Annual Mean PM2.5 Components Trace Elements (TEs) 50m Urban and 1km Non-Urban Area Grids for Contiguous U.S., 2000-2019, v1

The Annual Mean PM2.5 Components Trace Elements (TEs) 50m Urban and 1km Non-Urban Area Grids for Contiguous U.S., 2000-2019, v1 data set contains annual predictions of trace elements concentrations at a hyper resolution (50m x 50m grid cells) in urban areas and a high resolution (1km x 1km grid cells) in non-urban areas, for the years 2000 to 2019. Particulate matter with an aerodynamic diameter of less than 2.5 �m (PM2.5) is a human silent killer of millions worldwide, and contains many trace elements (TEs). Understanding the relative toxicity is largely limited by the lack of data. In this work, ensembles of machine learning models were used to generate approximately 163 billion predictions estimating annual mean PM2.5 TEs, namely Bromine (Br), Calcium (Ca), Copper (Cu), Iron (Fe), Potassium (K), Nickel (Ni), Lead (Pb), Silicon (Si), Vanadium (V), and Zinc (Zn). The monitored data from approximately 600 locations were integrated with more than 160 predictors, such as time and location, satellite observations, composite predictors, meteorological covariates, and many novel land use variables using several machine learning algorithms and ensemble methods. Multiple machine-learning models were developed covering urban areas and non-urban areas. Their predictions were then ensembled using either a Generalized Additive Model (GAM) Ensemble Geographically-Weighted-Averaging (GAM-ENWA), or Super-Learners. The overall best model R-squared values for the test sets ranged from 0.79 for Copper to 0.88 for Zinc in non-urban areas. In urban areas, the R-squared model values ranged from 0.80 for Copper to 0.88 for Zinc. The Coordinate Reference System (CRS) used in the predictions is the World Geodetic System 1984 (WGS84) and the Units for the PM2.5 Components TEs are ng/m^3. The data are provided in RDS tabular format, a file format native to the R programming language, but can also be opened by other languages such as Python.

本数据集《连续美国地区2000-2019年年度平均PM2.5成分微量元素（TEs）50米城市和1公里非城市区域网格，版本1》包含了对城市区域高分辨率（50米×50米网格单元）和非城市区域高分辨率（1公里×1公里网格单元）内微量元素浓度年度预测。空气中动力学直径小于2.5微米（PM2.5）的颗粒物质是全球数百万人的无声杀手，其中含有多种微量元素（TEs）。对其相对毒性的理解在很大程度上受到数据匮乏的限制。在本研究中，通过集成学习模型集合生成了约1630亿个预测，用以估算年度平均PM2.5微量元素浓度，包括溴（Br）、钙（Ca）、铜（Cu）、铁（Fe）、钾（K）、镍（Ni）、铅（Pb）、硅（Si）、钒（V）和锌（Zn）。大约600个监测点的数据与超过160个预测因子相结合，这些因子包括时间与地点、卫星观测、复合预测因子、气象协变量以及众多新颖的土地利用变量，通过多种机器学习算法和集成方法实现。针对城市区域和非城市区域，开发了多个机器学习模型。随后，使用广义加性模型（GAM）集合地理加权平均（GAM-ENWA）或超级学习器对它们的预测结果进行集成。测试集的整体最佳模型R平方值在非城市区域从铜的0.79变化至锌的0.88，在城市区域从铜的0.80变化至锌的0.88。预测中使用的坐标参考系统（CRS）为1984年世界大地测量系统（WGS84），PM2.5成分微量元素的单位为每立方米纳克（ng/m³）。数据以RDS表格格式提供，这是R编程语言的原生文件格式，但也可以被Python等语言打开。