Effective Environmental Management Research Articles

Driving Factors of NOx Emissions in China: Insights from Spatial Regression Analysis

China’s rapid industrialization and urbanization have led to significant nitrogen oxide (NOx) emissions, contributing to severe atmospheric pollution. Understanding the driving factors behind these emissions is crucial for effective pollution control and environmental management. Therefore, this study is an attempt to provide insights into the influence of socioeconomic factors and explore spatial dependencies of NOx emissions in China in 2022 employing spatial regression models (SRMs). Among the SRMs considered, the spatial Durbin model (SDM) is identified as the most suitable for analyzing regional NOx emissions. The study highlights the importance of controlling electricity consumption and vehicle emissions for addressing air pollution in Chinese regions. Specifically, a one billion kilowatt-hour increase in electricity consumption leads to approximately 549.6 tons of NOx emissions, and an increase of 1000 vehicles in a region results in an average increase of 7113.4 tons of NOx emissions in the same region. Furthermore, per capita consumption expenditure (PCEXP) and research and development (R&D) expenditure exhibit negative direct and spillover impacts. Contrary to previous studies, this research finds that changes in urban population density do not have a significant direct or indirect effect on NOx emissions within the studied areas. Moreover, we conducted additional investigations to assess the effectiveness of government action plans in reducing NOx emissions. Specifically, we evaluated the impact of Phases 1 and 2 of the Clean Air Action Plan, launched in 2013 and 2018, respectively, on the socioeconomic drivers of NOx emissions. Therefore, the data were modeled for the years 2013 and 2017 and compared to the results obtained for 2022. The findings indicate that over the entire period (2013–2022), the emission controls mandated by the action plan resulted in significant reductions in the impact of many of the studied NOx drivers. In conclusion, based on the results, this study presents recommendations to mitigate NOx emissions.

Analysis of the UV filter Benzophenone-3 assimilation in Crossostrea gigas oysters post-exposure in a controlled environment by LC-MS/MS

Benzophenone-3 (BP-3), utilized as a UV filter in cosmetic products, is an emerging contaminant that constitutes a threat to natural resources and environmental health. This study investigated the assimilation of the UV filter BP-3 in Crassostrea gigas oysters collected in Florianópolis, Santa Catarina, Brazil. Lyophilized oyster tissue extracts were prepared using the QuEChERS method, and LC-MS/MS was employed to determine the BP-3 concentration in the samples. The method was applied to specimens intentionally exposed to two concentrations of the contaminant, for different periods of exposure (1 and 7 days). Samples from treatment 1 (T1) were exposed to a concentration of 1 μg L−1 of the BP-3 standard, and samples from treatment 2 (T2) were exposed to a concentration of 100 μg L−1 of the BP-3 standard. The results revealed rapid absorption of BP-3, with an increase of 126% for lower concentrations, reaching 1.13 μg of BP-3 per gram of oyster tissue, and 17% for higher concentrations, reaching 34.6 μg of BP-3 per gram of oyster tissue after 7 days. The presence of BP-3 even in samples not directly exposed to the contaminant indicates its widespread environmental distribution. The rapid bioaccumulation suggests the need to consider seasonal variations, such as increased tourism in the summer. The validated analytical method demonstrated efficacy in quantifying BP-3, providing an integrated approach for long-term monitoring of pollution levels and their dynamic variations over time. In addition, variation in BP-3 levels in the samples may be related to transport patterns influenced by tides and discharges from septic system, highlighting the need to improve wastewater treatment. These findings underscore the necessity for continuous biomonitoring and effective environmental management to safeguard the health of marine ecosystems and humans.

