scholarly journals Water Quality of Inflows to the Everglades National Park over Three Decades (1985–2014) Analyzed by Multivariate Statistical Methods

2018 ◽  
Vol 15 (9) ◽  
pp. 1882 ◽  
Author(s):  
Lei Wan ◽  
Xiaohui Fan
Keyword(s):  
Water Quality ◽  
Best Management Practices ◽  
National Park ◽  
Management Practices ◽  
Temporal Trends ◽  
Principal Component ◽  
South Florida ◽  
Point Sources ◽  
Everglades National Park ◽  
Nitrogen And Phosphorus

The Everglades, a vast subtropical wetland, dominates the landscape of south Florida and is widely recognized as an ecosystem of great ecological importance. Data from seven inflow sites to the Everglades National Park (ENP) were analyzed over three decades (1985–2014) for temporal trends by the STL (integrated seasonal-trend decomposition using LOESS) method. A cluster analysis (CA) and principal component analysis (PCA) were applied for the evaluation of spatial variation. The results indicate that the water quality change trend is closely associated with rainfall. Increasing rainfall results in increasing flow and thus, decreasing concentrations of nitrogen and phosphorus. Based on 10 variables, the seven sampling stations were classified by CA into four distinct clusters: A, B, C, and D. The PCA analysis indicated that total nitrogen (TN) and total phosphorus (TP) are the main pollution factors, especially TN. The results suggest that non-point sources are the main pollution sources and best management practices (BMPs) effectively reduce organic nitrogen. However, TN and TP control is still the focus of future work in this area. Increasing the transfer water quantity can improve the water quality temporarily and planting submersed macrophytes can absorb nitrogen and phosphorus and increase the dissolved oxygen (DO) concentration in water, continuously improving the water quality.

Long-term Water Quality Trends after Implementing Best Management Practices in South Florida

2009 ◽  
Vol 38 (4) ◽  
pp. 1683-1693 ◽  
Author(s):  
Samira H. Daroub ◽  
Timothy A. Lang ◽  
Orlando A. Diaz ◽  
Sabine Grunwald
Keyword(s):  
Water Quality ◽  
Best Management Practices ◽  
Management Practices ◽  
South Florida ◽  
Best Management ◽  
Water Quality Trends

Iowa Statewide Stream Nitrate Loading: 2017-2018 Update

2019 ◽  
Vol 126 (1-4) ◽  
pp. 6-12 ◽  
Author(s):  
Christopher S. Jones ◽  
Keith E. Schilling
Keyword(s):  
Water Quality ◽  
Mississippi River ◽  
Best Management Practices ◽  
Management Practices ◽  
Nitrate Nitrogen ◽  
Nitrogen Loading ◽  
Point Sources ◽  
Nutrient Reduction ◽  
Stream Network ◽  
Discharge Data

In response to ongoing hypoxia in the Gulf of Mexico, several states in the Mississippi River basin have adopted nutrient reduction plans in recent years designed to arrest the flow of nitrogen (N) and phosphorus (P) from both point and non-point sources to the stream network. Iowa's Nutrient Reduction Strategy, implemented in 2012, aims to reduce stream loading of these nutrients by 45% within a yet-to-be-defined time frame. Because the state has chosen to integrate accountability into the strategy through the numerical objective, ongoing water monitoring is necessary to credibly measure progress. The primary objective of this research was to use water quality monitoring and discharge data to update statewide nitrate-nitrogen (NO3-N) loading using the combined data sets generated by in situ water quality sensors and traditional grab sample monitoring conducted by state government. Our research shows that the 5-year running annual average of nitrate-nitrogen loading continues to increase, and after the 2018 water year is 73% higher than that calculated in 2003. Loads from Iowa areas draining to the Missouri River are increasing more rapidly than loads from areas draining to the upper Mississippi River: 132% versus 55% since 2003. This shows that best management practices designed to stem the loss of nutrients from the corn-soybean system must be widely adopted and robustly designed for extreme environmental conditions if Iowa is to meet its water quality objectives.

Stimulating implementation of best management practices to reduce pesticide loads to surface water in a small agricultural catchment

2020 ◽  
Author(s):  
Piet Seuntjens ◽  
Ellen Pauwelyn ◽  
Els Belmans ◽  
Ingeborg Joris ◽  
Elien Dupon ◽  
...  
Keyword(s):  
Water Quality ◽  
Drinking Water ◽  
Surface Water ◽  
Best Management Practices ◽  
Management Practices ◽  
Point Sources ◽  
Mitigation Measures ◽  
Physical Analysis ◽  
Best Management ◽  
The Impact

