A New Model Using Multiple Feature Clustering and Neural Networks for Forecasting Hourly PM2.5 Concentrations,and Its Applications in China

Particulate matter with an aerodynamic diameter no greater than 2.5 lm(PM2.5)concentration forecasting is desirable for air pollution early warning.This study proposes an improved hybrid model,named multi-feature clustering decomposition(MCD)–echo state network(ESN)–particle swarm optimization(PSO),for multi-step PM2.5 concentration forecasting.The proposed model includes decomposition and optimized forecasting components.In the decomposition component,an MCD method consisting of rough sets attribute reduction(RSAR),k-means clustering(KC),and the empirical wavelet transform(EWT)is proposed for feature selection and data classification.Within the MCD,the RSAR algorithm is adopted to select significant air pollutant variables,which are then clustered by the KC algorithm.The clustered results of the PM2.5 concentration series are decomposed into several sublayers by the EWT algorithm.In the optimized forecasting component,an ESN-based predictor is built for each decomposed sublayer to complete the multi-step forecasting computation.The PSO algorithm is utilized to optimize the initial parameters of the ESN-based predictor.Real PM2.5 concentration data from four cities located in different zones in China are utilized to verify the effectiveness of the proposed model.The experimental results indicate that the proposed forecasting model is suitable for the multi-step high-precision forecasting of PM2.5 concentrations and has better performance than the benchmark models.

Deciphering urban traffic impacts on air quality by deep learning and emission inventory

Air pollution is a major obstacle to future sustainability,and traffic pollution has become a large drag on the sustainable developments of future metropolises.Here,combined with the large volume of real-time monitoring data,we propose a deep learning model,iDeepAir,to predict surface-level PM2.5 concentration in Shanghai megacity and link with MEIC emission inventory creatively to decipher urban traffic impacts on air quality.Our model exhibits high-fidelity in reproducing pollutant concentrations and reduces the MAE from 25.355μg/m^(3) to 12.283μg/m^(3) compared with other models.And identifies the ranking of major factors,local meteorological conditions have become a nonnegligible factor.Layer-wise relevance propagation(LRP)is used here to enhance the interpretability of the model and we visualize and analyze the reasons for the different correlation between traffic density and PM_(2.5) concentration in various regions of Shanghai.Meanwhile,As the strict and effective industrial emission reduction measurements implementing in China,the contribution of urban traffic to PM_(2.5) formation calculated by combining MEIC emission inventory and LRP is gradually increasing from 18.03%in 2011 to 24.37% in 2017 in Shanghai,and the impact of traffic emissions would be ever-prominent in 2030 according to our prediction.We also infer that the promotion of vehicular electrification would achieve further alleviation of PM_(2.5) about 8.45% by 2030 gradually.These insights are of great significance to provide the decision-making basis for accurate and high-efficient traffic management and urban pollution control,and eventually benefit people’s lives and high-quality sustainable developments of cities.