Air Pollution in China: Mapping of Concentrations and Sources
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{"title"=>"Air pollution in China: Mapping of concentrations and sources", "type"=>"journal", "authors"=>[{"first_name"=>"Robert A.", "last_name"=>"Rohde", "scopus_author_id"=>"8314032200"}, {"first_name"=>"Richard A.", "last_name"=>"Muller", "scopus_author_id"=>"55447500400"}], "year"=>2015, "source"=>"PLoS ONE", "identifiers"=>{"sgr"=>"84942874714", "pmid"=>"26291610", "pui"=>"606195262", "isbn"=>"10.1371/journal.pone.0135749", "scopus"=>"2-s2.0-84942874714", "doi"=>"10.1371/journal.pone.0135749", "issn"=>"19326203"}, "id"=>"a7ab2919-a169-3b86-b1d0-40f0412f053f", "abstract"=>"China has recently made available hourly air pollution data from over 1500 sites, including airborne particulate matter (PM), SO2, NO2, and O3. We apply Kriging interpolation to four months of data to derive pollution maps for eastern China. Consistent with prior findings, the greatest pollution occurs in the east, but significant levels are widespread across northern and central China and are not limited to major cities or geologic basins. Sources of pollution are widespread, but are particularly intense in a northeast corridor that extends from near Shanghai to north of Beijing. During our analysis period, 92% of the population of China experienced >120 hours of unhealthy air (US EPA standard), and 38% experienced average concentrations that were unhealthy. China's population-weighted average exposure to PM2.5 was 52 μg/m3. The observed air pollution is calculated to contribute to 1.6 million deaths/year in China [0.7-2.2 million deaths/year at 95% confidence], roughly 17% of all deaths in China.", "link"=>"http://www.mendeley.com/research/air-pollution-china-mapping-concentrations-sources", "reader_count"=>257, "reader_count_by_academic_status"=>{"Unspecified"=>13, "Professor > Associate Professor"=>4, "Researcher"=>29, "Student > Doctoral Student"=>11, "Student > Ph. D. Student"=>58, "Student > Postgraduate"=>18, "Student > Master"=>56, "Other"=>8, "Student > Bachelor"=>49, "Lecturer"=>4, "Lecturer > Senior Lecturer"=>1, "Professor"=>6}, "reader_count_by_user_role"=>{"Unspecified"=>13, "Professor > Associate Professor"=>4, "Researcher"=>29, "Student > Doctoral Student"=>11, "Student > Ph. D. Student"=>58, "Student > Postgraduate"=>18, "Student > Master"=>56, "Other"=>8, "Student > Bachelor"=>49, "Lecturer"=>4, "Lecturer > Senior Lecturer"=>1, "Professor"=>6}, "reader_count_by_subject_area"=>{"Unspecified"=>22, "Agricultural and Biological Sciences"=>8, "Arts and Humanities"=>3, "Philosophy"=>1, "Business, Management and Accounting"=>7, "Chemical Engineering"=>2, "Chemistry"=>10, "Computer Science"=>8, "Earth and Planetary Sciences"=>15, "Economics, Econometrics and Finance"=>25, "Energy"=>5, "Engineering"=>37, "Environmental Science"=>66, "Biochemistry, Genetics and Molecular Biology"=>3, "Nursing and Health Professions"=>4, "Materials Science"=>2, "Mathematics"=>1, "Medicine and Dentistry"=>5, "Design"=>2, "Physics and Astronomy"=>8, "Social Sciences"=>23}, "reader_count_by_subdiscipline"=>{"Materials Science"=>{"Materials Science"=>2}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>5}, "Social Sciences"=>{"Social Sciences"=>23}, "Physics and Astronomy"=>{"Physics and Astronomy"=>8}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>22}, "Environmental Science"=>{"Environmental Science"=>66}, "Chemical Engineering"=>{"Chemical Engineering"=>2}, "Arts and Humanities"=>{"Arts and Humanities"=>3}, "Design"=>{"Design"=>2}, "Engineering"=>{"Engineering"=>37}, "Chemistry"=>{"Chemistry"=>10}, "Energy"=>{"Energy"=>5}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>15}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>25}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>8}, "Computer Science"=>{"Computer Science"=>8}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>7}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>4}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>3}, "Philosophy"=>{"Philosophy"=>1}}, "reader_count_by_country"=>{"China"=>1, "Japan"=>1, "Ireland"=>1, "India"=>1}, "group_count"=>17}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2218352"], "description"=>"<p>Maps of average PM<sub>2.5</sub> concentration from this study (top) and two satellite-derived datasets restricted to the same region. The average over the 2008 to 2010 time interval was chosen for the satellite data due to the limitations of the available satellite data. Both the concentrations reported by van Donkelaar [<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135749#pone.0135749.ref028\" target=\"_blank\">28</a>] (middle) and those reported by de Sherbinin [<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135749#pone.0135749.ref030\" target=\"_blank\">30</a>] (bottom) rely on similar satellite observations of aerosol optical depth (obtained by NASA), but interpret those observations differently when determining pollutant concentration. The satellite-derived data was imported from concentration data files provided by their respected sources and rendered via MATLAB to use the same US EPA health category color scheme applied in Figs <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135749#pone.0135749.g002\" target=\"_blank\">2</a> and <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135749#pone.0135749.g003\" target=\"_blank\">3</a>.