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Zalzal J, Minet L, Brook J, Mihele C, Chen H, Hatzopoulou M. Capturing Exposure Disparities with Chemical Transport Models: Evaluating the Suitability of Downscaling Using Land Use Regression. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024. [PMID: 39092553 DOI: 10.1021/acs.est.4c03725] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
High resolution exposure surfaces are essential to capture disparities in exposure to traffic-related air pollution in urban areas. In this study, we develop an approach to downscale Chemical Transport Model (CTM) simulations to a hyperlocal level (∼100m) in the Greater Toronto Area (GTA) under three scenarios where emissions from cars, trucks and buses are zeroed out, thus capturing the burden of each transportation mode. This proposed approach statistically fuses CTMs with Land-Use Regression using machine learning techniques. With this proposed downscaling approach, changes in air pollutant concentrations under different scenarios are appropriately captured by downscaling factors that are trained to reflect the spatial distribution of emission reductions. Our validation analysis shows that high-resolution models resulted in better performance than coarse models when compared with observations at reference stations. We used this downscaling approach to assess disparities in exposure to nitrogen dioxide (NO2) for populations composed of renters, low-income households, recent immigrants, and visible minorities. Individuals in all four categories were disproportionately exposed to the burden of cars, trucks, and buses. We conducted this analysis at spatial resolutions of 12, 4, 1 km, and 100 m and observed that disparities were significantly underestimated when using coarse spatial resolutions. This reinforces the need for high-spatial resolution exposure surfaces for environmental justice analyses.
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Affiliation(s)
- Jad Zalzal
- Department of Civil & Mineral Engineering, University of Toronto, 35 St George Street, Toronto, Ontario M5S 1A4, Canada
| | - Laura Minet
- Department of Civil Engineering, University of Victoria, 3800 Finnerty Road, Victoria, British Columbia V8P 5C2, Canada
| | - Jeffrey Brook
- Dalla Lana School of Public Health, University of Toronto, 155 College Street, Toronto, Ontario M5T 3M7, Canada
| | - Cristian Mihele
- Air Quality Research Division, Environment and Climate Change Canada, 4905 Dufferin Street, North York, Ontario M3H 5T4, Canada
| | - Hong Chen
- Dalla Lana School of Public Health, University of Toronto, 155 College Street, Toronto, Ontario M5T 3M7, Canada
- Environmental Health Science and Research Bureau, Health Canada, 50 Colombine Driveway, Ottawa, Ontario K1A 0K9, Canada
- Public Health Ontario, 480 University Avenue, Toronto, Ontario M5G 1 V2, Canada
- ICES, 2075 Bayview Avenue, Toronto, Ontario M4N 3M5, Canada
| | - Marianne Hatzopoulou
- Department of Civil & Mineral Engineering, University of Toronto, 35 St George Street, Toronto, Ontario M5S 1A4, Canada
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2
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Kerr GH, Meyer M, Goldberg DL, Miller J, Anenberg SC. Air pollution impacts from warehousing in the United States uncovered with satellite data. Nat Commun 2024; 15:6006. [PMID: 39048550 PMCID: PMC11269699 DOI: 10.1038/s41467-024-50000-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Accepted: 06/25/2024] [Indexed: 07/27/2024] Open
Abstract
Regulators, environmental advocates, and community groups in the United States (U.S.) are concerned about air pollution associated with the proliferating e-commerce and warehousing industries. Nationwide datasets of warehouse locations, traffic, and satellite observations of the traffic-related pollutant nitrogen dioxide (NO2) provide a unique capability to evaluate the air quality and environmental equity impacts of these geographically-dispersed emission sources. Here, we show that the nearly 150,000 warehouses in the U.S. worsen local traffic-related air pollution with an average near-warehouse NO2 enhancement of nearly 20% and are disproportionately located in marginalized and minoritized communities. Near-warehouse truck traffic and NO2 significantly increase as warehouse density and the number of warehouse loading docks and parking spaces increase. Increased satellite-observed NO2 near warehouses underscores the need for indirect source rules, incentives for replacing old trucks, and corporate commitments towards electrification. Future ground-based monitoring campaigns may help track impacts of individual or small clusters of facilities.
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Affiliation(s)
- Gaige Hunter Kerr
- Department of Environmental and Occupational Health, George Washington University, Washington, DC, USA.
| | - Michelle Meyer
- International Council on Clean Transportation, Washington, DC, USA
| | - Daniel L Goldberg
- Department of Environmental and Occupational Health, George Washington University, Washington, DC, USA
| | - Joshua Miller
- International Council on Clean Transportation, Washington, DC, USA
| | - Susan C Anenberg
- Department of Environmental and Occupational Health, George Washington University, Washington, DC, USA
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3
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Bradley A, Croes BE, Harkins C, McDonald BC, de Gouw JA. Air Pollution Inequality in the Denver Metroplex and its Relationship to Historical Redlining. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:4226-4236. [PMID: 38380822 PMCID: PMC10919081 DOI: 10.1021/acs.est.3c03230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 01/30/2024] [Accepted: 01/31/2024] [Indexed: 02/22/2024]
Abstract
Prior studies have shown that people of color (POC) in the United States are exposed to higher levels of pollution than non-Hispanic White people. We show that the city of Denver, Colorado, displays similar race- and ethnicity-based air pollution disparities by using a combination of high-resolution satellite data, air pollution modeling, historical demographic information, and areal apportionment techniques. TROPOMI NO2 columns and modeled PM2.5 concentrations from 2019 are higher in communities subject to redlining. We calculated and compared Spearman coefficients for pollutants and race at the census tract level for every city that underwent redlining to contextualize the disparities in Denver. We find that the location of polluting infrastructure leads to higher populations of POC living near point sources, including 40% higher Hispanic and Latino populations. This influences pollution distribution, with annual average PM2.5 surface concentrations of 6.5 μg m-3 in census tracts with 0-5% Hispanic and Latino populations and 7.5 μg m-3 in census tracts with 60-65% Hispanic and Latino populations. Traffic analysis and emission inventory data show that POC are more likely to live near busy highways. Unequal spatial distribution of pollution sources and POC have allowed for pollution disparities to persist despite attempts by the city to rectify them. Finally, we identify the core causes of the pollution disparities to provide direction for remediation.
