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Yang J, Wang G, Zhang C. Forecast of Fine Particles in Chengdu under Autumn-Winter Synoptic Conditions. TOXICS 2023; 11:777. [PMID: 37755787 PMCID: PMC10535754 DOI: 10.3390/toxics11090777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 08/31/2023] [Accepted: 09/09/2023] [Indexed: 09/28/2023]
Abstract
We conducted an evaluation of the impact of meteorological factor forecasts on the prediction of fine particles in Chengdu, China, during autumn and winter, utilizing the European Cooperation in Science and Technology (COST)733 objective weather classification software and the Community Multiscale Air Quality model. This analysis was performed under four prevailing weather patterns. Fine particle pollution tended to occur under high-pressure rear, homogeneous-pressure, and low-pressure conditions; by contrast, fine particle concentrations were lower under high-pressure bottom conditions. The forecasts of fine particle concentrations were more accurate under high-pressure bottom conditions than under high-pressure rear and homogeneous-pressure conditions. Moreover, under all conditions, the 24 h forecast of fine particle concentrations were more accurate than the 48 and 72 h forecasts. Regarding meteorological factors, forecasts of 2 m relative humidity and 10 m wind speed were more accurate under high-pressure bottom conditions than high-pressure rear and homogeneous-pressure conditions. Moreover, 2 m relative humidity and 10 m wind speed were important factors for forecasting fine particles, whereas 2 m air temperature was not. Finally, the 24 h forecasts of meteorological factors were more accurate than the 48 and 72 h forecasts, consistent with the forecasting of fine particles.
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Affiliation(s)
- Jingchao Yang
- Institute of Plateau Meteorology, China Meteorological Administration, Chengdu 610072, China;
- Heavy Rain and Drought-Flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province, Chengdu 610072, China
| | - Ge Wang
- Institute of Plateau Meteorology, China Meteorological Administration, Chengdu 610072, China;
- Heavy Rain and Drought-Flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province, Chengdu 610072, China
| | - Chao Zhang
- Department of Atmospheric Science, School of Environmental Studies, China University of Geosciences, Wuhan 430074, China
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Ping L, Wang Y, Lu Y, Lee LC, Liang C. Tracing the sources of PM 2.5-related health burden in China. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2023; 327:121544. [PMID: 37030602 DOI: 10.1016/j.envpol.2023.121544] [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: 10/05/2022] [Revised: 03/29/2023] [Accepted: 03/30/2023] [Indexed: 06/19/2023]
Abstract
Fine particulate matter (PM2.5) poses a major environmental risk to human health. We estimated PM2.5-related premature deaths in 30 Chinese provinces in 2020 using an integrated exposure response model based on monitored concentrations and obtained regional and sectoral contributions based on the atmospheric transport of the atmospheric transport contribution matrix. From the perspective of regional- and sectoral-scale effects, the results revealed that 740,140 [95% confidence interval (CI):646,538-839,968] premature deaths were related to PM2.5 in 2020, mainly in East (30%), Central (18%), and North (15%) China. Manufacturing activity was found to be the major cause of PM2.5-related premature deaths, accounting for over 50% of the deaths. From the perspective of the interregional atmospheric transport effect, although local emissions were the major source of PM2.5-related premature deaths in all regions, non-local emissions contributed approximately 30%. The overall trend in the net atmospheric transport direction was from north to south. In particular, the Guangdong, Guangxi, and Hainan provinces of South China received contributions of more than 40% from non-local provinces, mainly from the East and Central China. Combined with economic data, the regions and sectors with the highest PM2.5-related premature deaths per unit output or consumption include the manufacturing and household sectors in North and Northeast China and transportation, agriculture, and electricity in Central China. Therefore, from the perspective of the above three impacts, although the potential impact of PM2.5 pollution on health in China has decreased with the decrease in PM2.5 concentration in the past decade owing to strict air pollution control, the central and northern parts of China are still the key areas requiring air pollution control. The health impacts of air pollution associated with the rapid development of China's manufacturing industry in the post-pandemic era cannot be ignored.
