Search Results (2,314)

Glyphosate (GLY), one of the most extensively applied broad-spectrum herbicides worldwide, frequently contaminates soil and aquatic ecosystems, posing serious threats to human health, non-target organisms, soil microbial communities, and environmental sustainability. In the present study, phytoremediation using Medicago sativa (MS) was evaluated for the removal of GLY from contaminated soil under greenhouse conditions, with remediation efficiency enhanced through inoculation with two bacterial bioagents, Bacillus sp. h10 (BS) and Pseudomonas aeruginosa KZFS4 (PA). Biochemical parameters, including superoxide dismutase (SOD), catalase (CAT), hydrogen peroxide (H₂O₂), and malondialdehyde (MDA), together with detoxification-related gene expression, were investigated in the roots and leaves of MS exposed to GLY stress. The combined application of MS with BS + PA, followed by MS + PA and MS + BS, significantly decreased GLY residues in soil and increased GLY accumulation in plant roots and leaves after 1, 3, 7, and 10 days compared with MS treatment alone. In vitro batch equilibrium experiments demonstrated that BS and PA desorbed 33.63 and 40.56 µg g⁻¹ of GLY, respectively, thereby enhancing its removal from soil. The persistence of GLY was highest in contaminated soil without treatment, exhibiting a half-life (t₁/₂) of 52.66 days, whereas the shortest half-life (6.69 days) was recorded in soil treated with MS combined with BS and PA relative to sterilized contaminated soil. Furthermore, inoculation with BS and PA markedly increased SOD and CAT activities in MS tissues, while significantly reducing H₂O₂ and MDA accumulation, indicating alleviation of oxidative stress. GLY exposure also triggered substantial upregulation of detoxification-associated genes, including cytochrome P450, glutathione S-transferases (GST), glycosyltransferases (GTs), and ABC transporters in MS. These findings demonstrate that the integration of BS and PA with phytoremediation effectively accelerates GLY dissipation and reduces pesticide-associated toxicity in contaminated soils and plants. Full article

Zymoseptoria tritici (Desm. Quaedvlieg & Crous), known as the wheat leaf spot disease agent, is a highly virulent fungus that induces blotch and necrosis on leaves. Although it is known to cause severe infections under humid conditions, recent observations suggest that it can also infect wheat leaves under drought and high-temperature conditions, possibly influenced by global warming. Recent findings showed that Z. tritici could easily tolerate various abiotic stresses, including drought, water stress, salinity, and temperature. It has been evident that the fungus can tolerate pesticide stress, as indicated by the increased frequency and number of pesticide applications throughout the growing season. Under stress conditions, the fungi, unlike crop plants, could easily tolerate stress by rapidly modifying gene expression and reducing spore production and mycelial growth without downregulating major biochemical components that play significant roles in pathogenicity and virulence. Z. tritici can accumulate melanin under stress conditions; therefore, an increase in pathogenicity under drought or salinity stress is not unexpected. Recent studies have shown that the pathogenicity of the fungus is increasing, and more virulent, toxin-producing pathogens might emerge in the future. Since drought and high-temperature stresses significantly affect crop plants, the adaptation of pathogenic microorganisms to these conditions could be inevitable if abiotic stress persists. Under these circumstances, the crop loss would be more pronounced. A critical aspect of this process is the assessment of DNA integrity in both wheat and the pathogen under drought stress conditions. The organism that better maintains DNA integrity is considered to exhibit greater drought tolerance. Therefore, our main target should be DNA health when developing or breeding new wheat varieties, considering double- or even multiple-stress conditions. We should finally state that we are very optimistic about generating highly stress-tolerant wheat varieties via metabolomic and proteomic approaches without compromising quality. However, the impact and combination of stress factors are becoming increasingly complex. Full article

