scholarly journals Fluctuations of Phytophthora and Pythium spp. in Components of a Recycling Irrigation System

Plant Disease ◽  
2003 ◽  
Vol 87 (12) ◽  
pp. 1500-1506 ◽  
Author(s):  
Elizabeth A. Bush ◽  
Chuanxue Hong ◽  
Erik L. Stromberg
Keyword(s):  
Water Quality ◽  
Plant Pathogens ◽  
Management Practices ◽  
Irrigation System ◽  
Phytophthora Capsici ◽  
Selective Media ◽  
Irrigation Reservoir ◽  
Phytophthora Spp ◽  
Detection Technology ◽  
Pollutant Discharge

Stringent standards of water quality have prompted many horticultural enterprises to limit pollutant discharge associated with nutrient and pesticide applications. Collecting and recycling effluent is a method that has been implemented by many operations to contain pollutants; however, plant pathogens may be spread through recycled effluent. In this study, Phytophthora and Pythium spp. present in a water-recycling irrigation system at a perennial container nursery in southwestern Virginia were characterized using filtering and baiting techniques with two selective media. Members of Phytophthora were identified to species, whereas Pythium spp. were identified to genus only. Pythium spp. were recovered more frequently and in greater numbers than Phytophthora spp. Phytophthora capsici, P. citricola, P. citrophthora, P. cryptogea, P. drechsleri, and P. nicotianae were recovered in filtering assays. Only P. cryptogea and P. drechsleri were identified from baits placed on the surface of the irrigation reservoir, whereas P. cactorum, P. capsici, P. citricola, P. citrophthora, P. cryptogea, and P. drechsleri were recovered at depths, specifically at 1 and 1.5 m. This research provides data for development of detection technology and management practices for plant pathogens in irrigation water and may lead to improvements in conventional assay protocols.

scholarly journals An Investigation of Stormwater Quality Variation within an Industry Sector Using the Self-Reported Data Collected under the Stormwater Monitoring Program

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3185
Author(s):  
Maryam Salehi ◽  
Khashayar Aghilinasrollahabadi ◽  
Mitra Salehi Esfandarani
Keyword(s):  
Water Quality ◽  
Management Practices ◽  
Monitoring Program ◽  
Principal Component ◽  
The Self ◽  
Stormwater Quality ◽  
Discharge Characteristics ◽  
Industry Sector ◽  
Pollutant Discharge ◽  
Reported Data

Storm runoff pollutants are among the major sources of surface water impairments, globally. Despite several monitoring programs and guidance on stormwater management practices, there are many streams still impaired by urban runoff. This study evaluates an industry sector’s pollutant discharge characteristics using the self-reported data collected under Tennessee Multi Sector Permit program. The stormwater pollutant discharge characteristics were analyzed from 2014 to 2018 for an industry sector involving twelve facilities in West Tennessee, USA. The data analysis revealed the presence of both organic and inorganic contaminants in stormwater samples collected at all twelve industrial facilities, with the most common metals being magnesium, copper, and aluminum. The principal component analysis (PCA) was applied to better understand the correlation between water quality parameters, their origins, and seasonal variations. Furthermore, the water quality indexes (WQIs) were calculated to evaluate the stormwater quality variations among studied facilities and seasons. The results demonstrated slight variations in stormwater WQIs among the studied facilities ranging from “Bad” to “Medium” quality. The lowest seasonal average WQI was found for spring compared to the other seasons. Certain limitations associated with the self-reported nature of data were identified to inform the decision makers regarding the required future changes.

A Survey of Nursery Water Quality Best Management Practices

HortScience ◽  
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.

Applying the Soil Water Assessment Tool to 5th Canadian Division Support Base Gagetown

2014 ◽  
Vol 49 (4) ◽  
pp. 372-385
Author(s):  
Shawn Burdett ◽  
Michael Hulley ◽  
Andy Smith
Keyword(s):  
Water Quality ◽  
Soil Water ◽  
Land Management ◽  
Management Practices ◽  
Assessment Tool ◽  
Anthropogenic Activities ◽  
Quality Model ◽  
Sediment Yields ◽  
Soil Water Assessment Tool ◽  
Water Assessment

