National Patterns in Environmental Injustice and Inequality: Outdoor NO2 Air Pollution in the United States
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{"title"=>"Segregation and Black/White differences in exposure to air toxics in 1990", "type"=>"generic", "authors"=>[{"first_name"=>"Russ", "last_name"=>"Lopez", "scopus_author_id"=>"7401491013"}], "year"=>2002, "source"=>"Environmental Health Perspectives", "identifiers"=>{"scopus"=>"2-s2.0-0036224242", "sgr"=>"0036224242", "issn"=>"00916765", "doi"=>"10.1371/journal.pone.0094431", "pmid"=>"11929740", "isbn"=>"0091-6765", "pui"=>"34436393"}, "keywords"=>["Air toxics", "Environmental justice", "Race", "Segregation"], "id"=>"62ab9b07-f5a3-3332-986e-af30c7c3ed49", "abstract"=>"I examined non-Hispanic Black and non-Hispanic White differences in exposure to noncriteria air pollutants in 44 U.S. Census Bureau-defined metropolitan areas with populations greater than one million, using data on air toxics concentrations prepared for the U.S. Environmental Protection Agency as part of its Cumulative Exposure Project combined with U.S. census data. I measured differences in exposure to air toxics through the calculation of a net difference score, which is a statistical measure used in income inequality analysis to measure inequality over the whole range of exposures. The scores ranged from 11.52 to 83.60. In every metropolitan area, non-Hispanic Blacks are more likely than non-Hispanic Whites to be living in tracts with higher total modeled air toxics concentrations. To assess potential reasons for such a wide variation in exposure differences, I performed a multiple regression analysis with the net difference score as the dependent variable. Independent variables initially included were as follows: the dissimilarity index (to measure segregation), Black poverty/White poverty (to control for Black/White economic differences), population density and percentage of persons traveling to work who drive to work (alone and in car pools), and percentage of workforce employed in manufacturing (factors affecting air quality). After an initial analysis I eliminated from the model the measures of density and the persons driving to work because they were statistically insignificant, they did not add to the predictive power of the model, and their deletion did not affect the other variables. The final model had an R(2) of 0.56. Increased segregation is associated with increased disparity in potential exposure to air pollution.", "link"=>"http://www.mendeley.com/research/segregation-blackwhite-differences-exposure-air-toxics-1990", "reader_count"=>56, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>6, "Librarian"=>1, "Student > Doctoral Student"=>6, "Researcher"=>8, "Student > Ph. D. Student"=>14, "Student > Postgraduate"=>2, "Student > Master"=>9, "Other"=>1, "Student > Bachelor"=>5, "Lecturer"=>1, "Professor"=>2}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>6, "Librarian"=>1, "Student > Doctoral Student"=>6, "Researcher"=>8, "Student > Ph. D. Student"=>14, "Student > Postgraduate"=>2, "Student > Master"=>9, "Other"=>1, "Student > Bachelor"=>5, "Lecturer"=>1, "Professor"=>2}, "reader_count_by_subject_area"=>{"Engineering"=>2, "Unspecified"=>3, "Environmental Science"=>10, "Biochemistry, Genetics and Molecular Biology"=>2, "Agricultural and Biological Sciences"=>2, "Medicine and Dentistry"=>5, "Psychology"=>1, "Social Sciences"=>24, "Decision Sciences"=>1, "Earth and Planetary Sciences"=>1, "Economics, Econometrics and Finance"=>5}, "reader_count_by_subdiscipline"=>{"Engineering"=>{"Engineering"=>2}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>5}, "Social Sciences"=>{"Social Sciences"=>24}, "Decision Sciences"=>{"Decision Sciences"=>1}, "Psychology"=>{"Psychology"=>1}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>1}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>5}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>2}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>2}, "Unspecified"=>{"Unspecified"=>3}, "Environmental Science"=>{"Environmental Science"=>10}}, "reader_count_by_country"=>{"Iran"=>1, "United States"=>4, "United Kingdom"=>1, "Israel"=>1}, "group_count"=>2}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/1464487"], "description"=>"<p>Concentrations shown are modeled by UA population tertile (linear regressions: <i>R<sup>2</sup></i>>0.98 [large UAs], >0.96 [medium UAs], >0.86 [small UAs], >0.47 [rural]; all models are statistically significant at <i>p</i><0.01<i>;</i> see <b>Tables S3–S18</b> in <b><a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0094431#pone.0094431.s002\" target=\"_blank\">File S1</a></b>). For visual display, plots use the population-weighted mean UA-specific dummy variable for each UA population tertile. Error bars show the 95% confidence intervals on linear regression model predictions. AD = average difference, UA = Urban Area. AD values shown are for interquartile range incomes ($25k, $75k) and for race-ethnicity groups with highest and lowest concentrations for that panel.