PestScreen: a screening approach for scoring and ranking pesticides by their environmental and toxicological concern

A combined approach for the estimation of groundwater leaching potential and environmental impacts of pesticides for agricultural lands

Pesticide pollution can degrade the quality of land, water, and air, and imposes risks on people, flora and fauna. Pesticides are frequently chosen based on their agricultural effectiveness and cost, rather than their potential environmental impacts. The aim of this study is to present a combined approach to estimate the leaching potential and environmental impacts of pesticides for agricultural lands. The pilot study area (PSA) is the agricultural plain of the Lower Aksu Stream Basin in Antalya - Turkey. The groundwater of the agricultural plain contributes to around half of the flow rate of the stream. The first phase of the combined approach involved pesticide monitoring to screen the target pesticides, the second phase involved evaluating the physicochemical properties of the target pesticides and estimating their leaching potential using several indices, and the third phase involved environmental impact analysis of the target pesticides using the Environmental Impact Quotient (EIQ) risk indicator. Additionally, Principal Component Analysis was conducted to examine the correlations between physicochemical parameters of pesticides and the leaching potential index results. By the application of the combined approach, 45 target pesticides were identified for the PSA and the indices were consistent to estimate similar leaching potentials for the same pesticides. Moreover, the computed EIQ-Field Use Rates provided valuable information to rank target pesticides according to their environmental impacts on consumer, farmworker and ecology. The applied methodology provided a practical approach to select pesticides with the least leaching potential and environmental impacts to protect water quality and ecosystem health.

Anti-oomycete ability of scopolamine against Phytophthora infestans, a terrible pathogen of potato late blight

BACKGROUND

Phytophthora infestans causes late blight, threatening potato production. The tropane alkaloid scopolamine from some industrial plants (Datura, Atropa, etc.) has a broad-spectrum bacteriostatic effect, but its effect on P. infestans is unknown.

RESULTS

In the present study, scopolamine inhibited the mycelial growth of phytopathogenic oomycete P. infestans, and the half-maximal inhibitory concentration (IC50 ) was 4.25 g L-1 . The sporangia germination rates were 61.43%, 16.16%, and 3.99% at concentrations of zero (control), 0.5 IC50 , and IC50 , respectively. The sporangia viability of P. infestans was significantly reduced after scopolamine treatment through propidium iodide and fluorescein diacetate staining, speculating that scopolamine destroyed cell membrane integrity. The detached potato tuber experiment demonstrated that scopolamine lessened the pathogenicity of P. infestans in potato tubers. Under stress conditions, scopolamine showed good inhibition of P. infestans, indicating that scopolamine could be used in multiple adverse conditions. The combination effect of scopolamine and the chemical pesticide Infinito on P. infestans was more effective than the use of scopolamine or Infinito alone. Moreover, transcriptome analysis suggested that scopolamine leaded to a downregulation of most P. infestans genes, functioning in cell growth, cell metabolism, and pathogenicity.

CONCLUSION

To our knowledge, this is the first study to detect scopolamine inhibitory activity against P. infestans. Also, our findings highlight the potential of scopolamine as an eco-friendly option for controlling late blight in the future. © 2023 Society of Chemical Industry.

A Pesticide Decision Support Tool to guide the selection of less environmentally harmful pesticides for the sugar cane industry

Pesticides applied to agricultural land have been shown to decrease the quality of water entering the Great Barrier Reef lagoon. This issue is addressed by the Reef 2050 Water Quality Improvement Plan which includes a pesticide reduction target. As part of a wider educational strategy, one method that could help meet the target is to provide stakeholders with information that assists in the selection and use of pesticide active ingredients (PAIs) that pose a lower risk to aquatic environments compared to those currently used. This study developed a Pesticide Decision Support Tool (PDST) in collaboration with stakeholders for the sugar cane industry. The PDST covers all PAIs registered and applied to sugar cane in Australia and four additional PAIs registered for use on crops grown in rotation with sugar cane. The PDST incorporates both the measure of mobility and persistence of a PAI and the measure of effect, which is based on the PAI application rate and ecotoxicity threshold value. The aquatic risk, which is the product of the measure of effect and the measure of mobility and persistence, is a measure of the likelihood that a PAI will reach the aquatic environment and cause harmful effects. Insecticide active ingredients (e.g., cadusafos, chlorpyrifos) posed the greatest aquatic risk, followed by herbicide active ingredients (e.g., MSMA, metolachlor), while fungicide AIs typically had a lower aquatic risk. An interactive spreadsheet allows characteristics, including application rate and tank mixes, to be considered when assessing the potential risk. While focusing on sugar cane, the results are equally appropriate to other crops that use the same PAIs provided the application rates are corrected to the new crop. In addition, the approach used in the PDST can be applied internationally and to any PAIs with sufficient toxicity, mobility, and persistence data.

