Spatial-temporal Variations Research Articles

Assessing sediment dynamics and retention services in the vulnerable mountain ecosystem of the Indian Himalayas.

Sediment loss and export pose significant global environmental issues, profoundly affecting water quality, soil fertility, and ecosystem stability, particularly in vulnerable mountain ecosystems like the Indian Himalayas. The present study used remote sensing data and the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) sediment delivery ratio (SDR) model to analyze spatial-temporal variations in soil loss (SL), sediment export (SE), and sediment retention (SR) capabilities in the South Shimla watershed, Himachal Pradesh, India, from 1993 to 2023. The findings showed significant changes in land use and land cover (LULC): evergreen forest and scrub land decreased sharply by 11.53% and 36.43%, respectively, while agricultural areas and built-up areas increased notably by 71.16% and 215.76%, respectively. Despite a decline of 19.18% in SL and 24.43% in SE, sediment loss and export varied across the study area, highlighting the heterogeneous nature of sediment dynamics. The overall retention capacity increased by 2.59%, with scrub forests playing a critical role in SR, while built-up areas showed the lowest retention. Northern and central sub-watersheds (SWs) experienced a significant decrease in retention capacity (from - 1.92 to - 11.6%), whereas those in the southern and eastern regions saw an increase in SR (from 3.69 to 28.24%). These results underscore the complex interactions between LULC changes, sediment dynamics, and retention services, highlighting the importance of preserving natural ecosystems and informing policy for landscape-based conservation and development planning in the vulnerable Himalayan region.

Spatiotemporal evolution and driving factors analysis of fractional vegetation coverage in the arid region of northwest China

The arid region of northwest China (ARNC) is the most ecologically fragile region in China, and is characterized by harsh natural conditions, severe soil erosion, and poor soil fertility. Understanding long-term vegetation changes in this region is critical for effective environmental monitoring and climate change adaptation. Fractional vegetation coverage (FVC) is a key parameter for characterizing the ecological conditions of the ARNC. However, the reliance on low-resolution FVC and NDVI data due to the lack of medium-resolution data has limited our understanding of the environmental dynamics in this region. Therefore, this study addressed this gap by utilizing Landsat data to generate FVC data, enabling a detailed investigation of the spatial-temporal variations and driving factors of vegetation in the ARNC from 2000 to 2020. The results indicated the following: (1) The FVC was generally low, with an average of 0.191. The FVC was greater in the northwest and lower in the southeast in terms of spatial distribution features. The trend of FVC change in ARNC showed significant spatial variability, with degradation outweighing improvement. (2) The coefficient of variation of FVC was 0.377, indicating significant temporal fluctuations, with more stable conditions in the northwest than in the southeast. (3) The spatial differentiation of the FVC in ARNC was primarily driven by land cover types, evapotranspiration, and precipitation, with explanatory powers exceeding 30 % each. This study is significant because it provides a comprehensive understanding of vegetation dynamics in one of China's most vulnerable regions, offering critical insights for ecological restoration, desertification control, and sustainable development. The findings underscore the importance of targeted ecological governance to address the challenges posed by environmental degradation in the ARNC.

Experimental study of a novel guided sequential immersion cooling system for battery thermal management

