Validation study of WindTrax reverse dispersion model coupled with a sensitivity analysis of model-specific settings

Tagliaferri, Francesca; Invernizzi, Marzio; Capra, Federica; Sironi, Selena

doi:10.1016/j.envres.2023.115401

Cited by 5 publications

(1 citation statement)

References 30 publications

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“…Currently, there are two main types of predictive modeling methods for atmospheric pollutant concentration: mechanism-based atmospheric pollutant predictive modeling and datadriven atmospheric pollutant predictive modeling [5][6][7]. Mechanism-based atmospheric pollutant predictive modeling is based on atmospheric dynamics and environmental chemistry [8,9]. It constructs mathematical models using equations to analyze the spatial and temporal distribution and diffusion of pollutants based on atmospheric pollutant emission source data and meteorological data [10,11].…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal data prediction of multiple air pollutants in multi-cities based on 4D digraph convolutional neural network

Wang,

Tang,

Wang

et al. 2023

PLoS ONE

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In response to the problem that current multi-city multi-pollutant prediction methods based on one-dimensional undirected graph neural network models cannot accurately reflect the two-dimensional spatial correlations and directedness, this study proposes a four-dimensional directed graph model that can capture the two-dimensional spatial directed information and node correlation information related to multiple factors, as well as extract temporal correlation information at different times. Firstly, A four-dimensional directed GCN model with directed information graph in two-dimensional space was established based on the geographical location of the city. Secondly, Spectral decomposition and tensor operations were then applied to the two-dimensional directed information graph to obtain the graph Fourier coefficients and graph Fourier basis. Thirdly, the graph filter of the four-dimensional directed GCN model was further improved and optimized. Finally, an LSTM network architecture was introduced to construct the four-dimensional directed GCN-LSTM model for synchronous extraction of spatio-temporal information and prediction of atmospheric pollutant concentrations. The study uses the 2020 atmospheric six-parameter data of the Taihu Lake city cluster and applies canonical correlation analysis to confirm the data’s temporal, spatial, and multi-factor correlations. Through experimentation, it is verified that the proposed 4D-DGCN-LSTM model achieves a MAE reduction of 1.12%, 4.91%, 5.62%, and 11.67% compared with the 4D-DGCN, GCN-LSTM, GCN, and LSTM models, respectively, indicating the good performance of the 4D-DGCN-LSTM model in predicting multiple types of atmospheric pollutants in various cities.

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