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Lakes not only play a crucial role in water supply, food production, and climate regulation [4] but also function as a cornerstone for socio-economic development. Space-based remote sensing was expected to revolutionize the monitoring of algal blooms and the water quality of large lakes [12], but it has proven difficult to draw statistically accurate pictures from such data [13]. In particular, (1) we must pay special attention and alert that the potential harmful effects and unrealized consequences of highly eutrophicated lake waters, e.g., novel hypertoxic viruses and new toxic chemical and organic compounds are overwhelming; (2) we should pay attention to the large long-distance trans-regional water drainage claimed to mitigate lake water pollution, as this process might result in abrupt changes in the established watershed ecosystems.

Cattle domestication and the complex histories of East Asian cattle breeds warrant further investigation. Through analysing the genomes of 49 modern breeds and eight East Asian ancient samples, worldwide cattle are consistently classified into five continental groups based on Y-chromosome haplotypes and autosomal variants. We find that East Asian cattle populations are mainly composed of three distinct ancestries, including an earlier East Asian taurine ancestry that reached China at least ~3.9 kya, a later introduced Eurasian taurine ancestry, and a novel Chinese indicine ancestry that diverged from Indian indicine approximately 36.6–49.6 kya. We also report historic introgression events that helped domestic cattle from southern China and the Tibetan Plateau achieve rapid adaptation by acquiring ~2.93% and ~1.22% of their genomes from banteng and yak, respectively. Our findings provide new insights into the evolutionary history of cattle and the importance of introgression in adaptation of cattle to new environmental challenges in East Asia. There are various indigenous cattle breeds in East Asia which have a complex history. Here, the authors analyse the genomes of 49 modern breeds and eight ancient samples and identify three distinct ancestries and multiple adaptive introgressions from other bovine species.

Zige Tangco is a meromictic saline lake located on the central Tibetan Plateau. Two parallel cores (ZGTC A-1 and ZGTC A-2) were collected from the lake at a water depth of 25 m during summer 2006. The chronology of core A-1 was reconstructed based on the Constant Initial Concentration (CIC) model of 210Pb and three accelerator mass spectrometry (AMS) ages from the chitin fragments. The hard water effect calibration of the sediment 14C age showed that the reservoir effect ranged from 1655 yr at 1950 AD to 1540 yr at 1610 AD. The hydrological variation in Zige Tangco during the past 800 yr was reconstructed using multi-proxies, including organic and carbonate content, stable isotopes of fine-grained carbonate minerals (< 38.5 μm) and grain-size distribution of the lake sediments. Our results show that there were strong fluctuations in the lake level between 1200 and 1820 AD, and at least three dry periods were recorded between 1235 and 1315 AD, 1410 and 1580 AD, and 1660 and 1720 AD characterized by high carbonate content, abrupt positive shifts of stable isotopes, and high sand content. The low-lake-level periods during the Little Ice Age (LIA) in Zige Tangco correspond to the lower δ 18O values in the Guliya ice core and the lower precipitation reconstructed from tree rings in Delingha. This demonstrated that the summer monsoon on the central Tibetan Plateau weakened during the dry and cold periods, whereas the winter monsoon strengthened. Relatively wetter periods or higher lake levels in Zige Tangco occurred at 1580-1650 AD and 1820-1900 AD. Negative shifts in stable isotopes were related to increased lake levels between 1800 and 1820 AD. Our results also showed that the summer monsoon precipitation on the central Tibetan Plateau was mainly controlled by solar activity during the past 800 yr.

Alpine permafrost plays a vital role in regional hydrology and ecology. Alpine permafrost is highly sensitive to climate change and human disturbance. The Muri area, which is located in the headwaters of the Datong River, northeast of the Tibetan Plateau, has undergone decadal mining, and the permafrost stability there has attracted substantial concerns. In order to decipher how and to what extent the permafrost in the Muri area has responded to the decadal mining in the context of climate change, daily MODIS land surface temperatures (LSTs) acquired during 2000–2024 were downscaled to 30 m × 30 m. The active layer thickness (ALT)–ground thaw index (DDT) coefficient was derived from in situ ALT measurements. An annual ALT of 30 m × 30 m spatial resolution was subsequently estimated from the downscaled LST for the Muri area using the Stefan equation. Validation of the LST and ALT showed that the root of mean squared error (RMSE) and the mean absolute error (MAE) of the downscaled LST were 3.64 °C and −0.1 °C, respectively. The RMSE and MAE of the ALT estimated in this study were 0.5 m and −0.25 m, respectively. Spatiotemporal analysis of the downscaled LST and ALT found that (1) during 2000–2024, the downscaled LST and estimated ALT delineated the spatial extent and time of human disturbance to permafrost in the Muri area; (2) human disturbance (i.e., mining and replantation) caused ALT increase without significant spatial expansion; and (3) the semi-arid climate, rough terrain, thin root zone and gappy vertical structure beneath were the major controlling factors of ALT variations. ALT, estimated in this study with a high resolution and accuracy, filled the data gaps of this kind for the Muri area. The ALT variations depicted in this study provide references for understanding alpine permafrost evolution in other areas that have been subject to human disturbance and climate change.

