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Zhang M, Chen W, Shen X, Zhao H, Gao C, Zhang X, Liu W, Yang C, Qin Y, Zhang S, Fu J, Tong D, Xiu A. Comprehensive and high-resolution emission inventory of atmospheric pollutants for the northernmost cities agglomeration of Harbin-Changchun, China: Implications for local atmospheric environment management. J Environ Sci (China) 2021; 104:150-168. [PMID: 33985718 DOI: 10.1016/j.jes.2020.11.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2020] [Revised: 11/03/2020] [Accepted: 11/03/2020] [Indexed: 06/12/2023]
Abstract
Using a bottom-up estimation method, a comprehensive, high-resolution emission inventory of gaseous and particulate atmospheric pollutants for multiple anthropogenic sectors with typical local sources has been developed for the Harbin-Changchun city agglomeration (HCA). The annual emissions for CO, NOx, SO2, NH3, VOCS, PM2.5, PM10, BC and OC during 2017 in the HCA were estimated to be 5.82 Tg, 0.70 Tg, 0.34 Tg, 0.75 Tg, 0.81Tg, 0.67 Tg, 1.59 Tg, 0.12 Tg and 0.26 Tg, respectively. For PM10 and SO2, the emissions from industry processes were the dominant contributors representing 54.7% and 49.5%, respectively, of the total emissions, while 95.3% and 44.5% of the total NH3 and NOx emissions, respectively, were from or associated with agricultural activities and transportation. Spatiotemporal distributions showed that most emissions (except NH3) occurred in November to March and were concentrated in the central cities of Changchun and Harbin and the surrounding cities. Open burning of straw made an important contribution to PM2.5 in the central regions of the northeastern plain during autumn and spring, while domestic coal combustion for heating purposes was significant with respect to SO2 and PM2.5 emissions during autumn and winter. Furthermore, based on Principal Component Analysis and Multivariable Linear Regression model, air temperature, relative humidity, electricity and energy consumption, and the urban and rural population were optimized to be representative indicators for rapidly assessing the magnitude of regional atmospheric pollutants in the HCA. Such indicators and equations were demonstrated to be useful for local atmospheric environment management.
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Affiliation(s)
- Mengduo Zhang
- Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Weiwei Chen
- Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China; University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Xiangjin Shen
- Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
| | - Hongmei Zhao
- Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
| | - Chengkang Gao
- Northeastern University School of Metallurgy, Shenyang 110819, China
| | - Xuelei Zhang
- Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
| | - Wei Liu
- Heilongjiang Provincial Academy of Environmental Sciences, Harbin 150090, China
| | - Chengjiang Yang
- Jilin Provincial Ecological Environment Monitoring Center, Changchun 130012, China
| | - Yang Qin
- Jilin Provincial Ecological Environment Monitoring Center, Changchun 130012, China
| | - Shichun Zhang
- Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
| | - Jing Fu
- Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
| | - Daniel Tong
- Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA 22030, USA; Cooperative Institute for Climate & Satellites, University of Maryland, College Park, MD 20740, USA
| | - Aijun Xiu
- Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
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Yang G, Zhao H, Tong DQ, Xiu A, Zhang X, Gao C. Impacts of post-harvest open biomass burning and burning ban policy on severe haze in the Northeastern China. Sci Total Environ 2020; 716:136517. [PMID: 32059315 DOI: 10.1016/j.scitotenv.2020.136517] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Revised: 01/02/2020] [Accepted: 01/03/2020] [Indexed: 06/10/2023]
Abstract
Open filed biomass burning is a major contributor to airborne particulate matter and reactive trace gases during the post-harvest season in the Northeastern China. Due to prevailing weather conditions and high emission density, this region is prone to the accumulation of air pollutants that often leads to severe haze events. In this study, we combined satellite and ground observations, and a regional air quality modeling system to quantify the contribution of open biomass burning to surface PM2.5 (particulate matter with diameter less than 2.5 µm) concentrations during a severe haze episode. During this period (November 1st - 4th, 2015), the average PM2.5 concentrations in Heilongjiang, Jilin, and Liaoning provinces reached 116.98 μg/m3, 98.60 μg/m3, and 70.17 μg/m3 respectively. Model simulations showed that open biomass burning contributed to 52.7% of PM2.5 concentrations over Northeast China. Using the differences in active fire spots as detected by the Visible Infrared Imaging Radiometer Suites (VIIRS) aboard the Suomi-NPP, we estimated that the burning ban enforced in 2018 have caused the PM2.5 concentrations to decrease from the 2015 level by 67.10%, 53.23%, and 10.06% in the Heilongjiang, Jilin, and Liaoning provinces respectively. Over the region, the burning ban proved to be effective in reducing fire emissions and lowering region-wide PM2.5 concentration by 48.1% during the post-harvest season.
