Search Results (366)

This study investigated pesticide residues in 580 vegetable samples collected from markets in Serbia, encompassing potatoes, carrots, celery, radishes, horseradish, ginger, onions, and leeks. In total, 33 distinct pesticides were detected using validated HPLC-MS/MS and GC-MS/MS analytical methods. Multiple residues were identified in 19 samples, while 29 samples exceeded established maximum residue levels (MRLs). Acute and chronic dietary risks were assessed for both adults and children. Although individual hazard quotients (HQs) for adults and children remained below the threshold of concern (HQ < 1), the cumulative acute risk reached up to 63.1% of the Acute Reference Dose (ARfD) for children and 51.1% ARfD for adults, with ginger and celery posing the highest risks. Similarly, cumulative chronic risks remained below the safety threshold, with the Acceptable Daily Intake (ADI) percentages reaching a maximum of 5.9% ADI for adults and increased vulnerability of 11.0% ADI among children. Monte Carlo simulations were applied to account for variability and uncertainty in chronic exposure estimates. The hazard index (HI) results showed that adverse health effects for both population groups remained within acceptable safety limits (HI < 1), although higher susceptibility was observed in children. Sensitivity analysis identified body weight and vegetable consumption rates as the most influential factors affecting chronic risk variability. Full article

Over the past years, increasing attention has been drawn to the adverse effects of agricultural pesticide use on pollinators, with honeybees being especially vulnerable. The aim of this study was to evaluate the levels of residues detectable and/or quantifiable of neonicotinoid pesticides and other pesticides in biological materials (bees, bee brood, etc.) and beehive products (honey, pollen, etc.) applied as seed dressings in rapeseed and sunflower plants in two growing seasons (2020–2021) in fields located in three agro-climatic regions in Romania. The study involved the comparative sampling of hive products (honey, pollen, adult bees, and brood) from experimental and control apiaries, followed by pesticide residue analysis in an accredited laboratory (Primoris) using validated chromatographic techniques (LC-MS/MS and GC-MS). Toxicological analyses of 96 samples, including bees, bee brood, honey, and pollen, confirmed the presence of residues in 46 samples, including 10 bee samples, 10 bee brood samples, 18 honey samples, and 8 pollen bread samples. The mean pesticide residue concentrations detected in hive products were 0.032 mg/kg in honey, 0.061 mg/kg in pollen, 0.167 mg/kg in bees, and 0.371 mg/kg in bee brood. The results highlight the exposure of honeybee colonies to multiple sources of pesticide residue contamination, under conditions where legal recommendations for the controlled application of agricultural treatments are not followed. The study provides relevant evidence for strengthening the risk assessment framework and underscores the need for adopting stricter monitoring and regulatory measures to ensure the protection of honeybee colony health. Full article

Background/Objectives: Pumpkin seed pulp from processing plants offers high nutritional value due to its rich β-carotene content, making it a potential functional feed ingredient. This study investigated the effects of pumpkin seed pulp, which has already been administered as livestock feed, on key physiological parameters in cattle, including the concentration of β-carotene in the blood measured during routine health monitoring. Methods: Here, pumpkin waste cultivated in various fields was processed into cattle feed (pumpkin seed pulp flakes, PSPFs) by grinding and drying, and residual pesticide (heptachlor) and β-carotene contents were measured. A pilot feeding trial was conducted with 13 cattle (7 in the treatment group and 6 in the control group) and blood component analysis was performed, and findings were contextualized with a literature review. Results: Heptachlor concentrations varied depending on the cultivation site of raw pumpkins. Among the six lots produced using raw materials sourced from fields not contracted by the Air Water Group—a collective of companies in which Air Water Inc. holds more than 51% ownership—three exceeded the regulatory limits for animal feed established in Japan. PSPFs contained high levels of β-carotene, as expected. Blood tests before and after the feeding trial indicated absorption of β-carotene in the cattle. Maintaining high plasma β-carotene concentrations in cattle has been associated with improved immune function and reproductive performance. Conclusions: Our study demonstrates that PSPFs are a promising, environmentally friendly, and natural β-carotene-rich feed ingredient. Tracing the cultivation fields of raw pumpkins can help ensure feed safety. Full article

