达州市城区环境空气质量变化趋势及CMAQ模型预报分析

利用Spearman秩相关系数法、污染日历图、浓度分析法和CMAQ预测模型研究了达州市城区2015-2019年空气质量状况.结果表明:2015-2019年,达州市城区O3浓度变化趋势为显著上升(P＜0.05),季度变化明显,8月易发生因O3超标导致的轻度污染状况;CO年均值变化趋势为显著降低(P＜0.05);NO2年均...

Analysis of Ambient Air Quality Variation Trend and CMAQ Model Forecast System in Urban Areas of Dazhou City

The air quality of Dazhou City in 2015-2019 was studied by using Spearman rank correlation coefficient method, pollution calendar, concentration analysis method and CMAQ prediction model. The results showed that the change trend of O₃ in the main urban area of Dazhou City in 2015-2019 was significantly increased (P<0.05), the quarterly change trend of O₃ concentration was obvious. In August, mild pollution caused by excessive O₃ was prone to occur. The change trend of CO annual concentration in 2015-2019 was significantly reduced (P<0.05). NO₂ annual average concentration showed an upward trend in the pass five years, but had not yet reached a significant level (P>0.05). The average annual concentration of SO₂, PM₁₀ and PM_2.5 showed a downward trend for the pass five years, but the change trend was not obvious (P>0.05). The heaviest pollution occurred in January and December 2019, and PM_2.5 exceeding the standard was the main cause. The situation of PM₁₀ and PM_2.5 annual average concentrations meeting national secondary standard was grim, and more than half of the days of the whole year PM_2.5 exceeded the national secondary standard of the annual average concentration, and the PM₁₀ was nearly half. The situation of the annual average concentration reaching the national secondary standard in 2019 was grim, and the NO₂ daily average concentration exceeded the annual average concentration secondary standard for 212 days in 2019. CMAQ model predicts different pollution indexes with different accuracy. The accuracy of CMAQ model to predict PM_2.5 concentration, primary pollutant and air quality grade is less than that of manual prediction, while the accuracy of AQI prediction is higher than that of manual prediction. The prediction comparison of more pollution indexes needs to be further studied.