Zhang Ping, Hong Bo, He Liang, Cheng Fei, Zhao Peng, Wei Cailiang, Liu Yunhui
School of Environmental and Chemical Engineering, Xi'an Polytechnic University, Xi'an 710048, China.
College of Landscape Architecture and Arts, Northwest A & F University, Yangling 712100, China.
Int J Environ Res Public Health. 2015 Sep 29;12(10):12171-95. doi: 10.3390/ijerph121012171.
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.
由于PM2.5对人群健康风险具有相对重要性和敏感性，其污染问题已日益引起公众关注。准确预测PM2.5污染及人群暴露风险对于制定有效的空气污染控制策略至关重要。我们通过耦合反向传播人工神经网络（BP-ANN）模型的优化算法与地理信息系统（GIS），对中国西安2013年、2020年和2025年的PM2.5浓度及人群暴露风险的时空变化进行了模拟和预测。结果表明，PM2.5浓度与国内生产总值、二氧化硫和二氧化氮呈正相关，而与人口密度、平均温度、降水量和风速呈负相关。对PM2.5浓度及其影响因素变量进行主成分分析，提取出四个成分，它们占总方差的86.39%。Levenberg-Marquardt（trainlm）算法和弹性（trainrp）算法的相关系数均大于0.8，trainrp算法和trainlm算法的一致性指数（IA）分别在0.541至0.863和0.502至0.803之间；平均偏差误差（MBE）和均方根误差（RMSE）表明预测值与观测值非常接近，且trainlm算法的精度优于trainrp算法。与2013年相比，2020年和2025年PM2.5浓度及人群污染暴露风险的时空变化有所降低。人群暴露于PM2.5的高风险区域主要分布在北部地区，该地区有市中心交通、丰富的商业活动和更多的废气排放。中度风险区位于与一些工业污染源相关的南部地区，而西部和东部地区主要为低风险区，这些地区主要是居民区和教育区。
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