</p>", "links"=>[], "tags"=>["Sources China", "air pollution", "Kriging interpolation", "epa", "analysis period", "1500 sites", "pollution maps", "PM 2.5", "O 3.", "air pollution data", "northeast corridor"], "article_id"=>1515136, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Robert A. Rohde", "Richard A. Muller"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0135749.g005", "stats"=>{"downloads"=>4, "page_views"=>48, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Comparison_of_PM_2_5_observations_to_satellite_data_/1515136", "title"=>"Comparison of PM<sub>2.5</sub> observations to satellite data.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-08-20 02:54:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/2218342"], "description"=>"<p>Maps of average pollutant concentration for PM<sub>2.5</sub>, PM<sub>10</sub>, and O<sub>3</sub> for eastern China (top row) and the Beijing to Shanghai corridor (bottom row). Concentrations are shown using color gradients and contour lines; the colors (green, yellow, etc.) represent US EPA qualitative health impacts. Pollution concentrations were computed as described in the text using hourly data and then the hourly concentration fields were averaged over the four month study duration. The resulting maps were rendered using MATLAB.</p>", "links"=>[], "tags"=>["Sources China", "air pollution", "Kriging interpolation", "epa", "analysis period", "1500 sites", "pollution maps", "PM 2.5", "O 3.", "air pollution data", "northeast corridor"], "article_id"=>1515131, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Robert A. Rohde", "Richard A. Muller"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0135749.g003", "stats"=>{"downloads"=>2, "page_views"=>32, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Average_air_pollution_maps_/1515131", "title"=>"Average air pollution maps.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-08-20 02:54:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/2218334"], "description"=>"<p>Map shows the locations of air quality monitoring sites in China and surrounding areas with sufficient hourly data to be included in this study. Selection criteria and data sources are described in <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0135749#pone.0135749.s001\" target=\"_blank\">S1 Text</a>. The map was prepared in MATLAB using political boundaries from the Global Database of Administrative Areas (version 2; <a href=\"http://gadm.org/\" target=\"_blank\">http://gadm.org/</a>).</p>", "links"=>[], "tags"=>["Sources China", "air pollution", "Kriging interpolation", "epa", "analysis period", "1500 sites", "pollution maps", "PM 2.5", "O 3.", "air pollution data", "northeast corridor"], "article_id"=>1515128, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Robert A. Rohde", "Richard A. Muller"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0135749.g001", "stats"=>{"downloads"=>3, "page_views"=>64, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Map_of_real_time_air_pollution_monitoring_stations_/1515128", "title"=>"Map of real-time air pollution monitoring stations.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-08-20 02:54:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/2218335"], "description"=>"<p>(Top) Time series of PM<sub>2.5</sub> concentration at Beijing extracted from the interpolated field. Red circles indicate times shown in bottom row. (Bottom) Maps of interpolated PM<sub>2.5</sub> concentration during a period of high pollution. Pollution concentrations were computed as described in the text from hourly data and maps were rendered in MATLAB. Concentrations are shown using color gradients and contour lines, where color tones (green, yellow, etc.) correspond to health impact categories defined by the US EPA. Bold circles show station locations with the observed value at each station indicated by the color within the circle.</p>", "links"=>[], "tags"=>["Sources China", "air pollution", "Kriging interpolation", "epa", "analysis period", "1500 sites", "pollution maps", "PM 2.5", "O 3.", "air pollution data", "northeast corridor"], "article_id"=>1515129, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Robert A. Rohde", "Richard A. Muller"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0135749.g002", "stats"=>{"downloads"=>5, "page_views"=>67, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Time_evolution_of_PM_2_5_pollution_in_the_vicinity_of_Beijing_/1515129", "title"=>"Time evolution of PM<sub>2.5</sub> pollution in the vicinity of Beijing.