Environmental Management in SD Alam Al Fath’s Bilingual Program: A Case Study

In the modern era, bilingual competencies not only enhance job opportunities and international collaboration but are also critical for effective environmental management in educational programs. Thus, environmental management of bilingual programs must be thoroughly examined and well-implemented. This article analyzes the policies and implementation of environmental management, along with influencing factors, at SD Alam Al Fath in Jambi City. The research employs a qualitative approach with a case study method developed from an interpretive paradigm. Through interviews, observations, documentation, and interactive analysis using Miles Huberman's approach, it is concluded that the environmental management of the bilingual program at SD Alam Al Fath in Jambi City is implemented through a well-structured series of daily, weekly, monthly, and yearly activities. There are five main supporting factors for these activities and three hindering factors: time constraints in the school environment, the lack of some supporting facilities, and various levels of language proficiency. This study suggests that student categorization can improve the effectiveness of bilingual programs in schools and similar programs elsewhere. However, if there is no possibility, inductive and expository strategies with direct and cooperative learning approaches could be preferred to improve the Arabic and English language proficiency of heterogeneous learners.

Coupling and interaction mechanism between green urbanization and tourism competitiveness based an empirical study in the Yellow River Basin of China

Exploring the spatial coupling relationship and interaction mechanism between green urbanization (GU) and tourism competitiveness (TC) is of great significance for promoting urban sustainable development. However, the lack of research on the interaction mechanism between GU and TC limits the formulation of effective environmental management policy and urban planning. Taking 734 counties in the Yellow River Basin (YRB) as the study area, this paper analyzes the spatial coupling relationship between GU and TC on the basis of comprehensive evaluation of GU and TC. Then, the interactive mechanism between GU and TC is systematically discussed, and the synergistic development strategy of the two is proposed. The results show that the GU level presents a multicore circle structure, with provincial capitals, prefecture-level urban districts and economically developed counties in east-central regions as high-value centers. The TC at county scale presents a multi-center spatial structure. Additionally, there is a significant positive spatial coupling between GU and TC in the YRB. The analysis further reveals that green urbanization level, social progress, population development, infrastructure construction, economic development quality, and eco-environmental protection has a observably influence on TC. Tourism competitiveness, service competitiveness, location competitiveness, resource competitiveness, market competitiveness, environmental influence, and talent competitiveness has a observably influence on GU. TC can promote GU, and the improvement of green urbanization level can support the development of tourism competitiveness. According to the spatial zoning method, 734 counties are divided into 6 categories, and the coordinated development strategy of GU and TC for each type of district is proposed.

Unveiling the eutrophication crisis: 20 years of nutrient development in Zhanjiang Bay, China

Coastal eutrophication is a major issue of marine pollution. The main factors controlling eutrophication must be identified to ensure effective marine environmental management according to the respective local conditions. Zhanjiang Bay (ZJB), located northwest of the South China Sea, is a semi-closed bay influenced by complex water flows and the development of surrounding cities. In this study, we investigated the development of nutrient concentrations and compositions in ZJB seawater over the past 20 years and the factors influencing eutrophication based on several field investigations from 2006 to 2022 and historical data. High concentrations of dissolved inorganic phosphorus (DIP) and dissolved inorganic nitrogen (DIN) were the main contributors to the severe long-term eutrophication in ZJB; however, light eutrophication was observed in the outer bay, primarily caused by chemical oxygen demand (COD) and DIP. The primary sources of COD and nutrients were riverine freshwater, sewage outfalls, mariculture and domestic effluents carried by rivers. Tidal effects diluted the nutrient concentrations in the bay with seawater from the outer bay, thereby playing a key role in nutrient redistribution. The DIN: DIP ratio of ZJB showed long-term nitrogen restriction and excess phosphorus, primarily owing to mariculture activities. Marine undertakings can exert various impacts on water quality. Eliminating illegal aquaculture and launching aquaculture tailwater treatment can improve water quality, whereas practices such as channel dredging may worsen it. This study demonstrates the intricate dynamics of the ZJB ecosystem and offers valuable insights for effective environmental management and conservation efforts.

