Long-term Time Scales Research Articles

Characterization of I/O Behaviors in Cloud Storage Workloads

As cloud platforms become increasingly popular, accurately understanding I/O behaviors in modern cloud storage is of paramount importance for system design and optimization. This paper sheds new light on the correlation of inter-arrival times of both read and write requests at the block level in four representative cloud storage workloads - AliCloud, Systor'17, MSRC and FIU. Our study reveals that I/O arrivals at the block level are very complex in modern cloud storage. There is a certain degree of correlation in the long-term timescale for request arrival intervals in AliCloud and Systor'17_read. Request arrival intervals in MSRC, FIU and Systor'17_write, however, are almost uncorrelated. The Gaussianity test confirms that I/O burstiness appears to be Gaussian in AliCloud_write and Systor'17_read, but the burstiness is non-Gaussian in other workloads. Importantly, we unfold the existence of self-similarity in cloud storage workloads with a certain degree of correlations, via visual evidence, the autocorrelation structure of the aggregated process of I/O request sequences, and Hurst parameter estimates. We further design an alpha-stable workload model for synthetic I/O generation, and the experimental results demonstrate that our model has an edge over conventional models in terms of accurately emulating I/O burstiness.

Using solicited research diaries to assess the restorative potential of exposure to inland blue space across time

Inland blue spaces, or freshwater environments, have been shown to provide people with positive mental health and wellbeing outcomes. Most inland blue space research focusing on wellbeing outcomes has so far been cross-sectional, utilising questionnaires and interviews. Therefore, there is significant uncertainty regarding the potential for inland waterways to benefit human populations over longer-term time scales. Across a sixteen-month data collection period, this study recruited four distinct sample groups to complete diaries for periods of three-months, focusing on inland blue space experiences in Scotland. The aim of the study was to use solicited diary methods to establish whether restorative exposure outcomes gained from visiting blue spaces may vary across time. Results from the diary data show that visiting freshwater areas consistently led to positive restorative outcomes, with minimal variation in restorative outcomes observed across time. Participants recorded three principal categories of blue space experiences; routine visits, getting fresh air; and day trips, with each visit type providing a range of restorative benefits. The findings highlight the potential for inland blue spaces to act as versatile public health resources and the need to consider long-term strategies to ensure these environments benefit populations across time.

Improved Giza pyramids construction algorithm for Modify the deep neural network-based method for energy demand forecasting

Accurate prediction of energy demand is crucial for improving services, reducing costs, and optimizing operations in energy systems. Deep neural networks (DNNs) have emerged as a popular method for energy demand forecasting. However, the performance of DNNs can be affected by data quality and hyperparameter selection. To address these concerns, this study proposes a novel energy demand forecasting technique that combines DNNs with an enhanced Giza pyramid construction methodology. The aim of this study is to provide a more reliable and effective approach for forecasting energy demand. The DNNs are employed to capture the complex relationships between input and output variables, while the Giza pyramids algorithm is utilized to optimal selection of hyperparameters of the network. Real-world energy demand data is used to evaluate the proposed approach, comparing it with state-of-the-art baseline models. The research methodology involves assessing the suggested approach using real-world energy demand information and conducting a comparative analysis with cutting-edge baseline models, including modified BP neural network (MBPNN), Neural Network based Genetic Algorithm (NNGA), and reinforcement learning and deep neural network (RLDNN). The IGPCA/CNN method outperforms other methods in energy prediction accuracy across short-term, medium-term, and long-term time scales. It achieves an MSE score of 0.564, lower than MBPNN, NNGA, and RLDNN. In medium-term prediction, it achieves an MSE score of 0.587, better than MBPNN, NNGA, and RLDNN. In long-term prediction, it achieves an MSE score of 0.629, lower than MBPNN and RLDNN. Further analysis and validation experiments are needed to ensure robustness and generalizability. Comparing the method with other state-of-the-art approaches can provide a comprehensive understanding of its superiority. The performance of the models is evaluated based on reliability and effectiveness in energy demand forecasting. The major conclusion of this study is that the proposed approach outperforms the initial models in accurately forecasting energy demand. The combination of DNNs and the improved Giza pyramid construction methodology results in enhanced performance, demonstrating superior reliability and effectiveness compared to other models. The study highlights the significance of accurate energy demand prediction for optimizing energy systems and reducing costs.

