Quantifying Multipollutant Health Impacts Using the Environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE): A Case Study in Atlanta, Georgia.
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| Title: | Quantifying Multipollutant Health Impacts Using the Environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE): A Case Study in Atlanta, Georgia. |
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| Authors: | Coffman, Evan1 coffman.evan@epa.gov, Rappold, Ana G.1, Nethery, Rachel C., Anderton, Jim, Amend, Meredith, Jackson, Melanie A., Roman, Henry, Fann, Neal, Baker, Kirk R., Sacks, Jason D.1 |
| Source: | Environmental Health Perspectives. Mar2024, Vol. 132 Issue 3, p037003-1-037003-9. 9p. |
| Subject Terms: | *Air pollution, *Environmental health, *Emissions (Air pollution), *Climate change, *Pollutants, *Ozone, *Carbon monoxide, *Environmental exposure, *Particulate matter, Asthma risk factors, Community health services, Risk assessment, Human services programs, Seasons, Hospital emergency services, Descriptive statistics, Medical appointments, Comparative studies, Confidence intervals, Children |
| Geographic Terms: | Georgia |
| Abstract: | BACKGROUND: Air pollution risk assessments do not generally quantify health impacts using multipollutant risk estimates, but instead use results from single-pollutant or copollutant models. Multipollutant epidemiological models account for pollutant interactions and joint effects but can be computationally complex and data intensive. Risk estimates from multipollutant studies are therefore challenging to implement in the quantification of health impacts. OBJECTIVES: Our objective was to conduct a case study using a developmental multipollutant version of the Environmental Benefits Mapping and Analysis Program—Community Edition (BenMAP-CE) to estimate the health impact associated with changes in multiple air pollutants using both a single and multipollutant approach. METHODS: BenMAP-CE was used to estimate the change in the number of pediatric asthma emergency department (ED) visits attributable to simulated changes in air pollution between 2011 and 2025 in Atlanta, Georgia, applying risk estimates from an epidemiological study that examined shortterm single-pollutant and multipollutant (with and without first-order interactions) exposures. Analyses examined individual pollutants (i.e., ozone, fine particulate matter, carbon monoxide, nitrogen dioxide (NO2), sulfur dioxide, and particulate matter components) and combinations of these pollutants meant to represent shared properties or predefined sources (i.e., oxidant gases, secondary pollutants, traffic, power plant, and criteria pollutants). Comparisons were made between multipollutant health impact functions (HIF) and the sum of single-pollutant HIFs for the individual pollutants that constitute the respective pollutant groups. RESULTS: Photochemical modeling predicted large decreases in most of the examined pollutant concentrations between 2011 and 2025 based on sector specific (i.e., source-based) estimates of growth and anticipated controls. Estimated number of avoided asthma ED visits attributable to any given multipollutant group were generally higher when using results from models that included interaction terms in comparison with those that did not. We estimated the greatest number of avoided pediatric asthma ED visits for pollutant groups that include NO2 (i. e., criteria pollutants, oxidants, and traffic pollutants). In models that accounted for interaction, year-round estimates for pollutant groups that included NO2 ranged from 27.1 [95% confidence interval (CI): 1.6, 52.7; traffic pollutants] to 55.4 (95% CI: 41.8, 69.0; oxidants) avoided pediatric asthma ED visits. Year-round results using multipollutant risk estimates with interaction were comparable to the sum of the single-pollutant results corresponding to most multipollutant groups [e.g., 52.9 (95% CI: 43.6, 62.2) for oxidants] but were notably lower than the sum of the single-pollutant results for some pollutant groups [e.g., 77.5 (95% CI: 66.0, 89.0) for traffic pollutants]. DISCUSSION: Performing a multipollutant health impact assessment is technically feasible but computationally complex. It requires time, resources, and detailed input parameters not commonly reported in air pollution epidemiological studies. Results estimated using the sum of single-pollutant models are comparable to those quantified using a multipollutant model. Although limited to a single study and location, assessing the trade-offs between a multipollutant and single-pollutant approach is warranted. [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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| Header | DbId: 8gh DbLabel: GreenFILE An: 176607523 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Quantifying Multipollutant Health Impacts Using the Environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE): A Case Study in Atlanta, Georgia. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Coffman%2C+Evan%22">Coffman, Evan</searchLink><relatesTo>1</relatesTo><i> coffman.evan@epa.gov</i><br /><searchLink fieldCode="AR" term="%22Rappold%2C+Ana+G%2E%22">Rappold, Ana G.</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Nethery%2C+Rachel+C%2E%22">Nethery, Rachel C.</searchLink><br /><searchLink fieldCode="AR" term="%22Anderton%2C+Jim%22">Anderton, Jim</searchLink><br /><searchLink fieldCode="AR" term="%22Amend%2C+Meredith%22">Amend, Meredith</searchLink><br /><searchLink fieldCode="AR" term="%22Jackson%2C+Melanie+A%2E%22">Jackson, Melanie A.</searchLink><br /><searchLink fieldCode="AR" term="%22Roman%2C+Henry%22">Roman, Henry</searchLink><br /><searchLink fieldCode="AR" term="%22Fann%2C+Neal%22">Fann, Neal</searchLink><br /><searchLink fieldCode="AR" term="%22Baker%2C+Kirk+R%2E%22">Baker, Kirk R.</searchLink><br /><searchLink fieldCode="AR" term="%22Sacks%2C+Jason+D%2E%22">Sacks, Jason D.</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Environmental+Health+Perspectives%22">Environmental Health Perspectives</searchLink>. Mar2024, Vol. 132 Issue 3, p037003-1-037003-9. 9p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Air+pollution%22">Air pollution</searchLink><br />*<searchLink fieldCode="DE" term="%22Environmental+health%22">Environmental health</searchLink><br />*<searchLink fieldCode="DE" term="%22Emissions+%28Air+pollution%29%22">Emissions (Air pollution)</searchLink><br />*<searchLink fieldCode="DE" term="%22Climate+change%22">Climate change</searchLink><br />*<searchLink fieldCode="DE" term="%22Pollutants%22">Pollutants</searchLink><br />*<searchLink fieldCode="DE" term="%22Ozone%22">Ozone</searchLink><br />*<searchLink fieldCode="DE" term="%22Carbon+monoxide%22">Carbon monoxide</searchLink><br />*<searchLink fieldCode="DE" term="%22Environmental+exposure%22">Environmental exposure</searchLink><br />*<searchLink fieldCode="DE" term="%22Particulate+matter%22">Particulate matter</searchLink><br /><searchLink fieldCode="DE" term="%22Asthma+risk+factors%22">Asthma risk factors</searchLink><br /><searchLink fieldCode="DE" term="%22Community+health+services%22">Community health services</searchLink><br /><searchLink fieldCode="DE" term="%22Risk+assessment%22">Risk assessment</searchLink><br /><searchLink fieldCode="DE" term="%22Human+services+programs%22">Human services programs</searchLink><br /><searchLink fieldCode="DE" term="%22Seasons%22">Seasons</searchLink><br /><searchLink fieldCode="DE" term="%22Hospital+emergency+services%22">Hospital emergency services</searchLink><br /><searchLink fieldCode="DE" term="%22Descriptive+statistics%22">Descriptive statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+appointments%22">Medical appointments</searchLink><br /><searchLink fieldCode="DE" term="%22Comparative+studies%22">Comparative studies</searchLink><br /><searchLink fieldCode="DE" term="%22Confidence+intervals%22">Confidence intervals</searchLink><br /><searchLink fieldCode="DE" term="%22Children%22">Children</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Georgia%22">Georgia</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: BACKGROUND: Air pollution risk assessments do not generally quantify health impacts using multipollutant risk estimates, but instead use results from single-pollutant or copollutant models. Multipollutant epidemiological models account for pollutant interactions and joint effects but can be computationally complex and data intensive. Risk estimates from multipollutant studies are therefore challenging to implement in the quantification of health impacts. OBJECTIVES: Our objective was to conduct a case study using a developmental multipollutant version of the Environmental Benefits Mapping and Analysis Program—Community Edition (BenMAP-CE) to estimate the health impact associated with changes in multiple air pollutants using both a single and multipollutant approach. METHODS: BenMAP-CE was used to estimate the change in the number of pediatric asthma emergency department (ED) visits attributable to simulated changes in air pollution between 2011 and 2025 in Atlanta, Georgia, applying risk estimates from an epidemiological study that examined shortterm single-pollutant and