| 基于地统计模型的上海大气污染物多建模方法的比较 |
| 摘要点击 3298 全文点击 883 投稿时间：2022-11-03 修订日期：2022-11-29 |
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| 中文关键词 PM2.5 NO2 地统计模型 偏最小二乘回归(PLS) 随机森林(RF) |
| 英文关键词 PM2.5 NO2 geostatistical model partial least squares regression(PLS) random forest(RF) |
| DOI 10.13227/j.hjkx.20231005 |
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| 中文摘要 |
| 地统计模型被广泛应用于环境空气污染物暴露模拟，但不同建模方法及其模拟结果之间的对比研究较少.基于上海2016~2019年55个环境空气监测点位的NO2和PM2.5观测数据，以及交通路网、排放源兴趣点和卫星数据等地统计变量，应用偏最小二乘回归（PLS）、监督学习线性回归（SLR）和机器学习随机森林（RF）这3种建模方法创建年暴露模型，并进一步应用普通克里金插值（OK）法分析模型残差，构建复合模型.应用交叉验证对模型的模拟效果进行检验，选取每一种建模方法的最优模型结构（是否应用OK）作为最终模型.结果表明，NO2模型中表现最好的是RF-OK （Rmse2为0.70~0.82）和PLS-OK模型（Rmse2为0.78~0.84）；PM2.5模型中PLS模型（Rmse2为0.62~0.71）优于SLR-OK （Rmse2为0.40~0.79）和RF-OK （Rmse2：0.31~0.56）模型.应用3种建模方法对上海1 km网格开展年暴露模拟和对比，NO2模型间模拟结果的相关性（r为0.82~0.91）高于PM2.5模拟结果的相关性（r为0.66~0.96）.基于3种模型2019年的模拟结果，评估了上海NO2和PM2.5的人群暴露水平. |
| 英文摘要 |
| Geostatistical models have been widely used in the exposure assessment of ambient air pollutants. However, few studies have focused on comparisons of modeling approaches and their prediction results. Here, we collected the NO2 and PM2.5 monitoring data from 55 sites in Shanghai from 2016 to 2019 and the geographic variables, such as road network, points of interest of emission locations, and satellite data were included. We used partial least squares regression (PLS), supervised linear regression (SLR), and random forest (RF) algorithms to develop spatial models and used ordinary kriging (OK) to develop a two-step model. We evaluated the models using a 5-fold cross validation method and selected the best model structure for each modeling approach between one-or two-step models that had been developed with or without OK. The results revealed that the best NO2 models were the RF-OK (Rmse2 was 0.70-0.82) and PLS-OK (Rmse2 was 0.78-0.84) models; the PLS model for PM2.5(Rmse2 was 0.62-0.71) outperformed the other PM2.5 models. We used the best models to predict annual exposures in Shanghai at a 1 km spatial scale and conducted the correlation analysis among the predictions of the best models. The results demonstrated that the NO2 predictions had higher correlation coefficients (r was 0.82-0.91) compared with those of the PM2.5 models (r was 0.66-0.96). Based on the exposure results predicted using the three models in 2019, we evaluated the cumulative population exposure concentrations for NO2 and PM2.5 in Shanghai. |
