Environmental Management Decisions Research Articles

Research on the mechanism and path of the coupling of digital technology and environmental regulation to promote urban green efficiency

Digital technology, renowned for its embeddedness and integration, revolutionizes governance ideas, institutions, and models. Based on the panel data of 285 cities in China from 2007 to 2021, a panel vector autoregressive model (PVaR) is built to reveal the interaction mechanism between digital technology and environmental regulation. The fixed-effects panel model explored the green impact of the coupling of digital technology and environmental regulation, considering the mediating role of industrial upgrading, factor allocation, and public participation. The results show that digital technology and environmental regulation promote each other. The coupling of digital technology and environmental regulation enhances urban green efficiency, especially in economically advanced and eastern regions. Industrial upgrading, factor allocation, and public participation mediate this effect. Therefore, government departments should build ecological civilization by improving environmental management decision-making, promoting the deep integration of digital technology and environmental regulation, and exploring a new path of urban green development.

Study of the Dynamic Adaptive Calculation Method for River Water Environmental Capacity

Controlling the total amount of river pollutant discharge is an important means of water resource protection and management, and it is also a necessary condition for ensuring the normal functioning of water areas. The total amount of pollutant discharge is closely related to the water environmental capacity (WEC). Shifting from the traditional method of calculating WEC to dynamic analyses and calculations, concerning practical applications, in this paper, a dynamic adaptive calculation method is proposed for the river WEC that considers the changes in adaptive demand and hydrological conditions. In this method, the dynamic WEC is represented by intervals based on dynamic changes in different spatial and temporal scales, various calculation methods, hydrological conditions, and parameters. According to the calculation results for the WEC, a variable interval was formed. Taking the Shaanxi section of the main stream of the Wei River as the research object, with the support of an integrated platform, the dynamic adaptive calculation of the WEC in the Shaanxi section of the Wei River was realized, and a corresponding simulation system was constructed. The verification results show that (1) the dynamic calculation of WEC can be realized by freely combining different model methods and calculation conditions; (2) the WEC is described using a variable interval, which has strong applicability and operability; and (3) the simulation system can quickly adapt to the changing needs of practical applications and provide managers with visual and credible decision support. The research results provide a theoretical basis for river water environment pollution prevention and environmental management decision-making and help in the high-quality development of the river basin.

A comprehensive review of soil organic carbon estimates: Integrating remote sensing and machine learning technologies

Abstract Purpose Accurately assessing soil organic carbon (SOC) content is vital for ecosystem services management and addressing global climate challenges. This study undertakes a comprehensive bibliometric analysis of global estimates for SOC using remote sensing (RS) and machine learning (ML) techniques. It showcases the historical growth and thematic evolution in SOC research, aiming to amplify the understanding of SOC estimation themes and provide scientific support for climate change adaptation and mitigation. Materials and Methods Employing extensive literature database analysis, bibliometric network analysis, and clustering techniques, the study reviews 1,761 articles on SOC estimation using RS technologies and 490 articles on SOC employing both RS and ML technologies. Results and Discussion The results indicate that satellite-based RS, particularly the Landsat series, is predominant for estimation of SOC and other associated studies, with North America, China, and Europe leading in evaluations with Africa is having low evaluations adopting RS technology. Trends in the research demonstrate an evolution from basic mapping to advanced topics such as carbon (C) sequestration, complex modeling, and big data utilization. Thematic clusters from co-occurrence analysis suggest the interplay between technology development, environmental surveys, soil properties, and climate dynamics. Conclusion The study highlights the synergy between RS and ML, with advanced ML techniques proving to be critical for accurate SOC estimation. These findings are crucial for comprehensive ecosystem SOC estimation, informed environmental management and strategic decision-making.

