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开发用于户外BTEX的每日1公里分辨率估计模型,使用随机森林与土地使用数据和气象变量进行评估

Chen-Yu Wang¹, Li-Hao Young¹, Bo-Ting Chen¹

¹Department of Occupational Safety and Health, College of Public Health, China Medical University, Taichung, Taiwan.

Journal of hazardous materials

|February 15, 2025

概括

人工智能模型预测台湾各地的,,乙烯和 (BTEX) 的每日空气污染水平. 这些模型将交通确定为关键来源,有助于健康风险评估和污染控制策略.

科学领域:

环境科学环境科学
数据科学数据科学数据科学
公共卫生公共卫生

背景情况:

,,乙和 (BTEX) 是具有重大健康风险的危险空气污染物.
传统的监测方法无法捕捉到BTEX的全部空间和时间变化.
机器学习和土地利用回归为室外BTEX度提供了增强的预测能力.

研究的目的:

使用机器学习开发高分辨率的每日BTEX模型.
利用台湾10年的每小时BTEX测量数据进行模型培训.
评估预测性能并确定户外BTEX的关键驱动因素.

主要方法:

使用随机森林算法每天开发1公里分辨率的BTEX模型.
纳入每小时BTEX测量 (2011-2020年),标准空气污染物,土地使用和气象数据.
使用10倍交叉验证,时间和空间验证技术评估模型性能.

主要成果:

所有BTEX组件的交叉验证R平方值都超过了0.8,o-xylen和m,p-xylen达到0.85.
时间验证的R平方值高于0.8,而空间验证范围从0.50到0.64.
确定了二氧化,道路,农作物,寺,商业和工业区作为重要的预测因素;交通成为主要的BTEX来源.

结论:

开发的机器学习模型准确预测室外BTEX水平,并确定台湾的热点.
交通是户外BTEX的主要原因之一,需要采取有针对性的交通控制措施.
这些模型为有关BTEX暴露和健康影响的流行病学研究提供了有价值的数据.

关键词:

在 BTEX 中使用 BTEX.估计模型的估计模型.机器学习是机器学习.空间和时间分布.挥发性有机化合物挥发性有机化合物

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