Environmental Management Tools Research Articles

More than half of the alien plants naturalised in the arid southeast of the Iberian Peninsula could be invasive

Having a list of alien plant species naturalised in an area and knowing their invasive potential (i.e. a post-border species risk assessment framework) and the precise locations where they are found, are now a priority as a management strategy to curb their spread, avoiding damage to ecosystems and saving management costs. This is especially important in arid ecosystems, which are particularly vulnerable to impacts due to their limited resources. Weed Risk Assessment systems (WRAs) analyse plant traits that influence their invasive potential through a set of questions whose answers score taxa according to their invasive potential. In this work, we identify potentially invasive plants inhabiting the arid southeast of the Iberian Peninsula, the driest region in Europe, by compiling alien plant species recorded in the wild and applying the Australian and New Zealand Weed Risk Assessment (AWRA) system. The AWRA applies scores that evaluate species characteristics related to biography, undesirable attributes and biology/ecology for establishment elsewhere. We provide the dataset obtained in the application of the AWRA test: a list of the alien plant species naturalised in the study area and their geographical distribution; the answers, scores and results of the test, as well as the scientific sources that support the existence of such characteristics in these species. We found that 64.4% of the 177 taxa assessed can be considered potential invaders. This database represents a useful and transparent tool for environmental managers to deal with the problem of plant invasions effectively. It can also be confronted with data from other areas of the world where these species are naturalised.

IoT-Based Tryptophan-like Fluorescence Portable Device to Monitor the Indicators for Microbial Quality by E. coli and Biochemical Oxygen Demand (BOD5)

Tryptophan-like fluorescence (TLF) is a key indicator of water contamination, particularly of microbial origin and biodegradable organic compounds. This study introduces an Internet of Things (IoT)-enabled portable device (IoT-TLF-PD) for real-time monitoring of microbial quality and biochemical oxygen demand (BOD5). The device was tested using surface water (S1), secondary wastewater (S2), and final wastewater effluents (S3). Results showed significant correlations between TLF intensity, Escherichia coli (E. coli) counts, and BOD5, with R2 values of 0.77 (S1), 0.61 (S2), and 0.76 (S3) for BOD5, and 0.60 (S2) to 0.68 (S3) for E. coli. Considering various water samples, a strong correlation was found between E. coli and BOD5 with TLF intensity normalized by total organic carbon (TOC) concentration (TLF intensity/TOC). The R2 value for E. coli was 0.92, and for BOD5, it was 0.77. This indicates the necessity of accounting for organic matter concentration when interpreting TLF intensity in water quality assessments. The study confirmed the potential of the IoT-TLF-PD to serve as a cost-effective, real-time indicator for assessing water quality, especially for detecting microbial contamination. This technology offers a valuable tool for environmental monitoring and water management.

Quantifying supply and demand of cultural ecosystem services from a dynamic perspective

This study introduces a dynamic perspective to assess Cultural Ecosystem Services (CES) supply and demand, addressing the limitations of previous research. We take a park as a case study, paying special attention to key CES types such as aesthetics, recreation, social relations, and education. We employed various data acquisition methods, including behavioral observations, and public participatory Geographic Information System mapping to collect data. Our research revealed that the park primarily offers aesthetics and recreational services, while the provision of social relations and educational services is relatively limited. This emphasizes the need for urban park planning to address a wider range of diverse needs, including social relations and educational activities. Furthermore, the study uncovers how the supply-demand ratios of different CES types vary throughout the day, offering valuable insights for the effective allocation of resources and their rational arrangement. The broad applicability and dynamic perspective of this method open up new possibilities for CES assessment in various contexts. This innovative method provides fresh perspectives and tools for urban planning and environmental management, allowing decision-makers to better meet the diverse CES needs of human societies.