Chlorophyll-a and suspended matter variability in a data-scarce coastal-estuarine ecosystem

Analyzing the variability of chlorophyll-a (Chl-a) and total suspended matter (TSM) in estuarine and coastal environments is crucial for understanding ecosystem health, guiding environmental management decisions, and evaluating climate change impacts. Satellite remote sensing offers a powerful tool for this analysis due to its extensive spatial and temporal coverage. Although several algorithms exist for complex coastal and estuarine waters, long-term datasets such as GlobColor's Ocean Color (OC5) and neural network (NN) algorithms are frequently used for robust variability analysis. This study uses the GlobColor NN algorithm to investigate the seasonal and inter-annual variability of Chl-a and TSM in a data-scare region, namely the Meghna estuary in Bangladesh and its adjacent coastal fringe. The other algorithm (i.e. OC5), while offers the longest time series, cannot be used in this region due to the high number of invalid pixels. Therefore, this study examines different environmental factors (i.e. sea surface temperature (SST), photosynthetically active radiation (PAR), rainfall, zonal (ZWC) and meridional (MWC) wind components, and ocean currents) and climatic indices (i.e., El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD)) to understand their influence on the seasonal and inter-annual variability of Chl-a and TSM derived from the GlobColor NN algorithm. Empirical orthogonal function analysis identifies the seasonal signal as dominant in the study region. The seasonal cycle of Chl-a is influenced by factors including MWC, TSM, SST, and rainfall. In contrast, TSM seasonal variations are primarily driven by rainfall and MWC. Post-monsoon Chl-a inter-annual fluctuations are mainly linked to TSM inter-annual variability, with secondary influences from monsoon rainfall and the winter ENSO index. Inter-annual changes in TSM are primarily associated with the winter ENSO index and monsoon rainfall. This research elucidates the primary mechanisms influencing Chl-a and TSM variability in the Meghna estuary and its adjacent coast, thus advancing the understanding of the dynamics in the study region. The information obtained through this study is valuable for scientists, policymakers, and stakeholders involved in the sustainable management of the Meghna estuary and its coastal resources, particularly in the context of climate change.

Advances in Understanding the Toxicity of 4-Hydroxyphenylpyruvate Dioxygenase-Inhibiting Herbicides.

4-Hydroxyphenylpyruvate dioxygenase inhibiting herbicides (HIHs) represent a recent class (HRAC group 27) of herbicides that offer many advantages, such as broad-spectrum activity, crop selectivity, and low resistance rates. However, emerging studies have highlighted the potential toxicity of HIHs in the environment. This review aims to provide a comprehensive summary of the toxicity of HIHs toward nontarget organisms, including plants, microorganisms, animals, and humans. Furthermore, the present work discusses the ecological roles of these organisms in the environment and their significance in agriculture. By shedding light on the toxicity of HIHs, this study seeks to raise awareness among end users, including environmentalists, researchers, and farmers, regarding the potential ecological implications of these herbicides. Hopefully, this knowledge can contribute to informed decision-making and sustainable practices in green agriculture and environmental management.

TEMDI: A Temporal Enhanced Multisource Data Integration model for accurate PM2.5 concentration forecasting

Accurate forecasting of PM2.5 concentration is crucial for implementing effective protective measures and mitigating the adverse health impacts of air pollution. To address the complex spatial propagation dynamics and temporal variations of PM2.5, we developed the Temporal Enhanced Multisource Data Integration (TEMDI) model. This innovative approach combines spatial modeling by a Graph Neural Network (GNN) to capture the intricate spatial propagation patterns based on multi-source data fusion, and a novel Time Series Enhancement (TSE) module that includes Ensemble Empirical Mode Decomposition (EEMD), Gated Recurrent Units (GRUs), and a self-attention mechanism to adequately manage the time series’ short-term and long-term trends. Our results demonstrate TEMDI’s superior performance, achieving exceptionally high Probability of Detection (POD) rates of 96.15%, 80.28%, and 71.86% for forecast horizons of 3, 36, and 72 h, respectively. Furthermore, our feature importance analysis reveals that multi-scale features extracted by the EEMD component become increasingly crucial as the prediction horizon extends. The TEMDI model’s ability to provide accurate, reliable PM2.5 forecasts and its enhanced interpretability position it as a valuable tool for guiding environmental policy and management decisions to safeguard public health.

