气候变化和使用对中国农田未来二氧化排放的协同效应
Wenjing Zhou1, Libo Chen2, Wenxin Cheng1
1Institute of Resource, Ecosystem and Environment of Agriculture, Nanjing Agricultural University, 1 Weigang, Nanjing, Jiangsu, China.
Journal of environmental management
|February 21, 2025
概括
中国的土壤氧化 (NO) 排放量是使用机器学习建模的,XGBoost表现最好. 预测显示到2050年可能会增加,但有针对性的管理可以减轻这些排放.
科学领域:
- 环境科学 环境科学
- 农业科学 农业科学
- 气候科学 气候科学
背景情况:
- 土壤的氧化 (NO) 排放有助于环境污染.
- 准确的量化对于制定减缓策略至关重要.
研究的目的:
- 使用机器学习构建和验证中国的土壤NO排放模型.
- 确定影响氧化物排放的关键因素,并预测未来的趋势.
- 评估管理策略在减排方面的潜力.
主要方法:
- 利用了中国各地476次现场测量的数据库.
- 采用了四种机器学习算法:XGBoost,随机森林,SVM和ANN.
- 使用独立观察数据验证模型性能.
主要成果:
- XGBoost 模型表现出最好的性能 (R2 = 0.67).
- 影响NO排放的关键变量是矿物的输入,土壤有机碳和空气温度.
- 预计2020年NO排放量为204.48 kt NO-N;预计到2050年增加2.9%-9.9%.
- 南方农业地区面临着最大的预计增长 (15%-31%).
结论:
- 机器学习,特别是XGBoost,提供了一个强大的方法来建模土壤NO排放.
- 未来的气候变化场景预测NO排放量增加,特别是在脆弱的南部地区.
- 适应性管理策略可以显著减少未来的NO排放 (15%-16.2%).
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