. This study proposes a novel mathematical model for designing a resilient and eco-friendly supply chain network under uncertainty. The proposed model applies the life cycle assessment (LCA) methodology and the cap-and-trade mechanism to decrease carbon emissions. To address demand uncertainty, the chance-constrained programming (CCP) method is utilized. The proposed model is validated by solving thirty-three test problems and comparing the results with a non-resilient model without pandemic considerations. Results demonstrate that the total costs of the resilient model during the pandemic exceed those of the non-resilient model, emphasizing the necessity for decision-makers to balance the benefits of a resilient supply chain network with the associated costs. Furthermore, total emissions of the resilient model are lower, highlighting the environmental advantages of resilience. Comparing circular and linear models, the circular supply chains represent an 8.8% cost reduction and a 12.1% emission reduction that is attributed to the recycling of returned items.
Optical fiber surface-enhanced Raman spectroscopy (SERS) probes offer a rapid and highly sensitive approach of pharmaceuticals from environmental sources. SERS hot spots are recognized to occur when two noble nanoparticles are brought close enough to each other. However, controlling the gap size between nanoparticles on the fiber end faces at the nanoscale remains a great challenge without expensive and bulky instruments. Here, we report a precisely and simply regulated method that employs laser-controlled capture of Polyvinylpyrrolidone-shaped silver particles to generate silver clusters with nanoscale dimensions and gaps on the fiber end faces. The produced SERS probes can detect Rhodamine B solutions down to concentrations of 10(-14) M, exhibiting excellent detection sensitivity and well reproducibility. The outstanding performance of these fiber SERS probes enables label-free monitoring of low concentrations at 0.01 mu g/mL of antibiotics in deionized water, and 0.1 mu g/mL of antibiotics in tap water and river water without complex pretreatment processes. The laser-induced SERS probe boasts rapid response, highly specific recognition, and cost-effective operation in the realm of environmental monitoring, offering an innovative solution for the efficient screening of trace antibiotic contamination.
Implementing zoned management for riparian buffer zones to control pollution is essential. Due to the inherent dynamic variability in pollution monitoring, the critical biogenic elements (C, N and P) to map the macro-scale pollution patterns within permissible buffer widths through the lens of stable background cause as well matter. In this study, an interpretable stepwise cluster analysis model was proposed for the biogenic element classification mapped by riparian buffer characteristics. The model could robustly identify the prior information from the buffer zones in multi-scale characteristics and effectively mitigate classification errors and bolster the credibility of the classification results. Pearl River Basin in China with 60 water-soil monitoring stations was taken as the study area for deeply searching landscape pollutants control schemes in riparian buffer zones. As data collection advances that will dynamically update classification value estimates, a more adaptive strategy to riparian buffer zone management can be achieved. The landscape of structure, connectivity and climate were leveraged in riparian buffers seek to reveal the hereditarian relationships behind the partitioning of the biogenic elements. The results indicated that the biogenic element classification could be mapped by riparian buffer characteristics with the optimal partition level of alpha in the interpretable stepwise cluster analysis model, and the results are valuable for supporting zoning adjustment of controllable characteristics on the existing riparian buffer management strategies.
This study compares two chemical transport models: the EMEP MSC-W (EMEP4PL) model at 4 km & times; 4 km resolution and the downscaling uEMEP model at 1 km & times; 1 km, 500 m & times; 500 m, and 100 m & times; 100 m, focusing on Poland. Daily PM2.5, NO2, and O-3 concentrations for 2022 were analyzed. The uEMEP model (1 km & times; 1 km) demonstrated significant performance improvements, particularly at over 80% of PM2.5 stations and 87% of NO2 stations, compared to EMEP4PL. The highest accuracy was achieved at 500 m & times; 500 m resolution. The uEMEP model outperformed EMEP4PL, with notable improvements at rural stations and weaker performance at urban traffic sites. Seasonal analysis revealed challenges in the summer months. The uEMEP model showed better accuracy, particularly for PM2.5 and NO2, both for concentrations above and below WHO recommendations. These findings underscore the importance of highresolution modeling for improving air quality predictions.
