Zou Bin, Wang Min, Wan Neng, Wilson J Gaines, Fang Xin, Tang Yuqi
School of Geosciences and Info-Physics, Central South University, Changsha, China, 410083,
Environ Sci Pollut Res Int. 2015 Jul;22(14):10395-404. doi: 10.1007/s11356-015-4380-3. Epub 2015 Mar 28.
Accurate measurements of PM2.5 concentration over time and space are especially critical for reducing adverse health outcomes. However, sparsely stationary monitoring sites considerably hinder the ability to effectively characterize observed concentrations. Utilizing data on meteorological and land-related factors, this study introduces a radial basis function (RBF) neural network method for estimating PM2.5 concentrations based on sparse observed inputs. The state of Texas in the USA was selected as the study area. Performance of the RBF models was evaluated by statistic indices including mean square error, mean absolute error, mean relative deviation, and the correlation coefficient. Results show that the annual PM2.5 concentrations estimated by the RBF models with meteorological factors and/or land-related factors were markedly closer to the observed concentrations. RBF models with combined meteorological and land-related factors achieved best performance relative to ones with either type of these factors only. It can be concluded that meteorological factors and land-related factors are useful for articulating the variation of PM2.5 concentration in a given study area. With these covariate factors, the RBF neural network can effectively estimate PM2.5 concentrations with acceptable accuracy under the condition of sparse monitoring stations. The improved accuracy of air concentration estimation would greatly benefit epidemiological and environmental studies in characterizing local air pollution and in helping reduce population exposures for areas with limited availability of air quality data.
随着时间和空间对细颗粒物（PM2.5）浓度进行准确测量对于减少不良健康后果尤为关键。然而，固定监测站点分布稀疏严重阻碍了有效表征观测浓度的能力。本研究利用气象和土地相关因素的数据，引入了一种基于径向基函数（RBF）神经网络的方法，用于根据稀疏的观测输入估计PM2.5浓度。美国德克萨斯州被选为研究区域。通过均方误差、平均绝对误差、平均相对偏差和相关系数等统计指标对RBF模型的性能进行了评估。结果表明，结合气象因素和/或土地相关因素的RBF模型估计的年PM2.5浓度明显更接近观测浓度。相对于仅包含其中一种因素的RBF模型，结合气象和土地相关因素的RBF模型表现最佳。可以得出结论，气象因素和土地相关因素有助于阐明给定研究区域内PM2.5浓度的变化。利用这些协变量因素，RBF神经网络能够在监测站点稀疏的情况下以可接受的精度有效估计PM2.5浓度。空气浓度估计精度的提高将极大地有利于流行病学和环境研究，以表征局部空气污染，并帮助减少空气质量数据有限地区的人群暴露。
