Optimizing the performance of conventional water treatment system using quantitative microbial risk assessment, Tehran, Iran

Drinking water QMRA and decision-making: Sensitivity of risk to common independence assumptions about model inputs

When assessing risk posed by waterborne pathogens in drinking water, it is common to use Monte Carlo simulations in Quantitative Microbial Risk Assessment (QMRA). This method accounts for the variables that affect risk and their different values in a given system. A common underlying assumption in such analyses is that all random variables are independent (i.e., one is not associated in any way with another). Although the independence assumption simplifies the analysis, it is not always correct. For example, treatment efficiency can depend on microbial concentrations if changes in microbial concentrations either affect treatment themselves or are associated with water quality changes that affect treatment (e.g., during/after climate shocks like extreme precipitation events or wildfires). Notably, the effects of erroneous assumptions of independence in QMRA have not been widely discussed. Due to the implications of drinking water safety decisions on public health protection, it is critical that risk models accurately reflect the context being studied to meaningfully support decision-making. This work illustrates how dependence between pathogen concentration and either treatment efficiency or water consumption can impact risk estimates using hypothetical scenarios of relevance to drinking water QMRA. It is shown that the mean and variance of risk estimates can change substantially with different degrees of correlation. Data from a water supply system in Calgary, Canada are also used to illustrate the effect of dependence on risk. Recognizing the difficulty of obtaining data to empirically assess dependence, a framework to guide evaluation of the effect of dependence is presented to enhance support for decision making. This work emphasizes the importance of acknowledging and discussing assumptions implicit to models.

Exposure assessment of nitrate and phenol derivatives in Tehran's water distribution system

The presence of organic and inorganic contaminants in drinking water is a global concern. Nitrate and phenol derivatives are examples of pollutants that could be of anthropogenic origin. They are associated with numerous health risks, underscoring the importance of monitoring their presence in drinking water. This study aimed to measure nitrate and phenol derivatives, including 2,4-Dichlorophenol (2,4-DCP), Pentachlorophenol (PCP), 2,4,5-Trichlorophenol (2,4,5-TCP), 2-Chlorophenol (2-CP), 4-Chlorophenol (4-CP), and phenol, in Tehran's water distribution system (WDS). The pollutants in Tehran's WDS were significantly and positively correlated with precipitation. The Hazard Quotient (HQ) and the Excess Lifetime Cancer Risk (ELCR) of the detected pollutants were estimated. The results showed that the regional mean of nitrate and PCP in Tehran's WDS were 35.58±8.71mg L-1 and 76.14±16.93 ng L-1 lower than the guideline values of 50 mg L-1 and 1000 ng L-1, respectively. Some districts exhibited nitrate concentration exceeding the allowable limit by a factor of 1.2 to 2.3. Consequently, the nitrate intake in some districts constituted approximately 50% of the reference dose. While PCP as a phenol derivative with more health concerns was identified in Tehran's WDS, the likelihood of its health effects was determined to be negligible.

Integrating life cycle assessment with quantitative microbial risk assessment for a holistic evaluation of sewage treatment plant

An integrated approach was employed in the present study to combine life cycle assessment (LCA) with quantitative microbial risk assessment (QMRA) to assess an existing sewage treatment plant (STP) at Roorkee, India. The midpoint LCA modeling revealed that high electricity consumption (≈ 576 kWh.day^-1) contributed to the maximum environmental burdens. The LCA endpoint result of 0.01 disability-adjusted life years per person per year (DALYs pppy) was obtained in terms of the impacts on human health. Further, a QMRA model was developed based on representative sewage pathogens, including E. coli O157:H7, Giardia sp., adenovirus, norovirus, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The public health risk associated with intake of pathogen-laden aerosols during treated water reuse in sprinkler irrigation was determined. A cumulative health risk of 0.07 DALYs pppy was obtained, where QMRA risks contributed 86 % of the total health impacts. The annual probability of illness per person was highest for adenovirus and norovirus, followed by SARS-CoV-2, E. coli O157:H7 and Giardia sp. Overall, the study provides a methodological framework for an integrated LCA-QMRA assessment which can be applied across any treatment process to identify the hotspots contributing maximum environmental burdens and microbial health risks. Furthermore, the integrated LCA-QMRA approach could support stakeholders in the water industry to select the most suitable wastewater treatment system and establish regulations regarding the safe reuse of treated water.

Risk assessment of Cryptosporidium intake in drinking water treatment plant by a combination of predictive models and event-tree and fault-tree techniques

Risk-informed decision making permits a more effective water safety management. In this framework, this article introduces the rationale and proposes a new approach to carry out a quantitative risk assessment along the water chain, from river source to tap water, by integrating predictive modelling combined with event-tree and fault-tree techniques. The model developed by this approach could not only account for normal but also for abnormal process conditions in the water treatment plant, as well as assess the real impact of the applied safety controls, such as turbidity control. A sensitivity study was conducted to determine the effect of considering a typical drinking water treatment plant (DWTP), i.e. coagulation, sedimentation and filtration with two turbidity controls (on intake and after filtration) on the risk of infection due to exposure to Cryptosporidium in tap water. The results showed that, with the current effectiveness of turbidity reduction in the DWTP, the first control did not minimise the annual risk of Cryptosporidium infection (3.6E-04) and only limiting turbidity after filtration to below 0.01NTU provided a clear reduction in risk (7.7E-05) at the cost of rejecting 60 % of the water after the control. The lowest risk was found when turbidity reduction was set at 4 logs (8.48E-06), although this means that the effectiveness of turbidity reduction should be greatly improved. It was therefore concluded that supplementing the current treatment with alternative barriers such as UV or ozone disinfection and/or implementing direct control of Cryptosporidium concentration should be considered.