AUDITORIA DE CONFORMIDADE LEGAL AMBIENTAL: PERSPECTIVAS, DESAFIOS E IMPORTÂNCIA

Environmental legal compliance audits have established themselves as an essential tool in modern environmental management, being increasingly used by companies and organizations that seek to ensure the adequacy of their activities to environmental standards. The audit process involves a systematic assessment of an organization’s processes, products and activities, verifying whether they comply with the legal requirements applicable by current environmental laws, provisions and standards. There are several types of environmental legal compliance auditing. This article seeks to explore in depth the main aspects related to environmental legal compliance audits, addressing their concepts, objectives, application methods, practical challenges and strategic importance in the current context in relation to legal compliance audit processes relevant to the NBR ABNT standard ISO 14.001:2015, Environmental Due Diligence and CONAMA Resolution 306/2002. Despite it is a powerful control tool, environmental legal compliance auditing faces practical challenges, which can compromise its effectiveness if not properly managed. Although the legal assessment of ISO 14001, environmental Due Diligence and the environmental audit of CONAMA Resolution 306 share the common objective of promoting environmental compliance, each has different focuses and is applicable in different contexts. ISO 14001 focuses on continuous and internal management, while environmental Due Diligence is focused on specific transactions and hidden risks. The CONAMA 306 audit offers a broader view of compliance and environmental performance, focusing on large projects and legal inspection. Thus, each approach presents specific advantages and challenges, being complementary in many business situations, especially when it comes to companies that deal with major environmental impacts.

Costos en la gestión ambiental: camino necesario a la sostenibilidad

Around the world, the importance of preserving the natural environment has been recognized, which has led to an increase in the expectations of various stakeholders involved. This process has driven the orientation that prioritizes sustainability. In this situation, the objective of this article is to promote environmental accounting as a necessary tool for environmental management, both in the business environment and on a global scale. The study consists of an exhaustive systematic review with a qualitative approach at a descriptive level through the review of scientific articles, consulted in databases such as: Scopus, Google academic, Scielo, Mendeley, among others. It is concluded that environmental management together with environmental accounting as a necessary tool to achieve sustainable development benefit society, the environment and the company in terms of promoting sustainability, corporate social responsibility, decision making, efficiency and competitive advantage.

Monitoring and Predicting Air Quality with IoT Devices

The growing concern about air quality and its influence on human health has prompted the development of sophisticated monitoring and forecast systems. This article gives a thorough investigation into forecasting the air quality index (AQI) with an Internet of Things (IoT) device that analyzes temperature, humidity, PM10, and PM2.5 levels. The dataset used for this analysis comprises 5869 data points across six critical parameters essential for accurate air quality prediction. The data from these sensors is sent to the ThingSpeak cloud platform for storage and preliminary analysis. The system forecasts AQI using a TensorFlow-based regression model, delivering real-time insights. The combination of IoT technology and machine learning improves the accuracy and responsiveness of air quality monitoring systems, making it a useful tool for environmental management and public health protection. This work presents comparatively the effectiveness of feedforward neural network models trained with the ‘adam’ and ‘RMSprop’ optimizers over different epochs, as well as the machine learning algorithm random forest with varying numbers of estimators to forecast AQI. The models were trained using both types of regression analysis: linear regression and random forest regression. The findings show that the model achieves a high degree of accuracy, with the predictions closely aligning with the actual AQI values, thus having the potential to significantly reduce the negative health impact associated with poor air quality, protecting public health and alerting users when pollution levels are higher than allowed. Specifically, the random forest model with 100 estimators delivers the best overall performance for both AQI 10 and AQI 2.5, achieving the lowest Mean Absolute Error (MAE) of 0.2785 for AQI 10 and 0.2483 for AQI 2.5. This integration of IoT technology and advanced predictive analysis addresses the significant worldwide issue of air pollution by identifying the pollution hotspots and allowing decision-makers for quick reactions, and the development of effective strategies to reduce pollution sources.

