Ecological Chemistry and Engineering S Feed

Integrating CCME-WQI and Machine Learning for Groundwater Quality in the Sedrata Plain

Sun, 11 Oct 2026 00:00:00 GMT

The quality of groundwater in the Sedrata plain in northeastern Algeria is under increasing pressure due to semi-arid climatic conditions, geological mineralisation and growing agricultural demand. This study provides a thorough physicochemical characterisation of phreatic aquifer waters collected from 25 open wells over four sampling periods (February, May and December 2023 and May 2024; n = 100), combining a traditional Water Quality Index (WQI) analysis with six machine learning (ML) prediction models. The CCME-WQI framework was applied using 12 physicochemical parameters. Six ML algorithms, Gradient Boosting (GB), Random Forest (RF), Support Vector Regression (SVR), Bayesian Regression (BR), AdaBoost (AdB) and K-Nearest Neighbours (K-NN) were trained and evaluated across 12 nested input-variable combinations, using the Recursive Feature Elimination with Linear Cross-Validation (RFE-L) methodology. Gradient Boosting achieved the highest predictive accuracy on the full variable set (R2 = 0.88 and RMSE = 4.89 in the test set), while SVR demonstrated the best generalisation ratio (R2 = 0.83 in the test set with only two variables removed). Best-subset regression identified a parsimonious nine-variable model (T, EC, Turb, HCO3–, Ca2+, Cl–, K+, Na+; Adj. R2 = 0.812) as the recommended input for operational WQI prediction. Chloride, calcium and electrical conductivity exhibited the strongest negative correlations with WQI, reflecting evaporite dissolution and ion exchange processes. These findings provide actionable guidance for the management of groundwater in semi-arid regions facing analogous hydrochemical and climatic constraints.

Artificial Intelligence Policy and Urban Carbon Emission Performance: Evidence from the Yangtze River Delta, China

Sun, 11 Oct 2026 00:00:00 GMT

Whether artificial intelligence (AI) policy translates into measurable urban decarbonisation remains uncertain: most evidence is firm- or robot-level, and transparent city-level policy evidence is scarce. This study treats the State Council’s “New Generation Artificial Intelligence Development Plan” and subsequent local responses as a quasi-natural experiment, using panel data on 26 Yangtze River Delta cities over 2010-2023. Four core cities (Shanghai, Nanjing, Hangzhou, Suzhou) are defined as the AI-policy treatment group, and a two-way fixed-effects difference-in-differences (DID) model estimates the effect of AI policy exposure on urban low-carbon performance, measured as a standardised inverse carbon-intensity indicator. The baseline DID coefficient is 0.493 and significant at the 5 % level, and the positive sign is stable when the onset year is set to 2018, the treatment group is expanded, the window is shortened, and Shanghai is excluded. The estimate is, however, sensitive to city-specific linear trends and to over-control specifications, so its magnitude should be read cautiously; event-study results are consistent with parallel pre-trends, with effects emerging gradually after implementation. Stepwise mechanism tests give indicative - not formal - evidence that industrial structure upgrading and green technological innovation act as transmission channels. Heterogeneity across cities is suggestive but not statistically confirmed, and cross-city spillovers are not robust across spatial weight matrices. AI policy thus appears associated with better urban carbon performance mainly through local channels, and implementation should prioritise practical low-carbon applications, green-innovation commercialisation, and locally differentiated strategies.

Multi-Objective Optimisation of Arable Land Allocation Within Environmental Carrying Capacity