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Affiliation(s)
- Alexander
C. Bradley
- University
of Colorado Boulder, Boulder, Colorado 80309, United States
- Cooperative
Institute for Research in Environmental Sciences, Boulder, Colorado 80309, United States
| | - Bart E. Croes
- Cooperative
Institute for Research in Environmental Sciences, Boulder, Colorado 80309, United States
| | - Colin Harkins
- Cooperative
Institute for Research in Environmental Sciences, Boulder, Colorado 80309, United States
- Chemical
Sciences Laboratory, National Oceanic and
Atmospheric Administration, Boulder, Colorado 80305, United States
| | - Brian C. McDonald
- Chemical
Sciences Laboratory, National Oceanic and
Atmospheric Administration, Boulder, Colorado 80305, United States
| | - Joost A. de Gouw
- University
of Colorado Boulder, Boulder, Colorado 80309, United States
- Cooperative
Institute for Research in Environmental Sciences, Boulder, Colorado 80309, United States
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4
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Wang J, Alli AS, Clark SN, Ezzati M, Brauer M, Hughes AF, Nimo J, Moses JB, Baah S, Nathvani R, D V, Agyei-Mensah S, Baumgartner J, Bennett JE, Arku RE. Inequalities in urban air pollution in sub-Saharan Africa: an empirical modeling of ambient NO and NO 2 concentrations in Accra, Ghana. ENVIRONMENTAL RESEARCH LETTERS : ERL [WEB SITE] 2024; 19:034036. [PMID: 38419692 PMCID: PMC10897512 DOI: 10.1088/1748-9326/ad2892] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Revised: 02/04/2024] [Accepted: 02/12/2024] [Indexed: 03/02/2024]
Abstract
Road traffic has become the leading source of air pollution in fast-growing sub-Saharan African cities. Yet, there is a dearth of robust city-wide data for understanding space-time variations and inequalities in combustion related emissions and exposures. We combined nitrogen dioxide (NO2) and nitric oxide (NO) measurement data from 134 locations in the Greater Accra Metropolitan Area (GAMA), with geographical, meteorological, and population factors in spatio-temporal mixed effects models to predict NO2 and NO concentrations at fine spatial (50 m) and temporal (weekly) resolution over the entire GAMA. Model performance was evaluated with 10-fold cross-validation (CV), and predictions were summarized as annual and seasonal (dusty [Harmattan] and rainy [non-Harmattan]) mean concentrations. The predictions were used to examine population distributions of, and socioeconomic inequalities in, exposure at the census enumeration area (EA) level. The models explained 88% and 79% of the spatiotemporal variability in NO2 and NO concentrations, respectively. The mean predicted annual, non-Harmattan and Harmattan NO2 levels were 37 (range: 1-189), 28 (range: 1-170) and 50 (range: 1-195) µg m-3, respectively. Unlike NO2, NO concentrations were highest in the non-Harmattan season (41 [range: 31-521] µg m-3). Road traffic was the dominant factor for both pollutants, but NO2 had higher spatial heterogeneity than NO. For both pollutants, the levels were substantially higher in the city core, where the entire population (100%) was exposed to annual NO2 levels exceeding the World Health Organization (WHO) guideline of 10 µg m-3. Significant disparities in NO2 concentrations existed across socioeconomic gradients, with residents in the poorest communities exposed to levels about 15 µg m-3 higher compared with the wealthiest (p < 0.001). The results showed the important role of road traffic emissions in air pollution concentrations in the GAMA, which has major implications for the health of the city's poorest residents. These data could support climate and health impact assessments as well as policy evaluations in the city.
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Affiliation(s)
- Jiayuan Wang
- Department of Environmental Health Sciences, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA, United States of America
| | - Abosede S Alli
- Department of Environmental Health Sciences, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA, United States of America
| | - Sierra N Clark
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
| | - Majid Ezzati
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
- Regional Institute for Population Studies, University of Ghana, Accra, Ghana
- Abdul Latif Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, United Kingdom
| | - Michael Brauer
- School of Population and Public Health, The University of British Columbia, Vancouver, Canada
| | | | - James Nimo
- Department of Physics, University of Ghana, Accra, Ghana
| | | | - Solomon Baah
- Department of Physics, University of Ghana, Accra, Ghana
| | - Ricky Nathvani
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
| | - Vishwanath D
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
| | - Samuel Agyei-Mensah
- Department of Geography and Resource Development, University of Ghana, Accra, Ghana
- Department of Civil and Environmental Engineering, Imperial College London, London, United Kingdom
| | - Jill Baumgartner
- Institute for Health and Social Policy, McGill University, Montreal, Canada
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Canada
| | - James E Bennett
- Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, United Kingdom
- MRC Centre for Environment and Health, School of Public Health, Imperial College London, London, United Kingdom
| | - Raphael E Arku
- Department of Environmental Health Sciences, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA, United States of America
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Baker KR, Valin L, Szykman J, Judd L, Shu Q, Hutzell B, Napelenok S, Murphy B, Connors V. Photochemical model assessment of single source NO 2 and O 3 plumes using field study data. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 903:166606. [PMID: 37640074 DOI: 10.1016/j.scitotenv.2023.166606] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Revised: 08/23/2023] [Accepted: 08/25/2023] [Indexed: 08/31/2023]
Abstract
Single source contribution to ambient O3 and PM2.5 has been estimated with photochemical grid models to support policy demonstrations for National Ambient Air Quality Standards, regional haze, and permit related programs. Limited field data exists to evaluate model representation of the spatial extent and chemical composition of plumes emitted by specific facilities. New tropospheric column measurements of NO2 and in-plume chemical measurements downwind of specific facilities allows for photochemical model evaluation of downwind plume extent, grid resolution impacts on plume concentration gradients, and source attribution methods. Here, photochemical models were applied with source sensitivity and source apportionment approaches to differentiate single source impacts on NO2 and O3 and compare with field study measurements. Source sensitivity approaches (e.g., brute-force difference method and decoupled direct method (DDM)) captured the spatial extent of NO2 plumes downwind of three facilities and the transition of near-source O3 titration to downwind production. Source apportionment approaches showed variability in terms of attributing the spatial extent of NO2 plumes and downwind O3 production. Each of the Community Multiscale Air Quality (CMAQ) source apportionment options predicted large O3 contribution from a large industrial facility in the flight transects nearest the facility when measurements and source sensitivity approaches suggest titration was outpacing production. In general, CMAQ DDM tends to attribute more O3 to boundary inflow and less to within-domain NOX and VOC sources compared to CMAQ source apportionment. The photochemical modeling system was able to capture single source plumes using 1 to 12 km grid resolution with best representation of plume extent and magnitude at the finer resolutions. When modeled at 1 to 12 km grid resolution, primary and secondary PM2.5 impacts were highest at the source location and decrease as distance increases downwind. The use of coarser grid resolution for single source attribution resulted in predicted impacts highest near the source but lower peak source specific concentrations compared to finer grid resolution simulations because impacts were spread out over a larger area.
