A multi-objective optimization tool for the selection and placement of BMPs for pesticide control

Abstract. Pesticides (particularly atrazine used in corn fields) are the foremost source of water contamination in many of the water bodies in Midwestern corn belt, exceeding the 3 ppb MCL established by the U.S. EPA for drinking water. Best management practices (BMPs), such as buffer strips and land management practices, have been proven to effectively reduce the pesticide pollution loads from agricultural areas. However, selection and placement of BMPs in watersheds to achieve an ecologically effective and economically feasible solution is a daunting task. BMP placement decisions under such complex conditions require a multi-objective optimization algorithm that would search for the best possible solution that satisfies the given watershed management objectives. Genetic algorithms (GA) have been the most popular optimization algorithms for the BMP selection and placement problem. Most optimization models also had a dynamic linkage with the water quality model, which increased the computation time considerably thus restricting them to apply models on field scale or relatively smaller (11 or 14 digit HUC) watersheds. However, most previous works have considered the two objectives individually during the optimization process by introducing a constraint on the other objective, therefore decreasing the degree of freedom to find the solution. In this study, the optimization for atrazine reduction is performed by considering the two objectives simultaneously using a multi-objective genetic algorithm (NSGA-II). The limitation with the dynamic linkage with a distributed parameter watershed model was overcome through the utilization of a BMP tool, a database that stores the pollution reduction and cost information of different BMPs under consideration. The model was used for the selection and placement of BMPs in Wildcat Creek Watershed (located in Indiana, for atrazine reduction. The most ecologically effective solution from the model had an annual atrazine concentration reduction of 30%, from the baseline with a BMP implementation cost of $18 million. The pareto-optimal fronts generated between the two optimized objective functions can be used to achieve desired water quality goals with minimum BMP implementation cost for the watershed.

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Pesticide Contamination of Groundwater in Virginia: BMP Impact Assessment

Water Science & Technology ◽

10.2166/wst.1993.0440 ◽

1993 ◽

Vol 28 (3-5) ◽

pp. 379-387 ◽

Cited By ~ 8

Author(s):

S. Mostaghimi ◽

P. W. McClellan ◽

R. A. Cooke

Keyword(s):

Water Quality ◽

Land Use ◽

Best Management Practices ◽

Water Samples ◽

Management Practices ◽

Water Quality Monitoring ◽

Pesticide Contamination ◽

Cropping Patterns ◽

Creek Watershed ◽

Surface And Groundwater

The Nomini Creek Watershed/Water Quality monitoring project was initiated in 1985, as part of the Chesapeake Bay Agreement of 1983, to quantify the impacts of agricultural best management practices (BMPs) on improving water quality. The watershed monitoring system was designed to provide a comprehensive assessment of the quality of surface and groundwater as influenced by changes in land use, agronomic, and cultural practices in the watershed over the duration of the project. The primary chemical characteristics monitored include both soluble and sediment-bound nutrients and pesticides in surface and groundwater. Water samples from 8 monitoring wells located in agricultural areas in the watershed were analyzed for 22 pesticides. A total of 20 pesticides have been detected in water samples collected. Atrazine is the most frequently detected pesticide. Detected concentrations of atrazine ranged from 0.03 - 25.56 ppb and occurred in about 26 percent of the samples. Other pesticides were detected at frequencies ranging from 1.6 to 14.2 percent of all samples collected and concentrations between 0.01 and 41.89 ppb. The observed concentrations and spatial distributions of pesticide contamination of groundwater are compared to land use and cropping patterns. Results indicate that BMPs are quite effective in reducing pesticide concentrations in groundwater.

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Multi-Objective Optimization for Selecting and Siting the Cost-Effective BMPs by Coupling Revised GWLF Model and NSGAII Algorithm

Water ◽

10.3390/w12010235 ◽

2020 ◽

Vol 12 (1) ◽

pp. 235 ◽

Cited By ~ 1

Author(s):

Zuoda Qi ◽

Gelin Kang ◽

Xiaojin Wu ◽

Yuting Sun ◽

Yuqiu Wang

Keyword(s):

