Unlocking the interactive role of GABA and NO in alleviating chlorpyrifos induced toxicity in Solanum melongena L. through modulation of physio-biochemical traits.

This study investigated the occurrence, spatial-seasonal distribution, and potential human health risks of organochlorine pesticide (OCP) residues in fish collected from eight locations along the Osun River, southwestern Nigeria. Seventeen OCP congeners, including γ-BHC, DDT and its metabolites (DDE and DDD), dieldrin, and heptachlor epoxide, were analyzed using gas chromatography equipped with an electron capture detector (GC-ECD). Descriptive and inferential statistics were employed, whereas human health risks were assessed using carcinogenic and noncarcinogenic indices. Total OCP concentrations in fish ranged from 0.219 to 2.555 mg/kg, with γ-BHC recording the highest concentration (0.994 mg/kg), exceeding the FAO/WHO permissible limit of 0.2 mg/kg. Hazard Index values exceeded the recommended safety threshold (HI > 1) at Oke Gada (1.315), Tesan (1.273), and Odo Oba (1.133), indicating possible health risks to fish consumers. Principal component analysis identified patterns among OCP congeners that may be consistent with historical pesticide application, environmental transformation processes, sediment-associated contamination, and other potential environmental inputs. Carcinogenic risk values exceeded the acceptable USEPA limit (1 × 10-4) at seven of the eight sampling sites, with the highest value observed at Tesan (1.27 × 10-3). Among the detected compounds, γ-BHC and heptachlor epoxide contributed most significantly to both carcinogenic and noncarcinogenic risks. The findings indicate that OCP contamination was present in fish from the Osun River during the study period and highlight the importance of continued environmental monitoring, effective pesticide management, and sustainable pest control practices to reduce potential exposure risks.

The application of pesticides often results in exposure and risks to non-target organisms. Among these organisms are non-target terrestrial plants (NTTPs), which are essential for maintaining healthy ecosystems. Despite the rigorous risk assessment process for approving pesticides used in Europe and many developed countries, the impacts of environmentally realistic mixtures remain insufficiently understood. This study evaluated the effects of 11 realistic mixtures on two plant species. Each mixture comprised 5 pesticides selected based on their estimated risk to terrestrial plants (from regulatory reports) and their frequency of detection in agricultural soils across 10 European countries and Argentina. Two standardized assays, root growth inhibition test (International Organization for Standardization [ISO] 11269-1) and seedling emergence and growth test (Organisation for Economic Co-operation and Development, test guideline 208), were conducted with wheat (Triticum aestivum) and lettuce (Lactuca sativa). Three concentrations were used: median measured environmental concentration (MEC), predicted environmental concentration (PEC), and five times PEC (5PEC). Significant effects were observed for ten of the eleven mixtures, and these were species-specific and endpoint-specific, likely reflecting variation in mixture composition and herbicide modes of action. Root growth was more sensitive than seedling emergence and shoot biomass. When considering both MEC and PEC treatments, root growth was affected in 9 mixtures for both species, while emergence and biomass were affected in 5 and 7 mixtures, respectively. These results indicate that realistic combinations of pesticide residues can cause deleterious effects on terrestrial plants even at concentrations below predicted environmental thresholds, where no adverse impact would ordinarily be expected. Given that current European risk assessments emphasize spray drift and single-compound exposures, consideration of mixture effects is crucial for adequately evaluating risks to NTTPs.

Monoclonal gammopathy of undetermined significance (MGUS) is an asymptomatic blood disorder that precedes multiple myeloma, and its etiology remains incompletely understood. Risk factors described in the literature include advanced age, male sex, Black race, a family history of plasma cell disorders, and the presence of chronic inflammatory or autoimmune conditions. Furthermore, direct exposure to pesticides has been proposed as a potentially modifiable risk factor. Accordingly, this study aimed to synthesize and evaluate the available evidence on MGUS prevalence and its possible relationship with occupational pesticide exposure among rural workers and pesticide applicators, including prevalence data when reported. A systematic review with meta-analysis was conducted according to Cochrane recommendations. Literature searches were conducted in October 2024 using the Medline (PubMed), Scopus, and Cochrane Library databases to identify relevant primary studies. Four eligible observational studies evaluating MGUS in participants directly exposed to pesticides were included. The studies reported positive associations between specific pesticides, such as aldrin, dieldrin, a carbon tetrachloride/carbon disulfide mixture, chlorothalonil, petroleum-derived products, and permethrin, and the occurrence of MGUS among rural workers. These findings were analyzed qualitatively due to methodological heterogeneity that precluded pooling of risk estimates. The meta-analysis, restricted to prevalence outcomes, showed that MGUS prevalence increases with age, suggesting age as an important risk factor. The scarcity of published studies and methodological heterogeneity highlight the need for further investigations to better characterize environmental and occupational risks associated with MGUS, which may inform future preventive strategies in higher-risk populations.