Immersion cooling exhibits superior cooling performance compared to traditional battery thermal management systems (BTMS). However, a significant challenge of immersion cooling is the spatial variation of temperature within both the coolant and lithium-ion batteries (LIBs). This research proposes a guided sequential immersion cooling (GSIC) BTMS to address this issue. Experimental studies were conducted to evaluate the heat dissipation performance of the GSIC structure under various conditions, including extreme loads. The results indicate that under the static flow immersion cooling (SFIC) scheme, cooling the tabs significantly influences the overall performance. Immersing the tabs can reduce the maximum battery temperature by 10.403 °C, although the spatial variation of temperature persists. Under forced flow immersion cooling (FFIC) conditions, increasing the coolant flow rate dissipates the heat generated by LIBs more effectively. Even at an extreme discharge rate of 5C, the maximum temperature remains below 45 °C. The average temperature reduction at the tabs is greater than the battery body, and with increased flow rates, the temperature difference between the two can be maintained within 1 °C under all conditions. As the flow rate keeps increasing, the average temperature at the tabs gets even lower than the battery body at low loads. This demonstrates that the GSIC BTMS can suppress the temperature rise at the tabs, which is a critical heat risk. Moreover, the temperature uniformity of the LIBs module is improved. As the flow rate increases, the temperature difference within the LIBs module decreases when the discharge rates is between 1C and 5C. Theoretical analysis confirms that increasing flow rates for low loads can suppress temperature rise, albeit with increased power consumption and reduced cooling efficiency. High flow rates are more suitable for high load conditions of the LIBs. These results validate the feasibility of the GSIC BTMS and provide new insights for the development of immersion cooling BTMS.

Occurrence Characteristics and Ecological Risk Assessment of Antibiotics in Reclaimed Water-receiving Rivers in Beijing

Taking Liangshui River, the reclaimed water-receiving river in Beijing, as the research area, the types, detection frequencies, and concentrations of 16 antibiotics in water and sediment were analyzed, and their temporal-spatial variation and occurrence characteristics were discussed. The results showed that nine and 13 target antibiotics were detected in the water and sediment of Liangshui River, with the antibiotic concentration ranges of ND-116.68 ng·L-1 and ND-235.42 ng·g-1, respectively. The main antibiotics in water were ofloxacin and clarithromycin, and the main antibiotic in sediment was ofloxacin. The total concentration of antibiotics in water and sediment showed a gradual decrease from the upstream to the downstream in the Liangshui River mainstream, and the concentration of antibiotics in tributaries was higher than that in the mainstream. The inflow of tributaries had an obvious impact on the antibiotic concentration in water for the Liangshui River but had little impact on its sediment. The total concentration of antibiotics in water and sediment during the dry season was generally higher than that during the wet season. The detected antibiotics with the highest concentration were quinolones in water during the wet season and macrolides in the dry season. Quinolones had the highest concentration in sediment in both seasons. The ecological risk assessment results showed that clarithromycin had a low risk in water in the dry season and sediment in both seasons, whereas the other antibiotics had no risk. The combined ecological risk and the most sensitive trophic level ecological risk assessment showed that all sampling sites had low risk or no risk, and the risk of the dry season was generally greater than that of the wet season. The risk values of some sampling points were close to the medium risk threshold during the dry season, which requires further attention.

A real-time assessment of hazardous atmospheric pollutants across cities in China and India

China and India are two of the fastest-growing developing economies covering about 35 % of the world’s population. Due to the extensive prevalence of air pollution across cities in China and India, contemporary assessment of atmospheric pollution through real-time and remote sensing observations is inadequate. The study aims to determine the spatial distribution and temporal variation of hazardous atmospheric pollutants across cities in China (Shanghai, Nanjing, Jinan, Zhengzhou and Beijing) and India (Kolkata, Asansol, Patna, Kanpur and Delhi). Ground observation data on CO, O3, PM2.5, PM10, NO2 and SO2 along with remote sensing data on AOD, CO, O3, BC, NO2, SO2 and dust surface mass concentrations are used to assess atmospheric pollution. This study examines daily, zonal and longitudinal pollutant distributions using Sentinel-5 P data and surface mass concentrations over the vertical column evaluated from NASA satellite data. The Mann-Kendall test and relative change methods have been implemented to assess pollutant trends while Sen’s Slope identifies the magnitude of change. The similarity test and data validation methods including NRMSE, PC and MBias have been employed to ensure consistency in analysing annual trends for each air pollutant in the datasets. Additionally, multiple correlation matrix analysis has been used to examine the associations among different pollutants from both datasets based on their annual averages. Remote sensing data reveals that eastern China and north-eastern India have the highest aerosol, BC, CO, NO2 and SO2 while western China and southern India lowest. Dust peaks in the west while O3 levels are highest in the northern part of China and India. Ground observation data indicates that Chinese cities have higher annual mean SO2 and O3 concentrations with yearly declines in PM2.5, PM10, NO2, SO2 and CO notably SO2. Indian cities witnessed overall increases in PM2.5, PM10, NO2 and SO2 from 2012 to 2019 with a slight decline in 2020 followed by a resurgence in 2023. The findings provide insights for implementing regional policy measures to reduce air pollution based on changes in pollutant behaviour. The study suggests that addressing atmospheric pollutants, particularly NO2, CO, PM2.5, PM10, and SO2 requires a comprehensive environmental policy framework involving central and state governments and enforcing stringent environmental protection laws.

