针对PM2.5和O3典型复合污染城市保定,利用PCT客观天气分型方法对秋冬季和春夏季京津冀地区的海平面气压场及10m风场进行天气分型,并通过后向轨迹(PSCF、CWT)及气象-空气质量模式(WRF-CAMx)对污染天气型下的PM2.5与O3进行来源解析,研究其贡献比例和传输路径,探究保定市重污染天气应对及不同天气型下复合污染防治策略.结果显示,保定市2019~2022年PM2.5年平均浓度呈下降趋势,下降幅度为26.9%,O3呈波动趋势,下降幅度仅为2.2%;月平均浓度呈现明显的季节性特点.秋冬季京津冀地区以高压场型为主,其中T5(东部高压后部,11.6%)、T6(鞍型场,11.6%)和T9(均压场,5.2%)是容易导致保定市PM2.5污染的污染天气型;春夏季以低压场为主,易导致保定市O3污染的天气类型为S3(均压场,12.9%)和S7(西北部低压后部,16.8%);在PM2.5污染天气型下,保定市的PM2.5来源贡献均以本地排放为主.T5(东部高压后部)中保定市的PM2.5其他贡献区域主要以邯郸、石家庄等西南路径为主;T6(鞍型场)中其他贡献区域主要以山西省和石家庄等西南路径为主;T9(均压场)中其他贡献区域主要以山西省及内蒙古地区等西北路径为主.在O3污染天气型下,保定市的O3本地贡献最高.在S3(均压场)下,其他地区贡献以保定周边城市传输影响为主;而S7(西北部低压后部)中其他地区贡献主要以河南省、邯郸、山西省、石家庄等西南路径输送为主.
Abstract
The typical compound pollution of PM2.5 and O3 in Baoding City was investigated in this study. Objective weather classification of sea-level pressure and 10m wind fields in the Beijing-Tianjin-Hebei region during the autumn-winter and spring-summer seasons was conducted using the Principal Component T-mode (PCT) method. Backward trajectory analysis (PSCF, CWT) and meteorological-air quality modeling system (WRF-CAMx) were applied to identify the source contributions and transport pathways of PM2.5 and O3 under pollution-related weather types. From 2019 to 2022, the annual average PM2.5 concentration in Baoding showed a significant decline of 26.9%, whereas O3 exhibited a fluctuating trend with a slight reduction of only 2.2%, indicating limited improvement. Monthly average concentrations of both pollutants demonstrated pronounced seasonal patterns. In autumn and winter, high-pressure systems dominated, and weather types T5 (eastern high-pressure rear, 11.6%), T6 (saddle-type, 11.6%), and T9 (isobaric field, 5.2%) were most frequently associated with PM2.5 pollution events. In contrast, low-pressure systems prevailed in spring and summer, with weather types S3 (isobaric field, 12.9%) and S7 (northwest low-pressure rear, 16.8%) contributing notably to O3 pollution. Local emissions were identified as the dominant source of PM2.5 during pollution weather types. Under T5, additional contributions originated mainly from Handan and Shijiazhuang; under T6, from Shanxi Province and Shijiazhuang; and under T9, from Shanxi and Inner Mongolia. For O3 pollution, local sources accounted for the largest share. Under S3, transport from surrounding cities played a secondary role, whereas under S7, contributions from southwestern regions, including Henan, Handan, Shanxi, and Shijiazhuang, were more prominent.
关键词
O3 /
PM2.5 /
来源解析 /
浓度权重分析法 /
潜在源贡献分析法 /
天气分型
Key words
concentration-weighted trajctory /
O3 /
PM2.5 /
potential source contribution function /
source apportionment /
weather classification
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基金
国家自然科学基金资助项目(52330002)
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