Best Management Practices ◽

Management Practices ◽

Cost Effective ◽

Control Measures ◽

Multi Objective Optimization ◽

Watershed Model ◽

Filter Strips ◽

Multi Objective ◽

Dissolved Nitrogen ◽

The Cost

Best management practices (BMPs) are an effective way to control water pollution. However, identification of the optimal distribution and cost-effect of BMPs provides a great challenge for watershed policy makers. In this paper, a semi-distributed, low-data, and robust watershed model, the Revised Generalized Watershed Loading Function (RGWLF), is improved by adding the pollutant attenuation process in the river channel and a bank filter strips reduction function. Three types of pollution control measures—point source wastewater treatment, bank filter strips, and converting farmland to forest—are considered, and the cost of each measure is determined. Furthermore, the RGWLF watershed model is coupled with a widely recognized multi-objective optimization algorithm, the non-dominated sorting genetic algorithm II (NSGAII), the combination of which is applied in the Luanhe watershed to search for spatial BMPs for dissolved nitrogen (DisN). Fifty scenarios were finally selected from numerous possibilities and the results indicate that, at a minimum cost of 9.09 × 107 yuan, the DisN load is 3.1 × 107 kg and, at a maximum cost of 1.77 × 108 yuan, the total dissolved nitrogen load is 1.31 × 107 kg; with the no-measures scenario, the DisN load is 4.05 × 107 kg. This BMP optimization model system could assist decision-makers in determining a scientifically comprehensive plan to realize cost-effective goals for the watershed.

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Do awareness-focussed approaches to mitigate diffuse pollution work? A study on combined behavioural and water quality evidence

10.5194/egusphere-egu2020-7640 ◽

2020 ◽

Author(s):

Murat Okumah

Keyword(s):

Water Quality ◽

Drinking Water ◽

Best Management Practices ◽

Management Practices ◽

Water Sources ◽

Quality Improvements ◽

Utility Company ◽

Pesticide Pollution ◽

Drinking Water Sources ◽

The Uk

<p>Recent efforts to tackle diffuse water pollution from agriculture (DWPA) have focussed on improving farmers&#8217; awareness under the expectation that this would contribute to adoption of best management practices (BMPs) and result in water quality improvements. To date, however, no study has studied the full awareness-behaviour-water quality pathway; with previous studies having mostly addressed the awareness-behaviour link relying on disciplinary approaches. Here, we investigated whether awareness-focussed approaches to mitigating DWPA work, addressing the pathway in full using a multidisciplinary approach. We did this by working with Welsh Water (a utility company in the UK) on their Weed wiper project which encourages farmers to consider &#8216;smarter&#8217; ways of weed, pest and disease control and promotes the safe storage, use and disposal of pesticides and thus safeguard drinking water sources. One aim of this project was to mitigate pesticide pollution in watercourses, through a free &#8216;weed wiper&#8217; hire trial. The main goal of the trial was to promote farmers&#8217; awareness and uptake of BMPs to tackle the rising concentrations of the pesticide MCPA (2-methyl-4-chlorophenoxyacetic acid) in drinking water sources in three catchments in Wales. Weed wipers are a proven and effective method of managing weeds and have multiple benefits. By wiping an herbicide directly onto weeds, weed wipers dramatically reduce spray drift in comparison to more traditional methods, such as boom or knapsack spraying. Using less chemical can save land managers money and reduce the risk to their health, water and the wider environment. Using factorial analysis of variance, we analysed MCPA concentrations from 2005 to 2019 for all water treatment works (WTWs) in the three catchments where the weed wiper trial had occurred and all the WTWs within three control catchments that had not been part of the trial but were in a similar location and of a similar characteristics. This was followed by semi-structured in-depth interviews with institutional stakeholders and farmers with varying degrees of exposure to the Weed wiper project. &#160;Results show that MCPA concentration for both treatment and control catchments had reduced following the weed wiper trial, however, considerably larger (38.9%) decreases were observed in the treatment catchments than in the control catchments (10%) and these differences were statistically significant (p<0.05, n= 2858). Results from the stakeholder interviews suggest that the weed wiper project had contributed to changes in behaviour and that these are very likely to have resulted in the water quality improvements. Further analysis revealed, however, that other psychosocial, agronomic, catchment and climate factors also influenced farmers&#8217; behaviour. Therefore, while awareness is an important step towards improving water quality, policymakers need to consider the role of these other variables in their interventions and how they interact with awareness. This research is the first one to cover the full awareness-behaviour-water quality pathway, and to combine different scientific disciplinary 'knowledges' with local non-scientific knowledge to explain water quality responses within the context of awareness-focussed interventions.&#160;</p>

