Literature DB >> 31422469

Chemical and physicochemical characterization of effluents from the tanning and textile industries in Bangladesh with multivariate statistical approach.

Md Aminul Ahsan1, Farjana Satter2, Md Abu Bakar Siddique3, Md Ahedul Akbor1, Shamim Ahmed1, Md Shajahan2, Rahat Khan4.   

Abstract

Industrial effluents are one of the foremost concerns relating to the anthropogenic environmental pollution. The effluents from the tanning and textile industries in Dhaka, Bangladesh, were characterized chemically and physicochemically with multivariate statistical techniques. The concentrations of heavy metals viz., Pb, Cd, Cr, Mn, Fe, Ni, Cu, and Zn were determined by atomic absorption spectrometer while concentrations of anions viz., F-, Cl-, NO2-, NO3-, and SO42- were measured by ion chromatograph. The physicochemical parameters viz., temperature, pH, electrical conductivity (EC), salinity, turbidity, dissolved oxygen (DO), and biological oxygen demand (BOD) were measured by a multiparameter meter while total suspended solids (TSS) and total dissolved solids (TDS) were measured gravimetrically. This study showed that effluents from both industries demonstrated high levels of TSS, TDS, EC, and heavy metals. Tannery effluents have lower pH and DO, and higher BOD, Cl-, SO42-, and Cr concentrations while textile dyeing effluents have higher pH, NO2-, and NO3- concentrations, compared to the standard limits promulgated by the Bangladesh government. Multivariate statistical techniques such as cluster analysis and principal component analysis along with the correlation matrices showed significant association among the measured parameters and identified pollution sources as well as effluent types in the study area which could be linked to the processes used in textile dying and tanning industries. This study will be useful for identifying pollutants emanating from the two industries and will guide future industrial aquatic studies where multiple industrial runoffs are concerned.

Entities:  

Keywords:  Environmental pollution; Heavy metals and anions; Industrial effluents; Multivariate statistical techniques; Physicochemical parameters

Mesh:

Substances:

Year:  2019        PMID: 31422469     DOI: 10.1007/s10661-019-7654-2

Source DB:  PubMed          Journal:  Environ Monit Assess        ISSN: 0167-6369            Impact factor:   2.513


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