Search Results (204)

Microplastics are increasingly recognized as contaminants of emerging concern in wastewater systems, where treatment plants act both as sinks and as point sources. However, Central Asian wastewater infrastructures are under-represented in the literature, and global syntheses are hindered by strong methodological heterogeneity (sampling regimes, size cut-offs, QA/QC). This PRISMA-guided critical review compiles and harmonizes data from 63 WWTP studies worldwide (402 matrix-stage observations), including the few available case studies from Kazakhstan and neighboring countries, to benchmark Central Asian plants against a global envelope and identify methodological and infrastructure gaps. Globally, influent concentrations cluster around a median ≈65 particles/L, while final/tertiary effluents show a median ≈2.2 particles/L. Median removal efficiency is 85.5% for secondary and 95.0% for tertiary/advanced trains, with ≈10³–10⁵ particles/kg DW typically retained in sludge. Across influent, effluent and sludge, fibers and fragments of PE, PP and PET dominate polymer morphology patterns, with similar PET/PE/PP signatures also reported in downstream river water. Central Asian influents fall within global interquartile ranges, but secondary-only facilities tend to yield effluents in the upper half of the global distribution. Overall, the review provides a first integrated, methodologically explicit assessment of WWTP microplastics in Central Asia and underscores the need for protocol harmonization, longitudinal monitoring, and targeted upgrades of polishing steps and sludge management in arid hydrosystems. Full article

Salinity is a key abiotic stress limiting crop growth. Accurate quantification of carbon budgets and their environmental responses is critical for sustainable cotton production, yet regional-scale assessments remain scarce. To clarify the evolutionary patterns and driving mechanisms of soil organic carbon (SOC) in saline cotton fields of arid Central Asia, this study focused on Xinjiang and modified the RothC model by integrating salinity adjustment factors and vegetation carbon decomposition indices, simulating SOC dynamics (1980–2022) with multi-source data. Results showed the improved model achieved high accuracy in capturing SOC dynamics in salinized cotton fields. Spatially, SOC exhibited high levels south of the Tianshan Mountains and low levels in southwestern Xinjiang; temporally, it showed an overall fluctuating upward trend, though both high- and low-value zones displayed localized declines. Geodetector analysis revealed fertilizer application as the primary driver of SOC spatial variation, followed by straw return, precipitation, and temperature, with most factors showing synergistic enhancement effects. Human management (fertilization and straw return) is the core regulator of SOC, and its synergy with natural factors shapes SOC spatiotemporal patterns. The salinization-adapted RothC model provides a novel framework for arid cotton field SOC simulation, offering scientific support for carbon pool optimization and sustainable agriculture in arid regions. Full article

Oxus “Scarface”, a small statuette from the Bactria–Margiana Archaeological Complex culture (Middle Bronze Age of Central Asia) preserved at the Metropolitan Museum (New York), wears a meteoritic iron headband like a comparable specimen preserved in Le Louvre Museum (Paris), as shown by XRF analyses of the headband. This implement could be crucial for the interpretation of these elusive figures. It could be the symbolic material for the retention of water by these monstrous creatures of the underworld, retainers of spring water, as is recorded in the Rig Veda, a myth in agreement with the problematics of agriculture in a semi-arid context. Accordingly, the scars across their faces are the deadly “split across the head” through which the water was released. The convergence of this culture of elamitic affinity with an Indo-Aryan myth suggests some hybridization between the two cultures. Full article

The resilience of arid ecosystems to climate change hinges on their carbon-water dynamics. This study investigates the spatiotemporal patterns of ecosystem water use efficiency (WUE) and its resilience in the ecologically vulnerable Yili-Balkhash Basin, a critical watershed in Central Asia. Contrary to a basin-wide trend of increasing WUE, we identify a significant decline in the WUE of high-productivity forest ecosystems. We demonstrate that this decline stems from a fundamental decoupling between the drivers of carbon (GPP) and water (ET) cycles during drought periods. While GPP shows a positive response to atmospheric aridity (vapor pressure deficit), likely driven by co-varying high radiation and temperature, ET remains primarily controlled by soil moisture and surface thermal conditions. This driver asynchrony results in ET-dominated control over WUE across 65.8% of the basin, rendering forests particularly vulnerable. Machine learning-based attribution reveals that ecosystem resilience is not determined by long-term drought legacy but by the combined effects of immediate thermal stress and a one-month ecological memory. Our findings highlight an emerging vulnerability of high-productivity forest ecosystems to atmospheric aridity and underscore the necessity of process-based frameworks for assessing ecosystem stability under a changing climate. Full article