Impacts of seasonally frozen soil hydrothermal dynamics on the watershed hydrological processes inferred from a spatially distributed numerical modelling approach

The freeze–thaw cycle over the surface seasonally frozen soil is an important soil hydrothermal dynamic process linking land surface processes and climatic changes in the cold regions. With the advancement of frozen soil hydrothermal dynamic studies and remote sensing technology, the simulation of frozen soil hydrological processes based on the distributed numerical model has become a hotspot to better understand the impact of frozen soil hydrothermal dynamics on the watershed hydrological processes in the cold regions over a large spatial scale. However, the quantitative analysis of the impact of seasonally frozen soil hydrothermal processes on watershed runoff at long-term time scales remained an unsolved issue in the field of frozen soil hydrology. Under the framework of the watershed distributed eco-hydrological model ESSI-3, a fully distributed frozen soil hydro-thermal processes integrated modeling system (FFIMS model) was established based on the coupled water and heat transferring mechanism for frozen soil hydro-thermal process simulations in the frozen surface or at a certain depth of a watershed. By coupling the FFIMS model with the distributed eco-hydrological model ESSI-3, the impacts of seasonally frozen soil hydrothermal processes on hydrological processes were investigated from the perspective of temporal-spatial domain with the simulated hydrothermal and hydrological processes for a long-term period from 2008 to 2016 over a watershed located in the cold region of Northeastern China. The results suggested that the soil freeze–thaw cycling posed different impacts with limited significance throughout the whole hydrological processes of the watershed in different seasons. Significant impacts on the hydrological processes were particularly observed in the thawing period of a year, when soil ice meltwater contributing to the discharge of the study watershed reached to about 35% in average in this period. ESSI-3 coupled with the FFIMS modelling system obviously improved the performance of the original ESSI-3 in cold region watershed simulations, and the averaged Nash efficiency coefficients obtained increased from almost 0 to 0.77 in the thawing period of a year. The study demonstrated the importance of application of spatially distributed numerical model with physical mechanism for seasonally frozen soil water and heat transfer process simulations.

The relationship between social media sentiment and house prices in China: Evidence from text mining and wavelet analysis

This study explores the dynamic relationship between house prices and social media sentiment in China, a housing sentiment index is constructed from social media reviews based on natural language processing techniques, and wavelet analysis is used to investigate the causal correlations from a new time-frequency perspective. We find that the sentiment index is negatively correlated with the fluctuation of house prices during the whole period, house prices and sentiment have bidirectional causality at the long-term timescale, and house prices causally affect sentiment at the short-term timescale. Furthermore, the sentiment significantly affects house prices in third-tier cities and the western regions.

Stratification variability in the Yucatan Channel and its relationship with the Loop Current System

The non-seasonal stratification variability in the upper 400 m of the Yucatan Channel (YC) and its relationship with the Loop Current System is studied based on the analysis of long-term timescales not yet resolved in observational studies. Daily outputs from a 22-year free-running simulation performed with the HYbrid Coordinate Ocean Model were used, and the variability modes of the squared buoyancy frequency were estimated using Empirical Orthogonal Functions. Intrinsic ocean dynamics is the primary determinant of the dominant spatial and temporal variability of hydrography and dynamics in the YC. The leading mode, described by a vertical dipole at the western YC section with timescales of 1.5-7.5 years, is mainly determined by the Yucatan Current (YCu) variability. There is a strong connection between YCu dynamics, the upper YC hydrography, the Loop Current (LC) intrusion into the Gulf of Mexico, the detachment of Loop Current Eddies (LCEs), and cyclonic anomalies surrounding the LC. Intense eastward displacements of the YCu are associated with intense stratification anomalies in the western YC, the presence of cyclonic anomalies surrounding the LC southwest margin, detachments of LCEs with larger-than-average diameter, and intense LC retreat; before these eddy detachments, the LC metric values are lower than their average ones. Atmospheric forcing induces more complexity in YC dynamics and the Loop Current System; it produces a significant high-frequency signal that modifies their low-frequency behavior. The relationships found in this work could contribute to a better understanding of the upwelling development on the eastern Yucatan shelf.