Atrazine exposure can dysregulate the immune system and increase the susceptibility against pathogens in honeybees in a dose-dependent manner

Recently, concerns regarding the impact of agrochemical pesticides on non-target organisms have increased. The effect of atrazine, the second-most widely used herbicide in commercial farming globally, on honeybees remains poorly understood. Here, we evaluated how atrazine impacts the survival of honeybees and pollen and sucrose consumption, investigating the morphology and mRNA expression levels of midgut tissue, along with bacterial composition (relative abundance) and load (absolute abundance) in the whole gut. Atrazine did not affect mortality, but high exposure (37.3 mg/L) reduced pollen and sucrose consumption, resulting in peritrophic membrane dysplasia. Sodium channels and chitin synthesis were considered potential atrazine targets, with the expression of various genes related to lipid metabolism, detoxification, immunity, and chemosensory activity being inhibited after atrazine exposure. Importantly, 37.3 mg/L atrazine exposure substantially altered the composition and size of the gut microbial community, clearly reducing both the absolute and relative abundance of three core gram-positive taxa, Lactobacillus Firm-5, Lactobacillus Firm-4, and Bifidobacterium asteroides. With altered microbiome composition and a weakened immune system following atrazine exposure, honeybees became more susceptible to infection by the opportunistic pathogen Serratia marcescens. Thus, considering its scale of use, atrazine could negatively impact honeybee populations worldwide, which may adversely affect global food security.

Screening priority pesticides for drinking water quality regulation and monitoring by machine learning: Analysis of factors affecting detectability

Proper selection of new contaminants to be regulated or monitored prior to implementation is an important issue for regulators and water supply utilities. Herein, we constructed and evaluated machine learning models for predicting the detectability (detection/non-detection) of pesticides in surface water as drinking water sources. Classification and regression models were constructed for Random Forest, XGBoost, and LightGBM, respectively; of these, the LightGBM classification model had the highest prediction accuracy. Furthermore, its prediction performance was superior in all aspects of Recall, Precision, and F-measure compared to the detectability index method, which is based on runoff models from previous studies. Regardless of the type of machine learning model, the number of annual measurements, sales quantity of pesticide for rice-paddy field, and water quality guideline values were the most important model features (explanatory variables). Analysis of the impact of the features suggested the presence of a threshold (or range), above which the detectability increased. In addition, if a feature (e.g., quantity of pesticide sales) acted to increase the likelihood of detection beyond a threshold value, other features also synergistically affected detectability. Proportion of false positives and negatives varied depending on the features used. The superiority of the machine learning models is their ability to represent nonlinear and complex relationships between features and pesticide detectability that cannot be represented by existing risk scoring methods.

Racial and social disparities in Ventura County, California related to agricultural pesticide applications and toxicity

Application of agricultural pesticides poses health concerns for farmworkers and for local communities due to pesticide drift from spraying or fumigation, pesticide volatilization into the air, contamination of household dust, as well as direct exposure for people who work in agriculture and their families. In this analysis of pesticide use records for Ventura County, California (USA) from 2016 to 2018, we identified the most prevalent toxicological effects of the pesticides applied. We also developed a cumulative toxicity index that incorporates specific toxicity endpoints for individual pesticides, the severity and strength of association for each endpoint, and the reliability of the data sources. Combining the toxicity index for each pesticide with the pounds applied within each square mile section in Ventura County, we calculated the total toxicity-weighted pesticide use and identified pesticides associated with higher potential risk to health. Analysis of U.S. Census data for Ventura County found a greater percentage of Hispanic/Latino, African American and Asian community members in township sections with a greater volume of pesticides applied and higher toxicity-weighted pesticide use. Similarly, areas with limited economic and social resources had elevated pesticide application overall and elevated toxicity-weighted pesticide use. The combination of toxicological and demographic analyses presented in this study provides information that can support the development of policies to protect public health from excessive exposure to pesticides and better environmental health protection for socially vulnerable populations.

A risk ranking of pesticides in Irish drinking water considering chronic health effects

This paper presents a novel scoring system which facilitates a relative ranking of pesticide risk to human health arising from contaminated drinking water. This method was developed to identify risky pesticides to better inform monitoring programmes and risk assessments. Potential risk was assessed considering pesticide use, chronic human health effects and environmental fate. Site-specific soil conditions, such as soil erodibility, hydrologic group, soil depth, clay, sand, silt, and organic carbon content of soil, were incorporated to demonstrate how pesticide fate can be influenced by the areas in which they are used. The indices of quantity of use, consequence and likelihood of exposure, hazard score and quantity-weighted hazard score were used to describe the level of concern that should be attributed to a pesticide. Metabolite toxicity and persistence were also considered in a separate scoring to highlight the contribution metabolites make to overall pesticide risk. This study presents two sets of results for 63 pesticides in an Irish case study, (1) risk scores calculated for the parent compounds only and (2) a combined pesticide-metabolite risk score. In both cases the results are assessed for two locations with differing soil and hydrological properties. The method developed in this paper can be adapted by pesticide users to assess and compare pesticide risk at site level using pesticide hazard scores. Farm advisors, water quality monitors, and catchment managers can apply this method to screen pesticides for human health risk at a regional or national level.