This review examines scientific and engineering strategies for adapting bituminous and asphalt concrete materials to the highly diverse climates of Central Asia. The region’s sharp gradients—from arid lowlands to cold mountainous zones—expose pavements to thermal fatigue, photo-oxidative aging, freeze–thaw cycles, and wind abrasion. Existing climatic classifications and principles for designing thermally and radiatively resilient pavements are summarized. Special emphasis is placed on linking binder morphology, rheology, and climate-induced transformations in composite bituminous systems. Advanced characterization methods—including dynamic shear rheometry (DSR), multiple stress creep recovery (MSCR), bending beam rheometry (BBR), and linear amplitude sweep (LAS), supported by FTIR, SEM, and AFM—enable quantitative correlations between phase composition, oxidative chemistry, and mechanical performance. The influence of polymeric, nanostructured, and biopolymeric modifiers on stability and durability is critically assessed. The review promotes region-specific material design and the use of integrated accelerated aging protocols (RTFOT, PAV, UV, freeze–thaw) that replicate local climatic stresses. A climatic rheological profile is proposed as a unified framework combining climate mapping with microstructural and rheological data to guide the development of sustainable and durable pavements for Central Asia. Key rheological indicators—complex modulus (G*), non-recoverable creep compliance (Jnr), and the BBR m-value—are incorporated into this profile. Full article

Westerlies Asia, which includes arid Central Asia (ACA) and arid West Asia (AWA), is characterized by water vapor transport primarily controlled by the westerlies. Recent studies have identified a dipole pattern in hydroclimate variability between ACA and AWA during both the Holocene and modern period. However, it remains unclear whether such a dipole pattern persisted over the past millennium. Our findings demonstrate that the PMIP4 multi-model simulations reveal a dipole precipitation pattern between arid Central Asia and arid West Asia over the past millennium. During the Little Ice Age (LIA), annual precipitation increased in ACA but decreased in AWA, while the opposite pattern occurred during the Medieval Climate Anomaly (MCA). This dipole precipitation pattern is attributed to seasonal differences: increased spring precipitation in ACA together with decreased summer precipitation in AWA shaped the annual precipitation anomaly during the Little Ice Age, with a reversed regime during the Medieval Climate Anomaly. Mechanistically, a negative North Atlantic Oscillation (NAO) phase during LIA springs shifted the westerly moisture transport southward, enhancing moisture supply to ACA and increasing the precipitation there. In contrast, during LIA summers, a positive NAO phase displaced the westerly northward, reducing moisture advection to AWA, while a strengthened Azores High promoted moisture outflow and descending motion, suppressing precipitation. These findings offer a paleo-hydroclimatic basis for anticipating alternating dry-wet regimes between subregions, which can inform adaptive water allocation strategies, drought and flood preparedness, and long-term infrastructure planning across Westerlies Asia in a warming world. Full article

