| 安徽省大气污染物时空分布特征及演化规律 |
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| 引用本文： | 王化杰,张波,胡昊,张永江.安徽省大气污染物时空分布特征及演化规律[J].环境科学研究,2018,31(4):628-641. |
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| 作者姓名： | 王化杰 张波 胡昊 张永江 |
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| 作者单位： | 1.安徽职业技术学院环境与化工学院, 安徽 合肥 230011 |
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| 基金项目： | 安徽高校省级自然科学研究重点项目（No.KJ2018A0185）；安徽省高校优秀青年人才支持项目（No.gxyq2018224）；重庆市基础科学与前沿技术研究项目（No.cstc2015jcyjA0002） |
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| 摘 要： | 在区域复合型大气污染逐渐常态化之下，联防联控治理的新模式已成为解决区域性大气污染的根本途径和有效措施.利用2015年冬季（2015年11月8日—2016年1月20日）、2016年冬季（2016年11月8日—2017年1月20日）安徽省16个城市大气污染物（NO2、SO2、CO、O3、PM10、PM2.5）浓度数据，结合耦合协调度模型、探索性空间数据分析和障碍度模型，分析大气污染物的时空格局特征，描述其演变规律和总体走向，诊断区域大气污染物中的首要障碍因子.结果表明:①安徽省大气污染物浓度水平具有时间波动性和空间非均衡性，NO2、O3、PM10和PM2.5指数水平表现为递增态势，整体呈现“两高一低”，即皖北高（0.050 3）、中部地区高（0.050 1）和皖南低（0.040 5）的态势，年际变化呈增长趋势，空间分异度变化较大；②安徽省大气污染物耦合度较高，基本维持在拮抗阶段（2015年冬季和2016年冬季耦合度年均值分别为0.480、0.479），皖北呈增加态势，而中、南部城市主要呈略微降低趋势；包括极度失调和严重失调两种类型（2015年冬季和2016年冬季协调度平均值分别为0.114、0.123）；③安徽省内各城市大气污染物在全省范围内热、冷点分布迥异，2015年冬季和2016年冬季主要经历了聚拢（NO2、O3向中部城市聚拢）和北迁（PM10、PM2.5往北迁）两个过程.研究显示，结合安徽省大气污染物障碍度测量分析，优化和量化区域大气污染物中的首要障碍因子，可为有效开展地区大气污染的防控治理及区域联动提供有利保障.
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| 关 键 词： | 大气污染物 耦合协调度 探索性空间数据分析 障碍度 安徽省 |
| 收稿时间： | 2017/8/21 0:00:00 |
| 修稿时间： | 2017/10/25 0:00:00 |
Evolution Characteristics and Spatial-Temporal Pattern of Air Pollutants in Anhui Province |
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| WANG Huajie,ZHANG Bo,HU Hao and ZHANG Yongjiang.Evolution Characteristics and Spatial-Temporal Pattern of Air Pollutants in Anhui Province[J].Research of Environmental Sciences,2018,31(4):628-641. |
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| Authors: | WANG Huajie ZHANG Bo HU Hao and ZHANG Yongjiang |
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| Affiliation: | School of Environment and Chemical Engineering, Anhui Vocational and Technical College, Heifei 230011, China;CAS Key Laboratory for Urban Pollutant Conversion, Department of Chemistry, University of Science and Technology of China, Hefei 230026, China,School of Environment and Chemical Engineering, Anhui Vocational and Technical College, Heifei 230011, China,CAS Key Laboratory for Urban Pollutant Conversion, Department of Chemistry, University of Science and Technology of China, Hefei 230026, China;Department of Architectural Engineering, Anhui Water Conservancy Technical College, Heifei 231603, China and Environmental Monitoring Center Station of Qianjiang, Chongqing 409099, China;School of Resources and Environment, Southwest University, Chongqing 400716, China |
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| Abstract: | Under the gradual normalization of regional composite atmospheric pollution, a novel model of joint prevention and control has emerged, which has become the fundamental way and effective measures to solve regional atmospheric pollution. Through the analysis of atmospheric pollutant concentration data (NO2, SO2, CO, O3, PM10 and PM2.5) from 16 cities of Anhui Province in the winter of 2015 and 2016, based on the coupled coordination model, exploratory spatial data analysis (ESDA) and obstacle degree model. The research reveals the spatial-temporal distribution of atmospheric pollutants, and describes its evolution and the overall trend, and the primary obstacle factor. The result shows:(1) Fluctuation and non-equilibrium of atmospheric pollutants concentration in Anhui Province, and index level of NO2, O3, PM10 and PM2.5 showed an increasing trend. As a whole, the result of "two highs and one low" shows that the high level index is in the northern (the annual mean of the index level is 0.0503) and central areas (the mean of the index level is 0.0501) of Anhui Province, while the southern Anhui region is low (the annual mean of the index level is 0.0405),and the spatial diversity varies greatly. (2) The coupling degree of atmospheric pollutants in Anhui Province is basically maintained in the antagonistic stage (the annual mean of coupling degree is 0.480 and 0.479 in 2015 and 2016, respectively), and an increasing trend is in the northern areas of Anhui Province, an opposite tendency is in the southern Anhui region. The coordinating degree mainly shows the extreme and serious disorder (the annual mean of coordinating degree is 0.114 and 0.123 in 2015 and 2016, respectively). (3) The distribution of heat and cold spots of atmospheric pollutants in different cities of Anhui province is significant different. In the two research years, the main experience:gather (NO2, O3 to the central city gathered) and northward migration (PM10 and PM2.5 moved north). (4) Finally, we can optimize and quantify the primary obstacle factors of regional by obstacle degree model, and it can provide a favorable guarantee for effective joint prevention and control of regional atmospheric pollution. |
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| Keywords: | air pollutants coupled cooperation exploratory spatial data analysis (ESDA) obstacle degree Anhui Province |
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