Outflows of Organic Halide Precursors from Forest Regions

1989 ◽  
Vol 21 (12) ◽  
pp. 1877-1880 ◽  
Author(s):  
S. Saito ◽  
K. Hattori ◽  
T. Okumura
Keyword(s):  
Water Quality ◽  
River Basin ◽  
Surface Soil ◽  
Soil Layer ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Deep Soil ◽  
Organic Halide ◽  
Forest Regions ◽  
Surface Soil Layer

Outflows of organic halide precursors (OXPs) from forest regions were studied in relation to water quality monitoring in the Yodo River basin. Firstly, the contribution of outflows from forest regions relative to the total was roughly estimated. Then equations for flows of these substances were formulated, divided into four different subflow categories: precipitation; throughfall; surface soil layer; and, deep soil layer. Finally, annual outflow loads were calculated for a test forest area.

Statistical and Data Mining Techniques for Understanding Water Quality Profiles in a Mining-Affected River Basin

2018 ◽  
Vol 9 (2) ◽  
pp. 1-19 ◽  
Author(s):  
Jose Simmonds ◽  
Juan A. Gómez ◽  
Agapito Ledezma
Keyword(s):  
Data Mining ◽  
Water Quality ◽  
River Basin ◽  
Suspended Solids ◽  
Water Quality Monitoring ◽  
High Flow ◽  
Quality Monitoring ◽  
Low Flow ◽  
Inverse Relation

This article contains a multivariate analysis (MV), data mining (DM) techniques and water quality index (WQI) metrics which were applied to a water quality dataset from three water quality monitoring stations in the Petaquilla River Basin, Panama, to understand the environmental stress on the river and to assess the feasibility for drinking. Principal Components and Factor Analysis (PCA/FA), indicated that the factors which changed the quality of the water for the two seasons differed. During the low flow season, water quality showed to be influenced by turbidity (NTU) and total suspended solids (TSS). For the high flow season, main changes on water quality were characterized by an inverse relation of NTU and TSS with electrical conductivity (EC) and chlorides (Cl), followed by sources of agricultural pollution. To complement the MV analysis, DM techniques like cluster analysis (CA) and classification (CLA) was applied and to assess the quality of the water for drinking, a WQI.

scholarly journals Quantifying the Information Content of a Water Quality Monitoring Network Using Principal Component Analysis: A Case Study of the Freiberger Mulde River Basin, Germany

Water ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 420 ◽  
Author(s):  
Thuy Hoang Nguyen ◽  
Björn Helm ◽  
Hiroshan Hettiarachchi ◽  
Serena Caucci ◽  
Peter Krebs
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
River Basin ◽  
Principal Component ◽  
Water Quality Monitoring ◽  
Component Analysis ◽  
Quality Monitoring ◽  
Critical Parameters ◽  
Spatial And Temporal Variation ◽  
Effectiveness Monitoring

Although river water quality monitoring (WQM) networks play an important role in water management, their effectiveness is rarely evaluated. This study aims to evaluate and optimize water quality variables and monitoring sites to explain the spatial and temporal variation of water quality in rivers, using principal component analysis (PCA). A complex water quality dataset from the Freiberger Mulde (FM) river basin in Saxony, Germany was analyzed that included 23 water quality (WQ) parameters monitored at 151 monitoring sites from 2006 to 2016. The subsequent results showed that the water quality of the FM river basin is mainly impacted by weathering processes, historical mining and industrial activities, agriculture, and municipal discharges. The monitoring of 14 critical parameters including boron, calcium, chloride, potassium, sulphate, total inorganic carbon, fluoride, arsenic, zinc, nickel, temperature, oxygen, total organic carbon, and manganese could explain 75.1% of water quality variability. Both sampling locations and time periods were observed, with the resulting mineral contents varying between locations and the organic and oxygen content differing depending on the time period that was monitored. The monitoring sites that were deemed particularly critical were located in the vicinity of the city of Freiberg; the results for the individual months of July and September were determined to be the most significant. In terms of cost-effectiveness, monitoring more parameters at fewer sites would be a more economical approach than the opposite practice. This study illustrates a simple yet reliable approach to support water managers in identifying the optimum monitoring strategies based on the existing monitoring data, when there is a need to reduce the monitoring costs.