Can ecological zoning act as an environmental management tool for protecting regional habitat quality: Causal evidence from the national key ecological function zone in China

Habitat degradation is one of the most serious environmental problems worldwide, which threatens human wellbeing and sustainable development. Exploring how to protect natural habitats has become a research hotspot in the environmental management field. Ecological zoning has been widely used in habitat protection, but its actual effectiveness has been rarely revealed, especially with the robust and reliable causal inference method. Therefore, taking the Yangtze River Economic Belt (YREB) in China as the study area, we first quantified habitat quality and changes at the district/county scale during 2005–2019. Then, we used the National Key Ecological Function Zone (NKEFZ) as a representative ecological zone scheme, and explored the effectiveness of the NKEFZ in protecting habitat quality with the Propensity Score Matching and Difference-in-Difference combined method, which is a representative causal inference method. We found that habitat quality showed an uneven distribution during the study period, and habitat quality of districts/counties within the NKEFZ was higher than that outside the NKEFZ with statistical significance. Most districts/counties experienced a habitat quality decrease, but the decrease within the NKEFZ is less than that outside the NKEFZ. We observed that the NKEFZ can protect habitat quality from the causal relationship perspective, but there was the spatio-temporal heterogeneity of the NKEFZ effectiveness. In terms of the spatial dimension, the NKEFZ was the most effective in the Lower Reach of the YREB. In terms of the temporal dimension, there was a 1-year time-lag effect of the NKEFZ effectiveness, and NKEFZ effectiveness experienced a sharp increase from 2016. The NKEFZ contributed to protecting habitat quality through multiple management strategies such as urban land quota, high pollution industry prohibition, and ecological compensation. Overall, this study verified that the ecological zone scheme can contribute to habitat quality protection, and emphasizes that the effectiveness of ecological zoning may vary across time and space. This work can support ecological zoning implementation with robust evidence, and provides a novel perspective for understanding the effectiveness of ecological zoning.

Integrating Fuzzy C-Means Clustering and Explainable AI for Robust Galaxy Classification

The classification of galaxies has significantly advanced using machine learning techniques, offering deeper insights into the universe. This study focuses on the typology of galaxies using data from the Galaxy Zoo project, where classifications are based on the opinions of non-expert volunteers, introducing a degree of uncertainty. The objective of this study is to integrate Fuzzy C-Means (FCM) clustering with explainability methods to achieve a precise and interpretable model for galaxy classification. We applied FCM to manage this uncertainty and group galaxies based on their morphological characteristics. Additionally, we used explainability methods, specifically SHAP (SHapley Additive exPlanations) values and LIME (Local Interpretable Model-Agnostic Explanations), to interpret and explain the key factors influencing the classification. The results show that using FCM allows for accurate classification while managing data uncertainty, with high precision values that meet the expectations of the study. Additionally, SHAP values and LIME provide a clear understanding of the most influential features in each cluster. This method enhances our classification and understanding of galaxies and is extendable to environmental studies on Earth, offering tools for environmental management and protection. The presented methodology highlights the importance of integrating FCM and XAI techniques to address complex problems with uncertain data.

Research of the nature of implementation of environmental innovations as a factor of ensuring competitiveness of hotel hospitality business

