Hasil untuk "Environmental effects of industries and plants"

Yourong Fan, Fang Wang, Abdallah Shaheen et al.

Black carbon (BC) deposition poses a critical environmental threat by accelerating cryospheric melt and imposing substantial health risks. However, the drivers governing its long-term dynamics remain poorly quantified, hindering effective environmental governance and sustainable planning. Here, we combine multi-source observations with machine learning to dissect the contributions of emissions and meteorology to BC deposition trends and interannual variability in China during 1980-2022. While emissions dominate multi-decadal trends, a significant multi-year meteorological anomaly temporarily decoupled deposition from emissions beginning in 2003—at which point wet deposition shifted from increasing to decreasing. This reversal preceded sustained emission reductions by several years and occurred five years earlier than the corresponding reversal in dry deposition. Factor separation further demonstrate that meteorological conditions overwhelmingly govern interannual variability across China, accounting for 24.20-54.07% of variance in dry deposition and 55.46-66.65% in wet deposition. Precipitation has emerged as the leading meteorological driver, explaining more variability in wet deposition across most Chinese subregions than any other factor. Our findings demonstrate that climate variability not only regulates year-to-year fluctuations but can reverse long-term deposition trends. This highlights the urgency of incorporating climate uncertainty into emission control strategies and sustainability assessment to achieve a reliable prediction of the environmental impact of BC.

Jinfei Zhang

Amid escalating environmental challenges and rapid digital transformation, sustained green innovation (SGI) has become a strategic imperative for manufacturing firms. Drawing on the knowledge-based view, this study examines whether and how firms’ digital innovation network embedding (DINE) enhances SGI through two complementary mechanisms—knowledge recombinative creation (KRC) and knowledge recombinative reuse (KRR). Using panel data on Chinese listed manufacturing firms from 2013 to 2021 and constructing patent co-application–based digital collaboration networks, we test the hypotheses with fixed-effects and instrumental-variable models. To mitigate endogeneity, DINE is instrumented by industry–province peer embedding (excluding the focal firm) and province-by-year fixed effects are absorbed to account for time-varying regional policy and institutional shocks. The results show that DINE significantly improves SGI, while KRC and KRR jointly mediate this relationship, explaining 37.5 % of the total effect. Robustness checks and endogeneity corrections support the validity of the findings. Moreover, the DINE–SGI linkage is stronger in technology-intensive industries, highlighting contextual contingencies in digital–green synergies. This study advances understanding of how digitally embedded knowledge flows sustain green innovation and offers practical implications for building digital collaboration ecosystems that support long-term sustainability in emerging economies.

Kunyu Liang, Xiaobin Jin, Xinxin Zhang et al.

Ensuring sufficient cropland is crucial for food security and social stability. Cropland compensation, encompassing cropland restoration and reclamation, is an essential means of enhancing food production. Although previous studies have examined the production and ecological impacts of these two pathways, systematic national-scale comparisons remain limited. Here, an integrated comparative framework combining machine learning, dynamic balance analysis, and impact assessment is constructed. First, the Maximum Entropy (MaxEnt) model was employed to assess cultivation suitability. Subsequently, potential areas for cropland restoration and reclamation in China were identified under the dynamic balance constraints. Furthermore, by setting different scenarios, the differential effects of two compensation pathways were simulated and compared. The results show that China has a considerable resource base for cropland compensation. China has a total of 44.95 million ha of cropland compensation potential areas, including 34.72 million ha for cropland restoration and 10.23 million ha for cropland reclamation. Both pathways contribute to increased food production levels but result in declining habitat quality. However, cropland restoration alleviates the trade-off between food production and habitat quality, whereas cropland reclamation can exacerbate it. In general, this study quantifies, for the first time at the national level, the potential areas and varying impacts of restoration and reclamation. It provides a unified basis for evaluating China's cropland compensation strategies and offers insights for sustainable agriculture and food security in densely populated or resource-constrained regions.