Multiple stressors associated with global change are influencing the phytoplankton taxonomic composition and biomass in plateau lakes, such as higher levels of ultraviolet radiation (UVR, 280–400 nm) and the risk of eutrophication. Although the restrictive effects of high UVR on phytoplankton are generally recognized, the effects will be impacted by the UVR levels of seasonal changes and the nutrient status. In this study, in situ phytoplankton productivity was measured seasonally under full solar radiation and only visible light (photosynthetically active radiation, PAR) conditions in the plateau oligotrophic Lake Fuxian, Southwest China. To determine the single effects of UVR and nutrient enrichment and their combined impact on phytoplankton communities, a mesocosm experiment (1 month) was conducted outdoors during late summer (rainy season). The interactive experiment was designed with two radiation treatments (UVR + PAR and PAR) and four nutrient treatments (raw water, nitrogen addition, phosphorus addition, nitrogen and phosphorus addition). Our results suggested that the response of phytoplankton to UVR depended largely on nutrient availability, which was embodied in the no significant responses of phytoplankton productivity and total biomass to UVR in the oligotrophic raw water from Lake Fuxian, but the positive responses of total biomass to UVR in the nutrient enrichment treatment. Furthermore, the response of phytoplankton to UVR was also taxa dependent. Chlorophytes and dinoflagellates showed positive response to UVR, whereas chrysophytes were quite sensitive to UVR; diatoms had no significant response to UVR under all nutrient conditions, while the response of cyanobacteria to UVR was highly dependent on nutrient status. This study highlights that the increase in nutrient concentrations in oligotrophic lake not only directly promotes the growth of phytoplankton but also combines with low UVR during summer to benefit the growth of eutrophic taxa based on the non-negative effect of UVR on total phytoplankton biomass and the positive effect on eutrophic taxa.

Geoscientists now identify coal layers using conventional well logs. Coal layer identification is the main technical difficulty in coalbed methane exploration and development. This research uses advanced quantile–quantile plot, self-organizing maps (SOM), k-means clustering, t-distributed stochastic neighbor embedding (t-SNE) and qualitative log curve assessment through three wells (X4, X5, X6) in complex geological formation to distinguish coal from tight sand and shale. Also, we identify the reservoir rock typing (RRT), gas-bearing and non-gas bearing potential zones. Results showed gamma-ray and resistivity logs are not reliable tools for coal identification. Further, coal layers highlighted high acoustic (AC) and neutron porosity (CNL), low density (DEN), low photoelectric, and low porosity values as compared to tight sand and shale. While, tight sand highlighted 5–10% porosity values. The SOM and clustering assessment provided the evidence of good-quality RRT for tight sand facies, whereas other clusters related to shale and coal showed poor-quality RRT. A t-SNE algorithm accurately distinguished coal and was used to make CNL and DEN plot that showed the presence of low-rank bituminous coal rank in study area. The presented strategy through conventional logs shall provide help to comprehend coal-tight sand lithofacies units for future mining.

The precise characterization of reservoir parameters is vital for future development and prospect evaluation of oil and gas fields. C-sand and B-sand intervals of the Lower Goru Formation (LGF) within the Lower Indus Basin (LIB) are proven reservoirs. Conventional seismic amplitude interpretation fails to delineate the heterogeneity of the sand-shale facies distribution due to limited seismic resolution in the Sawan gas field (SGF). The high heterogeneity and low resolution make it challenging to characterize the reservoir thickness, reservoir porosity, and the factors controlling the heterogeneity. Constrained sparse spike inversion (CSSI) is employed using 3D seismic and well log data to characterize and discriminate the lithofacies, impedance, porosity, and thickness (sand-ratio) of the C- and B-sand intervals of the LGF. The achieved results disclose that the CSSI delineated the extent of lithofacies, heterogeneity, and precise characterization of reservoir parameters within the zone of interest (ZOI). The sand facies of C- and B-sand intervals are characterized by low acoustic impedance (AI) values (8 × 106 kg/m2s to 1 × 107 kg/m2s), maximum sand-ratio (0.6 to 0.9), and maximum porosity (10% to 24%). The primary reservoir (C-sand) has an excellent ability to produce the maximum yield of gas due to low AI (8 × 106 kg/m2s), maximum reservoir thickness (0.9), and porosity (24%). However, the secondary reservoir (B-sand) also has a good capacity for gas production due to low AI (1 × 107 kg/m2s), decent sand-ratio (0.6), and average porosity (14%), if properly evaluated. The time-slices of porosity and sand-ratio maps have revealed the location of low-impedance, maximum porosity, and maximum sand-ratio that can be exploited for future drillings. Rock physics analysis using AI through inverse and direct relationships successfully discriminated against the heterogeneity between the sand facies and shale facies. In the corollary, we proposed that pre-conditioning through comprehensive petrophysical, inversion, and rock physics analysis are imperative tools to calibrate the factors controlling the reservoir heterogeneity and for better reservoir quality measurement in the fluvial shallow-marine deltaic basins.