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Preliminary Evaluation of Effects of Best Management Practices in the Black Earth Creek, Wisconsin, Priority Watershed

Water Science & Technology ◽

10.2166/wst.1993.0457 ◽

1993 ◽

Vol 28 (3-5) ◽

pp. 539-548 ◽

Cited By ~ 12

Author(s):

John F. Walker ◽

David J. Graczyk

Keyword(s):

Water Quality ◽

Mass Transport ◽

Best Management Practices ◽

Management Practices ◽

Nonpoint Source ◽

Transitional Period ◽

Preliminary Evaluation ◽

Best Management ◽

Creek Watershed ◽

Basin Scale

Nonpoint-source contamination accounts for a substantial part of the water quality problems in many watersheds. The Wisconsin Nonpoint Source Water Pollution Abatement Program provides matching money for voluntary implementation of various best management practices (BMPs). The effectiveness of BMPs on a drainage-basin scale has not been adequately assessed in Wisconsin by use of data collected before and after BMP implementation. The U.S. Geological Survey, in cooperation with the Wisconsin Department of Natural Resources, monitored water quality in the Black Earth Creek watershed in southern Wisconsin from October 1984 through September 1986 (pre-BMP conditions). BMP implementation began during the summer of 1989 and is planned to continue through 1993. Data collection resumed in fall 1989 and is intended to provide information during the transitional period of BMP implementation (1990-93) and 2 years of post-BMP conditions (1994-95). Preliminary results presented for two subbasins in the Black Earth Creek watershed (Brewery and Garfoot Creeks) are based on data collected during pre-BMP conditions and the first 3 years of the transitional period. The analysis includes the use of regressions to control for natural variability in the data and, hence, enhance the ability to detect changes. Data collected to date (1992) indicate statistically significant differences in storm mass transport of suspended sediment and ammonia nitrogen at Brewery Creek. The central tendency of the regression residuals has decreased with the implementation of BMPs; hence, the improvement in water quality in the Brewery Creek watershed is likely a result of BMP implementation. Differences in storm mass transport at Garfoot Creek were not detected, primarily because of an insufficient number of storms in the transitional period. As practice implementation continues, the additional data will be used to determine the level of management which results in significant improvements in water quality in the two watersheds. Future research will address techniques for including snowmelt runoff and early spring storms.

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Modeling effectiveness of broiler litter application method for reducing phosphorus and nitrogen losses

Hydrology Research ◽

10.2166/nh.2019.017 ◽

2019 ◽

Vol 50 (4) ◽

pp. 1047-1061 ◽

Cited By ~ 2

Author(s):

Palki Arora ◽

Jasmeet Lamba ◽

Puneet Srivastava ◽

Latif Kalin

Keyword(s):

Water Quality ◽

Best Management Practices ◽

Surface Runoff ◽

Management Practices ◽

Swat Model ◽

N Losses ◽

Broiler Litter ◽

Application Method ◽

Creek Watershed ◽

Total P

Abstract The linkages among the best management practices implemented at the field level and downstream water quality improvement at the watershed level are complex, because the processes that link management practices and watershed-level water quality span a range of scales. However, it is important to understand the effect of nutrient management strategies on watershed-level water quality because most of the water quality evaluation occurs at the watershed scale. The overall goal of this study was to quantify the effect of broiler litter application method (surface vs. subsurface application) on phosphorus (P) and nitrogen (N) losses in surface runoff using the Soil and Water Assessment Tool (SWAT) model. The research was conducted in the Big Creek watershed (8,024 ha) located in Mobile County, Alabama, USA. At the hydrological response unit level, the subsurface application of broiler litter to pastures reduced average annual (1991–2015) total P and N losses in surface runoff by 72% and 33%, respectively, compared to surface application of broiler litter. At the watershed outlet, subsurface application of broiler litter to pastures (covered 43% of the watershed area after the land use change scenario) reduced average annual (1991–2015) total P and N losses by 39% and 20%, respectively.