</p>", "links"=>[], "tags"=>["Atmospheric science", "atmospheric chemistry", "Air quality", "health care", "Environmental health", "chemistry", "Environmental chemistry", "pollutants", "Environmental engineering", "pollution", "within-rural", "population-weighted", "concentrations", "census", "ua"], "article_id"=>999319, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Lara P. Clark", "Dylan B. Millet", "Julian D. Marshall"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0094431.g001", "stats"=>{"downloads"=>4, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Within_urban_and_within_rural_population_weighted_mean_NO_2_concentrations_105_million_householders_by_Census_household_income_category_race_and_urban_category_large_UA_population_tertile_medium_UA_population_tertile_small_UA_population_tertile_or_rural_/999319", "title"=>"Within-urban and within-rural population-weighted mean NO<sub>2</sub> concentrations (105 million householders) by Census household income category, race, and urban category (large UA population tertile, medium UA population tertile, small UA population tertile, or rural).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-15 02:47:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/1464488"], "description"=>"<p>The left column shows differences in population-weighted mean NO<sub>2</sub> concentrations between low-income nonwhites (LIN) and high-income whites (HIW), with larger positive differences (red colors) indicating higher injustice (larger concentration difference between LIN and HIW). The right column shows the Atkinson Index, with higher values indicating greater inequality.</p>", "links"=>[], "tags"=>["Atmospheric science", "atmospheric chemistry", "Air quality", "health care", "Environmental health", "chemistry", "Environmental chemistry", "pollutants", "Environmental engineering", "pollution", "injustice", "inequality", "concentrations", "counties"], "article_id"=>999320, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Lara P. Clark", "Dylan B. Millet", "Julian D. Marshall"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0094431.g002", "stats"=>{"downloads"=>5, "page_views"=>32, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Environmental_injustice_and_inequality_in_residential_outdoor_NO_2_concentrations_for_US_regions_states_counties_and_urban_areas_/999320", "title"=>"Environmental injustice and inequality in residential outdoor NO<sub>2</sub> concentrations for US regions, states, counties and urban areas.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-04-15 02:47:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/1464489"], "description"=>"<p><sup><i>1</i></sup>Larger positive differences indicate greater injustice (concentrations are higher for low-income nonwhites than for high-income whites). A negative value denotes concentrations being lower for low-income nonwhites than for high-income whites.</p><p><sup><i>2</i></sup>Larger Atkinson Indices indicate greater inequality. Inequality aversion coefficient: ε = 0.75.</p><p><sup><i>3</i></sup>This analysis excludes counties that consist of 1 Block Group (<i>n = </i>29; total population = 21,500 people) or contain 0 low-income nonwhites and/or 0 high-income whites (<i>n</i> = 16; total population = 65,800 people).</p>", "links"=>[], "tags"=>["Atmospheric science", "atmospheric chemistry", "Air quality", "health care", "Environmental health", "chemistry", "Environmental chemistry", "pollutants", "Environmental engineering", "pollution", "injustice", "inequality", "metric"], "article_id"=>999321, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Lara P. Clark", "Dylan B. Millet", "Julian D. Marshall"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0094431.t003", "stats"=>{"downloads"=>5, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Environmental_injustice_and_inequality_metric_mean_population_weighted_mean_range_/999321", "title"=>"Environmental injustice and inequality metric mean (population-weighted mean) [range].", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-04-15 02:47:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/1464490"], "description"=>"<p><sup><i>1</i></sup>Difference in population-weighted mean concentration [Group 1 - Group 2]. For all rows, differences are statistically significant with <i>p</i><0.001.