<p>High-quality, safe, and sufficient drinking water is essential for life: we use it for drinking, food preparation and cleaning. Agriculture is the biggest source of pesticides and nitrate pollution in European fresh waters. Pesticide occurrences in rivers result from diffuse runoff from farmland or from point sources from the farmyard. Although many best management practices (BMPs) to mitigate these diffuse and point sources are developed and widely disseminated for several years, the effective implementation of mitigation measures in practice remains limited. Therefore, the Waterprotect project has been set up to improve the knowledge and awareness of the impact of crop protection products on the water quality among the many actors, to identify the bottlenecks for implementation of suitable BMPs and further develop new governance strategies to overcome these issues for a more effective drinking water protection. As all actors share the responsibility to deal with the water quality, government agencies (e.g. environmental agencies), private actors (e.g. drinking water company, input supplier, processing industry) and civil society actors (e.g. farmers) are involved in the project. Processes to cope with the problem are initiated in 7 action labs among which the Belgian Bollaertbeek action lab. The study area is a small agricultural catchment where surface water is used as intake to produce drinking water for the nearby city. The area is sensitive to erosion and based on a physical analysis and risk analysis of the catchment, the implementation of filling and cleaning places on individual farms and buffer strips along the watercourse are proposed as suitable measures to tackle the pollution problem. In order to implement them, mechanisms to increase the involvement of targeted farmers and alternative governance systems are studied. Results of the analysis of the water quality issues and the water governance system in the Belgian Bollaertbeek action lab and the strategies to try to improve the uptake of mitigation measures to improve water quality will be presented.</p>

Importance of diffuse pollution control in the Patzcuaro Lake Basin in Mexico

2008 ◽  
Vol 58 (11) ◽  
pp. 2179-2186 ◽  
Author(s):  
Marco Mijangos Carro ◽  
Jorge Izurieta Dávila ◽  
Antonieta Gómez Balandra ◽  
Rubén Hernández López ◽  
Rubén Huerto Delgadillo ◽  
...  
Keyword(s):  
Best Management Practices ◽  
Catchment Area ◽  
Management Practices ◽  
Point Sources ◽  
Diffuse Pollution ◽  
Lake Basin ◽  
Nitrogen And Phosphorus ◽  
Best Management ◽  
Critical Areas ◽  
Animal Farm

In the catchment area of the Lake Patzcuaro in Central Mexico (933 km2) the apportionments of erosion, sediment, nutrients and pathogen coming from thirteen micro basins were estimated with the purpose of identifying critical areas in which best management practices need to be implemented in order to reduce their contribution to the lake pollution and eutrophication. The ArcView Generalized Watershed Loading Functions model (AV-GWLF) was applied to estimate the loads and sources of nutrients. The main results show that the total annual contribution of nitrogen from point sources were 491 tons and from diffuse pollution 2,065 tons, whereas phosphorus loads where 116 and 236 tons, respectively during a thirty year simulation period. Micro basins with predominant agricultural and animal farm land use (56% of the total area) accounts for a high percentage of nitrogen load 33% and phosphorus 52%. On the other hand, Patzcuaro and Quiroga micro basins which comprise approximately 10% of the total catchment area and are the most populated and visited towns by tourist 686,000 people every year, both contributes with 10.1% of the total nitrogen load and 3.2% of phosphorus. In terms of point sources of nitrogen and phosphorus the last towns contribute with 23.5% and 26.6% respectively. Under this situation the adoption of best management practices are an imperative task since the sedimentation and pollution in the lake has increased dramatically in the last twenty years.

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.

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.

Agricultural best management practices and surface water improvement and management

1995 ◽  
Vol 31 (8) ◽  
pp. 109-121 ◽  
Author(s):  
D. L. Anderson ◽  
E. G. Flaig
Keyword(s):  
Water Management ◽  
Surface Water ◽  
Best Management Practices ◽  
Management Practices ◽  
Agricultural Runoff ◽  
South Florida ◽  
Lake Okeechobee ◽  
Runoff Water ◽  
Best Management ◽  
Management Plans

Restoration and enhancement of Lake Okeechobee and the Florida Everglades requires a comprehensive approach to manage agricultural runoff. The Florida Surface Water Improvement and Management (SWIM) Act of 1987 was promulgated to develop and implement plans for protecting Florida waters. The South Florida Water Management District was directed by Florida legislature to develop management plans for Lake Okeechobee (SWIM) and the Everglades ecosystem (Marjory Stoneman Douglas Everglades Protection Act of 1991). These plans require agriculture to implement best management practices (BMPs) to reduce runoff phosphorus (P) loads. The Lake Okeechobee SWIM plan established a P load reduction target for Lake Okeechobee and set P concentration limitations for runoff from non-point source agricultural sources. Agricultural water users in the Everglades Agricultural Area (EAA) are required to develop farm management plans to reduce P loads from the basin by 25%. The Everglades Forever Act of 1994 additionally emphasized linkage of these landscapes and consequent protection and restoration of the Everglades. Agricultural BMPs are being developed and implemented to comply with water management, environmental, and regulatory standards. Although BMPs are improving runoff water quality, additional research is necessary to obtain the best combination of BMPs for individual farms. This paper summarizes the development of comprehensive water management in south Florida and the agricultural BMPs carried out to meet regulatory requirements for Lake Okeechobee and the Everglades.

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.

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