Search Results (4,096)

The North China Craton hosts abundant skarn iron resources, yet the regional large-scale mineralization mechanism remains incompletely understood. The Litun deposit is a newly discovered skarn iron deposit in the North China Craton. Integrated field geological investigations, petrographic observations, fluid inclusion microthermometry and stable isotope geochemistry are applied to constrain evaporite contributions to metallogenic processes. Four mineralization stages are identified: skarn, oxide, sulfide, and carbonate. Early skarn-stage fluids are iron-rich magmatic hydrothermal fluids with high temperatures (498 to >550 °C), high salinities (18.6 to 59.4 wt% NaCl eqv.), and magmatic δ¹⁸O values of 8.3 to 10.8‰. Subsequent oxide to late carbonate stages record continuous infiltration of meteoric water, supported by H–O isotopic trends of rising meteoric water proportions. Pyrite from the magnetite ores has δ³⁴S_V-CDT values between 12.0 and 15.0‰, significantly higher than those of pyrite in the Litun diorite (−0.8 to 1.1‰), indicating the contributions of sulfur from evaporites (δ³⁴S_V-CDT 26.9 to 28.6‰) in the mineralization process. Moreover, vein pyrite formed in later stages displays even higher δ³⁴S values (17.3 to 20.9‰), demonstrating progressive enrichment of evaporite-derived sulfur as hydrothermal activity evolves. Synchronous rises in meteoric water fraction and evaporite sulfur proportion indicate evaporites are delivered into the ore-forming system via meteoric water mixing. The mixing of meteoric water containing dissolved evaporites and iron-rich magmatic-hydrothermal fluids may be the major mechanism of magnetite precipitation in the Litun deposit. Full article

Whole-plant soybean (WPS) is a high-protein forage resource, but its natural ensiling is often unsatisfactory due to low water-soluble carbohydrate content and high buffering capacity. This study investigated the effects of exogenous melatonin (ME) at 0 (CK), 5 (ME1), 10 (ME2), and 20 (ME3) mg/kg fresh matter on fermentation quality, chemical composition, in vitro rumen fermentation, and bacterial community structure of WPS silage. ME2 and ME3 had lower pH values and higher lactic acid contents than CK, with both treatments achieving pH values below 4.2. Crude protein concentration increased from 15.42% in CK to 19.96% in ME3, while neutral detergent fiber was lower in all ME treatments, and acid detergent fiber was lower in ME2 and ME3 than in CK. At 36 h, no overall treatment effect was detected for cumulative gas production, whereas in vitro dry matter digestibility differed only between ME2 and ME3. 16S rRNA gene sequencing revealed that ME altered the bacterial community, with community-weighted rrn copy number elevated in ME2 and ME3. Random forest analysis identified Enterococcus as the genus with the highest importance for treatment classification, and functional predictions indicated higher predicted abundances of amino acid biosynthesis pathways in ME treatment groups. These results indicate that ME has potential as an additive for improving WPS silage fermentation, but practical dosage recommendations require further validation through aerobic stability, animal performance, economic, and safety assessments. Full article

The sustainable valorization of spontaneous flora biomass for the recovery of high value-added phytochemicals represents a key opportunity within the circular bioeconomy, yet it remains constrained by the environmental limitations of conventional extraction solvents and the lack of data-driven optimization frameworks. In this study, Natural Deep Eutectic Solvents (NADES) composed of betaine and 1,3-propanediol were designed and applied as bio-based extraction media for the recovery of bioactive metabolites from Artemisia annua L. spontaneous biomass in the context of green extraction and sustainable resource utilization. Two liquid–solid extraction techniques, namely vortex-assisted extraction and ultrasound-assisted extraction, were evaluated. The influence of key process parameters, including the eutectic component molar ratio, water content, solid-to-liquid (S/L) ratio, extraction temperature, and extraction time, was systematically investigated. Results demonstrated that extraction efficiency was strongly dependent on both solvent composition and process conditions, with distinct optimum parameters for different phytochemical classes. Maximum total polyphenol content (52.08 mg GAE/mL) was achieved via ultrasound-assisted extraction at 20 °C for 15 min, using a 1:3 NADES ratio with 40% water dilution and S/L = 1:5, while the highest flavonoid yield (17.34 mg QE/mL) was obtained by vortex-assisted extraction for 45 min using a 1:6 NADES ratio under the same dilution and S/L conditions. To identify extraction conditions associated with improved process efficiency, a hybrid modeling approach combining deep neural networks with the Success-History-based Adaptive Differential Evolution (SHADE) algorithm was employed, enabling high-accuracy prediction of extraction performance across a broad parameter space. The proposed framework demonstrates the feasibility of integrating green solvent design with machine learning-driven process modeling for the efficient valorization of underutilized plant biomass, contributing to the development of resource-efficient, sustainable extraction protocols, consistent with principles of process intensification and resource-efficient extraction strategies. Full article