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Affiliation(s)
- Guangyi Yang
- Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China; University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Hongmei Zhao
- Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China.
| | - Daniel Q Tong
- Center for Spatial Information Science and Systems, George Mason University, VA 22030, USA; Department of Atmospheric, Oceanic and Earth Sciences, George Mason University, Fairfax, VA 22030, USA.
| | - Aijun Xiu
- Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China.
| | - Xuelei Zhang
- Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China.
| | - Chao Gao
- Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China; University of Chinese Academy of Sciences, Beijing 100049, China.
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Meng Q, Richmond-Bryant J, Lu SE, Buckley B, Welsh WJ, Whitsel EA, Hanna A, Yeatts KB, Warren J, Herring AH, Xiu A. Cardiovascular outcomes and the physical and chemical properties of metal ions found in particulate matter air pollution: a QICAR study. Environ Health Perspect 2013; 121:558-64. [PMID: 23462649 PMCID: PMC3673192 DOI: 10.1289/ehp.1205793] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2012] [Accepted: 03/04/2013] [Indexed: 05/04/2023]
Abstract
BACKGROUND This paper presents an application of quantitative ion character-activity relationships (QICAR) to estimate associations of human cardiovascular (CV) diseases (CVDs) with a set of metal ion properties commonly observed in ambient air pollutants. QICAR has previously been used to predict ecotoxicity of inorganic metal ions based on ion properties. OBJECTIVES The objective of this work was to examine potential associations of biological end points with a set of physical and chemical properties describing inorganic metal ions present in exposures using QICAR. METHODS Chemical and physical properties of 17 metal ions were obtained from peer-reviewed publications. Associations of cardiac arrhythmia, myocardial ischemia, myocardial infarction, stroke, and thrombosis with exposures to metal ions (measured as inference scores) were obtained from the Comparative Toxicogenomics Database (CTD). Robust regressions were applied to estimate the associations of CVDs with ion properties. RESULTS CVD was statistically significantly associated (Bonferroni-adjusted significance level of 0.003) with many ion properties reflecting ion size, solubility, oxidation potential, and abilities to form covalent and ionic bonds. The properties are relevant for reactive oxygen species (ROS) generation, which has been identified as a possible mechanism leading to CVDs. CONCLUSION QICAR has the potential to complement existing epidemiologic methods for estimating associations between CVDs and air pollutant exposures by providing clues about the underlying mechanisms that may explain these associations.
