关键词: 大气污染物, 大气质量状况, 时序变化, 空间分布, 监测站点, 组合指标, 相关分析, 季节性规律

Abstract:

After 2013, several serious haze pollution events in China have attracted wide attention from the public. Therefore, the Chinese government has developed a series of policies, measures, and laws for air pollution prevention and control. We select the air quality index and six key air pollutant observations in the national air environmental monitoring station network of China to analyze the changes trend and spatial pattern of the country-scaled atmospheric quality from 2015 to 2019. The annual and seasonal average concentrations of these parameters were used to investigate their relationships by using the ratios and correlation coefficients among them. The results showed that the concentration of PM_2.5, PM₁₀, SO₂, CO, NO₂, and AQI all decreased significantly. The annual average concentrations of them in 2019 decreased by 4.5%,3.84%,7.86%,3.74%, 0.95%, and 19.31%, respectively, compared with that in 2015. Only O₃ significantly increased by 0.79% in the same period. The average annual concentrations of PM₁₀, PM_2.5, O₃, NO₂, SO₂,CO, and AQI in northern China all are higher than those in southern China by 25.2%, 18.73%, 4.95%, 17.6%, 32.74%, 16.17%, and 28.3%, respectively. Besides, AQI and all six pollutants showed the seasonality. The seasonal variation pattern of O₃ exists with a higher concentration in summer and a lower concentration in winter, while the other five pollutants and AQI present the inverse seasonality. In general, PM_2.5 and O₃ are the most severe air pollutants in China during these five years. Furthermore, the relationships among different pollutants were found in the study period. The PM_2.5 concentration is significantly positively correlated with NO₂, SO₂ and CO (r>0.85, p<0.01), and O₃ is negatively correlated with its precursor NO₂ and CO (r>0.8, p<0.01).

Key words: key atmospheric pollutants, atmospheric quality, temporal trend, spatial pattern, monitoring sites, composite indicator, correlation analysis, seasonal pattern