T. Simpson, R. A. Badcock, T. Berry et al.

Levitated dipole reactors offer an attractive path towards economic fusion power generation. The intrinsic decoupling of the confining magnetic field-generating REBCO magnets and the vacuum vessel offer unparalleled accessibility and maintainability, allowing for high plant duty factors and theoretically low electricity prices. In order to achieve rapid deployment of fusion power to the grid, the use of the Deuterium-Tritium (DT) fuel cycle is required due to its lower required plasma triple products. Historically, designs of levitated dipole fusion power plants have targeted advanced fuels as a DT device was seen to be infeasible due to the high fluxes of 14.1 MeV neutrons on the superconducting core magnet. This study presents high level designs for two feasible first-of-a-kind (FOAK) DT levitated dipole fusion power plants, the larger of which produces 667 MW of fusion power and is predicted to produce 208 MW of net electric power. Both designs consist of a heavily neutron-shielded, high-field REBCO core magnet capable of producing peak magnetic field strengths of 23 T while keeping peak mechanical strains below 0.4%. The neutron shielding is comprised of a layered structure of tungsten and boron carbide, which allows for 92% of the heat deposited in the neutron shield to be radiated out to the first wall while still providing sufficient neutron attenuation to give adequate REBCO conductor lifetimes. The core magnet REBCO coil is comprised of a small "sacrificial" section and a larger semi-permanent section. The sacrificial section, comprising ~20% of the coil, will have a neutron damage limited lifetime of ~1 year, after which the core magnet will be quickly removed from the vacuum vessel and replaced. This allows the damaged core magnet to be refurbished and reused, reducing cost and allowing for economic fusion power generation from a DT levitated dipole reactor.

Sawant Sushant Anil, Dhananjayen and M. Sasi

Understanding aridity trends is crucial for climate adaptation strategies. This study analyzes the spatial and temporal fluctuations in aridity across Karnataka, India, over 63 years from 1958 to 2020 using the Aridity Index (AI). Monthly, seasonal, and annual AI values were calculated using precipitation and potential evapotranspiration data sourced from TerraClimate. The results indicate that approximately 74% (142,464 sq. km) of Karnataka is classified as dryland, ranging from semi-arid to dry subhumid zones, while 26% (49,416 sq. km) falls under more humid non-dryland areas. The Malnad and coastal regions are more humid compared to the predominantly semi-arid northern inland Karnataka. Temporal analysis between the periods 1958–1990 and 1991–2020 revealed that 6.24% of the land area shifted from semi-arid to dry subhumid, indicating increased moisture availability, whereas 0.43% shifted from dry subhumid to semi-arid, suggesting localized aridification. During the post-monsoon season, 14.12% of dryland areas transitioned to non-dryland, with substantial improvements in moisture availability observed in districts such as Uttara Kannada (59.21%) and Mandya (82.97%). Conversely, 1.5% of non-dryland areas converted to dryland, indicating localized decreases in water resources. Seasonal analysis revealed that 99.92% of the summer aridity status remained constant, while during the monsoon season, only 2.42% of dryland areas changed to non-dryland, reflecting stable monsoonal rainfall patterns. These findings highlight the significant influence of topography, monsoonal patterns, and water management on aridity dynamics in Karnataka. The study provides valuable insights for developing policies on climate adaptation, sustainable agriculture, and regional water resource management. Addressing the increasing trends in aridity is essential to reduce desertification risks and enhance the State’s resilience to climate change.

Yan'ge Yan, Shuiqin Zhang, Yingqiang Zhang et al.

Optimizing crop productivity while mitigating pollution requires a system-wide understanding of nitrogen (N) fertilizer fate and its loss pathways. However, regional variability in N fate and its linkage to yield response in China's wheat systems remains poorly quantified. We collected 4077 observations to analyze the effects of N fertilizer management, climate, and soil properties on wheat yield, N fate, and reactive nitrogen (Nr) losses using meta-analysis and machine learning. At the national scale, wheat yield increased by 65.40 ​% with N fertilization, with applied N partitioned into 41.56 ​% uptake, 29.66 ​% residual, and 38.81 ​% losses. The Nr losses comprised NH3 (9.35 ​%), N2O (0.73 ​%), NO (0.38 ​%), leaching (7.38 ​%), and runoff (4.68 ​%). At the regional scale, N uptake exhibited an increasing trend from north to south, whereas N residual and N loss gradually decreased. NH3 volatilization accounted for 91.76 ​% of total N loss in northern China (NC). In central China (CC), NH3 constituted 53.45 ​% of the losses, with N leaching accounting for 41.38 ​%. By contrast, southern China (SC) showed a more even distribution of losses across pathways. N application rate was the key determinant of N fate, whereas pH, mean annual precipitation, mean annual temperature, and bulk density had the greatest influence on Nr losses. Nationally, N uptake was the dominant driver of yield response, accounting for 54.06 ​% of the variation. Regionally, uptake remained the key factor in CC (37.83 ​%), whereas NC (27.33 ​% uptake; 16.09 ​% loss) and SC (15.20 ​% uptake; 12.37 ​% loss) showed substantial sensitivity to N loss. N residual had minimal impact in most regions but was significant in CC (11.66 ​%). Enhancing nitrogen uptake is the top priority in increasing wheat yield across different regions of China, while the role of fertilizer N loss and residual regionally varied. Accordingly, N management should prioritize loss reduction in NC and SC, and residual management in CC.