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Affiliation(s)
- Kirk R Baker
- U.S. Environmental Protection Agency, Research Triangle Park, NC, USA.
| | - Lukas Valin
- U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Jim Szykman
- U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Laura Judd
- NASA Langley Research Center, Hampton, VA, USA
| | - Qian Shu
- U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Bill Hutzell
- U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Sergey Napelenok
- U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
| | - Ben Murphy
- U.S. Environmental Protection Agency, Research Triangle Park, NC, USA
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6
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Kerr GH, Goldberg DL, Harris MH, Henderson BH, Hystad P, Roy A, Anenberg SC. Ethnoracial Disparities in Nitrogen Dioxide Pollution in the United States: Comparing Data Sets from Satellites, Models, and Monitors. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:19532-19544. [PMID: 37934506 DOI: 10.1021/acs.est.3c03999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2023]
Abstract
In the United States (U.S.), studies on nitrogen dioxide (NO2) trends and pollution-attributable health effects have historically used measurements from in situ monitors, which have limited geographical coverage and leave 66% of urban areas unmonitored. Novel tools, including remotely sensed NO2 measurements and estimates of NO2 estimates from land-use regression and photochemical models, can aid in assessing NO2 exposure gradients, leveraging their complete spatial coverage. Using these data sets, we find that Black, Hispanic, Asian, and multiracial populations experience NO2 levels 15-50% higher than the national average in 2019, whereas the non-Hispanic White population is consistently exposed to levels that are 5-15% lower than the national average. By contrast, the in situ monitoring network indicates more moderate ethnoracial NO2 disparities and different rankings of the least- to most-exposed ethnoracial population subgroup. Validating these spatially complete data sets against in situ observations reveals similar performance, indicating that all these data sets can be used to understand spatial variations in NO2. Integrating in situ monitoring, satellite data, statistical models, and photochemical models can provide a semiobservational record, complete geospatial coverage, and increasingly high spatial resolution, enhancing future efforts to characterize, map, and track exposure and inequality for highly spatially heterogeneous pollutants like NO2.
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Affiliation(s)
- Gaige Hunter Kerr
- Department of Environmental and Occupational Health, Milken Institute School of Public Health, George Washington University, Washington, District of Columbia 20052, United States
| | - Daniel L Goldberg
- Department of Environmental and Occupational Health, Milken Institute School of Public Health, George Washington University, Washington, District of Columbia 20052, United States
| | - Maria H Harris
- Environmental Defense Fund, 257 Park Avenue South, New York, New York 10010, United States
| | - Barron H Henderson
- U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27711, United States
| | - Perry Hystad
- College of Public Health and Human Sciences, Oregon State University, Corvallis, Oregon 97333, United States
| | - Ananya Roy
- Environmental Defense Fund, 257 Park Avenue South, New York, New York 10010, United States
| | - Susan C Anenberg
- Department of Environmental and Occupational Health, Milken Institute School of Public Health, George Washington University, Washington, District of Columbia 20052, United States
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7
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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. ENVIRONMENTAL HEALTH PERSPECTIVES 2023; 131:125003. [PMID: 38109120 PMCID: PMC10727036 DOI: 10.1289/ehp13063] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [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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8
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Yang X, Li P, Wang ZH. The impact of urban irrigation on the temperature-carbon feedback in U.S. cities. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 344:118452. [PMID: 37348305 DOI: 10.1016/j.jenvman.2023.118452] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 05/12/2023] [Accepted: 06/16/2023] [Indexed: 06/24/2023]
Abstract
Urban areas experience numerous environmental challenges, among which the anthropogenic emissions of heat and carbon are two major contributors, the former is responsible for the notorious urban heat effect, the latter longterm climate changes. Moreover, the exchange of heat and carbon dioxide are closely interlinked in the built environment, and can form positive feedback loops that accelerate the degradation of urban environmental quality. Among a handful countermeasures for heat and carbon mitigation, urban irrigation is believed to be effective in cooling, yet the understanding of its impact on the co-evolution of heat and carbon emission remains obscure. In this study, we conducted multiphysics urban climate modeling for all urban areas in the contiguous United States, and evaluated the irrigation-induced cooling and carbon mitigation. Furthermore, we assessed the impact of urban irrigation on the potential heat-carbon feedback loop, with their strength of coupling quantified by an advanced causal inference method using the convergent cross mapping algorithms. It is found that the impact of urban irrigation varies vastly in geographically different cities, with its local and non-local effect unraveling distinct pathways of heat-carbon feedback mechanism.
