利用可解释机器学习框架预测粤港澳大湾区酸雨

IF 3.9 3区 环境科学与生态学 Q2 ENVIRONMENTAL SCIENCES
Zeqin Huang , Jianyu Fu , Bingjun Liu , Xinfeng Zhao , Yun Zhang , Xiaofei Wang
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引用次数: 0

摘要

酸雨的特点是 pH 值低于 5.6,它是生态系统的一种严重自然干扰,威胁着全球生态系统、农业和人类社会的可持续性。然而,由于不断变化的环境具有显著的空间异质性,准确量化酸雨的驱动因素仍然具有挑战性。在此,我们建立了一个可解释的机器学习框架(MLF),以19个气象、空气污染物和地表变量作为模型输入,构建了2006-2021年粤港澳大湾区酸雨的pH值。MLF 包括用于酸沉降预测的极端梯度提升法(XGBoost)和用于解释因子重要性的 SHapley Additive exPlanations 法(SHAP)。结果表明,观测到的酸雨 pH 值的增加主要是受整个非洲大沙漠地区空气中二氧化硫(SO2)日最大浓度显著下降的控制,每个城市的相对贡献率从 16.2% 到 31.9% 不等。城市化率和靠近海岸程度的变化在预测酸雨 pH 值方面也起着重要作用。气象变量对酸雨预测的影响通常很小,所占比例一般低于 5%,这表明酸雨生成的物理过程非常复杂。这项研究加深了我们对高度发达地区酸雨驱动因素空间变异性的理解,为全球经常出现酸雨的地区提供了宝贵的见解和案例研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Acid rain prediction in the Guangdong-Hong Kong-Macao Greater Bay Area using an explainable machine learning framework

Acid rain, characterized by pH values lower than 5.6, is a critical natural disturbance of ecosystems, which threatens the sustainability of ecosystems, agriculture, and human society worldwide. However, accurately quantifying the driving factors of acid rain remains challenging due to a changing environment of significant spatial heterogeneity. Here, we established an explainable machine-learning framework (MLF) using 19 meteorological, air pollutant, and land surface variables as model input to construct the pH values of acid rain across the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) during 2006–2021. The MLF includes Extreme Gradient Boosting (XGBoost) for acid deposition prediction and a SHapley Additive exPlanations method (SHAP) for interpreting factor importance. The results indicated that the observed increases in pH values of acid rain are predominantly controlled by the significant decreases in maximum daily sulfur dioxide (SO2) concentration of air across GBA, with its relative contribution ranging from 16.2% to 31.9% for each city. Changes in the urbanization rate and the proximity to the coast also play significant roles in predicting the pH values of acid rain. Meteorological variables typically have minimal impact on acid rain predictions, with their contribution generally being less than 5%, indicating the complex physical process of acid rain generation. This study enhanced our comprehension of the spatial variability of acid rain drivers across a highly developed region, providing valuable insights and case studies for regions worldwide that frequently experience acid rain.

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来源期刊
Atmospheric Pollution Research
Atmospheric Pollution Research ENVIRONMENTAL SCIENCES-
CiteScore
8.30
自引率
6.70%
发文量
256
审稿时长
36 days
期刊介绍: Atmospheric Pollution Research (APR) is an international journal designed for the publication of articles on air pollution. Papers should present novel experimental results, theory and modeling of air pollution on local, regional, or global scales. Areas covered are research on inorganic, organic, and persistent organic air pollutants, air quality monitoring, air quality management, atmospheric dispersion and transport, air-surface (soil, water, and vegetation) exchange of pollutants, dry and wet deposition, indoor air quality, exposure assessment, health effects, satellite measurements, natural emissions, atmospheric chemistry, greenhouse gases, and effects on climate change.
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