Entomopathogenic fungi (EPF) are well established as biological control agents, but their emerging role as endophytes reveals a broader and more powerful function in crop protection. By colonizing plant tissues, endophytic entomopathogenic fungi (EEPF) create a dynamic tripartite interaction between plants, fungi, and herbivores, enabling systemic, plant-mediated pest suppression. This review synthesizes current knowledge on the behavioral and ecological responses of herbivorous arthropods to EEPF-colonized plants, with an emphasis on the mechanisms and implications for integrated pest management (IPM). Growing evidence indicates that EEPF consistently modify herbivore behavior and performance across diverse crops and insect taxa. Colonization frequently alters feeding, host selection, and oviposition, often deterring pests, although mediated responses may vary among fungal species, host plants, insect taxa, and environmental conditions. These responses are driven by EEPF-induced changes in plant chemistry, including shifts in volatile organic compounds (VOCs) and defensive metabolites. In parallel, EEPF impair insect fitness by delaying development, reducing survival, and lowering fecundity, thereby suppressing pest populations. These plant-mediated and behavioral changes extend to multitrophic interactions, potentially affecting associations with natural enemies and the transmission efficiency of some insect vectors of plant viruses. Despite rapid progress, critical gaps remain in resolving the mechanistic basis of these interactions and their stability under field conditions. Advancing the application of EEPF will require integrated approaches combining microbial ecology, chemical ecology, and insect behavioral biology. Harnessing these interactions offers a compelling pathway to reduce reliance on synthetic pesticides while enhancing the resilience and sustainability of agricultural systems. Full article

Pollinators are essential for maintaining ecosystem stability and agricultural productivity, yet their populations are in decline due to various stressors, including parasites, pathogens, climate change and pesticide exposure. Protecting pollinators has become a priority for environmental safety and food security. Regulatory authorities, including the European Food Safety Authority (EFSA), the Environmental Protection Agency (EPA) and the Organisation for Economic Co-operation and Development (OECD), have guidelines for pesticide risk assessment, but conventional testing methods are costly and time-consuming, limiting their applicability to large chemical datasets. Computational approaches, such as Structure–Activity Relationship (SAR) models, offer efficient alternatives by enabling the rapid screening of pesticides for potential toxicity to pollinators. In this study, we used a dataset of 357 compounds to develop a classification model based on structural alerts to predict oral acute toxicity in Apis mellifera. The model showed a higher Matthews Correlation Coefficient in the training set (0.82), with a moderate decay in the test set (0.56) likely due to applicability domain limits. Despite this, high balanced accuracy (0.80) and sensitivity (0.79) in the test set confirm the model as a reliable tool for the toxicological screening of pesticides. Full article

Pesticide use remains important in modern agriculture, vector control, and household pest management. However, exposure to pesticide active ingredients and residues remains a persistent public health concern. In addition to active ingredients, people may also be exposed to other constituents of commercial pesticide formulations, such as adjuvants and solvents, which can influence overall toxicity and health outcomes. These exposures may occur among direct applicators and may also affect other populations through contaminated air, water, soil, food, clothing, and household surfaces. This narrative review examines pesticide exposure from a community health perspective, emphasizing occupational, para-occupational, residential, dietary, drinking-water, airborne, and cumulative exposure pathways. It highlights vulnerable groups, including agricultural workers, children, pregnant women, women in agricultural communities, older adults, people with chronic illness, and marginalized rural populations. Evidence reviewed in this article links pesticide exposure with acute poisoning, respiratory effects, neurobehavioral and neurodevelopmental outcomes, cancer-related risks, reproductive and developmental effects, endocrine and metabolic disruption, cardiovascular outcomes, dermatological reactions, immune dysregulation, and biomarker-based subclinical changes. Beyond disease endpoints, pesticide exposure may also affect household income, education, mental well-being, food security, livelihoods, and intergenerational health. Major challenges include weak exposure assessment, underreporting, limited biomonitoring in low- and middle-income settings, and inconsistent community-level indicators. Strengthening surveillance, risk communication, integrated pest management, safer storage and disposal, protective regulation, and community-centered biomonitoring is essential to reduce pesticide-related health burdens. Full article