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Affiliation(s)
- Liying Ping
- School of Environmental Science and Engineering, Tianjin University, Tianjin, 300350, China
| | - Yuan Wang
- School of Environmental Science and Engineering, Tianjin University, Tianjin, 300350, China.
| | - Yaling Lu
- State Environmental Protection Key Laboratory of Environmental Planning and Policy Simulation, Chinese Academy for Environmental Planning, Beijing, 100012, China; The Center of Enterprise Green Governance, Chinese Academy for Environmental Planning, Beijing, 100012, China
| | - Lien-Chieh Lee
- School of Environmental Science and Engineering, Hubei Polytechnic University, Huangshi, 435003, China
| | - Chen Liang
- School of Environmental Science and Engineering, Tianjin University, Tianjin, 300350, China
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Sun L, Yang L, Wang D, Zhang T. Influence of the Long-Range Transport of Siberian Biomass Burnings on Air Quality in Northeast China in June 2017. SENSORS (BASEL, SWITZERLAND) 2023; 23:682. [PMID: 36679477 PMCID: PMC9861995 DOI: 10.3390/s23020682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 12/14/2022] [Accepted: 12/20/2022] [Indexed: 06/17/2023]
Abstract
Biomass burning (BB) emits a large volume of trace gases and aerosols into the atmosphere, which can significantly affect the earth's radiative balance and climate and has negative impacts on air quality and even human health. In late June 2017, an intense BB case, dominated by forest and savanna fires, occurred in Siberia, and it affected the air quality of Northeast China through long-range transport. Here, multisatellite remote-sensing products and ground-based PM2.5 measurements are used to evaluate the influence of the Siberian smoky plume on Northeast China. The results show that the BB was intense at the early stage when the daily fire count and average fire radiative power exceeded 300 and 200 MW, respectively. The maximum daily fire count reached 1350 in Siberia, and the peak value of instantaneous fire radiative power was as high as 3091.5 MW. High concentrations of CO and aerosols were emitted into the atmosphere by the BB in Siberia. The maximum daily mean values of the CO column concentration and aerosol optical depth (AOD) increased by 3 × 1017 molec·cm2 and 0.5 compared with that during the initial BB stage. In addition, the BB released a large number of absorptive aerosols into the atmosphere, and the UV aerosol index (UVAI) increased by five times at the peak of the event in Siberia. Under the appropriate synoptic conditions and, combined with pyroconvection, the smoky plume was lifted into the upper air and transported to Northeast China, affecting the air quality of Northeast China. The daily mean values of CO concentration, AOD, and UVAI in Northeast China increased by 6 × 1017 molec·cm2, 0.5, and 1.4, respectively, after being affected. Moreover, the concentration of the surface PM2.5 in Northeast China approximately doubled after being affected by the plume. The results of this study indicate that the air quality of Northeast China can be significantly affected by Siberian BBs under favorable conditions.