Sustainable agricultural development requires regional agricultural systems to balance output growth, resource efficiency, ecological protection, and long-term resilience. In mountainous and ecologically sensitive regions, identifying the development constraints and statistically derived marginal contribution thresholds of agriculture is essential for promoting green transformation and sustainable land use. Taking Yunnan Province, China, as a representative plateau mountainous agricultural region, this study uses provincial annual data from 1990 to 2023 to quantitatively identify the key drivers and threshold characteristics of agricultural development under multidimensional constraints. A multidimensional indicator system was constructed covering fiscal and investment support, agricultural production inputs, rural infrastructure, and labor and population conditions. Ridge regression was employed to address multicollinearity among explanatory variables, Bootstrap approximate inference was used to improve the robustness of coefficient estimation, and the SHAP interpretation framework was introduced to rank key driving factors and identify marginal contribution thresholds. By integrating ridge regression, Bootstrap approximate inference, SHAP-based contribution ranking, and threshold identification, the proposed framework advances prior agricultural sustainability studies by linking coefficient-based factor analysis with interpretable marginal contribution thresholds under conditions of high multicollinearity and multidimensional resource constraints. The results show that agricultural development in Yunnan is characterized by multidimensional resource and infrastructure constraints. Rural electricity consumption, total reservoir storage capacity, fixed asset investment in agriculture, forestry, animal husbandry and fisheries, local public fiscal budget expenditure, and agricultural population generally act as positive supporting factors. Rural electricity consumption is the most stable and core driver across the aggregate and three sectoral models. In contrast, pesticide and fertilizer inputs show significant negative associations in most models, suggesting that future agricultural development in Yunnan is unlikely to be sustainably supported by continued expansion of high-intensity chemical inputs. Sectoral heterogeneity is also evident: agriculture and animal husbandry are more dependent on energy, water resources, and mechanization, whereas forestry shows a more distinct operational structure. The SHAP dependence analysis identifies several statistically derived marginal contribution thresholds, including rural electricity consumption of approximately 6.055 billion kWh, total reservoir storage capacity of approximately 10.395 billion m³, total agricultural machinery power of approximately 19.8324 million kW, pesticide use of approximately 37,500 tons, and fertilizer application of approximately 1.5238 million tons. These values should be interpreted as empirical transition points in the modeled marginal contributions rather than definitive biophysical ecological limits. They indicate that the sustainability-related constraint structure of agricultural development in Yunnan is not a single output ceiling but a composite interval shaped by infrastructure support capacity, factor allocation conditions, and the declining marginal contribution of high-intensity chemical inputs. The findings provide directional quantitative evidence for sustainable agricultural governance, agricultural green transformation, and differentiated policy discussion in mountainous agricultural regions and offer reference implications for advancing SDG 2 and SDG 15 through the coordination of food-related production, resource use efficiency, and ecosystem conservation. The identified thresholds should be interpreted as model-derived marginal contribution transition points rather than operational policy cutoffs or directly enforceable ecological standards. Full article

The purpose of this paper is to present a techno-economic methodology for assessing the economic sustainability of offshore wind energy development within the Romanian exclusive economic zone (EEZ) of the Black Sea. The methodology illustrates nine key cases in this area that are grouped into three classes, each positioned at a greater distance from the Romanian coast and thus generating different environments given the water depth and wind climate. The data used for the analysis came from the ERA5 database and covered a 20-year span. Six types of wind turbines with capacities ranging from 5 to 9.5 MW were considered. In determining the levelized cost of energy (LCOE), the turbine with the highest production was considered, which turned out to be the Seimens Gamesa 8 MW, and for the economic model, the components related to both capital and operating costs were considered. Following the analysis, it was observed that the B2 site presents the best wind resources, also leading to the highest energy production of x. Regarding the LCOE analysis, values between 66.86 EUR/MWh and 87.39 EUR/MWh were obtained if the entire energy production is considered. Following the simulation with losses, the LCOE increases to values between 92.19 EUR/MWh and 121.85 EUR/MWh. Finally, an optimization calculation was also performed for the site with the highest LCOE considering another foundation time, after which the LCOE decreased to approximately 111.09 EUR/MWh, if we refer to energy production with losses. The results contribute to the economic sustainability evaluation of offshore wind projects in the Romanian Black Sea and influence future investment plans, sustainable energy planning, and renewable energy infrastructure development. Full article

