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使用可解释的机器学习框架,揭示了中国主要冬季小麦地区臭氧污染和农业生产率之间的复杂相互作用

Chenxi Du¹, Jie Pei², Zhaozhong Feng³

¹School of Geospatial Engineering and Science, Sun Yat-sen University, Zhuhai 519082, China.

The Science of the total environment

|September 16, 2024

概括

表面臭氧显著降低了中国的冬季小麦产量,造成高达22%的损失. 这种空气污染物是空气污染物.

科学领域:

环境科学,农业科学,大气化学,数据科学

背景情况:

表面臭氧是中国的主要大气污染物,影响农作物生产和粮食安全.
现有的研究重点是生理臭氧损伤,但区域影响和气候变化与气候变化相互作用的理解较少.

研究的目的:

研究臭氧及其与环境因素的相互作用对中国主要冬季小麦地区的作物生产的影响.
通过可解释的机器学习框架阐明驱动这些相互作用的机制.

主要方法:

采用了一个可解释的机器学习框架: eXtreme渐变增强 (XGBoost) 增强了SHapley添加式扩展 (SHAP).
开发了一个结构方程模型来分析相互作用机制.
评估了中国主要农业地区冬季小麦生产率的影响.

主要成果:

臭氧污染对冬季小麦的生产率产生了显著的负面影响 (r = -0.47,P < 0.001),损失在12.28%至22.09%之间.
臭氧的影响在空间上是异质的,山东省西部被确定为损害热点.
高气溶或高温与臭氧的联合影响加剧了作物损失;干旱可以减轻这些影响.

结论:

臭氧污染对中国冬季小麦产量构成重大威胁.
空气污染和气候变化因素 (温度,气溶,干旱) 的互动影响极大地影响作物生产率.
综合统计和可解释的AI框架为区域空气污染对作物的影响评估提供了一种有价值的方法.

关键词:

可解释的机器学习粮食安全粮食安全黄海海平原黄海海平原黄海海平原臭氧臭氧是什么?臭氧臭氧是一种冬季小麦冬季小麦

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