Chiral pesticides, comprising enantiomers with identical physical and chemical properties but different biological effects, call for extra efforts in comprehensively evaluating their risk assessment. However, the defect regarding sensitive analysis of chiral pesticides limits the progress of research. Therefore, an efficient and highly sensitive analytical method for the trace-level analysis of chiral pesticides is imperative. Herein, normal micellar electrokinetic chromatography (MEKC), sweeping-MEKC, and anion-selective exhaustive injection-sweeping-MEKC (ASEI-sweeping-MEKC) were optimized to separate and enrich three chiral phenoxy acid herbicides (cloprop, dichlorprop, and fenoprop). Normal MEKC with 30 mM γ-cyclodextrin, 50 mM SDS, pH 2.5, and 50 mM phosphate buffer background electrolyte (BGE) achieved ideal chiral separation of target herbicides. Then, sweeping-MEKC was modified to enhance the detection sensitivity by 4690 to 10,630-folds with the conditions of pH 2.5, 100 mM phosphate buffer high-conductivity buffer (HCB), 60 s injection time + 30 mM γ-CD, 50 mM SDS, pH 2.5, 50 mM phosphate buffer BGE + 120 s sample injection time. ASEI-sweeping-MEKC achieved further improvement of detection sensitivity by 241,000- to 673,000-fold. And the optimal ASEI-sweeping-MEKC conditions for the target chiral phenoxy acid herbicides were 20% ACN water plug, 30 s injection time + pH 2.5, 100 mM phosphate buffer HCB, 60 s injection time + 30 mM γ-CD, 50 mM SDS, pH 2.5, 50 mM phosphate buffer BGE + 12 min sample injection time. All in all, these findings have validated ASEI-sweeping-MEKC as an efficient and powerful analytical method for the trace-level analysis of the enantiomers of three chiral phenoxy acid herbicides. This approach enables a comprehensive risk assessment and provides guidance for the sustainable application of chiral pesticides. Full article

In this work, we developed a novel, ultrasensitive fluorescence sensing platform for determination of organophosphorus pesticides (OPs), using chlorpyrifos as a representative model analyte. The sensing strategy was constructed upon the key inner filter effect (IFE) between graphitic carbon nitride (g-C₃N₄) nanosheets and silver-coated gold core–shell nanoparticles (Au@Ag NPs). Initially, gold nanoparticles (Au NPs), silver nanoparticles (Ag NPs), and Au@Ag NPs were successfully synthesized, and their fluorescence quenching efficiencies toward g-C₃N₄ were systematically evaluated. Owing to the superior spectral overlap with the fluorescence emission of g-C₃N₄, Au@Ag NPs exhibited the most obvious quenching effect and were thereby selected as the optimal quencher for sensor fabrication. Then, acetylcholinesterase (AChE) catalyzed the hydrolysis of acetylthiocholine (ATCH) into thiocholine. The generated thiocholine then induced aggregation of Au@Ag NPs via electrostatic and Ag-S interactions, which reduced the IFE efficiency and ultimately restored the fluorescence of g-C₃N₄. In contrast, the presence of chlorpyrifos effectively inhibits AChE activity, thereby suppressing ATCH hydrolysis and the subsequent aggregation of Au@Ag NPs. The fluorescence intensity of g-C₃N₄ was quenched by Au@Ag NPs and the signal was low. Under optimal experimental conditions, the response signal was found to be proportional to chlorpyrifos (CPF). This work presents a rapid, cost-effective, and highly sensitive approach for CPF residue analysis, holding great potential for applications in food safety monitoring and environmental surveillance. Full article