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Management Scale Assessment of Practices to Mitigate Cattle Microbial Water Quality Impairments of Coastal Waters

Sustainability ◽

10.3390/su11195516 ◽

2019 ◽

Vol 11 (19) ◽

pp. 5516 ◽

Cited By ~ 3

Author(s):

David J. Lewis ◽

Dylan Voeller ◽

Tina L. Saitone ◽

Kenneth W. Tate

Keyword(s):

Water Quality ◽

Best Management Practices ◽

Management Practices ◽

Initial Period ◽

Quality Improvements ◽

Managerial Control ◽

Microbial Water Quality ◽

Cattle Movements ◽

Coastal Watersheds ◽

Creek Watershed

Coastal areas support multiple important resource uses including recreation, aquaculture, and agriculture. Unmanaged cattle access to stream corridors in grazed coastal watersheds can contaminate surface waters with fecal-derived microbial pollutants, posing risk to human health via activities such as swimming and shellfish consumption. Improved managerial control of cattle access to streams through implementation of grazing best management practices (BMPs) is a critical step in mitigating waterborne microbial pollution in grazed watersheds. This paper reports trend analysis of a 19-year dataset to assess long-term microbial water quality responses resulting from a program to implement 40 grazing BMPs within the Olema Creek Watershed, a primary tributary to Tomales Bay, USA. Stream corridor grazing BMPs implemented included: (1) Stream corridor fencing to eliminate/control cattle access, (2) hardened stream crossings for cattle movements across stream corridors, and (3) off stream drinking water systems for cattle. We found a statistically significant reduction in fecal coliform concentrations following the initial period of BMP implementation, with overall mean reductions exceeding 95% (1.28 log10)—consistent with 1—2 log10 (90–99%) reductions reported in other studies. Our results demonstrate the importance of prioritization of pollutant sources at the watershed scale to target BMP implementation for rapid water quality improvements and return on investment. Our findings support investments in grazing BMP implementation as an important component of policies and strategies to protect public health in grazed coastal watersheds.

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Modeling Water Quality and the Effects of Agricultural Best Management Practices in the Iowa River Basin

Water Science & Technology ◽

10.2166/wst.1985.0209 ◽

1985 ◽

Vol 17 (6-7) ◽

pp. 1141-1153 ◽

Cited By ~ 17

Author(s):

B. R. Bicknell ◽

Anthony S. Donigian ◽

T. A. Barnwell

Keyword(s):

Water Quality ◽

River Basin ◽

Best Management Practices ◽

Management Practices ◽

Agricultural Runoff ◽

Large River ◽

Water Quality Modeling ◽

Hydrological Simulation ◽

Best Management ◽

Creek Watershed

This paper describes a demonstration application of comprehensive hydrology and water quality modeling on a large river basin to evaluate the effects of agricultural nonpoint pollution and proposed best management practices (BMP). The model application combines detailed simulation of agricultural runoff and soil processes, including calculation of surface and subsurface pollutant transport to receiving water, with subsequent simulation of instream transport and transformation. The result is a comprehensive simulation of river basin water quality. The investigation of the Iowa River Basin described in this paper was part of a large study which included application and evaluation of the Hydrological Simulation Program - FORTRAN (HSPF) to both the data-intensive Four Mile Creek watershed and the Iowa River above Coralville Reservoir. In this study, the methodology developed on Four Mile Creek was extrapolated to the Iowa River Basin to demonstrate its applicability and functionality on a large river basin. Many model parameter values from Four Mile Creek were applied directly to the study area without adjustment while other parameters were modified based on available information and calibration. This study allowed the exploration of problems associated with modeling hydrology, sediment, and chemical fate and transport in a large river basin with varying meteorologic conditions, soils, and agricultural practices.

