scholarly journals Spatial and temporal trends in summertime climate and water quality indicators in the coastal embayments of Buzzards Bay, Massachusetts

2015 ◽  
Vol 12 (15) ◽  
pp. 13159-13192
Author(s):  
J. E. Rheuban ◽  
S. C. Williamson ◽  
J. E. Costa ◽  
D. M. Glover ◽  
R. W. Jakuba ◽  
...  
Keyword(s):  
Water Quality ◽  
Quality Indicators ◽  
Total Nitrogen ◽  
Temporal Trends ◽  
Nitrogen Loading ◽  
Quality Data ◽  
Water Quality Indicators ◽  
Chl A ◽  
Temperature And Precipitation ◽  
Spatial And Temporal Trends

Abstract. Degradation of coastal ecosystems by eutrophication is largely defined by nitrogen loading from land via surface and groundwater flows. However, indicators of water quality are highly variable due to a myriad of other drivers, including temperature and precipitation. To evaluate these drivers, we examined spatial and temporal trends in a 22 year record of summer water quality data from 122 stations in 17 embayments within Buzzards Bay, MA (USA), collected through a citizen science monitoring program managed by Buzzards Bay Coalition. To identify spatial patterns across Buzzards Bay's embayments, we used a principle component and factor analysis and found that rotated factor loadings indicated little correlation between inorganic nutrients and organic matter and chlorophyll a (Chl a) concentration. Factor scores showed that embayment geomorphology in addition to nutrient loading was a strong driver of water quality, where embayments with surface water inputs showed larger biological impacts than embayments dominated by groundwater influx. A linear regression analysis of annual summertime water quality indicators over time revealed that from 1992 to 2013, most embayments (15 of 17) exhibited an increase in temperature (mean rate of 0.082 ± 0.025 (SD) °C yr−1) and Chl a (mean rate of 0.0171 ± 0.0088 log10 (Chl a; mg m−3) yr−1, equivalent to a 4.0 % increase per year). However, only 7 embayments exhibited an increase in total nitrogen (TN) concentration (mean rate 0.32 ± 0.47 (SD) μM yr−1). Average summertime log10 (TN) and log10 (Chl a) were correlated with an indication that yield of Chl a per unit total nitrogen increased with time suggesting the estuarine response to TN may have changed because of other stressors such as warming, altered precipitation patterns, or changing light levels. These findings affirm that nitrogen loading and physical aspects of embayments are essential in explaining observed ecosystem response. However, climate-related stressors may also need to be considered by managers because increased temperature and precipitation may worsen water quality and partially offset benefits achieved by reducing nitrogen loading.

scholarly journals Spatial and temporal trends in summertime climate and water quality indicators in the coastal embayments of Buzzards Bay, Massachusetts

2016 ◽  
Vol 13 (1) ◽  
pp. 253-265 ◽  
Author(s):  
J. E. Rheuban ◽  
S. Williamson ◽  
J. E. Costa ◽  
D. M. Glover ◽  
R. W. Jakuba ◽  
...  
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Quality Indicators ◽  
Total Nitrogen ◽  
Temporal Trends ◽  
Nitrogen Loading ◽  
Water Quality Indicators ◽  
Chl A ◽  
Temperature And Precipitation ◽  
Spatial And Temporal Trends

Abstract. Degradation of coastal ecosystems by eutrophication is largely defined by nitrogen loading from land via surface water and groundwater flows. However, indicators of water quality are highly variable due to a myriad of other drivers, including temperature and precipitation. To evaluate these drivers, we examined spatial and temporal trends in a 22-year record of summer water quality data from 122 stations in 17 embayments within Buzzards Bay, MA (USA), collected through a citizen science monitoring program managed by Buzzards Bay Coalition. To identify spatial patterns across Buzzards Bay's embayments, we used a principle component and factor analysis and found that rotated factor loadings indicated little correlation between inorganic nutrients and organic matter or chlorophyll a (Chl a) concentration. Factor scores showed that embayment geomorphology in addition to nutrient loading was a strong driver of water quality, where embayments with surface water inputs showed larger biological impacts than embayments dominated by groundwater influx. A linear regression analysis of annual summertime water quality indicators over time revealed that from 1992 to 2013, most embayments (15 of 17) exhibited an increase in temperature (mean rate of 0.082 ± 0.025 (SD) °C yr−1) and Chl a (mean rate of 0.0171 ± 0.0088 log10 (Chl a; mg m−3) yr−1, equivalent to a 4.0 % increase per year). However, only seven embayments exhibited an increase in total nitrogen (TN) concentration (mean rate 0.32 ± 0.47 (SD) µM yr−1). Average summertime log10(TN) and log10(Chl a) were correlated with an indication that the yield of Chl a per unit total nitrogen increased with time suggesting the estuarine response to TN may have changed because of other stressors such as warming, altered precipitation patterns, or changing light levels. These findings affirm that nitrogen loading and physical aspects of embayments are essential in explaining the observed ecosystem response. However, climate-related stressors may also need to be considered by managers because increased temperature and precipitation may worsen water quality and partially offset benefits achieved by reducing nitrogen loading.