S. Monisha and S. P. Sangeetha

Heavy metal pollution released into the surface environment poses a significant threat, being hazardous to both the environment and living organisms. Phytoremediation thus appears as a viable technique to address heavy metal pollution in soils impacted by industrial effluents. To identify the growth performance of sunflower and cotton seeds under various concentrations of arsenic and chromium present in the tannery industrial wastewater in the Chengalpattu region, and to identify the accumulation of Arsenic(As)As and chromium (Cr) in the roots, shoots, and soil of these plants. This paper examined the usefulness of sunflower (Helianthus annuus) and cotton (Gossypium herbaceum) in eradicating Cr and As-polluted soils originating from tannery wastewater. In this experiment, Completely Randomized Block Design (CBRD) testing was performed, and the samples were analyzed using Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The accumulation of Cr in sunflowers was 120 mg.kg-1 in the roots and 25 mg.kg-1 in the above-ground parts. As accumulated to 85 mg.kg-1 in the roots and 15 mg.kg-1 in the above-ground parts. Similarly, cotton plants accumulated 90 mg.kg-1 of Cr in the roots and 20 mg.kg-1 in the above-ground parts. As accumulation in cotton plants was 100 mg.kg-1 in the roots and 30 mg.kg-1 in the aboveground parts. The study inferred that, in comparison to the other plants, the concentrations of Cr in sunflower roots were significantly higher, but cotton was found to have a better ability to take up As in the roots as well as in the aerial parts of the plant. It hence demonstrates the applicability of sunflower and cotton to support phytoremediation efforts sustainably within industrial environments to mitigate pollution and improve the quality of the soil.

Magdalena Zabek, Jose-Luis Galvez-Martos, Thaleia Konstantinou

The transition towards a circular economy in the built environment requires robust methodologies to evaluate carbon and material flows at the component level. This paper introduces Carbon Flow Analysis (CFA), an innovative approach that integrates Material Flow Analysis and Life Cycle Assessment to facilitate environmental decision- making for façade renovations. CFA systematically maps embodied carbon and material inputs within façade components, offering a transparent assessment of their circularity potential. The study further refines the selection process through a contextualization framework, which contrasts CFA results against environmental performance ranges derived from Environmental Product Declarations (EPDs) and environmental databanks. Findings demonstrate the variable role of secondary materials in reducing carbon emissions, due to the large variability of impact across materials and components. While CFA provides actionable insights into material selection for façade components, the study highlights the need for standardized circularity indicators and reliable databanks to enhance decision-making in architectural design. By combining quantitative carbon tracking with performance- based contextualization, this research contributes to the development of practical guidelines for achieving carbon-neutral façade renovations.

Benedikt Verkic, Lieve Göbbels, Kathrin Greiff

Through innovative exchange of resources, industrial symbiosis can contribute to the circular economy and decrease environmental impacts. Similarly, urban symbiosis focuses on synergies involving cities. Even though many literature reviews exist in the field of industrial symbiosis, overviews on currently used and suitable indicators for measuring environmental impacts are lacking. Therefore, this scoping review provides an overview and descriptive analysis of relevant environmental indicators in the field of industrial and urban symbiosis used in scientific and gray literature. To ensure a comprehensive and exhaustive overview, eco-industrial parks and circular economy on the meso level are included. The aim is to provide a solid basis for future industrial and urban symbiosis assessment frameworks to improve and accelerate the identification of individually appropriate indicators. In total 3349 indicators across 457 sources were identified and clustered into 624 comprehensive indicators. The indicators are evaluated regarding overall use and use over time, category, type and R-strategy. Our results showed that most indicators are available in the area of material, waste and water, followed by environment and emissions. The paper identified good coverage of high-circularity R-strategies but limited coverage for other strategies. These results form a solid basis for the development of holistic and standardized assessment frameworks in the field of industrial and urban symbiosis. The most used indicators could for instance serve as a basis for relevance when utilized by companies, industrial park operators, and urban administrations.