multipollutant (with and without first-order interactions) exposures. Analyses examined individual pollutants (i.e., ozone, fine particulate matter, carbon monoxide, nitrogen dioxide (NO2), sulfur dioxide, and particulate matter components) and combinations of these pollutants meant to represent shared properties or predefined sources (i.e., oxidant gases, secondary pollutants, traffic, power plant, and criteria pollutants). Comparisons were made between multipollutant health impact functions (HIF) and the sum of single-pollutant HIFs for the individual pollutants that constitute the respective pollutant groups. RESULTS: Photochemical modeling predicted large decreases in most of the examined pollutant concentrations between 2011 and 2025 based on sector specific (i.e., source-based) estimates of growth and anticipated controls. Estimated number of avoided asthma ED visits attributable to any given multipollutant group were generally higher when using results from models that included interaction terms in comparison with those that did not. We estimated the greatest number of avoided pediatric asthma ED visits for pollutant groups that include NO2 (i. e., criteria pollutants, oxidants, and traffic pollutants). In models that accounted for interaction, year-round estimates for pollutant groups that included NO2 ranged from 27.1 [95% confidence interval (CI): 1.6, 52.7; traffic pollutants] to 55.4 (95% CI: 41.8, 69.0; oxidants) avoided pediatric asthma ED visits. Year-round results using multipollutant risk estimates with interaction were comparable to the sum of the single-pollutant results corresponding to most multipollutant groups [e.g., 52.9 (95% CI: 43.6, 62.2) for oxidants] but were notably lower than the sum of the single-pollutant results for some pollutant groups [e.g., 77.5 (95% CI: 66.0, 89.0) for traffic pollutants]. DISCUSSION: Performing a multipollutant health impact assessment is technically feasible but computationally complex. It requires time, resources, and detailed input parameters not commonly reported in air pollution epidemiological studies. Results estimated using the sum of single-pollutant models are comparable to those quantified using a multipollutant model. Although limited to a single study and location, assessing the trade-offs between a multipollutant and single-pollutant approach is warranted. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab 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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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1289/EHP12969 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 9 StartPage: 037003-1 Subjects: – SubjectFull: Air pollution Type: general – SubjectFull: Environmental health Type: general – SubjectFull: Emissions (Air pollution) Type: general – SubjectFull: Climate change Type: general – SubjectFull: Pollutants Type: general – SubjectFull: Ozone Type: general – SubjectFull: Carbon monoxide Type: general – SubjectFull: Environmental exposure Type: general – SubjectFull: Particulate matter Type: general – SubjectFull: Asthma risk factors Type: general – SubjectFull: Community health services Type: general – SubjectFull: Risk assessment Type: general – SubjectFull: Human services programs Type: general – SubjectFull: Seasons Type: general – SubjectFull: Hospital emergency services Type: general – SubjectFull: Descriptive statistics Type: general – SubjectFull: Medical appointments Type: general – SubjectFull: Comparative studies Type: general – SubjectFull: Confidence intervals Type: general – SubjectFull: Children Type: general – SubjectFull: Georgia Type: general Titles: – TitleFull: Quantifying Multipollutant Health Impacts Using the Environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE): A Case Study in Atlanta, Georgia. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Coffman, Evan – PersonEntity: Name: NameFull: Rappold, Ana G. – PersonEntity: Name: NameFull: Nethery, Rachel C. – PersonEntity: Name: NameFull: Anderton, Jim – PersonEntity: Name: NameFull: Amend, Meredith – PersonEntity: Name: NameFull: Jackson, Melanie A. – PersonEntity: Name: NameFull: Roman, Henry – PersonEntity: Name: NameFull: Fann, Neal – PersonEntity: Name: NameFull: Baker, Kirk R. – PersonEntity: Name: NameFull: Sacks, Jason D. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 03 Text: Mar2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 00916765 Numbering: – Type: volume Value: 132 – Type: issue Value: 3 Titles: – TitleFull: Environmental Health Perspectives Type: main |
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