The widespread application of neonicotinoid pesticides (NEOs) has caused severe contamination of aquatic environments, posing serious threats to water resources and ecological sustainability. In this study, graphitic micro-mesoporous composite carbon (GMC) was synthesized via template-assisted method and employed for the adsorption of NEOs from water. Structural characterization confirmed that GMC possessed graphitized framework, well-developed micro-mesoporous composite architecture, and abundant functional groups, which enhanced its adsorption performance. The GMC exhibits excellent adsorption performance for NEOs, with adsorption equilibrium achieved within 120min. Batch adsorption experiments showed that the adsorption kinetics were consistent with both pseudo-first-order and pseudo-second-order models, while the adsorption isotherms followed the Freundlich model. This indicates that the adsorption process is spontaneous, endothermic, and tends toward physical adsorption. The maximum adsorption capacity for Thiamethoxam (THM) was achieved 1421.3mg/g at pH = 3, whereas that for Nitenpyram (NIM) was obtained 642.7mg/g at pH = 7, suggesting that the ionization degree of NEOs significantly affects the adsorption performance of GMCs. Five consecutive adsorption-desorption cycles validated the excellent stability and reusability of GMC. Co-existing ions and humic acid (HA) exerted negligible effects on adsorption performance. Furthermore, the GMC exhibits good adsorption performance in multi-pesticide coexistence systems and actual farmland surface water, demonstrating the adaptability of GMC to complex water matrices. This study reveals that the adsorption process of NEOs on GMCs is better described by interactions dominated by physical adsorption, driven by a variety of physical mechanisms, including strong π-π stacking, hydrogen bonding, and hydrophobic interactions. The excellent stability, sustainability, and reliability of GMC indicate its considerable potential for practical applications in water purification and environmental remediation.

Pesticides-induced histopathological and biochemical alterations in fish: Impacts and restoration strategies.

Rice is a fundamental food source for more than half of the global population, making stable yields and quality improvements vital for food security and sustainable agricultural development. Early infections of rice leaf diseases often exhibit subtle symptoms, while conventional control methods based on empirical judgment and routine pesticide application result in both yield losses and environmental pollution. A Multi-scale closed-loop tuning via spatial frequency collaborative sensitivity (MCCA-YOLO) model has been proposed in this paper with a multiscale closed-loop tuning and spatial frequency collaborative attention mechanism for the early detection and classification of rice crop diseases. MCCA-YOLO incorporates a closed-loop tuning compound network architecture that combines a dual-backbone feature extractor with a spatial frequency enhancement module to achieve system self-verification feedback, reducing transmission errors and enhancing the texture features of leaves. The framework implements a cross-scale weighted fusion and a deformable spatial hybrid attention enhanced bidirectional feature pyramid fusion network for dynamic feature adaptation, effectively accommodating the complex morphology of rice leaf lesions. By conducting comprehensive ablation studies and comparative experiments with existing techniques on the rice plant diseases v8 dataset, the proposed approach achieves a mean average precision (mAP) of 92.2%, outperforming well-established methods, while delivering superior precision (0.915) and recall (0.900). Extensive empirical validation of additional v9 and Rice Leaf Spot Disease (RLSD) datasets for rice plant diseases further demonstrates the model’s outstanding performance.

