研究论文 正式出版 版本 2 Vol 10 (3) : 248-256 2019
Analysis on the Characteristics of Temporal and Spatial Changes of Atmospheric PM2.5 and It's Influencing Factors in Xi’an from 2013 to 2017
: 2018 - 08 - 25
: 2018 - 09 - 01
: 2018 - 05 - 23
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关键词:西安市; PM2.5; 时空变化;气象要素
Abstract & Keywords
Abstract: Background, aim, and scope Xi’an City is located in the central basin of the Guanzhong Plain. The topographic conditions obstruct the airflow, which is not conducive to the spread of pollutants, and the mutual transmission of pollutants between cities has a greater impact. Those reasons made the air pollution in this region more serious. In addition, due to the increasingly serious atmospheric pollution in recent years, Xi’an is going to be listed in the key areas for air pollution control. In order to improve regional air quality and provide a good environment for the second "Belt and Road" Forum for International Cooperation to be held in Xi’an in 2019, it is necessary to understand the current pollution status of the city and its surrounding areas. Materials and methods Therefore, based on the data from 13 State-controlled air quality monitoring stations and meteorological data in Xi’an from July 2013 to December 2017, we analyzed the variation feature and correlation of the mass concentration of PM2.5, which can be changed with time and space, to provide reliable scientific data for further research, governance and control of PM2.5 pollution and early warning of air quality. Results The results reveal that PM2.5 concentration has obvious temporal and spatial patterns. In the interannual trend, from 2014 to 2017, with the implementation of various environmental policies, the overall PM2.5 concentration in Xi’an has a slight downward trend, and the 1st standard annual average daily qualified rate has gradually increased from 15.71% to 27.25%. In summer, PM2.5 pollution gradually improved, with the average concentration decreasing from 64.1 μg/m3 to 33.4 μg/m3, but high-polluted weather still remained in winter. Discussion In terms of monthly and quarterly changes, due to the low atmospheric pressure and low rainfall, the inversion layer is relatively thick, the height of the atmospheric boundary layer is low, and the effect of heating, the mass concentration in winter is higher compared to summer. In the daily changes, due to the difference in emissions caused by human activities, there is a "weekend effect" that the concentration on weekends and holidays is higher than that on the working day. In hourly fluctuation, due to the temperature difference between day and night, the change of the atmospheric boundary layer, and the influence of human activities emissions, there is a bimodal pattern which the concentration is higher in the morning and midnight, while lower in the afternoon. In the spatial distribution, the areas with the highest concentration of PM2.5 pollution are the central urban areas, northern and western area. The concentration in the northern city is higher than that in the southern area. The main urban area is significantly higher than the surrounding counties. This spatial difference is caused by internal emissions and external transport. Meteorological elements are important factors that restrict the dilution, diffusion, migration and transformation of pollutants in the atmosphere, and will affect the PM2.5 mass concentration to some extent. As the temperature is low, the inversion layer is relatively thick and the pollutants are difficult to diffuse in the atmosphere and easy to have secondary reactions, which made the concentration of PM2.5 has a certain negative correlation with the temperature. In overcast and in cloudy weather of autumn and winter, PM2.5 concentrations are high. When rainfall is heavy, the pollutants will be partially removed by wet deposition under the effect of rain and snow, and the concentration of PM2.5 is relatively low. The wind direction and wind speed will affect the horizontal migration of particles. Under windy conditions, the PM2.5 polluting situation is roughly inversely related to the wind speed. With the increase of wind power, the particulate matter diffusion conditions are getting better and the concentration is lower. However, under the static wind conditions, particulate matter transported by the regional transportation has less impact, the pollution mainly comes from the interior of the city, and the high-pollution weather is less. Conclusions In a word, urban atmospheric PM2.5 pollution is the result of the combined effects of anthropogenic emissions and weather. Recommendations and perspectives Meteorological conditions can affect air pollution to some extent, but man-made emissions are the root cause of PM2.5 pollution. At present, energy conservation and emission reduction are the most suitable methods for improving atmospheric conditions.
