中国重点城市 NO2和PM2.5的空间分布特征及影响因素研究
文献类型：学位论文
| 作者 | 张淑平 |
| 学位类别 | 硕士 |
| 答辩日期 | 2015-05 |
| 授予单位 | 中国科学院研究生院 |
| 授予地点 | 北京 |
| 导师 | 周伟奇 |
| 关键词 | NO2 PM2.5 空气污染 空间格局 气象因素 人口规模 |
| 其他题名 | Spatial Pattern of Typical Air Pollutants and Their Driving factors in Major Cities of China |
| 学位专业 | 生态学 |
| 中文摘要 | 目前我国众多城市在快速的城市化进程中，面临日益严峻的城市大气污染问题，引起各级政府和城市居民的广泛关注。二氧化氮（NO2）和细颗粒物（PM2.5）分别是城市传统光化学污染和新型污染（雾霾）的典型代表，尤其在污染较为严重的冬季，是评价城市大气污染程度的典型指标。本研究以 NO2和 PM2.5为研究对象，采用全国 114个重点城市在冬季的实时监测数据，分析了这两种典型污染物浓度的空间分布特征，明确了重点防治区域。并从人为因素和自然因素两方面，探讨影响 NO2和 PM2.5浓度的驱动机制，揭示了城市人口规模与气象因素对大气污染物浓度的影响。研究结果可为城市人口发展规模的规划管理与城市大气污染的防治提供决策提供科学依据，具有重要的科学和现实意义。主要结果如下： （1）我国城市冬季 NO2和PM2.5污染严重，区域分异特征显著。仅有21%的城市NO2浓度达到世界卫生组织（World Health Organization (WHO)）的城市年均浓度标准（40 μg/m3），所有城市的 PM2.5浓度均高于 WHO年均浓度标准（10μg/m3）。污染物的空间分布具有明显的区域特征：NO2的空间分布相对比较分散，PM2.5的空间分布有明显的“北高南低、内高外低”趋势。NO2的重点防治区域为天津、河北东南部和山东中部地区，PM2.5的重点防治区域为河北西南部和山东西部。 （2）城市人口规模对 NO2和 PM2.5的浓度影响显著，城市常住人口规模与NO2和PM2.5浓度呈倒“U”型关系。人口规模在1000到 1200万的城市，冬季平均 NO2和 PM2.5浓度最高（NO2: 69.28 μg/m3; PM2.5: 119.58 μg/m3）。人口规模小于1200万的城市，冬季NO2和PM2.5浓度随着城市规模的增加而显著升高（NO2:r=0.44, P<0.01; PM2.5: r=0.43, P<0.01）；人口规模大于 1200万的城市，NO2浓度与城市规模呈显著负相关关系（r=0.91, P<0.05），PM2.5浓度随城市规模增加有逐渐降低趋势，但统计上不显著。 （3）气象条件显著影响 NO2和 PM2.5浓度的日变化，且污染程度不同的城市，其影响因子的种类不同。影响石家庄市冬季NO2浓度的主要气象因素为降雨量；北京是相对湿度、平均风速和最低温；上海是日均温、相对湿度、平均风速、最高温和降雨量；深圳是日均温、相对湿度、平均风速和最高温；拉萨是平均风速、最高温和最低温。影响石家庄市冬季 PM2.5浓度的主要气象因素为湿度和风速；西安是湿度和平均风速；北京是湿度、日均温度、风速和最低温；太原市是日均温、湿度、最高温、最低温和最大持续风速；广州是日均温、湿度、风速和最低温。 （4）PM2.5浓度越高的地区，气象因素能够解释的PM2.5浓度变异越小。污染最严重的石家庄市，气象因素多元回归分析的 R2为 0.19，即气象因素可以解释 19%的 PM2.5日浓度变异；重污染区的西安市，多元回归分析 R2为0.24；中污染区的北京市气象因素多元回归分析 R2为 0.46；受到污染的太原市多元回归分析 R2为 0.52；污染程度最低的广州市象因素可以解释 40%的 PM2.5日浓度变异。气象因素对 PM2.5日浓度变异的解释能力有限。 |
| 英文摘要 | With the accelerated urbanization, many of the Chinese cities are increasingly facing serious problems of air pollutions. NO2 and PM2.5 are the two primary pollutants in many Chinese cities, particularly in winter. Here, we used real-time (every hour) ground measured NO2 and PM2.5concentration datasets collected in the winter of 2013 for 114 Chinese major cities to investigate the spatial distribution of these two pollutants. We also examined the relationships between the concentrations of air pollutants and the total population of the cities, as well as meteorological factors. We found: (1) The spatial distributions of the two pollutants had distinct regional characteristics. Only 21% (23 cities) of cities which have NO2 concentration meet the air quality guideline of World Health Organization (AQG of WHO; 40 μg/m3）, and no city had PM2.5 concentration lower than the AQG of WHO (10 μg/m3). Concentrations of PM2.5 in Northern China were higher than that in south of China, and the inland was higher than the east coastal areas. In particular, concentrations of PM2.5 were very high in the southwest of Hebei province and the west of Shandong province. Concentrations of NO2 pollutant were relatively high in Tianjin, the southeast of Hebei province and the middle of Shandong province. (2) An inverse “U” type relationship between air pollutant and urban population. Cities with population between 10-12 