Modeling spatial variation of gaseous air pollutants and particulate matters in a Metropolitan area using mobile monitoring data.
Modeling spatial variation of gaseous air pollutants and particulate matters in a Metropolitan area using mobile monitoring data.
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DOI：
10.1016/j.envres.2022.112858
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发表时间：
2022-07
影响因子：
8.3
通讯作者：
Zhu, Tong
中科院分区：
文献类型：
--
作者：
Xu, Jia;Yang, Wen;Bai, Zhipeng;Zhang, Renyi;Zheng, Jun;Wang, Meng;Zhu, Tong
在流行病学研究中，地质统计模型已被应用于评估细微规模的空气污染暴露。许多模型是为标准空气污染物而开发的，而不是其他未受监管的污染物(例如，超细颗粒、黑碳和苯)，这些污染物也可能对人体健康有害。我们的目标是使用6种算法建立管制和非管制空气污染物的空间模型，并比较它们的预测性能。使用带有快速响应监测仪的移动平台，于2008年7月至10月对北京、中国和北京的大气气态污染物(氮氧化物、一氧化碳、二氧化硫、臭氧、苯、甲苯、甲醇)和颗粒物(黑碳、比表面积、计数浓度和体积浓度)进行了为期30天的测量。在时间上调整背景浓度后，用于模型建立的移动监测数据被空间聚集到采样路线上间隔约600米的130个路段中。最好的空气污染物模型是由交通变量决定的，基于最高的交叉验证R2和不同算法中最低的均方根误差，交通变量可以解释60%以上的空间变化(范围从甲醇的0.61到臭氧的0.88)。在6种算法中，采用偏最小二乘回归降维算法和随机森林算法的空间模型性能优于其他算法。最好的模型对暴露的预测差异很大，空气污染物之间的空间模式截然不同。在整个城市的精细网格上，对于同一污染物，使用多种建模算法的预测彼此之间具有适度的相关性。暴露模型，特别是基于偏最小二乘算法和RF算法的暴露模型，捕捉到了短期平均浓度的空间变化，具有足够的预测有效性，可以应用于人体健康研究中有毒空气污染物暴露的评估。
Geo-statistical models have been applied to assess fine-scale air pollution exposures in epidemiological studies. Many of the models were developed for criteria air pollutants rather than others that have not been regulated (e.g., ultrafine particles, black carbon, and benzene) which may also be harmful to human health. We aim to develop spatial models for regulated and non-regulated air pollutants using 6 algorithms and compare their prediction performances. A mobile platform with fast-response monitors was used to measure gaseous air pollutants (nitrogen dioxides, carbon monoxide, sulfur dioxides, ozone, benzene, toluene, methanol) and particulate matters (black carbon, surface area, count- and volume-concentrations of ultrafine particles) in Beijing, China for 30 days from July to October 2008. Mobile monitoring data for model building were spatially aggregated into 130 road segments of approximately 600-meter interval on the sampling routes after temporal adjustment of background concentrations. The best models for the air pollutants were dominated by traffic variables, which explained more than 60% of the spatial variations (range: 0.61 for methanol to 0.88 for ozone) based on the highest cross-validation R2 and the lowest root mean square error among different algorithms. Amongst the 6 algorithms, the spatial models using partial least squares regression (PLS, a dimension reduction algorithm) and random forest (RF, a machine learning algorithm) algorithms outperformed the models with other algorithms. Exposure predictions from the best models varied substantially with distinct spatial patterns between the air pollutants. Predictions with multiple modeling algorithms were moderately correlated with each other for the same pollutant at the fine-scale grids across the city. Exposure models, especially based on PLS and RF algorithms, captured the spatial variation of short-term average concentrations, had adequate predictive validity, and could be applied to assess toxic air pollutant exposures in human health studies.
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影响因子：
11.4
作者：
Minet, Laura;Liu, Rick;Hatzopoulou, Marianne
通讯作者：
Hatzopoulou, Marianne
影响因子：
5
作者：
Brocco, D;Fratarcangeli, R;Ventrone, I
通讯作者：
Ventrone, I
影响因子：
10.4
作者：
Chen, Kai;Schneider, Alexandra;Strauch, K.
通讯作者：
Strauch, K.
DOI：
10.1080/15287390306357
发表时间：
2003-03-28
影响因子：
2.6
作者：
Bono, R;Scursatone, E;Gilli, G
通讯作者：
Gilli, G
影响因子：
11.4
作者：
Cohan, DS;Hakami, A;Russell, AG
通讯作者：
Russell, AG