Modeling transport and fate of heavy metals at the watershed scale: State-of-the-art and future directions

An integrated approach to prioritizing ecological restoration of abandoned mine lands based on cost-benefit analysis

How to increase the usable land area by adhering to environmentally friendly ecological restoration of mines with limited funds is a challenge that many cities are currently facing. Cost-benefit analysis (CBA) can provide efficient and effective restoration decisions for abandoned mine land (AML) ecological restoration with limited financial resources. Thus, this study proposes an integrated approach for coupling ecological benefits and restoration costs, including hotspots/coldspots analysis based on five ecosystem services (ESs), landscape connectivity analysis based on graph theory model, hidden costs, and project implementation costs to prioritize AML restoration. The study was conducted on 54 abandoned mine lands (AMLs) in Chaoyang city, the ecological security barrier of China's northern sand prevention belt (NSPB). The comprehensive analysis prioritized the restoration of AMLs into four levels, of which 9 mines were in priority I, where restoration was recommended as a priority, and 22 mines were in priority II, where restoration could be carried out within the affordability of funds. In addition, our model indicates areas with high ecological benefits, in which the ecological source area (7423.66 km2) and the ecosystem service hotspots area (2028.44 km2) are mostly distributed in the southwestern part of Chaoyang city, the two mountain ranges of Songling mountain and Nuruerhu mountain. This study provides scientific spatial guidance to ensure that the AMLs realizes effective restoration and management.

Unintended mitigation effect of air pollutant regulation on the aquatic cadmium: Evidence from the 11-FYPEP in China

This paper evaluates the unintended mitigation effect of air pollutant regulation on aquatic cadmium (Cd) emissions in the China's Eleventh Five-Year Plan for Environmental Protection (11-FYPEP), by employing a continuous Difference-in-Difference-in-Difference (DDD) estimator. We find that: (1) Although the 11-FYPEP did not target to reduce Cd emission, the implementation of 11-FYPEP reduced the emissions by 2.8 %. (2) The Cd emission is closely related to the industrial level, because the reduction of Cd is 6.1 % higher in areas with lower industrial output, and the mediating effect of the number of industrial enterprises accounts for 6.8 % of the Cd reduction. Based on our findings, implications like improving production efficiency and modifying industrial structure are proposed, as the 11-FYPEP achieves Cd reduction in an unsustainable way.

Long short-term memory models of water quality in inland water environments

Water quality is substantially influenced by a multitude of dynamic and interrelated variables, including climate conditions, landuse and seasonal changes. Deep learning models have demonstrated predictive power of water quality due to the superior ability to automatically learn complex patterns and relationships from variables. Long short-term memory (LSTM), one of deep learning models for water quality prediction, is a type of recurrent neural network that can account for longer-term traits of time-dependent data. It is the most widely applied network used to predict the time series of water quality variables. First, we reviewed applications of a standalone LSTM and discussed its calculation time, prediction accuracy, and good robustness with process-driven numerical models and the other machine learning. This review was expanded into the LSTM model with data pre-processing techniques, including the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise method and Synchrosqueezed Wavelet Transform. The review then focused on the coupling of LSTM with a convolutional neural network, attention network, and transfer learning. The coupled networks demonstrated their performance over the standalone LSTM model. We also emphasized the influence of the static variables in the model and used the transformation method on the dataset. Outlook and further challenges were addressed. The outlook for research and application of LSTM in hydrology concludes the review.