School of Resource and Environment Science, Wuhan University, Wuhan 430079, China.
Department of Geography and Environmental Studies, Central Michigan University, Mount Pleasant, MI 48859, USA.
Int J Environ Res Public Health. 2018 Jun 11;15(6):1228. doi: 10.3390/ijerph15061228.
This paper proposes a regression model using the Eigenvector Spatial Filtering (ESF) method to estimate ground PM concentrations. Covariates are derived from remotely sensed data including aerosol optical depth, normal differential vegetation index, surface temperature, air pressure, relative humidity, height of planetary boundary layer and digital elevation model. In addition, cultural variables such as factory densities and road densities are also used in the model. With the Yangtze River Delta region as the study area, we constructed ESF-based Regression (ESFR) models at different time scales, using data for the period between December 2015 and November 2016. We found that the ESFR models effectively filtered spatial autocorrelation in the OLS residuals and resulted in increases in the goodness-of-fit metrics as well as reductions in residual standard errors and cross-validation errors, compared to the classic OLS models. The annual ESFR model explained 70% of the variability in PM concentrations, 16.7% more than the non-spatial OLS model. With the ESFR models, we performed detail analyses on the spatial and temporal distributions of PM concentrations in the study area. The model predictions are lower than ground observations but match the general trend. The experiment shows that ESFR provides a promising approach to PM analysis and prediction.
本文提出了一种使用特征向量空间滤波 (ESF) 方法的回归模型来估算地面 PM 浓度。协变量来自遥感数据，包括气溶胶光学深度、归一化差异植被指数、地表温度、气压、相对湿度、行星边界层高度和数字高程模型。此外，模型中还使用了工厂密度和道路密度等文化变量。以长江三角洲地区为研究区域，我们构建了基于 ESF 的回归 (ESFR) 模型，时间尺度分别为 2015 年 12 月至 2016 年 11 月。结果表明，与经典的 OLS 模型相比，ESFR 模型有效地过滤了 OLS 残差中的空间自相关，提高了拟合优度指标，降低了残差标准误差和交叉验证误差。年度 ESFR 模型解释了 PM 浓度变化的 70%，比非空间 OLS 模型多 16.7%。我们利用 ESFR 模型对研究区域的 PM 浓度的时空分布进行了详细分析。模型预测值低于地面观测值，但与总体趋势相符。实验表明，ESFR 为 PM 分析和预测提供了一种很有前景的方法。
