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首页> 外文期刊>Environmental research >Evaluation of predictive capabilities of ordinary geostatistical interpolation, hybrid interpolation, and machine learning methods for estimating PM_(2.5) constituents over space
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Evaluation of predictive capabilities of ordinary geostatistical interpolation, hybrid interpolation, and machine learning methods for estimating PM_(2.5) constituents over space

机译：评估普通地统计插值，混合插值和机器学习方法的预测能力，以估计空间上的PM_（2.5）成分

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摘要

Numerous modeling approaches to estimate concentrations of PM2.5 components have been developed to derive better exposures for health studies, including geostatistical interpolation approaches, land use regression models and, models based on remote sensing technology. Recently, there have been some efforts to develop models based on machine learning algorithms. Each one of these exposure assessment methods has inherent uncertainties resulting in varying levels of exposure misclassification. To date, only a few studies have attempted to systematically compare exposure estimates from different PM2.5 constituent models. Our research addresses this gap, by comparing the predictive capabilities of ordinary geostatistical interpolation (Ordinary Kriging - OK), hybrid interpolation (combination of Empirical Bayesian Kriging and land use regression), and machine learning techniques (forest-based regression) for estimating PM2.5 constituents in Eastern Massachusetts in the United States. We compared the estimates of 10 ambient PM2.5 components, which included Al, Cu, Fe, K, Ni, Pb, S, Ti, V, and Zn. The OK model performed poorest for all PM2.5 components, with an R-2 under 0.30. The hybrid model presented a slight improvement, especially for Cu and Fe, for which the R-2 value increased to 0.62 and 0.59, respectively. These elements presented the highest R-2 value from the hybrid model. The forest model presented the best performance, with R-2 values higher than 0.7 for most of the particle components, including Cu, Fe, Ni, Pb, Ti, and V. Same as observed with the hybrid model, the forest model for Cu and Fe explained the highest concentration variance, with a R-2 value equal to 0.88 and 0.92, respectively. The forest model for K, S, and Zn performed poorest with an R-2 value of 0.54, 0.37, and 0.44, respectively. The results presented here can be useful for the environmental health community to more accurately estimate PM2.5 constituents over space.
机译：已经开发出许多用于估计PM2.5成分浓度的建模方法，以得出更好的健康研究暴露，包括地统计插值方法，土地利用回归模型以及基于遥感技术的模型。近来，已经进行了一些努力来开发基于机器学习算法的模型。这些接触评估方法中的每一种都有内在的不确定性，从而导致不同程度的接触错误分类。迄今为止，只有很少的研究试图系统地比较来自不同PM2.5组成模型的暴露估计。我们的研究通过比较普通地统计插值（Ordinary Kriging-OK），混合插值（Empirical Bayesian Kriging和土地利用回归的组合）和机器学习技术（基于森林的回归）来估计PM2的预测能力来解决这一差距。美国马萨诸塞州东部的5个选民。我们比较了10种环境PM2.5组分的估计值，其中包括Al，Cu，Fe，K，Ni，Pb，S，Ti，V和Zn。 OK模型在所有PM2.5组件中表现最差，R-2低于0.30。混合模型表现出轻微的改进，特别是对于铜和铁而言，其R-2值分别增加到0.62和0.59。这些元素代表了混合模型的最高R-2值。森林模型表现出最佳性能，大多数颗粒成分（包括Cu，Fe，Ni，Pb，Ti和V）的R-2值均高于0.7。与混合模型中观察到的相同，Cu的森林模型Fe和Fe解释了最大的浓度方差，R-2值分别等于0.88和0.92。 K，S和Zn的森林模型表现最差，R-2值分别为0.54、0.37和0.44。这里介绍的结果对于环境健康界更准确地估计整个空间中的PM2.5成分可能很有用。

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