Spatial–Temporal Variations and Driving Factors of the Albedo of the Qilian Mountains from 2001 to 2022

Surface albedo plays a pivotal role in the Earth’s energy balance and climate. This study conducted an analysis of the spatial distribution patterns and temporal evolution of albedo, normalized difference vegetation index (NDVI), normalized difference snow index snow cover (NSC), and land surface temperature (LST) within the Qilian Mountains (QLMs) from 2001 to 2022. This study evaluated the spatiotemporal correlations of albedo with NSC, NDVI, and LST at various temporal scales. Additionally, the study quantified the driving forces and relative contributions of topographic and natural factors to the albedo variation of the QLMs using geographic detectors. The findings revealed the following insights: (1) Approximately 22.8% of the QLMs exhibited significant changes in albedo. The annual average albedo and NSC exhibited a minor decline with rates of −0.00037 and −0.05083 (Sen’s slope), respectively. Conversely, LST displayed a marginal increase at a rate of 0.00564, while NDVI experienced a notable increase at a rate of 0.00178. (2) The seasonal fluctuations of NSC, LST, and vegetation collectively influenced the overall albedo changes in the Qilian Mountains. Notably, the highly similar trends and significant correlations between albedo and NSC, whether in intra-annual monthly variations, multi-year monthly anomalies, or regional multi-year mean trends, indicate that the changes in snow albedo reflected by NSC played a major role. Additionally, the area proportion and corresponding average elevation of PSI (permanent snow and ice regions) slightly increased, potentially suggesting a slow upward shift of the high mountain snowline in the QLMs. (3) NDVI, land cover type (LCT), and the Digital Elevation Model (DEM, which means elevation) played key roles in shaping the spatial pattern of albedo. Additionally, the spatial distribution of albedo was most significantly influenced by the interaction between slope and NDVI.

Spatial and Temporal Variations’ Characteristics of Extreme Precipitation and Temperature in Jialing River Basin—Implications of Atmospheric Large-Scale Circulation Patterns

In recent years, extreme climate events have shown to be occurring more frequently. As a highly populated area in central China, the Jialing River Basin (JRB) should be more deeply explored for its patterns and associations with climatic factors. In this study, based on the daily precipitation and atmospheric temperature datasets from 29 meteorological stations in JRB and its vicinity from 1960 to 2020, 10 extreme indices (6 extreme precipitation indices and 4 extreme temperature indices) were calculated. The spatial and temporal variations of extreme precipitation and atmospheric temperature were analyzed using Mann–Kendall analysis, to explore the correlation between the atmospheric circulation patterns and extreme indices from linear and nonlinear perspectives via Pearson correlation analysis and wavelet coherence analysis (WTC), respectively. Results revealed that among the six selected extreme precipitation indices, the Continuous Dry Days (CDD) and Continuous Wetness Days (CWD) showed a decreasing trend, and the extreme precipitation tended to be shorter in calendar time, while the other four extreme precipitation indices showed an increasing trend, and the intensity of precipitation and rainfall in the JRB were frequent. As for the four extreme temperature indices, except for TN10p, which showed a significant decreasing trend, the other three indices showed a significant increasing trend, and the number of low-temperature days in JRB decreased significantly, the duration of high temperature increased, and the basin was warming continuously. Spatially, the spatial variation of extreme precipitation indices is more obvious, with decreasing stations mostly located in the western and northern regions, and increasing stations mostly located in the southern and northeastern regions, which makes the precipitation more regionalized. Linearly, most of the stations in the extreme atmospheric temperature index, except TN10p, show an increasing trend and the significance is more obvious. Except for the Southern Oscillation Index (SOI), other atmospheric circulation patterns have linear correlations with the extreme indices, and the Arctic Oscillation (AO) has the strongest significance with the CDD. Nonlinearly, NINO3.4, Pacific Decadal Oscillation (PDO), and SOI are not the main circulation patterns dominating the changes of TN90p, and average daily precipitation intensity (SDII), maximum daily precipitation amount (RX1day), and maximum precipitation in 5 days (Rx5day) were most clearly associated with atmospheric circulation patterns. This also confirms that atmospheric circulation patterns and climate tend not to have a single linear relationship, but are governed by more complex response mechanisms. This study aims to help the relevant decision-making authorities to cope with the more frequent extreme climate events in JRB, and also provides a reference for predicting flood, drought and waterlogging risks.

