1
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Xu J, Zhao H, Zhang Y, Yang W, Wang X, Geng C, Li Y, Guo Y, Han B, Bai Z, Vedal S, Marshall JD. Reducing Indoor Particulate Air Pollution Improves Student Test Scores: A Randomized Double-Blind Crossover Study. Environ Sci Technol 2024. [PMID: 38647545 DOI: 10.1021/acs.est.3c10372] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/25/2024]
Abstract
Short-term exposure to air pollution is associated with a decline in cognitive function. Standardized test scores have been employed to evaluate the effects of air pollution exposure on cognitive performance. Few studies aimed to prove whether air pollution is responsible for reduced test scores; none have implemented a "gold-standard" method for assessing the association such as a randomized, double-blind intervention. This study used a "gold-standard" method─randomized, double-blind crossover─to assess whether reducing short-term indoor particle concentrations results in improved test scores in college students in Tianjin, China. Participants (n = 162) were randomly assigned to one of two similar classrooms and completed a standardized English test on two consecutive weekends. Air purifiers with active or sham (i.e., filter removed) particle filtration were placed in each classroom. The filtration mode was switched between the two test days. Linear mixed-effect models were used to evaluate the effect of the intervention mode on the test scores. The results show that air purification (i.e., reducing PM) was significantly associated with increases in the z score for combined (0.11 [95%CI: 0.02, 0.21]) and reading (0.11 [95%CI: 0.00, 0.22]) components. In conclusion, a short-term reduction in indoor particle concentration led to improved test scores in students, suggesting an improvement in cognitive function.
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Affiliation(s)
- Jia Xu
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Hong Zhao
- College of Computer Science, Nankai University, Tianjin 300071, China
| | - Yujuan Zhang
- Department of Family Planning, The Second Hospital of Tianjin Medical University, Tianjin 300211, China
| | - Wen Yang
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Xinhua Wang
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Chunmei Geng
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Yan Li
- College of Computer Science, Nankai University, Tianjin 300071, China
| | - Yun Guo
- College of Computer Science, Nankai University, Tianjin 300071, China
| | - Bin Han
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Zhipeng Bai
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Seattle, Washington 98105, United States
| | - Sverre Vedal
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Seattle, Washington 98105, United States
| | - Julian D Marshall
- Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington 98195, United States
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Thind MPS, Tessum CW, Marshall JD. Correction to "Environmental Health, Racial/Ethnic Health Disparity, and Climate Impacts of Inter-Regional Freight Transport in the United States". Environ Sci Technol 2024; 58:5186. [PMID: 38446129 PMCID: PMC10956488 DOI: 10.1021/acs.est.4c00052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Indexed: 03/07/2024]
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3
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Upadhya AR, Kushwaha M, Agrawal P, Gingrich JD, Asundi J, Sreekanth V, Marshall JD, Apte JS. Multi-season mobile monitoring campaign of on-road air pollution in Bengaluru, India: High-resolution mapping and estimation of quasi-emission factors. Sci Total Environ 2024; 914:169987. [PMID: 38211861 DOI: 10.1016/j.scitotenv.2024.169987] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/08/2023] [Revised: 12/30/2023] [Accepted: 01/05/2024] [Indexed: 01/13/2024]
Abstract
Mobile monitoring can supplement regulatory measurements, particularly in low-income countries where stationary monitoring is sparse. Here, we report results from a ~ year-long mobile monitoring campaign of on-road concentrations of black carbon (BC), ultrafine particles (UFP), and carbon dioxide (CO2) in Bengaluru, India. The study route included 150 unique kms (average: ~22 repeat measurements per monitored road segment). After cleaning the data for known instrument artifacts and sensitivities, we generated 30 m high-resolution stable 'data only' spatial maps of BC, UFP, and CO2 for the study route. For the urban residential areas, the mean BC levels for residential roads, arterials, and highways were ~ 10, 22, and 56 μg m-3, respectively. A similar pattern (highways being characterized by highest pollution levels) was also observed for UFP and CO2. Using the data from repeat measurements, we carried out a Monte Carlo subsampling analysis to understand the minimum number of repeat measures to generate stable maps of pollution in the city. Leveraging the simultaneous nature of the measurements, we also mapped the quasi-emission factors (QEF) of the pollutants under investigation. The current study is the first multi-season mobile monitoring exercise conducted in a low or middle -income country (LMIC) urban setting that oversampled the study route and investigated the optimum number of repeat rides required to achieve representative pollution spatial patterns characterized with high precision and low bias. Finally, the results are discussed in the context of technical aspects of the campaign, limitations, and their policy relevance for our study location and for other locations. Given the day-to-day variability in the pollution levels, the presence of dynamic and unorganized sources, and active government pollution mitigation policies, multi-year mobile measurement campaigns would help test the long-term representativeness of the current results.
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Affiliation(s)
| | | | - Pratyush Agrawal
- Center for Study of Science, Technology, and Policy, Bengaluru 560094, India
| | - Jonathan D Gingrich
- Civil, Architectural, and Environmental Engineering, University of Texas at Austin, TX 51250, United States of America
| | - Jai Asundi
- Center for Study of Science, Technology, and Policy, Bengaluru 560094, India
| | - V Sreekanth
- Center for Study of Science, Technology, and Policy, Bengaluru 560094, India.
| | - Julian D Marshall
- Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, United States of America
| | - Joshua S Apte
- Civil and Environmental Engineering, University of California, Berkeley, CA 94720, United States of America
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Gohlke JM, Harris MH, Roy A, Thompson TM, DePaola M, Alvarez RA, Anenberg SC, Apte JS, Demetillo MAG, Dressel IM, Kerr GH, Marshall JD, Nowlan AE, Patterson RF, Pusede SE, Southerland VA, Vogel SA. Response to "Comment on 'State-of-the-Science Data and Methods Need to Guide Place-Based Efforts to Reduce Air Pollution Inequity'". Environ Health Perspect 2024; 132:38002. [PMID: 38512316 PMCID: PMC10956668 DOI: 10.1289/ehp14705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2024] [Accepted: 02/22/2024] [Indexed: 03/22/2024]
Affiliation(s)
- Julia M. Gohlke
- Environmental Defense Fund, New York, New York, USA
- Department of Population Health Sciences, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA
| | | | - Ananya Roy
- Environmental Defense Fund, New York, New York, USA
| | | | | | | | - Susan C. Anenberg
- Department of Environmental and Occupational Health, George Washington University, Washington, District of Columbia, USA
| | - Joshua S. Apte
- Department of Civil and Environmental Engineering and School of Public Health, University of California, Berkeley, California, USA
| | | | - Isabella M. Dressel
- Department of Environmental Sciences, University of Virginia, Charlottesville, Virginia, USA
| | - Gaige H. Kerr
- Department of Environmental and Occupational Health, George Washington University, Washington, District of Columbia, USA
| | - Julian D. Marshall
- Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington, USA
| | | | - Regan F. Patterson
- Department of Civil and Environmental Engineering, University of California, Los Angeles, Los Angeles, California, USA
| | - Sally E. Pusede
- Department of Environmental Sciences, University of Virginia, Charlottesville, Virginia, USA
| | - Veronica A. Southerland
- Environmental Defense Fund, New York, New York, USA
- Department of Environmental and Occupational Health, George Washington University, Washington, District of Columbia, USA
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5
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Gohlke JM, Harris MH, Roy A, Thompson TM, DePaola M, Alvarez RA, Anenberg SC, Apte JS, Demetillo MAG, Dressel IM, Kerr GH, Marshall JD, Nowlan AE, Patterson RF, Pusede SE, Southerland VA, Vogel SA. State-of-the-Science Data and Methods Need to Guide Place-Based Efforts to Reduce Air Pollution Inequity. Environ Health Perspect 2023; 131:125003. [PMID: 38109120 PMCID: PMC10727036 DOI: 10.1289/ehp13063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 11/19/2023] [Accepted: 11/27/2023] [Indexed: 12/19/2023]
Abstract
BACKGROUND Recently enacted environmental justice policies in the United States at the state and federal level emphasize addressing place-based inequities, including persistent disparities in air pollution exposure and associated health impacts. Advances in air quality measurement, models, and analytic methods have demonstrated the importance of finer-scale data and analysis in accurately quantifying the extent of inequity in intraurban pollution exposure, although the necessary degree of spatial resolution remains a complex and context-dependent question. OBJECTIVE The objectives of this commentary were to a) discuss ways to maximize and evaluate the effectiveness of efforts to reduce air pollution disparities, and b) argue that environmental regulators must employ improved methods to project, measure, and track the distributional impacts of new policies at finer geographic and temporal scales. DISCUSSION The historic federal investments from the Inflation Reduction Act, the Infrastructure Investment and Jobs Act, and the Biden Administration's commitment to Justice40 present an unprecedented opportunity to advance climate and energy policies that deliver real reductions in pollution-related health inequities. In our opinion, scientists, advocates, policymakers, and implementing agencies must work together to harness critical advances in air quality measurements, models, and analytic methods to ensure success. https://doi.org/10.1289/EHP13063.
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Affiliation(s)
- Julia M. Gohlke
- Environmental Defense Fund, Washington, District of Columbia, USA
- Department of Population Health Sciences, Virginia Tech, Blacksburg, Virginia, USA
| | - Maria H. Harris
- Environmental Defense Fund, Washington, District of Columbia, USA
| | - Ananya Roy
- Environmental Defense Fund, Washington, District of Columbia, USA
| | | | - Mindi DePaola
- Environmental Defense Fund, Washington, District of Columbia, USA
| | - Ramón A. Alvarez
- Environmental Defense Fund, Washington, District of Columbia, USA
| | - Susan C. Anenberg
- Department of Environmental and Occupational Health, George Washington University, Washington, District of Columbia, USA
| | - Joshua S. Apte
- Department of Civil and Environmental Engineering, University of California, Berkeley, Berkeley, California, USA
- School of Public Health, University of California, Berkeley, Berkeley, California, USA
| | | | - Isabella M. Dressel
- Department of Environmental Sciences, University of Virginia, Charlottesville, Virginia, USA
| | - Gaige H. Kerr
- Department of Environmental and Occupational Health, George Washington University, Washington, District of Columbia, USA
| | - Julian D. Marshall
- Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington, USA
| | - Aileen E. Nowlan
- Environmental Defense Fund, Washington, District of Columbia, USA
| | - Regan F. Patterson
- Department of Civil and Environmental Engineering, University of California, Los Angeles, Los Angeles, California, USA
| | - Sally E. Pusede
- Department of Environmental Sciences, University of Virginia, Charlottesville, Virginia, USA
| | - Veronica A. Southerland
- Environmental Defense Fund, Washington, District of Columbia, USA
- Department of Environmental and Occupational Health, George Washington University, Washington, District of Columbia, USA
| | - Sarah A. Vogel
- Environmental Defense Fund, Washington, District of Columbia, USA
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Bekbulat B, Agrawal P, Allen RW, Baum M, Boldbaatar B, Clark LP, Galsuren J, Hystad P, L’Orange C, Vakacherla S, Volckens J, Marshall JD. Application of an Ultra-Low-Cost Passive Sampler for Light-Absorbing Carbon in Mongolia. Sensors (Basel) 2023; 23:8977. [PMID: 37960676 PMCID: PMC10647794 DOI: 10.3390/s23218977] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 10/29/2023] [Accepted: 11/02/2023] [Indexed: 11/15/2023]
Abstract
Low-cost, long-term measures of air pollution concentrations are often needed for epidemiological studies and policy analyses of household air pollution. The Washington passive sampler (WPS), an ultra-low-cost method for measuring the long-term average levels of light-absorbing carbon (LAC) air pollution, uses digital images to measure the changes in the reflectance of a passively exposed paper filter. A prior publication on WPS reported high precision and reproducibility. Here, we deployed three methods to each of 10 households in Ulaanbaatar, Mongolia: one PurpleAir for PM2.5; two ultrasonic personal aerosol samplers (UPAS) with quartz filters for the thermal-optical analysis of elemental carbon (EC); and two WPS for LAC. We compared multiple rounds of 4-week-average measurements. The analyses calibrating the LAC to the elemental carbon measurement suggest that 1 µg of EC/m3 corresponds to 62 PI/month (R2 = 0.83). The EC-LAC calibration curve indicates an accuracy (root-mean-square error) of 3.1 µg of EC/m3, or ~21% of the average elemental carbon concentration. The RMSE values observed here for the WPS are comparable to the reported accuracy levels for other methods, including reference methods. Based on the precision and accuracy results shown here, as well as the increased simplicity of deployment, the WPS may merit further consideration for studying air quality in homes that use solid fuels.
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Affiliation(s)
- Bujin Bekbulat
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, USA; (B.B.); (L.P.C.)
| | - Pratyush Agrawal
- Center for Study of Science, Technology & Policy, Bengaluru 560095, Karnataka, India; (P.A.); (S.V.)
| | - Ryan W. Allen
- Department of Health Sciences, Simon Fraser University, Burnaby, BC V5A 1S6, Canada;
| | | | - Buyantushig Boldbaatar
- School of Public Health, Mongolian National University of Medical Sciences, Ulaanbaatar 14210, Mongolia; (B.B.); (J.G.)
| | - Lara P. Clark
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, USA; (B.B.); (L.P.C.)
| | - Jargalsaikhan Galsuren
- School of Public Health, Mongolian National University of Medical Sciences, Ulaanbaatar 14210, Mongolia; (B.B.); (J.G.)
| | - Perry Hystad
- Department of Public Health and Human Sciences, Oregon State University, Corvallis, OR 97331, USA;
| | - Christian L’Orange
- Department of Mechanical Engineering, Colorado State University, Fort Collins, CO 80523, USA; (C.L.); (J.V.)
| | - Sreekanth Vakacherla
- Center for Study of Science, Technology & Policy, Bengaluru 560095, Karnataka, India; (P.A.); (S.V.)
| | - John Volckens
- Department of Mechanical Engineering, Colorado State University, Fort Collins, CO 80523, USA; (C.L.); (J.V.)
| | - Julian D. Marshall
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, USA; (B.B.); (L.P.C.)
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Bechle M, Millet DB, Marshall JD. Ambient NO 2 Air Pollution and Public Schools in the United States: Relationships with Urbanicity, Race-Ethnicity, and Income. Environ Sci Technol Lett 2023; 10:844-850. [PMID: 37840817 PMCID: PMC10569168 DOI: 10.1021/acs.estlett.3c00507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2023] [Revised: 08/18/2023] [Accepted: 08/21/2023] [Indexed: 10/17/2023]
Abstract
Schools may have important impacts on children's exposure to ambient air pollution, yet ambient air quality at schools is not consistently tracked. We characterize ambient air quality at home and school locations in the United States using satellite-based empirical model (i.e., land use regression) estimates of outdoor annual nitrogen dioxide (NO2). We report disparities by race-ethnicity and impoverishment status, and investigate differences by level of urbanicity. Average NO2 levels at home and school for racial-ethnic minoritized students are 18-22% higher than average (and 37-39% higher than for non-Hispanic, white students). Minoritized students are less likely than their white peers to live (0.55 times) and attend school (0.58 times) in areas below the World Health Organization's NO2 guideline. Predominantly minoritized schools (i.e., >50% minoritized students) are less likely than predominantly white schools (0.43 times) to be in locations below the guideline. Income and race-ethnicity impacts are intertwined, yet in large cities, racial disparities persist after controlling for income.
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Affiliation(s)
- Matthew
J. Bechle
- Department
of Civil & Environmental Engineering, University of Washington, 201 More Hall, Seattle, Washington 98195, United States
| | - Dylan B. Millet
- Department
of Soil, Water, and Climate, University
of Minnesota, 439 Borlaug
Hall, St. Paul, Minnesota 55108, United States
| | - Julian D. Marshall
- Department
of Civil & Environmental Engineering, University of Washington, 201 More Hall, Seattle, Washington 98195, United States
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Wang Y, Apte JS, Hill JD, Ivey CE, Johnson D, Min E, Morello-Frosch R, Patterson R, Robinson AL, Tessum CW, Marshall JD. Air quality policy should quantify effects on disparities. Science 2023; 381:272-274. [PMID: 37471550 DOI: 10.1126/science.adg9931] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/22/2023]
Abstract
New tools can guide US policies to better target and reduce racial and socioeconomic disparities in air pollution exposure.