Keywords: Xi’an; PM2.5; temporal and spatial variation; meteorological factor
PM2.5是指大气中所含有的空气动力学直径小于或等于2.5微米的颗粒物,也叫细颗粒物或可入肺颗粒物,具有比表面积大,活性强,容易吸附有毒物质的特点,是非均相反应的载体。PM2.5可以在大气中存在很长的时间,会对人体健康、生态环境、区域气候产生影响,是当前大气气溶胶研究的主要对象(Delfino et al,2005;Vasconcelos et al,1994;Jacobson,2001)。PM2.5可由多种气态及固态污染物转化而成,成分复杂,是直接反应大气污染情况的重要指标之一,也是西安地区的大气首要污染物。西安市位于关中平原(盆地)中部,总面积10108平方公里,包括11区2县,是西北地区最重要的经济中心之一。盆地地形易受阻塞而出现闭合环流风,对气流产生阻挡效应(李小飞等,2012),不利于污染物扩散,且城市之间污染物相互传输影响较大,会使区内污染加重。因此,西安地区大气污染受内外污染源与本地气象条件共同控制。在生态环境部发布通报的2017年74城市空气质量状况显示(中华人民共和国环境保护部,2018-01-18),西安市空气质量排名为倒数第四名,且西安市所处的汾渭平原由于近年来大气污染日益严重,或将被列入大气污染防治重点区域。为改善区域大气环境污染,为2019年西安市即将举办的第二届“一带一路”国际合作高峰论坛提供良好的会议环境,需先了解当前城市及周边污染现状及相关影响因素。本文选取西安城市及郊县区2013—2017年的PM2.5质量浓度监测数据进行统计分析,研究并探讨区域内PM2.5的时间、空间变化特征及气象条件和人为因素的相关影响,以为进一步研究、治理和控制本区域PM2.5污染以及空气质量情况预警预测提供可靠的科学依据。
1   资料与方法

图1   研究区域及监测点位
Fig.1 Research area and monitoring spot
1.1   资料
表1 西安市空气质量监测站点分布
Tab.1 The distribution of Xi’an air quality monitoring station
High voltage switch factory
Industrial park, near by the 2nd Ring Road.
Xingqing community
Living quarter, near by the Park.
Textile City
Industrial park.
Xiao Zhai
Business District, traffic hinge.
Xi’an Stadium
Business District, near by the Park.
West High-tech Development Zone
Science Park, near by the Park.
economic development Zone
administrative region, near by the Park.
Chang'an District
University campus.
Yan Liang District
Living quarter.
Lin Tong District
University campus.
Cao Tan
Near by the Park.
Qujiang Cultural Industry Group
Living quarter, near by the Park.
Guang Yun Tan
Near by the ecoregion.
