Search Results (2,475)

This study is based on time-series land use data of Xinjiang from 2000 to 2022. Using grid tools, bivariate autocorrelation models and other methods, we systematically analyzed the spatiotemporal variation characteristics of land use and ecosystem service value. The results show the following: Firstly, from 2000 to 2022, Xinjiang’s LUCC exhibits differentiated evolution characteristics: cropland, forestland, and built-up land expanded continuously, while the areas of grassland and unused land showed a steady reduction trend, and the area of water bodies showed a fluctuating growth pattern. Secondly, according to the calculation of carbon emissions from LUCC in Xinjiang from 2000 to 2022, the carbon emissions from LUCC have increased significantly, from 27.79 million tons in 2000 to 226.43 million tons in 2022, with built-up land being the main source of carbon emissions, but the continuous reduction in grassland area has led to the weakening of carbon sequestration capacity. Thirdly, from 2000 to 2022, Xinjiang’s ESV shows a fluctuating upward trend, increasing from 1880.528 billion yuan in 2000 to 1894.198 billion yuan in 2022, with grassland and water area being the core contributors to ESV, accounting for over 80% of the total contribution. Fourthly, in terms of spatial distribution, there is an overall negative correlation between the intensity of carbon emissions from LUCC and the intensity of ESV, mainly aggregated as “low–low” and “low–high”, with “high–low” aggregation primarily distributed in the desert areas of the Tarim Basin and Junggar Basin and “low–high” aggregation concentrated in the marginal mountainous areas and oasis regions of Xinjiang. The findings provide a solid scientific basis for the optimization of land use structure, the achievement of carbon emission reduction targets, and the protection of ecosystems in Xinjiang and similar arid regions worldwide. Full article

Secondary salinization in mulched drip-irrigated cotton fields of arid oasis–desert transition zones in Xinjiang imposes coupled root-zone constraints, including salt-induced aggregate structural degradation and ionic stress. However, field evidence remains limited on whether integrating a structure-oriented soil conditioner with humic acid can generate stable improvements across growing seasons. A two-year field experiment with a randomized block design (three replicates) was conducted to evaluate four treatments: control (CK), polyacrylamide (PAM, 30 kg ha⁻¹), humic acid (HA, 450 kg ha⁻¹), and PAM + HA. Soil physical and chemical properties and aggregate-size distribution were determined after harvest, while enzyme activities and root traits were assessed at the flowering–boll stage. Structural equation modeling (SEM) and random forest (RF) analysis were used to explore soil–root–yield linkages and identify key soil predictors associated with yield variation. Treatment effects were most evident in the 0–20 cm layer, with PAM + HA showing the greatest overall improvement. In the topsoil, PAM + HA lowered soil pH from 8.35 to 7.88 in 2024 (p < 0.05), increased soil organic carbon (SOC) to 4.29 g kg⁻¹ in 2025 (p < 0.01), and increased NO₃⁻–N to 25.51 and 30.27 mg kg⁻¹ in 2024 and 2025, respectively (both p < 0.05). PAM + HA also enhanced cellulase activity from 6.17 to 16.85 mg glucose g⁻¹ 72 h⁻¹ in 2024 and increased seed cotton yield to 6683.69 and 5996.89 kg ha⁻¹ in 2024 and 2025, with a 51.0% yield increase over CK in 2024. SEM showed that root development had the strongest direct positive effect on yield (β = 0.79, R² = 0.63; goodness of fit (GOF) = 0.74), while random forest identified alkaline phosphatase, cellulase, and NO₃⁻–N as the main yield predictors (out-of-bag R² (OOB R²) = 0.672, p = 0.01). This study elucidated the effects of the combined application of a structure-oriented soil conditioner and humic acid on the root-zone environment of mulched drip-irrigated cotton fields in arid regions, providing a theoretical basis for the coordinated regulation of soil structural improvement and nutrient activation in saline–sodic cotton fields. Full article