With the widespread application of new chemicals, the concentration of emerging contaminants (ECs), such as antibiotics, per- and polyfluoroalkyl substances (PFAS), microplastics, and new pesticides, in aquatic environments is on the rise. ECs such as those examined in the studies exhibit high toxicity, persistence, and a propensity for bioaccumulation, which can lead to significant risks for ecosystems and human health. Traditional water treatment technologies exhibit limited removal capabilities for ECs, whereas micro-nano bubbles (MNBs) exhibit great potential in the field of ECs treatment, due to their unique physicochemical properties. This article systematically reviews the research progress on the treatment of ECs using MNBs combined with other technologies, including physical methods (adsorption enhancement), chemical methods (ozonation, persulfate oxidation, photocatalysis, and material catalysis) and biological methods (microbial synergy). This review summarizes the research progress and mechanisms of MNBs combination technologies, outlining the critical knowledge gaps and future research perspectives to advance the rational design and engineering application of MNBs for ECs’ elimination in water treatment. Full article

Soil pollution constitutes a critical global environmental challenge driven by industrialization, intensive agriculture, urban expansion, mining, and the application of synthetic agrochemicals. This article presents seven annotated Python-based Machine Learning (ML) workflows for soil pollution assessment, structured around five contaminant groups: heavy metals, pesticides, microplastics, per- and polyfluoroalkyl substances (PFAS), and excess macronutrients. The contribution has three distinct components. First, a literature synthesis drawing on more than 100 peer-reviewed studies contextualizes each contaminant group within current spectroscopic, geochemical, and ML-based detection frameworks. Second, a conceptual six-step workflow links field sampling, ML-based analysis, and scenario-based risk modelling to soil ecosystem service (SES) assessment. Third, seven executable Python scripts—implementing Random Forest regression, XGBoost with SHAP explainability, 1-D Convolutional Neural Networks, LSTM time-series forecasting, PCA-based dimensionality reduction, Monte Carlo uncertainty propagation, and GeoPandas geospatial mapping—serve as illustrative demonstrations using a benchmark dataset. All reported performance metrics are derived from synthetic data and represent workflow demonstrations, not validated field results. Radionuclides are acknowledged as an important contaminant class but fall outside the defined scope of this study. The scripts are provided as reproducible templates for adaptation to real contaminated-site datasets. Full article

The changing climate raises the level of hydroclimatic non-stationarity and export of pollutants at the event scale in agricultural, mixed-land-use, and urbanizing watersheds. In this review, there is an emphasis on nitrogen, phosphorus, and sediment; however, selective references are made to pesticides, pathogens, microplastics, and wet-weather mixed-source processes when characteristics similar to event-driven transport, threshold exceedance, and adaptive control are identified. Drawing on a structured literature search of studies published from 2000 to December 2025, this narrative review synthesizes evidence from 138 selected references on how extreme rainfall, drought–rewetting, warming, and freeze–thaw processes alter source activation, hydrological connectivity, biogeochemical processing, and receiving-water hazards. Our resilience assessment is based on resistance, recovery, robustness, and persistence, which we interpret using exposure, sensitivity, and adaptive capacity. It is shown that standard average-load and fixed-baseline measurements may not detect short pollution pulses, cross-scenario failure, and long-term drift; operational measurement must thus involve event thresholds, recovery trajectories, tail-risk measures, and propagation of uncertainty. Extrapolation, interpretability, data demand, and applicability for data-sparse basins are used to compare process-based, data-driven, and hybrid models. Adaptation options are associated with measurable triggers as part of a monitoring–trigger–action cycle with location-specific instructions for monsoon-agricultural, cold-region, semi-arid and urban systems. The novel aspect of this framework is the integration of mechanism-based evidence, quantitative resilience indicators, model uncertainty, and adaptive governance into one decision-focused workflow. This sustainability-oriented framework advances long-term watershed management by linking water-quality protection and resilient development. Full article