In today's saturated market, chemical-based products often harm human health and the environment, while carbon emissions from production pose a significant challenge to sustainable manufacturing. Simultaneously, companies face competitive pressure to attract consumers through various offers. Against this backdrop, this study develops an optimal policy for an imperfect production system of green products, incorporating emission reduction, replacement, and rework strategies. Motivated by realistic business practices, the model treats an item's greening index as a time-dependent function. It also considers that the replacement period for faulty items has a non-linear impact on the system's total revenue. The model is subsequently analysed under two distinct cases based on the emission reduction policy applied. The time-dependent greening index presents a major challenge, as it reduces the average profit maximisation task to two distinct singular control problems. Due to the high non-linearity of these profit functions, six well-established metaheuristic algorithms are applied to solve the problems numerically. The key findings indicate that emission reduction leads to higher profitability. Furthermore, the greening index is shown to positively influence pricing while inversely affecting the optimal replacement duration. Finally, a sensitivity analysis provides several valuable managerial insights.
. The physical basis of grain size in polar ice crystals as a climate proxy has remained largely elusive. Here, this work investigates the influence of temperature, specifically between -30 and -3 degrees C, on grain evolution in firn samples from Summit, Greenland, simulating conditions encountered during the transport and storage of ice core samples. Utilizing 2-D optical micrographs, 3-D X-ray micro-computed tomography, and grain development models, the research reveals negligible variation in ice crystal grain size. Notably, the ability of these grains to return to their original size at a specific temperature, despite subsequent temperature changes, is referred to as the memorability of grain size on temperature (MoGSoT). This phenomenon can be attributed to the reduction in grain size caused by temperature gradient metamorphisms and the screw-step growth observed under isothermal conditions. In addition, sinusoidal signals incorporating six composite frequencies were developed to model temperature variations related to grain size, elucidating periodic climate cycles spanning multi-decadal, multi-centennial, and ten-millennial periods. Beyond establishing MoGSoT, which highlights the fundamental physics of polar ice grain size and its role in paleoclimate reconstruction, this work also emphasizes the intricate interactions between external factors, e.g., astronomical, orbital, solar, and planetary influences, and the internal dynamics of the climate system, including ocean-atmosphere oscillations, thereby offering new perspectives on the origins of climate change, whether anthropogenic or natural.
The solar CCHP system faces various uncertainties related to user energy demand, equipment energy output, and system energy provision, which are influenced by multiple factors. These uncertainties disrupt the balance between energy supply and demand and are exacerbated by climate change. Therefore, estimating extreme levels of user demand and assessing the performance of energy supply equipment under multiple uncertainties is critical to designing an appropriate energy supply scheme for solar CCHP system. This article innovatively integrates regional climate simulation, user demand prediction, equipment simulation, uncertainty identification and characterization, and operation optimization into a comprehensive framework, resulting in a combined uncertain operation optimization model for solar CCHP systems under climate change. Compared with conventional operation optimization models, the proposed model not only addresses oversimplification in traditional user demand prediction and equipment output simulation but also avoids the potential pitfalls of excessive reliance on subjective judgment while characterizing uncertain variables. The operation strategy generated by this model highlights the paradoxical relationship between system economic performance and energy supply reliability, emphasizing the significance of establishing and solving this dynamic and integrated optimization model under multiple uncertainties. In the context of global warming, this optimal energy distribution strategy effectively prevents cooling energy shortage in summer and heating energy oversupply in winter during extreme weather conditions. This proposed combined approach provides valuable insight and guidance for configuring and operating integrated energy systems in other regions worldwide.