Lake water level variation, reflecting the dynamic balance between water input and loss, is a crucial indicator of climate change and regional hydrological cycles. This is particularly significant in arid Central Asia, where lakes are vital surface water resources and key to ecosystem stability. This study systematically reconstructed water level changes of Lake Zaysan and Lake Ulungur from 2003 to 2024 using high-precision altimetry data from ICESat, CryoSat-2, and ICESat-2 satellites. Results indicate that Lake Zaysan experienced significant water level fluctuations of 5.01 m (21.01 Gt water mass change, where 1 Gt = 10⁹ metric tons) in 2010, 5.12 m (21.47 Gt) in 2013, and 3.53 m (14.80 Gt) in 2024. Lake Ulungur also exhibited notable water level changes during the same period. Our study reveals that water level variations in both lakes are primarily controlled by runoff processes. A highly significant positive correlation exists between lake level anomalies and discharge anomalies. Conversely, the low correlation between water levels and precipitation indicates a pronounced lagged effect of snowfall, as lake water level fluctuations are driven by a combination of spring snowmelt runoff and summer precipitation. Furthermore, these findings highlight the sensitive response of these Central Asian lakes to environmental changes under climate warming. Our study enriches observational data on regional lake dynamics and provides a scientific basis for water resource management and future climate adaptation strategies in arid regions. Full article

This study investigates the seasonal variability of water quality in the Yelek River Basin, Western Kazakhstan, using data from 2010 to 2025 that combine remote sensing, GIS, and hydrochemical monitoring data. This research addresses growing pressures on river systems from both natural and anthropogenic factors. Archival records from Kazhydromet and recent field measurements were analysed for dissolved oxygen, total suspended solids (TSSs), and total dissolved solids (TDSs), while satellite indices (NDWI, NDTI) provided spatiotemporal insights into turbidity. The results show clear seasonal contrasts: total suspended solids and turbidity rise sharply during spring floods due to snowmelt and erosion; water quality declines during summer–autumn low-flow periods under intensified human influence; and partial recovery occurs in winter when ice cover stabilises flow. Dissolved oxygen consistently indicates moderate pollution, while total dissolved solids (TDSs) remains within the “clean” range. Integration of satellite data with field observations enabled the development of a turbidity model and highlighted the lower river reaches as most vulnerable, where total suspended solids exceeded permissible limits. The findings confirm the value of combining remote sensing and GIS with traditional monitoring to capture long-term river water dynamics. This approach offers practical tools for sustainable water management, informs regional environmental policies, and provides transferable insights for semi-arid transboundary basins in Central Asia. Full article

Efficient water resource management in arid and semi-arid regions is a critical challenge due to persistent scarcity, climate change, and unsustainable agricultural practices. This review synthesizes recent advances in applying remote sensing (RS), geographic information systems (GIS), and machine learning (ML) to monitor, analyze, and optimize water use in vulnerable agricultural landscapes. RS is evaluated for its capacity to quantify soil moisture, evapotranspiration, vegetation dynamics, and surface water extent. GIS applications are reviewed for hydrological modeling, watershed analysis, irrigation zoning, and multi-criteria decision-making. ML algorithms, including supervised, unsupervised, and deep learning approaches, are assessed for forecasting, classification, and hybrid integration with RS and GIS. Case studies from Central Asia, North Africa, the Middle East, and the United States illustrate successful implementations across various applications. The review also applies the DPSIR (Driving Force–Pressure–State–Impact–Response) framework to connect geospatial analytics with water policy, stakeholder engagement, and resilience planning. Key gaps include data scarcity, limited model interpretability, and equity challenges in tool access. Future directions emphasize explainable AI, cloud-based platforms, real-time modeling, and participatory approaches. By integrating RS, GIS, and ML, this review demonstrates pathways for more transparent, precise, and inclusive water governance in arid agricultural regions. Full article