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-08-20 02:54:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/2218409", "https://ndownloader.figshare.com/files/2218410", "https://ndownloader.figshare.com/files/2218411", "https://ndownloader.figshare.com/files/2218412", "https://ndownloader.figshare.com/files/2218413", "https://ndownloader.figshare.com/files/2218414", "https://ndownloader.figshare.com/files/2218415", "https://ndownloader.figshare.com/files/2218416", "https://ndownloader.figshare.com/files/2218417", "https://ndownloader.figshare.com/files/2218418", "https://ndownloader.figshare.com/files/2218419", "https://ndownloader.figshare.com/files/2218420", "https://ndownloader.figshare.com/files/2218421", "https://ndownloader.figshare.com/files/2218422", "https://ndownloader.figshare.com/files/2218423", "https://ndownloader.figshare.com/files/2218424", "https://ndownloader.figshare.com/files/2218425", "https://ndownloader.figshare.com/files/2218426", "https://ndownloader.figshare.com/files/2218427", "https://ndownloader.figshare.com/files/2218428"], "description"=>"<div><p>China has recently made available hourly air pollution data from over 1500 sites, including airborne particulate matter (PM), SO<sub>2</sub>, NO<sub>2</sub>, and O<sub>3</sub>. We apply Kriging interpolation to four months of data to derive pollution maps for eastern China. Consistent with prior findings, the greatest pollution occurs in the east, but significant levels are widespread across northern and central China and are not limited to major cities or geologic basins. Sources of pollution are widespread, but are particularly intense in a northeast corridor that extends from near Shanghai to north of Beijing. During our analysis period, 92% of the population of China experienced >120 hours of unhealthy air (US EPA standard), and 38% experienced average concentrations that were unhealthy. China’s population-weighted average exposure to PM<sub>2.5</sub> was 52 μg/m<sup>3</sup>. The observed air pollution is calculated to contribute to 1.6 million deaths/year in China [0.7–2.2 million deaths/year at 95% confidence], roughly 17% of all deaths in China.</p></div>", "links"=>[], "tags"=>["Sources China", "air pollution", "Kriging interpolation", "epa", "analysis period", "1500 sites", "pollution maps", "PM 2.5", "O 3.", "air pollution data", "northeast corridor"], "article_id"=>1515179, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Robert A. Rohde", "Richard A. Muller"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0135749.s001", "https://dx.doi.org/10.1371/journal.pone.0135749.s002", "https://dx.doi.org/10.1371/journal.pone.0135749.s003", "https://dx.doi.org/10.1371/journal.pone.0135749.s004", "https://dx.doi.org/10.1371/journal.pone.0135749.s005", "https://dx.doi.org/10.1371/journal.pone.0135749.s006", "https://dx.doi.org/10.1371/journal.pone.0135749.s007", "https://dx.doi.org/10.1371/journal.pone.0135749.s008", "https://dx.doi.org/10.1371/journal.pone.0135749.s009", "https://dx.doi.org/10.1371/journal.pone.0135749.s010", "https://dx.doi.org/10.1371/journal.pone.0135749.s011", "https://dx.doi.org/10.1371/journal.pone.0135749.s012", "https://dx.doi.org/10.1371/journal.pone.0135749.s013", "https://dx.doi.org/10.1371/journal.pone.0135749.s014", "https://dx.doi.org/10.1371/journal.pone.0135749.s015", "https://dx.doi.org/10.1371/journal.pone.0135749.s016", "https://dx.doi.org/10.1371/journal.pone.0135749.s017", "https://dx.doi.org/10.1371/journal.pone.0135749.s018", "https://dx.doi.org/10.1371/journal.pone.0135749.s019", "https://dx.doi.org/10.1371/journal.pone.0135749.s020"], "stats"=>{"downloads"=>39, "page_views"=>52, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Air_Pollution_in_China_Mapping_of_Concentrations_and_Sources_/1515179", "title"=>"Air Pollution in China: Mapping of Concentrations and Sources", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2015-08-20 02:54:18"}
  • {"files"=>["https://ndownloader.figshare.com/files/2218350"], "description"=>"<p>Maps of average pollutant flux for PM<sub>2.5</sub>, PM<sub>10</sub>, SO<sub>2</sub>, and NO<sub>2</sub> for eastern China (top row) and the Beijing to Shanghai corridor (bottom row). Pollutant fluxes were computed as described in the text from changes in the interpolated hourly pollution fields along with contemporaneous wind and weather data. Due to sparse sampling and secondary transformations of pollutants in the atmosphere, apparent source fluxes are likely to appear more diffuse than the true emissions source.</p>", "links"=>[], "tags"=>["Sources China", "air pollution", "Kriging interpolation", "epa", "analysis period", "1500 sites", "pollution maps", "PM 2.5", "O 3.", "air pollution data", "northeast corridor"], "article_id"=>1515134, "categories"=>["Biological Sciences", "Ecology"], "users"=>["Robert A. Rohde", "Richard A. Muller"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0135749.g004", "stats"=>{"downloads"=>6, "page_views"=>39, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Air_pollution_source_maps_/1515134", "title"=>"Air pollution source maps.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-08-20 02:54:18"}

PMC Usage Stats | Further Information

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Relative Metric

{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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