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Affiliation(s)
- Li Sun
- Institute of Atmospheric Environment, China Meteorological Administration, Shenyang 110166, China
- Liaoning Weather Modification Office, Shenyang 110166, China
| | - Lei Yang
- Liaoning Meteorological Disaster Monitoring and Early Warning Centre, Shenyang 110166, China
| | - Dongdong Wang
- Institute of Atmospheric Environment, China Meteorological Administration, Shenyang 110166, China
| | - Tiening Zhang
- Liaoning Weather Modification Office, Shenyang 110166, China
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Liu F, Xing C, Su P, Luo Y, Zhao T, Xue J, Zhang G, Qin S, Song Y, Bu N. Source analysis of the tropospheric NO 2 based on MAX-DOAS measurements in northeastern China. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2022; 306:119424. [PMID: 35537554 DOI: 10.1016/j.envpol.2022.119424] [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: 04/20/2021] [Revised: 05/03/2022] [Accepted: 05/04/2022] [Indexed: 06/14/2023]
Abstract
Ground-based Multi-Axis Differential Optical Absorption Spectroscopy (Max-DOAS) measurements of nitrogen dioxide (NO2) were continuously obtained from January to November 2019 in northeastern China (NEC). Seasonal variations in the mean NO2 vertical column densities (VCDs) were apparent, with a maximum of 2.9 × 1016 molecules cm-2 in the winter due to enhanced NO2 emissions from coal-fired winter heating, a longer photochemical lifetime and atmospheric transport. Daily maximum and minimum NO2 VCDs were observed, independent of the season, at around 11:00 and 13:00 local time, respectively, and the most obvious increases and decreases occurred in the winter and autumn, respectively. The mean diurnal NO2 VCDs at 11:00 increased to at 08:00 by 1.6, 5.8, and 6.7 × 1015 molecules cm-2 in the summer, autumn and winter, respectively, due to increased NO2 emissions, and then decreased by 2.8, 4.2, and 5.1 × 1015 molecules cm-2 at 13:00 in the spring, summer, and autumn, respectively. This was due to strong solar radiation and increased planetary boundary layer height. There was no obvious weekend effect, and the NO2 VCDs only decreased by about 10% on the weekends. We evaluated the contributions of emissions and transport in the different seasons to the NO2 VCDs using a generalized additive model, where the contributions of local emissions to the total in the spring, summer, autumn, and winter were 89 ± 12%, 92 ± 11%, 86 ± 12%, and 72 ± 16%, respectively. The contribution of regional transport reached 26% in the winter, and this high contribution value was mainly correlated with the northeast wind, which was due to the transport channel of air pollutants along the Changbai Mountains in NEC. The NO2/SO2 ratio was used to identify NO2 from industrial sources and vehicle exhaust. The contribution of industrial NO2 VCD sources was >66.3 ± 16% in Shenyang due to the large amount of coal combustion from heavy industrial activity, which emitted large amounts of NO2. Our results suggest that air quality management in Shenyang should consider reductions in local NO2 emissions from industrial sources along with regional cooperative control.
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Affiliation(s)
- Feng Liu
- School of Environmental Science, Liaoning University, Shenyang, 110036, China
| | - Chengzhi Xing
- Key Lab of Environmental Optics & Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China
| | - Pinjie Su
- School of Environmental Science, Liaoning University, Shenyang, 110036, China
| | - Yifu Luo
- School of Environmental Science, Liaoning University, Shenyang, 110036, China
| | - Ting Zhao
- School of Environmental Science, Liaoning University, Shenyang, 110036, China
| | - Jiexiao Xue
- School of Environmental Science, Liaoning University, Shenyang, 110036, China
| | - Guohui Zhang
- School of Environmental Science, Liaoning University, Shenyang, 110036, China
| | - Sida Qin
- Liaoning Science and Technology Center for Ecological and Environmental Protection, Shenyang, 110161, China
| | - Youtao Song
- School of Environmental Science, Liaoning University, Shenyang, 110036, China
| | - Naishun Bu
- School of Environmental Science, Liaoning University, Shenyang, 110036, China; Key Laboratory of Wetland Ecology and Environment Research in Cold Regions of Heilongjiang Province, Harbin University, 150086, China.