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Affiliation(s)
- Yuzhou Wang
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, USA
| | - Joshua S Apte
- Department of Civil and Environmental Engineering, University of California, Berkeley, CA, USA
- School of Public Health, University of California, Berkeley, CA, USA
| | - Jason D Hill
- Department of Bioproducts and Biosystems Engineering, University of Minnesota, St Paul, MN, USA
| | - Cesunica E Ivey
- Department of Civil and Environmental Engineering, University of California, Berkeley, CA, USA
| | - Dana Johnson
- WE-ACT for Environmental Justice, Washington, DC, USA
| | | | - Rachel Morello-Frosch
- School of Public Health, University of California, Berkeley, CA, USA
- Department of Environmental Science, Policy and Management, University of California, Berkeley, CA, USA
| | - Regan Patterson
- Department of Civil and Environmental Engineering, University of California, Los Angeles, CA, USA
| | - Allen L Robinson
- Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Christopher W Tessum
- Department of Civil and Environmental Engineering, University of Illinois, Urbana-Champaign, IL, USA
| | - Julian D Marshall
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, USA
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Doubleday A, Blanco MN, Austin E, Marshall JD, Larson TV, Sheppard L. Characterizing Ultrafine Particle Mobile Monitoring Data for Epidemiology. Environ Sci Technol 2023; 57:9538-9547. [PMID: 37326603 DOI: 10.1021/acs.est.3c00800] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
Mobile monitoring is increasingly used to assess exposure to traffic-related air pollutants (TRAPs), including ultrafine particles (UFPs). Due to the rapid spatial decrease in the concentration of UFPs and other TRAPs with distance from roadways, mobile measurements may be non-representative of residential exposures, which are commonly used for epidemiologic studies. Our goal was to develop, apply, and test one possible approach for using mobile measurements in exposure assessment for epidemiology. We used an absolute principal component score model to adjust the contribution of on-road sources in mobile measurements to provide exposure predictions representative of cohort locations. We then compared UFP predictions at residential locations from mobile on-road plume-adjusted versus stationary measurements to understand the contribution of mobile measurements and characterize their differences. We found that predictions from mobile measurements are more representative of cohort locations after down-weighting the contribution of localized on-road plumes. Further, predictions at cohort locations derived from mobile measurements incorporate more spatial variation compared to those from short-term stationary data. Sensitivity analyses suggest that this additional spatial information captures features in the exposure surface not identified from the stationary data alone. We recommend the correction of mobile measurements to create exposure predictions representative of residential exposure for epidemiology.
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Affiliation(s)
- Annie Doubleday
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Magali N Blanco
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Elena Austin
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington 98195, United States
| | - Julian D Marshall
- Department of Civil & Environmental Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Timothy V Larson
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington 98195, United States
- Department of Civil & Environmental Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Lianne Sheppard
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington 98195, United States
- Department of Biostatistics, University of Washington, Seattle, Washington 98195, United States
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10
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Bramble K, Blanco MN, Doubleday A, Gassett AJ, Hajat A, Marshall JD, Sheppard L. Exposure Disparities by Income, Race and Ethnicity, and Historic Redlining Grade in the Greater Seattle Area for Ultrafine Particles and Other Air Pollutants. Environ Health Perspect 2023; 131:77004. [PMID: 37404015 PMCID: PMC10321236 DOI: 10.1289/ehp11662] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Revised: 05/15/2023] [Accepted: 06/01/2023] [Indexed: 07/06/2023]
Abstract
BACKGROUND Growing evidence shows ultrafine particles (UFPs) are detrimental to cardiovascular, cerebrovascular, and respiratory health. Historically, racialized and low-income communities are exposed to higher concentrations of air pollution. OBJECTIVES Our aim was to conduct a descriptive analysis of present-day air pollution exposure disparities in the greater Seattle, Washington, area by income, race, ethnicity, and historical redlining grade. We focused on UFPs (particle number count) and compared with black carbon, nitrogen dioxide, and fine particulate matter (PM 2.5 ) levels. METHODS We obtained race and ethnicity data from the 2010 U.S. Census, median household income data from the 2006-2010 American Community Survey, and Home Owners' Loan Corporation (HOLC) redlining data from the University of Richmond's Mapping Inequality. We predicted pollutant concentrations at block centroids from 2019 mobile monitoring data. The study region encompassed much of urban Seattle, with redlining analyses restricted to a smaller region. To analyze disparities, we calculated population-weighted mean exposures and regression analyses using a generalized estimating equation model to account for spatial correlation. RESULTS Pollutant concentrations and disparities were largest for blocks with median household income of < $ 20,000 , Black residents, HOLC Grade D, and ungraded industrial areas. UFP concentrations were 4% lower than average for non-Hispanic White residents and higher than average for racialized groups (Asian, 3%; Black, 15%; Hispanic, 6%; Native American, 8%; Pacific Islander, 11%). For blocks with median household incomes of < $ 20,000 , UFP concentrations were 40% higher than average, whereas blocks with incomes of > $ 110,000 had UFP concentrations 16% lower than average. UFP concentrations were 28% higher for Grade D and 49% higher for ungraded industrial areas compared with Grade A. Disparities were highest for UFPs and lowest for PM 2.5 exposure levels. DISCUSSION Our study is one of the first to highlight large disparities with UFP exposures compared with multiple pollutants. Higher exposures to multiple air pollutants and their cumulative effects disproportionately impact historically marginalized groups. https://doi.org/10.1289/EHP11662.
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Affiliation(s)
- Kaya Bramble
- Department of Industrial & Systems Engineering, College of Engineering, University of Washington, Seattle, Washington, USA
| | - Magali N. Blanco
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Seattle, Washington, USA
| | - Annie Doubleday
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Seattle, Washington, USA
| | - Amanda J. Gassett
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Seattle, Washington, USA
| | - Anjum Hajat
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, Washington, USA
| | - Julian D. Marshall
- Department of Civil & Environmental Engineering, College of Engineering, University of Washington, Seattle, Washington, USA
| | - Lianne Sheppard
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Seattle, Washington, USA
- Department of Biostatistics, School of Public Health, University of Washington, Seattle, Washington, USA
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11
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Swetschinski L, Fong KC, Morello-Frosch R, Marshall JD, Bell ML. Exposures to ambient particulate matter are associated with reduced adult earnings potential. Environ Res 2023:116391. [PMID: 37308068 DOI: 10.1016/j.envres.2023.116391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Revised: 05/27/2023] [Accepted: 06/09/2023] [Indexed: 06/14/2023]
Abstract
The societal costs of air pollution have historically been measured in terms of premature deaths (including the corresponding values of statistical lives lost), disability-adjusted life years, and medical costs. Emerging research, however, demonstrated potential impacts of air pollution on human capital formation. Extended contact with pollutants such as airborne particulate matter among young persons whose biological systems are still developing can result in pulmonary, neurobehavioral, and birth complications, hindering academic performance as well as skills and knowledge acquisition. Using a dataset that tracks 2014-2015 incomes for 96.2% of Americans born between 1979 and 1983, we assessed the association between childhood exposure to fine particulate matter (PM2.5) and adult earnings outcomes across U.S. Census tracts. After accounting for pertinent economic covariates and regional random effects, our regression models indicate that early-life exposure to PM2.5 is associated with lower predicted income percentiles by mid-adulthood; all else equal, children raised in high pollution tracts (at the 75th percentile of PM2.5) are estimated to have approximately a 0.51 decrease in income percentile relative to children raised in low pollution tracts (at the 25th percentile of PM2.5). For a person earning the median income, this difference corresponds to a $436 lower annual income (in 2015 USD). We estimate that 2014-2015 earnings for the 1978-1983 birth cohort would have been ∼$7.18 billion higher had their childhood exposure met U.S. air quality standards for PM2.5. Stratified models show that the relationship between PM2.5 and diminished earnings is more pronounced for low-income children and for children living in rural environments. These findings raise concerns about long-term environmental and economic justice for children living in areas with poor air quality where air pollution could act as a barrier to intergenerational class equity.
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Affiliation(s)
- Lucien Swetschinski
- Yale School of the Environment, Yale University, 195 Prospect Street, New Haven, CT, 06511, USA.
| | - Kelvin C Fong
- Department of Earth and Environmental Sciences, Dalhousie University, Halifax, Nova Scotia, Canada.
| | - Rachel Morello-Frosch
- Department of Environmental Science, Policy, and Management, University of California-Berkeley, Berkeley, CA, USA; School of Public Health, University of California-Berkeley, Berkeley, CA, USA.
| | - Julian D Marshall
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, USA.
| | - Michelle L Bell
- Yale School of the Environment, Yale University, 195 Prospect Street, New Haven, CT, 06511, USA.
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12
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Blanco MN, Doubleday A, Austin E, Marshall JD, Seto E, Larson TV, Sheppard L. Design and evaluation of short-term monitoring campaigns for long-term air pollution exposure assessment. J Expo Sci Environ Epidemiol 2023; 33:465-473. [PMID: 36045136 PMCID: PMC9971335 DOI: 10.1038/s41370-022-00470-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 08/12/2022] [Accepted: 08/15/2022] [Indexed: 06/02/2023]
Abstract
BACKGROUND Short-term mobile monitoring campaigns to estimate long-term air pollution levels are becoming increasingly common. Still, many campaigns have not conducted temporally-balanced sampling, and few have looked at the implications of such study designs for epidemiologic exposure assessment. OBJECTIVE We carried out a simulation study using fixed-site air quality monitors to better understand how different short-term monitoring designs impact the resulting exposure surfaces. METHODS We used Monte Carlo resampling to simulate three archetypal short-term monitoring sampling designs using oxides of nitrogen (NOx) monitoring data from 69 regulatory sites in California: a year-around Balanced Design that sampled during all seasons of the year, days of the week, and all or various hours of the day; a temporally reduced Rush Hours Design; and a temporally reduced Business Hours Design. We evaluated the performance of each design's land use regression prediction model. RESULTS The Balanced Design consistently yielded the most accurate annual averages; while the reduced Rush Hours and Business Hours Designs generally produced more biased results. SIGNIFICANCE A temporally-balanced sampling design is crucial for short-term campaigns such as mobile monitoring aiming to assess long-term exposure in epidemiologic cohorts. IMPACT STATEMENT Short-term monitoring campaigns to assess long-term air pollution trends are increasingly common, though they rarely conduct temporally balanced sampling. We show that this approach produces biased annual average exposure estimates that can be improved by collecting temporally-balanced samples.
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Affiliation(s)
- Magali N Blanco
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Hans Rosling Center for Population Health, 3980 15th Ave NE, Seattle, WA, 98195, USA.
| | - Annie Doubleday
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Hans Rosling Center for Population Health, 3980 15th Ave NE, Seattle, WA, 98195, USA
| | - Elena Austin
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Hans Rosling Center for Population Health, 3980 15th Ave NE, Seattle, WA, 98195, USA
| | - Julian D Marshall
- Department of Civil & Environmental Engineering, College of Engineering, University of Washington, 201 More Hall, Box 352700, Seattle, WA, 98195, USA
| | - Edmund Seto
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Hans Rosling Center for Population Health, 3980 15th Ave NE, Seattle, WA, 98195, USA
| | - Timothy V Larson
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Hans Rosling Center for Population Health, 3980 15th Ave NE, Seattle, WA, 98195, USA
- Department of Civil & Environmental Engineering, College of Engineering, University of Washington, 201 More Hall, Box 352700, Seattle, WA, 98195, USA
| | - Lianne Sheppard
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Hans Rosling Center for Population Health, 3980 15th Ave NE, Seattle, WA, 98195, USA.
- Department of Biostatistics, School of Public Health, University of Washington, Hans Rosling Center for Population Health, 3980 15th Ave NE, Seattle, WA, 98195, USA.
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13
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Liu J, Marshall JD. Spatial Decomposition of Air Pollution Concentrations Highlights Historical Causes for Current Exposure Disparities in the United States. Environ Sci Technol Lett 2023; 10:280-286. [PMID: 36938149 PMCID: PMC10019334 DOI: 10.1021/acs.estlett.2c00826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 02/14/2023] [Accepted: 02/15/2023] [Indexed: 06/18/2023]
Abstract
Racial-ethnic disparities in exposure to air pollution in the United States (US) are well documented. Studies on the causes of these disparities highlight unequal systems of power and longstanding systemic racism-for example, redlining, white flight, and racial covenants-which reinforced racial segregation and wealth gaps and which concentrated polluting land uses in communities of color. Our analysis is based on empirical estimates of ambient concentrations for two important pollutants (NO2 and PM2.5). We show that spatially decomposed concentrations can be used to infer and quantify types of root causes for local- to national-scale disparities. Urban-scale segregation is important yet reflects less than half of the overall national disparities. Other historical causes of national exposure disparities include those that led current populations of Black, Asian, and Hispanic Americans to live in larger cities; those outcomes are consistent with, for example, greater economic opportunity in large cities, land-takings from non-White farmers, and racism in homesteading and between-state migration. Our results suggest that contemporary national exposure disparities in the US reflect a broad set of historical local- to national-scale mechanisms-including racist laws and actions that include, but also extend beyond, urban-scale aspects-and offer a first attempt to quantify their relative importance.
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Affiliation(s)
- Jiawen Liu
- Department
of Civil and Environmental Engineering, University of Washington, Seattle, Washington 98125, United States
| | - Julian D. Marshall
- Department
of Civil and Environmental Engineering, University of Washington, Seattle, Washington 98125, United States
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14
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Thind MPS, Tessum CW, Marshall JD. Environmental Health, Racial/Ethnic Health Disparity, and Climate Impacts of Inter-Regional Freight Transport in the United States. Environ Sci Technol 2023; 57:884-895. [PMID: 36580637 PMCID: PMC9851153 DOI: 10.1021/acs.est.2c03646] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 12/08/2022] [Accepted: 12/08/2022] [Indexed: 06/17/2023]
Abstract
We quantify and compare three environmental impacts from inter-regional freight transportation in the contiguous United States: total mortality attributable to PM2.5 air pollution, racial-ethnic disparities in PM2.5-attributable mortality, and CO2 emissions. We compare all major freight modes (truck, rail, barge, aircraft) and routes (∼30,000 routes). Our study is the first to comprehensively compare each route separately and the first to explore racial-ethnic exposure disparities by route and mode, nationally. Impacts (health, health disparity, climate) per tonne of freight are the largest for aircraft. Among nonaircraft modes, per tonne, rail has the largest health and health-disparity impacts and the lowest climate impacts, whereas truck transport has the lowest health impacts and greatest climate impacts─an important reminder that health and climate impacts are often but not always aligned. For aircraft and truck, average monetized damages per tonne are larger for climate impacts than those for PM2.5 air pollution; for rail and barge, the reverse holds. We find that average exposures from inter-regional truck and rail are the highest for White non-Hispanic people, those from barge are the highest for Black people, and those from aircraft are the highest for people who are mixed/other race. Level of exposure and disparity among racial-ethnic groups vary in urban versus rural areas.
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Affiliation(s)
- Maninder P. S. Thind
- Department
of Civil and Environmental Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Christopher W. Tessum
- Department
of Civil and Environmental Engineering, University of Illinois at Urbana−Champaign, Urbana, Illinois 61801, United States
| | - Julian D. Marshall
- Department
of Civil and Environmental Engineering, University of Washington, Seattle, Washington 98195, United States
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15
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Blanco MN, Bi J, Austin E, Larson TV, Marshall JD, Sheppard L. Impact of Mobile Monitoring Network Design on Air Pollution Exposure Assessment Models. Environ Sci Technol 2023; 57:440-450. [PMID: 36508743 PMCID: PMC10615227 DOI: 10.1021/acs.est.2c05338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/01/2023]
Abstract
Short-term mobile monitoring campaigns are increasingly used to assess long-term air pollution exposure in epidemiology. Little is known about how monitoring network design features, including the number of stops and sampling temporality, impacts exposure assessment models. We address this gap by leveraging an extensive mobile monitoring campaign conducted in the greater Seattle area over the course of a year during all days of the week and most hours. The campaign measured total particle number concentration (PNC; sheds light on ultrafine particulate (UFP) number concentration), black carbon (BC), nitrogen dioxide (NO2), fine particulate matter (PM2.5), and carbon dioxide (CO2). In Monte Carlo sampling of 7327 total stops (278 sites × 26 visits each), we restricted the number of sites and visits used to estimate annual averages. Predictions from the all-data campaign performed well, with cross-validated R2s of 0.51-0.77. We found similar model performances (85% of the all-data campaign R2) with ∼1000 to 3000 randomly selected stops for NO2, PNC, and BC, and ∼4000 to 5000 stops for PM2.5 and CO2. Campaigns with additional temporal restrictions (e.g., business hours, rush hours, weekdays, or fewer seasons) had reduced model performances and different spatial surfaces. Mobile monitoring campaigns wanting to assess long-term exposure should carefully consider their monitoring designs.