Drought is one of the main climate-induced risks threatening agricultural sustainability in semi-arid regions. Northern Kazakhstan, a key grain-producing region in Central Asia, exhibits increasing vulnerability to droughts due to climatic variability and reliance on rainfed agriculture. This study evaluates the informativeness of drought indices based on the response of agricultural vegetation to dry conditions using remote sensing-based vegetation indices across Northern Kazakhstan from 1990 to 2024. Ground-based meteorological indices—the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), the Hydrothermal Coefficient (HTC), and the Modified China-Z Index (MCZI)—and vegetation indices—the Vegetation Condition Index (VCI), the Temperature Condition Index (TCI), and the Vegetation Health Index (VHI)—were analyzed using data from 11 representative meteorological stations. For the first time in Kazakhstan, the MCZI was calculated, demonstrating high sensitivity to local climate variability and strong agreement with the VHI. The SPI, MCZI, and HTC showed strong seasonal correlations with vegetation indices, whereas the SPEI had a weak correlation, limiting its applicability. The highest correlations (r ≥ 0.82) between meteorological and vegetation indices were recorded in summer, while spring and autumn were influenced by phenological and temperature factors. Persistent drying trends in the southern and southwestern areas contrasted with moderate wetting in the north. The combined use of the SPI, MCZI, HTC, and VHI proved effective for monitoring droughts. The results provide a reproducible foundation for local drought assessment and early warning systems, supporting climate-resilient agricultural planning and sustainable land and water resource management. The results also offer actionable insights to enhance adaptation strategies and support long-term agricultural and environmental sustainability in Central Asia and similar continental agroecosystems. Full article

Arid regions in Central Asia face persistent and increasing water scarcity, with groundwater serving as the primary source for drinking water, irrigation, and industry. The effective exploration and management of groundwater resources are critical, but are constrained by limited monitoring infrastructure and complex hydrogeological settings. This study investigates the Akbakay aquifer, a representative area within Central Asia with challenging hydrogeological conditions, to delineate potential zones for fresh groundwater exploration. A multi-criteria decision analysis was conducted by integrating the Analytical Hierarchy Process (AHP) with Geographic Information Systems (GIS), supported by remote sensing datasets. To address the subjectivity of weight assignment, the AHP results were further validated using Monte Carlo simulations and fuzzy logic aggregation (Fuzzy Gamma). The integrated approach revealed stable high-suitability groundwater zones that consistently stand out across deterministic, probabilistic, and fuzzy assessments, thereby improving the reliability of the groundwater potential mapping. The findings demonstrate the applicability of combined AHP–GIS methods enhanced with uncertainty analysis for sustainable groundwater resource management in data-scarce arid regions of Central Asia. Full article

Under global warming, drought frequency and its severity have risen notably, posing considerable challenges to vegetation growth. Central Asia (CA), recognized as the largest non-zonal arid zone globally, features dryland ecosystems that are particularly vulnerable to drought stress. This research examines how plant life in CA reacts to prolonged dry spells by analyzing multiple datasets, including drought indices and satellite-derived NDVI measurements, spanning four decades (1982–2022). This study also delves into the compound impact of drought, revealing how its influence on vegetation unfolds through both cumulative stress and delayed ecological responses. Based on the research results, the vegetation coverage in CA exhibited a notable rising tendency from 1982 to 1998. Specifically, it increased at a rate of 4 × 10⁻³ per year (p < 0.05). On the other hand, the direction of this trend shifted to a downward one during the period from 1999 to 2022. During this latter phase, the vegetation coverage decreased at a rate of −4 × 10⁻³ per year (p > 0.05). Vegetation changes in the study area underwent a fundamental reversal around 1998, shifting from widespread greening during 1982–1998 to persistent browning during 1999–2022. Specifically, 98.6% of the region underwent pronounced summer drought stress, which triggered a substantial rise in vegetation browning. The vegetation response to the accumulated and lagged effects of drought varied across seasons, with summer exhibiting the strongest sensitivity, followed by spring and autumn. The lagged effect of drought predominantly influences the vegetation during the growing season and spring, affecting 59.44% and 79.27% of CA, respectively. In contrast, the accumulated effect of drought is more prominent in summer and autumn, affecting 54.92% and 56.52% of CA. These insights offer valuable guidance for ecological restoration initiatives and sustainable management of dryland ecosystems. Full article

