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Chen L, Gao Y, Ma M, Wang L, Wang Q, Guan S, Yao X, Gao H. Striking impacts of biomass burning on PM 2.5 concentrations in Northeast China through the emission inventory improvement. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 318:120835. [PMID: 36496070 DOI: 10.1016/j.envpol.2022.120835] [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: 10/21/2022] [Revised: 11/23/2022] [Accepted: 12/05/2022] [Indexed: 06/17/2023]
Abstract
Biomass burning exerts substantial influences on air quality and climate, which in turn to further aggravate air quality. The biomass burning emissions in particular of the agricultural burning may suffer large uncertainties which limits the understanding of their impact on air quality. Based on an improved emission inventory of the Visible Infrared Imaging Radiometer Suite (VIIRS) relative to commonly used Global Fire Emissions Database (GFED), we thoroughly evaluate the impact of biomass burning on air quality and climate during the episodes of November 2017 in Northeast China which is rich in agriculture burning. The results first indicate substantial underestimates in simulated PM2.5 concentrations without the inclusion of biomass burning emission inventory, based on a regional air quality model Weather Research and Forecasting model and Community Multiscale Air Quality model (WRF-CMAQ). The addition of biomass burning emissions from GFED then reduces the bias to a certain extent, which is further reduced by replacing the agricultural fires data in GFED with VIIRS. Numerical sensitivity experiments show that based on the improved emission inventory, the contribution of biomass burning emissions to PM2.5 concentrations in Northeast China reaches 32%, contrasting to 15% based on GFED, during the episode from November 1 to 7, 2017. Aerosol direct radiative effects from biomass burning are finally elucidated, which not only reduce downward surface shortwave radiation and planetary boundary layer height, but also affect the vertical distribution of air temperature, wind speed and relative humidity, favorable to the accumulation of PM2.5. During November 1-7, 2017, the mean daily PM2.5 enhancement due to aerosol radiative effects from VIIRS_G is 16 μg m-3, a few times higher than that of 2.8 μg m-3 from GFED. The study stresses the critical role of biomass burning, particularly of small fires easily missed in the traditional low-resolution satellite products, on air quality.
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Affiliation(s)
- Lijiao Chen
- Frontiers Science Center for Deep Ocean Multispheres and Earth System, and Key Laboratory of Marine Environment and Ecology, Ministry of Education, Ocean University of China, and Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266100, China
| | - Yang Gao
- Frontiers Science Center for Deep Ocean Multispheres and Earth System, and Key Laboratory of Marine Environment and Ecology, Ministry of Education, Ocean University of China, and Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266100, China.
| | - Mingchen Ma
- Frontiers Science Center for Deep Ocean Multispheres and Earth System, and Key Laboratory of Marine Environment and Ecology, Ministry of Education, Ocean University of China, and Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266100, China
| | - Lili Wang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
| | - Qinglu Wang
- State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China
| | - Shuhui Guan
- Qilu University of Technology (Shandong Academy of Sciences), Shandong Computer Science Center (National Supercomputer Center in Jinan), Jinan, 250014, PR China
| | - Xiaohong Yao
- Frontiers Science Center for Deep Ocean Multispheres and Earth System, and Key Laboratory of Marine Environment and Ecology, Ministry of Education, Ocean University of China, and Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266100, China
| | - Huiwang Gao
- Frontiers Science Center for Deep Ocean Multispheres and Earth System, and Key Laboratory of Marine Environment and Ecology, Ministry of Education, Ocean University of China, and Qingdao National Laboratory for Marine Science and Technology, Qingdao, 266100, China
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Assessing the Effects of Urban Morphology Parameters on PM2.5 Distribution in Northeast China Based on Gradient Boosted Regression Trees Method. SUSTAINABILITY 2022. [DOI: 10.3390/su14052618] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The dispersion of urban pollutants is affected by the urban morphology parameters. The objective of this study was to investigate the correlation between PM2.5 distribution and urban morphology parameters in a cold-climate city in China. Field measurements were performed to record the PM2.5 concentration and microclimate parameters at 25 points in a 10 km2 urban area in Harbin, China. It was found that the maximum difference of PM2.5 concentration among the measuring points at the same time could be up to 69.03 μg/m3. In this study, a geographic information system (GIS) was used to extract and screen the urban morphology parameter data under reasonable buffer radius, the gradient boosted regression trees model (GBRT) was used to carry out the prediction experiment of PM2.5 concentration and explore the nonlinear influence of urban morphology factors on PM2.5 concentration. In addition, random forest (RF), decision trees (DT), and multiple linear regression (MLR) models were selected to compare the prediction accuracy of the GBRT model. The results show that the GBRT model has the highest accuracy, with R2 reaching 0.981; building density (57%) and average building height (49%) were the two most significant factors affecting PM2.5 concentration.
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Wang Y, Sun Y, Zhao G, Cheng Y. Air Quality in the Harbin-Changchun Metropolitan Area in Northeast China: Unique Episodes and New Trends. TOXICS 2021; 9:357. [PMID: 34941791 PMCID: PMC8707320 DOI: 10.3390/toxics9120357] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 12/09/2021] [Accepted: 12/15/2021] [Indexed: 11/16/2022]
Abstract
Because of the unique geographical, climate, and anthropogenic emission characteristics, it is meaningful to explore the air pollution in the Harbin-Changchun (HC) metropolitan area. In this study, the Air Quality Index (AQI) and the corresponding major pollutant were investigated for the HC cities, based on the air quality data derived from the China National Environmental Monitoring Center. The number of days with the air quality level of "good" gradually increased during recent years, pointing to an improvement of the air quality in HC. It was also found that ozone, a typical secondary pollutant, exhibited stronger inter-city correlations compared to typical primary pollutants such as carbon monoxide and nitrogen dioxide. In addition, for nearly all the HC cities, the concentrations of fine particulate matter (PM2.5) decreased substantially in 2020 compared to 2015. However, this was not the case for ozone, with the most significant increase of ozone observed for HC's central city, Harbin. This study highlights the importance of ozone reduction for further improving HC's air quality, and the importance of agricultural fire control for eliminating heavily-polluted and even off-the-charts PM2.5 episodes.
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Affiliation(s)
- Yulong Wang
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China; (Y.W.); (G.Z.)
| | - Youwen Sun
- Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China
| | - Gerong Zhao
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China; (Y.W.); (G.Z.)
| | - Yuan Cheng
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China; (Y.W.); (G.Z.)
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