研究报告
南国卫,孙虎.基于灰色关联模型对陕西省O3浓度影响因素分析[J].环境科学学报,2017,37(12):4519-4527
基于灰色关联模型对陕西省O3浓度影响因素分析
- Analysis of the driving factors of O3 in Shaanxi Province based on grey correlation model
- 基金项目：教育部科学技术重点项目（No.105152）
- 作者
- 单位
- 南国卫
- 1. 陕西师范大学地理科学与旅游学院, 西安 710119;2. 地理国家级实验教学示范中心, 陕西师范大学, 西安 710119
- 孙虎
- 1. 陕西师范大学地理科学与旅游学院, 西安 710119;2. 地理国家级实验教学示范中心, 陕西师范大学, 西安 710119
- 摘要：O3污染问题已经成为21世纪另一个环境课题.本次研究采用克里金插值法对2015年陕西省O3浓度的空间分布特征进行分析，同时构建了O3浓度的影响指标体系，运用灰色关联模型，综合分析了O3浓度与其影响指标因子的关联度，并探讨了各指标因子综合关联度的空间关联性和分异性.结果显示：1陕西省O3浓度呈"北高南低"的空间分布特征；2权重最大的指标层是O3污染来源（Wi=0.4331），其次是城市化与产业结构（Wi=0.3455），空气质量与自然因子最小（Wi=0.2215）；3各个指标因子与O3浓度关联度均为强度关联.空气质量与自然因子中，关联度较高的指标因子为CO、降水量、平均气温、日照时数；O3污染来源中，关联度较高的指标因子为等级公路里程、单位GDP能耗及工业用电量；城市化与产业结构中，关联度较高的指标因子为第二产业占地区总产值的比例、建成区绿地覆盖率和人均公园绿地面积；4O3污染来源对铜川、宝鸡、汉中等市O3浓度的影响较大；城市化与产业结构对宝鸡、咸阳、渭南、汉中等市的影响较大；空气质量与自然因子与陕西省各个城市O3浓度的关联度均为强度关联；5空气质量与自然因子中，CO、PM2.5、日照时数与O3浓度综合关联度较高.O3污染来源指标层中为机动车保有量、餐饮总额、烟（粉）尘排放量.城市化与产业结构中则是房屋建筑施工面积、建成区面积、人口密度.影响O3浓度的主要指标因子表现出较好的空间相关性与空间分异性.综合分析表明，灰色关联度模型能够有效地对O3浓度的主要影响因素作出分析与评价.
- Abstract：O3 pollution problem has become another environmental issue in the 21st century. In this study, the spatial distribution characteristics of O3 in Shaanxi Province were analyzed by Kriging interpolation method. Constructing the influence index system of O3 and using the gray correlation model to analyze the correlation degree between O3 and its influencing index factors, and to explore the spatial correlation and dissimilarity of the comprehensive correlation degree of each index factor, and to explore the spatial correlation and dissimilarity of the comprehensive correlation degree of each index factor. The research shows result as follows:1 The concentration data of O3 in the southern area is lower, while it is higher in the northern area. 2 The index layer with the greatest weight is the source of O3 pollution(Wi=0.4331), followed by urbanization and industrial structure(Wi=0.3455), and the weight of air quality and natural factor(Wi=0.2215) is the minimum. 3 The correlation degree between each index factors and O3 was all correlated with intensity. Among those of air quality and natural factors, CO, precipitation, average temperature, sunshine hours of high correlation; and for O3 pollution sources, the level of highway mileage, unit GDP energy consumption and industrial power associated with a higher degree; for urbanization and the industrial structure index layer, the secondary industry accounted for the regional total output value, built area green space coverage and per capita park green area of the correlation is higher. 4 O3 pollution source index layer have great influence on O3 in Tongchuan, Baoji and Hanzhong cities. The urbanization and industrial structure index layer have great influence on Baoji, Xianyang, Weinan and Hanzhong cities. The correlation between the air quality index layer and the O3 in each city of Shaanxi Province is intensity correlation. 5 CO, PM2.5, sunshine hours have a high correlation to O3 in Air quality and natural factor index layer. Among the source O3 pollution layer, motor vehicle ownership, total amount of food, smoke (powder) dust emissions in O3 pollution source indicator layer, housing construction area, built area, population density in Urbanization and industrial structure index layer and O3 with a higher degree of correlation. The main index factors influencing O3 show a better spatial correlation and spatial dissimilarity. The comprehensive analysis shows that the gray relational model can be used to effectively analyze and evaluate the main influencing factors of O3.
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