Development of Earth Gravity Models in A Sustainable Environment: An Overview of Nigeria’s Ecosystem

The evolution of Earth gravity models in Nigeria's ecological context is essential for advancing sustainable environmental stewardship. These models serve as crucial tools to understand the geological intricacies and environmental dynamics across Nigeria's diverse ecosystems. This article obtained data from previous existing data to emphasizes the significance of sustainable development, technological innovation, and the integration of sustainability principles into Earth gravity modeling efforts. The gravitational variations observed across Nigeria's ecosystems highlight the diverse geological features influencing gravitational anomalies. For instance, forests with dense vegetation exhibit gravitational variations ranging from -5.2 to -2.8 mGal, while coastal areas experiencing erosion and sea level rise show variations from -1.8 to 1.0 mGal. The article explores both advancements and challenges in developing Earth gravity models within Nigeria's unique environmental landscape, emphasizing the intersection of technology and sustainability. By leveraging advanced technologies and interdisciplinary methodologies, Earth gravity models contribute significantly to understanding the gravitational forces shaping the planet's surface and subsurface. Understanding the methodology behind Earth gravity models in a sustainable environment is crucial for effective environmental management and resource conservation, providing valuable tools for preserving Nigeria's diverse ecosystems for future generations. Through this integrated approach, Earth gravity models pave the way for enhanced environmental monitoring, land-use planning, and conservation strategies tailored to Nigeria's specific ecological challenges and opportunities.

Unravelling the Link Between Bilateral Investment Treaties and Environmental Sustainability in Sub-Saharan African Countries

This study investigates the practical relationship between foreign direct investments (FDI), bilateral investment treaties (BITs), income, energy consumption and CO2 emissions in sub-Saharan African (SSA) countries between 2000 and 2020. To address the challenges of heterogeneity, non-stationarity and cross-sectional dependence, we employed the dynamic common-correlated effects estimation method introduced by Chudik and Pesaran. This methodology provides reliable results for panel datasets of significant to moderate size. The results indicate a direct connection between FDI and CO2 emissions, providing evidence for the pollution haven theory. Notably, BITs also have a favourable and substantial impact on environmental degradation in SSA countries. These findings emphasise the need for effective environmental regulatory frameworks and management techniques. Achieving sustainable growth and economic development in SSA countries requires prioritising the adoption and implementation of carbon reduction strategies. SSA countries can attain balanced growth between economic progress and environmental preservation by reducing the negative effects of FDI inflows and BITs on environmental sustainability. This study expands the current knowledge base by providing insights into the complex relationship between FDI inflows, BITs, income, energy consumption and CO2 emissions in SSA countries. The findings offer valuable guidance to policymakers and stakeholders in formulating and executing effective environmental policies and regulations to promote sustainable development in the area.

Influence of large wood dynamics on flow and channel morphology in a forest stream

The riparian zone is a dynamic ecosystem formed by the interaction of aquatic and terrestrial components of rivers and serves as a source of the large wood (LW). We aimed to understand the LW dynamics and the LW influence on river morphology, sediment distribution, and flow velocity within three reaches of a low-order stream. The study was performed in a segment (770 m in length) of the Perdizes stream, located in Aparados da Serra National Park, southern Brazil. We framed the study by focusing three issues: i) the processes explaining the LW dynamics in the Araucaria Forest biome; ii) the effects of LW on the flow velocity along the study reaches, and iii) the presence of LW affecting the sediment longitudinal connectivity. The methodology consisted of: (i) collection of hydrological data (rainfall, discharge, and streamflow velocity), (ii) Perdizes stream characterization by topo-bathymetric survey and river-flow simulation with HEC-RAS 5.0.7 1D. and (iii) detailed field surveys of LW and bed sediments. The LW descriptions were performed at four moments (July 10, 2019; November 19, 2019; July 24, 2020; and December 18, 2020). Abundant LW, mainly from araucaria (Araucaria angustifolia (Bertol.) Kuntze), was observed before meanders. A notable reduction in LW volume and an increased proportion of araucaria, compared to deciduous trees, were observed within the channel. Mass balance analysis indicated fluctuations in riparian vegetation biomass, emphasizing the connectivity between vegetation and the river. Streamflow velocity decreased in LW presence, from occurrence to the margin. Bed sediment diameters were consistently smaller in cross-sections with LW presence. The accumulation of smaller sediments with LW indicated partial downstream sediment disconnectivity. HEC-RAS simulations confirmed increased LW deposition in lower velocity zones. This understanding of fluvial dynamics contributes to effective natural environment management, especially in the Araucaria Forest context.