1.2   评价方法
1.2.1   评价标准
以中华人民共和国环境保护部(2012)发布的《环境空气质量标准(GB 3095—2012)》中所规定的PM2.5浓度限值为标准,一、二级24小时均值浓度限值分别为35 μg/m3和75 μg/m3
1.2.2   数据处理
除日变化分析和空间对比外,其余均取24小时监测平均值做日均值进行对比及分析;分析和制图工具分别使用Excel 2013和Origin 2017。
2   PM2.5质量浓度变化规律
2.1   PM2.5污染现状及年际变化趋势
为了解近几年西安市PM2.5空气污染的整体现状和变化趋势,对西安市13个站点的日均PM2.5质量浓度变化(图2)和各年日均PM2.5质量浓度达标率(表2)进行了分析。西安市2013年7月—2017年12月总平均PM2.5浓度为73.5 μg/m3,其中日均最高值为599.2 μg/m3,出现在2013年12月;日均最低值为8.8 μg/m3,出现在2017年10月;日均值二级浓度限值达标率为68.8%,一级浓度限值达标率为22.54%。2014—2017年,随着各环保政策的实施,西安市各类能源消费量和能耗均在降低,整体PM2.5浓度有小幅的下降趋势,年日均一级、二级达标率分别升高了11.54%和5.09%。夏季PM2.5污染在逐步改善,平均浓度从64.1 μg/m3下降到33.4 μg/m3。但2016—2017年冬季,仍有28日PM2.5日均浓度高于200.0 μg/m3,且日均最高浓度达到了491.4 μg/m3,说明冬季仍然存在高污染天气。除PM2.5浓度的整体下降趋势外,相较前后各年,其在2014年及2015年冬季有明显的偏低现象(图2),季平均质量浓度分别为76.5 μg/m3和97.7 μg/m3。李晓配等(2017)认为,导致西安市2014年冬季PM2.5浓度偏低的主要原因之一是当季有利天气形势天数的增加。天气形势对区域内PM2.5浓度的影响将在后文详述。

图2   PM2.5日均浓度变化趋势
Fig.2 PM2.5 daily mean concentration trend
表2 PM2.5日均浓度达标率
Tab.2 The qualified rate of PM2.5 daily average concentration
National primary standard qualified rate
National secondary standard qualified rate
2.2   PM2.5季度、月度变化规律
对月平均值及四季平均值进行分析(图3),可见夏季(6—8月)大气PM2.5浓度最低,平均值为50.6 μg/m3;冬季(12—2月)最高,平均值为126.7 μg/m3,严重时冬季平均浓度可达到夏季平均浓度的2—3倍;秋季(9-11月)略高于春季(3—5月);浓度峰值通常出现在冬季1月份。产生这种现象的原因主要是因为冬季气温、气压低、降雨量少,逆温层较强较厚,大气边界层高度低(Stull,1988;吕立慧等,2017)。除自然原因外,北方冬季供暖也会导致大量污染物排放。污染物又难以上升扩散或沉降清除,从而聚集在盆地内部并发生二次反应,致使大气污染愈发严重。

图3   PM2.5日均浓度在月度、季度上的分布规律
箱式图显示了平均值、第10、第25、第50、第75和第90位百分位数;下文箱式图标记含义相同。The box plots indicate the mean concentration and the 10th, 25th, 50th, 75th, 90th percentiles. The markers have the same meaning in the following box plots.
Fig.3 The distribution of PM2.5 daily mean concentration in different month and season
2.3   PM2.5逐日变化的“周末效应”及“假日效应”
对周内各日及节假日(法定公休节假日)时的PM2.5日均浓度分布进行对比(图4)。可见周末和节假日PM2.5整体浓度相对于工作日有一定程度的偏高,这种现象称为PM2.5的“周末效应”和“假日效应”,主要由人类规律性活动引起的排放差异所致(李建东等,2015;Lough et al,2006)。在北京市(雷瑜等,2015)、成都市(谢雨竹等,2015)和厦门市(Niu et al,2013)等多数城市研究中,PM2.5质量浓度的周变化具有周末低、工作日高的特征;而在其他部分城市如南京(孙雪等,2017)等地,周末效应具有相反的变化模式。造成这种现象的主要原因是城市结构不同,受排放和人类活动影响的情况不同,周变化趋势也并不完全相同。近年来,随着西安市人民生活水平的提高,周末和节假日私家车出行规模大幅度增加,且在西安市采暖季的常态化限行政策中,对周末时段的机动车行驶无限制规定,这都会在一定程度上导致休息日机动车排放量增大,PM2.5质量浓度升高。

图4   PM2.5日均质量浓度的周变化模式及节假日对比
Fig.4 The daily mean PM2.5 mass concentration between workdays and holidays
2.4   PM2.5日变化规律
2.5   PM2.5各城区空间分布特征

图5   西安市各站点PM2.5 质量浓度24小时变化