The Linkage between Carbon Market and Green Bond Market: Evidence from Quantile Regression Based on Wavelet Analysis

The carbon market and the green bond market are important institutions for reducing greenhouse gas emissions and achieving economic low-carbon transformation. Accurately understanding the characteristics and correlations of the two markets is of great significance for promoting the achievement of the “dual carbon” goal. From the perspective of different time scales and market conditions, this study selected the maximal overlap discrete wavelet transform (MODWT) to decompose the price time series data of China’s carbon market and green bond market. The quantile Granger causality test was used to calculate the causal relationship between the two markets at different quantiles, and the association between the two markets was estimated based on quantile-to-quantile regression (QQR). The results show that, regardless of the time scale and market conditions, the Chinese carbon market is always the Granger cause of the green bond market. When the green bond market is in a slump state (i.e., in a “bear” market), it will have a certain negative impact on the carbon market in the short term, but in the medium and long term, the impact of the green bond market on the carbon market is positive. In addition, as the time scale increases, the synergistic effect between the green bond market and the carbon market becomes more and more significant. At medium- to long-term time scales, extreme market conditions can easily cause extreme shocks from the green bond market to the carbon market.

X-Ray Studies of Blazar 1ES 1959+650 Using Swift and XMM-Newton Satellite

High synchrotron energy peaked blazar 1ES 1959+650 was studied with the Swift and XMM-Newton satellites in a total of 127 observations during the period 2018 June–2020 December. We extensively studied its flux and spectral variability on intraday and long-term timescales. Discrete correlation function analysis between soft and hard X-ray bands indicates soft as well as hard lags. The results were used to constrain the magnetic field of the emitting region, which was found to be 0.64 ± 0.05 G. On long-term timescales, the distribution of fluxes shows lognormal behavior, which could be attributed to the minijets-in-a-jet model or might be due to the propagation of relativistic shocks down the jet. The spectral energy distribution around the synchrotron peak is well described by the log-parabola model. Spectral parameters like the peak energy E p , curvature β, and peak luminosity L p were derived from spectral analysis. Their correlations were studied to constrain the acceleration processes of the emitting particles. E p shows strong correlation with L p during the high state of the source, which indicates spectral changes might be caused by the variations of the average electron energy. Low values of the curvature parameter β and a weak correlation between E p and β indicate the coexistence of stochastic/statistical acceleration of electrons in the emitting region. Implications of other results are also discussed.

Characterisation of stellar activity of M dwarfs

Context. M dwarfs are active stars that exhibit variability in chromospheric emission and photometry at short and long timescales, including long cycles that are related to dynamo processes. This activity also impacts the search for exoplanets because it affects the radial velocities. Aims. We analysed a large sample of 177 M dwarfs observed with HARPS during the period 2003-2020 in order to characterise the long-term variability of these stars. We compared the variability obtained in three chromospheric activity indices (Ca II H & K, the Na D doublet, and Hα) and with ASAS photometry. Methods. We focused on the detailed analysis of the chromospheric emission based on linear, quadratic, and sinusoidal models. We used various tools to estimate the significance of the variability and to quantify the improvement brought by the models. In addition, we analysed complementary photometric time series for the most variable stars to be able to provide a broader view of the long-term variability in M dwarfs. Results. We find that most stars are significantly variable, even the quietest stars. Most stars in our sample (75%) exhibit a long-term variability, which manifests itself mostly through linear or quadratic variability, although the true behaviour may be more complex. We found significant variability with estimated timescales for 24 stars, and estimated the lower limit for a possible cycle period for an additional 9 stars that were not previously published. We found evidence of complex variability because more than one long-term timescale may be present for at least 12 stars, together with significant differences between the behaviour of the three activity indices. This complexity may also be the source of the discrepancies observed between previous publications. Conclusions. We conclude that long-term variability is present for all spectral types and activity level in M dwarfs, without a significant trend with spectral type or mean activity level.

