The spatial distribution of heavy metals, representative detection methods of heavy metals and related research trends are summarized.

Natural capital accounting is an essential prerequisite for the harmonious development of human beings and nature and benign interactions between economy and environment. Although the ecological footprint has significantly contributed to sustainability evaluation for many years, the traditional ecological footprint model is limited by lack of value accounting, incomplete account content, neglect of multi-functions of land, and geographical spatial heterogeneity. These limitations reduce the universality of this model. To improve the value accounting of the ecological footprint model, this study takes national hectares (nha) as the measurement unit and includes the freshwater and pollution footprints. The dynamic changes of natural capital from 2000 to 2018 were calculated and analyzed in 31 Chinese provinces. The main findings are summarized here. China's physical quantity of ecological footprint, ecological carrying capacity, and ecological deficit in 2018 was 4.03, 0.79, and -3.24 billion nha, respectively. The energy account contributed most of the physical quantity in the ecological footprint (72.12% of the total). From 2000 to 2018, the physical quantities of the per capita ecological footprint and the per capita ecological deficit increased at 5.49% and 10.08% per annum, respectively, while the physical quantity of the per capita ecological carrying capacity decreased by 0.55% per annum. The physical quantities of the per capita ecological footprint and per capita ecological deficit were spatially distributed, reducing in the order of East > Central > West. The spatial distribution of the physical quantity of the per capita ecological carrying capacity showed the opposite trend. By 2018, China's ecological footprint, ecological carrying capacity, and ecological deficit were valued at 18.09, 12.44 and CNY -5.65 trillion, respectively. Over the 2000-2018 period, the per capita ecological footprint and ecological carrying capacity increased by 495 and CNY 370 per annum, respectively, while the per capita ecological deficit expanded at CNY -125 per annum. The biological account contributed approximately 59.53% of the value quantity of the ecological footprint. Since the twenty-first century, the cumulative effect of excessive consumption has placed increasing pressure on China's ecosystems. On mainland China, only Tibet showed an ecological surplus in 2018. The ecological pressure index decreased in the order of East > Central > West. As Western China has developed extensively and its ecological deficit is rapidly expanding, this region deserves special attention. The most ecologically challenging regions in China are Shanghai, Tianjin, and Beijing. In contrast, Jilin, Qinghai, and Tibet impose low ecological pressure in China. These findings contribute to the standardization and localization of the ecological footprint model in China and provide a reference for regional resource management and ecological construction.

The Xinjiang Uyghur Autonomous Region of China is the largest arid region in Central Asia, and is heavily dependent on glacier melt in high mountains for water supplies. In this paper, glacier and climate changes in Xinjiang during the past decades were comprehensively discussed based on glacier inventory data, individual monitored glacier observations, recent publications, as well as meteorological records. The results show that glaciers have been in continuous mass loss and dimensional shrinkage since the 1960s, although there are spatial differences between mountains and sub-regions, and the significant temperature increase is the dominant controlling factor of glacier change. The mass loss of monitored glaciers in the Tien Shan has accelerated since the late 1990s, but has a slight slowing after 2010. Remote sensing results also show a more negative mass balance in the 2000s and mass loss slowing in the latest decade (2010s) in most regions. This needs further investigation on whether the slowing is general and continuing. In addition, glacier surging occurs more frequently in the Karakoram and Kunlun Mountains.

When the total nitrogen content in water sources exceeds the standard, it can promote the rapid proliferation of algae and other plankton, leading to eutrophication of the water body and also causing damage to the ecological environment of the water source area. Therefore, making timely and accurate predictions of water quality at the source is of vital importance. Since water quality data exhibit non-stationary characteristics, predicting them is quite challenging. This study proposes a novel hybrid deep learning model based on modal decomposition, ERSCB (EMD-RBMO-SVMD-CNN-BiGRU), to enhance the accuracy of water quality forecasting. The model first employs Empirical Mode Decomposition (EMD) technology to decompose the original water quality data. Subsequently, it quantifies the complexity of the subsequences obtained from EMD using Sample Entropy (SE) and further decomposes the most complex subsequences using Sequential Variational Mode Decomposition (SVMD). To address the matter of selecting balanced parameters in SVMD, this study introduces the Red-Billed Blue Magpie Optimization (RBMO) algorithm to optimize the hyperparameters of SVMD. On this basis, a forecasting model is constructed by integrating Convolutional Neural Networks (CNN) and Bidirectional Gated Recurrent Unit (BiGRU) networks. The experimental results show that, compared to existing water quality prediction models, the ERSCB model has an improved prediction accuracy of 4.0% and 3.1% for the KaShi River and GongNaiSi River areas, respectively.