This study examines the spatial distribution and temporal trend of PM2.5 constituent concentrations in California over two decades and evaluates spatial disparity of sub-population exposures, as well as the influence of interregional air pollution transport and other source contributions for PM2.5 constituent concentrations. The project builds machine learning models using existing ground measurement data, extensive satellite data, meteorological data, and other spatiotemporal data sources. The results will provide insight into PM2.5 mitigation strategies that takes into account social equity.
Street view imagery coupled with deep learning approach can accurately and efficiently measure eye-level street green space and distinguish vegetation types. Compared to predominant, traditional satellite imagery, street view data reflect different aspects and more details of natural environments. We applied this model to investigate socioeconomic disparities in in Los Angeles County, and found that lower SES and racial/ethnic minority communities had substantively less street green space.
Abstract:
Although increasing hidden layers can improve the ability of a neural network in modeling complex nonlinear relationships, deep layers may result in degradation of accuracy due to the problem of vanishing gradient. Accuracy degradation limits the applications of deep neural networks to predict continuous variables with a small sample size and/or weak or little invariance to translations. Inspired by residual convolutional neural network in computer vision, we developed an encoder-decoder full residual deep network to robustly regress and predict complex spatiotemporal variables. We embedded full shortcuts from each encoding layer to its corresponding decoding layer in a systematic encoder-decoder architecture for efficient residual mapping and error signal propagation. We demonstrated, theoretically and experimentally, that the proposed network structure with full residual connections can successfully boost the backpropagation of signals and improve learning outcomes. This novel method has been extensively evaluated and compared with four commonly used methods (i.e., plain neural network, cascaded residual autoencoder, generalized additive model, and XGBoost) across different testing cases for continuous variable predictions. For model evaluation, we focused on spatiotemporal imputation of satellite aerosol optical depth with massive nonrandomness missingness and spatiotemporal estimation of atmospheric fine particulate matter less than or equal to 2.5 um (PM2.5). Compared with the other approaches, our method achieved the state-of-the-art accuracy, had less bias in predicting extreme values, and generated more realistic spatial surfaces. This encoder-decoder full residual deep network can be an efficient and powerful tool in a variety of applications that involve complex nonlinear relationships of continuous variables, varying sample sizes, and spatiotemporal data with weak or little invariance to translation.
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