A novel multi-criteria framework for optimizing ecotoxicological effects and human health risks of neonicotinoid insecticides: Characterization, assessment and regulation strategies

The rapid increase of neonicotinoid insecticides (NNIs) leads to the resistance to target organisms and risks to non-target organisms in the ecosystem. Thus, we designed a multi-criteria framework for resistance to target organisms, exposure risks to non-target organisms under spraying and soil or seed treatment scenarios, and ruled out the NNIs on the priority control lists. The resistance and cross-resistance, as well as the toxicity (i.e., acute, chronic, and combined toxicities) were characterized and evaluated. Results showed that the cross-resistance between two NNIs (i.e., CLO and FLU) was 1.8 times higher than their single resistance. A medium to extra-high toxicity level of NNIs was found in non-target organisms. Regulation strategies for NNIs resistance and toxicity were also proposed. The best synergist blocking and control scheme for resistance and toxicity was screened out when three main synergists (i.e., TPP: DEM: PBO) with the ratio of 1:1:1. Four NNIs (i.e., NPM, IMI, ACE, TMX) used in grain crops and six NNIs (i.e., NPM, IMI, ACE, TMX, CLO, THI) used in vegetable crops were determined as the ruled-out pesticides on the priority control lists. This study highlights the adverse effects of NNIs on the ecosystem and human health which should not be overlooked.

Development of a data driven model to screen the priority control pesticides in drinking water based on health risk ranking and contribution rates

Pesticides are pollutants of high concern in drinking water. Several approaches aimed to promote pesticide risk management in drinking water have been brought forward by diverse ways, however, these methods usually take too many indicators into consideration, which are complex and non-universal. In this study, a more focused and data driven ranking model was proposed for the purpose of development of the priority control list in drinking water. By determining three parameters including the total health risks of dietary exposure pathways, drinking water contribution rates, and the drinking water health risks, pesticides could be divided into four categories including the priority control list, secondary control list, candidate control list, and non-regulatory list. As a case study, the proposed model was implemented for 23 pesticides detected in drinking water from 36 major cities across China during two major science and technology program for water pollution control and treatment. Totally 13 kinds of pesticides including carbofuran, dicofol, chlorpyrifos, 2,4-D, acetochlor, deltamethrin, dimethoate, heptachlor, parathion, hexachlorobenzene, DDT, hexachlorocyclohexane and atrazine are selected for priority control, methyl parathion, dichlorvos and chlorothalonil are recommended for secondary control, butachlor and malathion are classified into candidate control list, and fenobucarb is suggested to be removed from the pesticide control list.

Selection of priority pesticides in Japanese drinking water quality regulation: Validity, limitations, and evolution of a risk prediction method

Several risk scoring and ranking methods have been applied for the prioritization of micropollutants, including pesticides, and in the selection of pesticides to be regulated regionally and nationally. However, the effectiveness of these methods has not been evaluated in Japan. We developed a risk prediction method to select pesticides that have a high probability of being detected in drinking water sources where no monitoring data is available. The risk prediction method was used to select new pesticides for the 2013 Primary List in the Japanese Drinking Water Quality Guidelines. Here, we examined the effectiveness of the method on the basis of the results of water quality examinations conducted by water supply authorities across Japan, and studied ways to improve the risk prediction method. Of the 120 pesticides in the 2013 Primary List, 80 were detected in drinking water sources (raw water entering water treatment plants). The rates of detection of the newly selected pesticides and previously listed pesticides were not significantly different: 64% and 68%, respectively. When the risk predictor was revised to incorporate degradability of dry-field pesticides and current pesticide sales data, the rate of detection of pesticides selected as having a high risk of detection improved from 72% to 88%. We prepared regional versions of the Primary List using the revised risk predictors and verified their utility. The number of listed pesticides varied greatly by region, ranging from 32 to 73; all regional lists were much shorter than the national Primary List. In addition, 55% to 100% of the pesticides detected in each region were included in a Regional Primary List. This work verifies the ability of the risk prediction method to screen pesticides and select those with a high risk of detection.