Lakes on the Inner Mongolia–Xinjiang Plateau (IMXP) are increasingly vulnerable to eutrophication under climate change and human pressure, yet long-term monitoring remains limited by sparse field sampling. Here, we reconstruct multi-decadal trophic dynamics across the IMXP using Landsat time series and temporally transferable machine-learning models and further quantify the underlying natural and anthropogenic drivers. We compiled monthly in situ water-quality observations (chlorophyll-a, Chl-a; total phosphorus, TP; total nitrogen, TN; Secchi depth, SD; and permanganate index, COD_Mn;) and calculated the trophic level index (TLI). After rigorous quality control and monthly aggregation, we compiled a dataset of 1345 matched lake–month samples spanning 2000–2024, and divided it into a training set (n = 1076; ≤2019) and an independent test set (n = 269; 2020–2024) to evaluate temporal transferability. We utilized Google Earth Engine to generate monthly surface reflectance composites from Landsat 7 ETM+, Landsat 8 OLI, and Landsat 9 OLI-2. Four supervised regression algorithms—ridge regression (RR), support vector regression (SVR), random forest (RF), and eXtreme Gradient Boosting (XGBoost)—were trained to estimate TLI. On the independent test period, XGBoost performed best (R² = 0.780, RMSE = 3.290, MAE = 1.779), followed by RF (R² = 0.770, RMSE = 3.364), SVR (R² = 0.700, RMSE = 3.842), and RR (R² = 0.630, RMSE = 4.267); we then used XGBoost to reconstruct monthly and yearly TLI for 610 perennial grassland lakes from 2000 to 2024. From 2000 to 2024, the annual mean TLI (48–49) across the IMXP exhibited a statistically significant upward trend (slope = 0.0158 TLI yr⁻¹; 95% confidence interval (CI) = 0.0050–0.0267; p = 0.006). Meanwhile, spatial heterogeneity was distinct (TLI: 41.51–59.70). High values concentrated in endorheic and desert–oasis basins (e.g., Eastern Inner Mongolia Plateau, >51), whereas lower values characterized high-altitude regions (e.g., Yarkant River, <45). Overall, trends ranged from −0.49 to 0.51 yr⁻¹, increasing in 54% of lakes (15.6% significantly) and decreasing in 46% (15.4% significantly). Attribution analyses identified NDVI (33.92%) and temperature (21.67%) as dominant drivers (55.59% combined), followed by precipitation (13.99%) and human proxies (30.42% combined: population 10.66%, grazing 10.31%, built-up 9.45%). Across 53 sub-basins, NDVI was the primary driver in 28, followed by temperature (11), population (7), precipitation (3), grazing (3), and built-up land (1); notably, the top two drivers explained 56.6–87.1% of variations. TWFE estimates revealed bidirectional NDVI effects (significant in 31/53): positive associations in 22 basins were linked to nutrient retention, contrasting with negative effects in nine basins associated with agricultural return flows. Temperature effects were significant in 15 basins and predominantly negative (14/15), except for the Qiangtang Plateau. Overall, eutrophication risk across the IMXP lake region reflects the combined influences of climatic conditions, vegetation conditions, and human activities, with their relative contributions varying among basins. Full article

Accurate and timely cotton-field mapping is essential for irrigation management, water resource allocation, and regional yield assessment in arid irrigated agroecosystems. However, existing deep-learning-based crop mapping approaches generally lack interpretability and often exhibit performance variability across phenological stages, thereby limiting their reliability for operational deployment. To address these limitations, we developed an interpretable semantic segmentation framework for cotton mapping in the Wei-Ku Oasis, Xinjiang, China, under multi-source remote sensing conditions. The proposed model integrates Sentinel-2 surface reflectance, Sentinel-1 VV/VH backscatter, DEM, vegetation indices, and GLCM texture features. By incorporating a receptive-field enhancement mechanism together with an embedded feature-attribution module, the framework enables importance estimation of multi-source predictors within the network architecture, thereby providing intrinsic model interpretability. Under a unified training and evaluation protocol, the proposed model achieved an mIoU of 85.62% and an F1-score of 92.96% on the test set, outperforming U-Net, DeepLabV3+, and SegFormer baselines. Monthly classification results indicated that August provided the most discriminative acquisition window (mIoU = 85.54%, F1 = 92.83%), while June–July also maintained high recognition accuracy. Feature attribution results indicate that the importance of different predictors varies across phenological stages: Sentinel-2 red-edge bands remained highly influential throughout the growing season, NDVI/EVI exhibited increased contributions during June–August, SAR VH showed relatively higher importance during peak canopy development, and DEM maintained stable information contribution across all stages. Cross-year and cross-region experiments further demonstrated the model’s generalization capability, achieving an mIoU of 82.81% in same-region cross-year evaluation and 74.56% under cross-region transfer. Overall, the proposed segmentation framework improves classification accuracy while explicitly modeling and quantifying feature importance, providing a methodological reference for cotton-field mapping and acquisition timing selection in arid irrigated regions. Full article

