Rapid microbiology instrumentation for high-accuracy quantitative water safety assessments in remote and low-resource locations

Abstract

Microbial contamination of surface waters remains a significant public health concern, particularly in low- and middle-income countries (LMICs) and urban settings affected by combined sewer overflows (CSOs). Standard culture-based methods for monitoring fecal indicator bacteria (FIB)-- including membrane filtration (MF) and most probable number (MPN) assays--typically require laboratory infrastructure, do not provide same-day results, and systematically underestimate total FIB loads due to their inability to accurately quantify aggregate-bound bacteria. This study evaluates ALERT, an automated rapid method for culturable FIB quantification, as a viable field-deployable alternative to conventional laboratory techniques. ALERT employs a whole-sample approach that comprehensively captures both planktonic and aggregate-bound E. coli, thereby addressing critical limitations of traditional methods. Three instrument variants were assessed: the in-situ ALERT System V2, the portable ALERT LAB, and the handheld ALERT One, designed for single-sample analysis in resource-limited environments. Comparative field and laboratory trials conducted in Germany, France, and the UK demonstrate that ALERT delivers accuracy and precision equivalent to MPN methods, while providing faster results directly in the field and enabling high-frequency sampling. Use cases included storm event monitoring, CSO impact evaluation, and on-site validation and optimization of point-of-use water potabilization treatments. ALERT One was also successfully used by citizen scientist volunteers, highlighting its suitability for decentralized community-based monitoring. By enabling rapid, accurate, and comprehensive microbial water quality assessments, ALERT qualifies as a robust field alternative to laboratory testing. It supports enhanced water safety, empowers community-based surveillance and public engagement, and facilitates data-driven decision-making in both LMICs and high-income countries.