Chi Zuo, Martin B. Møller, Pablo Martínez-Nuevo et al.

While being disturbed by environmental noises, the acoustic masking technique is a conventional way to reduce the annoyance in audio engineering that seeks to cover up the noises with other dominant yet less intrusive sounds. However, misalignment between the dominant sound and the noise-such as mismatched downbeats-often requires an excessive volume increase to achieve effective masking. Motivated by recent advances in cross-modal generation, in this work, we introduce an alternative method to acoustic masking, aiming to reduce the noticeability of environmental noises by blending them into personalized music generated based on user-provided text prompts. Following the paradigm of music generation using mel-spectrogram representations, we propose a Blending Noises into Personalized Music (BNMusic) framework with two key stages. The first stage synthesizes a complete piece of music in a mel-spectrogram representation that encapsulates the musical essence of the noise. In the second stage, we adaptively amplify the generated music segment to further reduce noise perception and enhance the blending effectiveness, while preserving auditory quality. Our experiments with comprehensive evaluations on MusicBench, EPIC-SOUNDS, and ESC-50 demonstrate the effectiveness of our framework, highlighting the ability to blend environmental noise with rhythmically aligned, adaptively amplified, and enjoyable music segments, minimizing the noticeability of the noise, thereby improving overall acoustic experiences. Project page: https://d-fas.github.io/BNMusic_page/.

Jianping Yao, Son N. Tran

Plant leaf disease classification is an important task in smart agriculture which plays a critical role in sustainable production. Modern machine learning approaches have shown unprecedented potential in this classification task which offers an array of benefits including time saving and cost reduction. However, most recent approaches directly employ convolutional neural networks where the effect of the relationship between plant species and disease types on prediction performance is not properly studied. In this study, we proposed a new model named Multi-output Deep Supervised Classifier Chains (Mo-DsCC) which weaves the prediction of plant species and disease by chaining the output layers for the two labels. Mo-DsCC consists of three components: A modified VGG-16 network as the backbone, deep supervision training, and a stack of classification chains. To evaluate the advantages of our model, we perform intensive experiments on two benchmark datasets Plant Village and PlantDoc. Comparison to recent approaches, including multi-model, multi-label (Power-set), multi-output and multi-task, demonstrates that Mo-DsCC achieves better accuracy and F1-score. The empirical study in this paper shows that the application of Mo-DsCC could be a useful puzzle for smart agriculture to benefit farms and bring new ideas to industry and academia.

Jonas Nordström, Sigrid Denver

In this study we ask how a range of environmental sustainability adjustments that consumers find it easy to adopt affect the carbon footprint of their food consumption. The study is based on information about real purchases of food products and responses to a questionnaire about the various sustainability adjustments that the study participants apply and their concern about climate change. Based on principal component and regression analysis the results from the study indicate that sustainability adjustments such as organic consumption, buying domestically produced food and eating seasonal produce, as well as concern about climate change, are associated with a reduced carbon footprint from food consumption. The largest reductions were found for organic consumers. The results suggested that most committed organic consumers have a carbon footprint that is about one third smaller than that of consumers who seldom buy organic food products. The results also indicate that these voluntary sustainability adjustments are not sufficient to secure conformity with today’s goals for reduced greenhouse gas emissions.

Rameeja Shaik, Buddhadev Ghosh, Harish Chandra Barman, Arijit Rout and Pratap Kumar Padhy

In recent times, nanotechnology has experienced widespread acclaim across diverse sectors, including but not limited to tissue engineering, drug delivery systems, biosensors, and the mitigation and monitoring of environmental pollutants. The unique arrangement of carbon atoms in sp3 configurations within carbon nanomaterials endows them with exceptional physical, mechanical, and chemical characteristics, driving them to the forefront of materials research. Their appeal lies in their efficacy as superior adsorbents and their exceptional thermal resistance, making them versatile in various applications. The present review extensively explores a range of carbon-based nanomaterials, delving into their synthesis methods and examining their multifaceted applications in addressing environmental pollutants. It is crucial to emphasize that the popularity of carbon-based nanomaterials arises from their potential to serve as superior adsorbents, coupled with their outstanding thermal resistance properties. These attributes contribute to their applicability in diverse environmental contexts. Looking ahead, carbon-based nanomaterials are poised to emerge as environmentally friendly and cost-effective materials, representing promising and potential avenues for the advancement of sustainable technology.

