Search Results (29)

Pesticides have been widely applied in agricultural practices over the past decades to protect crops from pests and other harmful organisms. However, their extensive use results in the contamination of soil, water, and agricultural products, posing significant risks to human and environmental health. Exposure to pesticides can lead to skin irritation, respiratory disorders, and various chronic health problems. Moreover, pesticides frequently enter surface water bodies such as rivers and lakes through agricultural runoff and leaching processes. Therefore, developing effective analytical methods for the rapid and sensitive detection of pesticides in food and water is of great importance. Electrochemical sensing techniques have shown remarkable progress in pesticide analysis due to their high sensitivity, simplicity, and potential for on-site monitoring. Two-dimensional (2D) carbon nanomaterials have emerged as efficient electrocatalysts for the precise and selective detection of pesticides, owing to their large surface area, excellent electrical conductivity, and unique structural features. In this review, we summarize recent advancements in the electrochemical detection of pesticides using 2D carbon-based materials. Comprehensive information on electrode fabrication, sensing mechanisms, analytical performance—including sensing range and limit of detection—and the versatility of 2D carbon composites for pesticide detection is provided. Challenges and future perspectives in developing highly sensitive and selective electrochemical sensing platforms are also discussed, highlighting their potential for simultaneous pesticide monitoring in food and environmental samples. Carbon-based electrochemical sensors have been the subject of many investigations, but their practical application in actual environmental and food samples is still restricted because of matrix effects, operational instability, and repeatability issues. In order to close the gap between laboratory research and real-world applications, this review critically examines sensor performance in real-sample conditions and offers innovative approaches for in situ pesticide monitoring. Full article

Over the last 15 years, mixture risk assessment for food xenobiotics has evolved from conceptual discussions and simple screening tools, such as the Hazard Index (HI), towards operational, component-based and probabilistic frameworks embedded in major food-safety institutions. This review synthesizes methodological and regulatory advances in cumulative risk assessment for dietary “cocktails” of pesticides, contaminants and other xenobiotics, with a specific focus on food-relevant exposure scenarios. At the toxicological level, the field is now anchored in concentration/dose addition as the default model for similarly acting chemicals, supported by extensive experimental evidence that most environmental mixtures behave approximately dose-additively at low effect levels. Building on this paradigm, a portfolio of quantitative metrics has been developed to operationalize component-based mixture assessment: HI as a conservative screening anchor; Relative Potency Factors (RPF) and Toxic Equivalents (TEQ) to express doses within cumulative assessment groups; the Maximum Cumulative Ratio (MCR) to diagnose whether risk is dominated by one or several components; and the combined Margin of Exposure (MOET) as a point-of-departure-based integrator that avoids compounding uncertainty factors. Regulatory frameworks developed by EFSA, the U.S. EPA and FAO/WHO converge on tiered assessment schemes, biologically informed grouping of chemicals and dose addition as the default model for similarly acting substances, while differing in scope, data infrastructure and legal embedding. Implementation in food safety critically depends on robust exposure data streams. Total Diet Studies provide population-level, “as eaten” exposure estimates through harmonized food-list construction, home-style preparation and composite sampling, and are increasingly combined with conventional monitoring. In parallel, human biomonitoring quantifies internal exposure to diet-related xenobiotics such as PFAS, phthalates, bisphenols and mycotoxins, embedding mixture assessment within a dietary-exposome perspective. Across these developments, structured uncertainty analysis and decision-oriented communication have become indispensable. By integrating advances in toxicology, exposure science and regulatory practice, this review outlines a coherent, tiered and uncertainty-aware framework for assessing real-world dietary mixtures of xenobiotics, and identifies priorities for future work, including mechanistically and data-driven grouping strategies, expanded use of physiologically based pharmacokinetic modelling and refined mixture-sensitive indicators to support public-health decision-making. Full article

