An Integrated Vegetated Treatment System for Mitigating Imidacloprid and Permethrin in Agricultural Irrigation Runoff

Pyrethroid and neonicotinoid pesticides control an array of insect pests in leafy greens, but there are concerns about the off-site movement and potential water quality impacts of these chemicals. Effective on-farm management practices can eliminate aquatic toxicity and pesticides in runoff. This project evaluated an integrated vegetated treatment system (VTS), including the use of polyacrylamide (PAM), for minimizing the toxicity of imidacloprid and permethrin pesticides in runoff. The VTS incorporated a sediment trap to remove coarse particles, a grass-lined ditch with compost swales to remove suspended sediment and insecticides, and granulated activated carbon (GAC) or biochar to remove residual insecticides. Runoff was sampled throughout the VTS and analyzed for pesticide concentrations, and aquatic toxicity using the midge Chironomusdilutus and the amphipod Hyalella azteca. In simulated runoff experiments, the VTS reduced suspended sediment load by 88%, and imidacloprid and permethrin load by 97% and 99%, respectively. In runoff events from a conventionally grown lettuce field, suspended sediment load was reduced by 98%, and insecticide load by 99%. Toxicity was significantly reduced in approximately half of the simulated runoff events, and most of the lettuce runoff events. Integrated vegetated treatment systems that include components for treating soluble and hydrophobic pesticides are vital tools for reducing pesticide load and occurrence of pesticide-related toxicity.

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Land-Use/-Cover Changes and Their Effect on Soil Erosion and River Suspended Sediment Load in Different Landscape Zones of European Russia during 1970–2017

Water ◽

10.3390/w13121631 ◽

2021 ◽

Vol 13 (12) ◽

pp. 1631

Author(s):

Artyom V. Gusarov

Keyword(s):

Soviet Union ◽

Suspended Sediment ◽

Sediment Load ◽

Gully Erosion ◽

European Russia ◽

Relative Reduction ◽

Forest Steppe ◽

Suspended Sediment Load ◽

Cultivated Land ◽

Study Region

Contemporary trends in cultivated land and their influence on soil/gully erosion and river suspended sediment load were analyzed by various landscape zones within the most populated and agriculturally developed part of European Russia, covering 2,222,390 km2. Based on official statistics from the Russian Federation and the former Soviet Union, this study showed that after the collapse of the Soviet Union in 1991, there was a steady downward trend in cultivated land throughout the study region. From 1970–1987 to 2005–2017, the region lost about 39% of its croplands. Moreover, the most significant relative reduction in cultivated land was noted in the forest zone (south taiga, mixed and broadleaf forests) and the dry steppes and the semi-desert of the Caspian Lowland—about 53% and 65%, respectively. These territories are with climatically risky agriculture and less fertile soils. There was also a widespread reduction in agricultural machinery on croplands and livestock on pastures of the region. A decrease in soil/gully erosion rates over the past decades was also revealed based on state hydrological monitoring data on river suspended sediment load as one of the indicators of the temporal variability of erosion intensity in river basins and the published results of some field research in various parts of the studied landscape zones. The most significant reduction in the intensity of erosion and the load of river suspended sediment was found in European Russia’s forest-steppe zone. This was presumably due to a favorable combination of the above changes in land cover/use and climate change.

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Prediction of daily suspended sediment load (SSL) using new optimization algorithms and soft computing models

Soft Computing ◽

10.1007/s00500-021-05721-5 ◽

2021 ◽

Author(s):

Hamid Darabi ◽

Sedigheh Mohamadi ◽

Zahra Karimidastenaei ◽

Ozgur Kisi ◽

Mohammad Ehteram ◽

...

Keyword(s):

Suspended Sediment ◽

Soft Computing ◽

Sediment Load ◽

Optimization Algorithms ◽

Training Phase ◽

Suspended Sediment Load ◽

Engineering Structures ◽

Northern Iran ◽

Taylor Diagram ◽

Computing Models

AbstractAccurate modeling and prediction of suspended sediment load (SSL) in rivers have an important role in environmental science and design of engineering structures and are vital for watershed management. Since different parameters such as rainfall, temperature, and discharge with the different lag times have significant effects on the SSL, quantifying and understanding nonlinear interactions of the sediment dynamics has always been a challenge. In this study, three soft computing models (multilayer perceptron (MLP), adaptive neuro-fuzzy system (ANFIS), and radial basis function neural network (RBFNN)) were used to predict daily SSL. Four optimization algorithms (sine–cosine algorithm (SCA), particle swarm optimization (PSO), firefly algorithm (FFA), and bat algorithm (BA)) were used to improve the capability of SSL prediction of the models. Data from gauging stations at the mouth of the Kasilian and Talar rivers in northern Iran were used in the analysis. The selection of input combinations for the models was based on principal component analysis (PCA). Uncertainty in sequential uncertainty fitting (SUFI-2) and performance indicators were used to assess the potential of models. Taylor diagrams were used to visualize the match between model output and observed values. Assessment of daily SSL predictions for Talar station revealed that ANFIS-SCA yielded the best results (RMSE (root mean square error): 934.2 ton/day, MAE (mean absolute error): 912.2 ton/day, NSE (Nash–Sutcliffe efficiency): 0.93, PBIAS: 0.12). ANFIS-SCA also yielded the best results for Kasilian station (RMSE: 1412.10 ton/day, MAE: 1403.4 ton/day, NSE: 0.92, PBIAS: 0.14). The Taylor diagram confirmed that ANFIS-SCA achieved the best match between observed and predicted values for various hydraulic and hydrological parameters at both Talar and Kasilian stations. Further, the models were tested in Eagel Creek Basin, Indiana state, USA. The results indicated that the ANFIS-SCA model reduced RMSE by 15% and 21% compared to the MLP-SCA and RBFNN-SCA models in the training phase. Comparing models performance indicated that the ANFIS-SCA model could decrease MAE error compared to ANFIS-BA, ANFIS-PSO, ANFIS-FFA, and ANFIS models by 18%, 32%, 37%, and 49% in the training phase, respectively. The results indicated that the integration of optimization algorithms and soft computing models can improve the ability of models for predicting SSL. Additionally, the hybridization of soft computing models with optimization algorithms can decrease the uncertainty of models.

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