Multi-parametric water quality monitoring approach according to the WFD application in Evros trans-boundary river basin: priority pollutants

Desalination ◽  
2008 ◽  
Vol 226 (1-3) ◽  
pp. 306-320 ◽  
Author(s):  
Anastasia D. Nikolaou ◽  
Sureyya Meric ◽  
Demetris F. Lekkas ◽  
Vincenzo Naddeo ◽  
Vincenzo Belgiorno ◽  
...  
Keyword(s):  
Water Quality ◽  
River Basin ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Priority Pollutants

scholarly journals Real-time water quality monitoring through Internet of Things and ANOVA-based analysis: a case study on river Krishna

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Prasad M. Pujar ◽  
Harish H. Kenchannavar ◽  
Raviraj M. Kulkarni ◽  
Umakant P. Kulkarni
Keyword(s):  
Water Quality ◽  
Internet Of Things ◽  
Real Time ◽  
River Basin ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Quality Monitoring ◽  
Quality Analysis ◽  
Water Quality Analysis

AbstractIn this paper, an attempt has been made to develop a statistical model based on Internet of Things (IoT) for water quality analysis of river Krishna using different water quality parameters such as pH, conductivity, dissolved oxygen, temperature, biochemical oxygen demand, total dissolved solids and conductivity. These parameters are very important to assess the water quality of the river. The water quality data were collected from six stations of river Krishna in the state of Karnataka. River Krishna is the fourth largest river in India with approximately 1400 km of length and flows from its origin toward Bay of Bengal. In our study, we have considered only stretch of river Krishna flowing in state of Karnataka, i.e., length of about 483 km. In recent years, the mineral-rich river basin is subjected to rapid industrialization, thus polluting the river basin. The river water is bound to get polluted from various pollutants such as the urban waste water, agricultural waste and industrial waste, thus making it unusable for anthropogenic activities. The traditional manual technique that is under use is a very slow process. It requires staff to collect the water samples from the site and take them to the laboratory and then perform the analysis on various water parameters which is costly and time-consuming process. The timely information about water quality is thus unavailable to the people in the river basin area. This creates a perfect opportunity for swift real-time water quality check through analysis of water samples collected from the river Krishna. IoT is one of the ways with which real-time monitoring of water quality of river Krishna can be done in quick time. In this paper, we have emphasized on IoT-based water quality monitoring by applying the statistical analysis for the data collected from the river Krishna. One-way analysis of variance (ANOVA) and two-way ANOVA were applied for the data collected, and found that one-way ANOVA was more effective in carrying out water quality analysis. The hypotheses that are drawn using ANOVA were used for water quality analysis. Further, these analyses can be used to train the IoT system so that it can take the decision whenever there is abnormal change in the reading of any of the water quality parameters.

Monitoring and modelling mining impacts on water quality in Chindwin River Basin, Myanmar

2020 ◽  
Author(s):  
Thanapon Piman ◽  
Chayanis Krittasudthacheew ◽  
Shakthi K. Gunawardanaa ◽  
Sangam Shresthaa
Keyword(s):  
Water Quality ◽  
Heavy Metal ◽  
Drinking Water ◽  
Water Treatment ◽  
River Basin ◽  
Total Suspended Solid ◽  
Water Quality Monitoring ◽  
Suspended Solid ◽  
Quality Monitoring ◽  
Mining Activities

<p>The Chindwin River, a major tributary of the Ayeyarwady River in Myanmar, is approximately 850 km long with a watershed area of 115,300 km<sup>2</sup>. The Chindwin River is essential for local livelihoods, drinking water, ecosystems, navigation, agriculture, and industries such as logging and mining. Over the past two decades, Myanmar’s rapid economic development has resulted in drastic changes to socio-economic and ecological conditions in the basin. Water users in the basin reported that there is a rapid extension of gold and jade mining and they observed a noticeable decline in water quality along with increased sedimentation and turbidity. So far, however, Myanmar has not undertaken a comprehensive scientific study in the Chindwin River Basin to assess water quality and sources of water pollution and to effectively address issues of river basin degradation and concerns for public health and safety. This study aims to assess the status of water quality in the Chindwin River and the potential impact of mining activities on the water quality and loading through monitoring program and modeling approach. 17 locations in the upper, middle and lower parts of the Chindwin River Basin were selected for water quality monitoring. These sites are located near Homalin, Kalewa, Kani and Monywa townships where human activities and interventions could affect water quality. Water quality sampling and testing in the Chindwin River was conducted two times per year: in the dry season (May-June) and in the wet season (September-October) during 2015-2017. We monitored 21 parameters including heavy metals such as Lead (Pb), Mercury (Hg), Copper (Cu) and Iron (Fe). The observed values of Mercury in Uru River in the upper Chindwin River Basin which located nearby gold mining sites shown higher than the WHO drinking standard. This area also has high values of turbidity and Total Suspended Solid. The SHETRAN hydrological model, PHREEQC geochemical model and LOADEST model were used to quantify the heavy metal loads in the Uru River. Results from scenario analysis indicate an increase in Arsenic and Mercury load under increment of concentration due to expansions in mining areas. In both baseline and future climate conditions, the Uru downstream area shows the highest load effluent in both Arsenic and Mercury. These heavy metal loads will intensify the declining water quality condition in Chindwin River and can impact negatively on human health who use water for drinking. Therefore, we recommend that water quality monitoring should continue to provide scientific-evidence for decision-makers to manage water quality and mining activities properly.  Water treatment systems for drinking water are required to remove turbidity, Total Suspended Solid, and Mercury from raw water sources. Raising awareness of relevant stakeholders (local people, farmers, private sectors, etc.) is necessary as many people living in the Chindwin River Basin are using water directly from the river and other waterways without proper water treatment.</p>