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Affiliation(s)
- Xueli Yang
- School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ, USA
| | - Peiyuan Li
- Discovery Partners Institute, University of Illinois System, Chicago, IL, USA
| | - Zhi-Hua Wang
- School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe, AZ, USA.
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9
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Kelp MM, Fargiano TC, Lin S, Liu T, Turner JR, Kutz JN, Mickley LJ. Data-Driven Placement of PM 2.5 Air Quality Sensors in the United States: An Approach to Target Urban Environmental Injustice. GEOHEALTH 2023; 7:e2023GH000834. [PMID: 37711364 PMCID: PMC10499371 DOI: 10.1029/2023gh000834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 08/01/2023] [Accepted: 08/04/2023] [Indexed: 09/16/2023]
Abstract
In the United States, citizens and policymakers heavily rely upon Environmental Protection Agency mandated regulatory networks to monitor air pollution; increasingly they also depend on low-cost sensor networks to supplement spatial gaps in regulatory monitor networks coverage. Although these regulatory and low-cost networks in tandem provide enhanced spatiotemporal coverage in urban areas, low-cost sensors are located often in higher income, predominantly White areas. Such disparity in coverage may exacerbate existing inequalities and impact the ability of different communities to respond to the threat of air pollution. Here we present a study using cost-constrained multiresolution dynamic mode decomposition (mrDMDcc) to identify the optimal and equitable placement of fine particulate matter (PM2.5) sensors in four U.S. cities with histories of racial or income segregation: St. Louis, Houston, Boston, and Buffalo. This novel approach incorporates the variation of PM2.5 on timescales ranging from 1 day to over a decade to capture air pollution variability. We also introduce a cost function into the sensor placement optimization that represents the balance between our objectives of capturing PM2.5 extremes and increasing pollution monitoring in low-income and nonwhite areas. We find that the mrDMDcc algorithm places a greater number of sensors in historically low-income and nonwhite neighborhoods with known environmental pollution problems compared to networks using PM2.5 information alone. Our work provides a roadmap for the creation of equitable sensor networks in U.S. cities and offers a guide for democratizing air pollution data through increasing spatial coverage of low-cost sensors in less privileged communities.
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Affiliation(s)
- Makoto M. Kelp
- Department of Earth and Planetary SciencesHarvard UniversityCambridgeMAUSA
| | | | - Samuel Lin
- Department of Computer ScienceHarvard UniversityCambridgeMAUSA
| | - Tianjia Liu
- Department of Earth System ScienceUniversity of California, IrvineIrvineCAUSA
| | - Jay R. Turner
- Department of EnergyEnvironmental and Chemical EngineeringWashington UniversitySt. LouisMOUSA
| | - J. Nathan Kutz
- Department of Applied MathematicsUniversity of WashingtonSeattleWAUSA
| | - Loretta J. Mickley
- John A. Paulson School of Engineering and Applied SciencesHarvard UniversityCambridgeMAUSA
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10
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Wang Y, Liu P, Schwartz J, Castro E, Wang W, Chang H, Scovronick N, Shi L. Disparities in ambient nitrogen dioxide pollution in the United States. Proc Natl Acad Sci U S A 2023; 120:e2208450120. [PMID: 37036985 PMCID: PMC10120073 DOI: 10.1073/pnas.2208450120] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 03/08/2023] [Indexed: 04/12/2023] Open
Abstract
Average ambient concentrations of nitrogen dioxide (NO2), an important air pollutant, have declined in the United States since the enactment of the Clean Air Act. Despite evidence that NO2 disproportionately affects racial/ethnic minority groups, it remains unclear what drives the exposure disparities and how they have changed over time. Here, we provide evidence by integrating high-resolution (1 km × 1 km) ground-level NO2 estimates, sociodemographic information, and source-specific emission intensity and location for 217,740 block groups across the contiguous United States from 2000 to 2016. We show that racial/ethnic minorities are disproportionately exposed to higher levels of NO2 pollution compared with Whites across the United States and within major metropolitan areas. These inequities persisted over time and have worsened in many cases, despite a significant decrease in the national average NO2 concentration over the 17-y study period. Overall, traffic contributes the largest fraction of NO2 disparity. Contributions of other emission sources to exposure disparities vary by location. Our analyses offer insights into policies aimed at reducing air pollution exposure disparities among races/ethnicities and locations.
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Affiliation(s)
- Yifan Wang
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA30322
| | - Pengfei Liu
- School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA30322
| | - Joel Schwartz
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA02115
| | - Edgar Castro
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA02115
| | - Wenhao Wang
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA30322
| | - Howard Chang
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA30322
| | - Noah Scovronick
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA30322
| | - Liuhua Shi
- Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA30322
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11
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Zhang F, Li H, Xu W, Song G, Wang Z, Mao X, Wei Y, Dai M, Zhang Y, Shen Q, Fu F, Tan J, Ge L, He X, Yin T, Yang S, Li S, Yang P, Jia P, Zhang Y. Sulfur dioxide may predominate in the adverse effects of ambient air pollutants on semen quality among the general population in Hefei, China. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 867:161472. [PMID: 36638985 DOI: 10.1016/j.scitotenv.2023.161472] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2022] [Revised: 12/24/2022] [Accepted: 01/04/2023] [Indexed: 06/17/2023]
Abstract
Previous studies have reported potential adverse effects of exposure to ambient air pollutants on semen quality in infertile men, but studies on the general population have been limited and inconsistent, and the pollutants that play a major role remain unclear. This study aimed to explore the potential association between exposure to six air pollutants (PM2.5, PM10, NO2, SO2, O3 and CO) during different sperm development periods and semen quality among the general population, and to explore the interaction between different air pollutant exposures. We included 1515 semen samples collected from the Human Sperm Bank. We improved individuals' exposure level estimation by combining inverse distance weighting (IDW) interpolation with satellite remote sensing data. Multivariate linear regression models, restricted cubic spline functions and double-pollutant models were used to assess the relationship between exposure to six air pollutants and sperm volume, concentration, total sperm number and sperm motility. A negative association was found between SO2 exposure and progressive motility and total motility during 0-90 lag days and 70-90 lag days, and SO2 exposure during 10-14 lag days adversely affected sperm concentration and total sperm number. Sensitive analyses for qualified sperm donors and the double-pollutant models obtained similar results. Additionally, there were nonlinear relationships between exposure to PM, NO2, O3, CO and a few semen parameters, with NO2 and O3 exposure above the threshold showing negative correlations with total motility and progressive motility, respectively. Our study suggested that SO2 may play a dominant role in the adverse effects of ambient air pollutants on semen quality in the general population by decreasing sperm motility, sperm concentration and total sperm number. Also, even SO2 exposure lower than the recommended standards of the World Health Organization (WHO) could still cause male reproductive toxicity, which deserves attention.
