| 基于可解释性机器学习的2013~2023年青岛PM2.5主控因素 |
| 摘要点击 921 全文点击 102 投稿时间：2025-01-08 修订日期：2025-03-19 |
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| 中文关键词 PM2.5 长期变化 机器学习模型 政策 主控因素 |
| 英文关键词 PM2.5 long-term variation machine learning policy key drivers |
| DOI 10.13227/j.hjkx.20260202 |
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| 中文摘要 |
| 针对近年来PM2.5浓度降低放缓的问题，基于2013~2023年青岛市PM2.5浓度数据，结合空气污染物、气象和排放等因素，利用分阶段的可解释性机器学习模型预测其变化趋势，并探究不同政策驱动下PM2.5生成机制及主控因素的演变. 结果显示，青岛市ρ（PM2.5）从2013年的（56.3±43.66） μg·m-3下降到2023年的（30.2±24.50） μg·m-3，降幅达46.3%，主要由二次气溶胶的生成减少所驱动. 尤其在2017年之前，对工业和电力部门的末端治理显著减少了二次硫酸盐的生成，使PM2.5浓度下降最快，速率为-4.33 μg·（m3·a）-1. 之后PM2.5浓度降速放缓，可归因于硫酸盐、硝酸盐和铵盐（SNA）前体物浓度的非同步变化导致二次气溶胶对PM2.5生成的贡献增加. 机器学习模型显示NO2对PM2.5的贡献率增加了约6%而SO2的贡献可忽略，且当ρ（SO2）≤8 μg·m-3，PM2.5对NO2的敏感性增强；此外，气象因素对PM2.5的贡献率增加了约5.1%. PM2.5浓度季节变化特征大小表现为：冬季>春季>秋季>夏季，PM2.5浓度在夏季随年份下降最快，而受民用源一次排放增加影响，在冬季下降最慢. 因此，针对民用源控制能显著减少PM2.5一次排放，多污染物协调排放控制策略可有效降低PM2.5二次生成. |
| 英文摘要 |
| To elucidate the deceleration trend in PM2.5 concentration reduction and its formation mechanisms， this study analyzed PM2.5 concentration variations in Qingdao from 2013 to 2023， combined with factors such as air pollutants， meteorology， and emissions. A phased interpretable machine learning model was applied to predict the variations of PM2.5， investigate the formation mechanisms， and identify key drivers under evolving environmental policies. The results showed that PM2.5 concentration decreased from （56.3±43.66） μg·m-3 in 2013 to （30.2±24.50） μg·m-3 in 2023 in Qingdao， with a reduction of approximately 46.3%， primarily driven by the reduction in secondary aerosols. Notably， the fastest decline of PM2.5 occurred before 2017 at a rate of -4.33 μg·（m3·a）-1， mainly due to reduced formation of secondary sulfate from the end-of-pipe control in the industrial and power sectors. After 2017， the deceleration in the decline of PM2.5 concentrations stemmed from asynchronous reductions in sulfate， nitrate， and ammonium precursors， leading to enhanced secondary PM2.5 formation. The machine learning model indicated the enhanced sensitivity of PM2.5 to NO2 under low SO2 conditions， with the contribution of NO2 to PM2.5 concentrations increasing by approximately 6%， while that of SO2 became negligible. Additionally， meteorological factor contribution to PM2.5 concentrations increased by 5.1%. PM2.5 concentrations exhibited a seasonal variation in the order of winter > spring > autumn > summer. Influenced by increased primary emissions from the residential sector， winter PM2.5 concentrations showed the slowest decline over the years. Therefore， controlling residential sources can reduce primary PM2.5 emissions， and a coordinated multi-pollutant emission control strategy can effectively reduce secondary PM2.5 formation. |
