A new screening index for pesticides leachability to groundwater

Demir, Abdullah; Dilek, Filiz B.; Yetış, Ülkü

doi:10.1016/j.jenvman.2018.11.007

Cited by 44 publications

(19 citation statements)

References 21 publications

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“…Jay C. Means had found that the solubility of the compound and its K ow were significantly correlated with the sorption constant. 36 This result is consistent with our findings, with the substituted groups governing the compounds' interactions with the substrates.…”

Section: ■ Results and Discussionsupporting

confidence: 93%

“…Correlation analysis showed that K f was negatively correlated with water solubility (Figure c) and positively correlated with lg K ow (Figure d). Jay C. Means had found that the solubility of the compound and its K ow were significantly correlated with the sorption constant . This result is consistent with our findings, with the substituted groups governing the compounds’ interactions with the substrates.…”

Section: Resultssupporting

confidence: 92%

See 1 more Smart Citation

Phenyl Isocyanate-Modified Avermectin B1a Improves the Efficacy against Plant-Parasitic Nematode Diseases by Facilitating Its Soil Mobility

Jing

Zhang

Pan

et al. 2020

ACS Sustainable Chem. Eng.

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Root-knot nematodes are sedentary, soil-dwelling parasites that infect plant roots to disturb their nutrient and water utilization, causing significant yield losses. Avermectin is widely used in the field because of its excellent nematicidal activities; however, it is adsorbed by organic matter near the soil surface, resulting in poor migration in the soil, limiting its field performance. Many organic compound pesticides suffer from the same challenge. Six avermectin B1a (AVB1a) derivatives with phenyl carbamate groups were obtained and found to have increased water solubility and significantly decreased oil−water partition coefficients (K ow ) and soil adsorption coefficients (K f ). Two soil mobility experiments verified that all derivatives could move to a farther location compared with AVB1a. These derivatives could reduce nematode viability, though the nematicidal activities were lower by nearly 1.5−5-fold compared with AVB1a. In the field, the six derivatives exhibited excellent efficacy, 20−30% higher than that of the control compound. This research emphasizes the significance of uniform distribution in the soil by hydrophilic modification with phenyl carbamate that increased the delivery efficiency and bioavailability of the agent, thereby improving its efficacy.

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Section: ■ Results and Discussionsupporting

confidence: 93%

Section: Resultssupporting

confidence: 92%

Phenyl Isocyanate-Modified Avermectin B1a Improves the Efficacy against Plant-Parasitic Nematode Diseases by Facilitating Its Soil Mobility

Jing

Zhang

Pan

et al. 2020

ACS Sustainable Chem. Eng.

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“…In arid areas, the essentials of life, economic growth, agriculture, and industries highly rely on groundwater as essential natural storage to make up against a shortage of surface water [1]- [4]. While the climate change, rapid urban development, unsewered disposal, constructions, mining, and other industrial pollutants are constantly threatening the quality and quantity of groundwater; generally the oil and gas production, as well as agricultural practices of overusing pesticides and fertilizers more than any anthropogenic activities, are contributing to the groundwater contamination and scarcity [5]- [16]. Various salts (including nontoxic and toxic ions), minerals, heavy metals, microorganisms, pathogens, petroleum pollutants, and nutrients are among the major contaminators [17]- [22].…”

Section: Introductionmentioning

confidence: 99%

Groundwater Salinity Susceptibility Mapping Using Classifier Ensemble and Bayesian Machine Learning Models

et al. 2020

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Risk and susceptibility mapping of groundwater salinity (GWS) are challenging tasks for groundwater quality monitoring and management. Advancement of accurate prediction systems is essential for the identification of vulnerable areas in order to raise awareness about the potential salinity susceptibility and protect the groundwater and top-soil in due time. In this study, three machine learning models of Stochastic Gradient Boosting (StoGB), Rotation Forest (RotFor), and Bayesian Generalized Linear Model (Bayesglm) are developed for building prediction models and their performance evaluated in the delineation of salinity susceptibility maps. Both natural and human effective factors (16 features) were used as predictors for groundwater salinity modeling and were randomly divided into the training (80%) and testing (20%) datasets. The models were evaluated using testing datasets after calibration using the selected features by recursive feature elimination (RFE) method. The RFE indicated that modeling with 8 features had better performance among 1 to 16 features (Accuracy = 0.87). Results of the groundwater salinity prediction highlighted that StoGB had a good performance, whereas the RotFor and Bayesglm had an excellent performance based on the Kappa values (> 0.85). Although spatial prediction of the models was different, all of the models indicated that central parts of the region have a very high susceptibility which matches with agricultural areas, lithology map, the locations with low depth to groundwater, low slope, and elevation. Additionally, areas near to the Maharlu lake and locations with a high decline in groundwater are also located in the very high susceptibility zone, which can confirm the effects of saltwater intrusion. The susceptibility maps produced in this study are of utmost importance for water security and sustainable agriculture.

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“…A study using the Scopus database for the two-year period between 2018 and 2019 revealed a gap still present in studies on agrochemical contamination, given that the literature (RESHMA; SINDHU, 2019; KADAOUI; BOUALI; ARABI, 2019;GIMSING et al, 2019;HE et al, 2019;EJIOGU et al, 2019;FIDELIBUS;PULIDO-BOSCH, 2019;DEMIR;DILEK;YETIS, 2019;OKADERA et al, 2018;LIN;YAYA, 2018) has focused on the analysis of vulnerability to different variables, such as anthropic impacts, active ingredients of pesticides, coal mining, water salinity, pesticide leaching processes, saltwater infiltration, and the presence of nitrates, ammonia, and phosphates.…”

Section: Introductionmentioning

confidence: 99%

Groundwater vulnerability to agrochemical contamination

Soares

Silva

Filho

et al. 2020

Rev. Bras. Ciênc. Ambient. (Online)

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This research aimed at evaluating groundwater vulnerability to agrochemical contamination. To that end, we developed an index called Hydric Vulnerability and Agrochemical Contamination Index (HVACI), which integrates a geographic information system and fuzzy logic to measure catchment vulnerability to agrochemical contamination. Our case study investigates two sub-basins, the Baixo Jaguaribe and the Médio Jaguaribe, in the state of Ceará, Brazil. We built a logical relationship matrix involving economic and environmental information as a tool to enhance public managers' decision-making capabilities. Evaluation was based on four categories of vulnerability — high, medium-high, medium-low, and low —, and we found that the joint area of the Baixo Jaguaribe and Médio Jaguaribe sub-basins presented the following levels of risk contamination: 80.3% of the area had low vulnerability, 3.5% had medium-low vulnerability, 3.0% had medium-high vulnerability, and 13.2% had high vulnerability. Geographically, the municipalities with high vulnerability to contamination by pesticides were Aracati, Icapuí, Limoeiro do Norte, Tabuleiro do Norte, and Quixeré. Therefore, HVACI is an important tool for directing environmental management efforts toward areas identified as highly vulnerable to agrochemical contamination.

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A new screening index for pesticides leachability to groundwater

Cited by 44 publications

References 21 publications

Phenyl Isocyanate-Modified Avermectin B1a Improves the Efficacy against Plant-Parasitic Nematode Diseases by Facilitating Its Soil Mobility

Phenyl Isocyanate-Modified Avermectin B1a Improves the Efficacy against Plant-Parasitic Nematode Diseases by Facilitating Its Soil Mobility

Groundwater Salinity Susceptibility Mapping Using Classifier Ensemble and Bayesian Machine Learning Models

Groundwater vulnerability to agrochemical contamination

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