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    Tytuł:
    Evaluation of Spatio-Temporal Changes in Water Quality in the Middle Section of the Shatt Al-Arab River, Southern Iraq
    Autorzy:
    Moyel, Mohammad Salim
    Akbar, Manal Mohammad
    Hussain, Najah Abood
    Powiązania:
    https://bibliotekanauki.pl/articles/24201725.pdf
    Data publikacji:
    2023
    Wydawca:
    Polskie Towarzystwo Inżynierii Ekologicznej
    Tematy:
    Shatt al-Arab river
    middle section
    spatio-temporal change
    water quality index
    Opis:
    The Shatt al-Arab river is the main water source in the Basrah province, subjected to significant environmental and hydrological changes that have led to the complete degradation of its ecosystem, particularly the middle section. Nineteen water quality variables were selected to assess spatio-temporal changes in the middle section. Eight variables were chosen with the most significant impact on the Shatt al-Arab River water quality in calculating the water quality index (WQI). These variables were measured every month from December 2020 to November 2021 at five observation stations (Abu-Flous, Mhela, Baradeyea and Maqal) located on the main river and one on the Karmat Ali canal, which connects the Shatt al-Arab River with east Hammar marsh. The study was divided into two seasons of the year, the wet season (December 2020 – May 2021) and the dry season (July 2021 – November 2021), in the calculation of the WQI and its annual calculation. The results of the current study show deterioration in the values of most water quality variables, particularly those related to dissolved salts and organic and bacterial pollution. Water quality was also classified as poor on the WQI scale at all stations for the duration of the study. The results of the WQI indicated the deterioration in the quality of the water middle section, particularly during dry season. The degradation of the waters of the middle section of the Shatt al-Arab River is due to two main factors: increase salinity and organic pollution. In general, the Shatt al-Arab River and the middle section in particular, need comprehensive management, including a clear and expeditious plan to identify and address the degradation of the river’s environment, which has a great importance to all residents of the Basrah province.
    Źródło:
    Journal of Ecological Engineering; 2023, 24, 4; 297--311
    2299-8993
    Pojawia się w:
    Journal of Ecological Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Modeling Pollution Index Using Artificial Neural Network and Multiple Linear Regression Coupled with Genetic Algorithm
    Autorzy:
    Abdulkareem, Iman Ali
    Abbas, Abdulhussain A.
    Dawood, Ammar Salman
    Powiązania:
    https://bibliotekanauki.pl/articles/2068477.pdf
    Data publikacji:
    2022
    Wydawca:
    Polskie Towarzystwo Inżynierii Ekologicznej
    Tematy:
    Shatt Al-Arab river
    comprehensive pollution index
    multiple linear regression
    artificial neural network
    genetic algorithm
    Opis:
    Shatt Al-Arab River in Basrah province, Iraq, was assessed by applying comprehensive pollution index (CPI) at fifteen sampling locations from 2011 to 2020, taking into consideration twelve physicochemical parameters which included pH, Tur., TDS, EC, TH, Na+, K+, Ca+2, Mg+2, Alk., SO4-2, and Cl-. The effectiveness of multiple linear regression (MLR) and artificial neural network (ANN) for predicting comprehensive pollution index was examined in this research. In order to determine the ideal values of the predictor parameters that lead to the lowest CPI value, the genetic algorithm coupled with multiple linear regression (GA-MLR) was used. A multi-layer feed-forward neural network with backpropagation algorithm was used in this study. The optimal ANN structure utilized in this research consisted of three layers: the input layer, one hidden layer, and one output layer. The predicted equation of the comprehensive pollution index was created using the regression technique and used as an objective function of the genetic algorithm. The minimum predicted comprehensive pollution index value recommended by the GA-MLR approach was 0.3777.
    Źródło:
    Journal of Ecological Engineering; 2022, 23, 3; 236--250
    2299-8993
    Pojawia się w:
    Journal of Ecological Engineering
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Evaluation of water treatment plants quality in Basrah Province, by factor and cluster analysis
    Autorzy:
    Al Saad, Zainb A.A.
    Hamdan, Ahmed N.A.
    Powiązania:
    https://bibliotekanauki.pl/articles/293213.pdf
    Data publikacji:
    2020
    Wydawca:
    Instytut Technologiczno-Przyrodniczy
    Tematy:
    cluster analysis (CA)
    factor analysis (FA)
    multivariate statistics
    the Shatt Al Arab River
    water quality
    water treatment plant
    Opis:
    The Shatt Al Arab River (SAAR) is a major source of raw water for most water treatment plants (WTP’s) located along with it in Basrah province. This study aims to determine the effects of different variables on water quality of the SAAR, using multivariate statistical analysis. Seventeen variables were measured in nine WTP’s during 2017, these sites are Al Hussain (1), Awaissan (2), Al Abass (3), Al Garma (4), Mhaigran (5), Al Asmaee (6), Al Jubaila (7), Al Baradia (8), Al Lebani (9). The dataset is treated using principal component analysis (PCA) / factor analysis (FA), cluster analysis (CA) to the most important factors affecting water quality, sources of contamination and the suitability of water for drinking and irrigation. Three factors are responsible for the data structure representing 88.86% of the total variance in the dataset. CA shows three different groups of similarity between the sampling stations, in which station 5 (Mhaigran) is more contaminated than others, while station 3 (Al Abass) and 6 (Al Asmaee) are less contaminated. Electrical conductivity (EC) and sodium adsorption ratio (SAR) are plotted on Richard diagram. It is shown that the samples of water of Mhaigran are located in the class of C4-S3 of very high salinity and sodium, water samples of Al Abass station, are located in the class of C3-S1 of high salinity and low sodium, and others are located in the class of C4-S2 of high salinity and medium sodium. Generally, the results of most water quality parameters reveal that SAAR is not within the permissible levels of drinking and irrigation.
    Źródło:
    Journal of Water and Land Development; 2020, 46; 10-19
    1429-7426
    2083-4535
    Pojawia się w:
    Journal of Water and Land Development
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
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