Pesticide residues in fruits and vegetables pose a serious threat to food safety. Traditional detection methods have defects such as complex operation, high cost, and long detection time. Therefore, it is of great significance to develop rapid, non-destructive, and efficient detection technologies and equipment. In recent years, the combination of spectroscopic techniques and imaging technologies with machine learning algorithms has developed rapidly, providing a new attempt to solve this problem. This review focuses on the research progress of the combination of spectroscopic techniques (near-infrared spectroscopy (NIRS), hyperspectral imaging technology (HSI), surface-enhanced Raman scattering (SERS), laser-induced breakdown spectroscopy (LIBS), and imaging techniques (visible light (VIS) imaging, NIRS imaging, HSI technology, terahertz imaging) with machine learning algorithms in the detection of pesticide residues in fruits and vegetables. It also explores the huge challenges faced by the application of spectroscopic and imaging technologies combined with machine learning algorithms in the intelligent perception of pesticide residues in fruits and vegetables: the performance of machine learning models requires further enhancement, the fusion of imaging and spectral data presents technical difficulties, and the commercialization of hardware devices remains underdeveloped. This review has proposed an innovative method that integrates spectral and image data, enhancing the accuracy of pesticide residue detection through the construction of interpretable machine learning algorithms, and providing support for the intelligent sensing and analysis of agricultural and food products. Full article

Fruits and vegetables are crucial components of a healthy diet, which are susceptible to pests. Therefore, the application of pesticides is a basic manner of crop chemical protection. The aim of this study was a comprehensive analysis of pesticide occurrence in 1114 samples of fruits and vegetables. A unified multi-analytical protocol was used composed of primary–secondary amine/graphitized carbon black/magnesium sulfate to purify samples with diversified profile of interfering substances. Moreover, the obtained analytical data were used to evaluate the critical acute health risk in subpopulations of children and adults within European limits criteria. Out of 550 pesticides analyzed, 38 and 69 compounds were noted in 58.6% of fruits and 44.2% of vegetables, respectively. Acetamiprid (14.1% of all detections) and captan (11.3%) occurred the most frequently in fruits, while pendimethalin (10.6%) and azoxystrobin (8.6%) occurred the most frequently in vegetables. A total of 28% of vegetable and 43% of fruit samples were multiresidues with up to 13 pesticides in dill, reaching a final concentration of 0.562 mg kg⁻¹. Maximum residue level (MRL) was exceeded in 7.9% of fruits and 7.3% of vegetables, up to 7900% MRL for chlorpyrifos in dill (0.79 mg kg⁻¹). Notably, 8 out of 38 pesticides found in fruits (21%; 1.2% for carbendazim) and 24 out of 69 compounds in vegetables (35%, 7.4% for chlorpyrifos) were not approved in the EU. Concentrations of pesticides exceeding MRL were used to assess acute health risk for children and adults. Moreover, the incidence of acute health risk was proved for children consuming parsnip with linuron (156%). In other cases, it was below 100%, indicating that Polish food is safe. The work provides reliable and representative scientific data on the contamination of fruits and vegetables with pesticides. It highlights the importance of legislative changes to avoid the occurrence of not approved pesticides in the EU, increasing food and health safety. Full article

Air pollution caused by pesticide residues is an emerging concern in urban environments influenced by nearby agricultural activities. In this study, weekly air samples were collected between May 2018 and March 2020 in Strasbourg, France, to quantify 104 pesticides in both gas and particle phases using GC-MS/MS and LC-MS/MS. Herbicides and fungicides were the most frequently detected classes, appearing in 98% of both phases followed by insecticides. Key compounds such as metalaxyl-M, diphenylamine, and bifenthrin were present in over 90% of samples. Concentrations ranged from 2.5 to 63 ng m⁻³ weekly, with cumulative annual loads exceeding 1200 ng m⁻³. Gas–particle partitioning revealed that highly volatile compounds like azinphos-ethyl favored the gas phase, while less volatile ones like bifenthrin and tebuconazole partitioned >95% into particles. A third-degree polynomial regression (R² of 0.74) revealed a nonlinear relationship between Kₚ and particle-phase concentrations, highlighting a threshold above Kₚ of 0.025 beyond which compounds accumulate disproportionately in the particulate phase. Seasonal variability showed that 36% of the annual pesticide load occurred in autumn, with total airborne levels peaking near 400 ng m⁻³, while the lowest load occurred during summer. Principal component analysis identified rainfall and total suspended particles as major drivers of pesticide phase distribution. The inhalation health risk assessed yielded hazard index values < 1 × 10⁻⁷ for all population groups, suggesting negligible non-cancer risk. This study highlights the prevalence, seasonal dynamics, and partition behavior of airborne pesticides in urban air and underscores the need for regulatory attention to this overlooked exposure route. Full article