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Multi-Year Variation of Ozone and Particulate Matter in Northeast China Based on the Tracking Air Pollution in China (TAP) Data. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19073830. [PMID: 35409512 PMCID: PMC8997942 DOI: 10.3390/ijerph19073830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 03/18/2022] [Accepted: 03/22/2022] [Indexed: 12/10/2022]
Abstract
With the rapid development of economy and urbanization acceleration, ozone (O3) pollution has become the main factor of urban air pollution in China after particulate matter. In this study, 90th percentile of maximum daily average (MDA) 8 h O3 (O3-8h-90per) and PM2.5 data from the Tracking Air Pollution in China (TAP) dataset were used to determine the mean annual, seasonal, monthly, and interannual distribution of O3-8h-90per and PM2.5 concentrations in Northeast China (NEC). The O3-8h-90per concentration was highest in Liaoning (>100 μg/m3), whereas the highest PM2.5 concentration was observed mainly in urban areas of central Liaoning and the Harbin−Changchun urban agglomeration (approximately 60 μg/m3). The O3-8h-90per concentrations were highest in spring and summer due to more intense solar radiation. On the contrary, the PM2.5 concentration increased considerably in winter influenced by anthropogenic activities. In May and June, the highest monthly mean O3-8h-90per concentrations were observed in central and western Liaoning, about 170−180 μg/m3, while the PM2.5 concentrations were the highest in January, February, and December, approximately 100 μg/m3. The annual mean O3-8h-90per concentration in NEC showed an increasing trend, while the PM2.5 concentration exhibited an annual decline. By 2020, the annual mean O3-8h-90per concentration in southern Liaoning had increased considerably, reaching 120−130 μg/m3. From the perspective of city levels, PM2.5 and O3-8h-90per also showed an opposite variation trend in the 35 cities of NEC. The reduced tropospheric NO2 column is consistent with the decreasing trend of the interannual PM2.5, while the increased surface temperature could be the main meteorological factor affecting the O3-8h-90per concentration in NEC. The results of this study enable a comprehensive understanding of the regional and climatological O3-8h-90per and PM2.5 distribution at distinct spatial and temporal scales in NEC.
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Zhu J, Wang S, Dao X, Liu D, Wang J, Zhang S, Xue R, Tang G, Zhou B. Comparative observation of aerosol vertical profiles in urban and suburban areas: Impacts of local and regional transport. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 805:150363. [PMID: 34818754 DOI: 10.1016/j.scitotenv.2021.150363] [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: 07/06/2021] [Revised: 09/09/2021] [Accepted: 09/11/2021] [Indexed: 06/13/2023]
Abstract
Ground-based Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) instruments were used to carry out observation of aerosol in the urban and suburban areas of Shanghai from October 17 to November 21, 2019. Fudan University (FDU) site is a typical urban environment, surrounded by residential areas, commercial areas and arterial roads, while Dianshan Lake (DSL) site is a suburban environment with high vegetation coverage and no pollutant emission sources. The aerosol retrieved by MAX-DOAS was in good correlation with the observation of sun photometer and the PM2.5 concentration of the corresponding site, which demonstrates that the aerosol retrieved by MAX-DOAS is reliable and feasible. Comparing the mean aerosol extinction coefficient (AEC) profiles during the observation period between urban and suburban areas, it was found that the occurrence of high aerosol concentration at FDU was nearly 3 h later than that of DSL at suburban site. And the aerosol at DSL was concentrated at an altitude of 0.3- 0.5 km, with a mean peak value of 0.486 km-1, which was slightly higher than the peak AEC of 0.453 km-1 at FDU of 0.2- 0.4 km. The difference in aerosol characteristics between the two sites may be due to the fact that the influences of aerosol transport and boundary layer dynamics are different between the two sites. The backward trajectories analysis also presents that there were mutual transports of aerosol between urban and suburban areas, which affect the optical properties of the aerosol in these two sites. In a case of aerosol pollution, we visualized the transport pathway of aerosol from the western part of the North China Plain to Shanghai using AEC profiles and backward trajectories, providing the evidence that the local aerosol pollution in Shanghai was affected by long-distance transport.
