一个新的框架,将颗粒物污染减轻的数量归因于自然和社会经济驱动因素
Hao Cui1, Jian Li1, Yutong Sun2
1School of Geoscience and Technology, Zhengzhou University, Zhengzhou 450001, Henan, China.
The Science of the total environment
|March 24, 2024
概括
减少二氧化硫 (SO2),氧化 (NOx) 和尘埃排放,在中国31个城市显著改善了空气质量. 本研究量化了这些影响,以改善污染控制政策.
科学领域:
- 环境科学 环境科学
- 数据科学数据科学数据科学
- 城市规划 城市规划
背景情况:
- 准确量化空气质量驱动因素对于有效的污染控制和政策制定至关重要.
- 机器学习模型用于空气质量分析往往缺乏解释性,阻碍了对污染变化的归因.
- 了解对颗粒物的社会经济和自然影响对于城市管理至关重要.
研究的目的:
- 开发一个可解释的框架来量化社会经济和自然驱动因素的颗粒物减轻.
- 测量各种驱动因素对中国31个主要城市空气质量改善的全球和当地贡献.
- 确定影响颗粒物水平的驱动因素的关键值和相互作用影响.
主要方法:
- 利用Shapley添加式解释 (SHAP) 开发两个指数来衡量全球和本地驾驶员贡献.
- 从2014年到2021年,分析了中国31个城市的颗粒物 (PM2.5和PM10) 数据.
- 研究了社会经济和自然驱动因素对颗粒物度的独立和互动影响.
主要成果:
- 减少二氧化硫 (SO2),氧化 (NOx) 和尘埃排放占全球PM2.5减少的51.58%和PM10减少的51.96%.
- 在不同城市不同司机的贡献中观察到显著的异质性.
- 对诱导颗粒物水平转移的驱动因素确定了关键值.
结论:
- 开发的框架为空气质量驾驶员归因提供了可解释的见解.
- 减少SO2,NOx和尘埃的排放是改善中国城市空气质量的关键因素.
- 调查结果支持制定定制的,特定于地点的政策建议,以减轻空气污染.
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