<p class="MsoNormal" style="line-height: 120%; mso-pagination: widow-orphan; layout-grid-mode: char; mso-layout-grid-align: none; margin: 12.0pt 0cm 6.0pt 147.4pt;"><span lang="EN-US" style="font-size: 10.0pt; line-height: 120%; mso-fareast-font-family: 'Times New Roman'; color: black; mso-themecolor: text1; mso-font-kerning: 0pt; mso-fareast-language: JA;">In many countries of the world, sustainable tourism is developing rapidly, and therefore the application of innovative environmental technologies is becoming an integral part of the development strategies of hospitality industry enterprises, especially hotel business. This trend promotes the introduction of new management systems based on the principles of sustainable development, including resource-saving technologies. In the work, the existing environmental management tools are analyzed and comparative studies of the essence of the application of “green” innovations are conducted using the example of the hotel business enterprises of the Republic of Azerbaijan and the Russian Federation.</span><span lang="EN-US" style="font-size: 10.0pt; line-height: 120%; mso-fareast-font-family: 宋体; mso-fareast-theme-font: minor-fareast; color: black; mso-themecolor: text1; mso-font-kerning: 0pt;"> </span><span lang="EN-US" style="font-size: 10.0pt; line-height: 120%; mso-fareast-font-family: 'Times New Roman'; color: black; mso-themecolor: text1; mso-font-kerning: 0pt; mso-fareast-language: JA;">Studies have shown that the implementation of such innovations and eco-technologies in hotels demonstrates their commitment to environmental responsibility, improves the guest experience, helps save resources and acts as an additional advertising tool. Technological re-equipment and the use of modern materials aimed at reducing the burden on the environment ensure the economic efficiency of hotel business enterprises and increase their competitiveness.</span></p>

Prospects on the use of EIA as a Tool for Coastal Resources Conservation

The successful application of EIA in coastal resource conservation has underscored its potential as an effective environmental management tool. Traditional EIA processes often focus on the impacts of individual projects in isolation. However, coastal environments are influenced by multiple activities and stressors. There is a growing recognition of the importance of assessing cumulative impacts to ensure more holistic environmental management. It is essential to implement adaptive management practices that allow for ongoing monitoring and adjustment of mitigation strategies based on real-world outcomes. To enhance the effectiveness of EIA, therefore, it should be integrated with due considerations on environmental impacts at the policy, plan, and program levels, providing a broader context for individual project assessments. This integration can lead to more coherent and sustainable coastal management strategies.

Multifaceted anomaly detection framework for leachate monitoring in landfills

The imperative to preserve environmental resources has transcended traditional conservation efforts, becoming a crucial element for sustaining life. Our deep interconnectedness with the natural environment, which directly impacts our well-being, emphasizes this urgency. Contaminants such as leachate from landfills are increasingly threatening groundwater, a vital resource that provides drinking water for nearly half of the global population. This critical environmental threat requires advanced detection and monitoring solutions to effectively safeguard our groundwater resources. To address this pressing need, we introduce the Multifaceted Anomaly Detection Framework (MADF), which integrates Electrical Resistivity Tomography (ERT) with advanced machine learning models—Isolation Forest (IF), One-Class Support Vector Machines (OC-SVM), and Local Outlier Factor (LOF). MADF processes and analyzes ERT data, employing these hybrid machine learning models to identify and quantify anomaly signals accurately via the majority vote strategy. Applied to the Chaling landfill site in Zhuzhou, China, MADF demonstrated significant improvements in detection capability. The framework enhanced the precision of anomaly detection, evidenced by higher Youden Index values (≈ 6.216%), with a 30% increase in sensitivity and a 25% reduction in false positives compared to traditional ERT inversion methods. Indeed, these enhancements are crucial for effective environmental monitoring, where the cost of missing a leak could be catastrophic, and for reducing unnecessary interventions that can be resource-intensive. These results underscore MADF's potential as a robust tool for proactive environmental management, offering a scalable and adaptable solution for comprehensive landfill monitoring and pollution prevention across varied environmental settings.

A smartphone-based approach for comprehensive soil microbiome profiling.

The soil microbiome is crucial for nutrient cycling, health, and plant growth. This study presents a smartphone-based approach as a low-cost and portable alternative to traditional methods for classifying bacterial species and characterizing microbial communities in soil samples. By harnessing bacterial autofluorescence detection and machine learning algorithms, the platform achieved an average accuracy of 88% in distinguishing common soil-related bacterial species despite the lack of biomarkers, nucleic acid amplification, or gene sequencing. Furthermore, it successfully identified dominant species within various bacterial mixtures with an accuracy of 76% and three-level soil health identification at an accuracy of 80%-82%, providing insights into microbial community dynamics. The influence of other soil conditions (pH and moisture) was relatively minor, showcasing the platform's robustness. Various field soil samples were also tested with this platform at 80% accuracy compared with the laboratory analyses, demonstrating the practicality and usability of this approach for on-site soil analysis. This study highlights the potential of the smartphone-based system as a valuable tool for soil assessment, microbial monitoring, and environmental management.