The precise and timely identification of apple leaf diseases play a key role in targeted pesticide application in orchards. Conventional deep learning techniques encounter issues like the substantial size of model parameters and low detection accuracy across various disease scales in natural environments. To overcome these limitations, this paper presents YOLO-APLD, a lightweight algorithm for detecting apple leaf diseases, utilizing the improved YOLOv8n model. The proposed model incorporates four key improvements to enhance its detection performance. First, an EP-C2f enhancement module is embedded at the output of the backbone to strengthen the representation of local and structural features of damaged area, thereby achieving significant improvements in the recognition of morphologically complex diseases such as rust. Additionally, spatial intersection over union (SIoU) loss and focal loss are combined to form Focal-SIoU loss, which simultaneously optimizes bounding box regression and classification, thus enhancing the detection stability for hard-to-distinguish samples and few-shot categories including mosaic and brown spot. Meanwhile, a bidirectional feature pyramid network is adopted in the neck for efficient multiscale feature fusion, which strengthens the perceptual capability for both large-scale damaged area (powdery mildew and scab) and small-scale damaged area (Alternaria blotch and gray spot). Finally, a Slim-neck structure is employed to simplify the feature fusion architecture, reducing model size and accelerating inference speed. Comprehensive experiments demonstrate that YOLO-APLD achieves excellent performance while maintaining real-time capability, with precision, recall, mean average precision, and F1-score reaching 88.5, 84.3, 88.5, and 86.4%, respectively. Compared with YOLOv8n, these metrics show respective improvements of 1.7, 1.5, 0.8, and 1.6%. Meanwhile, floating point operations, parameter count, and model size are reduced by 22.2, 23.3, and 17.5%, respectively. The detection frame rate on edge computing devices reaches 90.3 f/s, indicating significantly accelerated inference speed. Additionally, testing performance on grape and tomato datasets further validates the generality of the proposed method. In summary, YOLO-APLD exhibits strong detection performance in the field of apple leaf disease detection and can provide practical technical support for precision pesticide application in orchards and on-site disease monitoring.

Phosphate-ester poly(ether trisiloxane) (PATS) surfactants have attracted considerable attention due to their excellent emulsifying ability, wetting performance, antistatic properties, and corrosion- and rust-inhibiting characteristics, which enable their wide application in daily chemicals, pesticides, pharmaceuticals, leather processing, and papermaking. In this study, 1,1,1,3,5,5,5-heptamethyltrisiloxane and allyloxy poly(oxyethylene ether) (APEG-400) were used as raw materials to synthesize the intermediate HPETS under the catalysis of chloroplatinic acid, followed by phosphorylation with phosphoric acid to obtain a PATS. The surface activity, thermodynamic properties, thermal stability, aggregation behavior, wetting ability, and foaming characteristics of the PATS/dodecyltrimethylammonium bromide (DTAB) mixed system were systematically investigated. The results indicate that PATS exhibits excellent surface activity, with a critical micelle concentration (CMC) of 5 g/L and a surface tension at CMC (γCMC) of 17.74 mN/m. Furthermore, thermodynamic analysis and aggregation studies reveal a pronounced synergistic interaction between PATS and DTAB. When the mixing ratio of PATS to DTAB is 1:2, the surface tension at CMC decreases to 15.57 mN/m and the CMC is reduced to 0.417 g/L, accompanied by enhanced wetting performance. In addition, PATS functions as an effective defoaming component, enabling efficient regulation of the foaming behavior of the DTAB system. These findings provide important insights into the interfacial behavior and synergistic mechanisms of trisiloxane-based surfactant mixtures, and highlight their potential applications in papermaking, textile processing, and industrial cleaning.

Malaria elimination efforts in sub-Saharan Africa are increasingly threatened by the spread of insecticide resistance in Anopheles gambiae sensu lacto (s.l.). The widespread use of insecticides in vector control and pesticides in agriculture has intensified resistance development in malaria vectors. This study examined the drivers of high insecticide resistance in Anopheles mosquitoes in Ghana's Coastal and Sahel Savannah ecological zones. Anopheles larvae were collected from eight sites across the Coastal Savannah (Tuba, Opeibea, Nima, and Teshie) and Sahel Savannah (Kpalsogu, Libga, Taha, and Kulaa) zones and reared to adults. WHO insecticide susceptibility intensity assays were performed on 3-5-day-old female Anopheles gambiae s.l. to assess phenotypic resistance. The polymerase chain reaction (PCR) technique was used to identify species within the An. gambiae s.l. and detect mutations at voltage-gated sodium channels (Vgsc- 1014F, Vgsc-1014S, Vgsc-1575Y) and acetylcholinesterase G119S-Ace-1 mutation. Alongside larval sampling, a survey was conducted to assess household use of insecticide-based vector control products and pesticide application in agriculture. Anopheles gambiae s.l. from the Coastal zone showed high resistance intensity to pyrethroids (Mortality rate: 50 to 85.9% at 10x). In the Sahel zone, moderate resistance intensity (Mortality rate: >98% at 10x) was observed. High resistance intensity to pirimiphos-methyl (80-90% at 10x) was recorded in vectors from Tuba and Opeibea. The prevalence of L1014F-kdr mutation was high (range: 0.81 to 1) in all study sites, while L1014S and N1575Y kdr mutations were present at low to moderate frequencies (range: 0 to 0.39). Household surveys revealed widespread use of pyrethroid-based mosquito control products (88.10%), including aerosol sprays (25.33%) and coils (53.95%). Farm surveys showed extensive application of pesticides containing organophosphates (16.2%), carbamates (11.29%), neonicotinoids (25.7%), and diamides (5.4%). This study reveals widespread pyrethroid and organophosphate resistance in Anopheles gambiae s.l,. which could likely be associated with intensive insecticide use in public health and agriculture. With resistance escalating, continuous surveillance and enhanced resistance management strategies are essential to sustain malaria control efforts in Ghana. Not applicable.