scholarly journals A GIS Methodology to Determine the Critical Regions for Mitigating Eutrophication in Large Territories: The Case of Jalisco, Mexico

2021 ◽  
Vol 13 (14) ◽  
pp. 8029
Author(s):  
Enrique Cervantes-Astorga ◽  
Oscar Aguilar-Juárez ◽  
Danay Carrillo-Nieves ◽  
Misael Sebastián Gradilla-Hernández
Keyword(s):  
Water Quality ◽  
Quality Indicators ◽  
Trophic State ◽  
Methodological Approach ◽  
The State ◽  
Geographical Information ◽  
Quality Data ◽  
Water Quality Indicators ◽  
Critical Regions ◽  
Over Time

Inadequate management practices for solid waste and wastewater are some of the main causes of eutrophication globally, especially in regions where intensive livestock, agricultural, and industrial activities are coupled with inexistent or ineffective waste and wastewater treatment infrastructure. In this study, a methodological approach is presented to spatially assess the trophic state of large territories based on public water quality databases. The trophic state index (TSI) includes total nitrogen, total phosphorus, chlorophyll A, chemical oxygen demand, and Secchi disk depth values as water quality indicators. A geographical information system (GIS) was used to manage the spatiotemporal attributes of the water quality data, in addition to spatially displaying the results of TSI calculations. As a case study, this methodological approach was applied to determine the critical regions for mitigating eutrophication in the state of Jalisco, Mexico. Although a decreasing trend was observed for the TSI values over time for most subbasins (2012–2019), a tendency for extreme hypereutrophication was observed in some regions, such as the Guadalajara metropolitan area and the Altos region, which are of high economic relevance at the state level. A correlation analysis was performed between the TSI parameters and rainfall measurements for all subbasins under analysis, which suggested a tendency for nutrient wash-off during the rainy seasons for most subbasins; however, further research is needed to quantify the real impacts of rainfall by including other variables such as elevation and slope. The relationships between the water quality indicators and land cover were also explored. The GIS methodology proposed in this study can be used to spatially assess the trophic state of large regions over time, taking advantage of available water quality databases. This will enable the efficient development and implementation of public policies to assess and mitigate the eutrophication of water sources, as well as the efficient allocation of resources for critical regions. Further studies should focus on applying integrated approaches combining on-site monitoring data, remote sensing data, and machine learning algorithms to spatially evaluate the trophic state of territories.

scholarly journals Using Optimized Two and Three-Band Spectral Indices and Multivariate Models to Assess Some Water Quality Indicators of Qaroun Lake in Egypt

2021 ◽  
Vol 13 (18) ◽  
pp. 10408
Author(s):  
Salah Elsayed ◽  
Mohamed Gad ◽  
Mohamed Farouk ◽  
Ali H. Saleh ◽  
Hend Hussein ◽  
...  
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Quality Indicators ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Water Quality Indicators ◽  
Chl A ◽  
Strong Relationships

Standard methods are limited for monitoring and managing water quality indicators (WQIs) in real-time and on a large scale. Consequently, there is an urgent need to use reliable, practical, swift, and cost-effective monitoring tools that can be easily deployed and assist decision makers in assessing key indicators relevant to surface water quality in a comprehensive manner. Surface water samples were collected and evaluated for water quality at 16 distinct sites across the Qaroun Lake in 2018 and 2019. Different WQIs, including total dissolved solids (TDS), transparency, total suspended solids (TSS), chlorophyll-a (Chl-a), and total phosphorus (TP), were tested for aquatic utilization. An integrated approach comprising WQIs, geospatial techniques, hyperspectral reflectance indices (SRIs) (commonly used SRIs, two-band and three-band SRIs (Spectral index calculated from water spectral reflectance of two or three wavelengths)), and partial least square regression (PLSR) models were used to assess the water quality of Qaroun Lake. According to the findings, the water quality attributes are polluted to varying degrees. The majority of commonly used SRIs presented moderately relationship with four WQIs (transparency, TSS, Chl-a, and TP) (R2 = 0.45 to 0.64), while the majority of newly two-band SRIs (NSRIs-2b) indicated moderate to strong relationships with WQIs (R2 = 0.51 to 0.74), and the majority of newly three band SRIs (NSRIs-3b) presented strong relationships with WQIs (R2 = 0.67 to 0.81). Broadly, the highest coefficients of determination were noticed with the NSRIs-3b followed by the NSRIs-2b and then the commonly used SRIs. For example, the NSRIs-3b (NDSI648,712,696) had stronger relationships with transparency, TSS, and Chl-a with R2 = 0.77, 0.66, and 0.81, respectively, than other SRIs. In addition, the NSRIs-3b (NDSI620,610,622) showed the highest R2 of 0.73 with TSS. The NSRIs-3b coupling with PLSR predicted the WQIs with satisfactory accuracy in the calibration (reach up R2 = 0.85) and validation (reach up R2 = 0.81) datasets. The overall findings of this research study showed that deriving an optimized NSRIs-3b from spectrum region and combining it with PLSR model could be a practical tool for managing water quality of the Qaroun Lake by accurately, timely, and non-destructively monitoring the WQIs.