Fig.5 PM2.5 concentration changes of each station of Xi’an in 24 hours(The range indicates mean concentration of Xi’an is 10th-90th, the ranges indicate each station is 25-75th)
2.6   气象条件的影响

图6   PM2.5浓度在不同气温和天气下的分布
Fig.6 PM2.5 concentration in different temperatures and weathers
(3)风向风速会影响大气中的污染物汇入和横向扩散。将西安地区的主导风向和对应的PM2.5日均质量浓度作对比(图7a),西安地区主导风向为NE,其次为E,占比为44.24%和32.97%,对应的PM2.5平均浓度为74.9 μg/m3和72.0 μg/m3。这说明通过东北风及东风汇入的污染物是影响西安市大气质量的重要因素。浓度最高的主导风向为N,占比为2.66%,对应的PM2.5平均浓度为86.7 μg/m3;浓度最低的主导方向为SW,占比仅为12.18%,对应的PM2.5平均浓度为64.0 μg/m3

图7   PM2.5浓度在不同风条件下的分布
Fig.7 PM2.5 concentration in different wind conditions
3   结论及展望
3.1   区域内PM2.5浓度变化特征
3.2   区域PM2.5污染影响因素及成因分析
曹军骥. 2012. 我国PM2.5污染现状与控制对策[J].地球环境学报, 3(5): 1030-1036. [Cao J J. 2012. Pollution status and control strategies of PM2.5 in China [J]. Journal of Earth Environment, 3(5): 1030-1036.]
雷瑜, 张小玲, 唐宜西, 等. 2015. 北京城区PM2.5及主要污染气体“周末效应”和“假日效应”研究[J]. 环境科学学报, 35(5): 1520-1528. [Lei Y, Zhang X L, Tang Y X, et al. 2015. Holiday effects on PM2.5 and other major pollutants in Beijing [J]. Acta Scientiae Circumstantiae, 35(5): 1520-1528.]
李建东, 铁学熙, 曹军骥. 2015. 城市地区PM2.5周末效应的初步研究[J].地球环境学报, 6(4): 224-230. [Li J D, Tie X X, Cao J J. 2015. A preliminary study of PM2.5 weekend effect in typical cities [J]. Journal of Earth Environment, 6(4): 224-230.]
李小飞, 张明军, 王圣杰, 等. 2012. 中国空气污染指数变化特征及影响因素分析[J].环境科学, 33(6): 1936-1943. [Li X F, Zhang M J, Wang S J, et al. 2012. Variation characteristics and influencing factors of air pollution index in China [J]. Environmental Science, 33(6): 1936-1943.]
李晓配, 贝耐芳, 赵琳娜. 2017. 气象条件对2013—2015 年冬季关中地区空气质量的影响[J]. 地球环境学报, 8(6): 516-523. [Li X P, Bei N F, Zhao L N. 2017. Influence of meteorological conditions on the wintertime air quality in the Guanzhong basin during 2013 to 2015 [J]. Journal of Earth Environment, 8(6): 516-523.]
刘洁, 张小玲, 徐晓峰, 等. 2008. 北京地区SO2、NOx、O3和PM2.5变化特征的城郊对比分析[J]. 环境科学, 29(4): 1059-1065. [ Liu J, Zhang X L, Xu X F, et al. 2008. Comparison analysis of variation characteristics of SO2, NOx, O3 and PM2.5 between rural and urban areas, Beijing [J]. Environmental Science, 29(4): 1059-1065. ]
吕立慧, 刘文清, 张天舒, 等. 2017. 基于激光雷达的京津冀地区大气边界层高度特征研究[J]. 激光与光电子学进展, 54(1): 44-50. [Lü L H, Liu W Q, Zhang T S, et al. 2017. Characteristics of boundary layer height in Jing-Jin-Ji area based on lidar [J]. Laser & Optoelectronics Progress, 54(1): 44-50.]