Temperature and humidity are critical abiotic factors shaping the survival and adaptation of insect pests. However, the molecular mechanisms underlying high-temperature tolerance under contrasting humidity conditions remain poorly understood, particularly in globally invasive species such as the tomato pinworm, Tuta absoluta. Previous studies have examined individual stressors, leaving interactive thermo-hygrometric effects on gene expression and survival insufficiently resolved. Here, we assessed the contribution of cytochrome P450 genes to thermal adaptation under low- and high-humidity conditions using transcriptome profiling combined with nanocarrier-mediated RNA interference (RNAi). Third-instar larvae were exposed to high temperature under low humidity (HT-LH: 40 °C, 50% RH) or high humidity (HT-HH: 40 °C, 75% RH) for eight hours. Survival declined from 97.5% in the control to 74.16% under HT-LH and 68.33% under HT-HH conditions. Transcriptome analysis revealed extensive differential gene expression, with 464 genes upregulated and 565 downregulated in HT-LH, and 1145 upregulated and 1166 downregulated in HT-HH. Functional annotation highlighted pathways linked to metabolic regulation, proteostasis, and detoxification, including multiple cytochrome P450-associated processes. RT-qPCR confirmed the upregulation (3–5 fold) of four P450 genes (CYP6AB327, CYP6ABF1b, CYP6AE214, and CYP9A306c) under high temperature across both humidity regimes. RNAi-mediated silencing of these genes significantly reduced larval survival, demonstrating their functional role in thermal-hygrometric stress tolerance across. Cytochrome P450 genes underpin the adaptive capacity of the tomato pinworm to high-temperature stress across contrasting humidity conditions, highlighting RNAi-based disruption of P450 function as a promising avenue for sustainable pest management under climate change scenarios. Full article

Surface water–groundwater interactions within oasis–desert ecotones of arid regions play a pivotal role in sustaining regional water security and ecological stability. Taking the Niya River Basin in Xinjiang, Northwest China, as a representative inland watershed, this study systematically elucidates the mechanisms and seasonal dynamics of surface water–groundwater coupling under the combined influences of natural processes and anthropogenic activities. A total of 68 surface water and groundwater samples were collected during the dry, normal, and wet hydrological periods. Integrated hydrochemical characterization, mineral saturation index analysis, and stable isotope (δ²H and δ¹⁸O) mass balance modeling were employed to quantify recharge contributions and unravel hydrogeochemical evolution pathways. Results indicate that the waters in the study area are predominantly brackish to saline, with consistent dominant ionic assemblages (SO₄²⁻ and Na⁺) across all hydrological periods, highlighting evaporite dissolution as the primary control on solute composition. Hydrochemical evolution is jointly regulated by evaporation concentration, water–rock interactions, and cation exchange processes. Surface water chemistry reflects the combined effects of silicate weathering and evaporite dissolution, whereas groundwater chemistry is mainly governed by evaporite dissolution coupled with pronounced cation exchange. Stable isotope signatures reveal substantial secondary evaporation of regional precipitation prior to recharge. Frequent bidirectional recharge between surface water and groundwater was observed, exhibiting distinct seasonal transitions. During the dry period, groundwater provides significant baseflow support to surface water (48.6% in the oasis zone and 54.3% in the desert zone). In the normal period, recharge direction reverses, with surface water becoming the dominant source of groundwater recharge (99.0% in the oasis zone and 76.6% in the desert zone). In the wet period, spatial heterogeneity becomes evident: surface water continues to dominate groundwater recharge in the oasis zone (92.7%), whereas groundwater recharge to surface water prevails in the desert zone (50.5%). This study identifies a seasonally dynamic “discharge–infiltration–zonal regulation” bidirectional recharge pattern in arid inland river systems. The findings advance the mechanistic understanding of hydrological connectivity reconstruction within oasis–desert ecotones and provide a scientific basis for optimized regional water resource allocation and groundwater salinization risk mitigation. Full article