Ensuring sustainable management of water, energy, and food (WEF) resources requires governance frameworks capable of addressing cross-sectoral interdependencies and policy fragmentation. This study evaluates the performance and coherence of national water, energy, and agricultural policies in Mali, South Africa, Malawi, and Tanzania, with a focus on their contribution to WEF nexus integration and energy transition pathways. A mixed-methods approach is applied, combining qualitative policy analysis, stakeholder consultations (n = 52), and a composite policy coherence index to assess cross-sectoral policy alignment across three river basins: the Bani River Basin (Mali), the Songwe River Basin (Malawi–Tanzania), and the Inkomati–Usuthu Water Management Area (South Africa). The results indicate that key water policy dimensions such as conservation, pollution control, and stakeholder participation demonstrate high performance (mean = 1.0) and strong coherence (SD = 0.0–0.1) across all countries. However, these values primarily reflect the presence of policy instruments rather than their effective implementation. Stakeholder evidence highlights persistent gaps in enforcement, coordination, and institutional capacity. In the energy sector, core infrastructure and participation policies exhibit high performance (mean = 1.0; SD = 0.0), while critical market instruments—including feed-in tariffs (FITs) and power purchase agreements (PPAs)—show moderate performance (mean = 0.6–0.8) and high variability (SD = 0.4–0.5), indicating regulatory inconsistency. In the agricultural sector, economic incentives achieve high performance (mean = 1.0; SD = 0.0), whereas sustainable practices such as agroecology, crop rotation, and organic fertilization remain weakly integrated (mean = 0.1–0.4; SD up to 0.5). Overall, the findings reveal that WEF nexus governance is characterized by strong structural policy alignment (mean = 0.8–1.0) but limited functional integration, reflecting a gap between policy design coherence and implementation effectiveness. Strengthening regulatory frameworks, improving cross-sectoral coordination, and enhancing investment mechanisms are critical for advancing resource efficiency and accelerating energy transition. The study provides a reproducible framework for assessing policy coherence and offers policy-relevant insights for integrated resource governance in Sub-Saharan Africa. Full article

Water yield is a key indicator for regional water resource assessment and directly concerns multidimensional socio-ecological sustainability. However, in arid and semi-arid regions, integrated long-term water yield simulation and nonlinear interpretation of driving factors remain insufficient. Therefore, Inner Mongolia was selected to analyze the spatial pattern and nonlinear driving mechanism of water yield depth for sustainable water resource management. Based on the InVEST model, water yield depth during 2001–2024 was simulated, and trend analysis was conducted. Annual XGBoost models with SHAP were used to explain nonlinear driver effects. Results showed a significant east-high and west-low pattern, with significantly increasing and decreasing areas accounting for 12.35% and 4.5%, respectively. Precipitation was the dominant driver, with higher ∣SHAP∣ values in wet years than in dry years. Zonal SHAP showed Pre led in all zones (48.8%, 63.5%, 37.7%), with secondary drivers shifting from forest/topography in the East to temperature in the West. SHAP values increased rapidly after precipitation exceeded thresholds of 200–300 mm in dry years and 400–500 mm in wet years. Under high precipitation, precipitation–non-forest interactions increased rapidly, whereas forest interactions changed little or became negative, showing a scissor-like divergence pattern. XGBoost reproduced the InVEST-simulated water yield depth well (R² = 0.91 ± 0.03). This workflow provides a reproducible pathway for water resource assessment in arid and semi-arid regions. Full article

This integrative critical review examines how biomass and bioenergy can contribute to energy diversification while accounting for constraints related to climate mitigation, energy security, resource availability, and technology readiness. The review combines a targeted literature synthesis with expert-informed insights from the international seminar Energy Transition and Biofuels held at the Federal University of Itajubá in October 2025. The seminar and COP30-related discussions were used as contextual and conceptual inputs, while peer-reviewed literature, policy documents, and technical reports provided the evidentiary basis for the analysis. The manuscript evaluates biomass and biofuels utilization, refinery integration, sustainable aviation fuels, biochar, BECCS, hydrogen synergies, life-cycle assessment, artificial intelligence, and logistics. The synthesis indicates that biomass is not a universal substitute for fossil fuels. Still, it has distinctive value in applications requiring renewable carbon, dispatchable energy, process heat, liquid fuels, carbon removal, and compatibility with existing infrastructure. The analysis also shows that these contributions are contingent on feedstock governance, land and water safeguards, logistics, fertilizer inputs, technology maturity, and verified life-cycle performance. The food–fuel discussion is therefore reframed as a context-specific problem of land-use, access, productivity, and governance rather than a simple competition between energy and food production. The study concludes that bioenergy can most credibly support the energy transition when deployed through differentiated pathways tailored to regional resources, sustainability constraints, and sector-specific decarbonization requirements. Full article