Due to the dramatic increase in pesticide usage and improper application, large amounts of unused pesticides enter the environment through paddy water, causing severe pesticide pollution. To find a rapid method for identifying pesticide types and predicting their concentrations, the dielectric properties frequency response of pesticides was analyzed in paddy water. A rapid detection method for typical pesticides such as chlorpyrifos, isoprothiolane, imidacloprid and carbendazim was studied based on their dielectric properties. In this paper, amplitude and phase frequency response data for blank paddy water samples and 15 types of paddy water samples containing pesticides were collected at 10 different temperatures. Principal component analysis (PCA) and competitive adaptive reweighted sampling (CARS) were used to extract characteristic frequencies. A species identification model based on support vector machine (SVM) for rapid detection of pesticides in paddy water was established using amplitude and phase frequency response data separately. Frequency response data of 431 sets from nine types of paddy water samples were divided into training and prediction sets in a 3:1 ratio, and a content prediction model based on artificial neural networks (ANN) with multiple inputs and single output was established using amplitude and phase frequency response data after CARS feature extraction. The experimental results show that both PCA-SVM and CARS-SVM species identification models established using amplitude and phase frequency response data have excellent identification effects, reaching over 90%. The PCA-SVM model based on phase frequency response data has the best identification effect for typical pesticides in paddy water with a prediction recognition accuracy range of 97.5–100%. The ANN content prediction model established using phase frequency response data performs well, and the highest R² prediction values of chlorpyrifos, isoprothiolane, imidacloprid and carbendazim in paddy water were 0.8249, 0.8639, 0.9113 and 0.8368 respectively. The research established a dielectric property detection method for the identification and content prediction of typical pesticides in paddy water, providing a theoretical basis for the hardware design of capacitive sensors based on dielectric property and the detection of pesticide residues in paddy water. This provides a new method and approach for pesticide residue detection, which is of great significance for scientific pesticide application and sustainable agricultural development. Full article

Quickly and accurately identifying weed areas is of great significance for improving weeding efficiency, reducing pesticide residues, protecting soil ecological environment, and increasing crop yield and quality. Targeting low detection efficiency in complex agricultural environments and inability of multispectral input in weed recognition of minor grain based on unmanned aerial vehicles (UAVs), a semantic segmentation model for buckwheat weeds based on MSU-Net (multispectral U-shaped network) was proposed to explore the influence of different band optimizations on recognition accuracy. Five spectral features—red (R), blue (B), green (G), red edge (REdge), and near-infrared (NIR)—were collected in August when the weeds were more prominent. Based on the U-net image semantic segmentation model, the input module was improved to adaptively adjust the input bands. The neuron death caused by the original ReLU activation function may lead to misidentification, so it was replaced by the Swish function to improve the adaptability to complex inputs. Five single-band multispectral datasets and nine groups of multi-band combined data were, respectively, input into the improved MSU-Net model to verify the performance of our method. Experimental results show that in the single-band recognition results, the B band performs better than other bands, with mean pixel accuracy (mPA), mean intersection over union (mIoU), Dice, and F1 values of 0.75, 0.61, 0.87, and 0.80, respectively. In the multi-band recognition results, the R+G+B+NIR band performs better than other combined bands, with mPA, mIoU, Dice, and F1 values of 0.76, 0.65, 0.85, and 0.78, respectively. Compared with U-Net, DenseASPP, PSPNet, and DeepLabv3, our method achieved a preferable balance between model accuracy and resource consumption. These results indicate that our method can adapt to multispectral input bands and achieve good results in weed segmentation tasks. It can also provide reference for multispectral data analysis and semantic segmentation in the field of minor grain crops. Full article