Navigating Data Uncertainty and Modeling Assumptions in Quantitative Microbial Risk Assessment in an Informal Settlement in Kampala, Uganda

Decision-makers in developing communities often lack credible data to inform decisions related to water, sanitation, and hygiene. Quantitative microbial risk assessment (QMRA), which quantifies pathogen-related health risks across exposure routes, can be informative; however, the utility of QMRA for decision-making is often undermined by data gaps. This work integrates QMRA, uncertainty and sensitivity analyses, and household surveys in Bwaise, Kampala (Uganda) to characterize the implications of censored data management, identify sources of uncertainty, and incorporate risk perceptions to improve the suitability of QMRA for informal settlements or similar settings. In Bwaise, drinking water, hand rinse, and soil samples were collected from 45 households and supplemented with data from 844 surveys. Quantified pathogen (adenovirus, Campylobacter jejuni, and Shigella spp./EIEC) concentrations were used with QMRA to model infection risks from exposure through drinking water, hand-to-mouth contact, and soil ingestion. Health risks were most sensitive to pathogen data, hand-to-mouth contact frequency, and dose-response models (particularly C. jejuni). When managing censored data, results from upper limits of detection, half of limits of detection, and uniform distributions returned similar results, which deviated from lower limits of detection and maximum likelihood estimation imputation approaches. Finally, risk perceptions (e.g., it is unsafe to drink directly from a water source) were identified to inform risk management.

Development of a quantitative chemical risk assessment (QCRA) procedure for contaminants of emerging concern in drinking water supply

The uncertainties on the occurrence, fate and hazard of Contaminants of Emerging Concern (CECs) increasingly challenge drinking water (DW) utilities whether additional measures should be taken to reduce the health risk. This has led to the development and evaluation of risk-based approaches by the scientific community. DW guideline values are commonly derived based on deterministic chemical risk assessment (CRA). Here, we propose a new probabilistic procedure, that is a quantitative chemical risk assessment (QCRA), to assess potential health risk related to the occurrence of CECs in DW. The QCRA includes uncertainties in risk calculation in both exposure and hazard assessments. To quantify the health risk in terms of the benchmark quotient probabilistic distribution, the QCRA estimates the probabilistic distribution of CECs concentration in DW based on their concentration in source water and simulating the breakthrough curves of a granular activated carbon (GAC) treatment process. The model inputs and output uncertainties were evaluated by sensitivity and uncertainty analyses for each step of the risk assessment to identify the most relevant factors affecting risk estimation. Dominant factors resulted to be the concentration of CECs in water sources, GAC isotherm parameters and toxicological data. To stress the potential of this new QCRA approach, several case studies are considered with focus on bisphenol A as an example CEC and various GAC management options. QCRA quantifies the probabilistic risk, providing more insight compared to CRA. QCRA proved to be more effective in supporting the intervention prioritization for treatment optimization to pursue health risk minimization.

Implementation of quantitative microbial risk assessment (QMRA) for public drinking water supplies: Systematic review

In the more than 15 years since its introduction, quantitative microbial risk assessment (QMRA) has become a widely used technique for assessing population health risk posed by waterborne pathogens. However, the variation in approaches taken for QMRA in relation to drinking water supply is not well understood. This systematic review identifies, categorises, and critically synthesises peer-reviewed and academic case studies of QMRA implementation for existing distributed public drinking water supplies. Thirty-nine English-language, peer-reviewed and academic studies published from 2003 to 2019 were identified. Key findings were synthesised in narrative form. The overall designs of the included studies varied widely, as did the assumptions used in risk calculation, especially in relation to pathogen dose. There was also substantial variation in the degree to which the use of location-specific data weighed with the use of assumptions when performing risk calculation. In general, the included studies' complexity did not appear to be associated with greater result certainty. Factors relating to pathogen dose were commonly influential on risk estimates whereas dose-response parameters tended to be of low relative influence. In two of the included studies, use of the 'susceptible fraction' factor was inconsistent with recognised guidance and potentially led to the underestimation of risk. While approaches and assumptions used in QMRA need not be standardised, improvement in the reporting of QMRA results and uncertainties would be beneficial. It is recommended that future authors consider the water supply QMRA reporting checklist developed for the current review. Consideration of the broad types of uncertainty relevant to QMRA is also recommended. Policy-makers should consider emergent discussion on acute microbial health-based targets when setting normative guidelines. The continued representation of QMRA case studies within peer-reviewed and academic literature would also enhance future implementation. Further research is needed on the optimisation of QMRA resourcing given the application context.