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Affiliation(s)
- Jian Zhu
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP(3)), Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Shanshan Wang
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP(3)), Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China; Institute of Eco-Chongming (IEC), No. 20 Cuiniao Road, Shanghai 202162, China.
| | - Xu Dao
- China National Environmental Monitoring Centre, Beijing 100012, China
| | - Duanyang Liu
- Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210008, China; Key Laboratory of Transportation Meteorology, China Meteorological Administration, Nanjing 210008, China
| | - Jie Wang
- Institutes of Physical Science and Information Technology, Anhui University, Hefei 230601, China; Information Materials and Intelligent Sensing Laboratory of Anhui Province, Hefei 230601, China
| | - Sanbao Zhang
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP(3)), Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Ruibin Xue
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP(3)), Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Guigang Tang
- China National Environmental Monitoring Centre, Beijing 100012, China
| | - Bin Zhou
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP(3)), Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China; Institute of Eco-Chongming (IEC), No. 20 Cuiniao Road, Shanghai 202162, China; Institute of Atmospheric Sciences, Fudan University, Shanghai 200433, China.
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Li W, Duan F, Zhao Q, Song W, Cheng Y, Wang X, Li L, He K. Investigating the effect of sources and meteorological conditions on wintertime haze formation in Northeast China: A case study in Harbin. THE SCIENCE OF THE TOTAL ENVIRONMENT 2021; 801:149631. [PMID: 34467910 DOI: 10.1016/j.scitotenv.2021.149631] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 08/09/2021] [Accepted: 08/09/2021] [Indexed: 06/13/2023]
Abstract
Heavy haze pollution has occurred frequently in the past few years in Northeast China during winters, which was distinct from other regions in China because of the particular meteorological conditions. In this study, we analyzed the temporal variation, source appointment, and influencing factors of PM2.5 from December 1, 2018 to February 28, 2019 in Harbin. The results showed obvious differences between the non-haze and haze periods. The source appointment based on a single-particle aerosol mass spectrometer showed that coal combustion, vehicle emissions, biomass burning, and secondary inorganic aerosols (SIAs) were the major contributors of PM2.5. It is interesting that from the non-haze to the haze period, contributions of coal combustion and SIAs increased (from 20.2% to 27.3%, and from 17.3% to 18.9%, respectively) while other sources decreased or increased little. It indicated the primary pollutants from heating supply were the most important contributor to haze formation due to the low temperature. Furthermore, from levels I (0 < PM2.5 ≤ 75 μg m-3) to III (115 < PM2.5 ≤ 150 μg m-3), SIAs increased from 15.3% to 19.4% (increased 4.1%), while coal combustion from 23.7% to 27.1% and increased 3.4%. It implied clearly that SIAs played a comparable role in the early stage of the evolution of haze episode as that of coal combustion. Combining data on prevailing winds and results of potential source contribution function indicated that PM2.5 during the haze period was primarily influenced by the air masses originating from the southwestern areas via regional transport. A positive correlation was observed between relative humidity (RH) and haze pollution when RH ≥ 60%, indicating that hygroscopic growth may be the principal factor promoting secondary formation. CAPSULE: Coal combustion was the most important source in Harbin due to the low temperature, and secondary aerosols promoted the early stage of the haze evolution.
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Affiliation(s)
- Wenguang Li
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
| | - Fengkui Duan
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China.
| | - Qing Zhao
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China; Tsing-huan smart source (Beijing) Technology Co., Ltd., Beijing 100084, China.