Spatial variability of temperature inside atoll lagoons assessed with Landsat-8 satellite imagery

Sea Surface Temperature (SST) maps are necessary for managing marine resources in a climate change context, but are lacking for most of the 598 world's atolls. We assessed the feasibility of using the Landsat-8 (L8) satellite to infer SST maps for four French Polynesia atolls of aquaculture interest in Tuamotu Archipelago, namely Takaroa, Raroia, Tatakoto, and Reao. Specifically, we (1) used sensors to measure in situ the range of spatial temperature differences recorded in these four atoll lagoons; (2) calibrated and assessed the performances of SST algorithms to estimate lagoon temperature from L8 signals; (3) generated temperature maps for the lagoons and compared spatial patterns of temperature obtained from these maps with patterns highlighted by in situ sensors. Good agreements between satellite and in situ temperature data were obtained, with better results achieved when using an atoll-by-atoll optimization (average bias = −0.26 °C; RMSE = 0.55 °C). However, we also show that the range of temperature inside atoll lagoons is low, and of the same order of magnitude than RMSE achieved with SST algorithms. Because of the L8 overpass time (∼9 a.m.) and the revisit time (16 days), L8 SST could not capture the entire range of spatial differences measured in situ in the four lagoons, but could capture spatial gradients and fronts better than with few in situ sensors. Considering the achieved accuracies and the actual temperature differences at the four study sites, we discuss the usefulness of L8 derived SST maps to assist fishery and aquaculture management in atoll lagoons, as well as the possible generalization to other lagoons.

The Distribution of Climate Comfort Duration for Forest Therapy Has Temporal and Regional Heterogeneity in Xinjiang

Climatic comfortability serves as a crucial factor in tourism decision making; however, there remains a gap in evaluating the climate comfort conditions specifically for forest therapy. We developed a new index—Forest Therapy Climate Comfort Index (FTCCI)—to evaluate the climate comfort conditions for forest therapy by integrating the Temperature (T), Temperature and Humidity Index (THI), and Wind Efficiency Index (WEI). A total of 26 potential forest therapy bases were selected from the protected areas in Xinjiang and divided into five clusters: Aksu cluster, Hami cluster, Altai cluster, Ili and its surrounding cluster, and Urumqi and its surrounding cluster. Based on the monthly observation data from 25 surface meteorological stations in Xinjiang, spanning from 1994 to 2023, employing the Co-Kriging interpolation method, we explored the spatial–temporal variation in FTCCI from June to September and made clear the climate comfort duration across 26 bases in Xinjiang. The results indicated that (1) The variation in T, THI, and WEI in 26 bases demonstrated a consistent pattern of temporal variation. July emerged as the optimal month, followed closely by August, with most indices in both months falling within the comfort level. Conversely, September proved to be the least favorable month due to frigid conditions and discomfort for the human body, whereas June’s sensation was slightly more tolerable. (2) The distribution of T, THI, and WEI showed regional heterogeneity. The Urumqi and its surrounding cluster displayed the most favorable conditions for forest therapy, whereas the Aksu cluster showed the poorest performance. (3) There were differences in both FTCCI and climate comfort duration among various clusters in Xinjiang. Overall, excluding Tomur Peak and Nalati (July and August), the remaining 24 bases offered ideal climate comfort conditions for forest therapy from mid to late June through August. Notably, the bases in Urumqi and its surrounding cluster had the longest climate comfort duration, ranging from 3.5 to 4 months. Therefore, reliance on the unique climate, resource, and geographical condition of each base is crucial in creating special forest therapy products that cater to the diverse health needs of tourists.