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Affiliation(s)
- Magali N Blanco
- Department of Environmental and Occupational Health Sciences, School of Public Health, Hans Rosling Center for Population Health, University of Washington, 3980 15th Avenue NE, Seattle, Washington98195, United States
| | - Jianzhao Bi
- Department of Environmental and Occupational Health Sciences, School of Public Health, Hans Rosling Center for Population Health, University of Washington, 3980 15th Avenue NE, Seattle, Washington98195, United States
| | - Elena Austin
- Department of Environmental and Occupational Health Sciences, School of Public Health, Hans Rosling Center for Population Health, University of Washington, 3980 15th Avenue NE, Seattle, Washington98195, United States
| | - Timothy V Larson
- Department of Environmental and Occupational Health Sciences, School of Public Health, Hans Rosling Center for Population Health, University of Washington, 3980 15th Avenue NE, Seattle, Washington98195, United States
- Department of Civil & Environmental Engineering, College of Engineering, University of Washington, 201 More Hall, Box 352700, Seattle, Washington98195, United States
| | - Julian D Marshall
- Department of Civil & Environmental Engineering, College of Engineering, University of Washington, 201 More Hall, Box 352700, Seattle, Washington98195, United States
| | - Lianne Sheppard
- Department of Environmental and Occupational Health Sciences, School of Public Health, Hans Rosling Center for Population Health, University of Washington, 3980 15th Avenue NE, Seattle, Washington98195, United States
- Department of Biostatistics, School of Public Health, Hans Rosling Center for Population Health, University of Washington, 3980 15th Avenue NE, Seattle, Washington98195, United States
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16
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Wang Y, Apte JS, Hill JD, Ivey CE, Patterson RF, Robinson AL, Tessum CW, Marshall JD. Location-specific strategies for eliminating US national racial-ethnic [Formula: see text] exposure inequality. Proc Natl Acad Sci U S A 2022; 119:e2205548119. [PMID: 36279443 PMCID: PMC9636929 DOI: 10.1073/pnas.2205548119] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 08/30/2022] [Indexed: 07/22/2023] Open
Abstract
Air pollution levels in the United States have decreased dramatically over the past decades, yet national racial-ethnic exposure disparities persist. For ambient fine particulate matter ([Formula: see text]), we investigate three emission-reduction approaches and compare their optimal ability to address two goals: 1) reduce the overall population average exposure ("overall average") and 2) reduce the difference in the average exposure for the most exposed racial-ethnic group versus for the overall population ("national inequalities"). We show that national inequalities in exposure can be eliminated with minor emission reductions (optimal: ~1% of total emissions) if they target specific locations. In contrast, achieving that outcome using existing regulatory strategies would require eliminating essentially all emissions (if targeting specific economic sectors) or is not possible (if requiring urban regions to meet concentration standards). Lastly, we do not find a trade-off between the two goals (i.e., reducing overall average and reducing national inequalities); rather, the approach that does the best for reducing national inequalities (i.e., location-specific strategies) also does as well as or better than the other two approaches (i.e., sector-specific and meeting concentration standards) for reducing overall averages. Overall, our findings suggest that incorporating location-specific emissions reductions into the US air quality regulatory framework 1) is crucial for eliminating long-standing national average exposure disparities by race-ethnicity and 2) can benefit overall average exposures as much as or more than the sector-specific and concentration-standards approaches.
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Affiliation(s)
- Yuzhou Wang
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195
| | - Joshua S. Apte
- Department of Civil and Environmental Engineering, University of California, Berkeley, CA 94720
- School of Public Health, University of California, Berkeley, CA 94720
| | - Jason D. Hill
- Department of Bioproducts and Biosystems Engineering, University of Minnesota, St. Paul, MN 55108
| | - Cesunica E. Ivey
- Department of Civil and Environmental Engineering, University of California, Berkeley, CA 94720
| | - Regan F. Patterson
- Center for Policy Analysis and Research, Congressional Black Caucus Foundation, Washington, DC 20036
| | - Allen L. Robinson
- Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Christopher W. Tessum
- Department of Civil and Environmental Engineering, University of Illinois, Urbana, IL 61801
| | - Julian D. Marshall
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195
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17
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Saha PK, Presto AA, Hankey S, Murphy BN, Allen C, Zhang W, Marshall JD, Robinson AL. National Exposure Models for Source-Specific Primary Particulate Matter Concentrations Using Aerosol Mass Spectrometry Data. Environ Sci Technol 2022; 56:14284-14295. [PMID: 36153982 DOI: 10.1021/acs.est.2c03398] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
This paper investigates the feasibility of developing national empirical models to predict ambient concentrations of sparsely monitored air pollutants at high spatial resolution. We used a data set of cooking organic aerosol (COA) and hydrocarbon-like organic aerosol (HOA; traffic primary organic PM) measured using aerosol mass spectrometry across the continental United States. The monitoring locations were selected to span the national distribution of land-use and source-activity variables commonly used for land-use regression modeling (e.g., road length, restaurant count, etc.). The models explain about 60% of the spatial variability of the measured data (R2 0.63 for the COA model and 0.62 for the HOA model). Extensive cross-validation suggests that the models are robust with reasonable transferability. The models predict large urban-rural and intra-urban variability with hotspots in urban areas and along the road corridors. The predicted national concentration surfaces show reasonable spatial correlation with source-specific national chemical transport model (CTM) simulations (R2: 0.45 for COA, 0.4 for HOA). Our measured data, empirical models, and CTM predictions all show that COA concentrations are about two times higher than HOA. Since COA and HOA are important contributors to the intra-urban spatial variability of the total PM2.5, our results highlight the potential importance of controlling commercial cooking emissions for air quality management in the United States.
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Affiliation(s)
- Provat K Saha
- Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
- Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Albert A Presto
- Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
- Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Steve Hankey
- School of Public and International Affairs, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Benjamin N Murphy
- Center for Environmental Measurement and Modeling, U.S. Environmental Protection Agency, Research Triangle Park, Durham, North Carolina 27709, United States
| | - Chris Allen
- General Dynamics Information Technology, Research Triangle Park, Durham, North Carolina 27711, United States
| | - Wenwen Zhang
- Department of Public Informatics, Rutgers University, New Brunswick, New Jersey 08901, United States
| | - Julian D Marshall
- Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Allen L Robinson
- Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
- Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
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18
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Tessum MW, Anenberg SC, Chafe ZA, Henze DK, Kleiman G, Kheirbek I, Marshall JD, Tessum CW. Sources of ambient PM 2.5 exposure in 96 global cities. Atmos Environ (1994) 2022; 286:119234. [PMID: 36193038 PMCID: PMC9297293 DOI: 10.1016/j.atmosenv.2022.119234] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 06/03/2022] [Accepted: 06/06/2022] [Indexed: 05/28/2023]
Abstract
To improve air quality, knowledge of the sources and locations of air pollutant emissions is critical. However, for many global cities, no previous estimates exist of how much exposure to fine particulate matter (PM2.5), the largest environmental cause of mortality, is caused by emissions within the city vs. outside its boundaries. We use the Intervention Model for Air Pollution (InMAP) global-through-urban reduced complexity air quality model with a high-resolution, global inventory of pollutant emissions to quantify the contribution of emissions by source type and location for 96 global cities. Among these cities, we find that the fraction of PM2.5 exposure caused by within-city emissions varies widely (μ = 37%; σ = 22%) and is not well-explained by surrounding population density. The list of most-important sources also varies by city. Compared to a more mechanistically detailed model, InMAP predicts urban measured concentrations with lower bias and error but also lower correlation. Predictive accuracy in urban areas is not particularly high with either model, suggesting an opportunity for improving global urban air emission inventories. We expect the results herein can be useful as a screening tool for policy options and, in the absence of available resources for further analysis, to inform policy action to improve public health.
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Affiliation(s)
- Mei W. Tessum
- Department of Agricultural and Biological Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States
| | - Susan C. Anenberg
- Department of Environmental and Occupational Health, George Washington University, Washington, DC, United States
| | - Zoe A. Chafe
- C40 Cities Climate Leadership Group Inc., New York, NY, United States
| | - Daven K. Henze
- Department of Mechanical Engineering, University of Colorado, Boulder, CO, United States
| | | | - Iyad Kheirbek
- C40 Cities Climate Leadership Group Inc., New York, NY, United States
| | - Julian D. Marshall
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, United States
| | - Christopher W. Tessum
- Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States
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19
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Qi M, Dixit K, Marshall JD, Zhang W, Hankey S. National Land Use Regression Model for NO 2 Using Street View Imagery and Satellite Observations. Environ Sci Technol 2022; 56:13499-13509. [PMID: 36084299 DOI: 10.1021/acs.est.2c03581] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Land use regression (LUR) models are widely applied to estimate intra-urban air pollution concentrations. National-scale LURs typically employ predictors from multiple curated geodatabases at neighborhood scales. In this study, we instead developed national NO2 models relying on innovative street-level predictors extracted from Google Street View [GSV] imagery. Using machine learning (random forest), we developed two types of models: (1) GSV-only models, which use only GSV features, and (2) GSV + OMI models, which also include satellite observations of NO2. Our results suggest that street view imagery alone may provide sufficient information to explain NO2 variation. Satellite observations can improve model performance, but the contribution decreases as more images are available. Random 10-fold cross-validation R2 of our best models were 0.88 (GSV-only) and 0.91 (GSV + OMI)─a performance that is comparable to traditional LUR approaches. Importantly, our models show that street-level features might have the potential to better capture intra-urban variation of NO2 pollution than traditional LUR. Collectively, our findings indicate that street view image-based modeling has great potential for building large-scale air quality models under a unified framework. Toward that goal, we describe a cost-effective image sampling strategy for future studies based on a systematic evaluation of image availability and model performance.
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Affiliation(s)
- Meng Qi
- School of Public and International Affairs, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Kuldeep Dixit
- School of Public and International Affairs, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Julian D Marshall
- Department of Civil & Environmental Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Wenwen Zhang
- Edward J. Bloustein School of Planning and Public Policy, Rutgers University, New Brunswick, New Jersey 08901, United States
| | - Steve Hankey
- School of Public and International Affairs, Virginia Tech, Blacksburg, Virginia 24061, United States
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20
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Clark LP, Harris MH, Apte JS, Marshall JD. National and Intraurban Air Pollution Exposure Disparity Estimates in the United States: Impact of Data-Aggregation Spatial Scale. Environ Sci Technol Lett 2022; 9:786-791. [PMID: 36118958 PMCID: PMC9476666 DOI: 10.1021/acs.estlett.2c00403] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 08/17/2022] [Accepted: 08/18/2022] [Indexed: 06/14/2023]
Abstract
Air pollution exposure disparities by race/ethnicity and socioeconomic status have been analyzed using data aggregated at various spatial scales. Our research question is this: To what extent does the spatial scale of data aggregation impact the estimated exposure disparities? We compared disparities calculated using data spatially aggregated at five administrative scales (state, county, census tract, census block group, census block) in the contiguous United States in 2010. Specifically, for each of the five spatial scales, we calculated national and intraurban disparities in exposure to fine particles (PM2.5) and nitrogen dioxide (NO2) by race/ethnicity and socioeconomic characteristics using census demographic data and an empirical statistical air pollution model aggregated at that scale. We found, for both pollutants, that national disparity estimates based on state and county scale data often substantially underestimated those estimated using tract and finer scales; in contrast, national disparity estimates were generally consistent using tract, block group, and block scale data. Similarly, intraurban disparity estimates based on tract and finer scale data were generally well correlated for both pollutants across urban areas, although in some cases intraurban disparity estimates were substantially different, with tract scale data more frequently leading to underestimates of disparities compared to finer scale analyses.
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Affiliation(s)
- Lara P. Clark
- Department
of Civil and Environmental Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Maria H. Harris
- Environmental
Defense Fund, New York, New York 10010, United States
| | - Joshua S. Apte
- Department
of Civil and Environmental Engineering, University of California Berkeley, Berkeley, California 94720, United States
- School
of Public Health, University of California
Berkeley, Berkeley, California 94720, United States
| | - Julian D. Marshall
- Department
of Civil and Environmental Engineering, University of Washington, Seattle, Washington 98195, United States
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21
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Blanco MN, Gassett A, Gould T, Doubleday A, Slager DL, Austin E, Seto E, Larson TV, Marshall JD, Sheppard L. Characterization of Annual Average Traffic-Related Air Pollution Concentrations in the Greater Seattle Area from a Year-Long Mobile Monitoring Campaign. Environ Sci Technol 2022; 56:11460-11472. [PMID: 35917479 PMCID: PMC9396693 DOI: 10.1021/acs.est.2c01077] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Growing evidence links traffic-related air pollution (TRAP) to adverse health effects. We designed an innovative and extensive mobile monitoring campaign to characterize TRAP exposure levels for the Adult Changes in Thought (ACT) study, a Seattle-based cohort. The campaign measured particle number concentration (PNC) to capture ultrafine particles (UFP), black carbon (BC), nitrogen dioxide (NO2), fine particulate matter (PM2.5), and carbon dioxide (CO2) at 309 roadside sites within a large, 1200 land km2 (463 mi2) area representative of the cohort. We collected about 29 two-minute measurements at each site during all seasons, days of the week, and most times of the day over a 1-year period. Validation showed good agreement between our BC, NO2, and PM2.5 measurements and monitoring agency sites (R2 = 0.68-0.73). Universal kriging-partial least squares models of annual average pollutant concentrations had cross-validated mean square error-based R2 (and root mean square error) values of 0.77 (1177 pt/cm3) for PNC, 0.60 (102 ng/m3) for BC, 0.77 (1.3 ppb) for NO2, 0.70 (0.3 μg/m3) for PM2.5, and 0.51 (4.2 ppm) for CO2. Overall, we found that the design of this extensive campaign captured the spatial pollutant variations well and these were explained by sensible land use features, including those related to traffic.
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Affiliation(s)
- Magali N. Blanco
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Hans Rosling Center for Population Health, 3980 15th Ave NE, Seattle, WA 98195, United States of America
| | - Amanda Gassett
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Hans Rosling Center for Population Health, 3980 15th Ave NE, Seattle, WA 98195, United States of America
| | - Timothy Gould
- Department of Civil & Environmental Engineering, College of Engineering, University of Washington, 201 More Hall, Box 352700, Seattle, WA 98195, United States of America
| | - Annie Doubleday
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Hans Rosling Center for Population Health, 3980 15th Ave NE, Seattle, WA 98195, United States of America
| | - David L. Slager
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Hans Rosling Center for Population Health, 3980 15th Ave NE, Seattle, WA 98195, United States of America
| | - Elena Austin
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Hans Rosling Center for Population Health, 3980 15th Ave NE, Seattle, WA 98195, United States of America
| | - Edmund Seto
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Hans Rosling Center for Population Health, 3980 15th Ave NE, Seattle, WA 98195, United States of America
| | - Timothy V. Larson
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Hans Rosling Center for Population Health, 3980 15th Ave NE, Seattle, WA 98195, United States of America
- Department of Civil & Environmental Engineering, College of Engineering, University of Washington, 201 More Hall, Box 352700, Seattle, WA 98195, United States of America
| | - Julian D. Marshall
- Department of Civil & Environmental Engineering, College of Engineering, University of Washington, 201 More Hall, Box 352700, Seattle, WA 98195, United States of America
| | - Lianne Sheppard
- Department of Environmental and Occupational Health Sciences, School of Public Health, University of Washington, Hans Rosling Center for Population Health, 3980 15th Ave NE, Seattle, WA 98195, United States of America
- Department of Biostatistics, School of Public Health, University of Washington, Hans Rosling Center for Population Health, 3980 15th Ave NE, Seattle, WA 98195, United States of America
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22
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Kushwaha M, Sreekanth V, Upadhya AR, Agrawal P, Apte JS, Marshall JD. Bias in PM 2.5 measurements using collocated reference-grade and optical instruments. Environ Monit Assess 2022; 194:610. [PMID: 35876898 DOI: 10.1007/s10661-022-10293-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/19/2021] [Accepted: 07/11/2022] [Indexed: 06/15/2023]
Abstract
Optical PM2.5 measurements are sensitive to aerosol properties that can vary with space and time. Here, we compared PM2.5 measurements from collocated reference-grade (beta attenuation monitors, BAMs) and optical instruments (two DustTrak II and two DustTrak DRX) over 6 months. We performed inter-model (two different models), intra-model (two units of the same model), and inter-type (two different device types: optical vs. reference-grade) comparisons under ambient conditions. Averaged over our study period, PM2.5 measured concentrations were 46.0 and 45.5 μg m-3 for the two DustTrak II units, 29.8 and 38.4 μg m-3 for DRX units, and 18.3 and 19.0 μg m-3 for BAMs. The normalized root square difference (NRMSD; compares PM2.5 measurements from paired instruments of the same type) was ~ 5% (DustTrak II), ~ 27% (DRX), and ~ 15% (BAM). The normalized root mean square error (NRMSE; compares PM2.5 measurements from optical instruments against a reference instrument) was ~ 165% for DustTrak II, ~ 74% after applying literature-based humidity correction and ~ 27% after applying both the humidity and BAM corrections. Although optical instruments are highly precise in their PM2.5 measurements, they tend to be strongly biased relative to reference-grade devices. We also explored two different methods to compensate for relative humidity bias and found that the results differed by ~ 50% between the two methods. This study highlights the limitations of adopting a literature-derived calibration equation and the need for conducting local model-specific calibration. Moreover, this is one of the few studies to perform an intra-model comparison of collocated reference-grade devices.