Application of Shannon Entropy in Assessing Changes in Precipitation Conditions and Temperature Based on Long-Term Sequences Using the Bootstrap Method

This study delves into the application of Shannon entropy to analyze the long-term variability in climate data, specifically focusing on precipitation and temperature. By employing data from 1901 to 2010 across 377 catchments worldwide, we investigated the dynamics of climate variables using the generalized extreme value (GEV) distribution and Shannon entropy measures. The methodology hinged on the robust bootstrap technique to accommodate the inherent uncertainties in climatic data, enhancing the reliability of our entropy estimates. Our analysis revealed significant trends in entropy values, suggesting variations in the unpredictability and complexity of climate behavior over the past century. These trends were critically assessed using non-parametric tests to discern the underlying patterns and potential shifts in climate extremes. The results underscore the profound implications of entropy trends in understanding climate variability and aiding the prediction of future climatic conditions. This research not only confirms the utility of Shannon entropy in climatological studies but also highlights its potential in enhancing our understanding of complex and chaotic climate systems. The study’s findings are vital for developing adaptive strategies in response to the evolving nature of climate extremes, thus contributing to more informed decision-making in environmental management and policy formulation.

Conflict Resolution through Indigenous Knowledge Systems

Abstract Indigenous knowledge is the local knowledge that is unique to a given culture or society. It is the basis for local decision-making in agriculture, medicine, food education, environmental management, craft skills, linguistics and conflict management. The study explored mengehacha, an indigenous system of conflict resolution among the Gumuz people in northwest Ethiopia, which aims to restore peace and harmony within the community when conflicts result from natural resource competition, exchange marriage, clan-based conflict and theft. Data was collected through semi-structured interviews, focus group discussions, non-participatory observation and document analysis, and analysed qualitatively. The indigenous mechanisms of conflict resolution were found to be relatively participatory and supplementary to the state justice system. It is recommended that indigenous systems of conflict management be supported and given equal treatment with the modern court system and that exchange marriage be curbed, since it is becoming a trigger for conflict eruption among the Gumuz.

A decision methodology for site-level ecosystem accounting

The United Nations System of Environmental-Economic Accounting Ecosystem Accounting (SEEA EA) framework is the international standard for ecosystem accounting. To date, application of SEEA EA has been predominantly at large scales, usually at landscape and national levels. However, many environmental management decisions are taken locally, in site-specific contexts. While the use of SEEA EA continues to develop at all scales, there is currently no widely endorsed methodology for employing SEEA EA at local scales, such as the site level. We present a methodology for developing site-level ecosystem accounts, describing the important decisions at each step of the process. We also provide two case studies that demonstrate the context-dependent nature of the decision-making process of ecosystem accounting at small scales. The two major challenges for site-level accounting are stakeholder engagement and data availability. As the use of SEEA EA continues to increase in policy and decision-making processes worldwide, there is a need for local-scale case studies that adapt this methodology across a broad range of contexts. Our case studies provide some of the first published examples of the application of SEEA EA at the site level and are intended to promote consistent implementation of ecosystem accounting across scales.

Trade-offs and Synergies between Economic and Environmental Cocoa Farm Management Decisions.

Optimizing sustainability among smallholder farms poses challenges due to inherent trade-offs. In the study of organic and conventional cocoa smallholder farming in Ghana, 398 farms are assessed using the Food and Agriculture Organsation of the United Nations (FAO) Sustainability Assessment of Food and Agriculture systems(SAFA) Guidelines and Sustainability Monitoring and Assessment Routine (SMART)-Farm Tool. Organic farming exhibited synergies in environmental aspects (e.g., soil quality, energy efficiency) and between biodiversity conservation and risk management. Conventional farming showed potential vulnerabilities, including trade-offs with long-range investments (e.g., chemical inputs) and species diversity. Both systems demand tailored approaches for short-term economic and environmental sustainability, aligning with community-wide long-term goals. To mitigate trade-offs in conventional farming, smallholders should adopt practices like material reuse, recycling, and recovery within their operations.