With the increasing prevalence of neurodegenerative diseases driven by population aging, imaging-based biomarkers are needed to quantify brain aging at an early stage. Brain age, which estimates structural brain aging relative to chronological age, has emerged as a useful indicator. Prior work has mainly used T1-weighted MRI with deep learning models such as convolutional neural networks (CNNs) or transformers; however, many approaches insufficiently capture three-dimensional structural continuity and localized anatomical patterns, and tissue-specific aging in gray matter (GM) and white matter (WM) is often treated as auxiliary. To address these limitations, we propose a 3D Global–Patch Transformer framework for brain age prediction that directly processes volumetric data while jointly learning global brain structure and local anatomical features. Our model runs global and patch pathways in parallel and explicitly incorporates GM and WM structural maps alongside T1-weighted MRI to encode tissue-specific aging signals. Experiments on multiple public datasets, including IXI and OASIS, show that the proposed method reduces mean absolute error (MAE) by approximately 10–15% compared with CNN-based and single-input transformer baselines, with notably improved performance in older populations, highlighting the value of tissue-level structural information for brain age estimation. Full article

Leveraging fungal consortia to degrade heavy oil is an emerging strategy for mitigating/cleaning up environmental pollution. However, many consortia are predominantly evaluated by measuring the biodegradation efficiency of heavy oil, with insufficient attention paid to the mechanistic underpinnings and metabolic pathways. In this study, heavy oil-degrading fungal consortia were developed for potential application in soil bioremediation. Whole-genome sequencing was used to predict the metabolic pathways and interspecific interactions driving heavy oil biodegradation. Three heavy oil-degrading fungal strains, designated Aspergillus corrugatus FH2, Aspergillus terreus FL4, and Alternaria alstroemeriae FW1, were isolated from oil sludge in the Karamay Oilfield in Xinjiang, China. Four consortia were constructed through the combination of two or three strains. The consortium F13 (FH2 + FW1) achieved 72.0% removal of heavy oil in a simulated bioremediation test over 30 days, which was more efficient than other consortia and single strains (59.5–68.5%). Notably, the mean degradation rate of long-chain alkanes (C₂₄–C₂₈) by F13 reached 95.9%. After F13 treatment, the major fractions of heavy oil showed considerable degradation, 87.4% for saturates, 92.0% for aromatics, 69.5% for resins, and 27.3% for asphaltenes. Genome annotation of FH2, FL4, and FW1 revealed the presence of core genes for degradation of n-alkanes and aromatics, e.g., CYP505, frmA, fadB, hmgA, ALDH, and ACSL. These functional genes encoded cross-lineage enzymes, enabling synergistic catabolism of C₁₃–C₂₈ alkanes and aromatics. Our findings indicated that the fungal consortium of A. corrugatus FH2 and Al. alstroemeriae FW1 has remarkable bioremediation potential for heavy oil-contaminated sites. This study provides molecular evidence for the design of targeted interventions to improve soil remediation efficiency with fungal consortia. Full article

Soil salinization is a major constraint on sustainable agriculture worldwide, highlighting the need for stress-tolerant plant growth-promoting rhizobacteria (PGPR) for salt-affected soils. A moderately halophilic and alkali-tolerant bacterium, Staphylococcus succinus NYN-1, was isolated from the rhizosphere soil of the halophyte Suaeda dendroides collected from a highly salinized site in Xinjiang, China. This study aimed to evaluate its salt–alkali tolerance and plant growth-promoting potential through integrated phenotypic characterization, pot experiments, and whole-genome analysis. NYN-1 grew over a broad salinity range [0–15% (w/v)] and pH range (6.0–11.0), and showed plant growth-promoting activities including organic phosphorus mineralization, inorganic phosphate solubilization, potassium solubilization, and NH₄⁺ production. In pot experiments under 300 mM NaCl, inoculation with NYN-1 significantly improved the growth performance of maize (Zea mays L.), cotton (Gossypium hirsutum L.), and sunflower (Helianthus annuus L.). Genome analysis identified multiple Na⁺/H⁺ antiporter-related genes and genes encoding compatible-solute transport systems that are consistent with adaptation to salt–alkali stress. The genome also harbors a broad set of genes related to phosphorus metabolism, as well as other plant growth-promoting functions, including potassium solubilization-related pathways and siderophore biosynthesis. Collectively, these findings identify S. succinus NYN-1 as a promising native halophilic PGPR candidate and a potential microbial resource for developing inoculant strategies in salt-affected agricultural systems. Full article