Agricultural pesticide use represents one of the most geographically patterned environmental systems, yet most prior research has focused largely on individual compounds rather than correlated exposure regimes. We conducted a cross-sectional analysis using modeled pesticide application intensity and Alzheimer's Disease (AD) dementia prevalence at the county-level across the United States. Stability-based Elastic Net screening and clustering were used to identify exposure groupings, and associations with AD prevalence were estimated using adjusted regression models. Out of 462 total pesticides screened, 112 demonstrated high selection stability and were grouped into 25 exposure clusters. Twenty clusters were significantly associated with AD dementia prevalence (p<0.05). The strongest positive associations were observed for a soil fumigation/nematicide system, an herbicide-dominant vegetation control regime, and a neuroactive insecticide system. Neuroactive insecticides and soil-intensive treatment systems were disproportionately represented among positively associated clusters, whereas systems dominated by phenoxy- and photosystem II inhibiting herbicides were more frequently aligned with inverse gradients. The fully adjusted model explained 59% of between-county variance (baseline R2 = 0.44). Findings suggest that pesticide mixtures are associated with geographic heterogeneity in AD dementia prevalence and warrant higher-resolution, longitudinal investigation.

Multi-rotor plant protection drones are extensively utilized in rice field operations. However, the airflow generated by the rotors not only disturbs the rice canopy but also affects the deposition of pesticide droplets. The investigation of the impact of rotor wind fields on the protection of rice crops has the potential to enhance the effectiveness of operations of this nature. The present study firstly acquired aerial imagery of plant protection drone operations via the utilization of aerial photography, and subsequently employed machine vision technology to investigate the disturbance patterns of rotor wind fields on rice canopies. Notably, there is currently no effective experimental method to observe the disturbance of rice canopies caused by the wind field generated by plant protection drone rotors, and machine vision processing is proven to be an effective approach, which is a key innovation of this study. This study innovatively adopts an integrated experimental-numerical approach, a distinct advancement over existing Unmanned Aerial Vehicle (UAV) spraying and airflow research that typically focuses on single experimental or simulation methods. Aerial photography and machine vision were used to explore canopy disturbance patterns, while reverse engineering combined with thrust tests and numerical simulations verified rotor model precision and analyzed droplet deposition. Core findings show that flight speeds of 3-4m/s enable effective overlap between canopy disturbance and droplet deposition zones, achieving optimal plant protection effects; higher speeds cause droplet drift and zone misalignment, reducing efficacy. In addition, this study explores the spray deposition of plant protection drones based on numerical simulation methods, and assists in judging the effect of plant protection operations through the coincidence of droplet deposition and canopy disturbance zones. This integrated approach and related findings provide a reliable technical basis for optimizing UAV flight parameters, and have certain reference significance for the technical research and development of plant protection drones and the setting of operation parameters.

Dermal exposure of bystanders and residents to direct spray drift in low crops during pesticide application.

A comprehensive assessment of a product's economic viability, along with a value chain analysis, is essential to identify opportunities for improvement, ascertain actual costs and margins, understand the interrelationships between activities, and enhance production efficiency. This study evaluates the economic viability and value chain of summer tomato cultivation, a sector gaining importance due to rising off-season demand and price premiums. A total of 50 summer tomato growers and 45 traders were selected using multi-stage sampling techniques from the Netrokona and Kishoreganj districts in Bangladesh. The results indicates that the adoption levels of ploughing, planting periods, seedling usage, pesticide application, and hormone use were high, whereas the use of urea, gypsum, MoP, boron, and zinc was low. The total cost of summer tomato cultivation was Tk. 2,75,606 per acre, while the average net return per acre was Tk. 3,43,364. The Benefit-Cost Ratio (BCR) of summer tomato cultivation was found to be 2.25, indicating that producing summer tomatoes is economically viable. Seven marketing channels were identified, with retailers contributing 42.58% of the overall value addition, marking the most significant contribution to the gross marketing margin. The summer tomato value chain in Bangladesh is sustainable due to its capacity to create income, encourage efficient resource utilization, enhance food security, and adapt to climatic changes. By maintaining investments in technology, training, and market systems, this value chain has the potential to flourish and contribute to a more resilient and inclusive agricultural economy.