scholarly journals Application of Rough Set Theory to Water Quality Analysis: A Case Study

Data ◽  
2018 ◽  
Vol 3 (4) ◽  
pp. 50 ◽  
Author(s):  
Maryam Zavareh ◽  
Viviana Maggioni
Keyword(s):  
Water Quality ◽  
Quality Indicators ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Specific Conductivity ◽  
Quality Analysis ◽  
Quality Data ◽  
Water Quality Data ◽  
Water Quality Indicators

This work proposes an approach to analyze water quality data that is based on rough set theory. Six major water quality indicators (temperature, pH, dissolved oxygen, turbidity, specific conductivity, and nitrate concentration) were collected at the outlet of the watershed that contains the George Mason University campus in Fairfax, VA during three years (October 2015–December 2017). Rough set theory is applied to monthly averages of the collected data to estimate one indicator (decision attribute) based on the remainder indicators and to determine what indicators (conditional attributes) are essential (core) to predict the missing indicator. The redundant attributes are identified, the importance degree of each attribute is quantified, and the certainty and coverage of any detected rule(s) is evaluated. Possible decision making rules are also assessed and the certainty coverage factor is calculated. Results show that the core water quality indicators for the Mason watershed during the study period are turbidity and specific conductivity. Particularly, if pH is chosen as a decision attribute, the importance degree of turbidity is higher than the one of conductivity. If the decision attribute is turbidity, the only indispensable attribute is specific conductivity and if specific conductivity is the decision attribute, the indispensable attribute beside turbidity is temperature.

scholarly journals Application of a Parallel Particle Swarm Optimization-Long Short Term Memory Model to Improve Water Quality Data

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1317 ◽  
Author(s):  
Jianzhuo Yan ◽  
Xinyue Chen ◽  
Yongchuan Yu ◽  
Xiaojuan Zhang
Keyword(s):  
Water Quality ◽  
Quality Indicators ◽  
Short Term Memory ◽  
Predictive Accuracy ◽  
Quality Data ◽  
Short Term ◽  
Water Quality Data ◽  
Swarm Optimization ◽  
Water Quality Indicators ◽  
Long Short Term Memory

Water quality data cleaning is important for the management of water environments. A framework for water quality time series cleaning is proposed in this paper. Considering the nonlinear relationships among water quality indicators, support vector regression (SVR) is used to forecast water quality indicators when some indicators are missing or when they show abnormal values at a certain point in time. Considering the time series of water quality information, long short-term memory (LSTM) networks are used to forecast water quality indicators when all indicators are missing at a certain point in time. A parallel model based on particle swarm optimization (PSO) and LSTM is realized based on a microservices architecture to improve the efficiency of model execution and the predictive accuracy of the LSTM networks. The performance of the model is evaluated in terms of the mean absolute error (MAE) and root-mean-square error (RMSE). Inlet water quality data from a wastewater treatment plant in Gaobeidian, Beijing, China is considered as a case study to examine the effectiveness of this approach. The experimental results reveal that this model has better predictive accuracy than other data-driven models because of smaller MAE and RMSE and has an advantage in terms of time consumption compared with standalone serial algorithms.

Application of a Pilot-Scale Pulsed Electrocoagulation system to OCC-Based Paper Mill Effluent

TAPPI Journal ◽  
2009 ◽  
Vol 8 (3) ◽  
pp. 14-20 ◽  
Author(s):  
YUAN-SHING PERNG ◽  
EUGENE I-CHEN WANG ◽  
SHIH-TSUNG YU ◽  
AN-YI CHANG
Keyword(s):  
Water Quality ◽  
Quality Indicators ◽  
Paper Mill ◽  
Pilot Scale ◽  
Operating Conditions ◽  
Optimal Dosage ◽  
Water Quality Indicators ◽  
Paper Mill Effluent ◽  
Treated Effluent ◽  
True Color