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Affiliation(s)
| | - V Sreekanth
- Center for Study of Science, Technology & Policy, Bengaluru, 560094, India.
| | | | | | - Joshua S Apte
- Civil and Environmental Engineering, University of California, Berkeley, CA, 94720, USA
| | - Julian D Marshall
- Civil and Environmental Engineering, University of Washington, Seattle, WA, 98195, USA
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Ranzani OT, Bhogadi S, Milà C, Kulkarni B, Balakrishnan K, Sambandam S, Garcia-Aymerich J, Marshall JD, Kinra S, Tonne C. Association of ambient and household air pollution with lung function in young adults in an peri-urban area of South-India: A cross-sectional study. Environ Int 2022; 165:107290. [PMID: 35594814 DOI: 10.1016/j.envint.2022.107290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 05/04/2022] [Accepted: 05/09/2022] [Indexed: 06/15/2023]
Abstract
OBJECTIVE Although there is evidence for the association between air pollution and decreased lung function in children, evidence for adolescents and young adults is scarce. For a peri-urban area in India, we evaluated the association of ambient PM2.5 and household air pollution with lung function for young adults who had recently attained their expected maximum lung function. METHODS We measured, using a standardized protocol, forced expiratory volume in the first second (FEV1) and forced vital capacity (FVC) in participants aged 20-26 years from the third follow-up of the population-based APCAPCS cohort (2010-2012) in 28 Indian villages. We estimated annual average PM2.5outdoors at residence using land-use regression. Biomass cooking fuel (a proxy for levels of household air pollution) was self-reported. We fitted a within-between linear-mixed model with random intercepts by village, adjusting for potential confounders. RESULTS We evaluated 1,044 participants with mean age of 22.8 (SD = 1) years (range 20-26 years); 327 participants (31%) were female. Only males reported use of tobacco smoking (9% of all participants, 13% of males). The mean ambient PM2.5 exposure was 32.9 (SD = 2.8) µg/m3; 76% reported use of biomass as cooking fuel. The adjusted association between 1 µg/m3 increase in PM2.5 was -27 ml (95% CI, -89 to 34) for FEV1 and -5 ml (95% CI, -93 to 76) for FVC. The adjusted association between use of biomass was -112 ml (95% CI, -211 to -13) for FEV1 and -142 ml (95% CI, -285 to 0) for FVC. The adjusted association was of greater magnitude for those with unvented stove (-158 ml, 95% CI, -279 to -36 for FEV1 and -211 ml, 95% CI, -386 to -36 for FVC). CONCLUSIONS We observed negative associations between ambient PM2.5 and household air pollution and lung function in young adults who had recently attained their maximum lung function.
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Affiliation(s)
- Otavio T Ranzani
- Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra, Barcelona, Spain; CIBER Epidemiología y Salud Pública, Madrid, Spain
| | | | - Carles Milà
- Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra, Barcelona, Spain; CIBER Epidemiología y Salud Pública, Madrid, Spain
| | - Bharati Kulkarni
- National Institute of Nutrition, Indian Council of Medical Research, Hyderabad, India
| | - Kalpana Balakrishnan
- Department of Environmental Health Engineering, Sri Ramachandra University (SRU), Chennai, India
| | - Sankar Sambandam
- Department of Environmental Health Engineering, Sri Ramachandra University (SRU), Chennai, India
| | - Judith Garcia-Aymerich
- Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra, Barcelona, Spain; CIBER Epidemiología y Salud Pública, Madrid, Spain
| | - Julian D Marshall
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, USA
| | - Sanjay Kinra
- Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, United Kingdom
| | - Cathryn Tonne
- Barcelona Institute for Global Health, ISGlobal, Barcelona, Spain; Universitat Pompeu Fabra, Barcelona, Spain; CIBER Epidemiología y Salud Pública, Madrid, Spain.
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24
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Islam MM, Wathore R, Zerriffi H, Marshall JD, Bailis R, Grieshop AP. Assessing the Effects of Stove Use Patterns and Kitchen Chimneys on Indoor Air Quality during a Multiyear Cookstove Randomized Control Trial in Rural India. Environ Sci Technol 2022; 56:8326-8337. [PMID: 35561333 DOI: 10.1021/acs.est.1c07571] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
We conducted indoor air quality (IAQ) measurements during a multiyear cookstove randomized control trial in two rural areas in northern and southern India. A total of 1205 days of kitchen PM2.5 were measured in control and intervention households during six ∼3 month long measurement periods across two study locations. Stoves used included traditional solid fuel (TSF), improved biomass, and liquefied petroleum gas (LPG) models. Intent-to-treat analysis indicates that the intervention reduced average 24 h PM2.5 and black carbon in only one of the two follow-up measurement periods in both areas, suggesting mixed effectiveness. Average PM2.5 levels were ∼50% lower in households with LPG (for exclusive LPG use: >75% lower) than in those without LPG. PM2.5 was 66% lower in households making exclusive use of an improved chimney stove versus a traditional chimney stove and TSF-exclusive kitchens with a built-in chimney had ∼60% lower PM2.5 than those without a chimney, indicating that kitchen ventilation can be as important as the stove technology in improving IAQ. Diurnal trends in real-time PM2.5 indicate that kitchen chimneys were especially effective at reducing peak concentrations, which leads to decreases in daily PM2.5 in these households. Our data demonstrate a clear hierarchy of IAQ improvement in real world, "stove-stacking" households, driven by different stove technologies and kitchen characteristics.
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Affiliation(s)
- Mohammad Maksimul Islam
- Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, North Carolina 27695-7908, United States
| | - Roshan Wathore
- Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, North Carolina 27695-7908, United States
| | - Hisham Zerriffi
- Department of Forest Resources Management, University of British Columbia, Vancouver, British Columbia V6T 1Z4, Canada
| | - Julian D Marshall
- Civil & Environmental Engineering, University of Washington, Seattle, Washington 98195-2700, United States
| | - Rob Bailis
- Stockholm Environmental Institute─US Centre, Somerville, Massachusetts 02144-1224, United States
| | - Andrew P Grieshop
- Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, North Carolina 27695-7908, United States
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Pond ZA, Hernandez CS, Adams PJ, Pandis SN, Garcia GR, Robinson AL, Marshall JD, Burnett R, Skyllakou K, Garcia Rivera P, Karnezi E, Coleman CJ, Pope CA. Cardiopulmonary Mortality and Fine Particulate Air Pollution by Species and Source in a National U.S. Cohort. Environ Sci Technol 2022; 56:7214-7223. [PMID: 34689559 DOI: 10.1021/acs.est.1c04176] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
The purpose of this study was to estimate cardiopulmonary mortality associations for long-term exposure to PM2.5 species and sources (i.e., components) within the U.S. National Health Interview Survey cohort. Exposures were estimated through a chemical transport model for six species (i.e., elemental carbon (EC), primary organic aerosols (POA), secondary organic aerosols (SOA), sulfate (SO4), ammonium (NH4), nitrate (NO3)) and five sources of PM2.5 (i.e., vehicles, electricity-generating units (EGU), non-EGU industrial sources, biogenic sources (bio), "other" sources). In single-pollutant models, we found positive, significant (p < 0.05) mortality associations for all components, except POA. After adjusting for remaining PM2.5 (total PM2.5 minus component), we found significant mortality associations for EC (hazard ratio (HR) = 1.36; 95% CI [1.12, 1.64]), SOA (HR = 1.11; 95% CI [1.05, 1.17]), and vehicle sources (HR = 1.06; 95% CI [1.03, 1.10]). HRs for EC, SOA, and vehicle sources were significantly larger in comparison to those for remaining PM2.5 (per unit μg/m3). Our findings suggest that cardiopulmonary mortality associations vary by species and source, with evidence that EC, SOA, and vehicle sources are important contributors to the PM2.5 mortality relationship. With further validation, these findings could facilitate targeted pollution regulations that more efficiently reduce air pollution mortality.
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Affiliation(s)
- Zachari A Pond
- Department of Economics, Brigham Young University, Provo, Utah 84602, United States
- Department of Agricultural and Resource Economics, University of California Berkeley, Berkeley, California 94720, United States
| | - Carlos S Hernandez
- Department of Civil and Environmental Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Peter J Adams
- Department of Civil and Environmental Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Spyros N Pandis
- Department of Chemical Engineering, University of Patras, Patras 26504, Greece
| | - George R Garcia
- Department of Economics, Brigham Young University, Provo, Utah 84602, United States
- Stanford Law School, Palo Alto, California 94305, United States
| | - Allen L Robinson
- Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Julian D Marshall
- Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Richard Burnett
- Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington 98195, United States
| | - Ksakousti Skyllakou
- Institute of Chemical Engineering Sciences, Foundation for Research and Technology Hellas, Patras 26504, Greece
| | - Pablo Garcia Rivera
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
| | - Eleni Karnezi
- Earth Sciences, Barcelona Supercomputing Center, Barcelona 08034, Spain
| | - Carver J Coleman
- Department of Economics, Brigham Young University, Provo, Utah 84602, United States
| | - C Arden Pope
- Department of Economics, Brigham Young University, Provo, Utah 84602, United States
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26
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Wang Y, Wang Y, Xu H, Zhao Y, Marshall JD. Ambient Air Pollution and Socioeconomic Status in China. Environ Health Perspect 2022; 130:67001. [PMID: 35674427 PMCID: PMC9175641 DOI: 10.1289/ehp9872] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 04/26/2022] [Accepted: 04/29/2022] [Indexed: 05/02/2023]
Abstract
BACKGROUND Air pollution disparities by socioeconomic status (SES) are well documented for the United States, with most literature indicating an inverse relationship (i.e., higher concentrations for lower-SES populations). Few studies exist for China, a country accounting for 26% of global premature deaths from ambient air pollution. OBJECTIVE Our objective was to test the relationship between ambient air pollution exposures and SES in China. METHODS We combined estimated year 2015 annual-average ambient levels of nitrogen dioxide (NO 2 ) and fine particulate matter [PM ≤ 2.5 μ m in aerodynamic diameter (PM 2.5 )] with national demographic information. Pollution estimates were derived from a national empirical model for China at 1 -km spatial resolution; demographic estimates were derived from national gridded gross national product (GDP) per capita at 1 -km resolution, and (separately) a national representative sample of 21,095 individuals from the China Health and Retirement Longitudinal Study (CHARLS) 2015 cohort. Our use of global data on population density and cohort data on where people live helped avoid the spatial imprecision found in publicly available census data for China. We quantified air pollution disparities among individual's rural-to-urban migration status; SES factors (education, occupation, and income); and minority status. We compared results using three approaches to SES measurement: individual SES score, community-averaged SES score, and gridded GDP per capita. RESULTS Ambient NO 2 and PM 2.5 levels were higher for higher-SES populations than for lower-SES population, higher for long-standing urban residents than for rural-to-urban migrant populations, and higher for the majority ethnic group (Han) than for the average across nine minority groups. For the three SES measurements (individual SES score, community-averaged SES score, gridded GDP per capita), a 1-interquartile range higher SES corresponded to higher concentrations of 6 - 9 μ g / m 3 NO 2 and 3 - 6 μ g / m 3 PM 2.5 ; average concentrations for the highest and lowest 20th percentile of SES differed by 41-89% for NO 2 and 12-25% for PM 2.5 . This pattern held in rural and urban locations, across geographic regions, across a wide range of spatial resolution, and for modeled vs. measured pollution concentrations. CONCLUSIONS Multiple analyses here reveal that in China, ambient NO 2 and PM 2.5 concentrations are higher for high-SES than for low-SES individuals; these results are robust to multiple sensitivity analyses. Our findings are consistent with the idea that in China's current industrialization and urbanization stage, economic development is correlated with both SES and air pollution. To our knowledge, our study provides the most comprehensive picture to date of ambient air pollution disparities in China; the results differ dramatically from results and from theories to explain conditions in the United States. https://doi.org/10.1289/EHP9872.
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Affiliation(s)
- Yuzhou Wang
- Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington, USA
| | - Yafeng Wang
- Institute of Social Survey Research, Peking University, Beijing, China
| | - Hao Xu
- Department of Earth System Science, Tsinghua University, Beijing, China
| | - Yaohui Zhao
- National School of Development, Peking University, Beijing, China
| | - Julian D. Marshall
- Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington, USA
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27
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Thakrar SK, Tessum CW, Apte JS, Balasubramanian S, Millet DB, Pandis SN, Marshall JD, Hill JD. Global, high-resolution, reduced-complexity air quality modeling for PM2.5 using InMAP (Intervention Model for Air Pollution). PLoS One 2022; 17:e0268714. [PMID: 35613109 PMCID: PMC9132322 DOI: 10.1371/journal.pone.0268714] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 05/05/2022] [Indexed: 11/19/2022] Open
Abstract
Each year, millions of premature deaths worldwide are caused by exposure to outdoor air pollution, especially fine particulate matter (PM2.5). Designing policies to reduce these deaths relies on air quality modeling for estimating changes in PM2.5 concentrations from many scenarios at high spatial resolution. However, air quality modeling typically has substantial requirements for computation and expertise, which limits policy design, especially in countries where most PM2.5-related deaths occur. Lower requirement reduced-complexity models exist but are generally unavailable worldwide. Here, we adapt InMAP, a reduced-complexity model originally developed for the United States, to simulate annual-average primary and secondary PM2.5 concentrations across a global-through-urban spatial domain: “Global InMAP”. Global InMAP uses a variable resolution grid, with horizontal grid cell widths ranging from 500 km in remote locations to 4km in urban locations. We evaluate Global InMAP performance against both measurements and a state-of-the-science chemical transport model, GEOS-Chem. Against measurements, InMAP predicts total PM2.5 concentrations with a normalized mean error of 62%, compared to 41% for GEOS-Chem. For the emission scenarios considered, Global InMAP reproduced GEOS-Chem pollutant concentrations with a normalized mean bias of 59%–121%, which is sufficient for initial policy assessment and scoping. Global InMAP can be run on a desktop computer; simulations here took 2.6–8.4 hours. This work presents a global, open-source, reduced-complexity air quality model to facilitate policy assessment worldwide, providing a screening tool for reducing air pollution-related deaths where they occur most.
