Effective Environmental Management Research Articles

A COMPREHENSIVE OVERVIEW OF MODERN ENVIRONMENTAL MANAGEMENT SOFTWARE

Effective environmental management is becoming increasingly important due to the anthropogenic impact on ecosystems and their components. Environmental Management Systems (EMS) are essential for heavy industries, helping to reduce the human footprint on the environment and optimize the use of natural resources. This paper examines various types of EMS, their characteristics, architecture, and computational models. Key functions of these systems include monitoring and data collection, data analysis and processing, compliance management, incident and deviation management, planning and goal setting, as well as communication and reporting. EMS address tasks related to environmental auditing, reporting, and managing environmental risks of production processes. Various systems on the market offer unique solutions for different industries. Successful implementation examples include companies like BASF and Valspar, which have saved significant resources by integrating EMS into their production processes. The conclusions highlight the importance of EMS for sustainable development and environmental safety, as well as the need for their continuous updating and adaptation to new challenges.

A Systematic Review of Urban Vegetation Research during 2000–2023: A Bibliometric Analysis

Urban vegetation is a fundamental element and the keystone of urban ecological systems. Therefore, better comprehending the patterns of change over time and space in this vegetation is crucial for more effective urban environment management to enhance urban habitat and counteract the negative impacts of accelerated urbanization. This study examined 1,081 articles listed in the Scopus database on urban vegetation between 2000 and 2023. After being exported in CSV format, these data were analyzed using VOSviewer for data visualization, Scopus Analytics for performance evaluation, MS Excel for organizing data in graphical form, and CiteSpace for examining the emergence of keywords. The objective was to offer guidance on current topics and forecast future research trends related to this theme. This analysis shows that in 2000, only 5 articles on urban vegetation were published, which has risen by 2840% to 142 in 2023, following a quadratic function (R2 = 0.9524). China is the country most interested in this topic, with 273 articles (25.25%). Thus, China and the United States are the most cooperative nations on the urban vegetation subject. Over the past 24 years, the environmental science field has contributed the most, with 33.3% of papers published. Concerning sources, the journal Urban Forestry and Urban Greening comes first with 73 articles and 2127 citations. The keyword “urban vegetation” is the most frequently used by authors (occurrences: 330, links: 115, and total link strength: 459). In terms of cocitations, an analysis of 75887 authors revealed that X. Li came out first with a total link strength of 21626 and 507 citations while G. D. Jenerette takes first place, concerning the co‐authorship with 11 publications and 899 citations. Moreover, the Chinese Academy of Sciences is the most effective and influential institution on the subject. Keyword emergence analysis revealed that urban growth, urban development, and ecosystem services have become current research hotspots with considerable impact over the past three years. This work has provided structured data, guidelines, and useful recommendations for further investigations, which can contribute to a better comprehension of the environmental features of urban vegetation.

Vegetation and inundation characteristics of waterbird breeding sites in the Murray–Darling Basin, Australia

Context The Murray–Darling Basin serves as a crucial habitat for aggregating waterbirds; however, decades of large-scale regulation of rivers and water resources have adversely affected waterbird breeding in the Basin. Aims To understand the characteristics of wetlands that attract and support aggregating waterbirds, focusing on identifying environmental conditions conducive to waterbird breeding. Methods In total, 52 wetland sites across the Murray–Darling Basin, with high waterbird abundances, were identified, of which 26 supported waterbird breeding. Classification models were developed using temporally static and dynamic environmental datasets to discern wetland characteristics associated with waterbird breeding. Key results Analyses showed that wetlands supporting waterbird breeding contained a maximum inundated area of ‘other shrublands’ exceeding 3.635 km2 and variation in normalised difference vegetation index, possibly reflective of a ‘boom and bust’ ecological response. Conclusions Understanding the habitat requirements of wetlands to prompt waterbird breeding is critical for effective environmental water management and conservation strategies. Implications Targeted wetland management and environmental water allocation to support waterbird breeding populations in the Murray–Darling Basin is essential for continued waterbird breeding. There is a need for continued research to refine management strategies and ensure the long-term sustainability of waterbird populations in the face of ongoing environmental challenges.

Impact of Community Participation on Environmental Management in Opobo/Nkoro Local Government Area of Rivers State, Nigeria.