This study aimed to refine the estimation of plant available water (PAW) by integrating satellite-derived data with limited soil moisture measurements in Millmeran, Queensland, Australia. The study focused on a paddock with a crop rotation of sorghum (summer) followed by barley (winter). MODIS 500m leaf area index (LAI) and the fraction of photosynthetically active radiation (fPAR) were used as inputs in two optional vegetation modules (Cover-based and LAI) of the HowLeaky soil water balance model. The model was calibrated and validated using MODIS 500m Actual Evapotranspiration (AET) data and PAW computed from limited soil moisture measurements at 20 physical locations measured on two occasions. Six testing candidate models were built for every pixel within the paddock by combining two inputs (Cover and LAI) and three output options (AET only, AET+PAW, and PAW only). Sensitivity analysis indicated that fPAR and residue cover are crucial in the Cover module, while 14 parameters, including the radiation use efficiency (rue), are important in the LAI module. Models integrating both ET and PAW enhanced prediction accuracy. Specifically, the InCover-OutPAW [NSE (calib) = 0.848; AICc = 68] and InCover-OutPAWET [NSE (calib) = 0.903; AICc = 59] excelled in predicting PAW, with the InLAI-OutPAWET showing the best overall performance [NSE (calib) = 0.913; AICc = 54]. Using only MODIS AET for calibration and validation failed to predict PAW profiles effectively in cover module-based adaptation, whereas a satisfactory level of performance could be observed in LAI-based adaptation, subject to accurate characterization of the LAI generic parameters. Integrating PAW as an observed variable significantly improved model accuracy, especially in capturing PAW variability post-rainfall. The study found a trade-off between precision and complexity, with models incorporating PAW measurements demonstrating improved prediction accuracy in a subhumid tropical setting of Australia.
Agricultural non-point source pollution (NPSP) has become a global concern requiring urgent attention, and high fertilizer application to cash crops often leads to more serious NPSP. We quantified the control effect of best management practices (BMPs) on total nitrogen (TN) and total phosphorus (TP) pollution in the Dongtiaoxi Watershed, with an emphasis on identifying critical source areas (CSAs), using the Soil Water Assessment Tool (SWAT) model. Although CSAs accounted for 11.78% of the whole watershed, they contributed to 23.43 and 21.72% of the TN and TP loads, respectively. In agricultural systems, planting cash crops is a key factor in the emergence of CSAs and NPSP; thus, it is necessary to improve the management of cash crops. Further, combined BMPs outperformed individual measures, achieving a 25.51% reduction in TN at the watershed scale. At the sub-watershed level (e.g., sub-watershed 27), combined BMPs reduced TN and TP by 50.11 and 20.00%, respectively. There are a large number of cash crops such as commercial bamboo forests in the study area that have been fertilized but were previously regarded as forest land. This can also lead to NPSP but may have been ignored in the past. Overall, our study classifies cash crops as agricultural land and improves the reliability of the results. The findings not only help to improve the water quality from the Taihu Basin but also provide technical support and a theoretical basis for government departments to improve the water environment.
. Two important policies have been discussed as ways to reduce dependency on fossil fuels: the promotion of renewable energy and the expansion of information and communication technology (ICT). Technological advancement reduces the cost of renewable energy, making the production of renewable energy more competitive. In this study, the nonlinear autoregressive distributed lag method is used to investigate the asymmetric impact of ICT on greenhouse gas emissions and renewable energy consumption, along with role of urban primacy in renewable energy use and reduction of greenhouse gas emissions. Wavelet coherence analysis was also used to explore the lead-lag relationship for the entire sample, which consisted of four panel groups (based on similar economic dynamics) of European countries. The results show that ICT penetration has a significant asymmetric effect on renewable energy in the short term in all European regions. However, in the long run, this asymmetric effect is significant only in the southern region. The results imply that, in the short term, increases the use of ICT decrease greenhouse gas emissions in the western, southern and northern regions of Europe. Overall, the impact of ICT use on greenhouse gas emissions is greatest in the western region, followed by the southern and northern regions, indicating that European regions have varying circumstances, challenges and capacities to address climate change mitigation and achieve sustainable development. Effective decision-making and comprehensive strategies for resilient urban development which hold urban governments and businesses more accountable will play a crucial role in dealing with climate change.