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Affiliation(s)
- Qingyu Meng
- School of Public Health, University of Medicine and Dentistry of New Jersey, Piscataway, New Jersey, USA
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Hanna AF, Yeatts KB, Xiu A, Zhu Z, Smith RL, Davis NN, Talgo KD, Arora G, Robinson PJ, Meng Q, Pinto JP. Associations between ozone and morbidity using the Spatial Synoptic Classification system. Environ Health 2011; 10:49. [PMID: 21609456 PMCID: PMC3117763 DOI: 10.1186/1476-069x-10-49] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2011] [Accepted: 05/24/2011] [Indexed: 05/19/2023]
Abstract
BACKGROUND Synoptic circulation patterns (large-scale tropospheric motion systems) affect air pollution and, potentially, air-pollution-morbidity associations. We evaluated the effect of synoptic circulation patterns (air masses) on the association between ozone and hospital admissions for asthma and myocardial infarction (MI) among adults in North Carolina. METHODS Daily surface meteorology data (including precipitation, wind speed, and dew point) for five selected cities in North Carolina were obtained from the U.S. EPA Air Quality System (AQS), which were in turn based on data from the National Climatic Data Center of the National Oceanic and Atmospheric Administration. We used the Spatial Synoptic Classification system to classify each day of the 9-year period from 1996 through 2004 into one of seven different air mass types: dry polar, dry moderate, dry tropical, moist polar, moist moderate, moist tropical, or transitional. Daily 24-hour maximum 1-hour ambient concentrations of ozone were obtained from the AQS. Asthma and MI hospital admissions data for the 9-year period were obtained from the North Carolina Department of Health and Human Services. Generalized linear models were used to assess the association of the hospitalizations with ozone concentrations and specific air mass types, using pollutant lags of 0 to 5 days. We examined the effect across cities on days with the same air mass type. In all models we adjusted for dew point and day-of-the-week effects related to hospital admissions. RESULTS Ozone was associated with asthma under dry tropical (1- to 5-day lags), transitional (3- and 4-day lags), and extreme moist tropical (0-day lag) air masses. Ozone was associated with MI only under the extreme moist tropical (5-day lag) air masses. CONCLUSIONS Elevated ozone levels are associated with dry tropical, dry moderate, and moist tropical air masses, with the highest ozone levels being associated with the dry tropical air mass. Certain synoptic circulation patterns/air masses in conjunction with ambient ozone levels were associated with increased asthma and MI hospitalizations.
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Affiliation(s)
- Adel F Hanna
- Institute for the Environment, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599, USA
| | - Karin B Yeatts
- Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599, USA
| | - Aijun Xiu
- Institute for the Environment, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599, USA
| | - Zhengyuan Zhu
- Department of Statistics, Iowa State University, Ames, Iowa, 50011, USA
| | - Richard L Smith
- Department of Statistics and Operations Research, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599, USA
| | - Neil N Davis
- Institute for the Environment, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599, USA
| | - Kevin D Talgo
- Institute for the Environment, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599, USA
| | - Gurmeet Arora
- HSBC Finance Corporation, Elmhurst, Illinois, 60126, USA
| | - Peter J Robinson
- Department of Geography, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, 27599, USA
| | - Qingyu Meng
- School of Public Health, University of Medicine and Dentistry of New Jersey, Piscataway, New Jersey, 08854, USA
| | - Joseph P Pinto
- U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, 27711, USA
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Abstract
Abstract
Although the development of soil, vegetation, and atmosphere interaction models has been driven primarily by the need for accurate simulations of long-term energy and moisture budgets in global climate models, the importance of these processes at smaller scales for short-term numerical weather prediction and air quality studies is becoming more appreciated. Planetary boundary layer (PBL) development is highly dependent on the partitioning of the available net radiation into sensible and latent heat fluxes. Therefore, adequate treatmentof surface properties such as soil moisture and vegetation characteristics is essential for accurate simulation of PBL development, convective and low-level cloud processes, and the temperature and humidity of boundary layer air. In this paper, the development ofa simple coupled surface and PBL model, which is planned for incorporation into the Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM4/5), is described. The soil-vegetation model is based on a simple force-restore algorithm with explicit soil moisture and evapotranspiration. The PBL model is a hybrid of nonlocal closure for convective conditions and eddy diffusion for all other conditions. A one-dimensional version of the model has been applied to several case studies from field experiments in both dry desert-like conditions (Wangara) and moist vegetated conditions(First International Satellite Land Surface Climatology Project Field Experiment) to demonstrate the model's ability to realistically simulate surface fluxes as well as PBL development. This new surface-PBL model is currently being incorporated into the MM4-MM5 system.
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Affiliation(s)
- Jonathan E. Pleim
- Atmospheric Sciences Modeling Division, Air Resources Laboratory, National Oceanic and Atmospheric Administration, Research Triangle Park, North Carolina
| | - Aijun Xiu
- Environmental Programs, MCNC, North Carolina Supercomputing Center, Research Triangle Park, North Carolina
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