The paper impact of community participation on environmental management focused on the importance of engaging local community members in the management of the environment. In the absence of proper environmental management, communities may engage in unsustainable practises which might be harmful to man and the environment, hence the need to get community members actively involved in the management of the environment. The study adopted the descriptive survey research design. Three objectives, three research questions and one hypothesis guided the study. The population of the study consist of 1000 members of community based organisations in Opobo/Nkoro Local Government Area of Rivers State, while the sample size consist of 100 members from the population which was randomly selected to get 10% of the population. A self structured questionnaire used for the study has a reliability index of 0.81. The data collected was analysed using the mean and standard deviation while t-test was used in testing the hypothesis at 0.05 significant level. From the analysed data the study revealed that community members got more involved in the management of their environment; that community participation positively influenced environmental management outcomes by fostering a sense of ownership and responsibilities and that communication, education, strong local leadership and adequate resources among others are factors affecting successful community participation in the management of the environment. Based on the findings it was recommended among others that regular educational programmes, workshops and training sessions be organised to empower community members with requisite knowledge and skills needed for effective environmental management.

Insights into size-segregated distribution of benzothiazoles in indoor aerosol from office environments

Understanding the impact of rubber-derived particles on indoor environmental quality is crucial for effective environmental management.

ВЗАИМОУВЯЗКА СЭМ И УПРАВЛЕНИЯ БИЗНЕС-ПРОЦЕССАМИ В СИСТЕМАХ ПОДДЕРЖКИ ПРИНЯТИЯ РЕШЕНИЙ

The relevance of the work is due to the fact that most enterprises, especially industrial ones, have a negative impact on the environment, which requires taking measures to reduce environmental risks and optimize management processes. Along with the growing attention of society to environmental issues and sustainable development, the need to develop and implement effective environmental management systems in organizations is becoming increasingly urgent. The implementation of business processes in environmental management can allow organizations to effectively manage environmental aspects in their activities.

Predicting and Navigating Environmental Transformations using Machine Learning

In today's world, as ecological challenges continue to rise, the importance of environmental monitoring and conservation cannot be overstated. These efforts play a vital role in countering the adverse impacts of human activities on our planet. The development of predictive models that can accurately predict the results of different conservation measures is essential for the success of these conservation efforts. This paper's objective is to create a Machine Learning (ML) model that predicts how environmental changes can unfold. This prediction helps in proactive monitoring and conservation planning, considering factors like deforestation, air pollution, sea level rise, temperature shifts, and forest preservation. The model relies on machine learning techniques, including AdaBoost, ETS, Prophet, SARIMA, LightGBM, and TL-BLSTM models, to analyze data, uncover trends, and inform decision-making. By offering a thorough assessment of the effects of conservation approaches, this model aids in shaping well-informed decisions regarding the distribution of resources. As a result, this process strengthens initiatives designed to protect biodiversity and the Earth's natural resources. Predicting changes in environmental variables is the main goal of this research, which will improve effective environmental management. In order to draw conclusions that are meaningful, this study compares several models for each of the four crucial factors.

Environmental fate and health risks of polycyclic aromatic hydrocarbons in the Yangtze River Delta Urban Agglomeration during the 21st century

Understanding the spatiotemporal distribution and behavior of Polycyclic Aromatic Hydrocarbons (PAHs) in the context of climate change and human activities is essential for effective environmental management and public health protection. This study utilized an integrated simulation system that combines land-use, hydrological, and multimedia fugacity models to predict the concentrations, transportation, and degradation of 16 priority-controlled PAHs across six environmental compartments (air, water, soil, sediment, vegetation, and impermeable surfaces) within one of the world's prominent urban agglomerations, the Yangtze River Delta Urban Agglomeration (YRDUA), under future Shared Socio-economic Pathways (SSP)-Representative Concentration Pathways (RCP) scenarios. Incremental lifetime carcinogenic risk for adults and children exposed to PAHs were also evaluated. The results show a declining trend in PAHs concentrations and associated health risks during the 21st century. Land use types, hydrological characteristics, population, and GDP, have significant correlations with the fate of PAHs. The primary removal for PAHs is determined to be driven by advection through air and water. PAHs covering on impermeable surfaces pose a relatively higher health risk compared to those in other environmental media. This study offers valuable insights into PAHs pollution in the YRDUA, aiming to ensure public health safety, with the potential for application in other urban areas.