This study employs density functional theory (DFT) to investigate the reaction mechanisms of NH3 selective catalytic reduction of NO on Ni-doped CeVO4 (200) surfaces, as well as the mechanism of sulfur poisoning, to understand the impact of Ni doping on the catalytic performance and sulfur resistance of CeVO4 catalysts. We systematically examined the NH3 -SCR mechanism on Ni-CeVO4 , including two consecutive NO reduction pathways. Simulations of the adsorption/dissociation behavior of NH3 , O-2, and NO molecules on the catalyst, both before and after doping were conducted. The results indicate that Ni doping significantly alters the electronic charge properties of the CeVO4 surface, markedly improving the adsorption characteristics of gas molecules and considerably lowering the dissociation energy barrier. In both CeVO4 and Ni-CeVO4 catalyst systems, the reaction follows the Eley-Rideal mechanism. The Ni doping reduces the activation barriers for the formation of intermediates NH2 and NH2NO, with NH2 formation identified as the rate-determining step. Additionally, the adsorption of SO(2 )and H2O and the dissociation pathways of ammonium sulfate before and after doping were studied. It was found that Ni doping considerably reduces the adsorption of H2O and SO(2 )and lowers the dissociation barrier of ammonium sulfate, thereby enhancing the catalyst's water and sulfur resistance.
. In this study, an inexact copula-based stochastic fractional model (ICSFP) approach is developed for supporting emergency evacuation management in response to nuclear power plant accidents. Based on an integration interval mathematical programming (IPP), fractional programming (FP) and joint chance-constraint programming (JCCP), ICSFP can systematically reflect various complexities in emergency evacuation systems such as multiple uncertainties and controversial targets. Specifically, the copula approach is introduced into ICSFP to reflect nonlinear dependence among random variables and further characterize the interaction among violations of single factors on the desired evacuation schemes and the associated risk levels. To demonstrate the effectiveness of the developed approach, ICSFP is applied to support emergency evacuation management subject to nuclear power plant accidents. The results indicate that the interactions among the traffic flows would pose apparent impacts on the desired evacuation plans, especially for the demand conditions (i.e., lower bound of unit cost). Comparison among the developed ICSFP model and traditional evacuation models (interval evacuation models and interval fractional evacuation model) suggests that ICSFP is advantageous in balancing conflicting objectives and reflecting interactions among dependent random variables to make trade-offs among system risks and unit costs.
The effects are assessed of climate change on the temperature, snow cover and precipitation on the Wapta Icefield, located in the Rocky Mountains between Alberta and British Columbia in western Canada. Using remote sensing data and regression analyses, the study focuses on spatial changes of the snow cover area during the warm months of June, July, and August from 1990 to 2021. Land-sat 5 and 8 satellite imagery are used to analyze environmental changes in the study region using Google Earth Engine (GEE) coding on the GEE platform. Normalized Difference Snow Index (NDSI), with a threshold of 0.4, and Normalized Difference Vegetation Index (NDVI) indices are used to detect snow-covered areas and identify vegetation areas, respectively. In addition, ERA5 and Global Precipitation Measurement (GPM) data are used to study trends in air temperature and precipitation changes. Examination of air temperature changes using ERA5 data from 1988 similar to 2019 shows an increase of 0.9 degrees C in average temperature for the area at the 80% significance level. The total precipitation in this region using (GPM) data from 2001 to 2021 shows a decrease in trends of precipitation. The results of the changes in snow cover in the warm months of the year within the period of 1990 to 2021 show a decrease of 45% with a significance level of 95%. Furthermore, the changes in the extent of vegetation during this same period show the extent of vegetation in the region has increased by 84% with a significance level of 95% and a-0.6 coefficient, indicating a relatively strong negative correlation between the snow cover and the vegetation cover, indicating an expansion of vegetation in the region with the continued loss of glacial ice.
Environmental management increasingly relies on rapid and precise information analysis to resolve critical environmental problems. This study evaluated the effectiveness of hyperparameter tuning and its impact on automatic environmental text classification performance using different Machine Learning (ML) classifiers and term-weighting schemes. Our results indicated that hyperparameter tuning generally enhanced classification performance, with the eXtreme Gradient Boosting (XGBoost) classifier showing the highest performance. The study also highlighted the trade-off between performance improvement and computational cost, i.e., enhanced classification accuracy at the expense of increased execution time. Notably, hyperparameter sensitivity varied among ML classifiers. For example, the Multinomial Naive Bayes classifier was less sensitive to hyperparameter tuning under certain term-weighting schemes. These findings provide new insights into the relationships between hyperparameter optimization, classification performance, and computational efficiency in environmental text classification. They offer valuable guidance for selecting and tuning classifiers to support better-informed decisions in environmental management.