Trends toward closure of white water recirculation loops in papermaking often lead to a need for system modifications. We conducted a pilot-scale study using pulsed electrocoagulation technology to treat the effluent of an old corrugated containerboard (OCC)-based paper mill in order to evaluate its treatment performance. The operating variables were a current density of 0–240 A/m2, a hydraulic retention time (HRT) of 8–16 min, and a coagulant (anionic polyacrylamide) dosage of 0–22 mg/L. Water quality indicators investigated were electrical con-ductivity, suspended solids (SS), chemical oxygen demand (COD), and true color. The results were encouraging. Under the operating conditions without coagulant addition, the highest removals for conductivity, SS, COD, and true color were 39.8%, 85.7%, 70.5%, and 97.1%, respectively (with an HRT of 16 min). The use of a coagulant enhanced the removal of both conductivity and COD. With an optimal dosage of 20 mg/L and a shortened HRT of 10 min, the highest removal achieved for the four water quality indicators were 37.7%, 88.7%, 74.2%, and 91.7%, respectively. The water qualities thus attained should be adequate to allow reuse of a substantial portion of the treated effluent as process water makeup in papermaking.

Spatio-temporal Variations in Water Quality Parameter Trends in River Waters

2018 ◽  
Vol 69 (10) ◽  
pp. 2940-2952 ◽  
Author(s):  
Martina Zelenakova ◽  
Pavol Purcz ◽  
Radu Daniel Pintilii ◽  
Peter Blistan ◽  
Petr Hlustik ◽  
...  
Keyword(s):  
Water Quality ◽  
Chemical Oxygen Demand ◽  
Quality Indicators ◽  
Water Resource Management ◽  
Oxygen Demand ◽  
Temporal Variations ◽  
Water Quality Parameter ◽  
Agricultural Activity ◽  
Water Quality Indicators ◽  
Nitrite Nitrogen

Evaluating trends in water quality indicators is a crucial issue in integrated water resource management in any country. In this study eight chemical and physical water quality indicators were analysed in seven river profiles in the River Laborec in eastern Slovakia. The analysed water quality parameters were biochemical oxygen demand (BOD5), chemical oxygen demand (CODCr), pH, temperature (t), ammonium nitrogen (NH4+-N), nitrite nitrogen (NO2--N), nitrate nitrogen (NO3--N), and total phosphorus (TP). Data from the monitored indicators were provided by the Ko�ice branch of the Slovakian Water Management Company, over a period of 15 years from 1999 to 2013. Mann�Kendall non-parametric statistical test was used for the trend analysis. Biochemical and chemical oxygen demand, ammonium and nitrite nitrogen content exhibit decreasing trends in the River Laborec. Decreasing agricultural activity in the area has had a significant impact on the trends in these parameters. However, NO2--N was the significant parameter of water quality because it mostly exceeds the limit value set in Slovak legislation, Regulation No. 269/2010 Coll. In addition, water temperature revealed an increasing trend which could be caused by global increase in air temperature. These results indicate that human activity significantly impacts the water quality.

scholarly journals Assessment of Automatically Monitored Water Levels and Water Quality Indicators in Rivers with Different Hydromorphological Conditions and Pollution Levels in Greece

Hydrology ◽  
2021 ◽  
Vol 8 (2) ◽  
pp. 86
Author(s):  
Angeliki Mentzafou ◽  
George Varlas ◽  
Anastasios Papadopoulos ◽  
Georgios Poulis ◽  
Elias Dimitriou
Keyword(s):  
Water Quality ◽  
Time Series ◽  
Quality Indicators ◽  
High Frequency ◽  
Automatic Monitoring ◽  
Water Levels ◽  
High Temporal Resolution ◽  
Water Quality Indicators ◽  
Pollution Levels ◽  
Riverine Ecosystems

Water resources, especially riverine ecosystems, are globally under qualitative and quantitative degradation due to human-imposed pressures. High-temporal-resolution data obtained from automatic stations can provide insights into the processes that link catchment hydrology and streamwater chemistry. The scope of this paper was to investigate the statistical behavior of high-frequency measurements at sites with known hydromorphological and pollution pressures. For this purpose, hourly time series of water levels and key water quality indicators (temperature, electric conductivity, and dissolved oxygen concentrations) collected from four automatic monitoring stations under different hydromorphological conditions and pollution pressures were statistically elaborated. Based on the results, the hydromorphological conditions and pollution pressures of each station were confirmed to be reflected in the results of the statistical analysis performed. It was proven that the comparative use of the statistics and patterns of the water level and quality high-frequency time series could be used in the interpretation of the current site status as well as allowing the detection of possible changes. This approach can be used as a tool for the definition of thresholds, and will contribute to the design of management and restoration measures for the most impacted areas.

Sign in / Sign up

Export Citation Format

Share Document