Abstract
PM2.5 pollution has become of increasing public concern because of its relative importance and sensitivity to population health risks. Accurate predictions of PM2.5 pollution and population exposure risks are crucial to developing effective air pollution control strategies. We simulated and predicted the temporal and spatial changes of PM2.5 concentration and population exposure risks, by coupling optimization algorithms of the Back Propagation-Artificial Neural Network (BP-ANN) model and a geographical information system (GIS) in Xi’an, China, for 2013, 2020, and 2025. Results indicated that PM2.5 concentration was positively correlated with GDP, SO2, and NO2, while it was negatively correlated with population density, average temperature, precipitation, and wind speed. Principal component analysis of the PM2.5 concentration and its influencing factors’ variables extracted four components that accounted for 86.39% of the total variance. Correlation coefficients of the Levenberg-Marquardt (trainlm) and elastic (trainrp) algorithms were more than 0.8, the index of agreement (IA) ranged from 0.541 to 0.863 and from 0.502 to 0.803 by trainrp and trainlm algorithms, respectively; mean bias error (MBE) and Root Mean Square Error (RMSE) indicated that the predicted values were very close to the observed values, and the accuracy of trainlm algorithm was better than the trainrp. Compared to 2013, temporal and spatial variation of PM2.5 concentration and risk of population exposure to pollution decreased in 2020 and 2025. The high-risk areas of population exposure to PM2.5 were mainly distributed in the northern region, where there is downtown traffic, abundant commercial activity, and more exhaust emissions. A moderate risk zone was located in the southern region associated with some industrial pollution sources, and there were mainly low-risk areas in the western and eastern regions, which are predominantly residential and educational areas.
Highlights
The risk of population exposure to airborne particulate pollutant PM2.5 has important scientific and practical significance for sustainable development [1,2,3]
PM2.5 concentration ranged from 67.26 μg/m3 to 100.48 μg/m3 in August 2013, with the maximum distribution in the north-central and north-eastern regions, and the minimum distribution in the central and north-central regions; the median PM2.5 concentration was in the western and south-eastern regions
0.8, which demonstrates that the Back Propagation-Artificial Neural Network (BP-artificial neural network models (ANN)) model can be used to predict PM2.5 concentrations in the study area; accuracy of the trainlm algorithm was higher than the trainrp algorithm
Summary
The risk of population exposure to airborne particulate pollutant PM2.5 has important scientific and practical significance for sustainable development [1,2,3]. The accurate forecast of PM2.5 is critical to its prevention and control [4,5,6], and simulation and prediction of the temporal and spatial variation of PM2.5 can improve atmospheric forewarning mechanisms. The temporal and spatial variation of PM2.5 is influenced by source and migration factors of the pollutant. It is difficult to obtain accurate temporal and spatial distribution characteristics of PM2.5 pollution; model simulation is the most efficient way to solve the problems of spatial analysis and prediction. PM2.5 concentration prediction methods include multivariate regression methods [7,8,9], genetic algorithms [10,11], grey [12] and Markov models [13,14], and artificial neural network models (ANN) [15,16]. An artificial neural network model is a network composed of a large number of neurons; compared with other forecasting models, the back propagation (BP) artificial neural network (BP-ANN)
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