The agricultural sector remains one of the most hazardous working environments, with viticulture posing particularly high risks due to repetitive manual tasks, pesticide exposure, and machinery operation. This study explores the potential of vision-based Artificial Intelligence (AI) systems to enhance occupational health and safety by evaluating their coherence with human expert assessments. A dataset of 203 annotated images, collected from 50 vineyards in Northern Italy, was analyzed across three domains: manual work activities, workplace environments, and agricultural machinery. Each image was independently assessed by safety professionals and an AI pipeline integrating convolutional neural networks, regulatory contextualization, and risk matrix evaluation. Agreement between AI and experts was quantified using weighted Cohen’s Kappa, achieving values of 0.94–0.96, with overall classification error rates below 14%. Errors were primarily false negatives in machinery images, reflecting visual complexity and operational variability. Statistical analyses, including McNemar and Wilcoxon signed-rank tests, revealed no significant differences between AI and expert classifications. These findings suggest that AI can provide reliable, standardized risk detection while highlighting limitations such as reduced sensitivity in complex scenarios and the need for explainable models. Overall, integrating AI with complementary sensors and regulatory frameworks offers a credible path toward proactive, transparent, and preventive safety management in viticulture and potentially other high-risk agricultural sectors. Furthermore, vision-based AI systems inherently act as optical sensors capable of capturing and interpreting occupational risk conditions. Their integration with complementary sensor technologies—such as inertial, environmental, and proximity sensors—can enhance the precision and contextual awareness of automated safety assessments in viticulture. Full article

People are exposed to pesticides daily through food, drinking water, and the environment, both in urban and rural settings. These chemicals, while offering economic and agricultural benefits through pest control and increased productivity, may pose a growing risk to human health and ecosystem biodiversity. While the European regulatory framework offers a robust foundation for risk assessment, significant limitations persist, especially in addressing cumulative exposure, low-dose effects, and chemical mixtures. This review focuses on selected scientific and regulatory challenges by reviewing recent European Food Safety Authority (EFSA) conclusions, Organization for Economic Co-operation and Development (OECD) test guidelines updates, and current European legislative approaches. Particular attention is given to the regulation of endocrine-disrupting and reprotoxic substances, highlighting progress and remaining gaps in implementation. A brief mention will also be made of immuno-toxic substances, for which no specific hazard class has yet been established. Building on official reports and peer-reviewed literature, this review provides a structured evaluation of the scientific and regulatory landscape, including underexplored issues like the transition to animal-free toxicology and integration of biomonitoring with health data. The goal is to propose realistic, evidence-based improvements to current frameworks using integrated, interdisciplinary approaches that connect toxicology, policy, and implementation science. A shift to a holistic, systems-based, and precautionary paradigm is vital to address emerging challenges and ensure strong protection of health and environment, as well as supporting the needs of the agricultural sector. Full article

Tree endotherapy has risen to prominence in the field of precision agriculture as an innovative and sustainable method of tree care, being respectful of both environmental protection and consumer health needs. A comprehensive review of the state of the art of research in this field has made it possible to spotlight the main advantages of tree infusion, which has undergone significant progress in step with technological innovation and an increased understanding of tree anatomy and physiology. The major criticalities associated with this technique, as well as the biological and technical–operational obstacles that still hinder its wider use, are also highlighted. What emerges is an innovative and rapidly expanding technique in tree care, in both the cultivation and phytosanitary management of fruit and ornamental trees. Some of the strengths of the endotherapy technique, such as the next-to-no water consumption, the strong reduction in the use of fertilizers and pesticides, the possibility of using biological control agents (BCAs) or other products of natural origin, the precision administration of the product inside the xylem of the tree, and the efficacy (20–90%) and persistence (1–2 years) of treatments, make it one of the cornerstones of sustainable tree protection at present. With a very low consumption of the “active ingredient”, endotherapy has a negligible impact on the external environment, minimizing the drift and dispersal of the active ingredient and thus limiting the exposure of non-target organisms such as beneficial insects, birds, and wildlife. The large-scale application of the technique would therefore also help to achieve an important goal in “climate-smart agriculture”, the saving of water resources, significantly contributing to climate change mitigation, especially in those areas of the planet where water is a precious resource. Full article

Bumblebees (Bombus spp.) are pivotal to sustaining biodiversity and enhancing agricultural productivity, thanks to their unique pollination mechanisms, including “buzz pollination”. Their ability to operate under adverse conditions—low temperatures and dim light—makes them essential for crops like tomatoes, peppers, and blueberries. This study synthesizes the ecological and behavioral traits of bumblebees, such as floral fidelity and vibration pollination, and explores their indispensable role in agricultural systems, particularly in greenhouse and open-field farming. By employing a bibliometric analysis, this review identifies critical research trends and emerging frontiers in bumblebee pollination, including their integration with precision agriculture technologies like remote sensing and artificial intelligence. Notably, there is increasing research on the impacts of climate change on bumblebee behavior and distribution, with studies focusing on how environmental stressors influence pollination efficiency. Additionally, the potential of using bumblebees in agroecological approaches and their role in enhancing crop resilience in changing climates are gaining traction. Moreover, it highlights the challenges posed by habitat loss, pesticide exposure, and climate change, emphasizing the urgency of conservation efforts. This study proposes interdisciplinary strategies for optimizing bumblebee pollination services, aiming to support sustainable agriculture and strengthen ecosystem resilience. The findings provide theoretical and practical insights for leveraging bumblebee pollination to achieve global food security and ecosystem stability. Full article