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Affiliation(s)
- Feng Zhang
- Reproductive Medical Center, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Hang Li
- Reproductive Medicine Center, Anhui Provincial Human Sperm Bank, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China; Department of Obstetrics and Gynecology, the First Affiliated Hospital of Anhui Medical University, NHC Key Laboratory of Study on Abnormal Gametes and Reproductive Tract, Anhui Medical University, Hefei, Anhui, China
| | - Wenting Xu
- School of Resource and Environmental Sciences, Wuhan University, Wuhan, Hubei, China; International Institute of Spatial Lifecourse Health (ISLE), Wuhan University, Wuhan, Hubei, China
| | - Ge Song
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, Hubei, China
| | - Zhanpeng Wang
- School of Resource and Environmental Sciences, Wuhan University, Wuhan, Hubei, China; International Institute of Spatial Lifecourse Health (ISLE), Wuhan University, Wuhan, Hubei, China
| | - Xiaohong Mao
- Reproductive Medicine Center, Anhui Provincial Human Sperm Bank, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China; Department of Obstetrics and Gynecology, the First Affiliated Hospital of Anhui Medical University, NHC Key Laboratory of Study on Abnormal Gametes and Reproductive Tract, Anhui Medical University, Hefei, Anhui, China
| | - Yiqiu Wei
- Reproductive Medical Center, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Mengyang Dai
- Reproductive Medical Center, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Yuying Zhang
- Reproductive Medical Center, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Qunshan Shen
- Reproductive Medicine Center, Anhui Provincial Human Sperm Bank, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China; Department of Obstetrics and Gynecology, the First Affiliated Hospital of Anhui Medical University, NHC Key Laboratory of Study on Abnormal Gametes and Reproductive Tract, Anhui Medical University, Hefei, Anhui, China
| | - Feifei Fu
- Reproductive Medicine Center, Anhui Provincial Human Sperm Bank, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China; Department of Obstetrics and Gynecology, the First Affiliated Hospital of Anhui Medical University, NHC Key Laboratory of Study on Abnormal Gametes and Reproductive Tract, Anhui Medical University, Hefei, Anhui, China
| | - Jing Tan
- Reproductive Medicine Center, Anhui Provincial Human Sperm Bank, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China; Department of Obstetrics and Gynecology, the First Affiliated Hospital of Anhui Medical University, NHC Key Laboratory of Study on Abnormal Gametes and Reproductive Tract, Anhui Medical University, Hefei, Anhui, China
| | - Lei Ge
- Reproductive Medicine Center, Anhui Provincial Human Sperm Bank, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China; Department of Obstetrics and Gynecology, the First Affiliated Hospital of Anhui Medical University, NHC Key Laboratory of Study on Abnormal Gametes and Reproductive Tract, Anhui Medical University, Hefei, Anhui, China
| | - Xiaojin He
- Reproductive Medicine Center, Anhui Provincial Human Sperm Bank, the First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China; Department of Obstetrics and Gynecology, the First Affiliated Hospital of Anhui Medical University, NHC Key Laboratory of Study on Abnormal Gametes and Reproductive Tract, Anhui Medical University, Hefei, Anhui, China
| | - Tailang Yin
- Reproductive Medical Center, Renmin Hospital of Wuhan University, Wuhan, Hubei, China
| | - Shujuan Yang
- West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China; International Institute of Spatial Lifecourse Health (ISLE), Wuhan University, Wuhan, Hubei, China
| | - Siwei Li
- School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, Hubei, China; State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, Hubei, China.
| | - Pan Yang
- Department of Public Health and Preventive Medicine, School of Medicine, Jinan University, Guangzhou, Guangdong, China.
| | - Peng Jia
- School of Resource and Environmental Sciences, Wuhan University, Wuhan, Hubei, China; Hubei Luojia Laboratory, Wuhan, Hubei, China; International Institute of Spatial Lifecourse Health (ISLE), Wuhan University, Wuhan, Hubei, China; School of Public Health, Wuhan University, Wuhan, Hubei, China.
| | - Yan Zhang
- Department of Clinical Laboratory, Institute of Translational Medicine, Renmin Hospital of Wuhan University, Wuhan, Hubei, China.
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12
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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. ENVIRONMENTAL SCIENCE & TECHNOLOGY 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] [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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13
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Zhou B, Zhang S, Xue R, Li J, Wang S. A review of Space-Air-Ground integrated remote sensing techniques for atmospheric monitoring. J Environ Sci (China) 2023; 123:3-14. [PMID: 36521992 DOI: 10.1016/j.jes.2021.12.008] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Revised: 11/24/2021] [Accepted: 12/08/2021] [Indexed: 06/17/2023]
Abstract
Currently, the three-dimensional (3D) distribution and characteristics of air pollution cannot be understood based on the application of any single atmospheric monitoring technology. Long-term, high-precision and large-scale 3D atmospheric monitoring might become practical by combining heterogeneous modern technologies; for this purpose, the Space-Air-Ground integrated system is a promising concept. In this system, optical remote sensing technologies employing fixed or mobile platforms are used as the main means for ground-based observations. Tethered balloons, unmanned aerial vehicles (UAV) and airborne platforms serve as the air-based observation segment. The final part, satellite remote sensing, corresponds to space-based observations. Aside from obtaining the 3D distribution of air pollution, research on emission estimation and pollution mechanisms has been extensively implemented based on the strengths of this system or some portion of it. Moreover, further research on the fusion of multi-source data, optimization of inversion algorithms, and coupling with atmospheric models is of great importance to the realization of this system.
