摘要
通过分析北京、天津、石家庄等7个城市53个监测点的2014年4月8日至2014年7月23日连续15周6种污染指标SO2、NO2、CO、O3、PM10、PM2.5的监测数据,运用相关系数法,借助SPSS软件确定了这些城市每种污染物的相关度。结果表明,北京与河北主要城市及天津在SO2、PM10、PM2.53个主要指标上具有强相关性。最后,为京、津、冀的协同治理空气污染提出一些建议和防范措施,以期为京津冀的协同治理空气污染提供帮助。
Based on the analysis of monitoring data on six pollution indexes of SO2, NO2, CO, O3, PM10 and PM2.5 from 53 monitoring points in 7 cities, including Beijing, Tianjin, Shijiazhuang, etc., from April 8 of 2014 to July 23 of 2014, this article adopted Pearson correlation coefficient method to determine the relevance among each pollutant of these cities with the help of SPSS. The results showed that such three leading indexes as SO2, PM10 and PM2.5 had strong correlation in Beijing, Tianjin and main cities of Hebei. Finally, some suggestions and preventive measures for the cooperative governance of air pollution in Beijing-Tianjin-Hebei Region were put forward, hoping this can help them.
基金
河北农业大学大学生科技创新基金(cxzr2014023)
河北农业大学理工基金重点项目(ZD201406)~~
关键词
Pearson
相关系数
线性插值
检测指标
空气质量
Pearson
Correlation coefficient
Linear interpolation
Monitoring indicators
Air quality