Search Results (54)

Food contact surfaces can be a source of food contamination. Bacteria of the genus Proteus are known as opportunistic pathogens, often associated with faecal contamination and decomposition of organic matter. This study aimed to isolate Proteus spp. from surface samples (of dimensions 5 cm² × 5 cm²). Three levels of artificially soiled aluminium foil were prepared using bacterial suspensions of Proteus hauseri ATCC 13315. Afterwards, the surface swabbing method for the detection of Proteus spp. was applied. The swab was homogenised with Eugon LT 100 broth, and 1 mL was transferred to the enrichment broth. After the incubation of the enrichment broth, streaking on the Brilliant Green Agar and Salmonella Shigella Agar was performed. The characteristic colonies were confirmed by biochemical reactions. The number of positive findings of Proteus hauseri on the applied level of contamination was used for calculation by the PODLOD_ver12.xls ECEL program by Wilrich and Wilrich. This program estimates the probability of detection (POD) function and the limit of detection (LOD) of qualitative microbiological methods. The results of the detection of Proteus hauseri in surface samples showed LOD₅₀ = 24.60 [48.96; 97.45] CFU in 1 mL of swab rinse, and LOD₉₅ = 106.30 [211.59; 421.15] CFU in 1 mL of swab rinse. The applied method for isolation of Proteus spp. from the surface samples can be used for well-contaminated surfaces. Full article

The unprecedented development of coastal cities in West Africa is marked by anarchic urbanization accompanied by ineffective environmental management, leading to water pollution. This study is conducted in the southern districts of Lomé, Togo, an area built on sandbars where inappropriate attitudes, behaviors, and inadequate hygiene and sanitation practices prevail. The objective of this study is to characterize the quality of groundwater in the study area. Bacteriological and physicochemical analyses were carried out on 11 wells in 10 districts in the southern districts during the four seasons of the year. The analysis shows that the groundwater is polluted in all seasons. Nitrate concentrations exceed 50 mg/L in 65% of the samples, while chloride levels surpassed 250 mg/L in 18% of the cases. Regardless of the season, the dominant facies is sodium chloride and potassium chloride. In all districts, the analysis of microbiological parameters including total germs (30 °C, 100/mL), total coliforms (30 °C, 0/mL), Escherichia coli (44 °C, 2/250 mL), fecal streptococci (0/100 mL), and anaerobic sulfite reducers (44 °C, 2/20 mL) reveals values exceeding the European Union standards (2007). Groundwater contamination is facilitated by the sandy nature of the soil, which increases its vulnerability to various pollutants. Togo continues to experience cholera outbreaks, aggravated by poor sanitation infrastructure and limited vaccination coverage. Public health efforts are directed toward improving sanitation and raising awareness about waterborne and non-communicable diseases. Full article

Soil health directly impacts food security, so investigating contaminants is a topic of interest for the anticipatory study of the action–effect correlation. This paper conducts a systematic literature review through seven analyses, identifying researchers’ interest in soil health using artificial intelligence tools. The first study examines the distribution of articles over the years to assess researchers’ interest in soil health, and subsequently, the same analysis is conducted regarding artificial intelligence (AI) methods. Additionally, the productivity of authors, the distribution of articles by country, relevant publications, and the frequency of keywords are analyzed to identify areas of interest associated with soil health. Subsequently, the branches of AI and examples of applications that have already been investigated in the specialized literature are identified, allowing areas that are currently underexplored to be pinpointed. This paper also proposes a specialized analysis using an algorithm specifically developed by the author for this investigation, which evaluates the interdisciplinary potential of the articles analyzed in the literature. In this way, the authors of the present research will propose new research directions that include machine learning, natural language processing, computer visualization, and other artificial intelligence techniques for monitoring soil contaminants. They will also suggest using these tools as preventive measures to minimize the negative impact of contaminants on the soil. The direct consequence is the protection of soil health and its effects on human health. Full article

