{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T04:10:01Z","timestamp":1777695001513,"version":"3.51.4"},"reference-count":61,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2019,2,16]],"date-time":"2019-02-16T00:00:00Z","timestamp":1550275200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2016YFC1302602"],"award-info":[{"award-number":["2016YFC1302602"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJERPH"],"abstract":"<jats:p>Air pollutants existing in the environment may have negative impacts on human health depending on their toxicity and concentrations. Remote sensing data enable researchers to map concentrations of various air pollutants over vast areas. By combining ground-level concentrations with population data, the spatial distribution of health impacts attributed to air pollutants can be acquired. This study took five highly populated and severely polluted provinces along the Huaihe River, China, as the research area. The ground-level concentrations of four major air pollutants including nitrogen dioxide (NO2), sulfate dioxide (SO2), particulate matters with diameter equal or less than 10 (PM10) or 2.5 micron (PM2.5) were estimated based on relevant remote sensing data using the geographically weighted regression (GWR) model. The health impacts of these pollutants were then assessed with the aid of co-located gridded population data. The results show that the annual average concentrations of ground-level NO2, SO2, PM10, and PM2.5 in 2016 were 31 \u00b5g\/m3, 26 \u00b5g\/m3, 100 \u00b5g\/m3, and 59 \u00b5g\/m3, respectively. In terms of the health impacts attributable to NO2, SO2, PM10, and PM2.5, there were 546, 1788, 10,595, and 8364 respiratory deaths, and 1221, 9666, 46,954, and 39,524 cardiovascular deaths, respectively. Northern Henan, west-central Shandong, southern Jiangsu, and Wuhan City in Hubei are prone to large health risks. Meanwhile, air pollutants have an overall greater impact on cardiovascular disease than respiratory disease, which is primarily attributable to the inhalable particle matters. Our findings provide a good reference to local decision makers for the implementation of further emission control strategies and possible health impacts assessment.<\/jats:p>","DOI":"10.3390\/ijerph16040579","type":"journal-article","created":{"date-parts":[[2019,2,17]],"date-time":"2019-02-17T22:11:50Z","timestamp":1550441510000},"page":"579","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Estimating Ground-Level Concentrations of Multiple Air Pollutants and Their Health Impacts in the Huaihe River Basin in China"],"prefix":"10.3390","volume":"16","author":[{"given":"Deying","family":"Zhang","sequence":"first","affiliation":[{"name":"Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China"},{"name":"School of Geographic Sciences, East China Normal University, Shanghai 200241, China"},{"name":"Joint Laboratory for Environmental Remote Sensing and Data Assimilation, East China Normal University and Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Shanghai 200241, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaixu","family":"Bai","sequence":"additional","affiliation":[{"name":"Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China"},{"name":"School of Geographic Sciences, East China Normal University, Shanghai 200241, China"},{"name":"Joint Laboratory for Environmental Remote Sensing and Data Assimilation, East China Normal University and Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Shanghai 200241, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunyun","family":"Zhou","sequence":"additional","affiliation":[{"name":"Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China"},{"name":"School of Geographic Sciences, East China Normal University, Shanghai 200241, China"},{"name":"Joint Laboratory for Environmental Remote Sensing and Data Assimilation, East China Normal University and Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Shanghai 200241, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Runhe","family":"Shi","sequence":"additional","affiliation":[{"name":"Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Shanghai 200241, China"},{"name":"School of Geographic Sciences, East China Normal University, Shanghai 200241, China"},{"name":"Joint Laboratory for Environmental Remote Sensing and Data Assimilation, East China Normal University and Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Shanghai 200241, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongyan","family":"Ren","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1016\/j.scitotenv.2017.12.102","article-title":"Spatial-temporal variation characteristics of air pollution in Henan of China: Localized emission inventory, WRF\/Chem simulations and potential source contribution analysis","volume":"624","author":"Liu","year":"2018","journal-title":"Sci. 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