In view of a constant growth in the human population on Earth, the provision of a necessary amount of high-quality food looks challenging. As over 10% of the crop yields are annually lost due to the presence of phytopathogens, the development of novel, eco-friendly methods of pest eradication might contribute to avoiding nutritional shortages. Here, we propose a controlled application of cold atmospheric pressure plasma (CAPP) generated in the form of an atmospheric pressure plasma jet (APPJ), for which we conducted multivariate optimization of the working parameters with the use of the design of experiments (DoE) in addition to the response surface methodology (RSM). After estimating the optimal operating conditions of APPJ, we determined the inactivation rates caused by 2 min CAPP exposure towards bacterial phytopathogens from three species Dickeya solani, Pectobacterium atrosepticum and Pectobacterium carotovorum artificially inoculated on the surface of plant seeds from four species. Logarithmic reductions, as a key result of this work, were enclosed in the range of 1.61–4.95 in the case of Cucumis sativus, Pisum sativum, and Vigna radiata, while for the bacteria-inoculated Zea mays seeds, lower antibacterial properties of APPJ equaling 0.86–1.12 logs were noted. The herein applied exposure to APPJ did not reveal any statistically significant detrimental effects on the germination of plant seeds, seed coat integrity, or early plant growth. Even plant growth promotion by 20.96% was observed for the APPJ-exposed Zea mays seeds. By applying colorimetric assays and optical emission spectrometry (OES), we determined the oxidative potential in addition to identifying the reactive oxygen species (ROS) ^•OH, ^•HO₂, ^•O₂⁻, O₃, and ¹O₂ and the reactive nitrogen species (RNS) N, NO₂, and NO₃ responsible for the antibacterial properties of APPJ. In summary, universal antiphytopathogenic properties of the APPJ treatment reached due to proper optimization of the working conditions were revealed against three bacterial strains from the family Pectobacteriaceae inoculated on the seeds from diverse plant species. The data presented herein may contribute to future development of the plasma agriculture field and provide alternatives to pesticides or the prevention-based control methods towards plant pathogenic bacteria. Full article

In recent years, there has been a significant increase in attention towards the use of pesticides, as evidenced by the introduction of regulations aimed at ensuring their safe and environmentally responsible application. Although this stricter legislative framework offers potential benefits, the challenges faced by farmers, particularly those in small-scale operations, in complying with occupational health and safety (OHS) requirements are considerable. To address this issue, a research project was promoted by the Italian Workers’ Compensation Authority (INAIL) aimed at developing a user-friendly software tool to support farmers in planning pesticide applications in safe conditions. This study summarizes the results of the research, which consisted of three main phases: the definition of the main parameters that characterize the farmers’ exposure based on the analysis of the literature; the development of a risk assessment model that integrates these determinants with data provided by producers for each authorized pesticide; and the development of software tool (called SICURPEST) for a preliminary risk assessment when using pesticides. This tool serves as a preliminary risk assessment method, offering a simplified model for practical use by farmers. Its initial verification was conducted through a case study and the results represent a step towards promoting safer pesticide practices, providing a solid basis for further validation. Full article

This study investigates the degradation characteristics of chlorpyrifos under individual exposure and compound exposure to multiple pesticides in both traditional constructed wetlands and electrochemically constructed wetland coupled systems, while also analyzing the microbial communities within the systems using high-throughput sequencing technology. The results show that the electric field can enhance the degradation performance of the system. The degradation effect of the coupled electrochemically constructed wetland coupled system is better than that of the traditional constructed wetland, while the compound exposure to multiple pesticides inhibits the degradation efficiency. Under the influence of pesticides, the diversity of microbial communities decreases towards the end of the system operation, and the electrochemically constructed wetland coupled system exhibits lower diversity compared to the traditional constructed wetland. Proteobacteria is the dominant phylum under compound exposure to multiple pesticides, while Firmicutes, Fusobacteria, Verrucomicrobia, Aeromonas, and Methylophilus are the dominant electrochemically active phyla and genera in the electrochemically constructed wetland coupled system. The impact of pesticides and the electric field results in a decrease in amino acid metabolism and carbohydrate metabolism functions, while membrane transport functions increase. The compound exposure to multiple pesticides has a more significant impact on the microbial community structure and functionality than the electric field. The results also lay a theoretical foundation for the expansion of pesticide degradation technology and constructed wetland treatment technology to new fields, which is of great significance in realizing the “zero direct discharge” of agricultural production wastewater, solving the problem of agricultural non-point source pollution and ensuring the availability of agricultural production. Full article

