Exposure assessment models for elemental components of particulate matter in an urban environment: A comparison of regression and random forest approaches.
Exposure assessment models for elemental components of particulate matter in an urban environment: A comparison of regression and random forest approaches.
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DOI：
10.1016/j.atmosenv.2016.11.066
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发表时间：
2017-03
期刊：
影响因子：
--
通讯作者：
Ryan P
中科院分区：
文献类型：
--
作者：
Brokamp C;Jandarov R;Rao MB;LeMasters G;Ryan P
利用土地利用模型对颗粒物（PM）的元素组分进行暴露评估是一个复杂的问题，因为在局部尺度上污染物浓度具有很大的时空变化。土地利用回归（LUR）模型可能无法捕捉污染物浓度和土地利用变量之间复杂的相互作用和非线性关系。大空间数据和机器学习方法的日益可用性为PM暴露评估模型的改进提供了机会。在这篇手稿中，我们的目标是开发一种新的土地利用随机森林（LURF）模型，并比较其准确性和精度的LUR模型的PM元素成分在城市的辛辛那提，俄亥俄州。辛辛那提儿童过敏和空气污染研究（CCAAPS）在24个采样站测量了小于2.5 μm的PM（PM2.5）和11种元素成分。超过50个不同的预测与交通，物理特征，社区社会经济特征，绿地，土地覆盖，和排放点源被用来构建LUR和LURF模型。交叉验证用于量化和比较模型性能。LURF和LUR模型是为CCAAPS研究区域的铝（Al）、铜（Cu）、铁（Fe）、钾（K）、锰（Mn）、镍（Ni）、铅（Pb）、硫（S）、硅（Si）、钒（V）、锌（Zn）和总PM2.5创建的。LURF利用了更多样化和更多的预测比LUR和LURF模型的Al，K，Mn，Pb，Si，Zn，TRAP，和PM2.5的预测因子都显示出至少5%的分数预测误差减少相比，他们的LUR模型。铝、铜、铁、钾、锰、铅、硅、锌、陷阱和PM2.5的LURF模型的交叉验证分数预测误差均小于30%。此外，LUR模型显示出差异暴露评估偏差，并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。
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.
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