孙雪, 罗小三, 陈燕, 等. 2017. 环境管理强化后南京市2013—2016年大气污染物的时空特征和气象影响[J]. 地球环境学报, 8(6): 506-515. [Sun X, Luo X S, Chen Y, et al. 2017. Spatio-temporal characteristics of air pollution in Nanjing during 2013 to 2016 under the pollution control and meteorological factors [J]. Journal of Earth Environment, 8(6): 506-515.]
西安市统计局, 国家统计局西安调查队. 2016. 西安统计年鉴:2016 [M]. 北京: 中国统计出版社. [Xi’an Municipal Bureau of Statistics, NBS Survey Office in Xi’an. 2016. Xi’an statistical yearbook: 2016 [M]. Beijing: China Statistics Press.]
西安市统计局, 国家统计局西安调查队. 2017. 西安统计年鉴:2017 [M]. 北京: 中国统计出版社. [Xi’an Municipal Bureau of Statistics, NBS Survey Office in Xi’an. 2017. Xi’an statistical yearbook: 2016 [M]. Beijing: China Statistics Press.]
谢雨竹, 潘月鹏, 倪长健, 等. 2015. 成都市区夏季大气污染物浓度时空变化特征分析[J]. 环境科学学报, 35(4): 975-983. [Xie Y Z, Pan Y P, Ni C J, et al. 2015. Temporal and spatial variations of atmospheric pollutants in urban Chengdu during summer [J]. Acta Scientiae Circumstantiae, 35(4): 975-983.]
中华人民共和国环境保护部. 2012. 环境空气质量标准(GB 3095—2012)[S]. 北京: 中国环境科学出版社. [Ministry of Environmental Protection of the People’s Republic of China. 2012. Ambient air quality standard (GB 3095—2012) [S]. Beijing: China Environmental Science Press.]
中华人民共和国环境保护部. 2018-01-18[2018-05-18]. 环境保护部通报2017年12月和1—12月重点区域和74个城市空气质量状况[EB/OL]. http://www.mep.gov.cn/gkml/hbb/qt/201801/t20180118_429903.htm. [Ministry of Environmental Protection of the People’s Republic of China. 2018-01-18[2018-05-18]. Ministry of Environmental Protection informs the air quality of key regions and 74 cities in December and January—December 2017 [EB/OL]. http:// www.mep.gov.cn/gkml/hbb/qt/201801/t20180118_429903.htm.]
Delfino R J, Sioutas C, Malik S. 2005. Potential role of ultrafine particles in associations between airborne particle mass and cardiovascular health [J]. Environmental Health Perspectives, 113(8): 934–946.
Jacobson M Z. 2001. Strong radiative heating due to the mixing state of black carbon in atmospheric aerosols [J]. Nature, 409(6821): 695-697.
Lough G C, Schauer J J, Lawson D R. 2006. Day-of-week trends in carbonaceous aerosol composition in the urban atmosphere [J]. Atmospheric Environment, 40(22): 4137-4149.
Niu Z C , Chen J S, Xu L L, et al. 2013. Application of the Environmental Internet of Things on monitoring PM2.5 at a coastal site in the urbanizing region of southeast China [J]. International Journal of Sustainable Development & World Ecology, 20(3): 231-237
Stull R B. 1988. An introduction to boundary layer meteorology [M]. Dordrecht: Springer.
Vasconcelos L A, Macias E S, White W H. 1994. Aerosol composition as a function of haze and humidity levels in the southwestern US [J]. Atmospheric Environment, 28(22): 3679–3691.
ZHAO Huiyizhe1,2,3
ZHOU Weijian1,2
NIU Zhenchuan1,2
FENG Tian1,2
中国科学院重点部署项目(Y722011017, Y22011480);生态环境部大气重污染成因与治理攻关项目(DQGG0105);国家基金委项目资助
Key Research Program of the Chinese Academy of Sciences (Y722011017, Y22011480); Air pollution causes and governance research projects of Ministry of Ecology and Environment of the People's Republic of China(DQGG0105); Supported by the National Natural Science Foundation of China
出版时间: 2018年5月23日 (版本2
Journal of Earth Environment