Heavy metal and cyanide contamination in soil presents serious environmental and ecological concerns due to their persistence, bioavailability, and toxicity to soil biota. In this study, a novel solid-phase direct contact bioassay kit was developed using immobilized Chlorella vulgaris spheres to evaluate the toxicity of soils contaminated with mercury (Hg²⁺), silver (Ag⁺), copper (Cu²⁺), and cyanide (CN⁻). The assay was designed using 25 mL glass vials in which algal spheres were directly exposed to spiked soils for 72 h without the need for pollutant extraction. Oxygen evolution in the headspace was measured as the primary endpoint, alongside optical density and chlorophyll a fluorescence (OJIP) to assess photosynthetic inhibition. The assay demonstrated high sensitivity and reproducibility, with strong correlations (R² > 0.93) between oxygen evolution and optical density. EC₅₀ values based on oxygen evolution were 4.43, 4.18, 3.10, and 61.3 mg/kg for Hg²⁺, Ag⁺, CN⁻, and Cu²⁺, respectively, and 7.8, 7.4, 2.9, and 29.7 mg/kg based on optical density. The relatively higher EC₅₀ for copper was attributed to its biological role as an essential micronutrient. OJIP transient profiles supported the observed photosynthetic inhibition, particularly under Hg²⁺, Ag⁺, and CN⁻ exposure. The present study overcomes the limitations of conventional chemical analyses by providing a rapid, low-cost, and ecologically relevant tool for direct soil toxicity assessment, with potential applications in environmental monitoring and contaminated site evaluation. Full article

Soil preservation from pollutants is essential for sustaining human and ecological health. This review explores the application of satellite imagery and machine learning (ML) techniques in detecting soil pollution, addressing recent advancements and key challenges in this field. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a comprehensive search across three major databases yielded 47 articles from an initial pool of 1018 publications spanning the last eight years. Among these, 34 studies focused on direct detection of soil pollutants, while 13 examined relationships between vegetation indicators and soil contaminants. This review evaluates various satellite platforms, highlights limitations of existing spaceborne sensors, and compares the effectiveness of ML models for soil pollution detection. Key challenges include the lack of standardization in datasets and methodologies, variations in evaluation metrics, and differences in algorithmic performance across studies. The findings emphasize the need for standardized frameworks and improved sensor capabilities to enhance detection accuracy. This work provides a foundation for future research, encouraging the integration of advanced ML models and multi-sensor satellite data for comprehensive soil pollution monitoring. Full article

Water scarcity presents a critical challenge to global sustainability, exacerbated by population growth, climate change, and environmental pollution. In this context, graywater reuse has emerged as a promising solution, offering substantial water savings with significant potential for agricultural applications. However, efficient treatment methods are essential to ensure safe reuse, as contaminants vary depending on the source. This study introduces a cyclic graywater treatment system that integrates both mechanical and biological filtration processes. A key feature of this system is the inclusion of Chenopodium quinoa, a resilient plant known for its phytoremediation potential, which enhances filtration efficiency and facilitates contaminant removal. The study examines the impact of treated graywater on soil and quinoa properties, focusing on its suitability for irrigation. The results show that the cyclic treatment system significantly improves graywater quality, enhancing the removal of biological and microbiological contaminants, such as BOD, with a significant decrease ranging from 31.33 mg O₂/L to 15.74 mg O₂/L is observed after treatment. For COD, the average values decreased from 102.64 mg O₂/L to 54.19 mg O₂/L after treatment, making the treated graywater compliant with Tunisian regulation NT 106.03 and WHO guidelines. Cyclic treatment significantly reduced the microbial load of graywater. For example, for E. coli, the average decreased from 0.87 log 10/100 mL in RGW to 0.58 log 10/100 mL in GWT3. The results demonstrate that the cyclic treatment process can predict the graywater quality beyond the three tested stages. This study highlights the potential of plant-based cyclic graywater treatment systems as an eco-friendly and scalable approach for sustainable water management in agriculture. Full article

Asbestos fibers are well-known carcinogens, and their rapid detection is critical for ensuring safety, protecting public health, and promoting environmental sustainability. In this work, short-wave infrared (SWIR) spectroscopy, combined with machine learning (ML), was evaluated as an environmentally friendly analytical approach for simultaneously distinguishing the asbestos type, asbestos-containing materials in various forms, asbestos-contaminated/-uncontaminated soil, and asbestos-contaminated/-uncontaminated cement, simultaneously. This approach offers a noninvasive and efficient alternative to traditional laboratory methods, aligning with sustainable practices by reducing hazardous waste generation and enabling in situ testing. Different chemometrics techniques were applied to discriminate the material classes. In more detail, partial least squares discriminant analysis (PLS-DA), principal component analysis-based discriminant analysis (PCA-DA), principal component analysis-based K-nearest neighbors classification (PCA-KNN), classification and regression trees (CART), and error-correcting output-coding support vector machine (ECOC SVM) classifiers were tested. The tested classifiers showed different performances in discriminating between the analyzed samples. CART and ECOC SVM performed best (${Recall}_M$ and ${Accuracy}_M$ equal to 1.00), followed by PCA-KNN (${Recall}_M$ of 0.98–1.00 and ${Accuracy}_M$ equal to 1.00). Poorer performances were obtained by PLS-DA (${Recall}_M$ of 0.68–0.72 and ${Accuracy}_M$ equal to 0.95) and PCA-DA (${Recall}_M$ of 0.66–0.70 and ${Accuracy}_M$ equal to 0.95). This research aligns with the United Nations’ Sustainable Development Goals (SDGs), particularly SDG 3 (Good Health and Well-Being), by enhancing human health protection through advanced asbestos detection methods, and SDG 12 (Responsible Consumption and Production), by promoting sustainable, low-waste testing methodologies. Full article