Mapping Terrestrial Conservation: Showcasing Model Assessment for Map Performance Evaluation

Habitat preservation, conservation, and protection of threatened species require careful site selection procedures. The presence of several site-selection models encourages model assessments to determine their usefulness and functionality for conservation purposes. Map model assessments are not conventionally conducted between site selection models to understand the nature of their decision-making strategies and prioritization. Therefore, the objective of this study was to determine the relationship between a recently published site selection model, the Safe Zone with MaxEnt, representing the Species Distribution Model, and the Forest Landscape Integrity Index, representing an intact forest habitat. As expected, there was considerable variation among the three models due to their different algorithms and themes of concern; nevertheless, these had some level of agreement in selecting potential areas for conservation. In addition, this study presented a convenient method for map data collection and the analysis required for a comprehensive understanding of the model agreements and relationships with other models. The nature of the decision making of the Safe Zone model and its potential utility for selecting Strict Protection Zones was presented. This study highlighted the intersection of geospatial analysis, conservation, and model evaluation. Finally, study emphasized the importance of informed decision-making in environmental planning and management through rigorous map assessment. The limitations of this study include the varied pixel resolutions of global and local maps, temporal predictions, and the fact that the models to which the SZ was compared are limited to models and maps available in the area of interest.

A simple and rapid method for measuring total free sulfides in marine sediments

AbstractThe quantitatively most important process by which organic matter in marine sediments is mineralized is performed by sulfate‐reducing bacteria, resulting in the accumulation of total dissolved (free) sulfide (S2− = H2S + HS− + S2−) in porewater. S2− is toxic to benthic animals and vascular plants and measurements serve as a proxy for the deleterious effects of organic enrichment on benthic habitat, biodiversity, and ecosystem function. Methodologies for measuring S2− in water have been pursued for at least a century, and standard approaches employ colorimetry (methylene blue and iodometric titration) and potentiometry. These standard methods require between 1 and 200 mL of porewater, which can be laborious to obtain. The ion‐selective electrode method is widely employed as a practical approach for sediment S2− analysis but lacks analytical robustness and is highly prone to measurement biases that misinform research and environmental management decisions. A technically simple method is described, based on direct UV spectrophotometry, for the near real‐time field analysis of small porewater samples. The procedure prevents known measurement biases associated with particulate sulfide interference, S2− volatilization and oxidation, and represents a practical approach for monitoring organic enrichment and classifying benthic ecological quality status. Porewater concentrations between 200 and 15,000 μmol L−1 can be measured and instrument calibration is highly stable. The method has the capacity to rapidly process and analyze sediment samples at low cost, which helps resolve the problem of chronic under‐sampling associated with the use of traditional S2− methods.

Sentinel-2 Perspective: Land Use and Land Cover Dynamics in East and West Karbi Anglong

This study delves into the dynamics of land use and land cover (LULC) in East and West Karbi Anglong using Sentinel-2 satellite imagery. In response to escalating environmental changes, remote sensing technologies, particularly Sentinel-2, offer crucial insights for sustainable land management. Through an analysis of LULC patterns, the study aims to inform evidence-based decisions for environmental conservation and resource management in the region. The present study highlights the utility of remote sensing in LULC assessments and acknowledges the necessity for customized approaches in regions with distinctive features such as Karbi Anglong. Methodologically, temporally stratified Sentinel-2 datasets were utilized, with meticulous pre-processing and accuracy assessments ensuring reliability. Results indicate notable shifts in LULC classes, including urban expansion and vegetation decline. Despite limitations, such as potential inaccuracies in data processing, the study emphasizes the importance of monitoring environmental changes and advocating for sustainable land management strategies.

High-resolution estimation of PM2.5 concentrations across China using multiple machine learning approaches and model fusion

Accurate estimation of fine particulate matter (PM2.5) concentrations can provide necessary data sources for epidemiological studies and environmental management decisions. However, the different sources and resolutions of satellite data and the limited number of monitoring stations decrease the accuracy of PM2.5 concentration estimation, which subsequently complicates the continuous estimation of high-resolution PM2.5. In this study, we evaluate and optimize five different machine learning methods, including cubist, random forest (RF), support vector machine (SVM), the generalized additive model (GAM), and Extreme Gradient Boosting (XGBoost), to perform spatial high-resolution (1 km) estimations of PM2.5 concentrations. Based on the optimization, two model fusion methods, i.e., the Bayesian model averaging and Granger–Ramanathan averaging algorithms, are applied and compared for estimating PM2.5 concentrations. Among the five single machine learning models, the RF model performed the best, with a coefficient of determination (R2) of 0.84, root mean square error (RMSE) of 5.09 μg/m3, and ratio of performance to interquartile distance (RPIQ) of 3.40. The cubist model ranks second in terms of performance (R2 = 0.82, RMSE = 5.37 μg/m3, and RPIQ = 3.23). The estimation accuracies of the five models decrease in the following order: RF > cubist > XGBoost > SVM > GAM. The fusion model performed better than the single model and reliably estimated the PM2.5 concentration (R2 = 0.86, RMSE = 5.04 μg/m3, and RPIQ = 3.44). The results show that the model fusion algorithms improved the overall performance of fine-level and long-term PM2.5 concentration estimation, and that the accuracies of the fusion models are significantly higher than those of the five machine learning models used individually. Fusion model is used to generate an annual PM2.5 concentration distribution map of China from 2017 to 2021. This study provides a reference for estimating PM2.5 concentrations and contributes to the formulation of targeted policies.

