Risk-based prioritization method for the classification of groundwater pesticide pollution from agricultural regions

Inhibitory Effect of Tea Saponin on Major Apple-Disease-Inducing Fungi

Plant secondary metabolites are well known for their biological functions in defending against pathogenic microorganisms. Tea saponin (TS), one type of secondary metabolite of the tea plant (Camellia sinensis), has been shown to be a valuable botanical pesticide. However, its antifungal activity in controlling the fungi Valsa mali, Botryosphaeria dothidea, and Alternaria alternata, which induce major diseases in apple (Malus domestica), has not been determined. In this study, we first determined that TS has higher inhibitory activity than catechins against the three types of fungi. We further utilized in vitro and in vivo assays to confirm that TS showed high antifungal activity against the three types of fungi, especially for V. mali and B. dothidea. In the in vivo assay, application of a 0.5% TS solution was able to restrain the fungus-induced necrotic area in detached apple leaves efficiently. Moreover, a greenhouse infection assay also confirmed that TS treatment significantly inhibited V. mali infection in leaves of apple seedlings. In addition, TS treatment activated plant immune responses by decreasing accumulation of reactive oxygen species and promoting the activity of pathogenesis-related proteins, including chitinase and β-1,3-glucanase. This indicated that TS might serve as a plant defense inducer to activate innate immunity to fight against fungal pathogen invasion. Therefore, our data indicated that TS might restrain fungal infection in two ways, by directly inhibiting the growth of fungi and by activating plant innate defense responses as a plant defense inducer.

Ecotoxicological risk assessment of 14 pesticides and corresponding metabolites to groundwater and soil organisms using China-PEARL model and RQ approach

Global use of pesticides brings uncertain risks to human and nontarget species via environmental matrix. Currently, various models for exposure risk assessment are developed and widely used to forecast the impact of pesticides on environmental organisms. In this study, five commonly used insecticides, seven herbicides and three fungicides were chosen to analyze the subsequent risks in groundwater in simulated scenarios using China-PEARL (Pesticide Emission Assessment at Regional and Local Scales) model. In addition, their exposure risks to soil organisms were characterized based on risk quotient (RQ) approach. The results indicated that 23.3% of the total 528 predicted environmental concentrations (PECs) of pesticides and respective metabolites in groundwater from six Chinese simulated locations with ten crops were above 10 μg L^-1. Furthermore, acceptable human risks of pesticides in groundwater were observed for all simulation scenarios (RQ < 1). Based on the derived PECs in soil short-term and long-term exposure simulation scenarios, all compounds were evaluated to be with acceptable risks to soil organisms, except that imidacloprid was estimated to be with unacceptable chronic risk (RQ = 27.5) to earthworms. Overall, the present findings provide an opportunity for a more-comprehensive understanding of exposure toxicity risks of pesticides leaching into groundwater and soil.

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.

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.

Socioeconomic status, ambidextrous learning, and farmers' adoption of biological control technology: evidence from 650 kiwifruit growers in China

BACKGROUND

Rural China is characterized as having different rates of economic growth. The resource and socioeconomic statuses of farm households greatly affect their productivity and the activities they engage in. The main objective in this study was to explore the mechanisms concerning how socioeconomic status of kiwifruit growers affects their adoption of biological control technology (BCT). To achieve this objective, field survey data from 650 kiwifruit farmers in specific kiwifruit growing areas of Shaanxi and Sichuan provinces in China were investigated. The binary probit model and Bootstrap dual mediated utility models served to assess socioeconomic status's effect on farmers' BCT adoption.

RESULTS

This study discovered a significant positive correlation between socioeconomic status and the adoption rate of biological control technology. Farmers of various socioeconomic status have significant differences in the rate of BCT adoption. This study's empirical analysis found that exploratory learning and exploitative learning under dual learning had a significant mediating effect on farmers' socioeconomic status when it came to BCT acceptance.