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A Survey of Nursery Water Quality Best Management Practices

HortScience ◽

10.21273/hortsci.33.3.501e ◽

1998 ◽

Vol 33 (3) ◽

pp. 501e-502

Author(s):

Cody J. White ◽

Michael A. Schnelle ◽

Gerrit W. Cuperus

Keyword(s):

Water Quality ◽

Best Management Practices ◽

Management Practices ◽

Cultural Practices ◽

Plant Materials ◽

Best Management ◽

Employee Safety ◽

Current Water ◽

Management Approaches ◽

Environmentally Sound

A survey was designed to assess high-risk areas with respect to environmental contamination, specifically how it relates to water quality. Oklahoma growers of all economic levels, retail and/or wholesale, were queried at their place of business for their current state of implementing best management practices (BMPs) and other strategic actions that could potentially affect current and future water quality standards. Specific areas such as the physical environment of the nursery, primary pesticides and fertilizers used, Integrated Pest Management (IPM) practices, and employee safety training were covered as well as other aspects germane to preserving and protecting current water quality and related environmental issues. More than 75 nurseries were surveyed and given the opportunity to participate in future training at Oklahoma State Univ. Results indicated that nurseries have not fully implemented many BMPs, but have adopted fundamental IPM approaches. The stage is set for the implementation of the next phase of expansion and refinement into ecologically based programs such as propagation and sale of low pesticide input plant materials, improved cultural practices, and the integration of environmentally sound management approaches. As an example, many growers are in the process of phasing out calendar-based pesticide application programs in favor of aesthetic and/or economic threshold-driven pesticide spray programs.

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A storm water retrofit plan for the mimico creek watershed

Water Science & Technology ◽

10.2166/wst.1999.0539 ◽

1999 ◽

Vol 39 (12) ◽

pp. 133-140

Author(s):

J. Y. Li ◽

D. Banting

Keyword(s):

Water Quality ◽

Quality Management ◽

Great Lakes ◽

Management Practices ◽

Water Quality Management ◽

Storm Water ◽

Planning Tool ◽

Creek Watershed ◽

Urbanized Areas ◽

Goals And Objectives

Storm water quality management in urbanized areas remains a challenge to Canadian municipalities as the funding and planning mechanisms are not well defined. In order to provide assistance to urbanized municipalities in the Great Lakes areas, the Great Lakes 2000 Cleanup Fund and the Ontario Ministry of the Environment commissioned the authors to develop a Geographic Information System planning tool for storm water quality management in urbanized areas. The planning tool comprises five steps: (1) definition of storm water retrofit goals and objectives; (2) identification of appropriate retrofit storm water management practices; (3) formulation of storm water retrofit strategies; (4) evaluation of strategies with respect to retrofit goals and objectives; and (5) selection of storm water retrofit strategies. A case study of the fully urbanized Mimico Creek wateshed in the City of Toronto is used to demonstrate the application of the planning tool.

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Comparison of Forecasting Models for Real-Time Monitoring of Water Quality Parameters Based on Hybrid Deep Learning Neural Networks

Water ◽

10.3390/w13111547 ◽

2021 ◽

Vol 13 (11) ◽

pp. 1547

Author(s):

Jian Sha ◽

Xue Li ◽

Man Zhang ◽

Zhong-Liang Wang

Keyword(s):

Neural Network ◽

Water Quality ◽

Deep Learning ◽

Real Time ◽

Best Management Practices ◽

Input Data ◽

Management Practices ◽

Quality Parameters ◽

Water Quality Parameters ◽

Real Time Monitoring

Accurate real-time water quality prediction is of great significance for local environmental managers to deal with upcoming events and emergencies to develop best management practices. In this study, the performances in real-time water quality forecasting based on different deep learning (DL) models with different input data pre-processing methods were compared. There were three popular DL models concerned, including the convolutional neural network (CNN), long short-term memory neural network (LSTM), and hybrid CNN–LSTM. Two types of input data were applied, including the original one-dimensional time series and the two-dimensional grey image based on the complete ensemble empirical mode decomposition algorithm with adaptive noise (CEEMDAN) decomposition. Each type of input data was used in each DL model to forecast the real-time monitoring water quality parameters of dissolved oxygen (DO) and total nitrogen (TN). The results showed that (1) the performances of CNN–LSTM were superior to the standalone model CNN and LSTM; (2) the models used CEEMDAN-based input data performed much better than the models used the original input data, while the improvements for non-periodic parameter TN were much greater than that for periodic parameter DO; and (3) the model accuracies gradually decreased with the increase of prediction steps, while the original input data decayed faster than the CEEMDAN-based input data and the non-periodic parameter TN decayed faster than the periodic parameter DO. Overall, the input data preprocessed by the CEEMDAN method could effectively improve the forecasting performances of deep learning models, and this improvement was especially significant for non-periodic parameters of TN.

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