Most of the water supplied in Brazil comes from water streams that may have higher values for apparent color than turbidity. Treatability trials were performed for color and turbidity removal to evaluate the advantages of coagulation during the charge neutralization mechanism when compared to sweep flocculation for water with those characteristics. There were three types of trials: conventional Jar Test with raw water, with and without filtration, and in a pilot Water Treatment Plant, direct downward filtration, with synthetic water. Auxiliary equipment such as Continuous Flocculation Monitoring Equipment (CFME) and image analysis were used to evaluate the growth of flocs. In the Jar Tests experiments, similar levels of color (61.49%) and turbidity (61.30%) removal were observed, with a lower dosage of coagulant (46 mg/L) in the charge neutralization mechanism compared to the ones with sweep flocculation (58.22% for color and 54.73% for turbidity removal with 52 mg/L of aluminium sulfate). Similar results were found on pilot plant. At filtration rates of 180 and 300 m³ m⁻² day⁻¹, sweep flocculation mechanism had shorter filtration cycle (<5 m³) compared to other mechanisms. Therefore, a change in the operation of Water Treatment plants that use a coagulant dosage associated with sweep flocculation can bring advantages such as the reduction of coagulant consumption and sludge productions, as well as the increase of chlorine disinfection and filtration cycles.

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.

This article addresses regional inequalities about critical healthcare infrastructure for coping with the COVID-19, based on geospatial analysis and compartmental modeling (SEIR). The study sites were the São Paulo Macro Metropolis (MMP) and the Metropolitan Region of Vale do Paraíba/Litoral Norte (RMVPLN). The results indicated the absence of ICU beds in more than 2/3 of the municipalities, in addition to the concentration in regional hubs, for which the migration of COVID-19 cases from neighboring municipalities should be considered. The analysis with the SEIR model, performed for scenarios with and without social isolation, showed that the isolation adopted in the regions was able to postpone the collapse of the health infrastructure, reducing the reproduction rate of the disease. The simulations indicated a reproduction rate of 0.95 in order to avoid collapse in the health infrastructure for RMVPLN in the analyzed period. In addition, for future resilience, it is essential to address inequalities in terms of health infrastructure available for public and private access.

Periodic evaluation of particulate contaminants in raw/untreated water is integral to assessing risk, establishing treatment requirements, and ensuring drinking water safety. However, pathogenic microorganisms and other discrete particles (e.g., microplastics) are not typically monitored with any regularity. When monitoring is required, recommended, or proactively used to evaluate the adequacy of treatment or assess treatment needs, there is a need for guidance on how to collect data and use them to maximize return on investment. The potentially increasing variability in source water quality associated with climate change emphasizes the importance of knowing contaminant concentrations to effectively manage risks. This work presents a framework to guide the development of monitoring protocols for particulate contaminants in water and the integration of monitoring data and quantitative microbial risk assessment into treatment decisions. The protozoa monitoring and risk-based compliance approach of a drinking water utility in Canada is presented along with 7 years of data. Guidance for determining sampling frequencies and locations is provided. It is shown that Cryptosporidium monitoring may be insufficient to inform treatment needs when Giardia cysts are more abundant in source water. This work underscores the importance of revisiting and enhancing monitoring practices for effective treatment and public health protection.

Granular media filtration remains a critical treatment process and regulatory requirement for managing pathogenic protozoa in drinking water. It is a dynamic process in which performance inherently varies. While research has focused on characterizing or maximizing (oo)cyst removal in individual filters, the risk implications of combinations of filters moving through different phases of the filter cycle (leading to temporal variation in plant‐scale performance) have not been described. Increasing threats from climate‐change‐exacerbated landscape disturbances leading to more variable source water quality emphasize the need for such evaluations. Here, a modeling framework was developed to investigate the impacts of individual filter performance variation on plant‐scale performance. It is shown that improving maximal removal during stable operation does not necessarily improve average performance. The effect of other design and operational strategies like increasing the number of filters or implementing proactive operations (e.g., avoiding breakthrough) are analyzed, thereby providing guidance for increasing treatment resilience.