This article presents an integrated assessment of pasture ecosystem degradation under conditions of extreme aridity in the Atyrau Region, where high livestock density, limited grazing capacity, and institutional fragmentation of land tenure exacerbate degradation risks. The study aimed to conduct a spatio-temporal analysis of pasture conditions and identify critical load zones to support sustainable management strategies. The methodology was based on a multi-factor Anthropogenic Load (AL) model integrating (1) calculation of pasture load (PL) using 2023 agricultural statistics with livestock numbers converted into livestock units; (2) spatial analysis of grazing concentration through Kernel Density Estimation in ArcGIS 10.8; (3) assessment of infrastructural accessibility (Accessibility Index, A_i); and (4) quantitative evaluation of institutional land use organization (Institutional Index, I_i). This integrative approach enabled the identification of stable, transitional, and critically overloaded zones and provided a cartographic basis for sustainable management. Results revealed persistent degradation hotspots within 3–5 km of water sources and settlements, while up to 40% of productive pastures remain excluded from use. The proposed AL model demonstrated high reproducibility and applicability for environmental monitoring and regional land use planning in arid regions of Central Asia. Full article

Extreme precipitation events, increasingly driven by climate change, are becoming more frequent and pose significant challenges to both the ecological environment and human society. Using the MSWEP data, this study constructed eight event-based extreme precipitation indicators so as to systematically analyze the spatiotemporal characteristics and dominant types of extreme precipitation across Central Asia and its three sub-regions from 1979 to 2023. The results revealed the following: (1) Extreme precipitation events exhibit a pronounced spatial preference for high-altitude areas, with the total number of events reaching up to 698 in these regions. (2) From 1979 to 1991, the frequency of extreme precipitation events has decreased in Central Asia (by 1.742 events per 13 years), while their duration has however increased (by 0.52 days per 13 years). The period from 1992 to 2009 experienced the most significant and widespread decline in the magnitude of extreme precipitation indicators. In contrast, from 2010 to 2023, all indicators—except for the event frequency (EF) and event intensity (EI)—have shown rising tendencies across the region. (3) Regarding the dominant event types, based on the proportion of extreme precipitation frequency across areas, the Southwestern Desert (SD) and northern Kazakhstan (NK) regions are characterized by a more prominent combination of rear-peak (TDP2) and front-peak (TDP1) events, whereas the southeastern mountains (SM) region is rather dominated by a combination of rear-peak (TDP2) and balanced-type (TDP3) events. (4) The EF and event duration (ED) are strongly associated with the Digital Elevation Model (DEM) and Aridity Index (AI). The spatial patterns of EF and ED are closely linked, with the sub-humid and mountainous regions demonstrating the highest frequency and longest duration of extreme precipitation events. Full article

The aim of our study is to achieve a comprehensive understanding of the global distribution of suitable habitats for Sophora alopecuroides L., as well as how these habitats might change in response to climate change. We employed the MaxEnt niche model to integrate distribution data from the Global Biodiversity Information Platform, incorporating 19 bioclimatic factors. This approach enabled us to predict the potential geographic distribution of S. alopecuroides L. worldwide under both current climatic conditions and future greenhouse gas emission scenarios. The results were visualized via ArcGIS 10.8 software. The findings indicate that currently, the suitable habitat for S. alopecuroides L. spans 12,897,100 km², with the majority situated in the arid regions of Central and Eastern Asia. The key environmental variables influencing its distribution are annual mean temperature, maximum temperature of the warmest month, precipitation of the warmest season, and mean temperature of the coldest season. For future climate projections, suitable habitats generally exhibit a shrinking trend. The most pronounced decrease is anticipated under the moderate greenhouse gas emission scenario (SSP245). However, under the high greenhouse gas emission scenario (SSP585), the suitable habitat area is projected to increase marginally by 2060. This dynamic change warning suggests that it is necessary to optimize climate adaptation strategies, strengthen ecological protection and restoration in suitable areas, so as to maintain the ecological service functions of S. alopecuroides L. in arid and semi-arid ecosystems, such as sand fixation and soil conservation, and maintain biodiversity, and provide basic guarantee for the sustainable development and utilization of its medicinal and forage resources. This study reveals the dynamic impact of climate change on the distribution of S. alopecuroides L. suitable areas, which not only provides a scientific basis for ecological restoration and S. alopecuroides L. resource protection in arid and semi-arid areas, but also has important practical significance for promoting the regional practice of the concept of sustainable development of “harmonious coexistence between man and nature.” Full article