million had the highest NO2 and PM2.5 concentration of 69.28 μg/m3 and 119.58 μg/m3, respectively. Significant positive correlations between urban population and the concentrations of NO2 (r=0.35,P<0.01), and PM2.5 (r=0.39, P<0.05) for cities with total population less than 12 million. In addition, for cities with population more than 12 million, the size of urban population had a significantly negative correlation with the concentration of NO2 (r=0.58, P<0.05) and PM2.5. While concentrations of NO2 and PM2.5 had a significantly positive correlation with population density for cities with population density less than 1000 person per sq. kilometer (NO2: r=0.23, P<0.05; PM2.5: r=0.36,P<0.01), concentrations of NO2 and PM2.5 were negatively correlated to population density for cities with population density more than 1000 person per sq. km (NO2:r=-0.61, P<0.05; PM2.5: r=0.63, P<0.01). (3) Meteorological factors had significant impacts on daily NO2 and PM2.5 concentrations, and the effects of meteorological variables on PM2.5 concentration varied by cities with different pollution levels. We found: (a) significantly positive correlations of PM2.5 concentrations with humidity (r=0.45, P<0.01), but negative with wind speed (r=-0.31, P<0.01) in Shijiazhuang; (b) significantly positive correlations of PM2.5 concentrations with humidity (r=0.44, P<0.01), but negative with wind speed (r=-0.29, P<0.01) in Xi’an; (c) significantly positive correlations of PM2.5 concentrations with humidity (r=0.62, P<0.01), temperature (r=0.28,P<0.05),minimum temperature (r=0.43, P<0.01), but negative with wind speed (r=-0.28,P<0.01) in Beijing; (d) significantly positive correlations of PM2.5 concentrations with humidity (r=0.29, P<0.01), temperature (r=0.50, P<0.01), minimum temperature (r=0.43, P<0.01), and maximum temperature (r=0.53, P<0.01), but negative with maximum sustained wind speed (r=-0.39, P<0.01) in Taiyuan; (e) significantly positive correlations of PM2.5 concentrations with humidity (r=-0.31, P<0.01),temperature (r=0.33, P<0.01), and minimum temperature (r=-0.35, P<0.01), but negative with wind speed (r=-0.34, P<0.01) in Guangzhou. （4）Variations in daily PM2.5 concentrations that could be explained by meteorological factors varied by cities. In general, more variations could be explained by meteorological factors in cities with lower concentrations of PM2.5 concentrations.For example, in the most heavily polluted city, Shijiazhuang, only 19% of the variations in daily PM2.5 concentrations could be explained by meteorological factors. However, 52% of the variations could be explained in the lightly polluted city of Taiyuan. The results can greatly improve our understanding on the spatial distribution of NO2 and PM2.5 in winter, and the driving factors, and thus provide important insights on air pollution control for major cities in China. |
| 源URL | [http://localhost/handle//34471]  |
| 专题 | 生态环境研究中心_城市与区域生态国家重点实验室 |
| 推荐引用方式 GB/T 7714 | 张淑平. 中国重点城市 NO2和PM2.5的空间分布特征及影响因素研究[D]. 北京. 中国科学院研究生院. 2015. |
入库方式： OAI收割
来源：生态环境研究中心
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