| | - Weiwei Song
- School of Environment, Harbin Institute of Technology, Harbin 150090, China
| | - Yuan Cheng
- School of Environment, Harbin Institute of Technology, Harbin 150090, China
| | - Xiaoyan Wang
- Environment Monitoring Center, Harbin 150090, China
| | - Lei Li
- Environment Monitoring Center, Harbin 150090, China
| | - Kebin He
- State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment, State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex, Beijing Key Laboratory of Indoor Air Quality Evaluation and Control, Tsinghua University, Beijing 100084, China
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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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9
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Fang C, Wang L, Li Z, Wang J. Spatial Characteristics and Regional Transmission Analysis of PM 2.5 Pollution in Northeast China, 2016-2020. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2021; 18:ijerph182312483. [PMID: 34886209 PMCID: PMC8657314 DOI: 10.3390/ijerph182312483] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 11/18/2021] [Accepted: 11/25/2021] [Indexed: 11/16/2022]
Abstract
Northeast China is an essential industrial development base in China and the regional air quality is severely affected by PM2.5 pollution. In this paper, spatial autocorrelation, trajectory clustering, hotspot analysis, PSCF and CWT analysis are used to explore the spatial pollution characteristics of PM2.5 and determine the atmospheric regional transmission pattern for 40 cities in Northeast China from 2016 to 2020. Analysis of PM2.5 concentration characteristics in the northeast indicates that the annual average value and total exceedance days of PM2.5 concentration in Northeast China showed a U-shaped change, with the lowest annual average PM2.5 concentration (31 μg/m3) in 2018, decreasing by 12.1% year-on-year, and the hourly PM2.5 concentration exploding during the epidemic lockdown period in 2020. A stable PM2.5 pollution band emerges spatially from the southwest to Northeast China. Spatially, the PM2.5 in Northeast China has a high degree of autocorrelation and a south-hot-north-cool characteristic, with all hotspots concentrated in the most polluted Liaoning province, which exhibits the H-H cluster pattern and hotspot per year. Analysis of the air mass trajectories, potential source contributions and concentration weight trajectories in Northeast China indicates that more than 74% of the air mass trajectories were transmitted to each other between the three heavily polluted cities, with the highest mean value of PM2.5 pollution trajectories reaching 222.4 μg/m3, and the contribution of daily average PM2.5 concentrations exceeding 60 μg/m3 within Northeast China. Pollution of PM2.5 throughout the Northeast is mainly influenced by short-range intra-regional transport, with long-range transport between regions also being an essential factor; organized integration is the only fundamental solution to air pollution.
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Affiliation(s)
| | | | | | - Ju Wang
- Correspondence: ; Tel.: +86-131-0431-7228
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10
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Wang Y, Sun Y, Zhang Z, Cheng Y. Spatiotemporal variation and source analysis of air pollutants in the Harbin-Changchun (HC) region of China during 2014-2020. ENVIRONMENTAL SCIENCE AND ECOTECHNOLOGY 2021; 8:100126. [PMID: 36157001 PMCID: PMC9488001 DOI: 10.1016/j.ese.2021.100126] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 09/10/2021] [Accepted: 09/11/2021] [Indexed: 06/16/2023]
Abstract
This study analyzed the characteristics of air pollution and specific pollution periods within the Harbin-Changchun (HC) metropolitan area during 2014-2020. Regarding annual, seasonal, and monthly variations of the six pollutants, the change trend in 11 cities of HC showed strong consistency in spatial distribution. The western cities (Songyuan, Daqing, and Siping) were vulnerable to dust storms from Inner Mongolia. The concentrations of all air pollutants, except O3-8h, showed downward fluctuation trends from 2014 to 2018 and remained stable from 2018 to 2020 in terms of annual variations. The inter-annual trend of significant reductions in SO2 and SO2/PM2.5 during the heating period indicated that strict emission reduction measures posed by the government were highly successful. The ratio of PM2.5/SO2 was used to identify open biomass burning (OBB), which showed a double peak (October-November (Oct-Nov), March-April (Mar-Apr)). The burning prohibition shifted the OBB from Oct-Nov to Mar-Apr.
