耦合多源环境要素的邵阳市PM2.5浓度空间分布模拟
Simulation of Spatial Distribution of PM2.5 Concentration in Shaoyang City Coupled With Multi-Source Environmental Factors
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摘要: 基于PM2.5浓度人工神经网络(ANN)空间模拟模型,实现了邵阳市PM2.5级模拟制图,模型效果表现良好,并实现邵阳市2016-2023年年均值空间分布制图,获取时空分布特征。模拟结果表明,耦合多源环境要素的ANN模型能够有效捕捉PM2.5浓度的时空变化特征,所构建的模型验证结果的拟合精度为0.82,均方根误差不超过6 μg/m3,说明模型能够反映污染物浓度的空间异质性,为科学制定污染防治措施提供依据。Abstract: Based on an artificial neural network (ANN) spatial simulation model coupled with multi-source environmental factors, a kilometer-resolution simulation mapping of PM2.5 concentration in Shaoyang City was achieved, and the spatial distribution characteristics of annual average values from 2016 to 2023 were obtained. The simulation results indicate that the ANN model can effectively capture the spatio-temporal variation characteristics of PM2.5 concentration. The model validation demonstrated a fitting accuracy of 0.82 and a root mean square error not exceeding 6 μg/m3. This model can accurately reflect the spatial heterogeneity of pollutant concentrations, providing a basis for the scientific formulation of pollution control measures.
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