The Curve Number (CN) remains one of the most widely applied parameters for estimating direct surface runoff. However, its conventional application based on watershed-aggregated tabulated values conceals hydrological variability in regions with contrasting soils and steep topographic gradients. A recurring limitation of distributed CN approaches is the absence of independent hydrological validation; most machine learning models are trained and evaluated against the same SCS-USDA lookup values used to construct the training target, a circular scheme that measures statistical agreement rather than physical credibility. This study develops a reproducible geospatial workflow for distributed CN estimation across continental Ecuador, combining official MAG land use, soil surface texture natural drainage, and topographic slope layers at 1:25,000 scale with a Random Forest regression model at 10 m spatial resolution. The CN reference raster was derived from official geo-pedological layers and independently validated, not against tabulated assumptions, but against observed hydrological behaviour. Field hydraulic characterization across four dominant land cover classes in the Guamote microwatershed (Chimborazo Province), combined with HEC-HMS (US Army Corps of Engineers, Davis, CA, USA) rainfall-runoff modelling over 41 years (1981–2021), confirmed a mean annual discharge of 0.1568 m³ s⁻¹ consistent with the tabulated CN assignments. To our knowledge, this is the first nationally distributed CN map with field-anchored hydrological benchmarking for an Andean country. The Random Forest model achieved an RMSE = 10.4, an R² = 0.42, and an NSE = 0.41, a performance consistent with published field-based CN estimation studies and expected given the inherent scatter of the SCS-USDA method under real-world conditions. Zonal CN comparisons confirmed a mean absolute error below 5 CN units across the Andean highland and Amazon watersheds; the Guamote watershed showed a mean ∆CN below 4 units against the field-calibrated model. Land use and surface texture emerged as the dominant CN predictors, with natural drainage providing critical discrimination in volcanic and poorly drained soil environments. The resulting 10 m national CN map offers a physically grounded, spatially explicit parameterization layer for distributed hydrological modeling and water resources planning across data-scarce Andean and tropical territories, with direct relevance for flood risk screening, irrigation planning, watershed conservation, and climate adaptation under SDG 6, SDG 11, SDG 13 and SDG 15. Full article

Local threshold limits translate abstract information on hydrological drought into concrete operational guidance for individual water resources. For reservoirs, surface water intakes, and groundwater sources that are important to a region, key monitored quantities and failure conditions are identified, critical levels corresponding to loss of function or unacceptable quality are derived, and an advance time to reach them is set. From these, threshold values analogous to flood stages are calculated, possibly varying over the year with the hydrological regime and demand. Implemented through regional drought plans and displayed in the HAMR (Hydrology–Agronomy–Meteorology–Retention drought monitoring and prediction system, hamr.chmi.cz), local threshold limits complement nationwide warnings by capturing the specific behaviour of each resource and enabling timely, proportionate and locally accepted drought management actions. Full article

Geothermal energy is a locally available, low-carbon resource that may support heat supply, building decarbonisation and regional energy diversification in non-volcanic crystalline settings. This study proposes an exploratory GIS-based approach for geothermal favourability mapping in the Guarda District, Central Portugal, where Hercynian granites, major fault systems and thermal and mineral water occurrences define a structurally controlled hydrogeothermal framework. Hydrogeochemical data from 54 groundwater abstraction points were integrated through silica-derived apparent geothermometric indicators, classical hydrothermal-parameter estimation and Empirical Bayesian Kriging Regression Prediction (EBKRP). Apparent silica-derived temperature indicators, circulation depth, geothermal gradient and theoretical thermal power were estimated, with log₁₀ transformed thermal power used as the dependent variable and distance to major mapped faults as the structural covariate. Apparent silica-derived temperature indicators range from 21.3 °C to 121.2 °C, with a mean of 64.6 °C, while estimated geothermal gradients range from 20.3 °C/km to 92.1 °C/km. Higher estimated values occur preferentially near NE–SW and NNW–SSE fault systems, suggesting that structural permeability may influence deep groundwater circulation. The interpretation explicitly acknowledges that, in low-temperature systems, dissolved silica may be influenced by chalcedony or amorphous silica control, as well as by cooling, mixing and incomplete re-equilibration during fluid ascent. The resulting map is interpreted as a screening-level favourability product, not as a definitive assessment of exploitable geothermal resources, and supports the prioritisation of future structural mapping, geophysical surveys, exploratory drilling, borehole temperature logging and applied geothermal assessment in fractured granitic terrains. Full article