Improving the representation of groundwater processes in a large-scale water resources model

ABSTRACT This study explores whether incorporating a more sophisticated representation of groundwater, and human–groundwater interactions, improves predictive capability in a large-scale water resource model. The Global Water Availability Assessment model (GWAVA) is developed to include a simple layered aquifer and associated fluxes (GWAVA-GW), and applied to the Cauvery River basin in India, a large, human-impacted basin with a high dependence on groundwater. GWAVA-GW shows good predictive skill for streamflow upstream of the Mettur dam: Kling-Gupta efficiency ≥ 0.3 for 91% of sub-catchments, and improved model skill for streamflow prediction compared to GWAVA over the majority of the basin. GWAVA-GW shows some level of predictive skill for groundwater levels over seasonal and long-term time scales, with a tendency to overestimate depth to groundwater in areas with high levels of groundwater pumping. Overall, GWAVA-GW is a useful tool when assessing water resources at a basin scale, especially in areas that rely on groundwater.

The Perturbation of Ozone and Nitrogen Oxides Impacted by Blue Jet Considering the Molecular Diffusion

This study investigated the diffusion impact on the chemical perturbation of NOx and O3 caused by the streamer and leader parts of a blue jet in the low stratosphere (18–30 km), using the coupling of a detailed stratospheric chemistry model and a typical diffusion model. The study found that diffusion significantly impacted the evolution of chemical perturbations at both short-term and long-term time scales after the blue jet discharge, with changes in NOx and O3 concentrations observed at different altitudes (18–28 km). At 18 km, the concentrations of NOx and N2O that account for diffusion start to decrease after 1 s, whereas those without diffusion remain at their peak concentrations. Meanwhile, O3 is slowly destroyed with less NOx, rather than dropping to an unrealistic low value immediately after the discharge without diffusion. The perturbation caused by the blue jet discharge disappears within a few tens of seconds at 18 km when molecular diffusion is considered. At 30 km, the chemical perturbation from four point sources was observed through changes in NO2 concentrations. However, the total concentration of NO2 perturbed by the streamer part discharge at the given surface was negligible when considering diffusion. Overall, this study provided a useful model tool for a more accurate assessment of the chemical effects of individual blue jets.

Dependence structure among rare earth and financial markets: A multiscale-vine copula approach

This paper examines the dynamic upper and lower tail dependence across rare earth metals, clean energy, gold, world equity, base metals, and crude oil markets at various time scales. Firstly, raw return series are decomposed into various time scales using the maximum overlapping discrete wavelet transform method, then the time-varying pairwise dependencies, accounting for the impact of the covariate (in our case, the rare earth stock index), are analysed using vine-copula. This so called multiscale-vine copula approach is applied to daily data from June 25, 2009 to October 7, 2022, covering the Covid-19 outbreak. The results show that, for raw returns, the rare earth market moderates the positive dependence between world equity and clean energy markets. At the short-term time scale, unlike other pairwise dependencies, rare earth eases the dependency between clean energies. During the Covid-19 pandemic period, the rare earth stock index significantly affects the correlation of the gold and oil markets and makes them more resilient to global health shocks. At the mid-term time scale, the impact of the rare earth index is more pronounced, for both the entire sample and during the Covid-19 outbreak, as the dynamic dependencies of most indices, such as clean energy-world equity, base metals-world equity, and crude oil-clean energy, significantly decline after accounting for the influence of rare earth metals. The main result at the long-term time scale is that the Covid-19 pandemic moderates the dependency of clean energy-gold even further when considering the impact of the rare earth stock index. In general, the rare earth stock index plays a significant role in easing the extent of dependency in the medium term during the entire sample and the pandemic. These findings provide some useful implications for heterogeneous investors and market participants operating at various time scales.