Computational and intelligence modeling analysis of pharmaceutical freeze drying for prediction of temperature in the process

Accurate temperature prediction is crucial for various scientific and engineering applications, yet it remains challenging due to the complex relationships between spatial coordinates and temperature variations. This study addresses this challenge by exploring advanced machine learning models to improve prediction accuracy, filling the research gap in optimizing predictive models for temperature estimation in freeze drying of pharmaceuticals. We employed Support Vector Regression (SVR), Poisson Regression (POR), and Adaptive Neuro-Fuzzy Inference System (ANFIS) models, each enhanced using the Cheetah Optimizer (CO), to estimate temperature based on spatial coordinates (X, Y, Z) in the domain of process. The methodology involved training and testing these models on a dataset comprising spatial and temperature data, with a focus on improving accuracy through optimization techniques. Validation of the models showed that the CO-SVR model outperformed the others, achieving an R2 score of 0.953, Mean Squared Error (MSE) of 9.893, and Mean Absolute Error (MAE) of 2.549. This represents a significant improvement over the CO-ANFIS model, which obtained an R2 of 0.863, MSE of 23.583, and MAE of 3.912. The CO-POR model showed the lowest predictive capability, with an R2 score of 0.753, MSE of 57.608, and MAE of 6.756. These findings underscore the effectiveness of the Cheetah Optimizer in enhancing the accuracy of SVR models for temperature prediction, suggesting its potential for broader applications in predictive modeling where accuracy is paramount.

GIS-Based Mapping of the Water Quality and Geochemical Assessment of the Ionic Behavior in the Groundwater Aquifers of Middle Ganga Basin, Patna, India

The study implemented Geographic Information System (GIS) techniques and multivariate hydrogeochemical analysis to evaluate the spatial-temporal and seasonal variation in the groundwater quality of Patna, India. For this purpose, sixty groundwater samples were collected and analyzed for major anions and cations during the pre-monsoon, monsoon, and post-monsoon seasons of 2019-2020. The physicochemical parameters such as pH, EC (Electrical Conductivity), TDS (Total Dissolved Solids), TH (Total Hardness), Ca2+, Mg2+, Na+, K+, HCO3-, Cl-, SO42- were considered to evaluate the water quality index. The result revealed degradation in groundwater quality from pre-monsoon (49.21) to post-monsoon (74.48). EC, TDS, TH, Mg2+, Na+, Ca2+, K+, and HCO3- ions were found accountable for high WQI values at various sampling sites during different seasons. Spatial maps showed that 45 % of the sampling stations exhibited poor quality in all three seasons, where the eastern part of the studied region was revealed to be the most affected area. The application of multivariate statistical methods and hydrogeochemical investigation has clearly defined the dominant role of the weathering process, and reverse ion exchange mechanism in controlling the aquifer’s ionic chemistry. Moreover, poor seepage system, and waste leachate from the surface have been found as the main cause of high levels of Na+, K+, and Cl- in the eastern part of Patna.