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Affiliation(s)
- Sumil K. Thakrar
- Department of Bioproducts & Biosystems Engineering, University of Minnesota, St Paul, Minnesota, United States of America
- Department of Applied Economics, University of Minnesota, St Paul, Minnesota, United States of America
- * E-mail: (SKT); (JDH)
| | - Christopher W. Tessum
- Department of Civil and Environmental Engineering, University of Illinois at Urbana−Champaign, Urbana, Illinois, United States of America
| | - Joshua S. Apte
- Department of Civil and Environmental Engineering, University of California, Berkeley, Berkeley, California, United States of America
- School of Public Health, University of California, Berkeley, California, United States of America
| | - Srinidhi Balasubramanian
- Department of Bioproducts & Biosystems Engineering, University of Minnesota, St Paul, Minnesota, United States of America
| | - Dylan B. Millet
- Department of Soil, Water, and Climate, University of Minnesota, St Paul, Minnesota, United States of America
| | - Spyros N. Pandis
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
- Department of Chemical Engineering, University of Patras, Patras, Greece
| | - Julian D. Marshall
- Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington, United States of America
| | - Jason D. Hill
- Department of Bioproducts & Biosystems Engineering, University of Minnesota, St Paul, Minnesota, United States of America
- * E-mail: (SKT); (JDH)
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28
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Lane H, Morello-Frosch R, Marshall JD, Apte JS. Historical Redlining Is Associated with Present-Day Air Pollution Disparities in U.S. Cities. Environ Sci Technol Lett 2022; 9:345-350. [PMID: 35434171 PMCID: PMC9009174 DOI: 10.1021/acs.estlett.1c01012] [Citation(s) in RCA: 105] [Impact Index Per Article: 52.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 02/18/2022] [Accepted: 02/22/2022] [Indexed: 05/02/2023]
Abstract
Communities of color in the United States are systematically exposed to higher levels of air pollution. We explore here how redlining, a discriminatory mortgage appraisal practice from the 1930s by the federal Home Owners' Loan Corporation (HOLC), relates to present-day intraurban air pollution disparities in 202 U.S. cities. In each city, we integrated three sources of data: (1) detailed HOLC security maps of investment risk grades [A ("best"), B, C, and D ("hazardous", i.e., redlined)], (2) year-2010 estimates of NO2 and PM2.5 air pollution levels, and (3) demographic information from the 2010 U.S. census. We find that pollution levels have a consistent and nearly monotonic association with HOLC grade, with especially pronounced (>50%) increments in NO2 levels between the most (grade A) and least (grade D) preferentially graded neighborhoods. On a national basis, intraurban disparities for NO2 and PM2.5 are substantially larger by historical HOLC grade than they are by race and ethnicity. However, within each HOLC grade, racial and ethnic air pollution exposure disparities persist, indicating that redlining was only one of the many racially discriminatory policies that impacted communities. Our findings illustrate how redlining, a nearly 80-year-old racially discriminatory policy, continues to shape systemic environmental exposure disparities in the United States.
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Affiliation(s)
- Haley
M. Lane
- Department
of Civil and Environmental Engineering, University of California, Berkeley, California 94720, United States
| | - Rachel Morello-Frosch
- School
of Public Health, University of California, Berkeley, California 94720, United States
- Department
of Environmental Science, Policy, and Management, University of California, Berkeley, California 94720, United States
| | - Julian D. Marshall
- Department
of Civil and Environmental Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Joshua S. Apte
- Department
of Civil and Environmental Engineering, University of California, Berkeley, California 94720, United States
- School
of Public Health, University of California, Berkeley, California 94720, United States
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29
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Lane HM, Morello-Frosch R, Marshall JD, Apte JS. Historical Redlining Is Associated with Present-Day Air Pollution Disparities in U.S. Cities. Environ Sci Technol Lett 2022; 9:345-350. [PMID: 35434171 DOI: 10.6084/m9.figshare.19193243] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 02/18/2022] [Accepted: 02/22/2022] [Indexed: 05/17/2023]
Abstract
Communities of color in the United States are systematically exposed to higher levels of air pollution. We explore here how redlining, a discriminatory mortgage appraisal practice from the 1930s by the federal Home Owners' Loan Corporation (HOLC), relates to present-day intraurban air pollution disparities in 202 U.S. cities. In each city, we integrated three sources of data: (1) detailed HOLC security maps of investment risk grades [A ("best"), B, C, and D ("hazardous", i.e., redlined)], (2) year-2010 estimates of NO2 and PM2.5 air pollution levels, and (3) demographic information from the 2010 U.S. census. We find that pollution levels have a consistent and nearly monotonic association with HOLC grade, with especially pronounced (>50%) increments in NO2 levels between the most (grade A) and least (grade D) preferentially graded neighborhoods. On a national basis, intraurban disparities for NO2 and PM2.5 are substantially larger by historical HOLC grade than they are by race and ethnicity. However, within each HOLC grade, racial and ethnic air pollution exposure disparities persist, indicating that redlining was only one of the many racially discriminatory policies that impacted communities. Our findings illustrate how redlining, a nearly 80-year-old racially discriminatory policy, continues to shape systemic environmental exposure disparities in the United States.
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Affiliation(s)
- Haley M Lane
- Department of Civil and Environmental Engineering, University of California, Berkeley, California 94720, United States
| | - Rachel Morello-Frosch
- School of Public Health, University of California, Berkeley, California 94720, United States
- Department of Environmental Science, Policy, and Management, University of California, Berkeley, California 94720, United States
| | - Julian D Marshall
- Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Joshua S Apte
- Department of Civil and Environmental Engineering, University of California, Berkeley, California 94720, United States
- School of Public Health, University of California, Berkeley, California 94720, United States
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30
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Liu J, Clark LP, Bechle MJ, Hajat A, Kim SY, Robinson AL, Sheppard L, Szpiro AA, Marshall JD. Disparities in Air Pollution Exposure in the United States by Race/Ethnicity and Income, 1990-2010. Environ Health Perspect 2021; 129:127005. [PMID: 34908495 PMCID: PMC8672803 DOI: 10.1289/ehp8584] [Citation(s) in RCA: 97] [Impact Index Per Article: 32.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
BACKGROUND Few studies have investigated air pollution exposure disparities by race/ethnicity and income across criteria air pollutants, locations, or time. OBJECTIVE The objective of this study was to quantify exposure disparities by race/ethnicity and income throughout the contiguous United States for six criteria air pollutants, during the period 1990 to 2010. METHODS We quantified exposure disparities among racial/ethnic groups (non-Hispanic White, non-Hispanic Black, Hispanic (any race), non-Hispanic Asian) and by income for multiple spatial units (contiguous United States, states, urban vs. rural areas) and years (1990, 2000, 2010) for carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), particulate matter with aerodynamic diameter ≤2.5μm (PM2.5; excluding year-1990), particulate matter with aerodynamic diameter ≤10μm (PM10), and sulfur dioxide (SO2). We used census data for demographic information and a national empirical model for ambient air pollution levels. RESULTS For all years and pollutants, the racial/ethnic group with the highest national average exposure was a racial/ethnic minority group. In 2010, the disparity between the racial/ethnic group with the highest vs. lowest national-average exposure was largest for NO2 [54% (4.6 ppb)], smallest for O3 [3.6% (1.6 ppb)], and intermediate for the remaining pollutants (13%-19%). The disparities varied by U.S. state; for example, for PM2.5 in 2010, exposures were at least 5% higher than average in 63% of states for non-Hispanic Black populations; in 33% and 26% of states for Hispanic and for non-Hispanic Asian populations, respectively; and in no states for non-Hispanic White populations. Absolute exposure disparities were larger among racial/ethnic groups than among income categories (range among pollutants: between 1.1 and 21 times larger). Over the period studied, national absolute racial/ethnic exposure disparities declined by between 35% (0.66μg/m3; PM2.5) and 88% (0.35 ppm; CO); relative disparities declined to between 0.99× (PM2.5; i.e., nearly zero change) and 0.71× (CO; i.e., a ∼29% reduction). DISCUSSION As air pollution concentrations declined during the period 1990 to 2010, absolute (and to a lesser extent, relative) racial/ethnic exposure disparities also declined. However, in 2010, racial/ethnic exposure disparities remained across income levels, in urban and rural areas, and in all states, for multiple pollutants. https://doi.org/10.1289/EHP8584.
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Affiliation(s)
- Jiawen Liu
- Department of Civil & Environmental Engineering, University of Washington, Seattle, Washington, USA
| | - Lara P Clark
- Department of Civil & Environmental Engineering, University of Washington, Seattle, Washington, USA
| | - Matthew J Bechle
- Department of Civil & Environmental Engineering, University of Washington, Seattle, Washington, USA
| | - Anjum Hajat
- Department of Epidemiology, University of Washington, Seattle, Washington, USA
| | - Sun-Young Kim
- Department of Cancer Control and Population Health, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang-si, Gyeonggi-do, Korea
| | - Allen L Robinson
- Department of Mechanical Engineering & Department of Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
| | - Lianne Sheppard
- Department of Biostatistics, University of Washington, Seattle, Washington, USA
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, Washington, USA
| | - Adam A Szpiro
- Department of Biostatistics, University of Washington, Seattle, Washington, USA
| | - Julian D Marshall
- Department of Civil & Environmental Engineering, University of Washington, Seattle, Washington, USA
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31
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Lu T, Marshall JD, Zhang W, Hystad P, Kim SY, Bechle MJ, Demuzere M, Hankey S. National Empirical Models of Air Pollution Using Microscale Measures of the Urban Environment. Environ Sci Technol 2021; 55:15519-15530. [PMID: 34739226 DOI: 10.1021/acs.est.1c04047] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
National-scale empirical models of air pollution (e.g., Land Use Regression) rely on predictor variables (e.g., population density, land cover) at different geographic scales. These models typically lack microscale variables (e.g., street level), which may improve prediction with fine-spatial gradients. We developed microscale variables of the urban environment including Point of Interest (POI) data, Google Street View (GSV) imagery, and satellite-based measures of urban form. We developed United States national models for six criteria pollutants (NO2, PM2.5, O3, CO, PM10, SO2) using various modeling approaches: Stepwise Regression + kriging (SW-K), Partial Least Squares + kriging (PLS-K), and Machine Learning + kriging (ML-K). We compared predictor variables (e.g., traditional vs microscale) and emerging modeling approaches (ML-K) to well-established approaches (i.e., traditional variables in a PLS-K or SW-K framework). We found that combined predictor variables (traditional + microscale) in the ML-K models outperformed the well-established approaches (10-fold spatial cross-validation (CV) R2 increased 0.02-0.42 [average: 0.19] among six criteria pollutants). Comparing all model types using microscale variables to models with traditional variables, the performance is similar (average difference of 10-fold spatial CV R2 = 0.05) suggesting microscale variables are a suitable substitute for traditional variables. ML-K and microscale variables show promise for improving national empirical models.
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Affiliation(s)
- Tianjun Lu
- Department of Earth Science & Geography, California State University Dominguez Hills, 1000 E. Victoria Street, Carson 90747, California, United States
| | - Julian D Marshall
- Department of Civil & Environmental Engineering, University of Washington, 201 More Hall, Seattle 98195, Washington, United States
| | - Wenwen Zhang
- Edward J. Bloustein School of Planning and Public Policy, Rutgers University, 33 Livingston Avenue, New Brunswick 08901, New Jersey, United States
| | - Perry Hystad
- College of Public Health and Human Sciences, Oregon State University, 2520 Campus Way, Corvallis 97331, Oregon, United States
| | - Sun-Young Kim
- Department of Cancer Control and Population Health, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang-si, Gyeonggi-do 10408, Korea
| | - Matthew J Bechle
- Department of Civil & Environmental Engineering, University of Washington, 201 More Hall, Seattle 98195, Washington, United States
| | - Matthias Demuzere
- Urban Climatology Group, Department of Geography, Ruhr-University Bochum, Bochum 44801, Germany
| | - Steve Hankey
- School of Public and International Affairs, Virginia Tech, 140 Otey Street, Blacksburg 24061, Virginia, United States
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Chambliss SE, Pinon CPR, Messier KP, LaFranchi B, Upperman CR, Lunden MM, Robinson AL, Marshall JD, Apte JS. Local- and regional-scale racial and ethnic disparities in air pollution determined by long-term mobile monitoring. Proc Natl Acad Sci U S A 2021; 118:e2109249118. [PMID: 34493674 PMCID: PMC8449331 DOI: 10.1073/pnas.2109249118] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2021] [Accepted: 07/26/2021] [Indexed: 11/18/2022] Open
Abstract
Disparity in air pollution exposure arises from variation at multiple spatial scales: along urban-to-rural gradients, between individual cities within a metropolitan region, within individual neighborhoods, and between city blocks. Here, we improve on existing capabilities to systematically compare urban variation at several scales, from hyperlocal (<100 m) to regional (>10 km), and to assess consequences for outdoor air pollution experienced by residents of different races and ethnicities, by creating a set of uniquely extensive and high-resolution observations of spatially variable pollutants: NO, NO2, black carbon (BC), and ultrafine particles (UFP). We conducted full-coverage monitoring of a wide sample of urban and suburban neighborhoods (93 km2 and 450,000 residents) in four counties of the San Francisco Bay Area using Google Street View cars equipped with the Aclima mobile platform. Comparing scales of variation across the sampled population, greater differences arise from localized pollution gradients for BC and NO (pollutants dominated by primary sources) and from regional gradients for UFP and NO2 (pollutants dominated by secondary contributions). Median concentrations of UFP, NO, and NO2 are, for Hispanic and Black populations, 8 to 30% higher than the population average; for White populations, average exposures to these pollutants are 9 to 14% lower than the population average. Systematic racial/ethnic disparities are influenced by regional concentration gradients due to sharp contrasts in demographic composition among cities and urban districts, while within-group extremes arise from local peaks. Our results illustrate how detailed and extensive fine-scale pollution observations can add new insights about differences and disparities in air pollution exposures at the population scale.
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Affiliation(s)
- Sarah E Chambliss
- Department of Civil, Architectural and Environmental Engineering, University of Texas at Austin, Austin, TX 78712
| | - Carlos P R Pinon
- Department of Civil, Architectural and Environmental Engineering, University of Texas at Austin, Austin, TX 78712
| | - Kyle P Messier
- National Toxicology Program, National Institute of Environmental Health Sciences, Durham, NC 27713
| | | | | | | | - Allen L Robinson
- Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Julian D Marshall
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195
| | - Joshua S Apte
- Department of Civil and Environmental Engineering, University of California, Berkeley, CA 94720;
- School of Public Health, University of California, Berkeley, CA 94720
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Kim SY, Pope AC, Marshall JD, Fann N, Sheppard L. Reanalysis of the association between reduction in long-term PM 2.5 concentrations and improved life expectancy. Environ Health 2021; 20:102. [PMID: 34517898 PMCID: PMC8439090 DOI: 10.1186/s12940-021-00785-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Accepted: 08/20/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND Much of the current evidence of associations between long-term PM2.5 and health outcomes relies on national or regional analyses using exposures derived directly from regulatory monitoring data. These findings could be affected by limited spatial coverage of monitoring data, particularly for time periods before spatially extensive monitoring began in the late 1990s. For instance, Pope et al. (2009) showed that between 1980 and 2000 a 10 μg/m3 reduction in PM2.5 was associated with an average 0.61 year (standard error (SE) = 0.20) longer life expectancy. That analysis used 1979-1983 averages of PM2.5 across 51 U.S. Metropolitan Statistical Areas (MSAs) computed from about 130 monitoring sites. Our reanalysis re-examines this association using modeled PM2.5 in order to assess population- or spatially-representative exposure. We hypothesized that modeled PM2.5 with finer spatial resolution provides more accurate health effect estimates compared to limited monitoring data. METHODS We used the same data for life expectancy and confounders, as well as the same analysis models, and investigated the same 211 continental U.S. counties, as Pope et al. (2009). For modeled PM2.5, we relied on a previously-developed point prediction model based on regulatory monitoring data for 1999-2015 and back-extrapolation to 1979. Using this model, we predicted annual average concentrations at centroids of all 72,271 census tracts and 12,501 25-km national grid cells covering the contiguous U.S., to represent population and space, respectively. We averaged these predictions to the county for the two time periods (1979-1983 and 1999-2000), whereas the original analysis used MSA averages given limited monitoring data. Finally, we estimated regression coefficients for PM2.5 reduction on life expectancy improvement over the two periods, adjusting for area-level confounders. RESULTS A 10 μg/m3 decrease in modeled PM2.5 based on census tract and national grid predictions was associated with 0.69 (standard error (SE) = 0.31) and 0.81 (0.29) -year increases in life expectancy. These estimates are higher than the estimate of Pope et al. (2009); they also have larger SEs likely because of smaller variability in exposure predictions, a standard property of regression. Two sets of effect estimates, however, had overlapping confidence intervals. CONCLUSIONS Our approach for estimating population- and spatially-representative PM2.5 concentrations based on census tract and national grid predictions, respectively, provided generally consistent findings to the original findings using limited monitoring data. This finding lends additional support to the evidence that reduced fine particulate matter contributes to extended life expectancy.
