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[山东半岛近地面O浓度的时空变化及潜在源区分析]

[Temporal and Spatial Variation in O Concentration Near the Surface of Shandong Peninsula and Analysis of Potential Source Areas].

作者信息

Li Le, Liu Min-Xia, Xiao Shi-Rui, Wang Si-Yuan, Mi Jia-le

机构信息

College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China.

出版信息

Huan Jing Ke Xue. 2022 Mar 8;43(3):1256-1267. doi: 10.13227/j.hjkx.202107003.

Abstract

The purpose of this study was to explore the temporal and spatial distribution characteristics and potential sources of ozone (O) in the Shandong Peninsula over a long period of time based on the analysis of the temporal and spatial changes in O concentration in Shandong Peninsula from 2005 to 2020. We used wavelet analysis, the entropy weight method, and correlation analysis to discuss O and its influencing factors and researched the potential sources of O in Shandong Peninsula. The results showed that:① in terms of the time pattern, the near-surface O in Shandong Peninsula showed a "triple peak" trend from 2005 to 2020, reaching the maximum value of[(40.48±7.64) μg·m] in 2010 and a minimum value of[(36.63±5.61) μg·m] in 2013. The season was expressed as:summer[(42.49±1.7) μg·m]>spring[(40.65±0.6) μg·m]>autumn[(36.47±0.7) μg·m]>winter[(36.46±0.3) μg·m]. ② In terms of the spatial pattern, the O concentration of Shandong Peninsula gradually increased with the increase in latitude from 2005 to 2020, showing the characteristics of high concentrations in the east and west and low in the middle region. During the 16-year evolution of the O concentration, there was a 1.5 a main oscillation period. ③The analysis of meteorological conditions revealed that O concentration was positively correlated with temperature, precipitation, relative humidity, and sunshine hours, whereas pressure and wind speed were negatively correlated. In the analysis of social factors, soot (dust) emissions were the most obvious factor affecting the third indicator, with a weight of 0.25. ④ Through simulating the trajectory of airflow from different regions (Ji'nan and Qingdao), it was found that the ocean airflow contributed 10.69% to Jinan and 48.94% to Qingdao. There was 64.04% of the long-distance air mass transmission path coming from the northwest, and 43.69% of the short-distance air mass transmission path was from the Bohai Sea and the Yellow Sea, followed by Shandong Province with 21.01%. ⑤ The analysis of potential sources of O showed that the potential sources of Ji'nan were mainly distributed in Jinzhou, Liaoning Province, northern Jiangsu Province, Hubei Province, and Anhui Province, with a WPSCF value >0.6, and Qingdao's WPSCF value of >0.6 was mainly distributed in the Yellow Sea area. The O contribution of Jining City, Linyi City, Xuzhou City, Huaibei City, and Lianyungang City was >40 μg·m. The area with >45 μg·m in Qingdao was mainly in the Yellow Sea. Through the analysis of potential sources in the Shandong Peninsula, particular attention should be paid to the supply of industrial sources in the surrounding areas and the marine sources provided by marine air pollution.

摘要

本研究旨在基于对2005年至2020年山东半岛臭氧(O)浓度时空变化的分析,探讨山东半岛长期以来臭氧的时空分布特征及潜在来源。我们运用小波分析、熵权法和相关分析来探讨臭氧及其影响因素,并研究山东半岛臭氧的潜在来源。结果表明:①在时间格局方面,2005年至2020年山东半岛近地面臭氧呈现“三峰”趋势,2010年达到最大值[(40.48±7.64)μg·m],2013年达到最小值[(36.63±5.61)μg·m]。季节表现为:夏季[(42.49±1.7)μg·m]>春季[(40.65±0.6)μg·m]>秋季[(36.47±0.7)μg·m]>冬季[(36.46±0.3)μg·m]。②在空间格局方面,2005年至2020年山东半岛臭氧浓度随纬度升高而逐渐增加,呈现出东西部高、中部低的特征。在臭氧浓度的16年演变过程中,存在一个1.5年的主振荡周期。③气象条件分析表明,臭氧浓度与温度、降水、相对湿度和日照时数呈正相关,而与气压和风速呈负相关。在社会因素分析中,烟尘(粉尘)排放是影响第三指标最明显的因素,权重为0.25。④通过模拟来自不同区域(济南和青岛)的气流轨迹,发现海洋气流对济南的贡献率为10.69%,对青岛的贡献率为48.94%。长距离气团传输路径有64.04%来自西北方向,短距离气团传输路径有43.69%来自渤海和黄海,其次是山东省内,占21.01%。⑤臭氧潜在来源分析表明,济南的潜在来源主要分布在辽宁省锦州市、江苏省北部、湖北省和安徽省,WPSCF值>0.6,青岛WPSCF值>0.6的区域主要分布在黄海海域。济宁市、临沂市、徐州市、淮北市和连云港市的臭氧贡献率>40 μg·m。青岛>45 μg·m的区域主要在黄海。通过对山东半岛潜在来源的分析,应特别关注周边地区工业源的供应以及海洋空气污染提供的海洋源。

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