Estuaries, such as the Pearl River Estuary (PRE), play a crucial role in hydrological exchange and material transport, serving as key zones for the discharge and accumulation of microplastics (MPs). Recent reports indicated that the PRE released approximately 66 tons of MPs annually into the South China Sea. While previous studies have examined the distribution of MPs in the PRE, the dynamic migration processes of MPs in the estuary, especially under changing environmental conditions, remain inadequately understood. To elucidate the migration and distribution characteristics of MPs under changing environmental conditions in PRE, the study established a MPs transport model integrated with the hydrodynamic module and Lagrangian particle tracking module. The established model accounted for the dynamic effects of runoff, sea level rise, and wind conditions, allowing for a more comprehensive simulation of MPs migration. The results demonstrated a high level of agreement between simulated and observed data, with R-2 values exceeding 0.95. The PRE exhibited marked seasonal variability in MPs distribution, with Shenzhen Bay (SZB) emerging as a persistent accumulation hotspot. This phenomenon stemmed from the synergistic effects of unique hydrodynamic constraints (flow velocities < 0.2 m/s) and intense anthropogenic pressures. Compared to adjacent regions, these conditions created a convergence-dominant regime that amplified MPs retention efficiency by 2.0 similar to 4.0 folds. Eight cases were designed to estimate the impacts of runoff, sea level rise, wind speed changes, and their interactions on the transport and distribution of MPs in the PRE in 2050 and 2070. These researches could highlight the complex interplay between hydrodynamic processes and climate change, underscoring the importance of considering multiple environmental factors when assessing MPs pollution in estuarine regions.
. Most developing countries face the dilemma of industrial development and environmental degradation. Cleaner production and effective government regulation have become key to environmental protection. However, the effectiveness of government regulation is challenged due to information asymmetry, etc., and with the development of digital economy, public supervision plays an increasingly important role. This paper constructed a tripartite evolutionary game model of local regulators, enterprises and the public. By analyzing the equilibrium points, it identified the key factors and influencing mechanisms among stakeholders in cleaner production regulation. The results show that public supervision primarily operates through the administrative intervention mechanism based on the penalty system and the reputation deterrent mechanism based on market constraints to incentivize a shift from 'collusion' to 'cooperation' among government and enterprises in the regulation process. Informal mechanisms such as 'hush money' and 'reporting rewards', which are common in reality, can have a crucial influence on the behavioral strategies of local regulators and firms.
Habitat quality is a key indicator of ecosystem services. However, current habitat quality assessment methods mainly depend on land-use types, which ignore the differences within the specific land-use type and have difficulty reflecting the actual situation of an ecosystem. Therefore, this study proposes an improved habitat quality assessment method that incorporates vegetation growth status by introducing the leaf area index (LAI). This method first uses the LAI to assess pixel-level habitat suitability and then incorporates threat indicators for refined habitat quality evaluation. Finally, the proposed method is used to assess habitat quality and its changes on the Qinghai-Tibet Plateau (QTP). The results show that the proposed method can effectively distinguish habitat suitability differences among pixels with the same land use type, enabling a more reasonable and precise evaluation of habitat quality. Habitat quality assessment on the QTP revealed that most regions improved between 2000 and 2020, except for urban areas, southeastern forests, and the Qiangtang region, where significant declines occurred. In particular, the Ruoergai Wetland, Qilian Mountains, Datong Beichuan River Source, and Yellow River Source exhibited greater improvements, with net habitat quality growth exceeding 30%. Furthermore, the proposed method has great potential for habitat quality assessment in other regions with various vegetation growth conditions, which will provide further support for environmental management.