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Affiliation(s)
- Sun-Young Kim
- Department of Cancer Control and Population Health, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang, Gyeonggi Korea
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA USA
| | - Arden C. Pope
- Department of Economics, Brigham Young University, Provo, UT USA
| | - Julian D. Marshall
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA USA
| | - Neal Fann
- Office of Air Quality, Planning and Standards, US Environmental Protection Agency, RTP, Durham, NC USA
| | - Lianne Sheppard
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA USA
- Department of Biostatistics, University of Washington, Seattle, WA USA
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Saha PK, Hankey S, Marshall JD, Robinson AL, Presto AA. High-Spatial-Resolution Estimates of Ultrafine Particle Concentrations across the Continental United States. Environ Sci Technol 2021; 55:10320-10331. [PMID: 34284581 DOI: 10.1021/acs.est.1c03237] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
There is growing evidence that ultrafine particles (UFP; particles smaller than 100 nm) are likely more toxic than larger particles. However, the health effects of UFP remain uncertain due in part to the lack of large-scale population-based exposure assessment. We develop a national-scale empirical model of particle number concentration (PNC; a measure of UFP) using data from mobile monitoring and fixed sites across the United States and a land-use regression (LUR) modeling framework. Traffic, commercial land use, and urbanicity-related variables explain much of the spatial variability of PNC (base model R2 = 0.77, RMSE = 2400 cm-3). Model predictions are robust across a diverse set of evaluations [random 10-fold holdout cross-validation (HCV): R2 = 0.72, RMSE = 2700 cm-3; spatially defined HCV: R2 = 0.66, RMSE = 3000 cm-3; evaluation against an independent data set: R2 = 0.54, RMSE = 2600 cm-3]. We apply our model to predict PNC at ∼6 million residential census blocks in the contiguous United States. Our estimates are annual average concentrations for 2016-2017. The predicted national census-block-level mean PNC ranges between 1800 and 26 600 cm-3 (population-weighted average: 6500 cm-3), with hotspots in cities and near highways. Our national PNC model predicts large urban-rural, intra-, and inter-city contrasts. PNC and PM2.5 are moderately correlated at the city scale, but uncorrelated at the regional/national scale. Our high-spatial-resolution national PNC estimates are useful for analyzing population exposure (socioeconomic disparity, epidemiological health impact) and environmental policy and regulation.
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Affiliation(s)
- Provat K Saha
- Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
- Department of Mechanical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, Pennsylvania 15213, United States
| | - Steve Hankey
- School of Public and International Affairs, Virginia Tech, Blacksburg, Virginia 24061, United States
| | - Julian D Marshall
- Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Allen L Robinson
- Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
- Department of Mechanical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, Pennsylvania 15213, United States
| | - Albert A Presto
- Center for Atmospheric Particle Studies, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, United States
- Department of Mechanical Engineering, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, Pennsylvania 15213, United States
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Domingo NGG, Balasubramanian S, Thakrar SK, Clark MA, Adams PJ, Marshall JD, Muller NZ, Pandis SN, Polasky S, Robinson AL, Tessum CW, Tilman D, Tschofen P, Hill JD. Air quality-related health damages of food. Proc Natl Acad Sci U S A 2021; 118:e2013637118. [PMID: 33972419 PMCID: PMC8158015 DOI: 10.1073/pnas.2013637118] [Citation(s) in RCA: 31] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Agriculture is a major contributor to air pollution, the largest environmental risk factor for mortality in the United States and worldwide. It is largely unknown, however, how individual foods or entire diets affect human health via poor air quality. We show how food production negatively impacts human health by increasing atmospheric fine particulate matter (PM2.5), and we identify ways to reduce these negative impacts of agriculture. We quantify the air quality-related health damages attributable to 95 agricultural commodities and 67 final food products, which encompass >99% of agricultural production in the United States. Agricultural production in the United States results in 17,900 annual air quality-related deaths, 15,900 of which are from food production. Of those, 80% are attributable to animal-based foods, both directly from animal production and indirectly from growing animal feed. On-farm interventions can reduce PM2.5-related mortality by 50%, including improved livestock waste management and fertilizer application practices that reduce emissions of ammonia, a secondary PM2.5 precursor, and improved crop and animal production practices that reduce primary PM2.5 emissions from tillage, field burning, livestock dust, and machinery. Dietary shifts toward more plant-based foods that maintain protein intake and other nutritional needs could reduce agricultural air quality-related mortality by 68 to 83%. In sum, improved livestock and fertilization practices, and dietary shifts could greatly decrease the health impacts of agriculture caused by its contribution to reduced air quality.
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Affiliation(s)
- Nina G G Domingo
- Department of Bioproducts and Biosystems Engineering, University of Minnesota, St. Paul, MN 55108
| | - Srinidhi Balasubramanian
- Department of Bioproducts and Biosystems Engineering, University of Minnesota, St. Paul, MN 55108
| | - Sumil K Thakrar
- Department of Bioproducts and Biosystems Engineering, University of Minnesota, St. Paul, MN 55108
| | - Michael A Clark
- Oxford Martin School, Nuffield Department of Population Health, University of Oxford, Oxford OX3 7DQ, United Kingdom
| | - Peter J Adams
- Department of Civil and Environmental Engineering, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Julian D Marshall
- Department of Civil & Environmental Engineering, University of Washington, Seattle, WA 98195
| | - Nicholas Z Muller
- Department of Engineering and Public Policy, Tepper School of Business, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Spyros N Pandis
- Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Stephen Polasky
- Department of Applied Economics, University of Minnesota, St. Paul, MN 55108
| | - Allen L Robinson
- Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Christopher W Tessum
- Department of Civil and Environmental Engineering, University of Illinois, Urbana, IL 61801
| | - David Tilman
- Department of Ecology, Evolution, and Behavior, University of Minnesota, St. Paul, MN 55108
| | - Peter Tschofen
- Department of Engineering and Public Policy, Tepper School of Business, Carnegie Mellon University, Pittsburgh, PA 15213
| | - Jason D Hill
- Department of Bioproducts and Biosystems Engineering, University of Minnesota, St. Paul, MN 55108;
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Bekbulat B, Apte JS, Millet DB, Robinson AL, Wells KC, Presto AA, Marshall JD. Changes in criteria air pollution levels in the US before, during, and after Covid-19 stay-at-home orders: Evidence from regulatory monitors. Sci Total Environ 2021; 769:144693. [PMID: 33736238 PMCID: PMC7831446 DOI: 10.1016/j.scitotenv.2020.144693] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 12/16/2020] [Accepted: 12/18/2020] [Indexed: 05/20/2023]
Abstract
The widespread and rapid social and economic changes from Covid-19 response might be expected to dramatically improve air quality. However, national monitoring data from the US Environmental Protection Agency for criteria pollutants (PM2.5, ozone, NO2, CO, PM10) provide inconsistent support for that expectation. Specifically, during stay-at-home orders, average PM2.5 levels were slightly higher (~10% of its multi-year interquartile range [IQR]) than expected; average ozone, NO2, CO, and PM10 levels were slightly lower (~30%, ~20%, ~27%, and ~1% of their IQR, respectively) than expected. The timing of peak anomaly, relative to the stay-at-home orders, varied by pollutant (ozone: 2 weeks before; NO2, CO: 3 weeks after; PM10: 2 weeks after); but, by 5-6 weeks after stay-at-home orders, the concentration anomalies appear to have ended. For PM2.5, ozone, CO, and PM10, no US state had lower-than-expected pollution levels for all weeks during stay-at-home-orders; for NO2, only Arizona had lower-than-expected levels for all weeks during stay-at-home orders. Our findings show that the enormous changes from the Covid-19 response have not lowered PM2.5 levels across the US beyond their normal range of variability; for ozone, NO2, CO, and PM10 concentrations were lowered but the reduction was modest and transient.
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Affiliation(s)
- Bujin Bekbulat
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, United States of America
| | - Joshua S Apte
- Department of Civil and Environmental Engineering, University of California, Berkeley, Berkeley, CA, United States of America and School of Public Health, University of California, Berkeley, Berkeley, CA, United States of America
| | - Dylan B Millet
- Department of Soil, Water, and Climate, University of Minnesota, St. Paul, MN, United States of America
| | - Allen L Robinson
- Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, United States of America
| | - Kelley C Wells
- Department of Soil, Water, and Climate, University of Minnesota, St. Paul, MN, United States of America
| | - Albert A Presto
- Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA, United States of America
| | - Julian D Marshall
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, United States of America.
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Tessum CW, Paolella DA, Chambliss SE, Apte JS, Hill JD, Marshall JD. PM 2.5 polluters disproportionately and systemically affect people of color in the United States. Sci Adv 2021; 7:7/18/eabf4491. [PMID: 33910895 DOI: 10.1126/sciadv.abf4491] [Citation(s) in RCA: 173] [Impact Index Per Article: 57.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Accepted: 03/10/2021] [Indexed: 05/17/2023]
Abstract
Racial-ethnic minorities in the United States are exposed to disproportionately high levels of ambient fine particulate air pollution (PM2.5), the largest environmental cause of human mortality. However, it is unknown which emission sources drive this disparity and whether differences exist by emission sector, geography, or demographics. Quantifying the PM2.5 exposure caused by each emitter type, we show that nearly all major emission categories-consistently across states, urban and rural areas, income levels, and exposure levels-contribute to the systemic PM2.5 exposure disparity experienced by people of color. We identify the most inequitable emission source types by state and city, thereby highlighting potential opportunities for addressing this persistent environmental inequity.
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Affiliation(s)
- Christopher W Tessum
- Department of Civil and Environmental Engineering, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA.
| | - David A Paolella
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, USA
| | - Sarah E Chambliss
- Department of Civil, Architectural and Environmental Engineering, University of Texas at Austin, Austin, TX 78712, USA
| | - Joshua S Apte
- Department of Civil and Environmental Engineering, University of California, Berkeley, Berkeley, CA 94720, USA
- School of Public Health, University of California, Berkeley, Berkeley, CA 94720, USA
| | - Jason D Hill
- Department of Bioproducts and Biosystems Engineering, University of Minnesota, St. Paul, MN 55108, USA
| | - Julian D Marshall
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA 98195, USA
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Islam MM, Wathore R, Zerriffi H, Marshall JD, Bailis R, Grieshop AP. In-use emissions from biomass and LPG stoves measured during a large, multi-year cookstove intervention study in rural India. Sci Total Environ 2021; 758:143698. [PMID: 33321364 DOI: 10.1016/j.scitotenv.2020.143698] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Revised: 11/06/2020] [Accepted: 11/06/2020] [Indexed: 06/12/2023]
Abstract
We conducted an emission measurement campaign as a part of a multiyear cookstove intervention trial in two rural locations in northern and southern India. 253 uncontrolled cooking tests measured emissions in control and intervention households during three ~3-month-long measurement periods in each location. We measured pollutants including fine particulate matter (PM2.5), organic and elemental carbon (OC, EC), black carbon (BC) and carbon monoxide (CO) from stoves ranging from traditional solid fuel (TSF) to improved biomass stoves (rocket, gasifier) to liquefied petroleum gas (LPG) models. TSF stoves showed substantial variability in pollutant emission factors (EFs; g kg-1 wood) and optical properties across measurement periods. Multilinear regression modeling found that measurement period, fuel properties, relative humidity, and cooking duration are significant predictors of TSF EFs. A rocket stove showed moderate reductions relative to TSF. LPG stoves had the lowest pollutant EFs, with mean PM2.5 and CO EFs (g MJdelivered-1) >90% lower than biomass stoves. However, in-home EFs of LPG were substantially higher than lab EFs, likely influenced by non-ideal combustion performance, emissions from food and possible influence from other combustion sources. In-home emission measurements may depict the actual exposure benefits associated with dissemination of LPG stoves in real world interventions.
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Affiliation(s)
- Mohammad Maksimul Islam
- Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, NC, USA
| | - Roshan Wathore
- Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, NC, USA
| | - Hisham Zerriffi
- Department of Forest Resources Management, University of British Columbia, Vancouver, British Columbia, Canada
| | - Julian D Marshall
- Civil & Environmental Engineering, University of Washington, Seattle, WA, USA
| | - Rob Bailis
- Stockholm Environmental Institute - US Centre, Somerville, MA, USA
| | - Andrew P Grieshop
- Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, NC, USA.
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Coleman NC, Ezzati M, Marshall JD, Robinson AL, Burnett RT, Pope CA. Fine Particulate Matter Air Pollution and Mortality Risk Among US Cancer Patients and Survivors. JNCI Cancer Spectr 2021; 5:pkab001. [PMID: 33644681 PMCID: PMC7898081 DOI: 10.1093/jncics/pkab001] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Revised: 08/26/2020] [Accepted: 01/05/2021] [Indexed: 01/04/2023] Open
Abstract
Background Exposure to fine particulate matter (PM2.5) air pollution has been linked to increased risk of mortality, especially cardiopulmonary and lung cancer mortality. It is unknown if cancer patients and survivors are especially vulnerable to PM2.5 air pollution exposure. This study evaluates PM2.5 exposure and risk for cancer and cardiopulmonary mortality in cohorts of US cancer patients and survivors. Methods A primary cohort of 5 591 168 of cancer patients and a 5-year survivor cohort of 2 318 068 was constructed using Surveillance, Epidemiology, and End Results Program data from 2000 to 2016, linked with county-level estimates of long-term average concentrations of PM2.5. Cox proportional hazards models were used to estimate PM2.5-mortality hazard ratios controlling for age-sex-race combinations and individual and county-level covariables. Results Of those who died, 26% died of noncancer causes, mostly from cardiopulmonary disease. Minimal PM2.5-mortality associations were observed for all-cause mortality (hazard ratio [HR] = 1.01, 95% confidence interval [CI] = 1.00 to 1.03) per 10 µg/m3 increase in PM2.5. Substantial adverse PM2.5-mortality associations were observed for cardiovascular (HR = 1.32, 95% CI = 1.26 to 1.39), chronic obstructive pulmonary disease (HR = 1.10, 95% CI = 1.01 to 1.20), influenza and pneumonia (HR = 1.55, 95% CI = 1.33 to 1.80), and cardiopulmonary mortality combined (HR = 1.25, 95% CI = 1.21 to 1.30). PM2.5-cardiopulmonary mortality hazard ratio was higher for cancer patients who received chemotherapy or radiation treatments. Conclusions Air pollution is adversely associated with cardiopulmonary mortality for cancer patients and survivors, especially those who received chemotherapy or radiation treatment. Given ubiquitous and involuntary air pollution exposures and large numbers of cancer patients and survivors, these results are of substantial clinical and public health importance.
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Affiliation(s)
- Nathan C Coleman
- Department of Economics, Brigham Young University, Provo, UT, USA
| | - Majid Ezzati
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Julian D Marshall
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, USA
| | - Allen L Robinson
- Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, PA, USA
| | | | - C Arden Pope
- Department of Economics, Brigham Young University, Provo, UT, USA
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Coleman NC, Burnett RT, Ezzati M, Marshall JD, Robinson AL, Pope CA. Fine Particulate Matter Exposure and Cancer Incidence: Analysis of SEER Cancer Registry Data from 1992-2016. Environ Health Perspect 2020; 128:107004. [PMID: 33035119 PMCID: PMC7546438 DOI: 10.1289/ehp7246] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 09/02/2020] [Accepted: 09/15/2020] [Indexed: 05/20/2023]
Abstract
BACKGROUND Previous research has identified an association between fine particulate matter (PM 2.5 ) air pollution and lung cancer. Most of the evidence for this association, however, is based on research using lung cancer mortality, not incidence. Research that examines potential associations between PM 2.5 and incidence of non-lung cancers is limited. OBJECTIVES The primary purpose of this study was to evaluate the association between the incidence of cancer and exposure to PM 2.5 using > 8.5 million cases of cancer incidences from U.S. registries. Secondary objectives include evaluating the sensitivity of the associations to model selection, spatial control, and latency period as well as estimating the exposure-response relationship for several cancer types. METHODS Surveillance, Epidemiology, and End Results (SEER) program data were used to calculate incidence rates for various cancer types in 607 U.S. counties. County-level PM 2.5 concentrations were estimated using integrated empirical geographic regression models. Flexible semi-nonparametric regression models were used to estimate associations between PM 2.5 and cancer incidence for selected cancers while controlling for important county-level covariates. Primary time-independent models using average incidence rates from 1992-2016 and average PM 2.5 from 1988-2015 were estimated. In addition, time-varying models using annual incidence rates from 2002-2011 and lagged moving averages of annual estimates for PM 2.5 were also estimated. RESULTS The incidences of all cancer and lung cancer were consistently associated with PM 2.5 . The incident rate ratios (IRRs), per 10 - μ g / m 3 increase in PM 2.5 , for all and lung cancer were 1.09 (95% CI: 1.03, 1.14) and 1.19 (95% CI: 1.09, 1.30), respectively. Less robust associations were observed with oral, rectal, liver, skin, breast, and kidney cancers. DISCUSSION Exposure to PM 2.5 air pollution contributes to lung cancer incidence and is potentially associated with non-lung cancer incidence. https://doi.org/10.1289/EHP7246.
