scholarly journals Ground water toxicity due to fluoride contamination in Southwestern Lahore, Punjab, Pakistan

Author(s):  
Abdullah Yasar ◽  
Tariq Javed ◽  
Firdaus Kausar ◽  
Jaweria Shamshad ◽  
Muhammad Umar Hayat Khan ◽  
...  

Abstract The prevalence of dental/bone deformities provides motivation for studying the distribution, severity and sources of the Fluoride (F−1). The ground water samples (n = 77) were collected, from the districts of Lahore and Kasur of approximately 750 Km2 area. The water was analyzed for fluoride (F−), pH, electric conductivity (EC), alkalinity and hardness. The results revealed F− concentration ranges from 0.25–21.3 mg. An inverse relation between depth and fluoride concentration was observed. On the basis of cluster analysis three zones were identified. Highly toxic zone was a strip of 15 km wide and 3 km long, along Multan road from Sunder to Phool Nagar bypass, with fluoride concentration (08–21.3 mg/l). The highly toxic zone inhabited a number of industrial units, disposing off their waste water through soaking pits. These units contribute pollution to the shallow water, which further penetrates to the surroundings. Hence the shallow water (depth of 45–50 feet) was the most contaminated. The intensity of toxic effects decreases from highly to mild toxic zone. It was concluded that the problem was actually associated with the industrial waste water. Therefore, to overcome the issue, measures of supplying fresh drinking water from the deep aquifer as well as treatment of industrial water is suggested. HIGHLIGHT Industry was actually responsible for fluoride toxicity in the region rather than natural sources.

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Taty Hernaningsih

Waste water treatment by industry usually uses chemicals that may lead to additional environmental pollution load. On the other hand, water demand increases and environmental regulations regarding waste water disposal requirements that apply more stringent. It is necessary for waste treatment technique that accommodate this requirement. Electrocoagulation process is a technique of wastewater treatment that has been chosen because the technique is environmentally friendly. This paper will review some of the research or application electrocoagulation process which is conducted on industrial waste water. Types of industrial waste water that is to be reviewed include: industries batik, sarongs, textiles, palm oil, slaughterhouses, food, leather tanning, laundry, pulp and paper. Overview reviewed in this research include the waste water treatment process in several processing variations such as: change in time, electricity and kind of electrodes. The results of the research with electrocoagulation process in the industry are the removal efficiency of TSS, COD, BOD5, Chrome, phosphate, surfactants, color turbidity influenced by several factors including time, strong current, voltage, distance and type of electrode and pH. The results of the study with electrocoagulation process in the industry is the removal efficiency of TSS, COD, BOD5, chromium, phosphate, surfactant, turbidity color that are influenced by several factors including time, strong current, voltage, distance and type of electrode and pH. It is hoped the information presented in this article can be a reference for similar research for the improvement of research on the process ektrokoagulasi.Key words: elektrocoagulation, removal eficiency, environmental friendly


2020 ◽  
Vol 13 ◽  
Author(s):  
Rishabha Malviya ◽  
Pramod Sharma ◽  
Akanksha Sharma

: Manuscript discussed about the role of polysaccharides and their derivatives in the removal of metal ions from industrial waste water. Quick modernization and industrialization increases the amount of various heavy metal ions in the environment. They can possess various disease in humans and also causes drastic environmental hazards. In this review the recent advancement for the adsorption of heavy metal ions from waste water by using different methods has been studied. Various natural polymers and their derivatives are act as effective adsorbents for the removal of heavy metal ions from the waste water released from the industries and the treated water released into the environment can decreases the chances of diseases in humans and environmental hazards. From the literature surveys it was concluded that the removal of heavy metal ions from the industrial waste water was important to decrease the environmental pollution and also diseases caused by the heavy metal ions. Graft copolymers were acts as most efficient adsorbent for the removal of heavy metal ions and most of these followed the pseudo first order and pseudo second order model of kinetics.


RSC Advances ◽  
2021 ◽  
Vol 11 (21) ◽  
pp. 12877-12884
Author(s):  
Yang Gui ◽  
Daniel J. Blackwood

Schematic description of Pb2+ removal based on capacitive electrochemical technique.


2013 ◽  
Vol 15 (4) ◽  
pp. 1474-1490 ◽  
Author(s):  
Ata Allah Nadiri ◽  
Elham Fijani ◽  
Frank T.-C. Tsai ◽  
Asghar Asghari Moghaddam

The study introduces a supervised committee machine with artificial intelligence (SCMAI) method to predict fluoride in ground water of Maku, Iran. Ground water is the main source of drinking water for the area. Management of fluoride anomaly needs better prediction of fluoride concentration. However, the complex hydrogeological characteristics cause difficulties to accurately predict fluoride concentration in basaltic formation, non-basaltic formation, and mixing zone. SCMAI predicts fluoride by a nonlinear combination of individual AI models through an artificial intelligent system. Factor analysis is used to identify effective fluoride-correlated hydrochemical parameters as input to AI models. Four AI models, Sugeno fuzzy logic, Mamdani fuzzy logic, artificial neural network (ANN), and neuro-fuzzy are employed to predict fluoride concentration. The results show that all of these models have similar fitting to the fluoride data in the Maku area, and do not predict well for samples in the mixing zone. The SCMAI employs an ANN model to re-predict the fluoride concentration based on the four AI model predictions. The result shows improvement to the CMAI method, a committee machine with the linear combination of AI model predictions. The results also show significant fitting improvement to individual AI models, especially for fluoride prediction in the mixing zone.


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