Clarification of generation mechanism of volatilization flux based on detailed analysis of transport phenomena near the ground surface and quantitative evaluation of influencing factors

Assessing human health risks associated with inhalation exposure of volatile chemical substances (VCSs) volatilized from contaminated soil requires quantitative evaluation of volatilization fluxes (VFs) and an understanding of how environmental factors impact VF generation. We developed a numerical model that considers advection-dispersion and VCSs volatilization in unsaturated soil, enabling VF prediction through parameter optimization using soil column tests. We conducted parametric analyses to assess how key parameters, such as soil particle size, contamination depth, temperature, and surface soil thickness affect VF generation. By analyzing VCS transport near the ground surface, we uncovered the mechanisms underlying VF generation. We also identified characteristic differences in VF generation behavior linked to soil particle size and gas saturation at the ground surface. Under specific soil particle size conditions, significant VF generation occurred even when contamination was deep underground. This was primarily observed when capillary effect was pronounced, and VCSs continued to be supplied to the ground surface through upward advection. Considering the significant impact of VF generation on human health, our parametric study provides valuable insights into relationships between different parameters and VF behavior, especially under varying ground surface temperatures and surface soil thicknesses. This study contributes to developing effective remediation and risk-reduction strategies. Environmental ImplicationThis research examines the environmental implications of volatile chemical substances (VCSs), including hazardous materials like benzene and trichloroethylene, in contaminated soil. VCSs pose health risks when they volatilize from soil. The study quantifies volatilization fluxes (VF) and elucidates the environmental factors affecting VF generation. These findings are vital for effective environmental management. By comprehending the mechanisms governing VF generation, particularly regarding soil properties like particle size, this research enhances the effectiveness of soil contamination remediation and risk reduction. It emphasizes the essential need for a comprehensive VCS assessment in contaminated soils to protect both human health and the environment.

Prototype of Forest and Land Fire Monitoring and Detection System Using IoT-Based WSN Technology

Forest and land fires are a recurrent issue in several Indonesian regions, necessitating advanced technological solutions for early detection and monitoring due to their significant impact. This research project focuses on developing a prototype system utilizing IoT-based Wireless Sensor Network (WSN) technology to detect forest and land fires. The prototype offers real-time remote monitoring of temperature, humidity, smoke levels, and wind speed in the forest through an Android app or web interface. To validate its performance, the prototype was compared to existing BMKG tools, with results showing minor temperature errors (2.41%) and a humidity error of 17.68%. The anemometer sensor exhibited a slight 4-second data transmission delay. Importantly, the prototype excelled in fire detection, effectively identifying temperature and humidity changes within a 2-meter radius or more indicative of fire outbreaks. This pioneering prototype promises to significantly enhance early warning and response mechanisms for forest and land fires in Indonesia, contributing to more effective environmental conservation and disaster management.

Improvement in Spatiotemporal Chl-a Data in the South China Sea Using the Random-Forest-Based Geo-Imputation Method and Ocean Dynamics Data

The accurate estimation of the spatial and temporal distribution of chlorophyll-a (Chl-a) concentrations in the South China Sea (SCS) is crucial for understanding marine ecosystem dynamics and water quality assessment. However, the challenge of missing values in satellite-derived Chl-a data has hindered obtaining complete spatiotemporal information. Traditional methods for deriving Chl-a are based on the modeling of measured sensor data and in situ measurements. Spatiotemporal imputation of Chl-a is difficult due to the inaccessibility of the measured Chl-a. In this study, we introduce an innovative approach that incorporates an ocean dynamics dataset and utilizes the random forest algorithm for predicting the Chl-a concentration in the SCS. The method combines the spatiotemporal feature pattern of Chl-a and the main influencing factors, and it introduces ocean dynamics data, which has a high correlation with the spatiotemporal distribution of Chl-a, as the input data through feature engineering. Also, we compared Random Forest (RF) with other Machine Learning (ML) methods. The results show that (1) ocean dynamics datasets can provide important data support for Chl-a imputation by capturing the impact of dynamical processes on ecological roles in the South China Sea. (2) The RF method is the superior imputation method for the reconstruction of Chl-a in the South China Sea, with better model performance and smaller errors. This study provides valuable insight for researchers and practitioners in choosing suitable machine learning methods for the imputation of the Chl-a concentration in the SCS, facilitating a better understanding of the region’s marine ecosystems and supporting effective environmental management.