Susana Leal, João Nascimento, Andriani Piki et al.

Higher education institutions have a role to play in developing sustainability skills and changing students' attitudes and behaviour towards sustainability issues and the Sustainable Development Goals (SDGs). This article aims to explore the knowledge, attitudes and behaviours of higher education students towards sustainability and understand how these vary in line with gender, age, level of education, the field of study and familiarity with the SDGs. A questionnaire survey was carried out among higher education students. A sample of 716 students from different European countries and Türkiye was obtained. The results show that the relationship between students' knowledge of sustainability and their behaviour towards sustainability issues is partly mediated by their attitudes towards sustainability. The practical implications of this study are that it highlights the need to strengthen education on sustainable development and the SDGs in all areas and at all levels of higher education and to provide sound training in this field from the moment students enter higher education. Although knowledge and attitudes towards sustainability are well developed, higher education institutions must train students to change their behaviour.

M. V. Vincy and R. Brilliant

The Vamanapuram River Basin (VRB) is home to a diverse range of plant species, including 152 distinct species from 50 botanical families. Poaceae, Leguminosae, Araceae, and Aseraceae are the most abundant, with 13 species. Euphorbiaceae, Acanthaceae, Apocynaceae, and Rubiaceae also contribute to the biodiversity hotspots. The VRB’s vegetation profile is characterized by a dynamic interplay of plant forms and ecological niches, with 74 herbs, 30 shrubs, 12 grasses, 1 liana, and 35 towering trees. The Poaceae family thrives in this environment due to hydrological factors. The sampling sites P6 and P5 exhibit high relative frequency and density, with key species like Macaranga peltata, Ficus hispida, and Swietenia macrophylla. Diversity indices like the Shannon-Wiener diversity index reaffirm the VRB’s tropical forest character. Beta-diversity patterns reveal unique plant species distribution dynamics among different panchayaths, emphasizing their ecological complexities. The study emphasizes the demand for specialized management and conservation techniques in this environmentally active region.

Nathan P. Lawrence, Seshu Kumar Damarla, Jong Woo Kim et al.

With the rise of deep learning, there has been renewed interest within the process industries to utilize data on large-scale nonlinear sensing and control problems. We identify key statistical and machine learning techniques that have seen practical success in the process industries. To do so, we start with hybrid modeling to provide a methodological framework underlying core application areas: soft sensing, process optimization, and control. Soft sensing contains a wealth of industrial applications of statistical and machine learning methods. We quantitatively identify research trends, allowing insight into the most successful techniques in practice. We consider two distinct flavors for data-driven optimization and control: hybrid modeling in conjunction with mathematical programming techniques and reinforcement learning. Throughout these application areas, we discuss their respective industrial requirements and challenges. A common challenge is the interpretability and efficiency of purely data-driven methods. This suggests a need to carefully balance deep learning techniques with domain knowledge. As a result, we highlight ways prior knowledge may be integrated into industrial machine learning applications. The treatment of methods, problems, and applications presented here is poised to inform and inspire practitioners and researchers to develop impactful data-driven sensing, optimization, and control solutions in the process industries.

Marsha Mariya Kappan, Joby George

A kind of pancreatic cancer called Pancreatic Ductal Adenocarcinoma (PDAC) is anticipated to be one of the main causes of mortality during past years. Evidence from several researches supported the concept that the oncogenic KRAS (Ki-ras2 Kirsten rat sarcoma viral oncogene) mutation is the major cause of pancreatic cancer. KRAS acts as an on-off switch that promotes cell growth. But when the KRAS gene is mutated, it will be in one position, allowing the cell growth uncontrollably. This uncontrollable multiplication of cells causes cancer growth. Therefore, KRAS was selected as the target protein in the study. Fifty plant-derived compounds are selected for the study. To determine whether the examined drugs could bind to the KRAS complex's binding pocket, molecular docking was performed. Computational analyses were used to assess the possible ability of tested substances to pass the Blood Brain Barrier (BBB). To predict the bioactivity of ligands a machine learning model was created. Five machine learning models were created and have chosen the best one among them for analyzing the bioactivity of each ligand. From the fifty plant-derived compounds the compounds with the least binding energies are selected. Then bioactivity of these six compounds is analyzed using Random Forest Regression model. Adsorption, Distribution, Metabolism, Excretion (ADME) properties of compounds are analyzed. The results showed that borneol has powerful effects and acts as a promising agent for the treatment of pancreatic cancer. This suggests that borneol found in plants like mint, ginger, rosemary, etc., is a successful compound for the treatment of pancreatic cancer.