Fungus gnats (Sciaridae) are one of the most devastating pests on mushroom farms. Generally, they are controlled using relatively inefficient physical means, like sticky or pheromone traps, or with chemical pesticides. Here, we have proposed an integrated fungus gnat control system combining a UV-A LED source at 365 nm and a high-power laser diode at 445 nm. The 365 nm UV-A LED serves a light trap, since previous studies have concluded that fungus gnats show maximum attraction in the range of 365–390 nm. The UV-A LED is also modulated at different frequencies, and the response of the gnats corresponding to these different frequencies was observed. We utilized an Arduino Uno microcontroller to run the integrated device, and a BASLER USB camera was used to capture the images. Our experiments indicated that a frequency of 40 Hz is the optimal choice for attracting the gnats most rapidly. Within 20 s of exposure, the UV-A LED operated at 40 Hz was found to trap approximately 80% of the gnats. In a restricted trapping zone measuring 2.5 × 2.5 × 3 cm³, our integrated module, which includes a 40 Hz modulated UV-A LED and a laser, resulted in a survival rate of only 50% for the total number of gnats. This outcome was accomplished through periodic 200 ms long exposures, amounting to a total duration of 2 min for a group of 100 gnats. Full article

Pesticide application in agricultural and residential areas is a worldwide practice. However, human pesticide poisoning and environmental pollution through pesticide residues remain a challenge in the developing world. The present study investigated the intensity of pesticide application in large-scale farms in Trans-Nzoia County to screen and prioritize the pesticides for potential human health and environmental risks. A cross-sectional survey involving 348 farmers was conducted in the study area, and data was analyzed using SPSS. Environmental Exposure Potential (EEP) and Toxicity Potentials (TP) were analyzed from the Pesticide Properties Database (PPDB). Majority (99.4%) of the farms surveyed apply various pesticide classes that include: organophosphates (34.78%), neonicotinoids (15.22%), carbamates (10.87%), pyrethroids (10.87%), organochlorines (8.7%), triazoles (6.5%), copper-based (4.34%), avermectines (2.17%), triazines (2.17%), and amidines (2.17%), with the use of organic manures (26.3%). Despite the high prevalence of pesticide application, only 48.28% of farms conduct soil quality monitoring, 77.3% of whom do not have clear records and schedules for conducting periodic soil analyses. There was a strong positive correlation between the acreage of operation and the use of herbicides in weed management (r = 0.77; p ≤ 0.05). In relation to degradation in the environment, 18.42% of the pesticides applied in the study area were persistent in soil sub-systems while 31.58% are persistent in water. Of the pesticides applied, 18.42% had high chances of bioconcentration in living tissues, 10.53% and 13.16% had the potential of contaminating groundwater and surface water resources, respectively. The ranked-order human toxicity potential associated with the used pesticides were teratogenicity (31.58%), neurotoxicity (28.95%), endocrine disruption (7.9%), carcinogenicity (7.9%), and mutagenicity (2.63%). However, 10.53% of the pesticides possess multiple toxicity potentials. Some farmers (53.70%) surveyed were not aware of the negative environmental impacts of pesticides with 59.50% having prior training on the use and handling of pesticides. Despite the availability of Personal Protective Equipment (PPEs) on larger farms, 31.9% of the farm workers do not adhere to their use during pesticide application. In conclusion, there is low awareness among farmers of human health and environmental risks associated with pesticide application. The study recommends training of farm managers, farm owners, and farm workers on pesticide handling and associated health and environmental effects. Full article

