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Exposure assessment models for elemental components of particulate matter in an urban environment: A comparison of regression and random forest approaches

机译：城市环境中颗粒物基本成分的暴露评估模型：回归和随机森林方法的比较

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Exposure assessment for elemental components of particulate matter (PM) using land use modeling is a complex problem due to the high spatial and temporal variations in pollutant concentrations at the local scale. Land use regression (LUR) models may fail to capture complex interactions and non-linear relationships between pollutant concentrations and land use variables. The increasing availability of big spatial data and machine learning methods present an opportunity for improvement in PM exposure assessment models. In this manuscript, our objective was to develop a novel land use random forest (LURF) model and compare its accuracy and precision to a LUR model for elemental components of PM in the urban city of Cincinnati, Ohio. PM smaller than 2.5 μm (PM2.5) and eleven elemental components were measured at 24 sampling stations from the Cincinnati Childhood Allergy and Air Pollution Study (CCAAPS). Over 50 different predictors associated with transportation, physical features, community socioeconomic characteristics, greenspace, land cover, and emission point sources were used to construct LUR and LURF models. Cross validation was used to quantify and compare model performance. LURF and LUR models were created for aluminum (Al), copper (Cu), iron (Fe), potassium (K), manganese (Mn), nickel (Ni), lead (Pb), sulfur (S), silicon (Si), vanadium (V), zinc (Zn), and total PM2.5 in the CCAAPS study area. LURF utilized a more diverse and greater number of predictors than LUR and LURF models for Al, K, Mn, Pb, Si, Zn, TRAP, and PM2.5 all showed a decrease in fractional predictive error of at least 5% compared to their LUR models. LURF models for Al, Cu, Fe, K, Mn, Pb, Si, Zn, TRAP, and PM2.5 all had a cross validated fractional predictive error less than 30%. Furthermore, LUR models showed a differential exposure assessment bias and had a higher prediction error variance. Random forest and other machine learning methods may provide more accurate exposure assessment.
机译：由于当地尺度污染物浓度的高时空变化，使用土地利用模型进行的颗粒物（PM）元素暴露评估是一个复杂的问题。土地利用回归（LUR）模型可能无法捕获复杂的相互作用以及污染物浓度与土地利用变量之间的非线性关系。大空间数据和机器学习方法的可用性不断提高，为改善PM暴露评估模型提供了机会。在本手稿中，我们的目标是建立一个新颖的土地使用随机森林（LURF）模型，并将其准确性和精度与俄亥俄州辛辛那提市城市PM中PM的基本成分的LUR模型进行比较。辛辛那提儿童过敏与空气污染研究（CCAAPS）在24个采样站测量了小于2.5μm的PM（PM2.5）和11种元素。与交通，自然特征，社区社会经济特征，绿地，土地覆盖和排放点源相关的50多种不同的预测因子用于构建LUR和LURF模型。交叉验证用于量化和比较模型性能。为铝（Al），铜（Cu），铁（Fe），钾（K），锰（Mn），镍（Ni），铅（Pb），硫（S），硅（Si）创建了LURF和LUR模型），钒（V），锌（Zn）和CCAAPS研究区域中的总PM2.5。与LUR和LURF模型相比，对于铝，钾，锰，铅，硅，锌，TRAP和PM2.5，LURF使用的预测因子数量更多，数量更多，与它们相比，其预测误差均降低了至少5％。 LUR模型。 Al，Cu，Fe，K，Mn，Pb，Si，Zn，TRAP和PM2.5的LURF模型的交叉验证分数预测误差均小于30％。此外，LUR模型显示出差异的暴露评估偏差，并且具有较高的预测误差方差。随机森林和其他机器学习方法可以提供更准确的暴露评估。

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