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利用基于物理和可解释的机器学习方法来量化雨水和空气污染物对湿沉积的相对贡献
1Department of Atmospheric Sciences, Yonsei University, Seoul 03722, Republic of Korea.
降雨比污染物度在限制湿沉积方面更为重要,特别是在污染地区. 这一发现对于理解生态系统影响和未来的环境变化至关重要.
科学领域:
- 大气化学 大气化学
- 环境科学 环境科学
- 生态系统动力学 生态系统动力学
背景情况:
- 湿沉积极大地影响着陆地和水生生态系统.
- 雨量和污染物度在湿沉积中的不同作用尚未完全量化.
- 这些贡献的区域差异仍然不清楚.
研究的目的:
- 量化区分降雨和污染物度对湿沉积的影响.
- 确定影响不同地区湿沉积的主要限制因素.
- 为未来湿沉积预测提供基础.
主要方法:
- 开发了一个基于物理的模型,使用简化的湿地清理方程.
- 采用可解释的机器学习 (随机森林与夏普利添加式扩展).
- 使用3D长期空气质量模拟 (2003-2019) 作为输入数据.
主要成果:
- 基于物理和机器学习的模型产生了一致的结果.
- 降雨被确定为比污染物度更为主导的限制因素.
- 在受污染的地区,雨水的贡献超过了污染物的3-4倍,在低排放地区超过了污染物的1.5-2.5倍.
结论:
- 降雨显著调节湿沉积,即使在排放低的地区,突出显示了污染物运输的作用.
- 在偏远地区,一旦降雨量超过一个值,湿沉积高原会出现,这两种因素的贡献都相当.
- 调查结果有助于解释年度间湿沉积变化,并预测排放控制和气候变化下的未来情景.
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