Implementation of the CREED approach for environmental assessments.

Environmental exposure data are a key component of chemical and ecological assessments, supporting and guiding environmental management decisions and regulations. Measures taken to protect the environment based on exposure data can have social and economic implications. Flawed information may lead to measures being taken in the wrong place or to important action not being taken. Although the advantages of harmonizing evaluation methods have been demonstrated for hazard information, no comparable approach is established for exposure data evaluation. The goal of Criteria for Reporting and Evaluating Exposure Datasets (CREED) is to improve the transparency and consistency with which exposure data are evaluated regarding usability in environmental assessments. Here, we describe the synthesis of the CREED process, and propose methods and tools to summarize and interpret the outcomes of the data usability evaluation in support of decision-making and communication. The CREED outcome includes a summary that reports any key gaps or shortcomings in the reliability (data quality) and relevance (fitness for purpose) of the data being considered. The approach has been implemented in a workbook template (provided asSupporting Information), for assessors to readily follow the workflow and create a report card for any given dataset. The report card communicates the outcome of the CREED evaluation and summarizes important dataset attributes, providing a concise reference pertaining to the dataset usability for a specified purpose and documenting data limitations that may restrict data use or increase environmental assessment uncertainty. The application of CREED is demonstrated through three case studies, which also were used during beta testing of the methodology. As experience with the CREED approach application develops, further improvements may be identified and incorporated into the framework. Such development is to be encouraged in the interest of better science and decision-making, and to make environmental monitoring and assessment more cost-effective. Integr Environ Assess Manag 2024;20:1019-1034. © 2024 SETAC.

Moving from ecological impacts to social vulnerability in data-scarce places

Managers need to trace social impacts and vulnerability caused by environmental change all the way to its driving forces to target key system components for intervention. However, most available scientific evidence deals with either the ecological impacts of direct drivers or the value of ecosystem benefits to people. Our matrix-based tool combines these types of evidence to make environmental management problems traceable through a structured yet flexible procedure. The tool consists of a series of matrices that sequentially link direct drivers of environmental change, landscapes, ecological conditions, benefits to people, and stakeholder types. Qualitative matrices result from the integration and synthesis of available evidence from literature reviews, and where data is scarce, these are used to elicit quantitative scores from expert opinion. Expert scoring of links and multiplication of matrices allow for estimating the impacts of each driver of environmental change on each stakeholder type and using this information as input to assess stakeholders’ vulnerability through impact-influence diagrams. Applying the tool to the Argentine Gran Chaco, a globally threatened ecoregion, yielded a transparent and reliable picture of this data-scarce place, with important management implications. Tracing stakeholder impacts back to direct drivers confirmed that further encroachment of cleared areas around indigenous lands will increase the vulnerability of this social group. Also, assessing confidence levels for every social-ecological link suggested that incentivizing peasant farmers to restore natural forage supply represents a management opportunity to reverse degradation. Our tool makes interdisciplinary frameworks of linked ecological and social systems operational so managers can use the best available knowledge of a place and account for uncertainty to make environmental management decisions.