Sun, 11 Oct 2026 00:00:00 GMT

Widespread abandonment of arable land, extensive cultivation practices, accumulation of fertiliser and pesticide-derived pollution jointly threaten sustainable use of regional arable land resources. This study addresses the allocation of a fixed regional arable land endowment between two operating entities with distinct behaviour: land contractors, who lease land, pursue profit, apply higher agrochemical inputs and may leave part of the leased area idle, and farmers, who cultivate their own land with higher yields and lower emissions. A tri-objective programming model is constructed that maximises the utilisation rate of arable land, maximises total grain yield and minimises the environmental pollution load, subject to the regional environmental carrying capacity, a government subsidy budget and a tiered penalty on idle land. Fertiliser and pesticide non-point-source pollution and crop-straw disposal: open burning versus return to the field, with the associated air pollutants, water pollutants and nitrogen losses, are converted into standard pollution equivalents following the NY/T 4157-2022 guideline. The model is solved with the NSGA-II algorithm, which outperforms GA and MOPSO on generational distance (0.0225), hypervolume (0.8832) and spread (0.0201). Sensitivity analysis shows that the subsidy coefficient for contractors is the dominant lever: it raises land utilisation and grain yield most strongly, but beyond a threshold it drives the largest increase in pollution load, while the waste-emission coefficients of the two entities jointly amplify environmental pressure. Based on the Pareto solution set, three differentiated regulation schemes: low-pollution priority, high-yield priority and balanced development, are proposed for coordinating subsidies, idle-land penalties and agrochemical application limits.

The Nonlinear Impact of Economic Growth on Carbon Emissions in China’s Yellow River Basin Using Difference-in-Differences Models

Sun, 11 Oct 2026 00:00:00 GMT

The present empirical investigation sets out to systematically unpack the multifaceted effects of aggregate economic expansion on anthropogenic carbon discharge across China’s vast Yellow River Basin, with targeted analytical emphasis placed on unpacking the non-monotonic, curved associations between advancing economic activity levels and greenhouse gas output, as well as the underlying causal pathways that generate these complex interplays. Considering that most of the existing studies focus on the direct economic-carbon emission relationship, this paper adopts a double-difference model to analyse the mediating effect of economic growth through factors such as industrial structure, urbanisation level and regional innovation capacity. The empirical results show that there is an inverted U-shaped nonlinear relationship between economic growth and carbon emissions, with carbon emissions rising in the early stage of economic development, but gradually declining as the economy matures and the industrial structure transforms. In addition, the optimisation of industrial structure, the acceleration of urbanisation and the enhancement of regional innovation capacity play an important mediating role between economic growth and carbon emissions. The theoretical significance of this study is that it enriches the research framework of the nonlinear relationship between economic growth and environmental pollution, and the practical significance is that it provides empirical support for the formulation of differentiated carbon emission policies. The results of the study provide an important policy basis for promoting the transition to a low-carbon economy and the sustainable development of the region.

Demand-Side Carbon Reduction Under Quota Constraints: Evidence from Green Experience

Sun, 11 Oct 2026 00:00:00 GMT

Under binding carbon quota policies, demand-side interventions are increasingly recognised as complementary to conventional technological abatement. This study develops an integrated behavioural emission accounting model linking green experience investment to consumer-level carbon reduction behaviour. By embedding behavioural response elasticity within a quota-constrained framework, the model quantifies how consumption intensity changes translate into behavioural emission reduction responses. Empirical analysis indicates a nonlinear threshold effect: demand-side investment beyond a critical elasticity threshold generates disproportionate emission responses. Mediation pathways involving trust and environmental values partially explain this behavioural response, while emotional heterogeneity moderates behavioural mitigation intensity across groups. Findings demonstrate that demand-side management can serve as an auxiliary behavioural mitigation pathway under cap-based systems, provided behavioural adjustments are integrated into formal accounting structures. This study contributes to environmental policy literature by linking micro-level behavioural dynamics with system-level reduction modelling.

Major and Trace Element Content of Heracleum sosnowskyi Manden., An Invasive Species in the Leningrad Region, Russia

Sun, 11 Oct 2026 00:00:00 GMT

Introduced to Europe in the early 20th century as an ornamental plant and to European Russia in the mid-20th century as a promising forage plant, Heracleum sosnowskyi Manden. (Sosnowsky’s hogweed, or giant hogweed; family Apiaceae or Umbelliferae), a common invasive species in the Leningrad Region (Vyborg District; 60.392455, 29.794482). In the 1950s, the Leningrad Region became the site of some of the first hogweed forage crops in the Soviet Union. Today, hogweed has spread across vast areas of this region, posing a threat to both human populations and agricultural lands. For the first time, a study was conducted using neutron activation analysis (NAA). This analytical method allowed the determination of 32 elements, including major and trace elements (Na, Mg, Al, Cl, K, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Zn, As, Se, Br, Rb, Sr, Mo, Sb, Cs, Ba, La, Ce, Sm, Tb, Hf, Ta, Th, and U). It was shown that many toxic elements not only do not accumulate but are present in lower concentrations than in the soil in which the plants grow.

