Groundwater is a key source of drinking water for 2 billion people around the world. Currently, groundwater is the major source of water for urban supply and for drinking in rural areas of Bangladesh. Despite the depletion in groundwater level due to high volume of extraction, groundwater is still preferable as drinking water source as surface water here is unhygienic. Recently, groundwater is being extracted from shallow wells to serve the Rohingya refugees at Cox’s Bazar, Bangladesh, due to insufficient and unpalatable surface water. As Iron (Fe) and Manganese (Mn) concentration in most areas of Bangladesh exceed the standard limit for drinking purposes, it’s important to monitor the quality of groundwater extracted for drinking. Both Fe and Mn occur through natural interactions of water and rock. They can pose health risk and lots of engineering and aesthetic problems if present at high concentration. Several methods such as Atomic Absorption Spectrophotometer (AAS), Inductively Coupled Plasma (ICP) and Colorimetric methods are used to measure Fe and Mn concentrations in water. These methods are often time consuming and require expensive devices, harmful chemical reagents and expert supervision. In this study, we used a quantitative approach, namely artificial neural networking (ANN), to estimate Fe and Mn in water using absorbance determined through UV-Vis spectrophotometer. UV-Vis Spectrophotometer is a relatively cheap, efficient, and portable device, which can provide absorbance of a spectrum instantly, requiring no additional chemical. We collected 52 groundwater samples, including both unfiltered and filtered ones, from Rohingya Camps. Absorbance of each sample in UV-Vis spectrophotometer were recorded for a spectrum covering wavelength of 200 nm to 1100 nm. Concentrations of Fe and Mn in these samples were determined using AAS method. An ANN model was developed to predict the Fe and Mn concentrations present in the samples from the measured absorbance values. From the model, we observed that the prediction models showed a strong correlation between Fe and absorbance values, reflected by the high R2-value for samples with relatively low concentrations of Fe (0.4 mg/L to 2 mg/L, R2 = 0.82) and Mn (0.008 mg/L to 0.3 mg/L, R2 = 0.91) than for samples with higher concentrations. Use of UV-Vis spectrophotometer coupled with application of artificial neural network may provide an efficient and cheap tool for groundwater quality monitoring for Fe and Mn.<br/>

Estimation of iron and manganese concentration in ground water using absorbance in UV-Vis spectrophotometer via artificial neural network

Macromolecular Chemistry: The Second Century

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ACS Spring 2020 National Meeting & Expo

The official archive and repository for all ACS Meetings & Expositions abstracts and select posters or presentations.