scholarly journals Heavy metals in water and surface sediments of the Fenghe River Basin, China: assessment and source analysis

2021 ◽  
Author(s):  
Pingping Luo ◽  
Chengyi Xu ◽  
Shuxin Kang ◽  
Aidi Huo ◽  
Jiqiang Lyu ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Water Quality ◽  
River Basin ◽  
Environmental Science ◽  
Pearson Correlation ◽  
Risk Index ◽  
Principal Component ◽  
Water Quality Monitoring ◽  
Shaanxi Province ◽  
Source Analysis

Abstract This paper combines environmental science, inorganic chemistry, water quality monitoring and other disciplines to analyze and assess the heavy metals in the water bodies and sediments of the Fenghe River Basin (FRB) in Shaanxi Province, and reveal their sources. Water Quality Index (WQI), Nemero Index (Pn), Geological Accumulation Index (I-geo) and Potential Ecological Risk Index (RI) are used to assess heavy metals in water and sediments. Pearson correlation analysis (CA), hierarchical cluster analysis (HCA), principal component analysis (PCA) and positive matrix factorization (PMF) models are used to study the relationship and source of heavy metals. The results show that most of the residual heavy metals in the water are below the corresponding environmental quality standards for surface water. Most of the heavy metals in the sediment exceed the background value of the soil.The factors or sources of heavy metals in water and sediment are revealed in detail through PMF models. The main sources of pollution in the region are urban construction and transportation, electronics industry, machinery manufacturing and tourism. In water, the average contribution rates of these four sources to heavy metals were 36.8%, 11.7%, 9.4% and 42.0%, and in sediments were 8.0%, 29.2%, 23.9% and 38.9%. Therefore, these sectors should be given sufficient attention.

scholarly journals Heavy Metals in Water and Surface Sediments of the Fenghe River Basin, China: Assessment and Source Analysis

2021 ◽  
Author(s):  
Pingping Luo ◽  
Chengyi Xu ◽  
Shuxin Kang ◽  
Aidi Huo ◽  
Jiqiang Lyu ◽  
...  
Keyword(s):  
Heavy Metals ◽  
Water Quality ◽  
River Basin ◽  
Ecological Risk ◽  
Surface Sediments ◽  
Pearson Correlation ◽  
Sampling Site ◽  
Source Analysis ◽  
Index Method ◽  
Study Results

Abstract This paper combines environmental science, inorganic chemistry, water quality monitoring and other disciplines, and uses several representative evaluation methods (WQI, Pn, I-geo, RI) for heavy metals in water and sediments. A preliminary assessment and source analysis of heavy metals (Cd, Cr, Fe, Mn, Zn, Cr, Ti, Ni, Cu, As, Pb, Sr) in water and surface sediments of the Fenghe River Basin, Shannxi Province, China was carried out in this study. Results indicate that most of the heavy metals in water are below national water quality standards. Exceptions include Mn, which exceeds national tertiary standards and Cr, which exceeds national drinking water standards. Most heavy metals in the sediments exceed the environmental standard values except Ni. Water quality index (WQI) and Nemero index (Pn) showed the same trend in contamination levels of sampling sites. According to the Geological Accumulation Index method (I-geo) and the Potential Ecological Risk Index method (RI), high concentrations of Cd poses a high ecological risk in some sampling locations. Pearson Correlation Analysis (CA), Hierarchical Clustering Analysis (HCA), Principal Component Analysis (PCA) and Positive Matrix Factorization (PMF) models are used to explore the relationships and sources of heavy metals. In general, upstream sources are similar, and middle and lower reaches are easily clustered into a large category except for some specific sampling points. For example, metals in sampling site FHK mainly come from surrounding residents and farms and heavy metals attributes in sampling site SLQ relate to the fact that municipal sewage is collected and treated. The factors or sources of heavy metals in water and sediment are revealed in detail through PMF models. In the water, the average contribution rate of these four source factors for heavy metals is 36.8%, 11.7%, 9.4% and 42.0%, while the average proportion of these four factors for heavy metals in sediment is 8.0%, 29.2%, 23.9% and 38.9% respectively. Results show that the main sources of pollution in the region are urban construction and transportation, electronics industry, machinery manufacturing, tourism and agriculture. These sectors should therefore be given sufficient attention in the prevention and management of heavy metal pollution.

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.

