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[关于上海空气污染物地理模型开发的多种建模方法的比较研究]

Ying-Han Wu¹, Jia Xu¹, Yu-Sen Duan²

¹Chinese Research Academy of Environmental Sciences, Beijing 100012, China.

Huan jing ke xue= Huanjing kexue

|October 12, 2023

概括

这项研究比较了上海空气污染暴露评估的地理统计模型. 随机森林和部分最小平方模型与普通Kriging显示了最好的二氧化 (NO2) 和颗粒物 (PM2.5) 预测的结果.

科学领域:

环境科学环境科学
地质统计学在地质统计学
空间分析空间分析

背景情况:

地理统计模型对于空气污染暴露评估至关重要.
有限的比较研究存在于不同的建模方法及其预测准确性.
了解模型性能是可靠曝光估计的关键.

研究的目的:

为了比较各种空间建模方法的性能,用于环境空气污染物暴露的评估.
评估不同的建模策略,包括采用普通Kriging (OK) 的单步和两步模型.
确定在上海预测二氧化 (NO2) 和颗粒物 (PM2.5) 度的最佳模型.

主要方法:

从上海55个地点 (2016-2019) 收集了NO2和PM2.5监测数据.
使用的地理变量 (道路网络,景点,卫星数据).
应用部分最小平方回归 (PLS),监督线性回归 (SLR) 和随机森林 (RF) 有或没有OK,通过5倍交叉验证进行评估.

主要成果:

对于NO2 (R2 0.70-0.84),RF-OK和PLS-OK模型表现出优异的性能.
PLS模型的性能优于其他PM2.5模型 (R2 0.62-0.71).
NO2预测显示的相关性比PM2.5 (r 0.66-0.96) 更高 (r 0.82-0.91).

结论:

该研究确定了在上海NO2和PM2.5暴露评估的最佳地缘统计模型.
模型比较为在空气质量研究中选择合适的方法提供了宝贵的见解.
预测的暴露情况基于对NO2和PM2.5.5的累积人口暴露度评估.

关键词:

二氧化 (NO2) 是一种有毒的物质.对于PM2.5来说,这是一个很好的例子.地质统计模型的地质统计模型.部分最小平方回归 (PLS)随机森林 (RF) 是一个随机的森林.

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