Continuous estimations of daily PM2.5 chemical components from temporally sparse monitoring data using a machine learning approach
Continuous estimations of daily PM2.5 chemical components from temporally sparse monitoring data using a machine learning approach
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DOI：
10.1016/j.apr.2022.101580
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发表时间：
2022-10
影响因子：
4.5
通讯作者：
Shin Araki;H. Shimadera;Masayuki Shima
中科院分区：
文献类型：
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作者：
Shin Araki;H. Shimadera;Masayuki Shima
高时空分辨率下细颗粒物（PM2.5）组分浓度的准确估算对于评估其对人类健康的影响至关重要。在这项研究中，我们开发的主要PM2.5组分（硫酸盐，硝酸盐，铵，元素碳和有机碳）的日浓度预测模型在日本关西地区，从2010年到2017年使用随机森林算法。我们的目标是建立一个建模方法，获得准确的每日估计PM2.5组分浓度使用时间稀疏的监测数据，仅涵盖15%的研究期间。我们使用气体和颗粒污染物浓度，化学传输模型输出，模拟气象参数，以及传统的土地利用和交通相关变量，以1 km × 1 km的分辨率进行每日估计。我们通过空间和时间交叉验证评估了我们的模型，并获得了各个成分的R2值为0.59-0.86。该模型再现了日常的变化，皮尔森的相关系数为0.75-0.88之间的估计值和独立的数据收集在连续监测。我们估计了2010 - 2017年PM2.5组分的日浓度，分辨率为1 km × 1 km。各分量的年变化趋势是通过对日估计值进行时空聚合得到的。我们的建模方法能够使用时间稀疏的监测数据准确估计每日PM2.5组分水平。估计浓度将进一步用于出生队列研究，以评估PM2.5组分的潜在健康影响。
Accurate estimates of the concentrations of fine particulate matter (PM2.5) components at high spatial and temporal resolution is essential for assessing their impact on human health. In this study, we developed prediction models of daily concentrations of major PM2.5components (sulfate, nitrate, ammonium, elemental carbon, and organic carbon) in the Kansai region, Japan, from 2010 to 2017 using the random forest algorithm. The objective is to establish a modeling approach for obtaining accurate daily estimates of PM2.5component concentrations using temporally sparse monitoring data covering only 15% of the study period. We used gaseous and particulate pollutant concentrations, chemical transport model outputs, simulated meteorological parameters, and conventional land use and traffic-related variables to produce daily estimations at 1 km × 1 km resolution. We evaluated our models via spatial and temporal cross-validation and obtainedR2values of 0.59–0.86 for individual components. The model reproduced the day-to-day variations well, with Pearson’s correlation coefficients of 0.75–0.88 between estimates and independent data collected at continuous monitors. We estimated the daily concentrations of PM2.5components from 2010 to 2017 at a 1 km × 1 km resolution. The annual trends of the components were obtained by temporally and spatially aggregating the daily estimations. Our modeling approach enabled accurate estimates of the daily PM2.5component levels using temporally sparse monitoring data. The estimated concentrations will be further utilized in a birth cohort study to assess the potential health impacts of PM2.5components.