To establish a reliable and accurate solid-phase extraction (SPE) pretreatment method for multi-class antibiotics in water and achieve simultaneous determination of 20 antibiotics, including tetracyclines, quinolones, and sulfonamides, key pretreatment parameters were optimized via single-factor experiments in this study. The optimized parameters included pH of acidified water samples, Na₂EDTA dosage, SPE cartridge type, operational conditions, and the type and volume of elution solvent. The validated method was further applied to analyze surface water samples collected from 16 sampling sites in Poyang Lake and its three typical tributaries (Ganjiang River, Jinjiang River, and Yuanhe River) to verify its practicability, reliability, and applicability. Results showed that the optimal pretreatment conditions were as follows: water samples were acidified to pH 3.0, added with 0.2 g Na₂EDTA for metal ion chelation, enriched using Oasis^® HLB cartridges at a loading flow rate of 8–10 mL/min, and dried for 5–30 min until no obvious liquid dripped from the cartridge tip, followed by elution with 12 mL of 0.1% (V:V) formic acid in methanol. Under these conditions, the spiked recoveries of 20 antibiotics in ultrapure water were generally above 80%, and most antibiotics exhibited recoveries exceeding 90%. In addition, the spatial distribution of antibiotic concentrations in the Poyang Lake watershed followed the following order: Jinjiang River > Yuanhe River > Ganjiang River > Poyang Lake. Sulfonamides, especially sulfamethoxazole with a maximum concentration of 250.08 ng·L⁻¹, were identified as the predominant pollutants in this basin. Full article

The Aksu River Basin, the main headwater of the Tarim River, contributes more than 70% of the main stream’s runoff and is therefore critical in maintaining hydrological stability in this arid river system. In recent decades, rapid oasis expansion and growing agricultural water withdrawals have intensified competition for surface and groundwater, posing increasing ecological risks to the downstream Tarim River Basin. To quantitatively characterize river–groundwater hydrological responses under intensive water use, we combined statistical analysis, field observations, and distributed hydrological modeling within a basin-scale conceptual framework. Multiple lines of evidence—water level monitoring, hydrochemical tracers, stable isotopes, and the integrated surface–groundwater model MIKE SHE—were used to identify river–groundwater interaction mechanisms in the Aksu alluvial plain. Results reveal a typical three-stage spatial exchange pattern: river recharge to groundwater in the upstream reach, groundwater discharge to the river in the midstream, and renewed river infiltration to groundwater downstream. The patterns inferred from water levels, hydrochemistry, and isotopes are broadly consistent, while water-level data better resolve left–right bank asymmetry. The MIKE SHE model supports the seasonal bidirectional exchange dynamics and reproduces runoff behavior with acceptable performance (RMSE and residual standard deviation within 20% of observed means and R² > 0.7 during both calibration (2010–2017) and validation (2018–2021)). The proposed multi-evidence framework captures the spatio-temporal variability of river–groundwater interactions in arid regions and provides spatially differentiated guidance for conjunctive surface–groundwater regulation and integrated water resources management in the Tarim River Basin. Full article

Populus euphratica is a critical constructive species in arid desert regions, serving as a “natural barrier” for oasis protection. The sustainable management of Populus euphratica forests is directly related to regional ecological security, and the fine identification of sparse Populus euphratica forests is essential for the conservation of natural Populus euphratica forests. Currently, most mapping studies on Populus euphratica distribution focus on the extraction of dense, contiguous Populus euphratica forests, with insufficient attention paid to the identification of sparse Populus euphratica forests. This study utilizes Gaofen-2 (GF-2) satellite imagery as the data source and takes a typical sparse Populus euphratica forests distribution area in the Tarim River Basin as the study site. It systematically evaluates the performance of nine mainstream deep learning models, including U-Net, DeepLabV3+, and SegFormer, in the task of sparse Populus euphratica forests identification. The results indicate that: (1) The false-color sample set, synthesized from near-infrared, red, and green bands, contributes to improved model accuracy. Compared to the true-color (red, green, blue bands) dataset, the average Intersection over Union (IoU) of the nine models shows a relative improvement of approximately 20%. (2) For the sparse Populus euphratica forests identification task based on the false-color dataset, four models—U-Net, U-Net++, MA-Net, and DeepLabV3+—exhibited excellent performance, with IoU exceeding 75%. (3) Using U-Net as the baseline model, this study integrated the max-pooling indices mechanism, atrous spatial pyramid pooling, and residual connection modules to construct a semantic segmentation network tailored for sparse Populus euphratica forests, named Sparse Populus euphratica Segmentation Network (SPS-Net). This model achieved an IoU of 80%, a relative improvement of approximately 6.3% over the baseline model, and demonstrated good stability in large-scale classification tests. The identification scheme for sparse Populus euphratica forests constructed using GF-2 imagery and deep learning models proposed in this study can provide effective technical support for the refined monitoring and protection of natural Populus euphratica forests. Full article