Understanding the spatiotemporal relationship between agricultural inputs and crop production is essential for sustainable agricultural management. Using provincial panel data from China from 2000 to 2022, this study integrates spatiotemporal analysis with the XGBoost-SHAP model to examine the nonlinear effects of agricultural machinery, fertilizers, pesticides, and plastic films on soybean, cereal, and tuber yields. The results show that China’s agricultural input system shifted around 2015 from input-intensive growth toward green transformation, with fertilizer, pesticide, and plastic-film use declining after this inflection point. Spatially, agricultural inputs and crop production show clear agglomeration and path dependence: machinery is concentrated in northern China, fertilizers and pesticides in eastern intensive farming regions, and plastic-film use in arid and cold regions, while soybean, cereal, and tuber production are mainly concentrated in Northeast China, the Northeast-Huang-Huai-Hai region, and Southwest China, respectively. The SHAP results reveal distinct crop-specific importance rankings and nonlinear threshold patterns. For soybean yield prediction, agricultural plastic film use contributes most strongly to the model output, followed by fertilizer application, pesticide use, and machinery power; its SHAP contribution turns negative beyond approximately 112.4 thousand tons. For cereal yield prediction, machinery power ranks first, followed by fertilizer application, pesticide use, and plastic-film use; its contribution becomes positive beyond approximately 28.34 million kW and then gradually levels off. For tuber yield prediction, fertilizer application is the dominant predictor, followed by pesticide use, machinery power, and plastic-film use; its contribution turns negative beyond approximately 1.35 million tons. These findings indicate that agricultural inputs have crop-specific nonlinear effects, and that input regulation should prioritize the most influential factors for each crop while considering their threshold ranges. The study provides a scientific basis for differentiated, crop-specific, and regionally adaptive agricultural input management. Full article

Nanoparticles of the aminopolysaccharide chitosan (ChNPs) are effective delivery platforms for biologically active substances for agrobiotechnological applications and hold great promise for solving precision problems in sustainable and efficient agriculture. This review presents an analysis of research publications during the past 20 years examining methods for producing ChNPs through ionotropic gelation using sodium tripolyphosphate for cross-linking macrochains, which are of practical interest for agriculture. Key aspects of the nanostructure formation process are analyzed, including the influence of the physicochemical characteristics of the aminopolysaccharide, the concentration and ratio of reagents, and ionic cross-linking conditions on the average size, size distribution (polydispersity), and zeta potential of nanoparticles. Particular attention is paid to several approaches proposed in the literature for determining optimal gelation conditions to obtain ChNPs with pre-specified size characteristics. Potential applications of nanostructured preparations based on these nanoparticles for agrobiochemical purposes are considered, including the encapsulation of antifungal, antiviral and antimicrobial agents, pesticides, NPK fertilizers, metal ions, plant extracts, essential oils, etc., to develop biodegradable stimulants for seed germination and plant growth, increased crop yields, and improved agricultural product quality. It is concluded that blocking the protonated amino groups of chitosan with tripolyphosphate anions is undesirable due to the reduced biological activity of the macromolecules and the nanostructured preparations obtained therefrom. An alternative approach for producing ChNPs with high biological activity with neither use of cross-linking agents nor encapsulation of agrochemicals is described. Full article

The agricultural industry largely relies on conventional pesticides to maintain healthy, pest-free crops. Application of conventional insecticides is the go-to method for cultivating important food crops, such as corn and sorghum, free of Spodoptera frugiperda (fall armyworm) infestations. However, conventional insecticides have purported negative environmental and health impacts. Natural plant extracts, such as essential oils, are viewed as a promising alternative to conventional insecticides. In the current study, Dysphania ambrosioides (epazote) essential oil was embedded into an artificial diet and fed at two different concentrations to fall armyworms during a 10-day period. Final weights of the 5% epazote treatment group were statistically less (F₆₃₄₃ = 136.2 p < 0.001) than control groups. The 5% epazote treatment group also experienced the highest mortality rate (62%) of any treatment group (X² = 831.4, DF = 6, p < 0.001). Findings suggest that epazote essential oil has potential as an effective, natural insecticidal ingredient. This research is of importance to the fields of agronomy and health sciences. Full article