Trichogramma dendrolimi, a key egg parasitoid for lepidopteran pest control, faces potential risks from neonicotinoid insecticides like acetamiprid used in integrated pest management (IPM). This study evaluated acetamiprid’s acute and sublethal toxicity to T. dendrolimi and assessed population-level risks via species sensitivity distribution (SSD). Acute toxicity assays using glass-vial residues revealed a 24 h LC₅₀ of 0.12 mg a.i. L⁻¹ for adults, three orders of magnitude below the maximum field rate (100 mg a.i. L⁻¹). Sublethal exposure (1/2–1/100 LC₅₀) significantly reduced parasitism and emergence rates (NOEC = 2.3 μg a.i. L⁻¹) but did not affect offspring survival. Acetamiprid also shortened offspring development at 11.5–57.5 μg a.i. L⁻¹. SSD analysis identified T. dendrolimi as the most sensitive parasitoid to acetamiprid (HC₅/HC₅₀) = 0.11/5.88 mg a.i. L⁻¹), with field rates (30–100 mg a.i. L⁻¹) indicating a potentially affected fraction (PAF) of 76.8–97.9%. These findings underscore the need to integrate sublethal effects into pesticide regulations to conserve parasitoid-mediated ecosystem services. Full article

Water pollution from pesticides is a major concern for regulatory agencies worldwide due to expensive detecting mechanisms, delays in the processing of results, and the complexity of the chemical analysis. However, the deployment of monitoring systems utilising the internet of things (IoT) and machine-to-machine communication technologies (M2M) holds promise in overcoming this major global challenge. In this current research, an IoT-based wireless sensor network (WSN) is successfully deployed in rural Kenya at the Kiu watershed, providing in situ pesticide detections and a real-time data visualisation of shallow wells. Kiu is an off-grid community located in an area of intensive agriculture, where residents face a high exposure to pesticides due to farming activities and a reliance on shallow wells for domestic water. The evaluation of path loss models utilising channel characteristics obtained from this study indicate a marked departure from the continuous signal decay with distance. Transmitted packets from deployed sensor nodes indicate minimal mutations of payloads, underscoring systems reliability and data transmission integrity. Additionally, the proposed design significantly reduces the time taken to deliver pesticide measurement results to relevant stakeholders. For the entire monitoring period, pesticide residues were not detected in the selected wells, an outcome validated with lab procedures. These results are attributed to prevailing dry weather conditions which limited the leaching of pesticides to lower layers reaching the water table. Full article

The evolution of global agriculture encourages the extensive use of pesticides although significant concerns regarding their impact on human health and the environment must be considered. The present paper highlights the presence and concentrations of various pesticide residues in fruits and vegetables available on Romanian markets. A total of 74 pesticide compounds authorized for agricultural use were identified and quantified in 620 randomly selected samples spanning a wide range of horticultural products by employing the QuEChERS extraction method and liquid chromatography–mass spectrometry (LC-MS/MS). The most often detected pesticides comprised boscalid and azoxystrobin, present in 42% and 37% of apple and strawberry samples, respectively, with mean concentrations of 0.12 mg/kg and 0.09 mg/kg. In cucumbers and tomatoes, difenoconazole and acetamiprid were predominant, detected in 35% and 40% of samples, with average residue amounts of 0.08 mg/kg and 0.07 mg/kg, respectively. Statistical analysis, achieved with Python 3.13.2, the pandas library (alongside descriptive statistics), and ANOVA, revealed significant variations in residue levels based on the product type and geographic origin. Boscalid and azoxystrobin were commonly encountered in apples and strawberries while difenoconazole and acetamiprid predominated in cucumbers and tomatoes. Even though the majority of pesticide residues conformed to EU maximum residue limits (MRLs), about 6% of samples, generally from imported products, displayed some residue concentrations approaching critical thresholds, with the highest exceedance observed for chlorpyrifos and lambda-cyhalothrin at concentrations of up to 0.25 mg/kg. This research provides a comprehensive overview of pesticide residues prevalence in Romania’s fresh product supply while, at the same time, supporting consumer awareness initiatives and evidencing the critical demand for continuous monitoring and strengthened regulatory frameworks for food safety. Full article