Historical trends of the ecotoxicological pesticide risk from the main grain crops in Rolling Pampa (Argentina)

We showed the results of the first long-term analysis (1987-2019) of pesticide (herbicides, fungicides and insecticides) impact in the Rolling Pampa, one of the main agricultural areas of Argentina. Using a clear and meaningful tool, based not only on acute toxicity but also on scaling up the results to total sown area, we identified time trends for both pesticide impact and the ecoefficiency of modal pesticide profiles. By the end of the time series, soybean showed a pesticide impact four times greater than maize crop in the studied area. However, the time trend in the subperiod (2012-2019) showed a sustainable pattern of pesticide use in soybean crop, with an improvement in its ecoefficiency. Oppositely, maize showed a relatively constant ecoefficiency value during most of the time series, suggesting a possible path towards an unsustainable cropping system. Findings from this study suggest that some efforts have to be made to improve the pest management decisions towards a more efficient pesticide profiles in maize crop and to keep improving the ecotoxicity pesticide profile in soybean crops because of its large sown area in the studied area.

Priority Setting for Management of Hazardous Biocides in Korea Using Chemical Ranking and Scoring Method

Biocides are non-agricultural chemical agents for the prevention of unhygienic pests. The worldwide demand for biocidal products has been rapidly increasing. Meanwhile, biocides have been causing negative health effects for decades, resulting in public health scares. Therefore, governments around the world have tried to strictly control biocides, and it is necessary to prioritize the health risks of biocides for efficient management. Chemical ranking and scoring (CRS) methods have been developed for the effective management of chemicals. However, existing methods do not use suitable variables to evaluate biocides, thus possibly underestimating or overestimating the actual health risks. We developed a new CRS method that reflects the exposure and toxicity characteristics of biocides. Eleven indicators were chosen as appropriate for prioritizing biocides, and scoring based on the globally harmonized system of classification and labeling of chemicals (GHS) improved the efficiency of the method. Correlations between individual indicators in this study were low (−0.151–0.325), indicating that each indicator was independent and well-chosen for prioritizing biocides. The effect of each indicator on the total score showed that carcinogenicity, mutagenicity, and reproductive toxicity (CMR) chemicals ranked high with r = 0.558. This result demonstrated that the most dangerous toxicants should play a more decisive role in the top ranking than the others. We expect that our method can be efficiently used to screen regulated biocides by prioritizing their health hazards, thus leading to better policy decision making about biocide use.

Integrated management of pesticides in an intensive agricultural area: a case study in Altinova, Turkey

Pesticide contamination of groundwater is a common critical problem faced by many countries due to excessive and unconscious applications. As a result of increasing concerns about pesticides, several qualitative and quantitative risk assessment models/indicators were developed and applied. In this study, a practical approach was presented for the protection of groundwater from pesticide contamination. The first phase is the assessment of the specific vulnerability of groundwater to pesticide contamination using the DRASTIC-Pesticide model. The second phase is the monitoring and assessment of groundwater quality to verify vulnerability assessment. The third phase is to assess the impacts of pesticides on the environment and human health using a commonly applied pesticide risk indicator, the environmental impact quotient (EIQ). The pilot study area (PSA) is the Altinova region at Antalya city of Turkey which exhibits intensive agricultural activities and covers an approximate surface area of 75 km². A total of 25 groundwater wells were chosen for monitoring of groundwater quality where four sessions of seasonal field measurements were conducted for 1 year. Two sessions of water quality analyses were also performed for many physical, bacteriological, and chemical parameters, including pesticides. Additionally, soil samples were analyzed, site surveys were conducted, and other relevant information (topographical, geological and meteorological characteristics, pesticide use and toxicological data sets, etc.) was gathered. The PSA was determined to have high vulnerability to contamination of groundwater, and it was verified through monitoring results. The commonly used pesticides in the PSA were ranked according to their risks to select pesticides with the least environmental impacts.

Critical review of methods for risk ranking of food-related hazards, based on risks for human health

This study aimed to critically review methods for ranking risks related to food safety and dietary hazards on the basis of their anticipated human health impacts. A literature review was performed to identify and characterize methods for risk ranking from the fields of food, environmental science and socio-economic sciences. The review used a predefined search protocol, and covered the bibliographic databases Scopus, CAB Abstracts, Web of Sciences, and PubMed over the period 1993-2013. All references deemed relevant, on the basis of predefined evaluation criteria, were included in the review, and the risk ranking method characterized. The methods were then clustered-based on their characteristics-into eleven method categories. These categories included: risk assessment, comparative risk assessment, risk ratio method, scoring method, cost of illness, health adjusted life years (HALY), multi-criteria decision analysis, risk matrix, flow charts/decision trees, stated preference techniques and expert synthesis. Method categories were described by their characteristics, weaknesses and strengths, data resources, and fields of applications. It was concluded there is no single best method for risk ranking. The method to be used should be selected on the basis of risk manager/assessor requirements, data availability, and the characteristics of the method. Recommendations for future use and application are provided.