In 2024 and 2025, field populations of Aphis gossypii were collected from eight regions in Xinjiang to monitor their resistance levels to five commonly used insecticides: sulfoxaflor, acetamiprid, imidacloprid, abamectin, and chlorpyrifos. The mutation frequencies of five sites in the acetylcholinesterase (AChE) gene (S431F, V332A, A302S, G221A, F139L) and three sites in the β1 subunit of the nicotinic acetylcholine receptor (nAChR) (R81T, V62I, K264E) were also analyzed. The results showed that from 2024 to 2025, the eight A. gossypii field populations exhibited the highest resistance to imidacloprid (primarily moderate to high resistance), followed by acetamiprid (all moderate resistance). Resistance to abamectin and sulfoxaflor was relatively low, but sulfoxaflor resistance increased rapidly (from low resistance in 2024 to moderate resistance in 2025). All populations remained consistently susceptible to chlorpyrifos. Gene analysis revealed that the mutation rate of S431F in the AChE gene was nearly 100%, while that of V332A remained stable at approximately 30%. The mutation rates of A302S and G221A showed a slight increase, whereas the F139L mutation rate was extremely low (<1.00%). In the β1 subunit of nAChR, the mutation rates of R81T and V62I remained stable at around 50%, and the K264E mutation rate was extremely low (<1.00%). This study clarifies the resistance evolution patterns of A. gossypii to different insecticides and the variation characteristics of key resistance genes in Xinjiang, providing a scientific basis for the integrated resistance management of A. gossypii and the rational selection of effective insecticides. Full article

The sustainability of date palm cultivation is a critical research focus due to its vital role in food security and the resilience of agricultural systems in arid regions. This study evaluates the sustainability of date palm farms by examining how distinct oasis typologies, traditional and modern, influence three core dimensions: agro-ecological, socio-territorial, and economic. Employing the IDEA framework (Indicateurs de Durabilité des Exploitations Agricoles) and statistical analyses, field data from 60 farms in Tunisia’s Tozeur and Kebili regions were assessed. The results revealed marked heterogeneity in sustainability performance, strongly structured by oasis type. Traditional oases exhibited higher agro-ecological sustainability but greater economic vulnerability, while modern oases proved more economically robust yet less environmentally sustainable. These findings highlight a clear trade-off between environmental integrity and economic performance, highlighting the need for differentiated strategies to enhance the overall sustainability of oasis agricultural systems. Full article

The Green Oasis (GRO) Project is a targeted urban greening intervention designed to evaluate the environmental and health impacts of compact, high-density plantings in dense built environments. Initiated in downtown Louisville, the project transformed Founders Square, a 0.64-acre sparsely planted park, into a microforest (“Trager Microforest”), a multilayered planting of 119 trees and more than 200 shrubs. The impact of this intervention is being assessed through a randomized crossover study in which participants walk in the microforest and a nearby impervious parking lot. Physiological outcomes include heart rate, heart rate variability, arterial stiffness, and stress biomarkers measured in saliva, urine, and sweat. Environmental conditions are continuously monitored by fixed and mobile weather stations, air pollution sensors, and biodiversity surveys. Baseline assessments were conducted in 2023 and 2024, with post-planting evaluations now underway (2025–). Power calculations indicate adequate sensitivity (n ≈ 40–50) to detect changes in cardiovascular stress responses in participants. Complementary ecological measurements include soil microbiome composition, greenhouse gas fluxes, and avian diversity. This study addresses critical gaps in understanding how small-scale, high-density greening interventions affect cardiovascular resilience, stress physiology, and microclimatic regulation. By integrating environmental, biological, and human health data, GRO establishes a comprehensive framework for evaluating the efficacy of urban microforests as nature-based solutions. The results are expected to inform urban planning, public health strategies, and climate adaptation policies, demonstrating how compact greening interventions can simultaneously mitigate heat, reduce pollution, enhance biodiversity, and promote human wellbeing in dense urban cores. Full article