Ethnic ecotourism: an alternative for the environmental sustainability of the Rancheria River delta, La Guajira

This study examined sustainable eco-ethno-tourism in the Ranchería River Delta Integrated Management District (DMI) as an environmental management tool to conserve and protect environmental and cultural aspects of the protected area. A mixed methodological strategy was implemented, combining documentary and descriptive research. On the other hand, the descriptive research involved unstructured interviews with experts to understand the conceptions and concepts related to the empowerment of eco-ethno-tourism for the sustainability of the Ranchería river delta, La Guajira. Aspects such as the characteristics of the Wayuu people, their social organization, ancestral history, governance, clans, myths, legends, customs, social fabric and fishing were addressed. The results obtained could serve as a tool for the management and planning of eco-etnotourism projects, thus strengthening conservation actions, sustainable use and management of mangroves and the adaptive capacity of coastal communities

Optimization of a PM2.5 automatic monitoring network based on the modeling approach – A case study in Long An province, Vietnam

Abstract Currently, fine particulate matter (PM2.5) pollution is one of the well-recognized serious environmental health risks. In particular, PM2.5 has remarkably impacted the human respiratory, cardiovascular, and circulatory systems. Vietnam has frequently ranked among the countries with the highest annual mean PM2.5 levels in Southeast Asia and worldwide. Thus, building a real-time continuous ambient PM2.5 monitoring network is an important environmental management tool to reduce health effects attributable to PM2.5 exposure. Nevertheless, measurement locations of an air quality monitoring (AQM) network depend strongly on meteorological conditions in the area and sensitivity to local primary emission sources, which significantly determines the AQM network’s operational efficiency. This study aims to estimate and optimize outdoor PM2.5 monitoring sites of an automatic air quality monitoring (AAQM) network using a system of low-cost sensors in Long An province, based on the spatio-temporal distribution assessment results of PM2.5 concentrations from WRF (Weather Research and Forecasting Model)/CMAQ (the Community Multiscale Air Quality) models. The prominent study outcomes have shown that the 2018 average PM2.5 concentration in Long An province exceeded the threshold of QCVN 05:2023/BTNMT (25 μg/m3) from 5.6 to 7.9 times. Moreover, this study has proposed four optimal monitoring sites (AQ1, AQ2, AQ3, and AQ4) for the automatically real-time PM2.5 observation in the areas of Tan An City, Can Giuoc District, the ring roads 3 and 4, and Kien Tuong Town. The study has provided an important scientific basis and support for local air quality management activities; furthermore, the newly proposed monitoring locations will complement the province’s periodic monitoring program towards 2025.

Enhanced SWAT calibration through intelligent range-based parameter optimization

Hydrological models are vital tools in environmental management. Weaknesses in model robustness for hydrological parameters transfer uncertainties to the model outputs. For streamflow, the optimized parameters are the primary source of uncertainty. A reliable calibration approach that reduces prediction uncertainty in model simulations is crucial for enhancing model robustness and reliability. The optimization of parameter ranges is a key aspect of parameter calibration, yet there is a lack of literature addressing the optimization of parameter ranges in hydrological models. In this paper, we introduce a parameter calibration strategy that applies a clustering technique, specifically the Self-Organizing Map (SM), to intelligently navigate the parameter space during the calibration of the Soil and Water Assessment Tool (SWAT) model for monthly streamflow simulation in the Baishan Basin, Jilin Province, China. We selected the representative algorithm, the Sequential Uncertainty Fitting version 2 (SUFI-2), from the commonly used SWAT Calibration and Uncertainty Programs for comparison. We developed three schemes: SUFI-2, SUFI-2-Narrowing Down (SUFI-2-ND), and SM. Multiple diagnostic error metrics were used to compare simulation accuracy and prediction uncertainty. Among all schemes, SM outperformed the others in describing watershed streamflow, particularly excelling in the simulation of spring snowmelt runoff (baseflow period). Additionally, the prediction uncertainty was effectively controlled, demonstrating the SM's adaptability and reliability in the interval optimization process. This provides managers with more credible prediction results, highlighting its potential as a valuable calibration tool in hydrological modeling.