Predicting the dynamics of per- and polyfluoroalkyl substances in coastal regions of Africa: vulnerability index and adverse ecological pathways from remote-sensed variables.

This study aimed to predict the dynamics of per- and polyfluoroalkyl substance (PFAS) contamination and ecological vulnerability within coastal regions of Africa utilizing time-averaged remote-sensed data patterns from 2020 to 2023. The analysis identified PFAS contamination hotspots along the coast of Africa, particularly in western Africa around Nigeria and in areas spanning Equatorial Guinea and Guinea-Bissau, with risk influenced by eastward wind patterns, overland runoff, and elevated aerosol optical depth (AOD) values. Regional trends indicated that variations in solar energy absorption and surface air temperature could influence PFAS dynamics in North Africa, South Africa, East Africa, and West Africa. In North Africa, intermediate overland runoff and lower sea-surface temperatures were observed. In South Africa, there were intermediate runoff levels and warmer sea-surface temperatures. East Africa experienced intermediate runoff as well. In West Africa, there was increased susceptibility to high overland runoff and aerosol-related PFAS contamination. From the weighted vulnerability index, significant disparities in environmental conditions across African coastal regions revealed that North Africa had relatively lower vulnerability, while West Africa had the highest susceptibility to per- and polyfluoroalkyl substance (PFAS) contamination. This study emphasizes the necessity for region-specific vulnerability index models and targeted mitigation strategies to address diverse ecological and health risks from PFAS contamination along the African coast. Regional and international collaboration, spearheaded by organizations such as the AU and ECOWAS, is essential, with tailored policies aligned with the SDGs, Agenda 2063, and NEPAD crucial for effective environmental management, urging policymakers to prioritize cooperation and resource sharing for comprehensive sustainability goals.

Assessing The Impact of Organizational Context on Learning Barrier Formation in Lima’s Private Universities

This research focuses on exploring the relationship between organizational learning barriers and organizational context in Private Universities in Lima, with the primary aim of elucidating how these elements interact and affect the learning dynamics within these institutions. Adopting a correlational cross-sectional non-experimental approach, a specific survey was designed and validated for application in the Peruvian context, gathering responses from 384 collaborators. The findings reveal that the most frequent barriers related to thought-action coherence highlight concerns about authenticity and sincerity in discussions, as well as a significant discrepancy between public and private opinions of workers. On the other hand, in the realm of organizational context, the critical factors identified include employees' perceptions of replaceability, fear of evaluations, deficiencies in communication channels, and the predominance of opinions from those with greater power. The conclusions point to the need to address these barriers to foster a more conducive environment for organizational learning, suggesting that effective knowledge and organizational environment management are fundamental for institutional development and adaptability.

WSN-Based Data Acquisition System for Collecting Environmental Pollution Factors for Green City

Implementation with a portable, intelligent weather station is to upgrade the system for environmental management and monitoring. This gadget measures and assesses meteorological and air quality data by real-time implementation through the Internet of Things technology to provide important environmental condition insights. The monitoring station's hardware parts include the ESP32 board, MQ2, MQ3, and MQ135 sensors for gas detection. Such an intelligent weather monitoring station can easily be deployed in different environments due to its portability and versatility. This small station size and its wireless link will enable effective data collection and transfer and integration into other systems of the present day. The existing environmental monitoring system takes the temperature and humidity and measures ammonia, benzene, alcohol, smoke, and carbon monoxide. Therefore, data is brought to the monitoring station, where it communicates in real time for the users to review and analyse on the online platform Blynk. It enables users to easily make data presentations and create notifications and dashboards using a mobile app of Blynk and the web platform, enabling them to create interfaces that meet their satisfaction easily. Users may also upload the uploaded data to a web server for remote access and analysis, which eases scalability and flexibility. Portable intelligent weather monitoring station has significant areas of influence on applications like public health, urban planning, and environmental monitoring, among others. It thus provides decision-makers with information on pollution management and climate resilience. This provides a sustainable and resilient future; it is approachable since the interface used in the system is accessible, befitting the two settings: urban and rural. The Intelligent Portable Weather Monitoring Station provides great usefulness in the disclosure, development, innovation of technology, and practice in the quest for meeting challenges of the environment with the promotion of sustainability. This not only allows smarter and more sustainable city development with effective environmental monitoring and management that includes IoT technologies but can also allow flexible data transmission facilities.