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Affiliation(s)
- Bin Zhou
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China; Institute of Eco-Chongming (IEC), Shanghai 202162, China; Zhuhai Fudan Innovation Institute, Zhuhai 519000, China; Institute of Atmospheric Sciences, Fudan University, Shanghai 200433, China.
| | - Sanbao Zhang
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Ruibin Xue
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Jiayi Li
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Shanshan Wang
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China; Institute of Eco-Chongming (IEC), Shanghai 202162, China.
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14
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Dressel I, Demetillo MA, Judd LM, Janz SJ, Fields KP, Sun K, Fiore AM, McDonald BC, Pusede SE. Daily Satellite Observations of Nitrogen Dioxide Air Pollution Inequality in New York City, New York and Newark, New Jersey: Evaluation and Application. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:15298-15311. [PMID: 36224708 PMCID: PMC9670852 DOI: 10.1021/acs.est.2c02828] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/09/2023]
Abstract
Urban air pollution disproportionately harms communities of color and low-income communities in the U.S. Intraurban nitrogen dioxide (NO2) inequalities can be observed from space using the TROPOspheric Monitoring Instrument (TROPOMI). Past research has relied on time-averaged measurements, limiting our understanding of how neighborhood-level NO2 inequalities co-vary with urban air quality and climate. Here, we use fine-scale (250 m × 250 m) airborne NO2 remote sensing to demonstrate that daily TROPOMI observations resolve a major portion of census tract-scale NO2 inequalities in the New York City-Newark urbanized area. Spatiotemporally coincident TROPOMI and airborne inequalities are well correlated (r = 0.82-0.97), with slopes of 0.82-1.05 for relative and 0.76-0.96 for absolute inequalities for different groups. We calculate daily TROPOMI NO2 inequalities over May 2018-September 2021, reporting disparities of 25-38% with race, ethnicity, and/or household income. Mean daily inequalities agree with results based on TROPOMI measurements oversampled to 0.01° × 0.01° to within associated uncertainties. Individual and mean daily TROPOMI NO2 inequalities are largely insensitive to pixel size, at least when pixels are smaller than ∼60 km2, but are sensitive to low observational coverage. We statistically analyze daily NO2 inequalities, presenting empirical evidence of the systematic overburdening of communities of color and low-income neighborhoods with polluting sources, regulatory ozone co-benefits, and worsened NO2 inequalities and cumulative NO2 and urban heat burdens with climate change.
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Affiliation(s)
- Isabella
M. Dressel
- Department
of Environmental Sciences, University of
Virginia, Charlottesville, Virginia 22904, United States
| | - Mary Angelique
G. Demetillo
- Department
of Environmental Sciences, University of
Virginia, Charlottesville, Virginia 22904, United States
| | - Laura M. Judd
- NASA
Langley Research Center, Hampton, Virginia 23681, United States
| | - Scott J. Janz
- NASA
Goddard Space Flight Center, Greenbelt, Maryland 20771, United States
| | - Kimberly P. Fields
- Carter
G. Woodson Institute for African American and African Studies, University of Virginia, Charlottesville, Virginia 22904, United States
| | - Kang Sun
- Department
of Civil, Structural and Environmental Engineering, University at Buffalo, Buffalo, New York 14260, United States
- Research
and Education in eNergy, Environment and Water (RENEW) Institute, University at Buffalo, Buffalo, New York 14260, United States
| | - Arlene M. Fiore
- Department
of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States
| | - Brian C. McDonald
- Chemical
Sciences Laboratory, NOAA Earth System Research
Laboratories, Boulder, Colorado 80305, United
States
| | - Sally E. Pusede
- Department
of Environmental Sciences, University of
Virginia, Charlottesville, Virginia 22904, United States
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15
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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. ENVIRONMENTAL SCIENCE & TECHNOLOGY 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] [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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16
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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. ENVIRONMENTAL SCIENCE & TECHNOLOGY LETTERS 2022; 9:786-791. [PMID: 36118958 PMCID: PMC9476666 DOI: 10.1021/acs.estlett.2c00403] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [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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17
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Riches NO, Gouripeddi R, Payan-Medina A, Facelli JC. K-means cluster analysis of cooperative effects of CO, NO 2, O 3, PM 2.5, PM 10, and SO 2 on incidence of type 2 diabetes mellitus in the US. ENVIRONMENTAL RESEARCH 2022; 212:113259. [PMID: 35460634 PMCID: PMC9413686 DOI: 10.1016/j.envres.2022.113259] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 03/08/2022] [Accepted: 04/03/2022] [Indexed: 06/02/2023]
Abstract
Air pollution (AP) has been shown to increase the risk of type 2 diabetes mellitus, as well as other cardiometabolic diseases. AP is characterized by a complex mixture of components for which the composition depends on sources and metrological factors. The US Environmental Protection Agency (EPA) monitors and regulates certain components of air pollution known to have negative consequences for human health. Research assessing the health effects of these components of AP often uses traditional regression models, which might not capture more complex and interdependent relationships. Machine learning has the capability to simultaneously assess multiple components and find complex, non-linear patterns that may not be apparent and could not be modeled by other techniques. Here we use k-means clustering to assess the patterns associating PM2.5, PM10, CO, NO2, O3, and SO2 measurements and changes in annual diabetes incidence at a US county level. The average age adjusted annual decrease in diabetes incidence for the entire US populations is -0.25 per 1000 but the change shows a significant geographic variation (range: -17.2 to 5.30 per 1000). In this paper these variations were compared with the local daily AP concentrations of the pollutants listed above from 2005 to 2015, which were matched to the annual change in diabetes incidence for the following year. A total of 134,925 daily air quality observations were included in the cluster analysis, representing 125 US counties and the District of Columbia. K-means successfully clustered AP components and indicated an association between exposure to certain AP mixtures with lower decreases on T2D incidence.