Heavy metal ion wastewater is extremely harmful to human health and the environment, while the adsorption materials used in traditional adsorption methods such as starch, are easily hydrolyzed, resulting in secondary water pollution. To address this issue, acrylic polymers were grafted onto magnetic starch (St/Fe3O4-g-pAA, St/Fe3O4-g-pHEMA, and St/Fe3O4-g-pGMA) to enhance adsorption properties for Cu(II). Under optimal conditions (dosage of adsorbent was 0.3 mL, initial copper ion concentration of 20 mg/L, pH 7, and adsorption time of 160 min), St/Fe3O4-g-pAA exhibited the highest adsorption capacity of 145 mg/g. St/Fe3O4-g-pGMA maintained 95.7% of its initial adsorption capacity after eight cycles, demonstrating excellent stability and renewable performance. The adsorption properties of St/Fe3O4-g-pHEMA showed minimal dependence on pH value. The non-dimensional constant separation factors (R-L) for St/ Fe3O4, St/Fe3O4-g-pHEMA, St/Fe3O4-gAA, and St/Fe3O4-g-pGMA were 0.226, 0.210, 0.321, and 0.172, respectively, all falling within the range of 0 similar to 1, suggesting a favorable and easy adsorption process for Cu(II). Overall, the adsorption processes of all three modified magnetic starch materials followed the Langmuir isotherm model and were better described by the pseudo-second-order kinetic model.
Drought has threatened sustainability in terrestrial ecosystems. Although a series of indices have been proposed, they have largely overlooked the performance in characterizing regional or local drought, which is significant in acknowledging the impacts of drought on regional carbon cycling. To extract site-specific optimal drought index for vegetation in China, vegetation net primary production (NPP) estimated by Terrestrial Ecosystem Carbon Flux model, was compared with 11 worldwide-used indices, through sensitivity analysis and Generalizes Estimation Equation method. It was found that NPP in northern areas were significantly sensitive and correlated to 7-11 drought indices, whereas southern areas were sensitive to 1-4 indices. Among the drought indices, moisture index was highlighted as the best one, followed by standardized precipitation index, effective drought index, drought reconnaissance index, and standardized precipitation evapotranspiration index. Using optimal drought indices for local applications, the dynamics of drought and its impact on vegetation were quantified from 2000 to 2022. A general increase in vegetation NPP and decrease in drought were demonstrated in major northern and eastern areas. Moderately-extremely drought led to > 20% and even > 40% loss in NPP across northern and northwestern areas, much larger than the loss in southern areas. Moreover, forests were exposed to a mean loss of-20.1% and standard deviation of 13.2% in NPP, smaller than croplands and grasslands. These results contribute to a better understanding of drought dynamic and coupling of vegetation production, and guide for planning mitigations in drought-prone hotpots to ensure region's resilience.
To enhance the crop production and soil fertility, the application of biochar (B) and nitrogen (N) has been practiced for many years. However, the effects of combined biochar (B) and nitrogen (N) treatments on the soil methanogenic community, methane (CH4) emissions, and nitrous oxide (N2O) emissions in paddy fields are not well known. Herein, we investigated the effects of four different B rates (0, 10, 20, and 30 ton ha -1) combined with two N levels (low: 135 kg ha−1; high: 180 kg ha−1) on CH4 and N2O emissions from rice fields, as well as the composition of the soil methanogenic community. The study was conducted at two locations - Guangxi University in Nanning (GXU) and Yulin City - across different years. Results showed that in both locations, B (30 ton ha-1) combined with both N (135 and 80 kg ha-1) improved soil pH (23 ~ 29%), soil organic carbon (25 ~ 32%), total N (24 ~ 28%), available phosphorus (16 ~ 18%), available potassium (78 ~ 85%), soil microbial biomass carbon (122 ~ 129%), microbial biomass N (110 ~ 133%), and the grain yield of rice (20 ~ 30%) compared with sole N-treated plots. Furthermore, increases in the B rate (> 20 ton ha-1) under both low and high N reduced N2O emissions in both locations and all seasons. However, CH4 emissions and the abundance of methanogenic archaea were increased in high B rate (30 ton ha−1) treatments under both N levels. CH4 emissions were 15 ~ 17% higher in GXU and Yulin City in the 20 ~ 30 ton B ha−1 treatments compared with the sole N treatments. Rice grain yields were highest, CH4 and N2O emissions were lowest, and soil characteristics were enhanced in the 10 ton B ha−1 with low N fertilizer treatment. The results of our study suggested that the use of 10 ton ha−1 combined with 135 kg N ha−1 is the most efficient method for enhancing soil fertility and rice yields while maintaining environmental sustainability.