Comparison of Recycling Modes of Closed-Loop Supply Chain Under Carbon Trading Mechanism

Sun, 11 Oct 2026 00:00:00 GMT

Under a cap-and-trade mechanism, this paper investigates the optimal recycling channel selection within a closed-loop supply chain (CLSC). We construct three distinct Stackelberg game models - manufacturer recycling (M mode), retailer recycling (R mode), and third-party recycling (T mode) - by incorporating decisions on forward carbon abatement technology investment and reverse logistics efforts into a unified framework. Analytical and numerical results demonstrate that government-allocated free carbon quotas enter the manufacturer’s profit function as an additive constant, shifting the absolute financial baseline without altering optimal decisions or mode rankings. Comparative static analyses show that while the direct manufacturer recycling (M mode) consistently yields overwhelming economic dominance across the feasible parameter space due to the elimination of reverse double-marginalisation, its environmental efficiency is conditional. Specifically, when the carbon intensity of remanufactured products exceeds the threshold or the consumer recycling price falls below the critical value, the retailer recycling (R mode) demonstrates stronger environmental resilience, generating higher per-unit carbon reductions. These findings indicate a critical trade-off between economic and environmental objectives in decentralised supply chains, suggesting that environmental regulations, transfer-pricing mechanisms, and channel structures must be coordinated through an integrated governance framework.

The Impact of Green Finance Policy on Corporate Carbon Emission Efficiency

Sun, 11 Oct 2026 00:00:00 GMT

Under the guidance of the “dual carbon” goals, enhancing carbon emission efficiency is the key to achieving coordinated development of economic growth and environmental protection. This paper takes the green finance reform and innovation pilot zones established in batches since 2017 as a quasi-natural experiment, and uses the multi-period difference-in-differences (DID) method to empirically examine the impact of green finance policies on the carbon emission efficiency of enterprises based on the data of Chinese A-share listed companies on the Shanghai and Shenzhen stock exchanges from 2013 to 2025. The research finds that green finance policies have significantly improved the carbon emission efficiency of enterprises in the pilot zones, with an average increase of about 5.4 percentage points, equivalent to 8.6 % of the sample mean. This conclusion remains valid after a series of robustness tests. Mechanism analysis indicates that the tightening of financing constraints (forcing effect) and the output of green patents are two core transmission paths, with the estimated indirect effects accounting for approximately 27.8 % and 31.5 % of the total effect, respectively. Heterogeneity analysis shows that the policy effect is more prominent in large-scale enterprises and heavily polluting industries. This paper deepens the understanding of the micro-environmental effects of green finance from the perspective of efficiency, reveals the internal logic of policy implementation, and provides empirical references and decision-making basis for improving the green finance system and driving high-quality low-carbon transformation of enterprises.

The Use of Waste Material from the Food and Agricultural Industries (Press Cake) as an Alternative, Renewable Solid Fuel

Sun, 11 Oct 2026 00:00:00 GMT

The research utilised by-products from the food and agricultural industry, which offer a promising, eco-friendly approach to waste utilisation and solid fuel production. With the growing global energy demand and the depletion of fossil fuel resources, renewable energy sources like biomass have gained significant importance. This study focused on investigating the pyrolysis of various press cakes from oil crops, such as rapeseed, flaxseed, apricot, and camelina. The pyrolysis process involves the conversion of biomass into biochar and volatile fractions, with potential applications in energy generation and other industries. The results showed that pyrolysis at 500 °C produced satisfactory char yields comparable to wood pyrolysis. As a result of pyrolysis, a solid fraction of 27 to 28 % was obtained. The primary challenge lies in the high ash content of press cake chars, which originates from the conversion of the entire mineral fraction into char. Nonetheless, press cake pyrolysis demonstrates promise as an eco-friendly waste utilisation method and a means to generate solid fuels. The char obtained from pyrolysis can be used directly as a substitute for solid fuels or as an additive during the formation of fuels, such as straw pellets. The second option seems more favourable due to the high ash content in the char. The main advantage of waste pyrolysis is that the waste is not sufficiently microbiologically stable without thermal treatment.