Projection of future long-term Aurelia coerulea biomass variability by regional moderate-temperature-duration approach in the Bohai and Yellow Seas

As temperature is regarded as a significant environmental factor affecting the intensity and frequency of jellyfish blooms, the projection of jellyfish peak biomass variation under global warming becomes essential for disaster prevention and mitigation on long-term time scales. The jellyfish Aurelia coerulea is distributed in worldwide oceans, particularly in the Bohai and Yellow Seas (BYSs), which are productive regions with substantial economic and climatic significance and have been suffered from A. coerulea blooms. Based on the theory of how A. coerulea reacts to various experimental temperature circumstances, recent studies have been proposed to determine the qualitative trends of A. coerulea blooms in response to climate change. However, the quantitative study of future A. coerulea biomass variability is a challenging task due to the uncertainty in the numerical relation between temperature and the biomasses of A. coerulea and the difficulty in determining the biomass considering the high mobility of jellyfishes. In this paper, we develop a moderate-temperature-duration approach to solve these two problems. The temperature suitability for all key life stages of A. coerulea during pelagic and benthic processes is involved in this approach, and we identify the major life stage that determines the peak biomass variation. The projection of future peak biomass of A. coerulea in BYSs is performed based on this approach and utilizing the long-term projection results of temperature changes by 2100 using the MPI-ESM-MR model from phase 5 of the Coupled Model Intercomparison Project (CMIP5) under the RCP4.5 scenario. The results indicate that global warming can certainly lead to the increase in A. coerulea biomass in some degree. The steady state of the highest peak biomass will be achieved during 2075∼2080, with an increase of 8.4%, 5.0%, and 11.5% in the Bohai Sea, the North Yellow Sea, and the South Yellow Sea, respectively, compared to the first steady state starting in 2020 detected by the regime shift. However, the result also shows that global warming cannot consistently lead to the increase of A. coerulea peak biomass. As the temperature rises to a large extent, the duration of moderate temperature ranges for strobilation and growth may be reduced, and thus resulting in the decrease of peak biomass instead.

An Analysis of Dynamic Correlations among Oil, Natural Gas and Ethanol Markets: New Evidence from the Pre- and Post-COVID-19 Crisis

After the economic shock caused by COVID-19, with relevant effects on both the supply and demand for energy assets, there was greater interest in understanding the relationships between key energy prices. In order to contribute to a deeper understanding of energy price relationships, this paper analyzes the dynamics between the weekly spot prices of oil, natural gas and benchmark ethanol in the US markets. The analysis period started on 23 June 2006 and ended on 10 June 2022. This study used the DMCA cross-correlation coefficient in a dynamic way, using sliding windows. Among the main results, it was found that: (i) in the post-pandemic period, oil and natural gas were not correlated, in both short- and long-term timescales; and (ii) ethanol was negatively associated with natural gas in the most recent post-pandemic period, especially in short-term scales. The results of the present study are potentially relevant for both market and public agents regarding investment diversification strategies and can aid public policies due to the understanding of the interrelationship between energy prices.

