Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90032, USA.
Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA 90032, USA; Department of Statistical Sciences and School of the Environment, University of Toronto, Toronto, Ontario, Canada.
Environ Int. 2022 Jul;165:107247. doi: 10.1016/j.envint.2022.107247. Epub 2022 Apr 18.
Due to a scarcity of routine monitoring of speciated particulate matter (PM), there has been limited capability to develop exposure models that robustly estimate component-specific concentrations. This paper presents the largest such study conducted in a single urban area. Using samples that were collected at 220 locations over two seasons, quasi-ultrafine (PM), accumulation mode fine (PM), and coarse (PM) particulate matter concentrations were used to develop spatiotemporal regression, machine learning models that enabled predictions of 24 elemental components in eight Southern California communities. We used supervised variable selection of over 150 variables, largely from publicly available sources, including meteorological, roadway and traffic characteristics, land use, and dispersion model estimates of traffic emissions. PM components that have high oxidative potential (and potentially large health effects) or are otherwise important markers for major PM sources were the primary focus. We present results for copper, iron, and zinc (as non-tailpipe vehicle emissions); elemental carbon (diesel emissions); vanadium (ship emissions); calcium (soil dust); and sodium (sea salt). Spatiotemporal linear regression models with 17 to 36 predictor variables including meteorology; distance to different classifications of roads; intersections and off ramps within a given buffer distance; truck and vehicle traffic volumes; and near-roadway dispersion model estimates produced superior predictions over the machine learning approaches (cross validation R-squares ranged from 0.76 to 0.92). Our models are easily interpretable and appear to have more effectively captured spatial gradients in the metallic portion of PM than other comparably large studies, particularly near roadways for the non-tailpipe emissions. Furthermore, we demonstrated the importance of including spatiotemporally resolved meteorology in our models as it helped to provide key insights into spatial patterns and allowed us to make temporal predictions.
由于常规监测特定颗粒物质 (PM) 的能力有限，因此开发能够稳健估计特定成分浓度的暴露模型的能力有限。本文介绍了在单一城市地区进行的此类最大规模的研究。使用在两个季节的 220 个地点收集的样本，准超细微粒 (PM)、积累模式细颗粒物 (PM) 和粗颗粒物 (PM) 浓度用于开发时空回归、机器学习模型，这些模型能够预测加利福尼亚州南部八个社区的 24 种元素成分。我们使用了超过 150 个变量的监督变量选择，这些变量主要来自公开来源，包括气象、道路和交通特征、土地利用以及交通排放的扩散模型估算。具有高氧化潜力（并且可能对健康有较大影响）或对主要 PM 源的重要标记物的 PM 成分是主要关注点。我们介绍了铜、铁和锌（作为非排气管车辆排放物）；元素碳（柴油排放物）；钒（船舶排放物）；钙（土壤尘埃）；和钠（海盐）的结果。具有 17 到 36 个预测变量的时空线性回归模型，包括气象；与不同道路分类的距离；给定缓冲区距离内的交叉口和支路；卡车和车辆交通量；以及近路的扩散模型估算值，其预测效果优于机器学习方法（交叉验证 R 平方范围从 0.76 到 0.92）。我们的模型易于解释，并且似乎比其他类似的大型研究更有效地捕捉了 PM 中金属部分的空间梯度，尤其是在道路附近的非排气管排放物。此外，我们证明了在我们的模型中包含时空分辨率气象的重要性，因为它有助于提供对空间模式的关键见解，并使我们能够进行时间预测。
