Sensitivitas dan Kelayakan Indeks Biotik Menggunakan Makroavertebrata untuk Menentukan Status Kesehatan Sungai

Water-quality monitoring using macroinvertebrates has been developed by several countries to determine their water qualities. Meanwhile in Indonesia, water quality monitoring has not been developed to adapt to Indonesia’s natural conditions. Some researchers use the existing biotic indices such as FBI, LQI, SIGNAL, and others. Therefore, this study aims to determine the status of water quality using several biotic indices and to compare the sensitivity and feasibility of indices on monitoring activities using simple matrix and Pearson correlation test. The interpretation results of FBI, LQI, and Singscore to determine water quality on each station in Brantas, Opak, Progo, and Cileungsi Rivers were different. The Pearson’s correlations test showed that the sensitivities are different between rivers. Those results are affected by the river conditions and also the activities around the rivers which release the pollution into the river. The FBI index showed the greatest score number of sensitivities among the other indexes. Keywords: macroinvertebrate, monitoring, river, sensitivity

Download Full-text

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 ◽

10.1155/2020/9570789 ◽

2020 ◽

Vol 2020 ◽

pp. 1-23 ◽

Cited By ~ 1

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.

Download Full-text

Seasonality and Correlations between Water Quality Parameters in the Lower Danube at Chiciu for the Period 2010-2012

Revista de Chimie ◽

10.37358/rc.20.2.7948 ◽

2020 ◽

Vol 71 (2) ◽

pp. 449-455

Author(s):

Rodica-Mihaela Frincu ◽

Cristian Omocea ◽

Cerasela-Iuliana Eni ◽

Eleonora-Mihaela Ungureanu ◽

Olga Iulian

Keyword(s):

Water Quality ◽

Dissolved Oxygen ◽

Social Economic ◽

Water Quality Monitoring ◽

Quality Parameters ◽

The Other ◽

Danube River ◽

Quality Monitoring ◽

The Social ◽

Pollution Loads

The Danube River receives tributaries with different pollution loads, according to the social-economic characteristics of the adjacent regions. Water quality monitoring data from Chiciu, Calarasi county, Romania, for the three-year period (2010-2012), were analysed using statistical methods in order to identify correlations between parameters, as well as their evolution during the study period. The analysis has confirmedpositive correlations between nitrates and total nitrogen and between ortho-phosphates and total phosphorus. Negative correlations were found between water temperatures on one side and dissolved oxygen and nitrates on the other side. These parameters have a seasonal evolution, with high temperatures and low dissolved oxygen and nitrates levels during summer periods. Linear regression highlights decreasing nutrients pollution during the study period, which may be due to improved wastewater treatment along Danube tributaries.

Download Full-text