A 209,000-year-old history of methane seepage activity controlled by multiple factors in the South China Sea

Methane seepage activity caused by gas hydrate dissociation plays an essential role in the global carbon cycle and climate change in Earth's history. However, the triggering mechanisms of gas hydrate dissociation remain elusive because of the lack of a relatively complete history of methane seeps on long-term timescales. Here, we present major and trace elements, stable C–O isotopes, and U–Th dating analyses for cold seep carbonates from drill cores in the South China Sea. Our results reveal a 209,00-year-old history of methane seepage activity, which involves multi-stage changes in seepage intensity and methane source (microbial or thermogenic). Chronological data suggest that hydrostatic pressure reduction and dynamic depositional environment mainly controlled the gas hydrate dynamics in the northwestern South China Sea, which is different from the gas hydrate dissociation in the northeastern South China Sea controlled by bottom water warming. This discrepancy may be caused by the differences in physicochemical conditions (e.g., water depth) in different sea areas. Therefore, the findings of this study help to further clarify the relationship between oceanic hydrate reservoirs and environmental change in other world regions.

Learning Rat-Like Behavioral Interaction Using a Small-Scale Robotic Rat.

In this paper, we propose a novel method for emulating rat-like behavioral interactions in robots using reinforcement learning. Specifically, we develop a state decision method to optimize the interaction process among 6 known behavior types that have been identified in previous research on rat interactions. The novelty of our method lies in using the temporal difference (TD) algorithm to optimize the state decision process, which enables the robots to make informed decisions about their behavior choices. To assess the similarity between robot and rat behavior, we use Pearson correlation. We then use TD-λ to update the state value function and make state decisions based on probability. The robots execute these decisions using our dynamics-based controller. Our results demonstrate that our method can generate rat-like behaviors on both short- and long-term timescales, with interaction information entropy comparable to that between real rats. Overall, our approach shows promise for controlling robots in robot-rat interactions and highlights the potential of using reinforcement learning to develop more sophisticated robotic systems.

Cloud Computing Solutions for Speeding-up the Small-and Medium Sized Enterprise (SME’s) Businesses in China

China has experienced rapid digitalization in the last decade. Cloud computing makes possible for different users to access data resources from any geographical location through the Internet. This new paradigm has the ability to benefit businesses by offering low-cost, flexible, and customizable solutions that provide companies significant competitive advantages in the strongly competitive business environment on long-term timescale. It can be essential for all business, but it is especially indispensable for small and medium-sized enterprises (SME’s) to make prosperity in today’s accelerating social, economic and technological changes. In recent years, the SME’s have allocated more budget to invest in implementing digitization and databased decision making processes as they have become more aware of the importance of technological development and managing information on boosting their competitiveness in values creation activities. However, they are restricted by the size of the business. Transformation of information construction and digitization process is still in its infant stage as a consequence of a shortage in experts and available resources. This situation has gradually changed with the advent of cloud computing technology. By leveraging cloud computing technology, SME’s could completely support the digital transformation process in an efficient and effective manner. Nevertheless, for those business organisations who make efforts for application of any cloud computing solution in their business processes, they have to face with some serious emerging questions. Whether the business deployed on the cloud provided by service providers has sufficient system robustness, and whether the data stored in the cloud has sufficient security. This review paper is aiming to provide comprehensive, relevant landscape about the different cloud computing solutions (Infrastructre as a Service -IaaS) Platform as a Service -PaaS; Software as a Service -SaaS) and services models (public, private and hybrid cloud). Besides that this study focuses on which cloud service model should SME’s choose and how Chinese SME businesses should take into account their own informational structure in the age of digital transformation improving businesses performance while minimizing operational expenses and risks at the same time

Inland water metabolic carbon processes and associated biological mechanisms that drive carbon source-sink instability