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Affiliation(s)
- Naomi O Riches
- University of Utah School of Medicine, Department of Biomedical Informatics, 421 Wakara Way #140, Salt Lake City, UT, 84108, USA; University of Utah Center of Excellence in Exposure Health Informatics, 27 S. Mario Capecci Dr. Bldg 379, Salt Lake City, UT, 84133, USA.
| | - Ramkiran Gouripeddi
- University of Utah School of Medicine, Department of Biomedical Informatics, 421 Wakara Way #140, Salt Lake City, UT, 84108, USA; University of Utah Center for Clinical and Translational Science, 27 S. Mario Capecci Dr. Bldg 379, Salt Lake City, UT, 84133, USA; University of Utah Center of Excellence in Exposure Health Informatics, 27 S. Mario Capecci Dr. Bldg 379, Salt Lake City, UT, 84133, USA.
| | - Adriana Payan-Medina
- University of Utah School of Medicine, Department of Biomedical Informatics, 421 Wakara Way #140, Salt Lake City, UT, 84108, USA; University of Utah Center of Excellence in Exposure Health Informatics, 27 S. Mario Capecci Dr. Bldg 379, Salt Lake City, UT, 84133, USA; University of Utah Office of Undergraduate Research, Sill Center 005, Salt Lake City, UT, 84112, USA.
| | - Julio C Facelli
- University of Utah School of Medicine, Department of Biomedical Informatics, 421 Wakara Way #140, Salt Lake City, UT, 84108, USA; University of Utah Center for Clinical and Translational Science, 27 S. Mario Capecci Dr. Bldg 379, Salt Lake City, UT, 84133, USA; University of Utah Center of Excellence in Exposure Health Informatics, 27 S. Mario Capecci Dr. Bldg 379, Salt Lake City, UT, 84133, USA.
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18
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Abstract
Nitrogen species present in the atmosphere, soil, and water play a vital role in ecosystem stability. Reactive nitrogen gases are key air quality indicators and are responsible for atmospheric ozone layer depletion. Soil nitrogen species are one of the primary macronutrients for plant growth. Species of nitrogen in water are essential indicators of water quality, and they play an important role in aquatic environment monitoring. Anthropogenic activities have highly impacted the natural balance of the nitrogen species. Therefore, it is critical to monitor nitrogen concentrations in different environments continuously. Various methods have been explored to measure the concentration of nitrogen species in the air, soil, and water. Here, we review the recent advancements in optical and electrochemical sensing methods for measuring nitrogen concentration in the air, soil, and water. We have discussed the advantages and disadvantages of the existing methods and the future prospects. This will serve as a reference for researchers working with environment pollution and precision agriculture.
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19
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Gardner-Frolick R, Boyd D, Giang A. Selecting Data Analytic and Modeling Methods to Support Air Pollution and Environmental Justice Investigations: A Critical Review and Guidance Framework. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:2843-2860. [PMID: 35133145 DOI: 10.1021/acs.est.1c01739] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Given the serious adverse health effects associated with many pollutants, and the inequitable distribution of these effects between socioeconomic groups, air pollution is often a focus of environmental justice (EJ) research. However, EJ analyses that aim to illuminate whether and how air pollution hazards are inequitably distributed may present a unique set of requirements for estimating pollutant concentrations compared to other air quality applications. Here, we perform a scoping review of the range of data analytic and modeling methods applied in past studies of air pollution and environmental injustice and develop a guidance framework for selecting between them given the purpose of analysis, users, and resources available. We include proxy, monitor-based, statistical, and process-based methods. Upon critically synthesizing the literature, we identify four main dimensions to inform method selection: accuracy, interpretability, spatiotemporal features of the method, and usability of the method. We illustrate the guidance framework with case studies from the literature. Future research in this area includes an exploration of increasing data availability, advanced statistical methods, and the importance of science-based policy.
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Affiliation(s)
- Rivkah Gardner-Frolick
- Department of Mechanical Engineering, University of British Columbia, Vancouver V6T 1Z4, Canada
| | - David Boyd
- Institute for Resources, Environment and Sustainability, University of British Columbia, Vancouver V6T 1Z4, Canada
| | - Amanda Giang
- Department of Mechanical Engineering, University of British Columbia, Vancouver V6T 1Z4, Canada
- Institute for Resources, Environment and Sustainability, University of British Columbia, Vancouver V6T 1Z4, Canada
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20
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Castillo MD, Kinney PL, Southerland V, Arno CA, Crawford K, van Donkelaar A, Hammer M, Martin RV, Anenberg SC. Estimating Intra-Urban Inequities in PM 2.5-Attributable Health Impacts: A Case Study for Washington, DC. GEOHEALTH 2021; 5:e2021GH000431. [PMID: 34765851 PMCID: PMC8574205 DOI: 10.1029/2021gh000431] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 08/19/2021] [Accepted: 10/08/2021] [Indexed: 05/05/2023]
Abstract
Air pollution levels are uneven within cities, contributing to persistent health disparities between neighborhoods and population sub-groups. Highly spatially resolved information on pollution levels and disease rates is necessary to characterize inequities in air pollution exposure and related health risks. We leverage recent advances in deriving surface pollution levels from satellite remote sensing and granular data in disease rates for one city, Washington, DC, to assess intra-urban heterogeneity in fine particulate matter (PM2.5)- attributable mortality and morbidity. We estimate PM2.5-attributable cases of all-cause mortality, chronic obstructive pulmonary disease, ischemic heart disease, lung cancer, stroke, and asthma emergency department (ED) visits using epidemiologically derived health impact functions. Data inputs include satellite-derived annual mean surface PM2.5 concentrations; age-resolved population estimates; and statistical neighborhood-, zip code- and ward-scale disease counts. We find that PM2.5 concentrations and associated health burdens have decreased in DC between 2000 and 2018, from approximately 240 to 120 cause-specific deaths and from 40 to 30 asthma ED visits per year (between 2014 and 2018). However, remaining PM2.5-attributable health risks are unevenly and inequitably distributed across the District. Higher PM2.5-attributable disease burdens were found in neighborhoods with larger proportions of people of color, lower household income, and lower educational attainment. Our study adds to the growing body of literature documenting the inequity in air pollution exposure levels and pollution health risks between population sub-groups, and highlights the need for both high-resolution disease rates and concentration estimates for understanding intra-urban disparities in air pollution-related health risks.