This study investigates the effect of high urban land temperatures on pesticide residue (PR) accumulation in cabbage and lettuce and on public health in the Greater Accra Metropolitan Area (GAMA) in Ghana. A comparative toxicological analysis regarding the food system was conducted with 66 farmers across three land surface temperatures: low (Atomic, n = 22), moderate (Ashaiman, n = 22), and high (Korle-Bu, n = 22). Pesticide residue concentrations were assessed using an ANOVA to examine spatial variations across sites. The results indicate a strong correlation between high land surface temperatures and pesticide residue accumulation, with lettuce recording significantly (p < 0.05) higher PR levels than cabbage. Several pesticides, including carbendazim (CBZ), Imidacloprid (IMI), Thiamethoxam (TMX), and Chlorpyrifos (CHL), exceeded the maximum residue limits (MRLs) set by the World Health Organization (WHO) and the European Union (EU) at moderate and high-temperature sites. carbendazim was the dominant pesticide detected, with a concentration of 19.0 mg/kg in lettuce, which far exceeded its maximum residue limit (MRL) of 0.10 mg/kg across all study sites. Statistical analyses (PERMANOVA) confirmed that land surface temperatures and pesticide types significantly influenced the PR concentrations. Public health risk assessments indicate that children are more vulnerable to pesticide exposure than adults. The toxicity hazard quotient (THQ) for organophosphate pesticides, particularly CHL and Dimethoate (DMT), exceeded safe thresholds at moderate and high-temperature sites. Full article

This study explores the use of carbon materials derived from Nocino walnut liqueur pomace residue for the removal of chlorpyrifos, a widely used organophosphate pesticide, from water. Carbon adsorbents were synthesized from young walnut biomass under different thermal and chemical treatment conditions, and their structural and surface properties were characterized using BET analysis, FTIR, SEM-EDX, Boehm titration, and zeta potential measurements. The materials exhibited distinct textural and chemical features, including high surface areas and varied surface functionalizations. Batch adsorption studies revealed that the chlorpyrifos removal followed pseudo-second-order kinetics and was best described by the Freundlich and Langmuir isotherms, indicating a combination of pore filling and physisorption via π-π and van der Waals interactions. The highest adsorption capacity of 45.2 ± 0.2 mg g⁻¹ was achieved at 30 °C. Thermodynamic analysis confirmed the process to be endothermic, spontaneous, and entropy-driven, with desolvation effects enhancing the performance at elevated temperatures. Dynamic filtration experiments validated the practical applicability of the materials, while moderate reusability was achieved through ethanol-based regeneration. These findings demonstrate the potential of walnut pomace-derived carbons as low-cost, renewable, and effective adsorbents for sustainable water decontamination. Full article

Terahertz spectroscopy (0.1~10 THz), as a new type of non-destructive testing method with both microwave and infrared characteristics, has shown remarkable potential in the field of food quality testing in recent years. Its unique penetration, high sensitivity, and low photon energy characteristics, combined with chemometrics and machine learning methods, provide an efficient solution for the qualitative and quantitative analysis of complex food ingredients. In this paper, we systematically review the principles of terahertz spectroscopy and its key applications in food testing, focusing on its research progress in pesticide residues, additives, biotoxins, and mold, adulteration identification, variety identification, and nutrient content detection. By integrating spectral data preprocessing, reconstruction algorithms, and machine learning model optimization strategies, this paper further analyzes the advantages and challenges of this technology in enhancing detection accuracy and efficiency. In addition, combined with the urgent demand for fast and nondestructive technology in the field of food detection, the future development direction of the deep integration of terahertz spectroscopy technology and artificial intelligence is envisioned, with a view to providing theoretical support and technical reference for food safety assurance and nutritional health research. Full article

Chlorpyrifos (CPF) has been extensively utilized in recent decades due to its highly efficient insecticidal properties. However, the widespread use of pesticides has posed new challenges to male reproduction. This study aims to explore the potential molecular mechanisms of male reproductive decline induced by CPF. We employ flow cytometry, qRT-PCR, Western blot, RNA sequencing, and bioinformatics analysis to investigate the potential molecular mechanisms involved in CPF-induced male reproductive damage in GC2_spd cells. Our results revealed that after 24 h of CPF treatment, the cell viability, cell cycle, apoptosis, and reactive oxygen species (ROS) accumulation of GC2_spd cells were significantly affected in vitro. RNA sequencing analysis data indicated that a total of 626 genes were differentially expressed compared to the DMSO group, especially for Efcab6, Nox3, and Cmpk2. These differential genes were mainly enriched in signaling pathways such as PI3K-AKT and glutamine metabolism. In addition, further validation through qRT-PCR, Western blot, and experiments involving the inhibition of intracellular ROS generation with N-acetylcysteine collectively confirmed that CPF induces male reproductive damage through the ROS/AKT/Efcab6 pathway. These studies elucidate potential targets and molecular mechanisms underlying CPF-induced male infertility, providing a theoretical basis for the prevention of male reproductive damage caused by pesticide residues. Full article