Priority pesticides in sediments of European coastal lagoons: A review

This review summarizes the legislation applied to pesticides and highlights the physicochemical properties of the past and recently listed PPs under Water Framework Directive (WFD). It reports important information regarding the analysis, occurrence and long-term screening of PPs in sediments of European coastal lagoons. Among the entire list of PPs, those analyzed have been the organochloride insecticides, such as lindane, hexachlorobenzene and DDT. Although flood events pointed to the possible redistribution of pesticides, which may increase under climate change conditions, few studies are available concerning PPs screening in sediments of European coastal lagoons. Data is scarce not only in terms of the number of listed PPs that have been analyzed but also in terms of their long-term screening. This lack of data on PP concentrations is probably a consequence of the fact that less importance is given to the sediments contamination/quality.

Environmental and human risk hierarchy of pesticides: A prioritization method, based on monitoring, hazard assessment and environmental fate

A pesticide prioritization approach was developed and implemented in the Pinios River Basin of Central Greece. It takes under consideration the Level of Environmental Risk containing information on the frequency of occurrence of pesticides above environmental thresholds, the intensity of this occurrence and the spatial distribution as well as information about the fate and behavior of pesticides in the environment and the potential to have adverse impact on humans' health. Original 3-year monitoring data from 102 Stationary Sampling Sites located on rivers and their tributaries, reservoirs, streams and irrigation/drainage canals giving rise to a collection of 2382 water samples resulting in 7088 data sets, were included in this integrated prioritization study. Among 302 monitored active ingredients, 119 were detected at least once and the concentrations found in the aquatic systems for 41% of compounds were higher than the respective lowest Predicted Non-Effect Concentration (PNEC) values. Sixteen and 5 pesticides were found with risk ratios (MECmax/PNEC) above 10 (high concern) and 100 (very high concern), respectively. However, pesticides with maximum Measured Environmental Concentration (MECmax) values exceeding by 1000 times the respective lowest PNEC values were also found which were considered of extremely high concern; in the latter group were included prometryn, chlorpyrifos, diazinon, λ-cyhalothrin, cypermethrin, α-cypermethrin deltamethrin, ethalfluralin and phosmet. The sensitivity of the analytical methods used in the monitoring study was considered inadequate to meet the toxicological endpoints for 32 pesticides. The widest distribution of occurrence in the Stationary Sampling Sites of the monitoring program was found for the pesticides, prometryn, fluometuron, terbuthylazine, S-metolachlor, chlorpyrifos, diphenylamine, acetochlor, alachlor, 2,4-D, etridiazole, imidacloprid and lindane (γ-ΗCH). Among the 27 priority pesticides included in the Directive 2013/39/EU, in the present study 13 pesticides were considered as candidates for River Basin Specific Pollutants (RBSP) for the River Basin of Pinios. Among the 30 pesticide specific pollutants that were established by the Greek authorities, in the present study only 6 were considered as candidates for RBSP for the river basin of Pinios. As a result of the implementation of the prioritization approach developed in this study a total of 71 pesticides were identified as being RBSP for the river basin of Pinios. The higher Level of Environmental Risk was found to be exerted by the organophosphorus insecticide chlorpyrifos, followed by lindane and prometryn. The present study provides background information for important decisions to be made concerning the selection of pesticides which should be included in the target analyte list of new monitoring and screening programs of surface water quality in the Pinios River Basin. In addition, the prioritization approach proposed here can be useful for the development of River Basin Management Plans.

A pesticide monitoring survey in rivers and lakes of northern Greece and its human and ecotoxicological risk assessment

