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Predicting Daily Urban Fine Particulate Matter Concentrations Using a Random Forest Model
Environmental Science & Technology ( IF 10.8 ) Pub Date : 2018-03-14 00:00:00 , DOI: 10.1021/acs.est.7b05381 Cole Brokamp 1, 2 , Roman Jandarov 3 , Monir Hossain 1 , Patrick Ryan 1, 2, 3
Environmental Science & Technology ( IF 10.8 ) Pub Date : 2018-03-14 00:00:00 , DOI: 10.1021/acs.est.7b05381 Cole Brokamp 1, 2 , Roman Jandarov 3 , Monir Hossain 1 , Patrick Ryan 1, 2, 3
Affiliation
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The short-term and acute health effects of fine particulate matter less than 2.5 μm (PM2.5) have highlighted the need for exposure assessment models with high spatiotemporal resolution. Here, we utilize satellite, meteorologic, atmospheric, and land-use data to train a random forest model capable of accurately predicting daily PM2.5 concentrations at a resolution of 1 × 1 km throughout an urban area encompassing seven counties. Unlike previous models based on aerosol optical density (AOD), we show that the missingness of AOD is an effective predictor of ground-level PM2.5 and create an ensemble model that explicitly deals with AOD missingness and is capable of predicting with complete spatial and temporal coverage of the study domain. Our model performed well with an overall cross-validated root mean squared error (RMSE) of 2.22 μg/m3 and a cross-validated R2 of 0.91. We illustrate the daily changing spatial patterns of PM2.5 concentrations across our urban study area made possible by our accurate, high-resolution model. The model will facilitate high-resolution assessment of both long-term and acute PM2.5 exposures in order to quantify their associations with related health outcomes.
中文翻译：
使用随机森林模型预测每日城市细颗粒物浓度
小于2.5μm（PM 2.5）的细颗粒物质对短期和急性健康的影响突出表明，需要具有高时空分辨率的暴露评估模型。在这里，我们利用卫星，气象，大气和土地利用数据来训练一个随机森林模型，该模型能够准确地预测每日的PM 2.5浓度，分辨率为1×1 km，覆盖整个七个县市区。与以前的基于气溶胶光密度（AOD）的模型不同，我们表明AOD的缺失是地面PM 2.5的有效预测指标并创建一个集成模型来明确处理AOD缺失，并能够在研究领域的完整时空覆盖范围内进行预测。我们的模型表现良好，总体交叉验证的均方根误差（RMSE）为2.22μg/ m 3，交叉验证的R 2为0.91。我们通过我们的精确，高分辨率模型说明了整个城市研究区域内PM 2.5浓度每日变化的空间格局。该模型将有助于对长期和急性PM 2.5暴露进行高分辨率评估，以量化其与相关健康结果的关联。
更新日期：2018-03-15
中文翻译：
使用随机森林模型预测每日城市细颗粒物浓度
小于2.5μm（PM 2.5）的细颗粒物质对短期和急性健康的影响突出表明，需要具有高时空分辨率的暴露评估模型。在这里，我们利用卫星，气象，大气和土地利用数据来训练一个随机森林模型，该模型能够准确地预测每日的PM 2.5浓度，分辨率为1×1 km，覆盖整个七个县市区。与以前的基于气溶胶光密度（AOD）的模型不同，我们表明AOD的缺失是地面PM 2.5的有效预测指标并创建一个集成模型来明确处理AOD缺失，并能够在研究领域的完整时空覆盖范围内进行预测。我们的模型表现良好，总体交叉验证的均方根误差（RMSE）为2.22μg/ m 3，交叉验证的R 2为0.91。我们通过我们的精确，高分辨率模型说明了整个城市研究区域内PM 2.5浓度每日变化的空间格局。该模型将有助于对长期和急性PM 2.5暴露进行高分辨率评估，以量化其与相关健康结果的关联。
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