Addressing groundwater contamination, this study applies machine learning (ML) algorithms to explore the spatiotemporal dynamics of hexavalent chromium (Cr[VI]) at the Hanford 100-Area. The research uses an extensive long-term monitoring dataset focused on groundwater wells and aquifers to enhance the understanding and management strategies of this complex environmental issue and predict the impact on aquifers due to the contamination in groundwater wells. The challenging nature of the task is due to various factors, such as the geological nature of the soil, pipeline leaks, and mobility of the particles that impact the speed of contamination. The findings demonstrate a random forest (ML)-based approach to predict the contaminant distributions accurately, thus significantly reducing uncertainties in contamination assessments and refining conceptual site models. This approach advances groundwater quality management and sets a precedent for future AI-driven environmental studies. Full article

Bioremediation is widely recognized as a promising and efficient approach for the elimination of Cd from contaminated paddy soils. However, the Cd removal efficacy achieved through this method remains unsatisfactory and is accompanied by a marginally higher cost. Cysteine has the potential to improve the bioleaching efficiency of Cd from soils and decrease the use cost since it is green, acidic and has a high Cd affinity. In this study, different combination modes of cysteine and microbial inoculant were designed to analyze their effects on Cd removal and the soil microbial community through the sequence extraction of Cd fraction and high-throughput sequencing. The results demonstrate that the mixture of cysteine and the microbial inoculant was the best mode for increasing the Cd removal efficiency. And a ratio of cysteine to microbial inoculant of 5 mg:2 mL in a 300 mL volume was the most economically efficient matching. The Cd removal rate increased by 7.7–15.1% in comparison with the microbial inoculant treatment. This could be ascribed to the enhanced removal rate of the exchangeable and carbonate-bound Cd, which achieved 94.6% and 96.1%, respectively. After the treatment, the contents of ammonium nitrogen (NH₃–N), total phosphorus (TP), available potassium (AK), and available phosphorus (AP) in the paddy soils were increased. The treatment of combinations of cysteine and microbial inoculant had an impact on the soil microbial diversity. The relative abundances of Alicyclobacillus, Metallibacterium, and Bacillus were increased in the paddy soils. The microbial metabolic functions, such as replication and repair and amino acid metabolism, were also increased after treatment, which benefitted the microbial survival and adaptation to the environment. The removal of Cd was attributed to the solubilizing, complexing, and ion-exchanging effects of the cysteine, the intra- and extracellular adsorption, and the production of organic acids of functional microorganisms. Moreover, cysteine, as a carbon, nitrogen, and sulfur source, promoted the growth and metabolism of microorganisms to achieve the effect of the synergistic promotion of microbial Cd removal. Therefore, this study underscored the potential of cysteine to enhance the bioremediation performance in Cd-contaminated paddy soils, offering valuable theoretical and technical insights for this field. Full article