Air Deposition of Heavy Metals in the Prishtina Region Using Mosses and Lichens as Bioindicators

Tue, 14 Jul 2026 00:00:00 GMT

Heavy metal pollution is an important environmental issue in urban and industrial areas because these elements persist in the environment and may cause ecological and health problems. The aim of this study was to investigate the atmospheric deposition of heavy metals (Cu, Mn, Ni, Pb, Zn, and Cr) in the Prishtina region (Kosovo) using mosses and lichens as bioindicators. A total of 24 moss samples (Hypnum cupressiforme) and 9 lichen samples (Cladonia portentosa) were collected between June and August 2025. The plant samples were digested using aqua regia and analysed by Inductively Coupled Plasma Atomic Emission Spectroscopy (ICP-AES). Multivariate statistical methods, including basic statistics, Pearson correlation, and principal component analysis (PCA), together with spatial distribution analysis of heavy metals, were applied to facilitate clearer data interpretation. To evaluate the degree of heavy metal contamination at individual sites and across the entire region, four pollution indices (CF, PLI, Eri, and PERI) were calculated. The results indicate that the study area is contaminated with nickel, chromium, and lead, mainly due to mining activities at the open-pit ferronickel mines in Golesh, emissions from the ferronickel smelter in Drenas, and heavy traffic along the Prishtina-Peja highway. The ecological risk index (Eri) for nickel in moss samples ranged from 6.00 to 336, while in lichen samples it ranged from 6.00 to 42.0. Copper showed Eri values of 6.67-16.67 in mosses and 3.33-5.00 in lichens. For lead, Eri values in moss samples ranged from 3.33 to 53.33, whereas in lichens they were below the detection limit. Based on moss data, 9 locations (39.1 %) were classified as moderately polluted, while 14 locations (60.9 %) fell into the unpolluted to moderately polluted category. In lichen samples, 3 locations (37.5 %) were classified as unpolluted and 5 locations (62.5 %) as unpolluted to moderately polluted. Overall, both the concentrations and pollution indices of heavy metals in mosses were 2-7 times higher than those observed in lichens at the same sampling locations. The smaller number of lichen samples compared with moss samples reflects their limited presence at several sampling sites, which represents a limitation of the study.

Bi-Objective Planning of Park Electrothermal Systems with Geothermal, Renewables and Storage

Tue, 14 Jul 2026 00:00:00 GMT

As wind and photovoltaic deployment in industrial parks approaches practical upper bounds, the central planning issue shifts from simply expanding renewable-electricity capacity to identifying where the remaining decarbonisation leverage exists. This study addresses this later-stage planning problem by developing a representative-day bi-objective planning model for an industrial park electricity-heat system integrating wind power, photovoltaic generation, geothermal heat utilisation, thermal energy storage (TES), battery storage, and bidirectional grid interaction, and solves it using multi-objective particle swarm optimisation. The results show that wind and photovoltaic capacities remain close to their feasible upper bounds across representative Pareto solutions, whereas the main structural variation occurs in geothermal deployment and thermal storage. From the carbon-oriented end to the economy-oriented end of the frontier, annualised capital expenditure (CAPEX) decreases from (2691.10 to 977.75) · 104 RMB, while carbon reduction decreases from (24.808 to 21.294) · 104 tCO₂ (t = tonne = 106 g = Mg = 103 kg) and the heat deficit ratio increases from 8.50 % to 36.46 %. The representative middle-frontier solution achieves a more balanced outcome, with an economic-efficiency index of 15.212, carbon reduction of 23.090 · 104 tCO₂, and a heat deficit ratio of 6.40 %. Winter representative-day operation shows that TES provides 48.8 % of heat demand, whereas medium- and high-temperature geothermal heat contribute 33.9 % and 10.7 % respectively; by contrast, geothermal electricity remains negligible. Additional heat-adequacy screening and TES cost-sensitivity analyses support the same interpretation. These results indicate that, once electricity-side renewable deployment is high, the main planning lever for balancing investment burden, decarbonisation performance, and heat-service adequacy shifts toward coordinated heat-side design in industrial parks.

