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基于机器学习的每小时和局部优化土壤逃尘排放清单方法

Lilai Song¹, Zhen Li¹, Jinqiu Zhang¹

¹Key Laboratory of Urban Air Particulate Pollution Prevention and Control of Ministry of Ecology and Environment, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China; CMA-NKU Cooperative Laboratory for Atmospheric Environment-Health Research, Tianjin 300350, China.

Journal of environmental sciences (China)

|August 21, 2025

概括

在中国北部,土壤逃尘 (SFD) 受到面积和湿度等裸土因素的重大影响. 这项研究优化了SFD排放计算和控制策略的模型.

科学领域:

环境科学
大气化学
地质科学

背景情况:

土壤逃尘 (SFD) 对中国北部的城市空气质量和生态系统产生重大影响.
SFD具有很高的空间时间变化和多样化的排放源.
目前对SFD的关键影响因素和定量方法的了解有限.

研究的目的:

通过可解释的机器学习来确定影响SFD排放的关键因素.
确定这些影响因素的相互作用和作用值.
提高SFD排放计算的量化方法.

主要方法:

使用可解释的机器学习技术来分析SFD数据.
确定主要影响因素及其相互作用.
使用局部参数优化风侵蚀方程模型.

主要成果:

确定了SFD影响因素的季节性变化.
发现包括面积和湿度在内的裸土源强度是一个重要的因素.
优化的风侵蚀方程模型显示每小时SFD排放的精度有所提高.

结论:

该研究提供了对SFD排放驱动因素的精细了解.
优化的参数化方案提高了SFD建模的准确性.
这些发现有助于制定有针对性的SFD预防和控制政策.

关键词:

排放量清单机器学习土壤逃离尘埃

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