Aditi Nidhi

Climate change is a widely debated topic in the 21st century, with various perspectives and opinions on its causes and potential remedies. Climate change risks have perplexed authorities and made protecting human life and health difficult. The elements that cause climate change, such as the combustion of fossil fuels, air pollutants, short-lived climatic pollutants, etc., have affected both the climate and human health. The Paris Agreement established several commitment periods that each nation was obligated to follow in accordance with their own individual capacities. This will assist in achieving greater human health and environmental benefits. To develop a robust climate change framework, WHO and other UN organizations have moved up to resolve these challenges. From the first international conference in 1988 to the current Conference of Parties, it has been concluded that “humanity is conducting an unintended, uncontrolled, globally pervasive experiment, the ultimate consequences of which could be second only to a global nuclear war.” The recent Katowice Agreement and the climate change package that was put in place demonstrate the seriousness required to resolve the issues of finance, loss and damage, and differentiation mechanisms, which were thoroughly discussed. The paper will focus on the existing legal solutions for providing climate justice to nations. The study will also look at the effectiveness of COP24 in executing adaptation and mitigation plans and adhering to the Paris Agreement in both text and spirit.

A. Khatri and R. Bhateria

Global agricultural production cannot catch the increasing population’s exigency. At different times, the world has faced food crises of varying intensity. Many steps have been taken after that to encounter the rising concerns. Nowadays, nanofertilizers are being experimented with as an alternative to conventional fertilizers. Nanofertilizers can be classified as macronutrients and micronutrients nanofertilizers. Synthesis of macronutrient nanofertilizers (nitrogen, phosphorus, potassium, calcium, magnesium, etc.) and micronutrient nanofertilizers (iron, boron, zinc, copper, silicon, etc.) can be done using chemical and green synthesis methods, which involves reducing agents, capping agents, dendrimers, microbial synthesis, solvents, and others. Composition of the nanofertilizers can be done using top-down and bottom-up approaches incorporating hydrocarbon polymer, dendrimers, microbes, etc., which decides their usage in various crops depending upon the requirement of the plant. Engineered nanofertilizers can improve crop yield by mitigating environmental pollution, environmental stress, and plant diseases. However, the unsystematic use of nanofertilizers can be a hurdle in its utilization. This article discusses various types of nanofertilizers with their unique properties and applications. Each category of nanofertilizers is explained considering their composition, particle size, concentrations applied, benefited plant species, and plant-growth enhancement aspects.

J. Grández, M. Oliva, E. Morales, M. Goñas, S. Chavez A. Guivin, L. Quiñones and M. Milla

The objective of the study was to evaluate the concentration of arsenic, chromium, cadmium, and lead in onion (Allium fistulosum and Allium cepa), tomato (Solanum lycopersicum), and celery (Apium graveolens) from two origins (local - Chachapoyas province and from the coast-province of Chiclayo) that are sold in the model market of the city of Chachapoyas. Six samples were taken on three different dates in November 2020, which were collected by non-probabilistic sampling (by convenience), which allowed choosing the most appropriate sample (according to its origin). For the determination of heavy metals, the Agilent 4100 MP-AES spectrometer was used. The concentration of As, Cr, and Cd in the vegetables remained below the Maximum Allowable Limits of the international standards with which they were compared; however, the concentration of Pb exceeded the Maximum Allowable Limits in all the samples analyzed, obtaining the lowest value in the celery samples from the local origin (0.15 mg.kg-1) and the highest value in the tomato samples from the coast (0.21 mg.kg-1). Therefore, it is concluded that only Pb is higher than the Maximum Allowable Limits with which it was compared.