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 Development of correlation-regression model between land use change and water quality indices in Ciliwung watershed

2018 ◽  
Vol 192 ◽  
pp. 02047
Author(s):  
Yopy Arfan ◽  
Dwita Sutjiningsih
Keyword(s):  
Water Quality ◽  
Regression Model ◽  
Pearson Correlation ◽  
Water Quality Monitoring ◽  
Test Method ◽  
Quality Indices ◽  
Water Quality Indices ◽  
Correlation Test ◽  
Impervious Cover ◽  
Water Ecosystems

Urbanization and industrialization lead to the change of land cover from pervious into impervious. This can impact environmental problems such as water quality degradation that affects human health and water ecosystems. The study aimed to develop a regression-correlation model between impervious cover in Ciliwung watershed and water quality indices in Ciliwung river. The correlation-regression model can be used to predict changes in the status of Ciliwung river water quality due to impervious cover changes. Methods of assessing the indices of water quality are CCME-WQI, NSF-WQI, and STORET within the period of 2005-2016. Monitoring locations from the most upstream to downstream are Atta’awun, Katulampa, Kedung Halang, Pondok Rajeg, Panus Bridge, Kelapa Dua, Condet, Kalibata, MT Haryono and Manggarai. Impervious cover data for each water quality monitoring location is processed using ArcGIS Software. Test of correlation significance between percentage of impervious cover and water quality indices using Pearson Correlation test method. The result of correlation test is significantly a strong inverse relationship between impervious cover and water quality indices. The result of regression test is trend line between impervious cover change and water quality indices that can be used to predict the change of water quality status in Ciliwung River.

Influence of Heavy Metals on the Growth of Chlorella vulgaris

2021 ◽  
Vol 15 (3) ◽  
pp. 374-379
Author(s):  
Bingbing Pang ◽  
Mingzhou Zeng ◽  
Wenjia Zhang ◽  
Fengcai Ye ◽  
Changhua Shang
Keyword(s):  
Heavy Metals ◽  
Water Quality ◽  
Growth Rate ◽  
Growth Inhibition ◽  
Chlorella Vulgaris ◽  
Specific Growth ◽  
Concentration Level ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Average Specific Growth Rate

Growth inhibition of chromium (Cr), cadmium (Cd) and lead (Pb) to fresh water microalga Chlorella vulgaris (C. vulgaris) FACHB-8 was examined. These results demonstrated that the concentration level (EC50 value) of three heavy metals (Cr, Cd and Pb) could be utilized as an indicator for evaluating the toxicities of Cr, Cd and Pb for microalga growth. The EC50 values of Cr for C. vulgaris were 0.22, 0.07 and 0.04 mg/L at 24, 48 and 72 h based on Algorithm 2 (%Ir, percent inhibition in average specific growth rate), respectively. The EC50 values of Cd for C. vulgaris were 2.76, 1.08 and 0.93 mg/L at 24, 48 and 72 h based on Algorithm 2, respectively. The EC50 values of Pb for C. vulgaris were 73.21, 65.02 and 48.38 mg/L at 24, 48 and 72 h based on Algorithm 2, respectively. The results laid a good foundation for the application of C. vulgaris in the water quality monitoring.

scholarly journals Use of Artificial Neural Networks and Multiple Linear Regression Model for the Prediction of Dissolved Oxygen in Rivers: Case Study of Hydrographic Basin of River Nyando, Kenya

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-23 ◽  
Author(s):  
Yashon O. Ouma ◽  
Clinton O. Okuku ◽  
Evalyne N. Njau
Keyword(s):  
Water Quality ◽  
Linear Regression ◽  
Dissolved Oxygen ◽  
Multiple Linear Regression ◽  
Pearson Correlation ◽  
Multiple Linear Regression Model ◽  
Water Quality Monitoring ◽  
Quality Parameters ◽  
Water Quality Parameters ◽  
Quality Monitoring

The process of predicting water quality over a catchment area is complex due to the inherently nonlinear interactions between the water quality parameters and their temporal and spatial variability. The empirical, conceptual, and physical distributed models for the simulation of hydrological interactions may not adequately represent the nonlinear dynamics in the process of water quality prediction, especially in watersheds with scarce water quality monitoring networks. To overcome the lack of data in water quality monitoring and prediction, this paper presents an approach based on the feedforward neural network (FNN) model for the simulation and prediction of dissolved oxygen (DO) in the Nyando River basin in Kenya. To understand the influence of the contributing factors to the DO variations, the model considered the inputs from the available water quality parameters (WQPs) including discharge, electrical conductivity (EC), pH, turbidity, temperature, total phosphates (TPs), and total nitrates (TNs) as the basin land-use and land-cover (LULC) percentages. The performance of the FNN model is compared with the multiple linear regression (MLR) model. For both FNN and MLR models, the use of the eight water quality parameters yielded the best DO prediction results with respective Pearson correlation coefficient R values of 0.8546 and 0.6199. In the model optimization, EC, TP, TN, pH, and temperature were most significant contributing water quality parameters with 85.5% in DO prediction. For both models, LULC gave the best results with successful prediction of DO at nearly 98% degree of accuracy, with the combination of LULC and the water quality parameters presenting the same degree of accuracy for both FNN and MLR models.

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
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