Pesticide contamination in urban runoff, stormwater, and peri-urban drainage networks is an increasing concern because of the persistence, mobility, and ecological toxicity of many pesticide residues and their transformation products. Nature-based solutions (NBSs), including constructed wetlands, bioretention systems, biofilters, and permeable reactive bio-barriers, provide low-energy and ecologically compatible platforms for urban water treatment; however, their performance is often constrained by limited sorption capacity, substrate saturation, variable hydraulic loading, and incomplete degradation of persistent pesticides. Biochar offers a multifunctional amendment for strengthening these systems because its tunable porosity, surface functionality, mineral composition, redox activity, and microbial habitat-forming capacity can support pesticide adsorption, catalytic transformation, and biodegradation. This review critically evaluates biochar-integrated NBSs for pesticide-contaminated urban water systems by linking biochar production and modification strategies with pesticide removal mechanisms, biochar–microbe interactions, engineered treatment configurations, and field-scale applicability. A comparative synthesis is provided across material-level mechanisms, system-level performance, machine learning-assisted prediction, techno-economic feasibility, life-cycle impacts, and environmental risk considerations. By integrating material properties, removal mechanisms, NBS configurations, predictive modeling, sustainability assessment, and risk considerations, this review provides a broader comparative basis than previous studies focused mainly on individual aspects of biochar-based pesticide remediation. Future priorities include standardized biochar production, long-term field validation, spent-biochar management, ecotoxicological assessment, and data-driven optimization of biochar-assisted NBSs. Full article

Urban plant communities are widely regarded as important nature-based solutions for climate mitigation, yet their actual carbon benefits remain uncertain: vegetation growth is accompanied by carbon emissions from construction and long-term maintenance, and existing assessments rarely integrate community succession, interspecific competition, and maintenance-related emissions within a consistent life-cycle framework. To address these limitations, this study developed a dynamic succession-based life-cycle simulation model to project the 50-year carbon source–sink transitions of 150 typical urban plant communities in Tianjin, China. The model updates plant structural attributes—diameter at breast height, crown width, and tree height—iteratively by linking individual plant growth to environmental suitability and neighborhood competition through a Plant Health Index. Simulated structural trajectories were coupled with biomass equations and carbon content coefficients to estimate aboveground carbon sequestration, while construction and maintenance emissions were quantified using life cycle assessment, enabling evaluation of modeled net carbon balance rather than gross carbon sequestration alone. Under the modeled 50-year scenario, most communities were projected to act as carbon sources during the early stage but gradually shifted toward carbon sinks as biomass accumulated; 86.1% of the communities were projected to become net carbon sinks after 50 years (a scenario-based projection under specified growth, maintenance, and emission assumptions). The highest modeled net carbon balance reached 3186.08 kg·C·ha⁻¹, whereas the weakest community remained a slight carbon source at −81.21 kg·C·ha⁻¹. Vertical structural complexity and species richness were the strongest positive predictors of modeled net carbon balance, followed by three-dimensional green quantity and canopy closure. Among maintenance processes, fertilization was the dominant emission source, followed by pesticide application and irrigation; comparative scenario analysis showed that resource-saving maintenance consistently improved projected net carbon balance relative to high-maintenance management. These results suggest that low-carbon planting design should prioritize locally adapted species, multi-layered vertical structures, and adaptive maintenance over simply maximizing planting density or minimizing inputs. The results represent scenario-based projections of aboveground vegetation carbon balance; belowground biomass, soil carbon, litter carbon, dead organic matter, and parameter uncertainty were not fully incorporated, and future studies should address these limitations to improve the robustness and transferability of the proposed framework. Full article