A dataset of spatial-temporal water color variations in regulating reservoirs of Jiaodong Water Transfer Project during 1990–2022

A dataset of spatial-temporal water color variations in regulating reservoirs of Jiaodong Water Transfer Project during 1990–2022

Spatial–temporal prediction model for unsteady near-wall flow around cylinder based on hybrid neural network

A hybrid neural network based on Densely Connected Convolutional Networks (DenseNet), Convolutional Long Short-Term Memory Neural Network (ConvLSTM), and Deconvolutional Neural Network (DeCNN) is employed to predict unsteady flow fields. The utilization of DenseNet makes the model more compact and makes the prediction of three-dimensional flow affordable. The ConvLSTM is implemented to predict multiple future time steps which improves prediction efficiency. The proposed model transforms the time sequences of velocity and pressure fields into uniform spatial–temporal topology as input and captures nonlinear feature information in the spatial–temporal domain. Numerical simulations are conducted for the flow around cylinder at different Reynolds numbers and the near-wall flow around cylinder with different gap ratios, and training samples for the neural network inputs are established. The predicted results are compared with the numerical simulation results, showing good agreement. From the prediction cycle, it can be seen that good prediction results can be maintained in the first three prediction cycles. The prediction results of the three-dimensional unsteady flow around a cylinder near a plane wall, exhibit remarkable accuracy, successfully capturing the evolution of turbulent vortex structures. This signifies that the prediction model is highly effective in capturing the spatial–temporal variations of complex unsteady flows.

Experimental investigation on pool boiling heat transfer enhancement using reticular bi-conductive surfaces

Pool boiling has been a research focus over the decades because of its superior heat dissipation performance. This paper provides insights into the underlying pool boiling enhancement mechanism of bi-conductive surface which is an effective modification approach. In this study, a new type of bi-conductive surface is created by embedding a reticular structure made of low-conductive epoxy into a high-conductive copper substrate. Because of the enormous difference in conductivity, the heat flux almost exclusively flows through copper. The existence of epoxy creates periodic spatial surface temperature variations and induces ordered liquid and vapor paths. The influence of the width and depth of epoxy on pool boiling is studied by conducting heat transfer experiments. According to the results, all bi-conductive surfaces exhibit critical heat flux (CHF) increases in copper zone and the improvement is more significant with the increase of epoxy width. However, the depth of the epoxy has no significant effect on pool boiling. At low superheat, the heat transfer coefficient (HTC) in copper zone is relevant to the thermal flow rate of the independent copper zone. At high superheat, bi-conductive surfaces with wider epoxy have higher HTC in copper zone. The results show that for the bi-conductive surface with 0.75 mm width of epoxy, replacing 44% of the high-conductive copper with low-conductive epoxy results in a 270% CHF increase in copper zone and a 108% increase in the maximum total effective thermal power.

Spatial-temporal distribution characteristics of surface water pollutants and their potential sources in Ngari, China.

The Ngari region has many important rivers and is critical to water resource security and water resource continuity in China and even in adjoining Asian countries. However, the spatial distribution and monthly variation in local water quality have been poorly understood until recently. In this study, the spatial-temporal variations of 12 water quality parameters, including pH, dissolved oxygen (DO), permanganate index (IMn), chemical oxygen demand (COD), five-day biochemical oxygen demand (BOD5), ammonia nitrogen (NNH3), total nitrogen (Ntotal), total phosphorus (Ptotal), copper (Cu), fluoride (F), arsenic (As) and cadmium (Cd), were determined from samples collected monthly at 22 water cross-sectional sites in the Ngari region in 2020. The surface water pollution in the southern Ngari region was the most serious, and the water pollution level in winter was higher than that in the other seasons. As (0.0781 ~ 0.6154 mg/L) and F (1.05 ~ 4.64 mg/L) were the main exceedance factors derived from the recharge of high arsenic and fluoride geothermal water and weathering of As and F-bearing minerals. The hazard quotient and carcinogenic risk for As and F at the five contaminated sampling sites indicated potential health risks and even carcinogenicity to local populations. The hydrochemistry types of the lakes and rivers in the Ngari region were mainly chloride water and carbonate water. The results from this study can provide a scientific basis for the prevention and control of surface water pollution in the Ngari region and contribute to subsequent research on the ecology of water bodies.

