Sohrabi Zohreh, Maleki Jamshid
Department of Geomatics Engineering, Faculty of Civil Engineering and Transportation, University of Isfahan, Isfahan, Iran.
Sci Rep. 2025 Jul 1;15(1):21449. doi: 10.1038/s41598-025-05332-2.
With the rapid advancement of urbanization and industrialization in cities, air pollution has become one of the significant environmental challenges and issues in many countries. The concentration of particulate matter with an aerodynamic diameter of less than 2.5 µm (PM), recognized as the main air pollutant in Iran, penetrates the respiratory system through inhalation, causing respiratory and cardiovascular diseases, reproductive disorders, central nervous system disturbances, and cancer. Accurate and high spatiotemporal modeling of air pollutant concentrations is crucial for air quality management and exposure assessment in epidemiological studies. Air quality monitoring stations provide valuable data on air pollution levels at specific locations, but they have limitations in fully capturing the air quality across entire areas and cities in a country. Meanwhile, the rapid growth of computational technologies and the availability of air quality data have enabled researchers to develop complex models using deep learning for modeling various air pollutant concentrations. In this research, PM pollutant concentration modeling for monthly continuous distribution estimation has been implemented and evaluated based on deep learning models. The results of the presented models were compared to select the model with the best performance. Additionally, the influence of the parameters used on pollutant concentration levels was analyzed. In this study, we applied deep learning techniques, including Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Convolutional Long Short-Term Memory (ConvLSTM), to model PM concentrations for continuous monthly distribution estimation. We utilized satellite data, ground-based observations, and meteorological parameters as input features. The models were evaluated using Root Mean Square Error (RMSE) and the coefficient of determination (R). The ConvLSTM model outperformed others with an RMSE of 4.95 µg/m and an R of 91.24%. Sensitivity analysis indicated that among 18 input parameters, population density, AOD, NO, and rainfall had the most significant impact on PM concentrations.
随着城市城市化和工业化的快速推进，空气污染已成为许多国家面临的重大环境挑战和问题之一。空气动力学直径小于2.5微米的颗粒物（PM）浓度，被认为是伊朗的主要空气污染物，通过吸入进入呼吸系统，导致呼吸道和心血管疾病、生殖紊乱、中枢神经系统紊乱和癌症。准确且高时空分辨率的空气污染物浓度建模对于空气质量管控以及流行病学研究中的暴露评估至关重要。空气质量监测站提供特定地点空气污染水平的宝贵数据，但在全面捕捉一个国家整个地区和城市的空气质量方面存在局限性。与此同时，计算技术的快速发展以及空气质量数据的可得性使研究人员能够利用深度学习开发复杂模型来模拟各种空气污染物浓度。在本研究中，基于深度学习模型对月度连续分布估计的PM污染物浓度建模进行了实施和评估。比较了所提出模型的结果以选择性能最佳的模型。此外，还分析了所使用参数对污染物浓度水平的影响。在本研究中，我们应用了深度学习技术，包括多层感知器（MLP）、卷积神经网络（CNN）、长短期记忆网络（LSTM）和卷积长短期记忆网络（ConvLSTM），对连续月度分布估计的PM浓度进行建模。我们利用卫星数据、地面观测数据和气象参数作为输入特征。使用均方根误差（RMSE）和决定系数（R）对模型进行评估。ConvLSTM模型表现优于其他模型，RMSE为4.95微克/立方米，R为91.24%。敏感性分析表明，在18个输入参数中，人口密度、气溶胶光学厚度（AOD）、一氧化氮（NO）和降雨量对PM浓度影响最为显著。