Unmanned aerial spraying systems (UASSs) are increasingly used in crop protection. There are, however, considerable concerns regarding pesticide drift during their field application, which poses potential environmental risks. Reducing drift while maintaining efficiency is therefore critical. In this study, an octocopter UASS with centrifugal nozzles was tested under varying spray parameters using a modified ISO 22866 method. The effects of droplet size, flight speed and flight height on target deposition and downwind drift were evaluated under two crosswind conditions, and UASS performance was compared with that of a conventional boom sprayer. The downwind cumulative drift percentage of the UASS is 62.8-811.7%, which is 1.5-17.9 times that of boom sprayers, depending on the operational parameters of the UASS. Droplet size, flight speed and flight height had significant effects on UASS drift, while flight height was the most critical in determining droplet drift and deposition. Droplet size exerted a stronger influence on airborne drift than flight speed, while neither droplet size nor flight speed had a significant impact on target-area deposition. Lowering the flight height to below 3 m should be considered first to reduce drift risk during operations. This study provides quantitative evidence on the influence of spray parameters on UASS drift behavior and highlights strategies to mitigate drift risks. The findings offer valuable guidance for parameter optimization in UASS field spraying, thereby supporting environmentally safer pesticide applications and promoting cleaner agricultural production. © 2026 Society of Chemical Industry.

Effect of operational parameters of UAV spraying simulation platform on droplet characteristics and prediction using PSO-ANN for maize crop

Assessment of applicators' exposure to pesticides during knapsack mist sprayer application in rice fields.

Physiological and microbial alterations induced by pesticides in agricultural systems: A bioassay- and 16S rRNA-based approach.

Spatial and temporal effects of fungicide inputs on resistance selection were evaluated by conducting two field studies and by leveraging publicly available pesticide application data along with previously published fungicide resistance survey results. The trials were conducted to evaluate the short-term impact of fungicide programs on the development of resistance in Botrytis cinerea, the causal agent of Botrytis fruit rot in strawberries, where strawberry plots were treated with various single and rotational fungicide regimens over a six-week period. Resistance in B. cinerea isolates was assessed across five sampling dates using discriminatory dose assays. After the consecutive applications, resistance frequencies tended to remain stable to boscalid and fludioxonil but increased sharply for fenhexamid. After a 6-week period with no fungicide inputs, resistance frequencies to fenhexamid tended to decrease, while resistance frequency to pyraclostrobin was stable. The findings suggest varying responses in resistance selection and fitness costs depending on the mode of action that the populations were exposed to. The public fungicide application data showed that intensive strawberry production regions with high fungicide inputs were hot spots for fungicide resistance selection. The findings emphasize the need for strategic fungicide use and resistance management.

Botrytis cinerea is a widespread plant pathogenic ascomycete that causes grey mould in over 1400 species and impacts global crop yields. Double-stranded RNA (dsRNA)-induced gene silencing is a promising technology for pest control, but efficient delivery remains a major challenge. This work presents a star polycation (SPc)-based nano-delivery platform that enhances dsRNA internalization and enables effective control of B. cinerea. SPc assembles with dsRNA via electrostatic interactions, hydrogen bonding and Van der Waals forces, forming stable nanoscale complexes. SPc facilitates dsRNA uptake into fungal mycelia by 2.19-fold, primarily via two activated routes: endocytosis and transmembrane transport, which increase vesicle and particle numbers by 3.74- and 1.87-fold, respectively. Six genes (pkc, ypt10, pil1, mfs1, mfs2 and mfs3) in these two routes play crucial roles in the delivery process. RNA interference, chemical inhibition and mutation experiments demonstrate that the two routes interact to optimize the cellular uptake of SPc-loaded dsRNA. Finally, a high-efficiency RNA fungicide is developed, comparable to commercial fungicide in protecting cucumber leaves and tomato fruits. This study reveals the synergistic mechanism of nanocarrier-mediated gene delivery and identifies key genes, supporting the design and application of RNA pesticides.