近几年我国典型城市AQI变化特征

论文总字数：17602字

目 录

1引言 5

2数据来源和研究方法 6

2.1数据来源 6

2.2研究方法 6

3结果与分析 7

3.1 AQI的变化特征 7

3.1.1 AQI的年变化 7

3.1.2 AQI的季节变化 8

3.1.3 AQI的月变化 10

3.2污染天数及首要污染物对比 13

3.2.1污染天数对比 13

3.2.2首要污染物对比 14

3.3 AQI指数与气象条件的关系 15

3.3.1 AQI与气象要素相关性分析 15

3.3.2基于气象要素的AQI回归方程建立与拟合效果检验 16

4结论 17

参考文献 17

致谢 19

近几年我国典型城市的AQI变化特征

李衡

, China

Abstract: The study used the daily AQI （Air quality Index） data of eight typical cities in Beijing, Shanghai, Guangzhou, Nanjing, Wuhan, Chengdu, Zhengzhou, and Harbin from 2015 to 2017 to study the annual, seasonal, and monthly variations characteristics of AQI in each city. The number of days for each youthful degree of pollution and above levels in the cities and the seasonal variation characteristics of primary pollutants in the first-tier cities were counted. The results show that the pollution days in Beijing, Shanghai, Nanjing, Wuhan, Chengdu, Zhengzhou and Harbin in 2017 have decreased compared to 2015, and the annual average value of AQI has decreased. Seasonal variations characteristics of AQI basically show that pollution is light in summer and autumn, and winter pollution is heavy. The large value of AQI in Beijing, Shanghai, Chengdu and Zhengzhou in the summer of 2017 was mainly caused by the ozone 8-hour pollution incident. The frequency of primary pollutants in the summer is 8 hours, and the frequency of primary pollutants in the spring, autumn, and winter is highest, and the probability that the primary pollutant in the winter is NO2 is also higher. The study also gives the correlation coefficient between the daily mean value of AQI and the meteorological elements such as temperature, humidity, atmospheric pressure and wind speed in each month of 2017 in Beijing. And selects meteorological elements with good correlation to establish the regression equation of AQI and tests the fitting results. The results show that the regression equation has a good effect on the overall trend of the AQI changes in Beijing and the average, but the ability to fit the extreme values ​​is poor, and the fitting results tend to be even.

Key words: AQI variations characteristics; Primary pollutants; Meteorological elements; Regression equation

1引言

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