ENVIRONMENTAL AND ECONOMIC ASSESSMENT OF COMPOST BARN SYSTEM IN MILK PRODUCTION IN BRAZIL

Intensifying animal productivity in specific areas is recommended to reduce or mitigate greenhouse gas emissions associated with these production systems. This study evaluates the environmental and economic impacts of six dairy farm using an intensive Compost Bedding Pack Barns system in Minas Gerais, Brazil. A Life Cycle Assessment grounded in the IPCC 2006 guidelines, was conducted as a valuable tool in environmental management, providing a comprehensive understanding of the environmental costs from feed cultivation to milk distribution. The Life Cycle Assessment results showed an average carbon footprint of 1.48 kg of CO2 eq. per liter of fat- and protein-corrected milk. Alongside this, an economic and financial viability assessment using investment analysis tools indicated that, despite initial investments and high fixed and variable costs, the system presents strong economic results. These findings support the premise that intensifying productive systems can mitigate environmental impacts while ensuring financial profitability, enhancing the sustainability and competitiveness of Brazil's dairy industry.

Using Machine Learning Models for Short-Term Prediction of Dissolved Oxygen in a Microtidal Estuary

This paper presents a comprehensive approach to predicting short-term (for the upcoming 2 weeks) changes in estuarine dissolved oxygen concentrations via machine learning models that integrate historical water sampling, historical and upcoming 2-week meteorological data, and river discharge and discharge metrics. Dissolved oxygen is a critical indicator of ecosystem health, and this approach is implemented for the Neuse River Estuary, North Carolina, U.S.A., which has a long history of hypoxia-related habitat degradation. Through meticulous data preprocessing and feature selection, this research evaluates the predictions of dissolved oxygen concentrations by comparing a recurrent neural network with four other models, including a Multilayer Perceptron, Long Short-Term Memory, Gradient Boosting, and AutoKeras, through sensitivity experiments. The input predictors to our prediction models include water temperature, turbidity, chlorophyll-a, aggregated river discharge, and aggregated wind based on eight directions. By emphasizing the most impactful predictors, we streamlined the model-building processes and built a hindcast system from 2015 to 2019. We found that the recurrent neural network model was most effective in predicting the dissolved oxygen concentrations, with an R2 value of 0.99 at multiple stations. Different from our machine learning hindcast models that used observed upcoming meteorological and discharge data, an actual forecast system would use forecasted meteorological and discharge data. Therefore, an actual operational forecast may have lower accuracy than the hindcast, as determined by the accuracy of the predicted meteorological and discharge data. Nevertheless, our studies enhance our understanding of the factors influencing dissolved oxygen variability and set the basis for the implementation of a predictive tool for environmental monitoring and management. We also emphasized the importance of building station-specific models to improve the prediction results.