CONCLUSION

Results show that the rate of BCT adoption is related to farmers' socioeconomic status and dual learning mode, which provides new insights for understanding how farmers implement new technology. This study will help agricultural extension departments increase their awareness of BCT adoption by farmers, and the development of diverse learning approaches in response to differences in socioeconomic status of farmers may significantly increase their likelihood to implement BCT. © 2021 Society of Chemical Industry.

Leaching behaviour appraisal of eight persistent herbicides on a loam soil amended with different composted organic wastes using screening indices

The addition of organic wastes is a common agronomic practice in some Mediterranean regions to increase soil organic matter. In addition, they consume high amounts of agrochemicals. Hand-packed soil columns were used to evaluate the effect of three different composted organic soil amendments (agro-forestry, agro-industrial and animal manure) on the leachability of eight persistent herbicides. A new leaching index based on the amounts recovered from leachates and referred as Experimental Leaching Index (ELI) is proposed according to the mean annual precipitation in a specific place. This index is compared with others such as Groundwater Ubiquity Score (GUS), Relative Leaching Potential Index (RLPI) and Leachability Index (LIX), which only include degradation (DT₅₀) and sorption (K_OC) parameters. According to ELI, metribuzin is very mobile in all cases, while terbuthylazine, chlorotoluron and isoproturon present high leachability only in unamended soil reducing their leaching potential in amended soils. Aclonifen, oxyfluorfen, trifluralin and pendimethalin behave in all cases as immobile (non-leacher) compounds.

A new screening index for pesticides leachability to groundwater

Given the fact that pesticides exist in the aquatic environment at very low concentrations, it is clear that their analysis require expensive analytical techniques. Water authorities in Turkey are in need of assessing the likelihood of pesticide occurrence in groundwater in order to identify minimum number of pesticides that would be included in monitoring programs. To this purpose, the pesticides used in Turkey are ranked and those having higher leaching potentials are identified using pesticide screening leaching indexes. A total of 15 indexes (AF, AFR/AFT, Hamaker's RF, Briggs's RF, LPI, VI, LIX, GUS, Hornsby, LEACH, MLEACH, PLP, GWCP, LIN and GLI) was adopted and leaching potentials of 157 pesticides used in Turkey were estimated. Because each index is based on different pesticide/soil characteristics, each produced a different ranking. In order to emphasize variation in rankings and bring out a strong pattern, the statistical technique of Principal Component Analysis was used and a new composite index named as "YASGEP-P" was developed, the most relevant components (indices) were identified and the corresponding factor scores were calculated. This new index came out as a composite of GUS, LIX, MLEACH, LIN, Briggs's RF, Hamaker's RF, PLP and AFR indices. It was seen that all these indices except AFR are almost equally dominant and increase the value of YASGEP-P index, whereas AFR is also dominant but causes a decrease in YASGEP-P index value. The new index developed tends to discriminate between the relatively more soluble/less sorbable and more sorbable/less soluble pesticides. With the use of this composite index, the pesticides used in Turkey were sorted from the most leachable to least leachable and the priority pesticides to be monitored in the groundwaters were identified.

Quantity based indicators fail to identify extreme pesticide risks

As a matter of policy, minimizing human health and environmental risks associated with pesticide use is a major challenge but necessary for improving agricultural sustainability. Efficient and effective policies that encourage the use of less risky pesticides, such as pesticide taxes, necessitate a precise and realistic quantification of potential adverse effects. Various indicators are currently utilized in policies and they focus mainly on a purely quantitative dimension of the pesticides used, which can lead potentially to unfavorable outcomes of pesticide policies. A unique dataset applied to pesticide use by Swiss farmers in winter wheat and potato production, demonstrates that on average the two most important quantitative indicators show a significant correlation with pesticide risks as expressed by the Danish Load Indicator. However, they have almost no explanatory power for extreme risks (e.g. most intensive use patterns for pesticides with unfavorable toxicity profiles). Results remain stable over a range of aggregation levels, from application- to farm-level indicators of pesticide use. These findings render the commonly used, quantitative indicators ineffective to reduce potential environmental and human health risks of pesticides and, in the worst case, lead to misinformed market-based pesticide policies consequential to National Action Plans.