弥合时间差距:基于人工智能的时间缩小,将每两周的NH3缩小到每天的规模,并具有空间可转移性
Saman Malik1, Eunjin Kang1, Yoojin Kang1
1Department of Civil, Urban, Earth, and Environmental Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Republic of Korea.
Journal of hazardous materials
|July 11, 2025
概括
机器学习模型准确地预测了氨 (NH3) 度,将每两周的数据缩小到美国的每日估计. 这提供了关键的高分辨率空气质量监测,对于环境和健康评估至关重要.
科学领域:
- 环境科学 环境科学
- 大气化学 大气化学
- 数据科学数据科学数据科学
背景情况:
- 氨 (NH3) 是一种严重的气体污染物,对环境和健康产生重大影响.
- 随着NH3度的增加,需要高分辨率的监测,但每日地面数据有限.
研究的目的:
- 开发和评估用于预测每两周NH3度的机器学习框架.
- 为了降低两周一次的NH3估计,在整个美国的每日分辨率.
- 评估模型性能,确定关键预测因素,并评估空间可转移性.
主要方法:
- 开发了深度神经网络 (DNN),随机森林和光梯度增强机器模型.
- 综合NH3柱度,气象数据,土地覆盖,牲畜数据和地面测量.
- 采用空间交叉验证,独立测试和沙普利增量解释 (SHAP) 分析.
主要成果:
- 在DNN模型中,R=0.79,RMSE=0.98 μg/m3和IoA=0.83在9公里分辨率下表现出卓越的性能.
- 确定了时间因素 (年度的一天,气象) 和空间因素 (土地覆盖面,牛群密度) 作为关键预测因素.
- 在英国进行测试时,模型显示空间可转移性.
结论:
- 机器学习有效地弥合了NH3监测的时间数据差距.
- 使用这些先进的框架,可以生成高分辨率的每日NH3估计值.
- 该研究支持改善空气质量评估和环境管理策略.
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