基于机器学习算法的不同空间尺度,研究中国PM2.5的影响因素
Meiru Chen1, Jun Liu1, Biwu Chu2
1Laboratory of Atmospheric Environment and Pollution Control, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China; University of Chinese Academy of Sciences, Beijing 100049, China.
Environment international
|May 20, 2025
概括
中国的颗粒物 (PM2.5) 污染受到各种因素的影响. 机器学习模型揭示了污染物度和气象条件是关键驱动因素,在控制策略方面存在区域差异.
科学领域:
- 环境科学 环境科学
- 大气化学 大气化学
- 数据科学数据科学数据科学
背景情况:
- 颗粒物 (PM2.5) 污染是中国的一个重大环境挑战,表现出复杂的空间变化.
- 了解PM2.5的驱动因素对于制定有效的污染控制策略至关重要.
研究的目的:
- 确定和量化影响中国2015年至2022年平均年PM2.5水平的关键因素.
- 为了比较机器学习模型在城市和电网层面的性能,用于PM2.5预测.
- 为有针对性的PM2.5缓解政策提供特定区域的见解.
主要方法:
- 采用光梯度增强机 (LightGBM) 模型进行预测分析.
- 利用夏普利添加式解释 (SHAP) 方法来解释模型发现并确定影响因素.
- 在城市和精细电网层面分析了PM2.5数据,以评估空间分辨率的影响.
主要成果:
- 纳入污染物度 (NO2,CO,SO2) 显著改善了模型性能 (R2从0.79到0.93).
- 更高的空间分辨率 (网格级模型) 进一步提高了预测准确性 (R2 0.960.99).
- 城市PM2.5是由污染物度 (49.3%) 驱动的,而非城市地区更受气象因素 (温度,降水) 的影响.
- 区域分析显示,在长江三角洲 (YRD) 和珍珠河三角洲 (PRD) 中,主要排放控制PM2.5,而大气氧化能力限制控制在北京-天津-河北 (BTH),芬威平原 (FWP) 和四川盆地 (SCB).
结论:
- 机器学习,特别是LightGBM与SHAP,为分析复杂的空气污染数据提供了强大的工具.
- 调查结果强调了针对PM2.5控制的特定区域战略的必要性,考虑到不同的影响因素.
- 这项研究为决策者提供了有价值的数据驱动的见解,以制定更有效,更有针对性的空气污染减缓计划.
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