Predicting Fine Particulate Matter (PM2.5) in the Greater London Area: An Ensemble Approach using Machine Learning Methods
Predicting Fine Particulate Matter (PM2.5) in the Greater London Area: An Ensemble Approach using Machine Learning Methods
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DOI：
10.3390/rs12060914
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发表时间：
2020-03-01
期刊：
影响因子：
5
通讯作者：
Schwartz, Joel
中科院分区：
文献类型：
--
作者：
Yazdi, Mahdieh Danesh;Kuang, Zheng;Schwartz, Joel
长期以来，估计空气污染暴露一直是环境健康研究人员面临的挑战。技术进步和新颖的机器学习方法使我们能够扩大暴露模型的地理范围和准确性，使其成为进行健康研究和识别污染热点的宝贵工具。在这里，我们使用融合了卫星气溶胶光学厚度(AOD)、土地利用和气象数据的集成机器学习方法，创建了大伦敦地区2005年1月1日至2013年12月31日每日PM2.5水平的预测模型。这些预测是在3960个网格单元上以1公里x 1公里的尺度进行的。该集成包括来自三个不同机器学习器的预测：随机森林(RF)、梯度助推机(GBM)和k近邻(KNN)方法。我们的整体模型表现非常好，交叉验证的R-2为10倍，为0.828。在三个机器学习器中，随机森林的性能优于GBM和KNN。我们的模型特别擅长预测PM2.5水平的逐日变化，样本外的时间R-2为0.882。然而，它预测空间变异性的能力较弱，R-2为0.396。我们认为这是由于该地区污染物水平的空间差异较小。
Estimating air pollution exposure has long been a challenge for environmental health researchers. Technological advances and novel machine learning methods have allowed us to increase the geographic range and accuracy of exposure models, making them a valuable tool in conducting health studies and identifying hotspots of pollution. Here, we have created a prediction model for daily PM2.5 levels in the Greater London area from 1st January 2005 to 31st December 2013 using an ensemble machine learning approach incorporating satellite aerosol optical depth (AOD), land use, and meteorological data. The predictions were made on a 1 km x 1 km scale over 3960 grid cells. The ensemble included predictions from three different machine learners: a random forest (RF), a gradient boosting machine (GBM), and a k-nearest neighbor (KNN) approach. Our ensemble model performed very well, with a ten-fold cross-validated R-2 of 0.828. Of the three machine learners, the random forest outperformed the GBM and KNN. Our model was particularly adept at predicting day-to-day changes in PM2.5 levels with an out-of-sample temporal R-2 of 0.882. However, its ability to predict spatial variability was weaker, with a R-2 of 0.396. We believe this to be due to the smaller spatial variation in pollutant levels in this area.