Sewage-based surveillance for COVID-19 has been described in multiple countries and multiple settings. However, nearly all are based on testing sewage treatment plant inflows and outflows using structured sewage networks and treatment systems. Many resource-limited countries worldwide have open canals, lakes and other such waste-contaminated water bodies that act as a means of sewage effluent discharge. These could serve as hyperlocal testing points for detecting COVID-19 incidence using the effluents from nearby communities. However, a sensitive, robust and economical method of SARS-CoV-2 RNA detection from open waste contaminated water bodies in resource-constrained regions is currently lacking. A protocol employed in Bangalore, India, where SARS-CoV-2 RNA levels were evaluated using two open canal systems during the first and second waves in the present study. SARS-CoV-2 RNA was measured using two strategies: a modified TrueNATTM microchip-based rapid method and traditional real-time reverse transcription-PCR (rRT-PCR), which were compared for analytical sensitivity, cost and relative ease of use. SARS-CoV-2 RNA levels were detected at lower levels during the earlier half compared to the later half of the first wave in 2020. The opposite trend was seen in the second wave in 2021. Interestingly, the change in RNA levels corresponded with the community burden of COVID-19 at both sites. The modified TrueNATTM method yielded concordant results to traditional rRT-PCR in sensitivity and specificity and cost. It provides a simple, cost-effective method for detecting and estimating SARS-CoV-2 viral RNA from open-water sewage canals contaminated with human excreta and industrial waste that can be adopted in regions or countries that lack structured sewage systems.
This paper presents an integrated methodology that allows determining the probability of noncompliance for a given wastewater treatment plant. The methodology applies fault-tree analysis, which uses failure probabilities of individual components, to predict the overall system failure probability. The methodology can be divided into two parts : risk identification and risk quantification. In risk identification, the key components in the system are determined by analyzing the contribution of individual component failures toward system failure (i.e., noncompliance). In risk quantification, a fault-tree model is constructed for the particular system, component failure probabilities are estimated, and the fault-tree model is evaluated to determine the probability of occurrence of the top event (i.e., noncompliance). A list can be developed that ranks critical events on the basis of their contributions to the probability of noncompliance. Such a ranking should assist managers to determine which components require most attention for a better performance of the entire system. A wastewater treatment plant for treating metal-bearing rinse water from an electroplating industry is used as an example to demonstrate the application of this methodology.
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