</p>", "links"=>[], "tags"=>["Atmospheric science", "atmospheric chemistry", "Air quality", "health care", "Environmental health", "chemistry", "Environmental chemistry", "pollutants", "Environmental engineering", "pollution", "population-weighted", "concentrations"], "article_id"=>999322, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Lara P. Clark", "Dylan B. Millet", "Julian D. Marshall"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0094431.t002", "stats"=>{"downloads"=>9, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparisons_between_population_weighted_mean_NO_2_concentrations_for_specific_populations_/999322", "title"=>"Comparisons between population-weighted mean NO<sub>2</sub> concentrations for specific populations.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-04-15 02:47:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/1464491"], "description"=>"<p><sup><i>1</i></sup>Population totals may be less than 100% because of rounding, nonresponses in Census data, and category definitions (e.g., population >25 years old is 66% of total population).</p><p><sup><i>2</i></sup>Each race-ethnicity category in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0094431#pone-0094431-t001\" target=\"_blank\"><b>Table 1</b></a> includes people who reported a single race category and non-Hispanic ethnicity (i.e., “White” category is “White alone; non-Hispanic”), except for the “Hispanic” category, which includes people who reported any race(s) and Hispanic ethnicity, and the “Black Hispanic” category, which includes people who reported Black race alone and Hispanic ethnicity.</p>", "links"=>[], "tags"=>["Atmospheric science", "atmospheric chemistry", "Air quality", "health care", "Environmental health", "chemistry", "Environmental chemistry", "pollutants", "Environmental engineering", "pollution", "ppb"], "article_id"=>999323, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Lara P. Clark", "Dylan B. Millet", "Julian D. Marshall"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0094431.t001", "stats"=>{"downloads"=>5, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Population_weighted_mean_NO_2_concentration_in_ppb_percent_of_total_population_1_/999323", "title"=>"Population-weighted mean NO<sub>2</sub> concentration in ppb (percent of total population<i>1</i>).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-04-15 02:47:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/1464492", "https://ndownloader.figshare.com/files/1464494"], "description"=>"<div><p>We describe spatial patterns in environmental injustice and inequality for residential outdoor nitrogen dioxide (NO<sub>2</sub>) concentrations in the contiguous United States. Our approach employs Census demographic data and a recently published high-resolution dataset of outdoor NO<sub>2</sub> concentrations. Nationally, population-weighted mean NO<sub>2</sub> concentrations are 4.6 ppb (38%, <i>p</i><0.01) higher for nonwhites than for whites. The environmental health implications of that concentration disparity are compelling. For example, we estimate that reducing nonwhites’ NO<sub>2</sub> concentrations to levels experienced by whites would reduce Ischemic Heart Disease (IHD) mortality by ∼7,000 deaths per year, which is equivalent to 16 million people increasing their physical activity level from inactive (0 hours/week of physical activity) to sufficiently active (>2.5 hours/week of physical activity). Inequality for NO<sub>2</sub> concentration is greater than inequality for income (Atkinson Index: 0.11 versus 0.08). Low-income nonwhite young children and elderly people are disproportionately exposed to residential outdoor NO<sub>2</sub>. Our findings establish a national context for previous work that has documented air pollution environmental injustice and inequality within individual US metropolitan areas and regions. Results given here can aid policy-makers in identifying locations with high environmental injustice and inequality. For example, states with both high injustice and high inequality (top quintile) for outdoor residential NO<sub>2</sub> include New York, Michigan, and Wisconsin.</p></div>", "links"=>[], "tags"=>["Atmospheric science", "atmospheric chemistry", "Air quality", "health care", "Environmental health", "chemistry", "Environmental chemistry", "pollutants", "Environmental engineering", "pollution", "patterns", "injustice", "united", "states"], "article_id"=>999324, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Lara P. Clark", "Dylan B. Millet", "Julian D. Marshall"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0094431.s002", "https://dx.doi.org/10.1371/journal.pone.0094431.s001"], "stats"=>{"downloads"=>42, "page_views"=>20, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/National_Patterns_in_Environmental_Injustice_and_Inequality_Outdoor_NO_2_Air_Pollution_in_the_United_States/999324", "title"=>"National Patterns in Environmental Injustice and Inequality: Outdoor NO<sub>2</sub> Air Pollution in the United States", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-04-15 02:47:49"}

PMC Usage Stats | Further Information

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