珠江三角洲秋季典型O3污染的气象条件及贡献量化

利用自组织神经网络分析法,对珠江三角洲2013~2017年秋季海平面气压和全风速场进行大样本客观分型.结果表明,影响O₃的天气类型共有7种,由污染程度高低分为高、中、低3类,对应的平均O₃超标率分别为32.3%, 12.0%和4.2%.对比2017年和2016年秋季O₃污染天气分型下促发O₃污染的气象因子差异,2017年秋季高污染型O₃天气形势出现的天数比2016年增多,且中污染型天气形势出现时,2017年的局地污染气象条件更为不利.采用WRF-CAMx模式通过改变气象场输入来量化气象条件贡献,并用实测变化减去气象变化以推算排放贡献.结果表明,气象条件变化导致O₃浓度上升的贡献率为29.8%,而排放的变化引起O₃浓度下降的贡献率为7.1%.在2017年秋季开展的O₃污染防治专项行动指导下的珠江三角洲O₃前体物控制措施,有效缓解了部分由于不利气象条件而引起的O₃污染浓度上升.不利气象条件是导致2017年秋季O₃浓度升高的重要成因.

Meteorological conditions and contribution quantification of typical ozone pollution during autumn in Pearl River Delta

The self-organizing map (SOM) analysis method was applied to conduct synoptic classification regarding surface ozone pollution in Pearl River Delta (PRD) using the meteorological reanalysis data (i.e., sea level pressure and u, v wind) in autumn from 2013 to 2017. The results showed that there were 7 synoptic types regarding O₃ pollution, which were divided into severe, moderate and mild categories by the pollution degree, with the corresponding ratios of excessive-standard O₃ being 32.3%, 12.0%, and 4.2%, respectively. Comparing the meteorological factors aggravating O₃ pollution under different weather-types regarding O₃ pollution between 2017 and 2016, the number of days with severe O₃ pollution weather-type increased in autumn of 2017 relative to 2016. When the moderate pollution weather-type occurred, the local meteorological conditions became more unfavorable in 2017. The WRF-CAMx model was utilized to quantify the contribution of meteorological conditions by altering the meteorological inputs with fixed emissions, and the contribution of emissions was estimated by subtracting the meteorological contribution from the changes in observation. The results showed that the changes in meteorological conditions led to an increase of 29.8% in O₃ concentrations, while the emission changes contributed to a decrease of 7.1%. The special O₃ pollution prevention and control action was launched in autumn of 2017, and the O₃ precursor control measures in PRD have effectively reduced the O₃ concentration. The emission control has alleviated the enhancement of O₃ pollution caused by the adverse meteorological conditions. Unfavorable weather conditions were the leading cause of the increase in O₃ concentration in the autumn of 2017.