Aluminum toxicity in acidic soils threatens rice (Oryza sativa L.) cultivation, hindering agricultural productivity. This study explores the potential of plant growth-promoting rhizobacteria (PGPR) as a novel and sustainable approach to mitigate aluminum stress in rice. Two rice varieties, INIAP-4M and SUPREMA I-1480, were selected for controlled laboratory experiments. Seedlings were exposed to varying aluminum concentrations (0, 2, 4, 8, and 16 mM) in the presence of four PGPR strains: Serratia marcescens (MO4), Enterobacter asburiae (MO5), Pseudomonas veronii (R4), and Pseudomonas protegens (CHAO). The INIAP-4M variety exhibited greater tolerance to aluminum than SUPREMA I-1480, maintaining 100% germination up to 4 mM and higher vigor index values. The study revealed that rhizobacteria exhibited different responses to aluminum concentrations. P. protegens and S. marcescens showed the highest viability at 0 mM (2.65 × 10¹⁰ and 1.71 × 10¹⁰ CFU mL⁻¹, respectively). However, P. veronii and S. marcescens exhibited the highest viability at aluminum concentrations of 2 and 4 mM, indicating their superior tolerance and adaptability under moderate aluminum stress. At 16 mM, all strains experienced a decrease, with P. protegens and E. asburiae being the most sensitive. The application of a microbial consortium significantly enhanced plant growth, increasing plant height to 73.75 cm, root fresh weight to 2.50 g, and leaf fresh weight to 6 g compared to the control (42.75 cm, 0.88 g, and 3.63 g, respectively). These findings suggest that PGPR offer a promising and sustainable strategy to bolster rice resilience against aluminum stress and potentially improve crop productivity in heavy metal-contaminated soils. Full article

To predict the behavior of aromatic contaminants (ACs) in complex soil–plant systems, this study developed machine learning (ML) models to estimate the root concentration factor (RCF) of both traditional (e.g., polycyclic aromatic hydrocarbons, polychlorinated biphenyls) and emerging ACs (e.g., phthalate acid esters, aryl organophosphate esters). Four ML algorithms were employed, trained on a unified RCF dataset comprising 878 data points, covering 6 features of soil–plant cultivation systems and 98 molecular descriptors of 55 chemicals, including 29 emerging ACs. The gradient-boosted regression tree (GBRT) model demonstrated strong predictive performance, with a coefficient of determination (R²) of 0.75, a mean absolute error (MAE) of 0.11, and a root mean square error (RMSE) of 0.22, as validated by five-fold cross-validation. Multiple explanatory analyses highlighted the significance of soil organic matter (SOM), plant protein and lipid content, exposure time, and molecular descriptors related to electronegativity distribution pattern (GATS8e) and double-ring structure (fr_bicyclic). An increase in SOM was found to decrease the overall RCF, while other variables showed strong correlations within specific ranges. This GBRT model provides an important tool for assessing the environmental behaviors of ACs in soil–plant systems, thereby supporting further investigations into their ecological and human exposure risks. Full article

With increasing urbanization and industrialization, soil and forest resources are facing considerable pressure, as well as the demand for water for domestic, agricultural, and industrial activities. Therefore, it is essential to conduct regular assessments of water quality and ensure that water is consistently maintained in the context of ecosystem services (ESs). Our objective was to apply the driving forces–pressures–state–impacts–responses (DPSIR) model to understand the cause–effect relationships and interactions with anthropogenic pressures on deforestation and water quality in the Talgua River watershed and associated valley and plain areas in central-eastern Honduras. Physicochemical and microbiological analyses were conducted to determine the water quality index (NSF–WQI) and other contamination indexes. The results identified high contamination by coliforms, up to 920.00 NPM/100 mL, and high levels of contamination by organic matter (ICOMO, 0.65), solids (ICOSUS, 0.79), mineralization (ICOMI, 0.99), and the presence of bacteria (BPI, 8.50), as well as the development of eutrophication processes (ICOTRO), resulting in generally low water quality. These problems were caused by the socio-demographic and economic growth of the area, as well as the high demand for water, vulnerability to climate change, and intense agro-livestock and industrial activity, which led to deforestation processes, changes in land use, and contamination of natural water bodies that impacted the overexploitation of aquifers. After applying the DPSIR model, strategies are proposed for the management and administration of the watershed aimed at preserving the water, soils, and forest resources, while promoting stakeholder, business, education sector, and public administration participation. Full article