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Affiliation(s)
| | | | - Majid Ezzati
- Medical Research Council–Public Health England Centre for Environment and Health, School of Public Health, Imperial College London, London, UK
| | - Julian D. Marshall
- Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington, USA
| | - Allen L. Robinson
- Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA
| | - C. Arden Pope
- Department of Economics, Brigham Young University, Provo, Utah, USA
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Coleman NC, Burnett RT, Higbee JD, Lefler JS, Merrill RM, Ezzati M, Marshall JD, Kim SY, Bechle M, Robinson AL, Pope CA. Cancer mortality risk, fine particulate air pollution, and smoking in a large, representative cohort of US adults. Cancer Causes Control 2020; 31:767-776. [PMID: 32462559 DOI: 10.1007/s10552-020-01317-w] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2020] [Accepted: 05/18/2020] [Indexed: 12/13/2022]
Abstract
PURPOSE Air pollution and smoking are associated with various types of mortality, including cancer. The current study utilizes a publicly accessible, nationally representative cohort to explore relationships between fine particulate matter (PM2.5) exposure, smoking, and cancer mortality. METHODS National Health Interview Survey and mortality follow-up data were combined to create a study population of 635,539 individuals surveyed from 1987 to 2014. A sub-cohort of 341,665 never-smokers from the full cohort was also created. Individuals were assigned modeled PM2.5 exposure based on average exposure from 1999 to 2015 at residential census tract. Cox Proportional Hazard models were utilized to estimate hazard ratios for cancer-specific mortality controlling for age, sex, race, smoking status, body mass, income, education, marital status, rural versus urban, region, and survey year. RESULTS The risk of all cancer mortality was adversely associated with PM2.5 (per 10 µg/m3 increase) in the full cohort (hazard ratio [HR] 1.15, 95% confidence interval [CI] 1.08-1.22) and the never-smokers' cohort (HR 1.19, 95% CI 1.06-1.33). PM2.5-morality associations were observed specifically for lung, stomach, colorectal, liver, breast, cervix, and bladder, as well as Hodgkin lymphoma, non-Hodgkin lymphoma, and leukemia. The PM2.5-morality association with lung cancer in never-smokers was statistically significant adjusting for multiple comparisons. Cigarette smoking was statistically associated with mortality for many cancer types. CONCLUSIONS Exposure to PM2.5 air pollution contributes to lung cancer mortality and may be a risk factor for other cancer types. Cigarette smoking has a larger impact on cancer mortality than PM2.5 , but is associated with similar cancer types.
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Affiliation(s)
- Nathan C Coleman
- Department of Economics, Brigham Young University, 142 FOB, Provo, UT, 84602, USA
| | | | - Joshua D Higbee
- Department of Economics, University of Chicago, Chicago, IL, USA
| | - Jacob S Lefler
- Department of Agricultural and Resource Economics, University of California, Berkeley, CA, USA
| | - Ray M Merrill
- Department of Public Health, Brigham Young University, Provo, UT, USA
| | - Majid Ezzati
- MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College, London, London, UK
| | - Julian D Marshall
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, USA
| | - Sun-Young Kim
- Department of Cancer Control and Population Health, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang-si, Gyeonggi-do, Korea
| | - Matthew Bechle
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, USA
| | - Allen L Robinson
- Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, PA, USA
| | - C Arden Pope
- Department of Economics, Brigham Young University, 142 FOB, Provo, UT, 84602, USA.
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Chambliss SE, Preble CV, Caubel JJ, Cados T, Messier KP, Alvarez RA, LaFranchi B, Lunden M, Marshall JD, Szpiro AA, Kirchstetter TW, Apte JS. Comparison of Mobile and Fixed-Site Black Carbon Measurements for High-Resolution Urban Pollution Mapping. Environ Sci Technol 2020; 54:7848-7857. [PMID: 32525662 DOI: 10.1021/acs.est.0c01409] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
Urban concentrations of black carbon (BC) and other primary pollutants vary on small spatial scales (<100m). Mobile air pollution measurements can provide information on fine-scale spatial variation, thereby informing exposure assessment and mitigation efforts. However, the temporal sparsity of these measurements presents a challenge for estimating representative long-term concentrations. We evaluate the capabilities of mobile monitoring in the represention of time-stable spatial patterns by comparing against a large set of continuous fixed-site measurements from a sampling campaign in West Oakland, California. Custom-built, low-cost aerosol black carbon detectors (ABCDs) provided 100 days of continuous measurements at 97 near-road and 3 background fixed sites during summer 2017; two concurrently operated mobile laboratories collected over 300 h of in-motion measurements using a photoacoustic extinctiometer. The spatial coverage from mobile monitoring reveals patterns missed by the fixed-site network. Time-integrated measurements from mobile lab visits to fixed-site monitors reveal modest correlation (spatial R2 = 0.51) with medians of full daytime fixed-site measurements. Aggregation of mobile monitoring data in space and time can mitigate high levels of uncertainty associated with measurements at precise locations or points in time. However, concentrations estimated by mobile monitoring show a loss of spatial fidelity at spatial aggregations greater than 100 m.
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Affiliation(s)
- Sarah E Chambliss
- Department of Civil, Architectural and Environmental Engineering, University of Texas at Austin, Austin, Texas 78712, United States
| | - Chelsea V Preble
- Department of Civil and Environmental Engineering, University of California, Berkeley, Berkeley, California 94720, United States
- Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Julien J Caubel
- Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
- Department of Mechanical Engineering, University of California, Berkeley, Berkeley, California 94720, United States
| | - Troy Cados
- Department of Civil and Environmental Engineering, University of California, Berkeley, Berkeley, California 94720, United States
- Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Kyle P Messier
- Department of Civil, Architectural and Environmental Engineering, University of Texas at Austin, Austin, Texas 78712, United States
- Environmental Defense Fund, Austin, Texas 78701, United States
| | - Ramón A Alvarez
- Environmental Defense Fund, Austin, Texas 78701, United States
| | - Brian LaFranchi
- Aclima, Inc., 10 Lombard Street, San Francisco, California 94111, United States
| | - Melissa Lunden
- Aclima, Inc., 10 Lombard Street, San Francisco, California 94111, United States
| | - Julian D Marshall
- Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington 98195, United States
| | - Adam A Szpiro
- Department of Biostatistics, University of Washington, Seattle, Washington 98195, United States
| | - Thomas W Kirchstetter
- Department of Civil and Environmental Engineering, University of California, Berkeley, Berkeley, California 94720, United States
- Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States
| | - Joshua S Apte
- Department of Civil, Architectural and Environmental Engineering, University of Texas at Austin, Austin, Texas 78712, United States
- Department of Civil and Environmental Engineering, University of California, Berkeley, Berkeley, California 94720, United States
- School of Public Health, University of California, Berkeley, Berkeley, California 94720, United States
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Sanchez M, Milà C, Sreekanth V, Balakrishnan K, Sambandam S, Nieuwenhuijsen M, Kinra S, Marshall JD, Tonne C. Personal exposure to particulate matter in peri-urban India: predictors and association with ambient concentration at residence. J Expo Sci Environ Epidemiol 2020; 30:596-605. [PMID: 31263182 DOI: 10.1038/s41370-019-0150-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2018] [Revised: 03/11/2019] [Accepted: 05/01/2019] [Indexed: 05/03/2023]
Abstract
Scalable exposure assessment approaches that capture personal exposure to particles for purposes of epidemiology are currently limited, but valuable, particularly in low-/middle-income countries where sources of personal exposure are often distinct from those of ambient concentrations. We measured 2 × 24-h integrated personal exposure to PM2.5 and black carbon in two seasons in 402 participants living in peri-urban South India. Means (sd) of PM2.5 personal exposure were 55.1(82.8) µg/m3 for men and 58.5(58.8) µg/m3 for women; corresponding figures for black carbon were 4.6(7.0) µg/m3 and 6.1(9.6) µg/m3. Most variability in personal exposure was within participant (intra-class correlation ~20%). Personal exposure measurements were not correlated (Rspearman < 0.2) with annual ambient concentration at residence modeled by land-use regression; no subgroup with moderate or good agreement could be identified (weighted kappa ≤ 0.3 in all subgroups). We developed models to predict personal exposure in men and women separately, based on time-invariant characteristics collected at baseline (individual, household, and general time-activity) using forward stepwise model building with mixed models. Models for women included cooking activities and household socio-economic position, while models for men included smoking and occupation. Models performed moderately in terms of between-participant variance explained (38-53%) and correlations between predictions and measurements (Rspearman: 0.30-0.50). More detailed, time-varying time-activity data did not substantially improve the performance of the models. Our results demonstrate the feasibility of predicting personal exposure in support of epidemiological studies investigating long-term particulate matter exposure in settings characterized by solid fuel use and high occupational exposure to particles.
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Affiliation(s)
- Margaux Sanchez
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
| | - Carles Milà
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
| | - V Sreekanth
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, United States
| | - Kalpana Balakrishnan
- Department of Environmental Health Engineering, Sri Ramachandra University (SRU), Chennai, India
| | - Sankar Sambandam
- Department of Environmental Health Engineering, Sri Ramachandra University (SRU), Chennai, India
| | - Mark Nieuwenhuijsen
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain
- Universitat Pompeu Fabra (UPF), Barcelona, Spain
- CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
| | - Sanjay Kinra
- Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Julian D Marshall
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, United States
| | - Cathryn Tonne
- Barcelona Institute for Global Health (ISGlobal), Barcelona, Spain.
- Universitat Pompeu Fabra (UPF), Barcelona, Spain.
- CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain.
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Clark LP, Sreekanth V, Bekbulat B, Baum M, Yang S, Baylon P, Gould TR, Larson TV, Seto EYW, Space CD, Marshall JD. Developing a Low-Cost Passive Method for Long-Term Average Levels of Light-Absorbing Carbon Air Pollution in Polluted Indoor Environments. Sensors (Basel) 2020; 20:E3417. [PMID: 32560462 PMCID: PMC7348734 DOI: 10.3390/s20123417] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 06/11/2020] [Accepted: 06/13/2020] [Indexed: 01/03/2023]
Abstract
We propose a low-cost passive method for monitoring long-term average levels of light-absorbing carbon air pollution in polluted indoor environments. Building on prior work, the method here estimates the change in reflectance of a passively exposed surface through analysis of digital images. To determine reproducibility and limits of detection, we tested low-cost passive samplers with exposure to kerosene smoke in the laboratory and to environmental pollution in 20 indoor locations. Preliminary results suggest robust reproducibility (r = 0.99) and limits of detection appropriate for longer-term (~1-3 months) monitoring in households that use solid fuels. The results here suggest high precision; further testing involving "gold standard" measurements is needed to investigate accuracy.
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Affiliation(s)
- Lara P. Clark
- Department of Civil & Environmental Engineering, University of Washington, Seattle, WA 98195, USA; (L.P.C.); (V.S.); (B.B.); (S.Y.); (T.R.G.); (T.V.L.); (C.D.S.)
| | - V. Sreekanth
- Department of Civil & Environmental Engineering, University of Washington, Seattle, WA 98195, USA; (L.P.C.); (V.S.); (B.B.); (S.Y.); (T.R.G.); (T.V.L.); (C.D.S.)
- Center for Study of Science, Technology & Policy, Bengaluru 560094, India
| | - Bujin Bekbulat
- Department of Civil & Environmental Engineering, University of Washington, Seattle, WA 98195, USA; (L.P.C.); (V.S.); (B.B.); (S.Y.); (T.R.G.); (T.V.L.); (C.D.S.)
| | | | - Songlin Yang
- Department of Civil & Environmental Engineering, University of Washington, Seattle, WA 98195, USA; (L.P.C.); (V.S.); (B.B.); (S.Y.); (T.R.G.); (T.V.L.); (C.D.S.)
- Astronaut Center of China, Beijing 100094, China
| | - Pao Baylon
- Department of Atmospheric Sciences, University of Washington, Seattle, WA 98195, USA;
| | - Timothy R. Gould
- Department of Civil & Environmental Engineering, University of Washington, Seattle, WA 98195, USA; (L.P.C.); (V.S.); (B.B.); (S.Y.); (T.R.G.); (T.V.L.); (C.D.S.)
| | - Timothy V. Larson
- Department of Civil & Environmental Engineering, University of Washington, Seattle, WA 98195, USA; (L.P.C.); (V.S.); (B.B.); (S.Y.); (T.R.G.); (T.V.L.); (C.D.S.)
- Department of Environmental & Occupational Health Sciences, University of Washington, Seattle, WA 98195, USA;
| | - Edmund Y. W. Seto
- Department of Environmental & Occupational Health Sciences, University of Washington, Seattle, WA 98195, USA;
| | - Chris D. Space
- Department of Civil & Environmental Engineering, University of Washington, Seattle, WA 98195, USA; (L.P.C.); (V.S.); (B.B.); (S.Y.); (T.R.G.); (T.V.L.); (C.D.S.)
| | - Julian D. Marshall
- Department of Civil & Environmental Engineering, University of Washington, Seattle, WA 98195, USA; (L.P.C.); (V.S.); (B.B.); (S.Y.); (T.R.G.); (T.V.L.); (C.D.S.)
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Sergi BJ, Adams PJ, Muller NZ, Robinson AL, Davis SJ, Marshall JD, Azevedo IL. Optimizing Emissions Reductions from the U.S. Power Sector for Climate and Health Benefits. Environ Sci Technol 2020; 54:7513-7523. [PMID: 32392045 DOI: 10.1021/acs.est.9b06936] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Improved air quality and human health are often discussed as "co-benefits" of mitigating climate change, yet they are rarely considered when designing or implementing climate policies. We analyze the implications of integrating health and climate when determining the best locations for replacing power plants with new wind, solar, or natural gas to meet a CO2 reduction target in the United States. We employ a capacity expansion model with integrated assessment of climate and health damages, comparing portfolios optimized for benefits to climate alone or both health and climate. The model estimates county-level health damages and accounts for uncertainty by using a range of air quality models (AP3, EASIUR, and InMAP) and concentration-response functions (American Cancer Society and Harvard Six Cities). We find that reducing CO2 by 30% yields $21-68 billion in annual health benefits, with an additional $9-36 billion possible when co-optimizing for climate and health benefits. Additional benefits accrue from prioritizing emissions reductions in counties with high population exposure. Total health benefits equal or exceed climate benefits across a wide range of modeling assumptions. Our results demonstrate the value of considering health in climate policy design and the need for interstate cooperation to achieve additional health benefits equitably.