Economic globalization-energy diversification nexus and implications for environmental management in China

Every nation on earth has the responsibility to implement effective environmental management measures for sustainable environmental quality. In doing so, this study scrutinizes the relationship between economic globalisation and energy diversification in the Chinese economy from 1995 to 2022 for designing and implanting policies for environmental management. It uses industrialization, foreign direct investment, foreign remittances, and information & communication technology as supplementary factors into augmented energy diversification demand function. This empirical analysis shows cointegration between the variables, with economic globalisation positively impacting energy diversification. Factors such as foreign direct investment, foreign remittances, and information & communication technology contribute to energy diversity. However, industrialization has an adverse relationship with energy diversification. The relationship forms an inverted-U shaped between economic globalization and energy diversification. Our causality analysis indicates that economic globalization positively causes energy diversification. This study also reveals a reciprocal and beneficial cause-and-effect association between foreign direct investment and energy diversification. Lastly, foreign remittances and information & communication technologies positively cause energy diversification.

Harnessing artificial intelligence‐driven industrial robotics for sustainability: Insights from leading green economies

AbstractIn 2023, global temperatures witnessed an alarming escalation, reaching an unprecedented 1.46°C above preindustrial levels, marking it as the hottest year on record. Simultaneously, atmospheric carbon dioxide surpassed 420 ppm, exceeding a stability maintained for over 6000 years by more than double. This troubling surge in CO2 intensifies global warming, leading to an increased frequency of extreme weather events and contributing to 24% of global deaths attributed to environmental concerns. These alarming environmental challenges demand urgent attention and the implementation of innovative policies. Responding to this imperative, the study examines the impact of artificial intelligence‐based industrial robotics (AIIR) and other control variables such as green energy, green finance, and green energy investment on CO2 emissions in economies supporting green initiatives, including Canada, Denmark, China, Japan, New Zealand, Norway, Sweden, and Switzerland. Using monthly data from 2008 to 2021 and a novel nonlinear autoregressive distributed lag approach, the results indicate that AIIR significantly reduces CO2 emissions in the sample economies. Additionally, green energy, green finance, and green energy investment also significantly decrease CO2 emissions. The study's outcomes bear policy implications for decision‐makers in the sampled economies, offering tangible insights for effective environmental management.

Toxicity and speciation of inorganic arsenics and their adverse effects on in vivo endpoints and oxidative stress in the marine medaka Oryzias melastigma

Here, we investigate the effects of acute and chronic exposure to arsenate (AsV) and arsenite (AsIII) in the marine medaka Oryzias melastigma. In vivo effects, biotransformation, and oxidative stress were studied in marine medaka exposed to the two inorganic arsenics for 4 or 28 days. An investigation of embryonic development revealed no effect on in vivo parameters, but the hatching rate increased in the group exposed to AsIII. Exposure to AsIII also caused the greatest accumulation of arsenic in medaka. For acute exposure, the ratio of AsV to AsIII was higher than that of chronic exposure, indicating that bioaccumulation of inorganic arsenic can induce oxidative stress. The largest increase in oxidative stress was observed following acute exposure to AsIII, but no significant degree of oxidative stress was induced by chronic exposure. During acute exposure to AsV, the increase in the enzymatic activity of glutathione-S-transferase (GST) was twice as high compared with exposure to AsIII, suggesting that GST plays an important role in the initial detoxification process. In addition, an RNA-seq–based ingenuity pathway analysis revealed that acute exposure to AsIII may be related to cell-cycle progression. A network analysis using differentially expressed genes also revealed a potential link between the generation of inflammatory cytokines and oxidative stress due to arsenic exposure.