Life cycle assessment of cross laminated timber: a systematic literature review

Cross Laminated Timber (CLT) is an engineered wood material that has been used in civil construction in structural functions previously reserved for materials such as steel or concrete. As it is a renewable material and promotes carbon sequestration, its use in civil construction is linked to mitigating global warming promoted by the industry. However, the increase in demand for this material raises questions about sustainability and what the environmental burden would be resulting from its mass production. Life Cycle Assessment (LCA) is an environmental management tool that can analyze this product chain as it maps and quantifies emissions and resource consumption from the production process, providing support for the calculation and analysis of environmental impacts. The goal of this study was to observe how the most recent research has been developed methodologically in terms of scope and what are the main scientific gaps presented in LCA studies of CLT material throughout its life cycle. To this end, a systematic literature review was carried out based on the Systematic Search Flow (SSF) method by Ferenhof and Fernandes (2014). The review points out the growing interest of the scientific community in the environmental profile of the product and the scope definitions were mostly cradle-to-grave study boundaries; the most applied functional unit is m² of building; impact analysis focus on quantifying global warming potential, energy resource consumption and carbon sequestration. The studies point to the end of life and the accounting of the material's biogenic emissions as scientific gaps, as well as the lack of studies with Brazilian data.

Environmental protection tax, green innovation, and environmental, social, and governance performance

This study investigates the impact of China's Environmental Protection Tax Law, implemented as a quasi-natural experiment, on the environmental, social, and governance (ESG) performance of listed industrial enterprises from 2012 to 2022. The objective is to gain a better understanding of how this tax influences corporate behavior toward sustainability, addressing a gap in the literature regarding the effect on nonfinancial performance indicators. By employing a dataset of 9077 observations from the Wind and CSMAR databases, we determine whether the tax leads to improved ESG outcomes and fosters green innovation within these enterprises. The findings reveal that the tax significantly enhances the ESG performance of firms, particularly in environmental aspects, by encouraging green innovations. Moreover, the tax's impact is more pronounced in nonstate-owned enterprises than in state-owned ones. These results suggest that similar fiscal policies can effectively drive corporate sustainability; hence, policymakers should leverage economic tools for environmental management.

Responses of plankton community to threshold metal concentrations of cadmium and lead in a mesocosm experiment at Bay of Bengal

Metals are essential at trace levels to aquatic organisms for the function of many physiological and biological processes. But their elevated levels are toxic to the ecosystem and even brings about shifts in the plankton population. Threshold limits such as Predicted No Effect Concentration (PNEC - 0.6 μg/l of Cd; 2.7 μg/l of Pb), Criterion Continuous Concentration (CCC - 3.0 μg/l of Cd; 4.5 μg/l of Pb) and Criterion Maximum Concentration (CMC - 23 μg/l of Cd; 130 μg/l of Pb) prescribed for Indian coastal waters were used for the study. Short-term mesocosm experiments (96 h) were conducted in coastal waters of Visakhapatnam to evaluate responses of the planktonic community on exposure to threshold concentrations of cadmium and lead for the first time. Four individual experimental bags of 2500 L capacity (Control, PNEC, CCC & CMC) were used for the deployment and ambient water samples were analysed simultaneously to evaluate the impacts of the threshold levels in the natural waters. Chaetoceros sp. were dominant group in the control system whereas, Prorocentrum sp. Ceratium sp. Tintinopsis sp. Chaetoceros sp. and Skeletonema sp. were major groups in the test bags. Throughout the experiment the phytoplankton community did not show any significant differences with increased nutrients and plankton biomass (Chl-a <8.64 mg/m3). Positive response of plankton community was observed in the experimental bags. High abundance of diatoms were observed in PNEC, CCC & CMC bags at 48 h and the abundance decreased with shift in the species at 72–96 h. The catalase activity in phytoplankton (5.99 nmol/min/ml) and the zooplankton (4.77 nmol/min/ml) showed induction after exposure to PNEC. The present mesocosm study is confirmed that short-term exposure to threshold metal concentration did not affects the phytoplankton community structure in PNEC, but CCC and CMC affects the community structure beyond 24 h. The insights from this study will serve as a baseline information and help develop environmental management tools. We believe that long-term mesocosm experiments would unravel metal detoxification mechanisms at the cellular level and metal transfer rate at higher trophic levels in real-world environment.