide (CO) may be associated with cardiovascular disease outcomes, but epidemiological evidence of this link is limited. The number and distribution of ground-level regulatory agency monitors are insufficient to characterize fine-scale variations in CO concentrations. OBJECTIVES: To develop a daily, high-resolution ambient CO exposure prediction model at the city scale. METHODS: We developed a CO prediction model in Baltimore, Maryland, based on a spatiotemporal statistical algorithm with regulatory agency monitoring data and measurements from calibrated low-cost gas monitors. We also evaluated the contribution of three novel parameters to model performance: high-resolution meteorological data, satellite remote sensing data, and copollutant (PM2.5, NO2, and NOx) concentrations. RESULTS: The CO model had spatial cross-validation (CV) R² and root-mean-square error (RMSE) of 0.70 and 0.02 parts per million (ppm), respectively; the model had temporal CV R² and RMSE of 0.61 and 0.04 ppm, respectively. The predictions revealed spatially resolved CO hot spots associated with population, traffic, and other nonroad emission sources (e.g., railroads and airport), as well as sharp concentration decreases within short distances from primary roads. DISCUSSION: The three novel parameters did not substantially improve model performance, suggesting that, on its own, our spatiotemporal modeling framework based on geographic features was reliable and robust. As low-cost air monitors become increasingly available, this approach to CO concentration modeling can be generalized to resource-restricted environments to facilitate comprehensive epidemiological research. [ABSTRACT FROM AUTHOR]
Copyright of Environmental Health Perspectives is the property of National Institute of Environmental Health Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Within-City Variation in Ambient Carbon Monoxide Concentrations: Leveraging Low-Cost Monitors in a Spatiotemporal Modeling Framework.
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  Data: <searchLink fieldCode="AR" term="%22Bi%2C+Jianzhao%22">Bi, Jianzhao</searchLink><relatesTo>1</relatesTo><i> jbi6@uw.edu</i><br /><searchLink fieldCode="AR" term="%22Zuidema%2C+Christopher%22">Zuidema, Christopher</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Clausen%2C+David%22">Clausen, David</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Kirwa%2C+Kipruto%22">Kirwa, Kipruto</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Young%2C+Michael+T%2E%22">Young, Michael T.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Gassett%2C+Amanda+J%2E%22">Gassett, Amanda J.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Seto%2C+Edmund+Y%2E+W%2E%22">Seto, Edmund Y. W.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Sampson%2C+Paul+D%2E%22">Sampson, Paul D.</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Larson%2C+Timothy+V%2E%22">Larson, Timothy V.</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AR" term="%22Szpiro%2C+Adam+A%2E%22">Szpiro, Adam A.</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Sheppard%2C+Lianne%22">Sheppard, Lianne</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22Kaufman%2C+Joel+D%2E%22">Kaufman, Joel D.</searchLink><relatesTo>1,5,6</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Environmental+Health+Perspectives%22">Environmental Health Perspectives</searchLink>. Sep2022, Vol. 130 Issue 9, p097008-1-097008-11. 11p. 2 Charts, 2 Graphs, 2 Maps.
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  Data: <searchLink fieldCode="DE" term="%22Maryland%22">Maryland</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: BACKGROUND: Based on human and animal experimental studies, exposure to ambient carbon monoxide (CO) may be associated with cardiovascular disease outcomes, but epidemiological evidence of this link is limited. The number and distribution of ground-level regulatory agency monitors are insufficient to characterize fine-scale variations in CO concentrations. OBJECTIVES: To develop a daily, high-resolution ambient CO exposure prediction model at the city scale. METHODS: We developed a CO prediction model in Baltimore, Maryland, based on a spatiotemporal statistical algorithm with regulatory agency monitoring data and measurements from calibrated low-cost gas monitors. We also evaluated the contribution of three novel parameters to model performance: high-resolution meteorological data, satellite remote sensing data, and copollutant (PM2.5, NO2, and NOx) concentrations. RESULTS: The CO model had spatial cross-validation (CV) R² and root-mean-square error (RMSE) of 0.70 and 0.02 parts per million (ppm), respectively; the model had temporal CV R² and RMSE of 0.61 and 0.04 ppm, respectively. The predictions revealed spatially resolved CO hot spots associated with population, traffic, and other nonroad emission sources (e.g., railroads and airport), as well as sharp concentration decreases within short distances from primary roads. DISCUSSION: The three novel parameters did not substantially improve model performance, suggesting that, on its own, our spatiotemporal modeling framework based on geographic features was reliable and robust. As low-cost air monitors become increasingly available, this approach to CO concentration modeling can be generalized to resource-restricted environments to facilitate comprehensive epidemiological research. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Environmental Health Perspectives is the property of National Institute of Environmental Health Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – Type: doi
        Value: 10.1289/EHP10889
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        Text: English
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        PageCount: 11
        StartPage: 097008-1
    Subjects:
      – SubjectFull: Air pollution
        Type: general
      – SubjectFull: Carbon monoxide
        Type: general
      – SubjectFull: Risk factors of environmental exposure
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      – SubjectFull: Relative medical risk
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      – SubjectFull: Population geography
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      – SubjectFull: Risk assessment
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      – SubjectFull: Descriptive statistics
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      – SubjectFull: Research funding
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      – SubjectFull: Prediction models
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      – SubjectFull: Data analysis software
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      – SubjectFull: Algorithms
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      – SubjectFull: Baroclinicity
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      – SubjectFull: Maryland
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      – TitleFull: Within-City Variation in Ambient Carbon Monoxide Concentrations: Leveraging Low-Cost Monitors in a Spatiotemporal Modeling Framework.
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              Text: Sep2022
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