This research focuses on the health risks caused by heavy metal (HM) environmental pollution. Soil, water, corn, rice, and patients’ hair samples from Daping Village, Yunnan Province, China, were analyzed for seven selected HMs. Geoaccumulation index ( I geo ), pollution indexes (PI), and the Nemerow integrated pollution index (PN) were used to evaluate pollution levels. We employed principal component analysis (PCA), correlation analysis (CA), and spatial distribution to identify the source and distribution characteristics of HMs in soil. Health risks of HMs and exposure pathways were accessed by calculating the hazard quotient (HQ) and hazard index (HI). The I geo , PI, and PN results show that cadmium (Cd) and arsenic (As) pollution is severe in soil, while other pollution is relatively little. PCA, CA, and spatial distribution show that HMs may be derived from black shale weathering and enrichment. Residents’ drinking water is relatively safe. Arsenic is the element most threatening to local residents (HI = 3.8). Soil (HI = 3.55) ingestion and plant (HI = 1.67) ingestion are the primary exposure pathways to HMs. This unusual disease may be caused by children’s relatively low immunity and long-term exposure to As. We must enhance the protection of children and encourage avoiding soil contact as much as possible. Our results highlight the importance of investigating HM pollution from geological sources and blocking potential exposure pathways.

The identification of small scale faults (SSFs) and fractures provides an improved understanding of geologic structural features and can be exploited for future drilling prospects. Conventional SSF and fracture characterization are challenging and time-consuming. Thus, the current study was conducted with the following aims: (a) to provide an effective way of utilizing the seismic data in the absence of image logs and cores for characterizing SSFs and fractures; (b) to present an unconventional way of data conditioning using geostatistical and structural filtering; (c) to provide an advanced workflow through multi-attributes, neural networks, and ant-colony optimization (ACO) for the recognition of fracture networks; and (d) to identify the fault and fracture orientation parameters within the study area. Initially, a steering cube was generated, and a dip-steered median filter (DSMF), a dip-steered diffusion filter (DSDF), and a fault enhancement filter (FEF) were applied to sharpen the discontinuities. Multiple structural attributes were applied and shortlisted, including dip and curvature attributes, filtered and unfiltered similarity attributes, thinned fault likelihood (TFL), fracture density, and fracture proximity. These shortlisted attributes were computed through unsupervised vector quantization (UVQ) neural networks. The results of the UVQ revealed the orientations, locations, and extensions of fractures in the study area. The ACO proved helpful in identifying the fracture parameters such as fracture length, dip angle, azimuth, and surface area. The adopted workflow also revealed a small scale fault which had an NNW–SSE orientation with minor heave and throw. The implemented workflow of structural interpretation is helpful for the field development of the study area and can be applied worldwide in carbonate, sand, coal, and shale gas fields.

Understanding the seasonal variation characteristics and trends in water quality is one of the most important aspects for protecting and conserving lakes. Lake Yangzong water quality parameters and nutrients, including water temperature, dissolved oxygen (DO), pH, conductivity, Chlorophyll-a, phycocyanin, total nitrogen (TN) and total phosphorus (TP), were monitored in different seasons from 2015 to 2021. Based on the monitoring data, the temporal and spatial variations of various parameters were analyzed. The results showed that Lake Yangzong is a warm monomictic lake. The Pearson correlation coefficient and correlation analysis showed water quality parameters were significantly correlated and probably affected by temperature. Cyanobacteria were at risk of blooming in spring and autumn. The contents of TN and TP in winter were significantly higher than in summer, especially TN, with both reaching a peak at the epilimnion and hypolimnion in December 2020 (TN = 1.3 mg/L, TP = 0.06 mg/L). We also observed a dual risk of endogenous release and exogenous input. Therefore, strengthening the supervision for controlling eutrophication caused by human activities and endogenous release is urgently needed.