Evaluation of PM2.5 spatio-temporal variability and hotspot formation using low-cost sensors across urban-rural landscape in lucknow, India

The high-resolution spatio-temporal monitoring through low-cost sensors (LCS) provides important insights into the dynamics of localized pollution patterns and in conceptualizing effective environmental management and public health interventions. We assessed the role of meteorology and local sources on spatially distributed PM2.5 pollutants measured from a unique urban-rural scaled network of LCS in Lucknow, India. The city-wide average PM2.5 annual cycle ranged between 29 ± 7 μg/m3 during the monsoon to 64 ± 30 μg/m3 during the rest of the year. During non-monsoon seasons, >60% of air mass trajectories indicated regional level transportations primarily from the north-western plains of the Indo-Gangetic basin, with peak weighted concentration weighted trajectory (WCWT, scale: 0–100) value estimated at >70. To analyze the impact of local sources, we designed a statistical classification method to divide each seasonal distribution into five incremental concentration groups ranging from no risk zone to hotspots. The hotspots were identified both within urban and rural regions with their average concentration measured as 23 μg/m3 and 26 μg/m3 higher than the rest of the regions in pre-monsoon (2 hotspots) and post-monsoon (4 hotspots) season, respectively. Further analysis shows that the nighttime concentrations were much higher in several locations (up to 40% in pre-monsoon and 54% in post-monsoon compared to their daytime levels), indicating the greater impact of local sources in the presence of low boundary layer height. A diurnal trend analysis along with conditional bivariate probability function (CBPF) was performed to interpret the characteristics and locations of the dominant sources. The study highlights the importance of a dense network of LCS to scale air quality monitoring in spatially heterogeneous environments and as a futuristic tool for PM2.5 exposure-based studies.

Exploring How Environmental Accounting Impact Company Value: A Literature Review

This research aims to adress the persistent environmental challenges in Indonesia, drawing attention to its low Environmental Performance Index (EPI) ranking and escalating carbon emissions. Despite governmental regulations, many corporations continue to inadequately comply. By using the literature review method, this research explores the contemporary understanding of corporate environmental practices and their impact on corporate value, revealing inconsistent findings. The study emphasizes the potential positive impact of effective environmental management, urging companies to view environmental costs as strategic investments in corporate reputation and long-term value. In conclusion, the study advocates for future research to delve into the intricate mechanisms through which environmental factors influence corporate value across industries, regions, and emerging trends in sustainable finance. This research aspires to provide valuable insights for refining policies and business practices in Indonesia, fostering sustainable development and environmental responsibility

Microplastics in sediment and surface water from an island ecosystem in Bay of Bengal

Microplastics (MPs) have garnered global attention as emerging pollutants in aquatic and terrestrial ecosystems. Despite their significance, studies on MP pollution have overlooked a biodiverse island ecosystem in the northeast Bay of Bengal. Hence, the current study is a pioneering effort to delve into this issue with the island. This research embodies the first comprehensive report exploring the presence of MP pollution in sediment and surface water and their influencing factors along Sandwip island in the northeast Bay of Bengal. The average MP concentration was 305 ± 37.16 (items/kg) in sediment and 106.14 ± 22.57 (items/m3) in surface water. Fragments emerged as the predominant type in sediment (78.77%) and surface water (54.64%) samples. Fourier Transform Infrared Spectroscopy identified three plastic polymers, the most abundant being polyethylene (56%) and polypropylene (41%). Anthropogenic activities, particularly fishing practices, improper waste disposal, and inadequate waste management strategies, were pinpointed as potential sources of MP contamination on the island. MP concentrations in water and sediment correlated positively with pH and organic matter (p < 0.000), indicating important factors influencing MP distribution. The spatial distribution and hotspots of MPs followed significant human routes. By shedding light on the extent of MPs' presence and their potential sources, this study contributes essential insights that can inform effective environmental management strategies for the island's future well-being.