<p>Due to their complexity, inland water carbon (C) cycling processes significantly impact the C source-sink stability status of terrestrial ecosystems over short-term, long-term, and geological timescales. Stable C source-sink processes primarily include terrestrial biospheric production, lithospheric organic carbon (OC) oxidization, rock weathering, and riverine C transport. Conversely, the effect of metabolic C processes on the C source-sink status of inland waterbodies is not stable. Therefore, inland water metabolic C processes may cause significant C sink underestimations, which relevant studies have largely ignored. A new way to account for this missing inland water C sink is an in-depth understanding of the metabolic C processes and associated driving effects of biological regulation mechanisms on the C source-sink status. This new approach can help to more accurately quantify the global ecosystem C budget. The purpose of this review is threefold: (i) to clarify metabolic C processes and associated biological regulation mechanisms of inland waterbodies; (ii) to systematically analyze C cycling processes and associated C source-sink statuses of inland waterbodies at different timescales; (iii) to reveal driving mechanisms of metabolic C processes on C source-sink stability in inland waterbodies. Doing so will help us better understand how to more accurately calibrate C source-sink functions globally while also garnering an in-depth understanding of the role that terrestrial ecosystems play in C neutralization under global climate change.</p>

A multidiscipline collaborative optimization approach with acceleration strategies for generator maintenance scheduling in large hydrothermal power systems considering uncertainty of natural inflows

Generator maintenance scheduling (GMS) is an important task in the dispatch and operation of hydrothermal power systems. On a long-term timescale, the uncertainty of natural inflows makes GMS essentially a stochastic optimization problem. The sample average approximation (SAA) method is used to convert the stochastic hydro and thermal generator maintenance model into a large-scale deterministic optimization problem, but it is difficult to solve. This paper proposes a multidisciplinary collaborative optimization approach with acceleration strategies (MCO-AS) for GMS, where the deterministic optimization problem is decomposed into a system-level optimization problem and discipline-level optimization subproblems through decoupling of the nonanticipative constraints and the maintenance variable consistency constraints. Three acceleration strategies, replacement and linearization, information pooling, and parallel calculation, are proposed and incorporated. Finally, computational experiments on a real large hydrothermal power system are conducted, and the results illustrate the effectiveness of the approach and acceleration strategies proposed in this paper compared to the traditional SAA method as a benchmark. • Stochastic maintenance scheduling is considered in large hydrothermal power systems. • A multidisciplinary collaborative approach is designed to decompose the problem. • Three strategies are proposed to further accelerate the solution. • Results on a real-world power system show the effectiveness of the proposed method.

A reactive transport model designed to predict the environmental footprint of an ‘in-situ recovery’ uranium exploitation

Worldwide, most uranium production relies on the ‘in situ recovery’ (ISR) extraction technique. This consists of dissolving the ore using a leaching solution (acid or alkaline) directly within the deposit through a series of injection and extraction wells. Due to the nature of the injected ISR solutions, the water quality of the aquifer could be affected. Reactive transport modeling is a powerful tool for predicting fluid flow and geochemical reactions in ISR reservoirs. In this study we present a coupled 3D environmental geochemical model (EGM) (based on the HYTEC reactive transport software), capable of predicting the physico-chemical conditions in an acid-leaching ISR uranium mine and its environmental footprint on the aquifer in the years following the closure of the production block. The model was validated at the KATCO mine (Kazakhstan) on two different and independent production blocks, over 10 years after their closure. The model shows that incorporating two main geochemical processes, (1) cationic sorption on clay surfaces (smectite-beidellite) and (2) precipitation of gypsum (CaSO4.2H2O), successfully reproduces the measured well data (pH, acidity and SO4) over short- and long-term time scales. Clay surface sites remain mostly saturated in protons during the production phase. Simulations show that sorbed protons on the clay surfaces maintains the acid conditions for a longer period of time. The environmental impact model was also compared to a pre-existing model specifically developed for production simulation purposes: differences are observed as expected for the uranium production, but also for the impact distances, due to differences in the considered reactive mineralogical paragenesis. Thus, the choice of geochemical model should be made with due regard for the desired objectives. This work will assist the mine operator by providing a tool capable of assessing both the short- and long-term environmental footprints of the ISR production operation conditions and of identifying the best remediation strategy.