整合可解释的人工智能和因果推理,揭示中国地区空气质量驱动因素
Zhiyuan Fu1, Xiao Yang2, Yike Ma1
1School of Resources Environment and Tourism, Anyang Normal University, Anyang, 455000, China.
Journal of environmental management
|June 21, 2025
概括
中国的空气质量因地区而异,受当地气候和工业因素的影响. 一个新的因果关系框架显示,温度和降水改善了空气质量,而SO2排放和工业恶化了空气质量,需要有针对性的排放控制.
科学领域:
- 环境科学 环境科学
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
背景情况:
- 空气污染是全球主要的健康问题.
- 中国面临着复杂的空气质量挑战,因为污染驱动因素的空间异质性.
- 统一的治理策略对于不同的区域污染动态是不够的.
研究的目的:
- 引入因果分析-检测-解释-预测-值 (CADEPT) 框架,用于对空气质量的多级因果推断.
- 研究中国空气质量指数 (AQI) 的驱动力和未来演变.
- 确定局部的因果机制,并为目标空气质量控制策略提供信息.
主要方法:
- 利用了与城市,社会经济和气候数据集集集成的全国空气质量监测数据 (2014-2022年).
- 采用了空间异质性分析 (Local Moran's I) 和因果推理技术 (SHAP,TabPFN).
- 进行值分析和基于场景的模拟,用于AQI预测.
主要成果:
- 检测到AQI的显著空间聚类,气候,排放和工业对区域特定的影响.
- 确定了变量的非线性,区域特定的贡献;气候通常减轻了污染,而工业加剧了污染.
- 量化因果影响:温度和降水增加改善了AQI,而SO2排放和工业扩张降低了AQI.
- 在关键值时发现了气象条件和排放因子之间的协同放大效应.
结论:
- 促进低碳转型和协调减排对于持续改善全国空气质量至关重要.
- CADEPT框架有效地捕捉了复杂环境问题的局部因果机制.
- 定制的,特定于地区的战略对于解决中国异质空气污染局面至关重要.
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