Plant protection control based on the spray application of plant protection products is a very complex task depending on a series of factors, among which droplet size is the most influential for deposition and pesticide effectiveness. In fact, the adoption of the correct droplet size can ensure that the required dose reaches the target area and is not wasted, minimizes the off-target losses due to evaporation, drift and run-off and, at the same time, enhances the operator’s safety in terms of inhalation, ingestion and dermal exposure. In this paper, after defining some mean characteristic diameters helpful for a description of a drop population and focusing on the main drop size distribution functions for the statistical characterization of sprays, a critical analysis of known methods, both intrusive and non-intrusive, for drop size measurement is carried out by reviewing the literature. Among intrusive methods, the liquid immersion method and the use of water-sensitive papers are discussed, whereas, among non-intrusive methods, laser-based systems (laser diffraction, phase Doppler particle analysis) and high-speed imaging (shadowgrapy) are presented. Both types of method, intrusive and non-intrusive, can be used in machine-learning-based approaches exploiting regression techniques and neural network analysis. Full article

Droplet size spectrum is a key factor in pesticide application because it affects the biological efficacy of a treatment in terms of target coverage, environmental impact in terms of evaporation, drift and run-off, and operator’s safety in terms of inhalation and dermal exposure. Droplet measurement methods based upon image analysis have to face the “binarization” or “segmentation” process, by which the objects of interest (the droplets) are extracted from the background. Segmentation is carried out by choosing appropriate threshold values, mostly based on the operator’s experience. In this study, images of droplets of an air induction nozzle TVI 8002 at four pressures (0.3, 0.5, 1.0, and 1.5 MPa) were obtained using the liquid immersion method. Each image was processed multiple times, firstly by using a “reference” threshold value based on the operator’s experience and then by using 11 different threshold values, chosen in the range of around ±5% of the reference threshold and based upon the average gray level of the image. For each threshold value, the corresponding spray parameters (volumetric diameters, mean diameters, Sauter diameters, and numeric diameters) were analyzed. The results showed that spray parameters had a statistically significant linear trend with respect to the threshold values in most cases. However, in absolute terms, variations were almost always less than 1.0% of reference values. This result allows considering the image acquisition system used in the present study as an automatic tool able to select the threshold according to the gray level of the image, making the whole segmentation process faster, more objective, and less dependent on the operator’s experience. Full article

Objective: This study aimed to develop a nomogram for the risk assessment of any type of birth defect in offspring using a large birth-defect database in Northwest China. Methods: This study was based on a birth-defect survey, which included 29,204 eligible women who were pregnant between 2010 and 2013 in the Shaanxi province of Northwest China. The participants from central Shaanxi province were assigned to the training group, while the subjects from the south and north of Shaanxi province were assigned to the external validation group. The primary outcome was the occurrence of any type of birth defect in the offspring. A multivariate logistic regression model was used to establish a prediction nomogram, while the discrimination and calibration were evaluated by external validation. Results: The multivariate analyses revealed that household registration, history of miscarriages, family history of birth defects, infection, taking medicine, pesticide exposure, folic acid supplementation, and single/twin pregnancy were significant factors in the occurrence of birth defects. The area under the receiver operating characteristic curve (AUC) in the prediction model was 0.682 (95% CI 0.653 to 0.710) in the training set. The validation set showed moderate discrimination, with an AUC of 0.651 (95% CI 0.614 to 0.689). Additionally, the prediction model had a good calibration (HL χ² = 8.106, p= 0.323). Conclusions: We developed a nomogram risk model for any type of birth defect in a Chinese population based on important modifying factors in pregnant women. This risk-prediction model could be a tool for clinicians to assess the risk of birth defects and promote health education. Full article

Dermal transfer of pesticide residues to human skin due to contact with treated crops, treated surfaces, or contaminated surfaces is an important route of exposure for operators, workers and possibly for bystanders and residents. However, information on dermal transfer data is limited and mainly available for workers. The aim of the present study has been to generate both dermal exposure and transfer data related for operators involved in sequential tasks of mixing/loading and application of pesticides in a southern EU zone greenhouse. Exposure measurements were based on the principles of the whole-body dosimetry (WBD) method involving the use of cotton coveralls and gloves as dosimeters. Six field trials were conducted in three tomato greenhouses, on the island of Crete, Greece. The study results showed that the contribution of existing pesticide deposits on the treated crops, i.e., from an application conducted earlier the same day, was in the range of 8–16% for the application task and 0.9–18% for the mixing/loading task in relation to the measured total exposure to this pesticide during a short-term sequential application. The results of this study have been incorporated in the GAOEM (Greenhouse Agricultural Operator Exposure Model) included in the updated EFSA Guidance on the assessment of exposure of operators, workers, residents and bystanders in risk assessment of plant protection products. The low values of the pesticide amount penetrating the coverall (actual dermal exposure) in all cases highlight and confirm the need for the use of appropriate personal protective equipment (PPE) for operator safety. Full article