Assessment of Toxic Elements in Atmospheric Deposition in Azerbaijan Using Moss Biomonitoring Methods

Tue, 14 Jul 2026 00:00:00 GMT

This study investigated atmospheric heavy metal deposition across three western regions of Azerbaijan - Goygol, Gadabay, and Dashkasan - within the framework of the UNECE International Cooperative Programme on Effects of Air Pollution on Natural Vegetation and Crops (ICP Vegetation), employing moss biomonitoring as the primary investigative approach. Two dominant moss species, Hylocomium splendens and Pleurozium schreberi, were collected and analysed for a total of 44 elements using a combination of neutron activation analysis (NAA), atomic absorption spectroscopy (AAS), and particle-induced X-ray emission (PIXE). Interelemental relationships were examined through factor analysis, which yielded four distinct factors representing major source groups. When Azerbaijani moss data were compared against Norwegian reference values, elevated concentrations of most heavy metals - including Mg, Al, V, Fe, As, Mo, Cd, La, and U - were recorded in the sampled material. Mosses were utilised as passive bioindicators to quantify elemental pollutant loads in atmospheric deposits and characterise the general pollution status of each study area. Geospatial distribution maps were generated to delineate the most heavily polluted zones and establish links to known contamination sources. Elemental concentrations in mosses collected from Dashkasan and Gadabay were found to substantially exceed those measured in Goygol samples, reflecting the stronger anthropogenic influence in the former regions. In addition to industrial and agricultural pressures, local geological characteristics - specifically lithology and soil composition - were identified as contributing factors governing spatial variability in elemental distribution.

Development of Coal Magnetic Activated Carbon for Cd(II) Removal: Adsorption Isotherm and Kinetic Modelling

Tue, 14 Jul 2026 00:00:00 GMT

The metal coating process produces wastes containing heavy metals such as cadmium that are harmful to health. One method to remove cadmium is adsorption. This research developed an adsorber, specifically magnetic activated carbon derived from coal (CMAC). Activated carbon is modified with a Fe3O4 composite to make it magnetic and reusable. The objective of this investigation is to optimise the key parameters of activator concentration and thermal activation conditions for the fabrication of high-quality activated carbon. The investigation will evaluate the characteristics of the resulting carbon and its cadmium removal efficiency. The experimental adsorption data were fitted to both the Freundlich and Langmuir isotherm models and evaluated using kinetic equations. Chemical activators were used in a mixture of phosphoric and hydrochloric acid (1: 1) at concentrations of 0.5 M - 1.25 M. Physical activation was carried out at (873 - 1173) K. The optimal activation temperatures were identified as 1123 K and 1173 K. The CMAC removal percentage for Cd was 98.10 %. The Freundlich isothermal adsorption equation shows n = 0.4530 and KF = 3.9823 mg/g with R2 = 0.9691. The adsorption data were well-fitted by the Langmuir model, which yielded a maximum monolayer adsorption capacity qₘ of 238.10 mg/g and an affinity constant KL of 0.0787 L/mg, supported by a high coefficient of determination (R² = 0.9082). Furthermore, the adsorption kinetics model yielded a pseudo-second-order equation. The regeneration test over five cycles showed decreases in Cd removal efficiency of 97.04 %, 81.89 %, 42.75 %, 13.83 %, and 9.71 %, respectively, indicating limited stability of the adsorbent after repeated use.

Adaptive Fuzzy Interference System for Quantifying the Effect of Urban Green Space on Human Health

Tue, 14 Jul 2026 00:00:00 GMT

Urban green areas consist of intentionally preserved plantations, parks, and natural elements available to the public for recreation and relaxation. These places are good for people’s health because they provide places that encourage physical activity, stress relief, and mental recovery. To evaluate their influence, urban green areas must be systematically categorised based on their measurable impact on human health. In this research, an Effect Quantifying Process using a Fuzzy Inference System (EQP-FIS) is used to figure out what effect this has by looking at two main factors: health improvements that have been shown to happen and the functional effect of being close to green spaces. The method finds the degree of connectivity between environmental effects and health-related recommendations. Low connectivity indicates limited health benefits, prompting targeted suggestions for improvement, whereas high connectivity supports the growth or replication of effective green spaces. The model’s performance is verified using recommendation scores, effect rates, data availability, and analysis time.

