• 关键词：PM₁₀和PM_2.5  后向轨迹  聚类分析  潜在源区  西宁

• 基金项目：国家自然科学基金（No.41705121，41665008）；青海省自然科学基金（No.2017-ZJ-944Q）；青海省气象局重点项目（No.QXZD2021-08）

• 刘娜
• 青海省人工影响天气办公室, 青海省防灾减灾重点实验室, 西宁 810001

• 余晔
• 中国科学院西北生态环境资源研究院, 寒旱区陆面过程与气候变化重点实验室, 兰州 730000

• 张莉燕
• 青海省人工影响天气办公室, 青海省防灾减灾重点实验室, 西宁 810001

• 王启花
• 青海省人工影响天气办公室, 青海省防灾减灾重点实验室, 西宁 810001

• 马学谦
• 青海省人工影响天气办公室, 青海省防灾减灾重点实验室, 西宁 810001

• 摘要：利用HYSPLIT模式计算了2016—2018年西宁市逐日72 h气团后向轨迹，采用聚类分析方法，结合同期颗粒物PM₁₀和PM_2.5质量浓度数据，分析逐年和3年平均西宁市颗粒物输送特征及差异，运用潜在源贡献因子分析法（PSCF）和浓度权重轨迹分析法（CWT）对影响西宁市PM₁₀和PM_2.5质量浓度的污染潜在源区及不同潜在源区贡献进行了分析.结果表明，2016—2018年，西宁市颗粒物最主要输送路径源自青海北部的聚类2、甘肃中部的聚类6和甘肃东部的聚类8，占同期总轨迹比例分别为28.1%、27.4%和27.5%；3年平均则源自青海北经青海东折回西宁的聚类2，占比45.3%.最主要输送路径对应颗粒物质量浓度最低，输送距离较短、垂直高度较低、气团移速较慢；影响气团由西北向偏东转变，3年平均则以西北气团为主.2018年源自甘肃经青海东至西宁的短距离输送处于突出地位，所含轨迹占总轨迹的比例高达49.6%.PM₁₀和PM_2.5主要输送路径和污染路径由较长距离向较短距离过渡，较长距离输送路径出现比例逐年较小.PM_2.5/PM₁₀小于0.3时，主要输送路径与PM₁₀污染轨迹有很好的对应关系；PM_2.5/PM₁₀大于0.6时，主要输送路径与PM_2.5污染轨迹有较好的对应关系.PSCF和CWT分析发现，影响西宁市颗粒物质量浓度的主要污染潜在源区分布在新疆南部和青海北部，对PM₁₀质量浓度贡献大于100 μg·m^-3，对PM_2.5质量浓度贡献大于45 μg·m^-3.潜在源区分布年变化差异明显，2016年最广，2018年最小.印度北部主要贡献源区虽分布范围逐年减小，但在2017年局部贡献增大，对PM₁₀贡献超250 μg·m^-3，对PM_2.5贡献超60 μg·m^-3.主要贡献区周边区域及西宁至兰州一带为中等贡献源区，对PM₁₀贡献为50~100 μg·m^-3，对PM_2.5贡献为15~45 μg·m^-3.

• Abstract：The HYSPLIT model was used to identify the transport pattern and pathways of particulate matter pollution in Xining by clustering 72 h backward trajectories during 2016-2018. Potential source regions and their contributions were defined with the help of potential source contribution function (PSCF) and concentration-weighted trajectory (CWT) combined with daily PM₁₀ and PM_2.5 mass concentrations. The most important transport pathways were the cluster 2 originated from northern Qinghai Province, the cluster 6 from central Gansu, the cluster 8 from eastern Gansu, and the cluster 2 from northern and eastern Qinghai arriving at Xining, accounting for 28.1%,27.4%,27.5% and 45.3% of the total trajectories in each year and during the whole study period. The most important transport pathways correspond to the air mass trajectories with the lowest concentration, shorter distance, lower vertical height and slower speed. The air masses changed from the northwest to the easterly wind during 2016-2018, while the northwest air masses were dominated during the campaign. The short distance pathways from Gansu to Xining across eastern Qinghai accounted for up to 49.6% in 2018. The main transported and polluted pathways showed that the variation characteristics from longer to shorter distances during the campaign. Meanwhile, the probability of longer distance transport were increasingly smaller from 2016 to 2018. The pathways with PM_2.5/PM₁₀ less than 0.3 had the best relationship between the main transport and the PM₁₀ pollution trajectories. In addition, the better relationship was found to be in PM_2.5/PM₁₀ more than 0.6 pathways between the main transport and the PM_2.5 pollution trajectories. PSCF and CWT analysis indicated that the main potential regions were located in southern Xinjiang and northern Qinghai, with the contribution to PM₁₀ and PM_2.5 loadings more than 100 and 45 μg·m^-3. The differences in the potential source regions distribution were significant during 2016-2018. Their areas were the widest in 2016, while the smallest in 2018. The distribution of the main potential source regions from northern India increasingly decreased from 2016 to 2018, while the contribution increased with more than 250 (60) μg·m^-3 to PM₁₀ (PM_2.5) loadings in 2017. The surrounding of the main potential source regions and Xining-Lanzhou cities were medium contribution areas,with 50~100 μg·m^-3 to PM₁₀ and 15~45 μg·m^-3 to PM_2.5, respectively.

• Key words：PM₁₀ and PM_2.5  backward trajectory  cluster analysis  potential source  Xining

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