scholarly journals Key Issues for Water Quality Monitoring in the Zambezi River Basin in Mozambique in the Context of Mining Development

2015 ◽  
Vol 07 (05) ◽  
pp. 430-447 ◽  
Author(s):  
Clemêncio M. Carlos Nhantumbo ◽  
Rolf Larsson ◽  
Dinis Juízo ◽  
Magnus Larson
Keyword(s):  
Water Quality ◽  
River Basin ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Key Issues ◽  
Zambezi River Basin ◽  
Zambezi River

Combining Ecohydrological Catchment Modelling and Water Quality Monitoring to Assess Surface Water Pollution in the Swist River Basin

2020 ◽  
Author(s):  
Alexander Ahring ◽  
Marvin Kothe ◽  
Christian Gattke ◽  
Ekkehard Christoffels ◽  
Bernd Diekkrüger
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
River Basin ◽  
Surface Waters ◽  
Water Quality Monitoring ◽  
Pollutant Emissions ◽  
Quality Monitoring ◽  
Monitoring Data ◽  
Quality Data

<p>Inland surface waters like rivers, streams, lakes and reservoirs are subject to anthropogenic pollutant emissions from various sources. These emissions can have severe negative impacts on surface water ecology, as well as human health when surface waters are used for recreational activities, irrigation of cropland or drinking water production. In order to protect aquatic ecosystems and freshwater resources, the European Water Framework Directive (WFD) sets specific quality requirements which the EU member states must meet until 2027 for every water body.</p><p>Implementing effective measures and emission control strategies requires knowledge about the important emission pathways in a given river basin. However, due to the abundance of pollution sources and the heterogeneity of emission pathways in time and space, it is not feasible to gain this knowledge via water quality monitoring alone. In our study, we aim to combine SWAT ecohydrological modelling and long term water quality monitoring data to establish a spatially differentiated nitrogen emission inventory on the sub-catchment scale. SWAT (short for Soil and Water Assessment Tool) is a semi-distributed, dynamic and process-driven watershed model capable of simulating long term hydrology as well as nutrient fluxes on a daily time step.</p><p>The study area is the Swist river basin in North Rhine-Westphalia (Germany). Belonging to the Rhine river system, the Swist is the largest tributary of the Erft River and drains a basin area of approximately 290 km². As part of its legal obligations and research activities, the Erftverband local waterboard collects a large variety of long term monitoring data in the Swist river catchment, which is available for this study. This includes operational data from the wastewater treatment plants in the watershed, discharge data from four stream gauging stations, river water quality data from continuous and discontinuous monitoring, groundwater quality data as well as quality data from surface, sub-surface and tile drainage runoff from various land uses.</p><p>Our contribution will be made up of two equal parts: First, we will present our water quality monitoring activities in the catchment and the related data pool outlined above, with special emphasis on recent monitoring results from agricultural tile drainages. Apart from nutrients and other pollutants, the data suggests considerable inputs of herbicide transformation products like Chloridazon-Desphenyl (maximum concentration measured: 15 µg/l) via this pathway. Second, we will explain how we integrate the monitoring data into the SWAT simulations and how we tackle related challenges like parameter equifinality (meaning that multiple parameter sets can yield similar or identical model outputs). The overall goal is to take all possible emission pathways into consideration, including those often neglected in past SWAT studies, like tile drainages and combined sewer overflows (CSO). As the Swist catchment is affected by groundwater extraction due to lignite mining in the Lower Rhine Bay area, we will discuss how this is considered during SWAT model setup and calibration, and will present first simulation results concerning catchment hydrology.</p>

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