Studies on the Prevalence Rate of Neoplastic Growth in Fish Johniuss Dussumieri (Valenciennes, 1833) of Karachi Coast Due to Polluted Sea Environment

Background: With the growing concern over the impacts of environmental pollution on marine life, this study addresses the critical issue of neoplastic growth in fish populations. The increasing prevalence of tumors in fish due to contaminated marine environments has raised alarms about ecosystem health and public health implications. Objective: The primary objective of this research was to assess the prevalence and seasonal variation of neoplastic growth in fish populations, particularly focusing on the impact of environmental pollution. Methods: Fish specimens were collected from various markets and examined for neoplastic growth. The study employed observational techniques, statistical analyses including ANOVA and Duncan's Multiple Range Test, and assessed seasonal variations from January 2019 to December 2022. Results: A total of 2,373 fish were examined, with 193 cases of neoplastic growth identified, indicating a substantial prevalence of tumors. The statistical analysis revealed significant variations in tumor prevalence, with a notable correlation to environmental factors. Conclusion: The study concludes that environmental pollution significantly impacts the prevalence of neoplastic growth in fish populations. This has profound implications for marine ecology and public health, emphasizing the need for effective environmental management and pollution control measures.

Spatial and Cross-Sectoral Transfer of Air Pollutant Emissions from the Fleet Electrification in China by 2030.

Fleet electrification shifts emission sources from the tailpipe to electricity generation and automotive supply chains subsequently, with emission transfer among regions. Such a spatial and cross-sectoral transfer of air pollutant emissions might embody uncertain environmental benefits spatially, which has not been comprehensively quantified, mainly due to the complexity of manufacturing processes of electric vehicle (EV) components (e.g., battery). We developed a hybrid life cycle assessment by combining inventory data of major processes and cross-sectoral input-output information and identified how China's EV deployment would influence the spatial redistribution of air pollutant emissions currently (2017) and in the future (2030). The results indicate that fleet electrification could readily reduce life cycle nitrogen oxides (NOx) and nonmethane volatile organic compound (NMVOC) emissions by 12-93%, and the reductions are estimated to be concentrated in major cities and urban agglomerations. However, increased demand for electricity and power battery production could increase PM2.5 and SO2 emissions in 17-55% of grids under all the scenarios, which emerge in coal-rich (e.g., Inner Mongolia, Shanxi) and industrial (e.g., Shandong, Henan, Jiangsu) provinces. By tracing the upstream, 31-55% of vehicle-cycle emissions are from deep supply chains but exhibit diverse sources. It suggests the necessity to relieve emissions leakage of fleet electrification by synchronizing effective environmental management across multiple sectors through EV supply chains.

Investigating the Relationship between Plant Species Composition and Topography in the Tomeyama Landslide: Implications for Environmental Education and Sustainable Management in the Happo-Shirakami Geopark, Japan

The Tomeyama landslide in the Happo-Shirakami Geopark, Japan, has interesting and important geomorphological and geoecological characteristics. Understanding these characteristics is crucial for environmental education and sustainable management in the geopark. In this study, we quantified the characteristics of the landslide, including its precise topography and vegetation. We used high-resolution 2.5 m-mesh ALOS World 3D topographic data to define the topography of the landslide. We also surveyed plant species composition and cover in four plots (three on the upper slope and one on the convex lower foot slope), each measuring 20 m × 20 m. Our findings reveal that the landslide is sited on a northwest-facing slope, 250 m below the ridge top, and has a horseshoe-shaped main scarp with a height of 40 m. Two smaller secondary scarps and their corresponding displaced landslide blocks suggest reactivation since the main landslide event. In the upper slope plots, 40–55 plant species were identified, including 14–16 species associated with the Japanese beech forest and 2–5 species related to the Pterocarya rhoifolia forest. In the lower slope plot, 70 plant species were identified, including 14 species from the Japanese beech forest and 11 from the Pterocarya rhoifolia forest. The upper slope plant community belongs to the Japanese beech forest; however, categorizing the lower slope community is challenging, although more Pterocarya rhoifolia forest species are present compared with the upper slope. These results suggest that certain plant species have adapted to the diverse topography created by the landslide. These findings improve the understanding of landslide topography and plant community composition with respect to environmental factors and thereby support effective environmental education and sustainable management in the Happo-Shirakami Geopark.

