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Understanding the driving mechanism of natural conditions and human activities on wetland ecosystem service value (WESV) change is important for the sustainable development of wetland resources. The existing studies often analyze the direct effects of natural conditions and human activities on WESV, however, the interaction mechanism of them on WESV is still unclear. Therefore, based on the remote sensing image data of Liaoning Province China in 1990–2020, this paper combined Emergy with Partial least squares-structural equation model (PLS-SEM) to evaluate main WESV and explore the interaction mechanism between socio-economic development factor (F sed), climate factor (F cl), environment factor (F en)and terrain factor (F te) on WESV change. The results showed that the provisioning (WESV P) and regulating (WESV R) services value increased by 6.93 E+14 and 3.01 E+14 solar emjoules (sej), respectively, while the cultural services value (WESV C) decreased by 1.50 E+14sej. Overall, the total value of wetland services value (WESV T) increased by 8.45 E+14sej during the study period, with a growth rate of 37.88%. F sed was the most significant factor driving WESV growth, with path coefficient of 0.53, followed by F en and F te , with path coefficient of 0.23 and 0.20, respectively, while F cl had the greatest negative effect on WESV, with path coefficient of −0.18. From the perspective of the interaction between each factor, F cl weakened the positive effect of F sed and F en on WESV, at the same time, F sed weakened the positive effect of F te on WESV. Through the indirect influence of F te , the adverse effect of F cl on WESV was further enhanced. This study innovatively revealed the interaction mechanism of natural conditions and human activities on WESV change, and the results could provide scientific basis for the management and sustainable development of wetland resources. [Display omitted] • Emergy theory was applied to calculate the main wetland ecosystem services value (WESV). • The interaction mechanism between natural conditions and human activities on WESV change was revealed by PLS-SEM. • F sed played an important role in the indirect effect of WESV change, and the interaction between F cl and F en exacerbated their negative effect on WESV change. [ABSTRACT FROM AUTHOR]

The urban agglomeration area is a heavy disaster area of PM2.5 pollution, and the problem of PM2.5 pollution seriously affects the natural environment and public health. Accurate prediction of PM2.5 concentrations in urban agglomerations is the basis for effective environmental management. Deep learning methods such as Bi-directional Long Short-Term Memory (BiLSTM) and Convolutional Neural Network (CNN) have been applied to PM2.5 concentration prediction in urban agglomerations. However, the existing research methods have low prediction accuracy and poor practicality, and lack a universal method to accurately predict PM2.5 concentrations in 19 urban agglomerations of China. Based on this, this paper combines BiLSTM and CNN, and introduces a Learning Rate Schedule (LRS) to propose a hybrid model of LRS + BiLSTM + CNN. This model combines the advantages of BiLSTM and CNN to extract temporal and spatial features of PM2.5 data. In addition, LRS is introduced in the process of model training, which dynamically adjusts the learning rate of the network model and reduces the training cost of the model. Finally, the daily average datasets of six air qualities from nineteen urban agglomerations of China are used as examples, and seven benchmark network models, CNN, Long Short-Term Memory, BiLSTM, CNN + LSTM, LSTM + CNN, and CNN + BiLSTM, are considered for validation experiments. The results show that the prediction effect of the proposed model is significantly better than that of the benchmark model in 17 urban agglomerations, such as Mid-southern Liaoning urban agglomerations and Central Yunnan urban agglomeration. Compared with the above seven benchmark models, the prediction accuracy is improved by 12.7%, 8.9%, 12.4%, 12.8%, 16.4%, 19.7%, 9.5%; 10.2%, 24.2%, 14.4%, 17.9%, 18.4%, 19.9% and 2.6% respectively. The LRS is fully applicable to nineteen urban agglomerations, which has clear effects on the optimization of the network model. LRS + BiLSTM + CNN has high accuracy and practicality in predicting PM2.5 concentrations in urban agglomeration areas in China, and can provide effective technical support for environmental governance of urban agglomeration areas. [ABSTRACT FROM AUTHOR]