A pesticide monitoring study covering the main rivers and lakes of Northern Greece (Macedonia, Thrace and Thessaly) was undertaken. A total of 416 samples were collected over a 1.5-year sampling period (September 1999- February 2001) from six rivers and ten lakes. The water samples were analyzed with an off-line solid phase extraction technique coupled with a gas chromatography ion trap mass spectrometer using an analytical method for 147 pesticides and their metabolites, including organochlorines, organophosphates, triazines, chloroacetanilides, pyrethroids, carbamates, phthalimides and other pesticides (herbicides, insecticides and fungicides). Based on the pesticide survey results, a human health carcinogenic and non-carcinogenic risk assessment was conducted for adults and children. Ecotoxicological risk assessment was also conducted using default endpoint values and the risk quotient method. Results showed that the herbicides metolachlor, prometryn, alachlor and molinate, were the most frequently detected pesticides (29%, 12.5%, 12.5% and 10%, respectively). They also exhibited the highest concentration values, often exceeding 1 μg/L. Chlorpyrifos ethyl was the most frequently detected insecticide (7%). Seasonal variations in measured pesticide concentrations were observed in all rivers and lakes. The highest concentrations were recorded during May-June period, right after pesticide application. Concentrations of six pesticides were above the maximum allowable limit of 0.1 μg/L set for drinking water. Alachlor, atrazine and a-HCH showed unacceptable carcinogenic risk estimates (4.5E-06, 4.6E-06 and 1.3E-04, respectively). Annual average concentrations of chlorpyriphos ethyl (0.031 μg L), dicofol (0.01 μg/L), dieldrin (0.02 μg/L) and endosulfan a (0.065 μg/L) exceeded the EU environmental quality standards. The risk quotient estimates for the insecticides chorpyrifos ethyl, diazinon and parathion methyl and herbicide prometryn were above acceptable risk values. The coupling of monitoring data to probabilistic human and ecotoxicological risk estimates could find use by Greek regulatory authorities, proposing effective pollution management schemes.

Differential responses of the antioxidant system of ametryn and clomazone tolerant bacteria

The herbicides ametryn and clomazone are widely used in sugarcane cultivation, and following microbial degradation are considered as soil and water contaminants. The exposure of microorganisms to pesticides can result in oxidative damage due to an increase in the production of reactive oxygen species (ROS). This study investigated the response of the antioxidant systems of two bacterial strains tolerant to the herbicides ametryn and clomazone. Bacteria were isolated from soil with a long history of ametryn and clomazone application. Comparative analyses based on 16S rRNA gene sequences revealed that strain CC07 is phylogenetically related to Pseudomonas aeruginosa and strain 4C07 to P. fulva. The two bacterial strains were grown for 14 h in the presence of separate and combined herbicides. Lipid peroxidation, reduced glutathione content (GSH) and antioxidant enzymes activities were evaluated. The overall results indicated that strain 4C07 formed an efficient mechanism to maintain the cellular redox balance by producing reactive oxygen species (ROS) and subsequently scavenging ROS in the presence of the herbicides. The growth of bacterium strain 4C07 was inhibited in the presence of clomazone alone, or in combination with ametryn, but increased glutathione reductase (GR) and glutathione S-transferase (GST) activities, and a higher GSH concentration were detected. Meanwhile, reduced superoxide dismutase (SOD), catalase (CAT) and GST activities and a lower concentration of GSH were detected in the bacterium strain CC07, which was able to achieve better growth in the presence of the herbicides. The results suggest that the two bacterial strains tolerate the ametryn and clomazone herbicides with distinctly different responses of the antioxidant systems.

Biomonitoring of pesticides by pine needles--chemical scoring, risk of exposure, levels and trends

Vegetation is a useful matrix for the quantification of atmospheric pollutants such as semi-volatile organic compounds (SVOCs). In particular, pine needles stand out as effective biomonitors due to the excellent uptake properties of their waxy layer. Having previously validated an original and reliable method to analyse pesticides in pine needles, our work team set the objective of this study to determine the levels of 18 pesticides in Pinus pinea needles collected in 12 different sampling sites in Portugal. These compounds were selected among a total of 70 pesticides by previous chemical scoring, developed to assess their probability to occur in the atmosphere. The risk of exposure was evaluated by the binomial chemical score/frequency of occurrence in the analysed samples. Levels and trends of the chemical families and target of the pesticides were obtained regarding the type of land occupation of the selected sites, including the use of advanced statistics (principal component analysis, PCA). Finally, some correlations with several characteristics of the sampling sites (population, energy consumption, meteorology, etc.) were also investigated.

Selecting pesticides for inclusion in drinking water quality guidelines on the basis of detection probability and ranking

Pesticides released into the environment may pose both ecological and human health risks. Governments set the regulations and guidelines for the allowable levels of active components of pesticides in various exposure sources, including drinking water. Several pesticide risk indicators have been developed using various methodologies, but such indicators are seldom used for the selection of pesticides to be included in national regulations and guidelines. The aim of the current study was to use risk indicators for the selection of pesticides to be included in regulations and guidelines. Twenty-four risk indicators were created, and a detection rate was defined to judge which indicators were the best for selection. The combination of two indicators (local sales of a pesticide for the purposes of either rice farming or other farming, divided by the guideline value and annual precipitation, and amended with the scores from the physical and chemical properties of the pesticide) gave the highest detection rates. In this case study, this procedure was used to evaluate 134 pesticides that are currently unregulated in the Japanese Drinking Water Quality Guidelines, from which 44 were selected as pesticides to be added to the primary group in the guidelines. The detection probability of the 44 pesticides was more than 72%. Among the 102 pesticides currently in the primary group, 17 were selected for withdrawal from the group.

