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2015—2019 年天津市大气污染物时空变化特征及成因分析 被引量:37

Temporal and Spatial Variation Characteristics and Origin Analysis of AirPollutants in Tianjin from 2015 to 2019
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摘要 基于2015—2019年天津市大气监测点的污染物逐时质量浓度数据,分析了天津市PM2.5、PM10、SO2、CO、NO2和O36种大气污染物的时空变化特征,揭示了天津市大气污染的变化情况,为全市大气环境污染治理与改善提供理论参考。结果表明,2015—2019年天津市PM2.5、PM10、SO2年均质量浓度和CO 24 h平均第95百分位数变化均呈下降趋势;NO2年均质量浓度变化呈“单峰型”,先上升后下降;O3_8 h_max第90百分位数变化整体上呈显著上升趋势。从月变化来看,PM2.5、PM10、SO2、CO和NO2表现为“冬高夏低”;O3月变化呈“夏高冬低”。通过SPSS软件分析,PM2.5与CO相关系数为0.862,相关程度较强,表明天津市大气污染中PM2.5与CO的贡献或有协同性。O3与NO2相关性较高,其原因与光化学反应有关。气象因素中,温度和日照时长对天津市大气污染影响较大。汽车尾气排放等移动源污染比煤炭燃烧、发电和工业生产等固定源污染的贡献更大。细颗粒物是天津市颗粒物大气污染的主要贡献者。不同污染物的空间分布变化存在差异。PM2.5和PM10始终呈“西高东低”。SO2和CO的空间分布格局变化较大,重污染地区逐渐转移到城郊村镇地区。NO2空间分布格局呈“城镇高、乡村低”,并且随时间推移市区污染缓解明显,滨海新区核心区渐成污染重点。O3污染整体上西部和北部地区较严重,O3污染程度逐年加剧。目前,天津市大气污染得到了有效缓解,NO2质量浓度波动较大但2019年NO2年均质量浓度较2015年变化不大,机动车尾气排放仍是天津市大气污染的重要贡献源。NO2等污染物经光化学反应又能有效促进O3生成。因此控制机动车污染排放是当下天津市大气污染治理中亟待解决的问题。 This study explored the spatial and temporal characteristics of 6 kinds air pollutants(PM2.5,PM10,SO2,CO,NO2 and O3)in Tianjin from 2015 to 2019 based on ground-based measurements data.It provides scientific basis for the improving-of air quality in Tianjin.We found that from 2015 to 2019,the average annual mass concentration of PM2.5,PM10,SO2 and the 95th percentile of mean CO in 24 hours showed a downward trend in Tianjin;the change of annual mass concentration of NO2 was“single peak”,it increased first and then decreased;the 90th percentile of O3_8 h_max showed a significant upward trend.In terms of monthly variations,the PM2.5,PM10,SO2,CO and NO2 showed“winter high and summer low”and the O3 showed“summer high and winter low”.The correlation coefficient between PM2.5 and CO was 0.862,which indicated that the contribution of PM2.5 and CO in air pollution in Tianjin might be synergistic.The correlation between O3 and NO2 was high,and the reason was related to photochemical reaction.Among meteorological factors,temperature and sunshine duration had great influence on air pollution in Tianjin.Mobile source pollution such as automobile exhaust emissions contributed more than fixed source pollution such as coal combustion,power generation and industrial production.Fine particulate matter was the main contributor to air pollution of particulate matter in Tianjin.There were differences in the spatial distribution of different pollutants.The spatial distribution pattern of PM2.5 and PM10 were always“west high east low”.The spatial pattern of SO2 and CO changed greatly,and the heavy pollution area gradually transferred to the suburban village and town area.The spatial distribution of NO2 was“high in town and low in countryside”,and it could be seen that the air quality improved in Tianjin urban area during these years,and the core area of Binhai new area had gradually become the center of heavy pollution.O3 in the western and northern regions was more serious than other regions,the level of O3 pollution was increasing in recent years.The air pollution in Tianjin had been effectively improved,the mass concentration of NO2 fluctuated significantly,but the average annual mass concentration of NO2 in 2019 did not change much compared with 2015.The vehicle exhaust emission as the main source of NO2 was still an important contribution source of air pollution in Tianjin.The photochemical reaction of NO2 and other pollutants can effectively promote O3 formation.Therefore,controlling the emission of motor vehicle pollution is the most urgent problem in the air pollution control in Tianjin.
作者 沈楠驰 周丙锋 李珊珊 赵文慧 王丽丽 董洁 赵文吉 SHEN Nanchi;ZHOU Bingfeng;LI Shanshan;ZHAO Wenhui;WANG Lili;DONG Jie;ZHAO Wenji(College of Resources Environment and Tourism,Capital Normal University,Beijing 100048,China;Beijing Municipal Research Institute of Environmental Protection,Beijing 100037,China;Beijing Municipal Environmental Monitoring Center,Beijing 100048,China)
出处 《生态环境学报》 CSCD 北大核心 2020年第9期1862-1873,共12页 Ecology and Environmental Sciences
基金 国家重点研发计划项目(2018YFC0706004,2018YFC0706000) 北京市自然科学基金项目(8202024)。
关键词 天津市 大气污染物 时空变化 相关性分析 特征分析 Tianjin atmospheric pollutants temporal and spatial changes correlation analysis characteristic analysis
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