A hydrologic and water quality model is sought to establish an approach to land management decisions for a Canadian Army training base. Training areas are subjected to high levels of persistent activity creating unique land cover and land-use disturbances. Deforestation, complex road networks, off-road manoeuvres, and vehicle stream crossings are among major anthropogenic activities observed to affect these landscapes. Expanding, preserving and improving the quality of these areas to host training activities for future generations is critical to maintain operational effectiveness. Inclusive to this objective is minimizing resultant environmental degradation, principally in the form of hydrologic fluctuations, excess erosion, and sedimentation of aquatic environments. Application of the Soil Water Assessment Tool (SWAT) was assessed for its ability to simulate hydrologic and water quality conditions observed in military landscapes at 5th Canadian Division Support Base (5 CDSB) Gagetown, New Brunswick. Despite some limitations, this model adequately simulated three partial years of daily watershed outflow (NSE = 0.47–0.79, R2 = 0.50–0.88) and adequately predicted suspended sediment yields during the observation periods (%d = 6–47%) for one highly disturbed sub-watershed in Gagetown. Further development of this model may help guide decisions to develop or decommission training areas, guide land management practices and prioritize select landscape mitigation efforts.

Pesticide Contamination of Groundwater in Virginia: BMP Impact Assessment

1993 ◽  
Vol 28 (3-5) ◽  
pp. 379-387 ◽  
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.

A storm water retrofit plan for the mimico creek watershed

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.

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

Water ◽  
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.

scholarly journals A robotic irrigation system for urban gardening and agriculture

2019 ◽  
Vol 50 (4) ◽  
pp. 198-207
Author(s):  
Ioannis Gravalos ◽  
Avgoustinos Avgousti ◽  
Theodoros Gialamas ◽  
Nikolaos Alfieris ◽  
Georgios Paschalidis
Keyword(s):  
Water Content ◽  
Food Systems ◽  
Management Practices ◽  
Irrigation System ◽  
Robotic System ◽  
Urban Gardening ◽  
Irrigation Treatments ◽  
Comparison Of The Results ◽  
Irrigation Technologies ◽  
Sustainable Irrigation

Water supply limits and continued population growth have intensified the search for measures to conserve water in urban gardening and agriculture. The efficiency of water use is depended on performance of the irrigation technologies and management practices. In this study, a robotic irrigation system was developed that consists of a moving bridge manipulator and a sensor-based platform. The manipulator constructed is partly using open-source components and software, and is easily reconfigurable and extendable. In combination to the sensor-based platform this custommade manipulator has the potential to monitor the soil water content (SWC) in real time. The irrigation robotic system was tested in an experimental soil tank. The total surface of the soil tank was divided by a raster into 18 equal quadrants. The water management for maintaining water content in the soil tank within tolerable lower limit (refill point) was based on three irrigation treatments: i) quadrants whose SWC is below the refill point are irrigated; ii) quadrants are irrigated only when the daily mean SWC of the tank is below the refill point and only for those whose actual SWC is lower than that limit; and iii) quadrants are irrigated every two days with constant amount of water. A comparison of the results of the three irrigation treatments showed that the second treatment gave less irrigation events and less applied water. Finally, we could conclude that the performance of the fabricated robotic system is appropriate and it could play an important role in achieving sustainable irrigation into urban food systems.

Integrated modelling under uncertainty in watershed-level assessment and management

2007 ◽  
Vol 56 (8) ◽  
pp. 31-39 ◽  
Author(s):  
J.H. Ham ◽  
C.G. Yoon ◽  
K.W. Jung ◽  
J.H. Jang
Keyword(s):  
Water Quality ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Watershed Management ◽  
Management Practices ◽  
Control Measures ◽  
Integrated Modelling ◽  
Quality Prediction ◽  
Water Quality Prediction ◽  
Modelling System

Uncertainty in water quality model predictions is inevitably high due to natural stochasticity, model uncertainty, and parameter uncertainty. An integrated modelling system (modified-BASINS) under uncertainty is described and demonstrated for use in receiving-water quality prediction and watershed management. A Monte Carlo simulation was used to investigate the effect of various uncertainty types on output prediction. Without pollution control measures in the watershed, the concentrations of total nitrogen (T-N) and total phosphorus (T-P) in the Hwaong Reservoir, considering three uncertainty types, would be less than about 4.4 and 0.23 mg L−1, respectively, in 2012, with 90% confidence. The effects of two watershed management practices, wastewater treatment plants (WWTP) and constructed wetlands (WETLAND), were evaluated. The combined scenario (WWTP + WETLAND) was the most effective at improving reservoir water quality, bringing concentrations of T-N and T-P in the Hwaong Reservoir to less than 3.4 and 0.14 mg L−1, 24 and 41% improvements, respectively, with 90% confidence. Overall, the Monte Carlo simulation in the integrated modelling system was practical for estimating uncertainty and reliable in water quality prediction. The approach described here may allow decisions to be made based on the probability and level of risk, and its application is recommended.

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