Integrated assessment of offshore wind and wave power resources in mainland Portugal

This study presents a comprehensive analysis of offshore wind and wave energy resources in mainland Portugal, aiming to facilitate sustainable energy exploitation. The study integrates the spatial distribution and temporal variability of those energy resources, as well as the interplay between wind and wave patterns and co-location feasibility. A 44-year dataset (1979–2022), derived from ERA5 reanalysis wind and SWAN wave model across the concession zones designated by the Portuguese government (labelled as P1 to P6) are utilised. The results indicate promising prospects for wind and wave power densities in these zones, emphasising the suitability of the offshore renewable energy extraction. Additionally, analysis revealed fluctuations in wind and wave power variability among the selected locations, with concession zone P3 (Leixões) emerging as a promising candidate due to its relatively lower variability and stable distribution patterns of both resources. The analysis on co-location feasibility demonstrated the potential for efficient energy extraction in location P5 proving to be the most favourable in wind-wave resource integration. Moreover, time lag analysis highlighted opportunities for optimising energy capture efficiency. Overall, this study provides valuable insights into the sustainable exploitation of wind and wave power resources, contributing to deliver informed decision-making for renewable energy investment.

Spatial–temporal variations of sediment transport rate and driving factors in Shule River Basin, northwest China

The sediment content and transport rate of rivers are crucial indicators reflecting soil erosion, water quality, and water resource management in a region. Studying changes in river sediment transport rates within a basin is essential for evaluating water quality, restoring water ecosystems, and implementing soil and water conservation measures. This study focused on the Shule River Basin and utilized various methods such as moving average, cumulative anomaly, Mann–Kendall mutation test, Mann–Kendall (M–K) trend test, Sen’s slope estimation, Correlation analysis, wavelet analysis, R/S analysis, ARCGIS10.7 interpolation, non-uniformity coefficient, and concentration to analyze data from hydrologic stations at Changmapu (CMP), Panjiazhuang (PJZ), and Dangchengwan (DCW). The research examined the temporal and spatial characteristics of sediment transport rates and identified key driving factors. Findings revealed significant increases in annual sediment transport rates at CMP and PJZ by 12.227 and 4.318 kg/s (10a)−1, respectively, while DCW experienced a decrease of 0.677 kg/s (10a)−1. The sediment transport rate of the three stations had a sudden change around 1994. The average annual sediment transport rates displayed distinct cycles, with CMP, PJZ, and DCW showing cycles of 51a, 53a, and 29a respectively. Additionally, while CMP and PJZ exhibited a continuous upward trend in sediment transport rates, DCW showed a consistent decline. The annual average sediment transport rates of CMP, PJZ, and DCW were 1305.43 kg/s, 810.06 kg/s, and 247.80 kg/s, respectively. These research findings contribute to enhancing the comprehension of sediment dynamics in the arid region of northwest China and offer a theoretical basis for the restoration and management of ecological environments in similar areas in the future.

Semi-analytical solutions for land subsidence due to groundwater withdrawal

This paper introduces novel semi-analytical models tailored for estimating land subsidence resulting from groundwater extraction in confined aquifers. These models offer high scalability, allowing them to be applied to various well configurations and pumping schedules. Their development involves the numerical integration of two key analytical solutions: the “nucleus of strain” (NoS) (Mindlin and Chen, 1950), which represents a localised zone within the aquifer where a unit change in pore pressure leads to deformation and subsequent surface displacement, and the classic Theis equation (Theis, 1935) for the pore pressure changes induced by a constant-rate well pumping from a laterally unbounded aquifer. These integrations yield surface displacement components, both horizontal and vertical, expressed as functions of two dimensionless spatial–temporal variables, which encompass aquifer depth, thickness, well placement, pumping schedules, and critical hydro-geomechanical parameters like hydraulic conductivity, porosity, vertical compressibility, and water compressibility. Proposed are two distinct modelling approaches: one employing a lookup table (LT) derived from numerical integration results, and the other providing direct closed-form surface displacement solutions by fitting LT data with “hinge models”, which use piecewise-linear functions linked by sigmoidal curves for computational efficiency. In both cases, surface displacement components are estimated by plugging in the dimensionless variables. Conditions of variable pumping from multiple wells can be addressed by applying superposition of solutions. In essence, these semi-analytical models offer swift computational capabilities for understanding and forecasting land subsidence dynamics. Their scalability makes them adaptable to a wide array of well configurations and scheduling scenarios, rendering them valuable for numerous applications. They are particularly significant for providing preliminary estimates of the impacts of groundwater development, conducting “what-if” tests, and performing sensitivity analyses to identify key factors affecting land subsidence risk. This underscores the importance of these models in sustainable groundwater resource management and in mitigating land subsidence and its associated consequences.

