The excessive exploitation of groundwater not only destroys the dynamic balance between coastal aquifer and seawater but also causes a series of geological and environmental problems. Groundwater level prediction provides an efficient way to solve these intractable ecological problems. Although several hydrological numerical models have been employed to conduct prediction, no study has accurately predicted the groundwater level change under the consideration of groundwater exploitation, especially in coastal aquifers. This is due to the characteristics of spatially and temporally complex hydrological processes. This study proposes a novel data-driven method based on the combination of time series analysis and a machine learning method for accurately predicting the variation of groundwater level in a coastal aquifer under the influence of groundwater exploitation. The partial autocorrelation function and continuous wavelet coherence were used to analyze the monitoring data of groundwater level at three wells, which indicated that the historical monitored data and the dataset of precipitation could be considered as the input variables to construct the hydrological model. Then, three models based on the different inputs were constructed, namely, the LSTM, PACF-LSTM, and PACF-WC-LSTM models. The performances of the three models were compared by the calculation of four error metrics. The results showed that the performance of the PACF-LSTM and PACF-WC-LSTM models was better than that of the LSTM model and that the PACF-WC-LSTM model achieved the best prediction performance. Accurately predicting the variation of groundwater level provides the basis for managing groundwater resources and preserving the ecological environment.

Mining provides basic materials and energy for human life and supports economic and social prosperity and development. The decoupling of mining carbon emissions from economic development is an important way of achieving China’s carbon peaking and carbon neutrality goals. This study uses the Tapio decoupling model to measure the relationship between China’s economic development and carbon emissions from 2001 to 2018. It analyzes the overall industry as well as its subdivisions and identifies the factors driving carbon emissions with help from the improved Kaya identity and LMDI decomposition models. The results show that, except for the unstable situation in the oil and natural gas mining industry, the other mining divisions have attained strong decoupling and have become stable, showing a continuous positive trend. On the whole, the mining product smelting and processing industry has achieved a major transformation, moving from negative decoupling to weak decoupling, but there are great differences between different sub-sectors. The overall consumption of China’s mining products, and the incremental carbon emissions have continued to decline, while economic development has shifted from inefficient expansion to high-quality economic development, although without reaching the ideal state. The economic factor and energy intensity effects are the key factors in increasing and restraining carbon emissions, respectively, and their influence should not be ignored. This study aims to provide a decision-making basis for China’s mining industry, that it might carry out carbon emission reduction planning, and promote the clean and efficient construction of the industry and the green and high-quality development of the economy.

Sustainability Utilization of the Fault-Controlled Wentang Geothermal Field With Hydrogeological Numerical Model at Site Scale

The Ailaoshan orogenic belt is one of the most significant orogenic belts in the southeastern margin of the Qinghai–Tibet Plateau. The widely developed magmatic rocks in this belt preserve the multi-stage tectonic evolution records of the South China Plate. As an important response to the Rodinia breakup tectonic event, the study of Neoproterozoic magmatic rocks in the area is of great significance for reconstructing the Neoproterozoic tectonic process of the Ailaoshan orogenic belt and the tectonic evolution of the South China Plate. Petrology, geochemistry, zircon U-Pb, and Lu-Hf isotopes of the Daping pluton in the Ailaoshan orogenic belt are studied in this paper. The Daping pluton is mainly divided into gabbros and granites. Gabbros and granites belong to the sub-alkaline series, which are relatively enriched in large ion lithophilic elements and depleted in high-field strength elements. The ΣREE contents of the gabbro are low with enrichment in LREEs and depletion in HREEs, and the degree of differentiation of light and heavy rare earth is low, with positive δEu and weak negative δCe anomalies. The ΣREE contents of the granite are low with enrichment in LREEs and depletion in HREEs, and the degree of light and heavy rare earth differentiation is high, with medium–weak negative δEu and weak positive δCe anomalies, suggesting an A₂-type granite with A₁-A₂ transition characteristics. The weighted average age of the gabbro is 816.1 ± 4.1 Ma (MSWD = 0.11), with zircon ε_Hf(t) values of −7.5–5.5. The magma source is a mixture of an ancient crust source and a new mantle source; the weighted average age of the syenogranite is 783.7 ± 8.1 Ma (MSWD = 1.4), with zircon ε_Hf(t) values of −4.3–0.4. The magma source is mainly ancient crustal material (Ailaoshan Group), mixed with a small amount of mantle-derived material; the weighted average age of the monzogranite is 754.8 ± 6.1 Ma (MSWD = 3.0), with positive zircon ε_Hf(t) values of 1.65–10.36. The magma source is a mixture of a large number of mantle-derived materials and a small number of crust-derived materials (Ailaoshan Group). The Daping pluton was formed in the transitional tectonic environment from post-collision to intraplate continental margin rift, corresponding to the Rodinia breakup process in the western margin of the South China Plate.