Applying Multivariate Analysis and Organic Pollution Index to Evaluate Mexa and Cheffia Dams, Algeria

Tue, 14 Jul 2026 00:00:00 GMT

This study provides a comprehensive assessment of surface water quality in the Mexa and Cheffia dams located in the El Tarf region, northeastern Algeria. The work focuses on evaluating pollution levels, organic contamination, and nutrient status using several indices. Sampling was carried out at two different sites over a 12-month period (May 2024 to April 2025). The Water Pollution Index (WPI) classified all seasons in both reservoirs as “very highly polluted”, reflecting strong mineralisation and ionic enrichment. The Organic Pollution Index (OPI) indicated predominantly high to very high organic pollution, with Cheffia showing 91.7 % high organic pollution and 8.3 % very high organic pollution, and Mexa presenting 100 % high organic pollution. The Nutrient Pollution Index (NPI) revealed strong seasonal contrasts, ranging from moderate pollution (0.66-2.47) in summer-autumn to extremely high nutrient enrichment (up to 7.44) in winter and spring. Trophic evaluation based on the TN : TP ratio showed seasonal variability, with 50 % of the observations corresponding to oligotrophic conditions, while the remaining observations exhibited eutrophic to hypertrophic states, especially in Mexa. Overall, the integrated index assessment highlights that both reservoirs are subjected to substantial mineral, organic, and nutrient pressures, with Mexa displaying greater variability and Cheffia showing relatively more stability across indices.

Purification and Thermochemical Conversion of Ammonium Sulphate: A Pathway to Sustainable Waste Recycling

Tue, 14 Jul 2026 00:00:00 GMT

Ammonium sulphate produced as a by-product of the coke-chemical process at JSC “QARMET” (Kazakhstan) contains impurities such as tarry substances and pyridine-based sulphates. These impurities cause adverse effects, including strong odour, caking, and atypical appearance, while pyridine sulphates also increase acidity (pH < 5). This study investigates the purification of substandard ammonium sulphate to expand its potential applications. Mechanisms for producing complex fertilisers from the purified product were examined. The results provide a fundamental theoretical and technological basis for developing new production lines for chemical products, including compound fertilisers. Thermochemical interactions between potassium carbonate and molten ammonium bisulphate were studied. It was found that at equimolar ratios of 2 NH4HSO4 to K2CO3, ammonium potassium sulphate (NH4KSO4) forms in the temperature range of (157-260) °C; above 260 °C, potassium sulphate and ammonium sulphate are produced, with concurrent pyrolysis of ammonium sulphate to ammonium bisulphate and ammonia. Upon cooling, various double bisulphates form, depending on the ratio of potassium sulphate to ammonium bisulphate.

Spatiotemporal Variations and Emission Characteristics of CO2 Fluxes in Deep Large Reservoirs

Tue, 14 Jul 2026 00:00:00 GMT

Climate change driven by greenhouse gas emissions is a global priority. Deep large reservoirs represent significant potential emission sources, yet their greenhouse gas source-sink dynamics and driving mechanisms remain inadequately understood. This study targeted three representative deep reservoirs of the upper Yellow River (Longyangxia (LYX), Lijiaxia (LJX), and Liujiaxia (LJXX)). Comparative analysis of water column conditions during the stratification period (summer) and mixing period (winter) was conducted. We quantified the spatial distribution of dissolved partial pressure of CO2 (pCO2) and the water-air interface exchange flux (FCO₂), elucidating their spatiotemporal patterns and carbon source-sink attributes. Key findings include: (1) Concentration and Flux Characteristics: Mean pCO2 was 765.00 μatm in summer and 739.00 μatm in winter. Corresponding mean FCO₂ was 12.273 mmol/(m2·d) and 9.111 mmol/(m2·d). (2) Regulatory Mechanisms: Thermal stratification, climatic fluctuations, and reservoir-specific properties influenced pCO2 by modulating carbon cycling pathways and carbonate equilibria. (3) Source-Sink Nature: All three reservoirs exhibited significant spatial gradients and seasonal variations in pCO2 and FCO₂, collectively functioning as sources of the CO2. This study provides critical theoretical support for quantifying the carbon footprint of hydropower projects.