Histopathological effects of long-term exposure to realistic concentrations of cadmium in the hepatopancreas of Sparus aurata juveniles

In recent decades, cadmium has emerged as an environmental stressor in aquatic ecosystems due to its persistence and toxicity. It can enter water bodies from various natural and anthropogenic sources and, once introduced into aquatic systems, can accumulate in sediments and biota, leading to bioaccumulation and biomagnification in the food chain. For this reason, the effects of cadmium on aquatic life remain an area of ongoing research and concern. In this paper, a multidisciplinary approach was used to assess the effects of long-term exposure to an environmental concentration on the hepatopancreas of farmed juveniles of sea bream, Sparus aurata. After determining metal uptake, metallothionein production was assessed to gain insight into the organism's defence response. The effects were also assessed by histological and ultrastructural analyses. The results indicate that cadmium accumulates in the hepatopancreas at significant concentrations, inducing structural and functional damage. Despite the parallel increase in metallothioneins, fibrosis, alterations in carbohydrate distribution and endocrine disruption were also observed. These effects would decrease animal fitness although it did not translate into high mortality or reduced growth. This could depend on the fact that the animals were farmed, protected from the pressure deriving from having to search for food or escape from predators. Not to be underestimated is the return to humans, as this species is edible. Understanding the behaviour of cadmium in aquatic systems, its effects at different trophic levels and the potential risks to human health from the consumption of contaminated seafood would therefore be essential for informed environmental management and policy decisions.

Biosensors for ocean acidification detection

Ocean acidification is a global environmental problem that significantly impacts Marine ecosystems and biodiversity. The traditional chemical analysis method has the problems of complex equipment and high cost in ocean acidification monitoring. In recent years, fluorescent protein biosensor technology, as an innovative monitoring method, has provided a new solution for the real-time detection of ocean acidification. Compared with traditional chemical analysis methods, fluorescent protein biosensors have the advantages of simple operation, high sensitivity and low cost. Current studies have demonstrated the potential of fluorescent protein biosensors for ocean acidification monitoring. The researchers designed a variety of fluorescent protein biosensors and conducted indoor and outdoor experimental validation. These results show that fluorescent protein biosensors can detect ocean acidification quickly and accurately and maintain stable performance under different environmental conditions. Further studies are needed to verify the consistency and reliability of fluorescent protein biosensors and traditional chemical analysis methods for ocean acidification monitoring. Future research directions include further improving the performance of the fluorescent protein biosensor, increasing its sensitivity and stability, and verifying its application in real Marine environments. This will help establish a better monitoring network for ocean acidification and provide a reliable scientific basis for Marine environmental protection and management decisions. The development and application of fluorescent protein biosensor technology will provide important support and guidance for us to better understand the impact of ocean acidification.

Integration of environmental indicators into the management decision-making system

Climate change demands integrating environmental indicators into management decision-making systems, highlighting shifts towards environmental safety and stricter emissions regulations. This research develops a scheme for such integration, filling the gap in literature on the relationship between environmental factors and management decisions. Literature analysis and data examination through scientific and mathematical methods, including linear and multicriteria analysis, have led to a model emphasizing the importance of embedding environmental considerations into decision-making. The findings highlight the role of multi-criteria decisions in various conditions and establish a comprehensive model for the interplay between economic and environmental indicators, enhancing eco-efficiency in management practices. This approach introduces a novel method for incorporating environmental sustainability into management strategies, underscoring its significance for ongoing research and application in eco-efficient management decision-making.

Integration of Data and Predictive Models for the Evaluation of Air Quality and Noise in Urban Environments.

This work addresses assessing air quality and noise in urban environments by integrating predictive models and Internet of Things technologies. For this, a model generated heat maps for PM2.5 and noise levels, incorporating traffic data from open sources for precise contextualization. This approach reveals significant correlations between high pollutant/noise concentrations and their proximity to industrial zones and traffic routes. The predictive models, including convolutional neural networks and decision trees, demonstrated high accuracy in predicting pollution and noise levels, with correlation values such as R2 of 0.93 for PM2.5 and 0.90 for noise. These findings highlight the need to address environmental issues in urban planning comprehensively. Furthermore, the study suggests policies based on the quantitative results, such as implementing low-emission zones and promoting green spaces, to improve urban environmental management. This analysis offers a significant contribution to scientific understanding and practical applicability in the planning and management of urban environments, emphasizing the relevance of an integrated and data-driven approach to inform effective policy decisions in urban environmental management.