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Affiliation(s)
- Brian J Sergi
- Department of Engineering & Public Policy, Carnegie Mellon University, Pittsburgh, PA 15213, United States
| | - Peter J Adams
- Department of Engineering & Public Policy, Carnegie Mellon University, Pittsburgh, PA 15213, United States
- Department of Civil & Environmental Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, United States
| | - Nicholas Z Muller
- Department of Engineering & Public Policy, Carnegie Mellon University, Pittsburgh, PA 15213, United States
- Tepper School of Business, Carnegie Mellon University, Pittsburgh, PA 15213, United States
- National Bureau of Economic Research, Cambridge, MA 02138, United States
| | - Allen L Robinson
- Department of Engineering & Public Policy, Carnegie Mellon University, Pittsburgh, PA 15213, United States
- Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, United States
| | - Steven J Davis
- Department of Earth System Science, University of California, Irvine, CA 92697 United States
- Department of Civil & Environmental Engineering, University of California, Irvine, CA 92697, United States
| | - Julian D Marshall
- Department of Civil & Environmental Engineering, University of Washington, Seattle, WA 98115, United States
| | - Inês L Azevedo
- Department of Engineering & Public Policy, Carnegie Mellon University, Pittsburgh, PA 15213, United States
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Ranzani OT, Milà C, Sanchez M, Bhogadi S, Kulkarni B, Balakrishnan K, Sambandam S, Sunyer J, Marshall JD, Kinra S, Tonne C. Personal exposure to particulate air pollution and vascular damage in peri-urban South India. Environ Int 2020; 139:105734. [PMID: 32361533 PMCID: PMC7267772 DOI: 10.1016/j.envint.2020.105734] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Revised: 04/01/2020] [Accepted: 04/08/2020] [Indexed: 06/11/2023]
Abstract
OBJECTIVE Air pollution is a leading preventable risk factor for cardiovascular diseases. Previous studies mostly relied on concentrations at residence, which might not represent personal exposure. Personal air pollution exposure has a greater variability compared with levels of ambient air pollution, facilitating evaluation of exposure-response functions and vascular pathophysiology. We aimed to evaluate the association between predicted annual personal exposure to PM2.5 and black carbon (BC) and three vascular damage markers in peri-urban South India. METHODS We analyzed the third wave of the APCAPS cohort (2010-2012), which recruited participants from 28 villages. We used predicted personal exposure to PM2.5 and BC derived from 610 participant-days of 24 h average gravimetric PM2.5 and BC measurements and predictors related to usual time-activity. Outcomes included carotid intima-media thickness (CIMT), carotid-femoral pulse wave velocity (cf-PWV) and augmentation index (AIx). We fit linear mixed models, adjusting for potential confounders and accounting for the clustered data structure. We evaluated nonlinear associations using generalized additive mixed models. RESULTS Of the 3017 participants (mean age 38 years), 1453 (48%) were women. The average PM2.5 exposure was 51 µg/m3 (range 13-85) for men, and 61 µg/m3 (range 40-120) for women, while the average BC was 4 µg/m3 (range 3-7) for men and 8 µg/m3 (range 3-22) for women. A 10 μg/m3 increase of PM2.5 was positively associated with CIMT (0.026 mm, 95% CI 0.014, 0.037), cf-PWV (0.069 m/s, 95% CI 0.008, 0.131) and AIx (0.8%, 95% CI 0.3, 1.3) among men. The exposure-response function for PM2.5 and AIx among men showed non-linearity, particularly within the exposure range dominated by tobacco smoking and occupational exposures. Both PM2.5 and BC were positively associated with AIx among women (0.6%, 95% CI 0.2, 1.0, per 10 μg/m3 PM2.5; 0.5%, 95% CI 0.1, 0.8, per 2 μg/m3 BC). CONCLUSIONS Personal exposure to particulate matter was associated with vascular damage in a peri-urban population in South India. Personal exposure to particulate matter appears to have gender-specific effects on the type of vascular damage, potentially reflecting differences in sources of personal exposure by gender.
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Affiliation(s)
- Otavio T Ranzani
- Barcelona Institute for Global Health (ISGlobal), Universitat Pompeu Fabra, CIBER Epidemiología y Salud Pública, Barcelona, Spain
| | - Carles Milà
- Barcelona Institute for Global Health (ISGlobal), Universitat Pompeu Fabra, CIBER Epidemiología y Salud Pública, Barcelona, Spain
| | - Margaux Sanchez
- Barcelona Institute for Global Health (ISGlobal), Universitat Pompeu Fabra, CIBER Epidemiología y Salud Pública, Barcelona, Spain
| | | | - Bharati Kulkarni
- National Institute of Nutrition, Indian Council of Medical Research, Hyderabad, India
| | - Kalpana Balakrishnan
- Department of Environmental Health Engineering, Sri Ramachandra University (SRU), Chennai, India
| | - Sankar Sambandam
- Department of Environmental Health Engineering, Sri Ramachandra University (SRU), Chennai, India
| | - Jordi Sunyer
- Barcelona Institute for Global Health (ISGlobal), Universitat Pompeu Fabra, CIBER Epidemiología y Salud Pública, Barcelona, Spain
| | - Julian D Marshall
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, USA
| | - Sanjay Kinra
- Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Cathryn Tonne
- Barcelona Institute for Global Health (ISGlobal), Universitat Pompeu Fabra, CIBER Epidemiología y Salud Pública, Barcelona, Spain.
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Higbee JD, Lefler JS, Burnett RT, Ezzati M, Marshall JD, Kim SY, Bechle M, Robinson AL, Pope CA. Estimating long-term pollution exposure effects through inverse probability weighting methods with Cox proportional hazards models. Environ Epidemiol 2020; 4:e085. [PMID: 32656485 PMCID: PMC7319228 DOI: 10.1097/ee9.0000000000000085] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Accepted: 01/19/2020] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Fine particulate matter (PM2.5) is associated with negative health outcomes in both the short and long term. However, the cohort studies that have produced many of the estimates of long-term exposure associations may fail to account for selection bias in pollution exposure as well as covariate imbalance in the study population; therefore, causal modeling techniques may be beneficial. METHODS Twenty-nine years of data from the National Health Interview Survey (NHIS) was compiled and linked to modeled annual average outdoor PM2.5 concentration and restricted-use mortality data. A series of Cox proportional hazards models, adjusted using inverse probability weights, yielded causal risk estimates of long-term exposure to ambient PM2.5 on all-cause and cardiopulmonary mortality. RESULTS Covariate-adjusted estimated relative risks per 10 μg/m3 increase in PM2.5 exposure were estimated to be 1.117 (1.083, 1.152) for all-cause mortality and 1.232 (1.174, 1.292) for cardiopulmonary mortality. Inverse probability weighted Cox models provide relatively consistent and robust estimates similar to those in the unweighted baseline multivariate Cox model, though they have marginally lower point estimates and higher standard errors. CONCLUSIONS These results provide evidence that long-term exposure to PM2.5 contributes to increased mortality risk in US adults and that the estimated effects are generally robust to modeling choices. The size and robustness of estimated associations highlight the importance of clean air as a matter of public health. Estimated confounding due to measured covariates appears minimal in the NHIS cohort, and various distributional assumptions have little bearing on the magnitude or standard errors of estimated causal associations.
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Affiliation(s)
- Joshua D. Higbee
- Department of Economics, University of Chicago, Chicago, Illinois
| | - Jacob S. Lefler
- Department of Agricultural and Resource Economics, University of California – Berkeley, Berkeley, California
| | | | - Majid Ezzati
- MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
| | - Julian D. Marshall
- Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington
| | - Sun-Young Kim
- Department of Cancer Control and Population Health, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang-si, Gyeonggi-do, Korea
| | - Matthew Bechle
- Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington
| | - Allen L. Robinson
- Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, Pennsylvania
| | - C. Arden Pope
- Department of Economics, Brigham Young University, Provo, Utah
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Milà C, Curto A, Dimitrova A, Sreekanth V, Kinra S, Marshall JD, Tonne C. Identifying predictors of personal exposure to air temperature in peri-urban India. Sci Total Environ 2020; 707:136114. [PMID: 31863998 DOI: 10.1016/j.scitotenv.2019.136114] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/15/2019] [Revised: 12/12/2019] [Accepted: 12/12/2019] [Indexed: 06/10/2023]
Abstract
Characterizing personal exposure to air temperature is critical to understanding exposure measurement error in epidemiologic studies using fixed-site exposure data and to identify strategies to protect public health. To date, no study evaluating personal air temperature in the general population has been conducted in a low-and-middle income country. We used data from the CHAI study consisting of 50 adults monitored in up to six non-consecutive 24 h sessions in peri-urban south India. We quantified the agreement and association between fixed-site ambient and personal air temperature, and identified predictors of personal air temperature based on housing assessment, self-reported, GPS, remote sensing, and wearable camera data. Mean (SD) daytime (6 am-10 pm) average personal air temperature was 31.2 (2.6) °C and mean nighttime (10 pm-6 am) average temperature was 28.8 (2.8) °C. Agreement between average personal air and fixed-site ambient temperatures was limited, especially at night when personal air temperatures were underestimated by fixed-site temperatures (MBE = -5.6 °C). The proportion of average personal nighttime temperature variability explained by ambient fixed-site temperatures was moderate (R2mar = 0.39); daytime associations were stronger for women (R2mar = 0.51) than for men (R2mar = 0.3). Other predictors of average nighttime personal air temperature included residential altitude, ceiling height, and household income. Predictors of average daytime personal air temperature included roof materials, GPS-tracked altitude, time working in agriculture (for women), and time travelling (for men). No biomass cooking, urban heat island, or greenspace effects were identified. R2mar between ambient fixed-site and personal air temperature indicate that ambient fixed-site temperature is only a moderately useful proxy of personal air temperature in the context of peri-urban India. Our findings suggest that people living in houses at lower altitude, with lower ceiling height and asbestos roofing sheets might be more vulnerable to heat. We also identified households with higher income, women working in agriculture and men with long commutes as disproportionately exposed to high temperatures.
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Affiliation(s)
- Carles Milà
- ISGlobal, Universitat Pompeu Fabra, CIBER Epidemiología y Salud Pública, Barcelona, Spain
| | - Ariadna Curto
- ISGlobal, Universitat Pompeu Fabra, CIBER Epidemiología y Salud Pública, Barcelona, Spain
| | - Asya Dimitrova
- ISGlobal, Universitat Pompeu Fabra, CIBER Epidemiología y Salud Pública, Barcelona, Spain
| | - V Sreekanth
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, USA; Center for Study of Science, Technology & Policy, Bengaluru 560 094, India
| | - Sanjay Kinra
- Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Julian D Marshall
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, USA
| | - Cathryn Tonne
- ISGlobal, Universitat Pompeu Fabra, CIBER Epidemiología y Salud Pública, Barcelona, Spain.
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Kim SY, Bechle M, Hankey S, Sheppard L, Szpiro AA, Marshall JD. Concentrations of criteria pollutants in the contiguous U.S., 1979 - 2015: Role of prediction model parsimony in integrated empirical geographic regression. PLoS One 2020; 15:e0228535. [PMID: 32069301 PMCID: PMC7028280 DOI: 10.1371/journal.pone.0228535] [Citation(s) in RCA: 62] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2019] [Accepted: 01/17/2020] [Indexed: 12/20/2022] Open
Abstract
National-scale empirical models for air pollution can include hundreds of geographic variables. The impact of model parsimony (i.e., how model performance differs for a large versus small number of covariates) has not been systematically explored. We aim to (1) build annual-average integrated empirical geographic (IEG) regression models for the contiguous U.S. for six criteria pollutants during 1979–2015; (2) explore systematically the impact on model performance of the number of variables selected for inclusion in a model; and (3) provide publicly available model predictions. We compute annual-average concentrations from regulatory monitoring data for PM10, PM2.5, NO2, SO2, CO, and ozone at all monitoring sites for 1979–2015. We also use ~350 geographic characteristics at each location including measures of traffic, land use, land cover, and satellite-based estimates of air pollution. We then develop IEG models, employing universal kriging and summary factors estimated by partial least squares (PLS) of geographic variables. For all pollutants and years, we compare three approaches for choosing variables to include in the PLS model: (1) no variables, (2) a limited number of variables selected from the full set by forward selection, and (3) all variables. We evaluate model performance using 10-fold cross-validation (CV) using conventional and spatially-clustered test data. Models using 3 to 30 variables selected from the full set generally have the best performance across all pollutants and years (median R2 conventional [clustered] CV: 0.66 [0.47]) compared to models with no (0.37 [0]) or all variables (0.64 [0.27]). Concentration estimates for all Census Blocks reveal generally decreasing concentrations over several decades with local heterogeneity. Our findings suggest that national prediction models can be built by empirically selecting only a small number of important variables to provide robust concentration estimates. Model estimates are freely available online.
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Affiliation(s)
- Sun-Young Kim
- Department of Cancer Control and Population Health, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang-si, Gyeonggi-do, Korea
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, United States of America
- * E-mail:
| | - Matthew Bechle
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, United States of America
| | - Steve Hankey
- School of Public and International Affairs, Virginia Polytechnic Institute and State University, Blacksburg, VA, United States of America
| | - Lianne Sheppard
- Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, United States of America
- Department of Biostatistics, University of Washington, Seattle, WA, United States of America
| | - Adam A. Szpiro
- Department of Biostatistics, University of Washington, Seattle, WA, United States of America
| | - Julian D. Marshall
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, United States of America
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Ranzani OT, Milà C, Sanchez M, Bhogadi S, Kulkarni B, Balakrishnan K, Sambandam S, Sunyer J, Marshall JD, Kinra S, Tonne C. Association between ambient and household air pollution with carotid intima-media thickness in peri-urban South India: CHAI-Project. Int J Epidemiol 2020; 49:69-79. [PMID: 31605119 PMCID: PMC7124504 DOI: 10.1093/ije/dyz208] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/23/2019] [Indexed: 01/29/2023] Open
Abstract
BACKGROUND Evidence linking ambient air pollution with atherosclerosis is lacking from low- and middle-income countries. Additionally, evidence regarding the association between household air pollution and atherosclerosis is limited. We evaluated the association between ambient fine particulate matter [particulate matter with an aerodynamic diameter of ≤2.5 µm (PM2.5)] and biomass fuel use on carotid intima-media thickness (CIMT), a surrogate of atherosclerosis, in India. METHODS We analysed the third follow-up of the Andhra Pradesh Children and Parent Study cohort (2010-2012), which recruited participants from 28 peri-urban villages. Our primary outcome was mean CIMT, measured using a standardized protocol. We estimated annual average PM2.5 outdoors at residence using land-use regression. Biomass cooking fuel was self-reported. We fitted a within-between linear-mixed model adjusting for potential confounders. RESULTS Among 3278 participants (48% women, mean age 38 years), mean PM2.5 was 32.7 [range 24.4-38.2] µg/m3, and 60% used biomass. After confounder adjustment, we observed positive associations between within-village variation in PM2.5 and CIMT in all participants [1.79%, 95% confidence interval (CI), -0.31 to 3.90 per 1 µg/m3 of PM2.5] and in men (2.98%, 95% CI, 0.23-5.72, per 1 µg/m3 of PM2.5). Use of biomass cooking fuel was associated with CIMT in all participants (1.60%, 95% CI, -0.46 to 3.65), especially in women with an unvented stove (6.14%, 95% CI, 1.40-10.89). The point-estimate for the PM2.5 association was larger in sub-groups with higher cardiometabolic risk profile. CONCLUSIONS Ambient and household air pollution were positively associated with CIMT in a peri-urban population of India, although with limited precision for some estimates. We observed differences in the association between ambient and household air pollution and CIMT by gender.
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Affiliation(s)
- Otavio T Ranzani
- Barcelona Institute for Global Health, Universitat Pompeu Fabra, CIBER Epidemiología y Salud Pública, Barcelona, Spain
| | - Carles Milà
- Barcelona Institute for Global Health, Universitat Pompeu Fabra, CIBER Epidemiología y Salud Pública, Barcelona, Spain
| | - Margaux Sanchez
- Barcelona Institute for Global Health, Universitat Pompeu Fabra, CIBER Epidemiología y Salud Pública, Barcelona, Spain
| | | | - Bharati Kulkarni
- National Institute of Nutrition, Indian Council of Medical Research, Hyderabad, India
| | - Kalpana Balakrishnan
- Department of Environmental Health Engineering, Sri Ramachandra University (SRU), Chennai, India
| | - Sankar Sambandam
- Department of Environmental Health Engineering, Sri Ramachandra University (SRU), Chennai, India
| | - Jordi Sunyer
- Barcelona Institute for Global Health, Universitat Pompeu Fabra, CIBER Epidemiología y Salud Pública, Barcelona, Spain
| | - Julian D Marshall
- Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, USA
| | - Sanjay Kinra
- Department of Non-communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK
| | - Cathryn Tonne
- Barcelona Institute for Global Health, Universitat Pompeu Fabra, CIBER Epidemiología y Salud Pública, Barcelona, Spain
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