Revealing neglected hotspots for China’s quality-induced water scarcity

A dependable assessment of quality-induced water scarcity (QualWS) is essential for tackling the issue and achieving sustainable development goals. The conventional Emission-based grey water footprint (GWF) may over- or under-estimate QualWS, as it solely focuses on local pollutant emissions while disregarding other influential factors, such as water body self-purification capacity, transboundary water flows and the potential under- or over-estimation of water pollution emissions. To address this limitation, we propose the State-based GWF to reflect the quality status of local water resources accurately. The indicator is applied in annual and monthly QualWS assessments at the provincial scale in China. In 2021, 19 provinces were identified as QualWS hotspots, comprising seven moderate and 12 slight hotspots for at least one pollutant. Notably, the State-based assessment revealed eight previously overlooked hotspots undetected by conventional methods. Furthermore, total phosphorus emerged as the most critical water pollutant, followed by total nitrogen and chemical oxygen demand. Our assessment presents an innovative perspective for understanding QualWS and establishes a scientific basis for effective aquatic environment management.

Ecotoxicity of trace metal enrichment and the degrees of contaminated sediment and water from Riparian communities in Rivers State, Nigeria

This study assessed trace metal enrichment and contamination levels in Tema, Sangama, and Degema communities in Rivers State, Nigeria. Samples were collected monthly from October 2021 to April 2022. Trace metals such as Pb, Cu, Fe, Cd, Zn, and As were analysed using an Atomic Absorption Spectrophotometer (AAS). The contamination factor, degree of contamination and enrichment factor for trace metals were used to evaluate the impact of pollution levels. There was a descending order of heavy metal concentrations in water at the three stations: Fe > Cu > Zn > Cd > Pb. Sediment heavy metal concentrations were descending from Fe > Zn > Cu > Pb > Cd at the same stations. Heavy metal levels were consistently higher at Sangama. The result revealed that compared to Pb and Zn, Cd contamination was moderate to considerable. Fe contamination was relatively low, but Cu contamination was moderate to high. Water and sediment were contaminated to varying degrees. The contamination levels of Pb, Zn, and Cu were low to moderate. Sangama, Tema, and Degema all had varying degrees of contamination, with some areas having higher contamination levels. This study recommends the need for effective environmental management practices in these coastal marine wetlands.

Multi-step ahead dissolved oxygen concentration prediction based on knowledge guided ensemble learning and explainable artificial intelligence

Accurate water quality prediction is crucial for effective environmental management and decision-making. However, previous studies have solely relied on historical data to simulate water quality, overlooking the potential discrepancies between predicted values and actual observations. Additionally, the opacity of machine learning models has posed challenges to the credibility of their predictions. Hence, considering the excellent nonlinear fitting ability of ensemble tree models, especially the Categorical Boosting (Catboost) model, this study proposes a knowledge-guided Catboost (KGCatboost) model for predicting the dissolved oxygen concentration, one of the vital water quality indicators, in 15 river sections of the Yangtze River Basin in Yunnan Province, China. Furthermore, to enhance the model’s interpretability, we employ the SHapley Additive exPlanations (SHAP) method to analyze the contributions of each input variable within the water body. The results demonstrate that on the test set of each dataset, the mean Nash-Sutcliffe Efficiency (NSE) value of KGCatboost is 0.874, which has improved by 0.34% and 3.07% compared to Catboost and eXtreme Gradient Boosting (Xgboost). In addition, this study reveals that pH has the most significant impact on DO concentrations. Specifically, as the pH increased, the DO concentration increased significantly. A regulatory mechanism has also been developed to alleviate the hazards caused by low DO concentrations. The KGCatboost model can provide valuable guidance for water resource management processes.