PURE: a web-based decision support system to evaluate pesticide environmental risk for sustainable pest management practices in California

Farmers, policy makers, and other stakeholders seek tools to quantitatively assess pesticide risks for mitigating pesticide impacts on ecosystem and human health. This paper presents the Pesticide Use Risk Evaluation (PURE) decision support system (DSS) for evaluating site-specific pesticide risks to surface water, groundwater, soil, and air across pesticide active ingredient (AI), pesticide product, and field levels. The risk score is determined by the ratio of the predicted environmental concentrations (PEC) to the toxicity value for selected endpoint organism(s); except that the risk score for the air is calculated using the emission potential (EP), which is a pesticide product property for estimating potential volatile organic compound (VOC) emissions by California Environmental Protection Agency (CEPA). The risk scores range from 0 to 100, where 0 represents negligible risk while 100 means the highest risk. The procedure for calculating PEC in surface water was evaluated against monitoring data for 41 pesticide AIs, with a statistically significant correlation coefficient of r=0.82 (p<0.001). In addition, two almond fields in the Central Valley, California were evaluated for pesticide risks as a case study, where the commonly acknowledged high-risk pesticides gained high risk scores. Simazine, one of the most frequently detected pesticides in groundwater, was scored as 74 (the moderate high risk class) to groundwater; and chlorpyrifos, one of the frequently detected pollutants in surface water, was scored as 100 (the high risk class) to surface water. In support of pesticide risk quantitative assessment and use of reduced-risk pesticide selection, the PURE-DSS can be useful to assist growers, pesticide control advisors, and environmental protection organizations in mitigating pesticide use impacts on the environment.

A concurrent neuro-fuzzy inference system for screening the ecological risk in rivers

PURPOSE

A conceptual model to assess water quality in river basins was developed here. The model was based on ecological risk assessment principles, and incorporated a novel ranking and scoring system, based on self-organizing maps, to account for the likely ecological hazards posed by the presence of chemical substances in freshwater. This approach was used to study the chemical pollution in the Ebro River basin (Spain), whose currently applied environmental indices must be revised in terms of scientific accuracy.

METHODS

Ecological hazard indexes for chemical substances were calculated by pattern recognition of persistence, bioaccumulation, and toxicity properties. A fuzzy inference system was proposed to compute ecological risk points (ERP), which are a combination of the ecological hazard to aquatic sensitive organisms and environmental concentrations. By aggregating ERP, changes in water quality over time were estimated.

RESULTS

The proposed concurrent neuro-fuzzy model was applied to a comprehensive dataset of the network controlling the levels of dangerous substances, such as metals, pesticides, and polycyclic aromatic hydrocarbons, in the Ebro river basin. The approach was verified by comparison versus biological monitoring. The results showed that water quality in the Ebro river basin is affected by presence of micro-pollutants.

CONCLUSIONS

The ERP approach is suitable to analyze overall trends of potential threats to freshwater ecosystems by anticipating the likely impacts from multiple substances, although it does not account for synergies among pollutants. Anyhow, the model produces a convenient indicator to search for pollutant levels of concern.

Selecting analytical target pesticides in monitoring: Sensitivity analysis and scoring

Measuring river water concentrations of all pesticides applied in a catchment area is a daunting task. This study aims to develop new score tables for selecting analytical target pesticides. Sensitivity analyses were conducted using a diffuse pollution hydrologic model to quantitatively evaluate the influence of pesticide properties (e.g., log K(OC), degradability [half-life]) on concentrations of rice-farming pesticides in river water. Using the results of the analyses, score tables were systematically designed for the pesticide properties such that the sum of the scores for a particular pesticide, designated as the contamination index, was proportional to the expected/predicted concentration of that pesticide in river water. The contamination indexes for pesticides applied in three river basins were calculated and compared with the corresponding observed pesticide concentrations. Correlations between contamination indexes and observed concentrations were fairly good. Pesticides were ranked according to the quotients obtained by dividing the pesticide concentrations predicted from the contamination indexes by the corresponding drinking-water quality guideline values, and pesticide candidates to be monitored were successfully selected on the basis of a threshold quotient.

The role of allelopathy in agricultural pest management

Allelopathy is a naturally occurring ecological phenomenon of interference among organisms that may be employed for managing weeds, insect pests and diseases in field crops. In field crops, allelopathy can be used following rotation, using cover crops, mulching and plant extracts for natural pest management. Application of allelopathic plant extracts can effectively control weeds and insect pests. However, mixtures of allelopathic water extracts are more effective than the application of single-plant extract in this regard. Combined application of allelopathic extract and reduced herbicide dose (up to half the standard dose) give as much weed control as the standard herbicide dose in several field crops. Lower doses of herbicides may help to reduce the development of herbicide resistance in weed ecotypes. Allelopathy thus offers an attractive environmentally friendly alternative to pesticides in agricultural pest management. In this review, application of allelopathy for natural pest management, particularly in small-farm intensive agricultural systems, is discussed.