Spatiotemporal Evolution and Regional Differences of Urban Ecological Resilience in the Middle Yangtze River City Cluster

Tue, 14 Jul 2026 00:00:00 GMT

This study evaluates the spatio-temporal evolution and regional disparities of urban ecological resilience (UER) in the middle Yangtze River city cluster (MYR city cluster) from 2010 to 2022. Based on a “resistance-adaptability-restoration” framework, we construct a multi-indicator evaluation model and apply the entropy weight method to measure resilience levels. Spatial autocorrelation analysis (Moran’s I and LISA) identifies clustering patterns, while Markov chains and Dagum’s Gini coefficient reveal dynamic transitions and regional differences. Finally, the geodetector method is used to explore influencing factors. Results show that overall UER has improved, with restoration surpassing resistance and adaptability, though fluctuations remain. Spatially, high-value clusters are concentrated in the Poyang Lake area, while low-value clusters are located in the Wuhan City Circle, with evident “club convergence” and path dependence. Regional disparities in UER are narrowing, but intra-regional imbalance persists, especially in the Wuhan City Circle and Changzhutan City Cluster. Precipitation, topographic relief, and investment in pollution control emerge as the dominant drivers, with interactive effects stronger than single-factor influences. Policy recommendations include strengthening ecological compensation, tailoring resilience-building strategies to geographic conditions, and enhancing cross-regional governance. These findings offer direct scientific support for ecological engineering decisions and pollution-control planning in the MYR city cluster, thereby contributing to sustainable development and ecological management in the Yangtze River Economic Belt.

A Highly Sensitive Detection Method for Five Organophosphorus Pesticide Residues in Tea Based on the Combined Technology of SPME-LC-MS/MS

Tue, 14 Jul 2026 00:00:00 GMT

Excessive pesticide residues seriously threaten the safety of agricultural products and the health of consumers. As a widely consumed beverage, the monitoring of pesticide residues in tea is of vital importance. To improve the sensitivity and accuracy of organophosphorus pesticide residue detection in tea, an integrated method combining solid-phase microextraction pretreatment and high-performance liquid chromatography-tandem mass spectrometry analysis was researched and developed. The research optimised the sample pretreatment process. Tea powder was extracted by vortex using a mixed solvent of ethyl acetate and n-hexane with a volume ratio of 9 : 1. The isotope internal standard method was used for quantification. By optimising the chromatographic conditions and mass spectrometry parameters, the detection methods for five target pesticides were established, and the linear range, quantification limit, accuracy and precision of the methods were systematically evaluated. The results show that the linear relationship of the five organophosphorus pesticides is good within the range of (5.0 - 500.0) ng/mL, and the quantitative limit of the method is extremely low, ranging from 0.0011 mg/kg to 0.0026 mg/kg. The correlation coefficients of the standard curves were all greater than 0.99, and the average recovery rates ranged from 72 % to 109 %. The results show that the pretreatment of the research method is simple, rapid and has low solvent consumption, effectively improving the detection efficiency and accuracy. The research provides efficient and reliable analytical means for ensuring the quality and safety of tea, safeguarding consumers’ health and promoting the sustainable development of the tea industry.

Machine Learning Analysis of Coastal Water Pollution in China: Drivers and Complex Relationships

Sat, 18 Apr 2026 00:00:00 GMT

This study investigates the nonlinear effects of socioeconomic, pollution control, and technological innovation factors on seawater pollution using a Generalised Additive Model (GAM) and panel data from 11 Chinese coastal provinces (2018-2023). Key findings indicate: (1) Chemical Oxygen Demand Emissions (CODE) from direct marine discharge sources showed a temporal pattern of “initial increase, subsequent decrease, and gradual stabilisation”, with significant spatial heterogeneity - Zhejiang recorded the highest emissions, Tianjin the lowest; (2) GAM revealed significant nonlinear relationships between most variables and CODE; (3) Factor impacts exhibited distinct range-dependent characteristics. These findings provide a scientific basis for identifying key pollution sources and formulating differentiated control strategies, leading to targeted policy recommendations.