Nativeness is a binary concept —Invasiveness and its management are not

In interdisciplinary fields such as biodiversity conservation or invasion science —where multiple perspectives from diverse disciplines often need to converge for effective environmental management, it is crucial to minimise terminological confusion in order to understand and transmit concepts accurately. The diversity of perspectives can exert a substantial influence on defining key terms in those interdisciplinary fields, potentially resulting in confusion. A lively topic within invasion science concerns the definitions of nativeness, non-nativeness, and invasiveness. While some academics dismiss the nativeness concept because it cannot be objectively defined, others advocate for its categorization, and a third perspective posits it as a binary term. Here we argue the inherent binary nature of nativeness, even when our capacity to observe is challenging. Nativeness (and consequently, non-nativeness) is an intrinsic and binary property of a species (i.e. the set of populations of a species) in a place, which should remain a central piece of information in biodiversity conservation and ecosystem restoration. In contrast, invasiveness, which relies on quantitative metrics (including abundance, spread, or impacts), should not be defined on binary terms. This underscores the importance of offering diverse, context-specific management strategies to deal with it. We illustrate the consistency of nativeness' binary nature and the need to rely on diverse management options to address different invasion scenarios with the example of the freshwater crayfish in the Iberian Peninsula.

Predicting nitrous oxide contaminants in Cauvery basin using region-based convolutional neural network

Nitrous oxide (N2O) in riverbeds affects hydrological processes by contributing to the greenhouse effect, indicating poor water quality, disrupting biogeochemical cycling, and linking to eutrophication. Elevated N2O levels signal environmental issues, impacting aquatic life and necessitating precise forecasting for effective environmental management and reduced greenhouse gas emissions. Precisely forecasting nitrous oxide (N2O) emissions from riverbeds is paramount for effective environmental management, given its significant potency as a greenhouse gas. This study focuses on the difficulties related to spatial feature extraction and modeling accuracy in predicting N2O in riverbeds in Tamil Nadu. To address the obstacles, the research suggests utilizing the Deep Learning Based Prediction of Nitrous Oxide Contaminants (DL-PNOC), which studies the N2O contaminants in water using Region-based Convolutional Neural Network (RCNN) for spatial feature extraction, to predict nitrous oxide contaminants. The study is centered on the Cauvery River Basin located in Tamil Nadu, where the emission of N2O is a matter of environment. The outcomes encompass the specialized N2O contaminant model for riverbeds and the implementation of RCNN achieves precise N2O forecasting. The DL-PNOC approach combines a contaminant model with RCNN deep learning techniques to capture spatial characteristics and predict N2O pollutants accurately. Furthermore, using the River Bed Dynamics Simulator reinforces the dependability of the findings. The DL-PNOC approach has exhibited encouraging results, as evidenced by the following metrics: a high IoU of 88.66%, precision of 88.96%, recall of 90.03%, F1 score of 89.22%, and low RMSE and MAE values of 9.14% and 7.59%, respectively. The findings highlight the efficacy of the DL-PNOC approach in precisely forecasting N2O pollutants in river sediments.

Quantitative assessment, molecular docking and novel metabolic pathways reveal the interaction mechanisms between norfloxacin biodegradation and environmental implications

Norfloxacin (NOR) is widely used in medicine and animal husbandry, but its accumulation in the environment poses a substantial threat to ecological and human health. Traditional physical, chemical, and rudimentary biological methods often fall short in mitigating NOR contamination, necessitating innovative biological approaches. This study proposes an engineered bacterial consortium found in marine sediment as a strategy to enhance NOR degradation through inter-strain co-metabolism of diverse substrates. Strategically supplementing the engineered bacterial consortium with exogenous carbon sources and metal ions boosted the activity of key degradation enzymes like laccase, manganese peroxidase, and dehydrogenase. Iron and amino acids demonstrated synergistic effects, resulting in a remarkable 70.8% reduction in NOR levels. The innovative application of molecular docking elucidated enzyme interactions with NOR, uncovering potential biodegradation mechanisms. Quantitative assessment reinforced the efficiency of NOR degradation within the engineered bacterial consortium. Four metabolic routes are herein proposed: acetylation, defluorination, ring scission, and hydroxylation. Notably, this study discloses distinctive, co-operative metabolic pathways for NOR degradation within the specific microbial community. These findings provide new ways of understanding and investigating the bioremediation potential of NOR contaminants, which may lead to the development of more sustainable and effective environmental management strategies.