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Affiliation(s)
- Maria D. Castillo
- George Washington University Milken Institute School of Public HealthWashingtonDCUSA
| | | | - Veronica Southerland
- George Washington University Milken Institute School of Public HealthWashingtonDCUSA
| | - C. Anneta Arno
- District of Columbia Department of HealthOffice of Health EquityWashingtonDCUSA
| | - Kelly Crawford
- District of Columbia Department of Energy & EnvironmentAir Quality DivisionWashingtonDCUSA
| | - Aaron van Donkelaar
- Department of Physics and Atmospheric ScienceDalhousie UniversityHalifaxNSCanada
- Center for Aerosol Science and EngineeringWashington University in St. LouisSt. LouisMOUSA
| | - Melanie Hammer
- Center for Aerosol Science and EngineeringWashington University in St. LouisSt. LouisMOUSA
| | - Randall V. Martin
- Department of Physics and Atmospheric ScienceDalhousie UniversityHalifaxNSCanada
- Center for Aerosol Science and EngineeringWashington University in St. LouisSt. LouisMOUSA
| | - Susan C. Anenberg
- George Washington University Milken Institute School of Public HealthWashingtonDCUSA
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Duncan BN, Malings CA, Knowland KE, Anderson DC, Prados AI, Keller CA, Cromar KR, Pawson S, Ensz H. Augmenting the Standard Operating Procedures of Health and Air Quality Stakeholders With NASA Resources. GEOHEALTH 2021; 5:e2021GH000451. [PMID: 34585034 PMCID: PMC8456713 DOI: 10.1029/2021gh000451] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 07/21/2021] [Accepted: 08/23/2021] [Indexed: 06/13/2023]
Abstract
The combination of air quality (AQ) data from satellites and low-cost sensor systems, along with output from AQ models, have the potential to augment high-quality, regulatory-grade data in countries with in situ monitoring networks and provide much needed AQ information in countries without them, including Low and Moderate Income Countries (LMICs). We demonstrate the potential of free and publicly available USA National Aeronautics and Space Administration (NASA) resources, which include capacity building activities, satellite data, and global AQ forecasts, to provide cost-effective, and reliable AQ information to health and AQ professionals around the world. We provide illustrative case studies that highlight how global AQ forecasts along with satellite data may be used to characterize AQ on urban to regional scales, including to quantify pollution concentrations, identify pollution sources, and track the long-range transport of pollution. We also provide recommendations to data product developers to facilitate and broaden usage of NASA resources by health and AQ stakeholders.
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Affiliation(s)
| | - Carl A. Malings
- NASA Goddard Space Flight CenterGreenbeltMDUSA
- Universities Space Research AssociationColumbiaMDUSA
| | - K. Emma Knowland
- NASA Goddard Space Flight CenterGreenbeltMDUSA
- Universities Space Research AssociationColumbiaMDUSA
| | - Daniel C. Anderson
- NASA Goddard Space Flight CenterGreenbeltMDUSA
- Universities Space Research AssociationColumbiaMDUSA
| | - Ana I. Prados
- NASA Goddard Space Flight CenterGreenbeltMDUSA
- University of Maryland Baltimore CountyBaltimoreMDUSA
| | - Christoph A. Keller
- NASA Goddard Space Flight CenterGreenbeltMDUSA
- Universities Space Research AssociationColumbiaMDUSA
| | | | | | - Holli Ensz
- Bureau of Ocean Energy ManagementSterlingVAUSA
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Abstract
We leverage the unparalleled changes in human activity during COVID-19 and the unmatched capabilities of the TROPOspheric Monitoring Instrument to understand how lockdowns impact ambient nitrogen dioxide (NO2) pollution disparities in the United States. The least White communities experienced the largest NO2 reductions during lockdowns; however, disparities between the least and most White communities are so large that the least White communities still faced higher NO2 levels during lockdowns than the most White communities experienced prior to lockdowns, despite a ∼50% reduction in passenger vehicle traffic. Similar findings hold for ethnic, income, and educational attainment population subgroups. Future strategies to reduce NO2 disparities will need to target emissions from heavy-duty vehicles. The unequal spatial distribution of ambient nitrogen dioxide (NO2), an air pollutant related to traffic, leads to higher exposure for minority and low socioeconomic status communities. We exploit the unprecedented drop in urban activity during the COVID-19 pandemic and use high-resolution, remotely sensed NO2 observations to investigate disparities in NO2 levels across different demographic subgroups in the United States. We show that, prior to the pandemic, satellite-observed NO2 levels in the least White census tracts of the United States were nearly triple the NO2 levels in the most White tracts. During the pandemic, the largest lockdown-related NO2 reductions occurred in urban neighborhoods that have 2.0 times more non-White residents and 2.1 times more Hispanic residents than neighborhoods with the smallest reductions. NO2 reductions were likely driven by the greater density of highways and interstates in these racially and ethnically diverse areas. Although the largest reductions occurred in marginalized areas, the effect of lockdowns on racial, ethnic, and socioeconomic NO2 disparities was mixed and, for many cities, nonsignificant. For example, the least White tracts still experienced ∼1.5 times higher NO2 levels during the lockdowns than the most White tracts experienced prior to the pandemic. Future policies aimed at eliminating pollution disparities will need to look beyond reducing emissions from only passenger traffic and also consider other collocated sources of emissions such as heavy-duty vehicles.
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