Groundwater is an essential freshwater resource in the Western Himalayas, where increasing anthropogenic pressure and environmental variability are raising concerns regarding groundwater quality and water security. However, regionally integrated assessments of groundwater-quality variability across the Western Himalayan states remain limited. This study evaluates groundwater quality across Jammu and Kashmir, Himachal Pradesh, and Uttarakhand using groundwater-monitoring data obtained from the Central Ground Water Board (CGWB). A total of 338 observation wells monitored during 2019–2022 were analyzed using the weighted arithmetic Water Quality Index (WQI) based on Bureau of Indian Standards (BIS) and World Health Organization (WHO) drinking-water guidelines. Spatial and temporal variability were examined through hydrochemical, correlation, and geospatial analyses. The results reveal substantial regional and district-level variability in groundwater quality across the Western Himalayas. Groundwater in Himachal Pradesh and Uttarakhand is predominantly classified as excellent to good, whereas Jammu and Kashmir exhibit greater hydrochemical heterogeneity and localized groundwater deterioration. Elevated WQI values are concentrated within foothill and valley-transition districts, while high-altitude recharge zones generally maintain lower WQI values. Hydrochemical analyses indicate that groundwater-quality variability is primarily associated with mineralization processes, lithological controls, and localized anthropogenic influences. Temporal analysis further indicates moderate groundwater-quality improvement between 2019 and 2022, particularly in parts of Jammu and Kashmir. Overall, the findings demonstrate that groundwater systems across the Western Himalayas remain largely controlled by hydrogeological conditions but are increasingly modified by localized anthropogenic pressures. Strengthened groundwater monitoring, protection of recharge zones, and targeted management of vulnerable foothill and valley-transition environments will be essential for sustaining long-term water security in this climate-sensitive mountain region. Full article

Standardized drought indices such as the Standardized Precipitation Index (SPI) are widely used in both monitoring and climate-change impact assessments. However, SPI values are not uniquely defined unless the reference climatology used for standardization is explicitly stated and justified−a methodological issue that becomes critical under non-stationary climate conditions. Here, we present a methodological assessment of how reference-climatology strategy affects SPI-based drought projections under climate change, using Türkiye’s 26 major basins as a hydroclimatically diverse testbed. These assessments inform sustainable water resources planning, agricultural adaptation, and climate-resilient infrastructure design under non-stationary climate. Daily precipitation projections from 56 GCM-RCM pairs (EURO-CORDEX EUR-11, 0.11° (approximately 12 km at the mid-latitudes of the study domain); CMIP5 RCP8.5) were bias-corrected against ERA5-Land and aggregated to basin means. We computed SPI-9 and compared two commonly used reference strategies: (i) a fixed historical baseline (1970–2005), applied consistently to both historical and future periods (fixed-baseline SPI); and (ii) a period-specific baseline (period-specific SPI; future SPI values are standardized to the climatology of the future evaluation period itself). Using the same climate simulations, the two strategies yield markedly different drought projections. At the country scale, end-of-century drought time reaches 458 months under the fixed-baseline strategy, whereas the period-specific strategy indicates 393 drought months. Corresponding severity summaries are likewise stronger under fixed-baseline standardization. The contrast is even stronger in several Mediterranean basins, where fixed-baseline standardization produces persistently severe drought conditions. These results show that SPI-based drought projections are substantially sensitive to the choice of reference-climatology strategy, and that the same climate ensemble can support materially different drought narratives depending on how anomalies are standardized. Because the two strategies differ in both reference-timing and calibration-window length (36 versus 95 years), the headline contrast should be interpreted as a combined effect rather than as a pure baseline-timing result. In the present implementation, the period-specific strategy uses a single future calibration period (2006–2100), so the comparison should be interpreted as a stress test of reference framing under non-stationary climate rather than as an equal-length baseline experiment. An equal-length late-baseline sensitivity check (1970–2005 versus 2065–2100; both spanning 36 years) shows that the fixed-to-late-baseline contrast is larger than the fixed-to-period-specific contrast in 25 of 27 spatial units, including a 3.0-fold amplification at the national scale, indicating that the reference-timing effect persists when calibration-window length is held constant. Because the analysis is based on a CMIP5-driven RCP8.5 ensemble, the numerical projections should be interpreted as a high-end stress-test envelope rather than as the most likely outcome. We therefore recommend that drought projection studies explicitly report the reference-climatology strategy, justify the calibration window, and distinguish between analyses designed to quantify change relative to a historical climate and analyses designed to describe anomalies relative to an evolving future climate. These methodological choices have direct implications for sustainable water resources management and drought-risk preparedness in water-stressed Mediterranean systems, and contribute to broader sustainability targets such as Sustainable Development Goal 6 (Clean Water and Sanitation), SDG 13 (Climate Action), and SDG 15 (Life on Land). Full article

