Development of bacteria-based bioassays for arsenic detection in natural waters.

Abstract

Arsenic contamination of natural waters is a worldwide concern, as the drinking water supplies for large populations can have high concentrations of arsenic. Traditional techniques to detect arsenic in natural water samples can be costly and time-consuming; therefore, robust and inexpensive methods to detect arsenic in water are highly desirable. Additionally, methods for detecting arsenic in the field have been greatly sought after. This article focuses on the use of bacteria-based assays as an emerging method that is both robust and inexpensive for the detection of arsenic in groundwater both in the field and in the laboratory. The arsenic detection elements in bacteria-based bioassays are biosensor-reporter strains; genetically modified strains of, e.g., Escherichia coli, Bacillus subtilis, Staphylococcus aureus, and Rhodopseudomonas palustris. In response to the presence of arsenic, such bacteria produce a reporter protein, the amount or activity of which is measured in the bioassay. Some of these bacterial biosensor-reporters have been successfully utilized for comparative in-field analyses through the use of simple solution-based assays, but future methods may concentrate on miniaturization using fiberoptics or microfluidics platforms. Additionally, there are other potential emerging bioassays for the detection of arsenic in natural waters including nematodes and clams.

DOI: 10.1007/s00216-009-2785-x

Statistics

010020020102011201220132014201520162017
Citations per Year

254 Citations

Semantic Scholar estimates that this publication has 254 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@article{Diesel2009DevelopmentOB, title={Development of bacteria-based bioassays for arsenic detection in natural waters.}, author={Elizabeth Diesel and Madeline Schreiber and Jan Roelof van der Meer}, journal={Analytical and bioanalytical chemistry}, year={2009}, volume={394 3}, pages={687-93} }