Assessment and interpretation of river water quality in Little Akaki River using multivariate statistical techniques

被引:17
|
作者
Yilma, M. [1 ]
Kiflie, Z. [1 ]
Windsperger, A. [2 ]
Gessese, N. [3 ]
机构
[1] Addis Ababa Univ, Sch Chem & Bioengn, Environm Engn Stream, Addis Ababa, Ethiopia
[2] Inst Ind Okol, Rennbahnstr 29B, A-3100 St Polten, Austria
[3] Global Dev Solut LLc, Addis Ababa, Ethiopia
关键词
Addis Ababa; Domestic; Industrial; Pollution; Waste; SURFACE-WATER; PRINCIPAL COMPONENT; TEMPORAL VARIATIONS; BASIN; POLLUTION; EVOLUTION; DISTRICT; NITROGEN; CLUSTER; INDIA;
D O I
10.1007/s13762-018-2000-8
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Indiscriminant waste disposal is limiting the usability of the Little Akaki River in Addis Ababa, Ethiopia. Besides, there are inadequate comprehensive studies on the river principally due to insufficient research fund. Therefore, in this study, water quality investigation that is both regular and economical is sought. In October and November 2015, twenty-seven locations were sampled from the river and tributaries. Multivariate statistical tools were engaged to investigate data from measurements and laboratory analysis. Consequently, cluster analysis divided sampled sites into three according to level of their pollution. This indicates that water quality variation was caused because of the difference in land-use conditions. In addition, for the spatial analysis of the three pollution groups, backward stepwise approach of discriminant analysis was identified to provide data reduction (87.5%) to two parameters resulting in 85.2% correct assignment. The principal component analysis/factor analysis identified ten parameters accounting for 81.9% of total variation. However, data reduction was not significant. The factors that were latent and identified from the principal components' varimax rotation suggest that variation in water quality was caused mainly by domestic sewage. The outcomes show that the methods can be applied to evaluate the river water quality variation using three monitoring sites and ten parameters: total nitrogen, total suspended solids, total ammonia, chemical oxygen demand, nitrite, total phosphorus, phosphate, nitrate, biological oxygen demand and electrical conductivity. This, in consequence, requires lesser cost and effort and hence paves way for more affordable, regular water quality evaluation of Little Akaki River.
引用
收藏
页码:3707 / 3720
页数:14
相关论文
共 50 条
  • [21] Assessment and interpretation of surface water quality in Jhelum River and its tributaries using multivariate statistical methods
    Sarvat Gull
    Shagoofta Rasool Shah
    Ayaz Mohmood Dar
    Environmental Monitoring and Assessment, 2023, 195
  • [22] Assessment and interpretation of surface water quality in Jhelum River and its tributaries using multivariate statistical methods
    Gull, Sarvat
    Shah, Shagoofta Rasool
    Dar, Ayaz Mohmood
    ENVIRONMENTAL MONITORING AND ASSESSMENT, 2023, 195 (06)
  • [23] Assessment of water quality using multivariate techniques in River Sosiani, Kenya
    Achieng', A. O.
    Raburu, P. O.
    Kipkorir, E. C.
    Ngodhe, S. O.
    Obiero, K. O.
    Ani-Sabwa, J.
    ENVIRONMENTAL MONITORING AND ASSESSMENT, 2017, 189 (06)
  • [24] Assessment of water quality using multivariate techniques in River Sosiani, Kenya
    A. O Achieng’
    P. O Raburu
    E. C Kipkorir
    S. O Ngodhe
    K.O Obiero
    J Ani-Sabwa
    Environmental Monitoring and Assessment, 2017, 189
  • [25] The water quality management in the Nakdong River watershed using multivariate statistical techniques
    Suhee Han
    Eungseock Kim
    Sangdan Kim
    KSCE Journal of Civil Engineering, 2009, 13 : 97 - 105
  • [26] The Water Quality Management in the Nakdong River Watershed using Multivariate Statistical Techniques
    Han, Suhee
    Kim, Eungseock
    Kim, Sangdan
    KSCE JOURNAL OF CIVIL ENGINEERING, 2009, 13 (02) : 97 - 105
  • [27] Spatial and temporal variations of river water quality using multivariate statistical techniques
    Alssgeer, Hassan M. A.
    Kamarudin, Mohd Khairul Amri
    Abu Samah, Mohd Armi
    Toriman, Mohd Ekhwan
    Gasim, Muhammad Barzani
    Hanafiah, Marlia M.
    Alubyad, Laila O. M.
    Saudi, Ahmad Shakir Mohd
    Maulud, Khairul Nizam
    Wahab, Noorjima Abd
    Bati, Siti Nor Aisyah
    Erhayem, Mohamed
    DESALINATION AND WATER TREATMENT, 2022, 269 : 106 - 122
  • [28] SURFACE WATER QUALITY ASSESSMENT USING MULTIVARIATE STATISTICAL TECHNIQUES (CASE STUDY: TALAR RIVER, IRAN)
    Shahedi, Kaka
    Kaveh, Alireza
    Habibnejad, Mahmoud
    Ghorbani, Jamshid
    SOCIOINT14: INTERNATIONAL CONFERENCE ON SOCIAL SCIENCES AND HUMANITIES, 2014, : 352 - 363
  • [29] Assessment of water quality using multivariate statistical techniques: A case study of the Nakdong river basin, Korea
    Park, Seongmook
    Kazama, Futaba
    Lee, Shunhwa
    Environmental Engineering Research, 2014, 19 (03) : 197 - 203
  • [30] Multivariate statistical techniques for the assessment of surface water quality of Fuji River Basin, Japan
    Shrestha, S.
    Kazama, F.
    5th World Water Congress: Water Services Management, 2006, 6 (05): : 59 - 67