Achieving simultaneous improvements in grain yield and water–nitrogen use efficiency remains a major challenge in high-density summer maize production. Therefore, this study investigated how drip fertigation (DI) regulates soil nitrogen spatial distribution, plant nitrogen translocation, and ultimately resource use efficiency. A two-year field experiment (2023–2024) was conducted in Tai’an, Shandong, China, using a split-plot design. Two water–fertilizer management regimes, conventional border irrigation (BI) and drip fertigation (DI), were assigned to the main plots, while eight planting densities (15,000–120,000 plants ha⁻¹) were allocated to the subplots. Two summer maize cultivars, Denghai 605 (DH605) and MY73, were evaluated. Compared with BI, DI significantly increased grain yield as well as water and nitrogen use efficiency. For DH605 and MY73, grain yield increased by 7.3% and 3.8%, respectively, accompanied by increases of 18.4% and 16.3% in WUE and 7.4% and 3.5% in NPFP. DI enhanced nitrogen accumulation within the 0–20 cm root zone while reducing nitrate-N residues in the 20–60 cm soil layer, thereby improving the spatial distribution and availability of root-zone nitrogen. Consequently, DI increased nitrogen translocation from vegetative organs to grains, as reflected by higher NTA, NTE, and NHI values, which promoted grain nitrogen accumulation and improved nitrogen use efficiency. Notably, DI did not significantly affect nitrogen uptake efficiency (NU_pE), suggesting that the improvement in nitrogen utilization efficiency (NU_tE) was driven primarily by enhanced nitrogen remobilization from vegetative organs to grains rather than by increased nitrogen uptake. Overall, drip fertigation improved grain yield, water use efficiency, and nitrogen use efficiency in high-density summer maize by optimizing root-zone nitrogen availability and promoting post-silking nitrogen translocation to grains. Full article

The oxidative potential (OP) of atmospheric particulate matter serves as an effective indicator for assessing the health risks posed by reactive oxygen species (ROS). Existing studies have mainly focused on conventional particulate matter including PM_2.5, whereas systematic investigations into the OP of road dust in coal–resource–based cities are still limited. Taking Huainan City, China as the study area, this paper explored the characteristics and influencing factors of OP in water–soluble fractions of road dust from different functional zones. The results indicated that the OP of water-soluble fractions in road dust from Huainan City was 0.162 ± 0.079 pmol/min/μg, with the value in the coal mining zone being significantly lower than that in the commercial and industrial zones. The average concentration of water–soluble organic carbon (WSOC) was 67.3 ± 59.4 mg/kg, with lower levels observed in the coal mining and power plant zones. WSOC was primarily dominated by fulvic acid–like (C1) and tryptophan–like (C2) components. C1 prevailed in coal mining, power plant, and other functional zones, whereas C2 was dominant in commercial, park and residential zones. Overall, the WSOC showed a mixed-source signature dominated by endogenous sources and characterized by a low degree of humification. The total concentration of water–soluble heavy metals in road dust was 43.46 mg/kg, dominated by Fe, Sr, Cu, Ba, and Mn, with relatively lower concentrations observed in the industrial and coal mining zones. The influencing factors of OP exhibited differentiation among functional zones: in industrial zones, it was regulated by As, Mn, TC (total carbon), WSOC and its fluorescent components, while in non-industrial zones, it was closely associated with Co, TC, and WSOC. These findings indicate that road dust toxicity and its key chemical drivers in coal mining and power plant zones of coal resource–based cities exhibit distinctive characteristics. This study provides a scientific basis for the precise management of road dust pollution and the prevention of associated health risks. Full article