Spatial distribution, temporal variations and source of titanium dioxide nanoparticles in Taihu Lake, China

The detrimental impacts of titanium dioxide nanoparticles (TiO2NPs) on the ecosystem and organisms have aroused great public concerns. However, the information on their concentration in the real aquatic environment is still limited, hindering the rational evaluation of their potential hazards. In this study, water samples from Taihu Lake were collected in June and November 2023, to investigate the spatial distribution and temporal variations of TiO2NPs. Using phosphorylated Fe3O4 particles based magnetic solid phase extraction and ICP-MS determination, high concentrations of TiO2NPs were detected in the western and northern regions of Taihu Lake. These areas contribute to 83 % of the total runoff into the lake. Total Ti levels were typically higher in November than in June, but no marked seasonal difference was observed for TiO2NPs. Different shapes of TiO2NPs with both smooth and rough surfaces were observed in the surface water. To further distinguish whether these TiO2NPs were sourced from the natural background or anthropogenic sources, the ratios of Ti to other rare elements including Nb were calculated. In November, the Ti/Nb ratios at most sampling sites were significantly higher than those in June, indicating that a large amount of engineered TiO2NPs are discharged into Taihu Lake during the summer and autumn seasons. Our study contributes to the understanding of contamination levels, spatial distribution, and temporal variation of TiO2NPs in lake systems, and provides valuable data for their further risk assessment.

Spatial-temporal distributions of phytoplankton shifting, chlorophyll-a, and their influencing factors in shallow lakes using remote sensing

Understanding phytoplankton dynamics is crucial for assessing water ecosystem health. However, traditional in-situ investigations often fall short of capturing the spatial-temporal variations of phytoplankton community structure and biomass on large scales. This study introduces a novel approach that harnesses the power of machine learning models coupled with Sentinel-2 satellite data to predict phytoplankton shifting and chlorophyll-a (Chla) in three large shallow lakes in central China from 2016 to 2021. We employed an array of machine learning algorithms, including the extreme gradient boosting method (XGBoost), random forest (RF), support vector machine (SVM), and K-nearest neighbor (KNN) to retrieve Chla and phytoplankton groups clustered by hierarchical cluster analysis based on the Bray-Curtis similarity index (HCA). The influences of environmental factors, including meteorology and nutrients, on phytoplankton dynamics were then explored. Our results identified four distinct phytoplankton groups, each exhibiting unique structural characteristics according to the HCA. The RF and XGBoost, utilizing top-of-atmosphere reflectance, provided the most accurate estimations of phytoplankton shifting and Chla, respectively. The red edge-red ratio emerged as a key variable in both models, underlining the significance of red edge bands in phytoplankton monitoring. We mapped spatial-temporal patterns of phytoplankton group occurrence and average Chla concentration across the three lakes. Our findings indicated that eutrophication extended periods of cyanobacteria domination and algal blooms, particularly in bays and nearshore areas. Redundancy analysis and classification and regression tree model highlighted temperature as a significant driver of phytoplankton shifting, while nutrient levels exhibited stronger influences on Chla. Our study underscores the potential of integrating machine learning models with Sentinel-2 data to enhance the monitoring and prediction of phytoplankton dynamics in shallow lakes. This approach offers valuable insights for the early detection of algal blooms and informed management strategies, contributing to the preservation and sustainable management of aquatic ecosystems.