Occurrence and fate of the herbicide glyphosate and its degradate aminomethylphosphonic acid in the atmosphere

This is the first report on the ambient levels of glyphosate, the most widely used herbicide in the United States, and its major degradation product, aminomethylphosphonic acid (AMPA), in air and rain. Concurrent, weekly integrated air particle and rain samples were collected during two growing seasons in agricultural areas in Mississippi and Iowa. Rain was also collected in Indiana in a preliminary phase of the study. The frequency of glyphosate detection ranged from 60 to 100% in both air and rain. The concentrations of glyphosate ranged from <0.01 to 9.1 ng/m(3) and from <0.1 to 2.5 µg/L in air and rain samples, respectively. The frequency of detection and median and maximum concentrations of glyphosate in air were similar or greater to those of the other high-use herbicides observed in the Mississippi River basin, whereas its concentration in rain was greater than the other herbicides. It is not known what percentage of the applied glyphosate is introduced into the air, but it was estimated that up to 0.7% of application is removed from the air in rainfall. Glyphosate is efficiently removed from the air; it is estimated that an average of 97% of the glyphosate in the air is removed by a weekly rainfall ≥ 30 mm.

Life cycle toxicity assessment of pesticides used in integrated and organic production of oranges in the Comunidad Valenciana, Spain

The relative impacts of 25 pesticides including acaricides, fungicides, herbicides, insecticides, and post-harvest fungicides, used in the production of oranges in Spain were assessed with current life cycle impact assessment (LCIA) tools. Chemical specific concentrations were combined with pesticide emission data and information on chemical toxicity to assess human toxicity and freshwater ecotoxicity impacts. As a case study, the relative impacts of two orange production systems in the region of Valencia, integrated pest management (IP) and organic production (OP), were assessed. The evaluation of active ingredients showed that on average acaricides have the highest human toxicity impact scores, while for freshwater ecotoxicity insecticides show the highest impact. In both impact categories the lowest impact scores were calculated for herbicides. In the production of 1 kg of orange fruits, where several kinds of pesticides are combined, results show that post-harvest fungicides can contribute more than 95% to the aggregate human toxicity impacts. More than 85% of aquatic ecotoxicity is generated by fungicides applied before harvest. The potential to reduce impacts on freshwater ecosystems is seven orders of magnitude, while impacts on human health can be reduced by two orders of magnitude. Hence, this stresses the importance of a careful pre-selection of active ingredients. In both impact categories, organic production represents the least toxic pest-control method.

Pesticide uptake in potatoes: model and field experiments

A dynamic model for uptake of pesticides in potatoes is presented and evaluated with measurements performed within a field trial in the region of Boyacá, Colombia. The model takes into account the time between pesticide applications and harvest, the time between harvest and consumption, the amount of spray deposition on soil surface, mobility and degradation of pesticide in soil, diffusive uptake and persistence due to crop growth and metabolism in plant material, and loss due to food processing. Food processing steps included were cleaning, washing, storing, and cooking. Pesticide concentrations were measured periodically in soil and potato samples from the beginning of tuber formation until harvest. The model was able to predict the magnitude and temporal profile of the experimentally derived pesticide concentrations well, with all measurements falling within the 90% confidence interval. The fraction of chlorpyrifos applied on the field during plant cultivation that eventually is ingested by the consumer is on average 10(-4)-10(-7), depending on the time between pesticide application and ingestion and the processing step considered.

A fuzzy expert system for soil characterization

As soil is a natural resource not always renewable, the risk characterization of contaminated soils is an issue of great interest. Artificial Intelligence (AI), based on Decision Support Systems (DSSs), has been developed for a wide range of applications in contaminated soil management. Decision trees have already shown to be easy to interpret and able to treat large scale applications. Fuzzy logic gives an improvement in the perturbations and the variance of the training data, due to the elasticity of fuzzy set formalism. In this study, we have developed a classificatory tool applied to characterize contaminated soil in function of human and environmental risks. Knowledge engineering for constructing the Soil Risk Characterization Decision Support System (SRC-DSS) involves three stages: knowledge acquisition, conceptual design and system implementation. A total of 26 parameters were divided into three groups to facilitate the configuration of the expert system: source attributes, transfer vector attributes, and local properties. Sixteen case studies were evaluated with the SRC-DSS. In comparison with other techniques, the results of the current study have shown that SRC-DDS is an excellent tool to classify and characterize soils according to the associated risk.