Hazardous petroleum wastes are an inevitable source of environmental pollution. Leachates from these wastes could contaminate soil and potable water sources and affect human health. The management of acid tars, as a byproduct of refining and petrochemical processes, represented one of the major hazardous waste problems in Romania. Acid tars are hazardous and toxic waste and have the potential to cause pollution and environmental damage. The need for the identification, study, characterization, and subsequently either the treatment, valorization, or elimination of acid tars is determined by the fact that they also have high concentrations of hydrocarbons and heavy metals, toxic for the storage site and its neighboring residential area. When soil contamination with acid tars occurs, sustainable remediation techniques are needed to restore soil quality to a healthy production state. Therefore, it is necessary to ensure a rapid but robust characterization of the degree of contamination with hydrocarbons and heavy metals in acid tars so that appropriate techniques can then be used for treatment/remediation. The first stage in treating these acid tars is to determine its properties. This article presents a software program that uses machine learning to estimate selected properties of acid tars (pH, Total Petroleum Hydrocarbons—TPH, and heavy metals). The program uses the Automatic Machine Learning technique to determine the Machine Learning algorithm that has the lowest estimation error for the given dataset, with respect to the Mean Average Error and Root Mean Squared Error. The chosen algorithm is used further for properties estimation, using the R² correlation coefficient as a performance criterion. The dataset used for training has 82 experimental points with continuous, unique values containing the coordinates and depth of acid tar samples and their properties. Based on an exhaustive search performed by the authors, a similar study that considers machine learning applications was not found in the literature. Further research is required because the method presented therein can be improved because it is dataset dependent, as is the case with every ML problem. Full article

Environmental biotechnology needs solutions that are associated with a low budget and cleaner remediation, and which are connected to resources and energetic valorization, to be able to encourage a circular bioeconomy. A prospective resolution for heavy-metal-contaminated soils is the application of phytoremediation approaches merged with bioenergy generation using the resulting biomass. Sunflower (Helianthus annuus) has been studied as a feedstock for biodiesel generation, and appears to be very attractive for biogas and bioethanol production. The current study reports an innovative energetic valorization approach of H. annuus biomass derived from the application of a phytoremediation strategy devised to remove Zn and Cd from an industrially contaminated soil (599 mg Zn kg⁻¹ and 1.2 mg Cd kg⁻¹)—and its comparison to the analysis of the same energetic valorization pathway for sunflower plants growing in an agricultural non-contaminated soil. After plant harvesting, bioethanol was produced from the aboveground tissues, and applied in the transesterification of the oil obtained through seed extraction for the generation of biodiesel. Also, biogas production was assessed through the root’s biomass anaerobic digestion. Similar yields of oil extraction—0.32 and 0.28 mL g⁻¹ DW—were obtained when using seeds from H. annuus cultured in contaminated and non-contaminated soils, respectively. The production yield of bioethanol was superior using biomass from the agricultural non-contaminated soil (0.29 mL g⁻¹ DW) when compared to the industrial metal-contaminated soil (0.20 mL g⁻¹ DW). Zinc was measured in minor levels in bioethanol and oil (ca. 1.1 and 1.8 mg mL⁻¹, correspondingly) resulting from the biomass cultivated in the industrialized soil, whereas Cd was not detected. The production yield of biogas was superior when using root biomass from H. annuus cultivated in agricultural non-contaminated soil (VS max. ca. 104 mL g⁻¹) when compared to the one deriving from the industrial contaminated soil (VS max ca. 85 mL g⁻¹). Generally, results demonstrate that substantial production yields of the tested biofuels were attained from biomass resulting from phytoremediation, corroborating this integrated original approach as a valuable alternative for the phytoremediation of HM-polluted soils and as an important strategy for plant biomass valorization. Full article

Mining waste is an obvious source of environmental pollution due to the presence of heavy metals, which can contaminate soils, water resources, sediments, air, and people living nearby. The F-(Ba-Pb-Zn) deposit of Hammam Zriba located in northeast Tunisia, 8 km southeast of Zaghouan was intensively exploited from 1970 to 1992. More than 250,000 m³ of flotation tailings were produced and stored in the open air in three dumps without any measure of environmental protection. Thus, in this paper, mineralogical and chemical characterization, especially the sulfide and carbonate phases, were carried out to evaluate the potential for acid mining drainage (AMD) and metal leaching (ML). Conventional analytical methods (XRD, XRF, SEM) have revealed that this mining waste contains on average 34.8% barite–celestine series, 26.6% calcite, 23% quartz, 6.3% anglesite, 4.8% fluorite, 2.1% pyrite, and 0.4% sphalerite. The content of sulfides is less important. The tailing leaching tests (AFNOR NFX 31-210 standard) did not generate acidic leachate (pH: 8.3). The acidity produced by sulfide oxidation was neutralized by calcite present in abundance. Furthermore, the leaching tests yielded leachates with high concentrations of heavy metals, above the authorized thresholds. This high mobilization rate in potential toxic elements (PTE) represents a contamination risk for the environment. Full article