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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
| | - Youwen Sun
- Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei, 230031, China
| | - Zhiqing Zhang
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin, 150090, China
| | - Yuan Cheng
- State Key Laboratory of Urban Water Resource and Environment, School of Environment, Harbin Institute of Technology, Harbin, 150090, China
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Variation Characteristics and Transportation of Aerosol, NO2, SO2, and HCHO in Coastal Cities of Eastern China: Dalian, Qingdao, and Shanghai. REMOTE SENSING 2021. [DOI: 10.3390/rs13050892] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This paper studied the method for converting the aerosol extinction to the mass concentration of particulate matter (PM) and obtained the spatio-temporal distribution and transportation of aerosol, nitrogen dioxide (NO2), sulfur dioxide (SO2), and formaldehyde (HCHO) based on multi-axis differential optical absorption spectroscopy (MAX-DOAS) observations in Dalian (38.85°N, 121.36°E), Qingdao (36.35°N, 120.69°E), and Shanghai (31.60°N, 121.80°E) from 2019 to 2020. The PM2.5 measured by the in situ instrument and the PM2.5 simulated by the conversion formula showed a good correlation. The correlation coefficients R were 0.93 (Dalian), 0.90 (Qingdao), and 0.88 (Shanghai). A regular seasonality of the three trace gases is found, but not for aerosols. Considerable amplitudes in the weekly cycles were determined for NO2 and aerosols, but not for SO2 and HCHO. The aerosol profiles were nearly Gaussian, and the shapes of the trace gas profiles were nearly exponential, except for SO2 in Shanghai and HCHO in Qingdao. PM2.5 presented the largest transport flux, followed by NO2 and SO2. The main transport flux was the output flux from inland to sea in spring and winter. The MAX-DOAS and the Copernicus Atmosphere Monitoring Service (CAMS) models’ results were compared. The overestimation of NO2 and SO2 by CAMS is due to its overestimation of near-surface gas volume mixing ratios.
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Contribution of Regional PM2.5 Transport to Air Pollution Enhanced by Sub-Basin Topography: A Modeling Case over Central China. ATMOSPHERE 2020. [DOI: 10.3390/atmos11111258] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The Twain-Hu basin (THB), covering the lower plain of Hubei and Hunan provinces in Central China, has experienced severe air pollution in recent years. However, the terrain effects of such sub-basin on air quality over the THB have been incomprehensibly understood. A heavy PM2.5 pollution event occurred over the THB during 4–10 January 2019. By using the observations and WRF-Chem simulations, we investigated the underlying mechanisms of sub-basin effects on the air pollution with several sensitivity experiments. Observationally, air pollution in the western THB urban area with an average PM2.5 concentration of 189.8 μg m−3, which was more serious than the eastern urban area with the average PM2.5 concentration of 106.3 μg m−3, reflecting a different influence of topography on air pollution over the THB. Simulation results revealed that the terrain effect can contribute 12.0% to increasing the PM2.5 concentrations in the western THB, but slightly mitigate the pollution extent in the eastern THB with the contribution of −4.6% to PM2.5 during the heavy pollution episode. In particular, the sub-basin terrain was conducive to the accumulation of PM2.5 by regional transport with the contribution of 39.1 %, and contrarily lowered its local pollution by −57.0% via the enhanced atmospheric boundary layer height and ventilation coefficients. Given a heavy air pollution episode occurring over the THB, such inverse contribution of terrain effects reflected a unique importance of sub-basin topography in regional transport of air pollutants for air pollution in central China.