Uncovering the factors that affect earthquake insurance uptake using supervised machine learning

The escalating threat of natural disasters to public safety worldwide underlines the crucial role of effective environmental risk management tools, such as insurance. This is particularly evident in the case of earthquakes that occurred in Oklahoma between 2011 and 2020, which were linked to wastewater injection, underscoring the need for earthquake insurance. In this regard, from a survey of 812 respondents in Oklahoma, USA, we used supervised machine learning techniques (i.e., logit, ridge, least absolute shrinkage and selection operator (LASSO), decision tree, and random forest classifiers) to identify the factors that influence earthquake insurance uptake and to predict individuals who would acquire earthquake insurance. Our findings reveal that influential factors that affect earthquake insurance uptake include demographic factors such as older age, male gender, race, and ethnicity. These were found to significantly influence the decision to purchase earthquake insurance. Additionally, individuals residing in rental properties were less likely to purchase earthquake insurance, while longer residency in Oklahoma had a positive influence. Past experience of earthquakes was also found to positively influence the decision to purchase earthquake insurance. Both decision trees and random forests demonstrated good predictive capabilities for identifying earthquake insurance uptake. Notably, random forests exhibited higher precision and robustness, emerging as an encouraging choice for earthquake insurance modeling and other classification problems. Empirically, we highlight the importance of insurance as an environmental risk management tool and emphasize the need for awareness and education on earthquake insurance as well as the use of supervised machine learning algorithms for classification problems.

Utilizing Machine Learning within Artificial Intelligence to Enhance Dissolved Oxygen Estimation in the Mississippi River via Temperature-Driven Polynomial Regression

Abstract: In this research, a comprehensive examination of Dissolved Oxygen (DO) levels in the Mississippi River is undertaken, employing a Polynomial Regression model drivenby temperature data for predictive estimation. Dissolved oxygen serves as a pivotal indicator of water quality and the health of aquatic ecosystems, making its accurate forecasting crucial for effective environmental monitoring and management. By utilizingtemperature as a primary predictor, this study seeks to advance our understanding of the intricate relationship between temper- ature and DO within the context of the Mississippi River. The research involves an extensive dataset comprising measurements of water temperature and dissolved oxygen collected over an extended duration. A Polynomial Regression model is employed to establish a mathematical link between temperature and DO, thus providing a predictive tool for estimating DO levels at specific locations along the river. The model’s performance is subjected to a rigorous evaluation, involving the assessment of diverse statistical metrics and validation techniques. The research outcomes yield valuable insights into the dynamics of DO in the Mississippi River, emphasizing the pivotal role of temperature as a primary driver of DO fluctuations. This study introduces a practical and efficient method for the monitoring and prediction of DO levels, which can be instrumental in the preservation and sustainable management of this vital aquatic ecosystem. Moreover, it makes a meaningful contribution to the broader realm of water quality assessment and has the potential to inform policies and practices aimed at ensuring the environmental well- being of the Mississippi River.

Quantifying Bioaccumulation Factors of Food Cultivars in the Vicinity of Coal Mining: A Study in Ngwo, Akwuke, and Udi Communities, Enugu State, Nigeria

Heavy metal contamination resulting from coal mining activities poses a substantial threat to both environmental stability and human health. This study delves into the bioaccumulation factors of various food cultivars within the coal mining regions of Ngwo, Akwuke, and Udi Communities in Enugu State, Nigeria. Heavy metals, including lead, cadmium, nickel, manganese, and chromium, have been recognized as significant contaminants responsible for various detrimental health effects. The presence of these metals in the environment, particularly in soil, water, and subsequently in food crops, leads to indirect human exposure through the food chain, thereby triggering several health risks. This research focuses on assessing the levels of heavy metal bioaccumulation in food cultivars and their potential implications for human health. Samples of leafy vegetables and tubers were collected from different locations within the communities and processed following recommended methods. The digestion process involved the use of concentrated acids and subsequent analysis through Atomic Absorption Spectrophotometry (AAS) to quantify the concentration of heavy metals. Bioaccumulation factors (BAF) were determined by establishing the ratio of metal concentrations in the plants to those in water. Results indicated significant variations in bioaccumulation factors among different cultivars and study sites. For instance, the bioaccumulation of manganese and chromium in pumpkin from Akwuke was notably higher compared to Udi and Ngwo, while lead and cadmium levels were significantly higher in Udi. Similar trends were observed in scent leaf and cassava samples, suggesting differential metal accumulation based on cultivar type and geographical location. The findings underscore the concerning levels of heavy metal bioaccumulation, especially high cadmium accumulation in certain food cultivars, posing potential health hazards to communities reliant on these crops. This study emphasizes the critical need for effective environmental management strategies and water treatment practices in these regions to mitigate health risks associated with heavy metal exposure through food consumption. Keywords: Heavy metals, Bioaccumulation factors, Food cultivars, Coal mining, Environmental health, Nigeria.