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Zheng Y, Che H, Xia X, Wang Y, Yang L, Chen J, Wang H, Zhao H, Li L, Zhang L, Gui K, Yang X, Liang Y, Zhang X. Aerosol optical properties and its type classification based on multiyear joint observation campaign in north China plain megalopolis. CHEMOSPHERE 2020; 273:128560. [PMID: 34756345 DOI: 10.1016/j.chemosphere.2020.128560] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 09/22/2020] [Accepted: 10/04/2020] [Indexed: 06/13/2023]
Abstract
Since haze and other air pollution are frequently seen in the North China Plain (NCP), detail information on aerosol optical and radiative properties and its type classification is demanded for the study of regional environmental pollution. Here, a multiyear ground-based synchronous sun photometer observation at seven sites on North China Plain megalopolis from 2013 to 2018 was conducted. First, the annual and seasonal variation of these characteristics as well as the intercomparsion were analyzed. Then the potential relationships between these properties with meteorological factors and the aerosol type classification were discussed. The results show: Particle volume exhibited a decreasing trend from the urban downtown to suburban and the rural region. The annual average aerosol optical depth at 440 nm (AOD440) varied from ∼0.43 to 0.86 over the NCP. Annual average single-scattering albedo at 440 nm (SSA440) varied from ∼0.89 to 0.93, indicating a moderate to slight absorption capacity. Average absorption aerosol optical depth at 440 nm (AAOD440) varied from ∼0.07 to 0.10. The absorption Ångström exponent (AAE) (∼0.89-1.40) indicated the multi-types of absorptive matters originated form nature and anthropogenic emission. The discussion of aerosol composition showed a smaller particle size of aerosol from biomass burning and/or fossil foil consumption with enhanced aerosol scattering and enlarged light extinction. Aerosol classification indicated a large percentage of mixed absorbing aerosol (∼20%-49%), which showed increasing trend between relative humidity (RH) with aerosol scattering and dust was an important environmental pollutant compared to southern China.
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Affiliation(s)
- Yu Zheng
- State Key Laboratory of Severe Weather (LASW) and Key Laboratory of Atmospheric Chemistry (LAC), Chinese Academy of Meteorological Sciences, CMA, Beijing, 100081, China
| | - Huizheng Che
- State Key Laboratory of Severe Weather (LASW) and Key Laboratory of Atmospheric Chemistry (LAC), Chinese Academy of Meteorological Sciences, CMA, Beijing, 100081, China.
| | - Xiangao Xia
- Laboratory for Middle Atmosphere and Global Environment Observation (LAGEO), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, 100029, China; School of Geoscience, University of Chinese Academy of Science, Beijing, 100049, China
| | - Yaqiang Wang
- State Key Laboratory of Severe Weather (LASW) and Key Laboratory of Atmospheric Chemistry (LAC), Chinese Academy of Meteorological Sciences, CMA, Beijing, 100081, China
| | - Leiku Yang
- School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo, 454000, China
| | - Jing Chen
- Shijiazhuang Meteorological Bureau, Shijiazhuang, 050081, China
| | - Hong Wang
- State Key Laboratory of Severe Weather (LASW) and Key Laboratory of Atmospheric Chemistry (LAC), Chinese Academy of Meteorological Sciences, CMA, Beijing, 100081, China
| | - Hujia Zhao
- Institute of Atmospheric Environment, China Meteorological Administration, Shenyang, 110016, China
| | - Lei Li
- State Key Laboratory of Severe Weather (LASW) and Key Laboratory of Atmospheric Chemistry (LAC), Chinese Academy of Meteorological Sciences, CMA, Beijing, 100081, China
| | - Lei Zhang
- State Key Laboratory of Severe Weather (LASW) and Key Laboratory of Atmospheric Chemistry (LAC), Chinese Academy of Meteorological Sciences, CMA, Beijing, 100081, China
| | - Ke Gui
- State Key Laboratory of Severe Weather (LASW) and Key Laboratory of Atmospheric Chemistry (LAC), Chinese Academy of Meteorological Sciences, CMA, Beijing, 100081, China
| | - Xianyi Yang
- State Key Laboratory of Severe Weather (LASW) and Key Laboratory of Atmospheric Chemistry (LAC), Chinese Academy of Meteorological Sciences, CMA, Beijing, 100081, China
| | - Yuanxin Liang
- State Key Laboratory of Severe Weather (LASW) and Key Laboratory of Atmospheric Chemistry (LAC), Chinese Academy of Meteorological Sciences, CMA, Beijing, 100081, China
| | - Xiaoye Zhang
- State Key Laboratory of Severe Weather (LASW) and Key Laboratory of Atmospheric Chemistry (LAC), Chinese Academy of Meteorological Sciences, CMA, Beijing, 100081, China
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