Monitoring and modelling to support wastewater system management in developing mega-cities

Modeling spatiotemporal domestic wastewater variability: Implications for measuring treatment efficiency

Continuously measuring the efficiency of wastewater treatment plants is crucial to progress in sanitation management. Regulations for decentralized wastewater treatment plants (WWTP) can include rudimentary specifications for sporadic sampling, unencouraging continuous monitoring, and missing crucial domestic wastewater (DW) variability, especially in low- and middle-income countries. However, few studies have focused on modeling and understanding spatiotemporal DW variability. We developed and calibrated an agent-based model (ABM) to understand spatial and temporal DW variability, its role in estimated WWTP efficiency, and provide recommendations to improve sampling regulations. We simulated DW variability at various spatial and temporal resolutions in Santa Ana Atzcapotzaltongo, Mexico, focusing on chemical oxygen demand (COD) and total suspended solids (TSS). The model results show that DW variability increases at higher spatiotemporal resolutions. Without a proper understanding of DW variability, treatment efficiency can be overestimated or underestimated by as much as 25% from sporadic sampling. Sensor measurements at 6-min intervals over 3 hours are recommended to overcome uncertainty resulting from temporal variability during heavy drinking water demand in the morning. Reporting of sewage catchment areas, population sizes, and sampling times and intervals is recommended to compare WWTP efficiencies to overcome uncertainty resulting from spatiotemporal variability. The proposed model is a useful tool for understanding DW variability. It can be used to estimate the impact of spatiotemporal variability when measuring WWTP efficiencies, support improvements to sampling regulations for decentralized sanitation, and alternatively for designing and operating WWTPs.

Evaluation of sampling frequency impact on the accuracy of water quality status as determined considering different water quality monitoring objectives

Water quality sampling is a key element in tracking water quality monitoring objectives. However, frequencies adapted by different agencies might not be sufficient to provide an accurate indication of water quality status. In this study, data from low- and high-resolution water quality datasets were analyzed to determine the extent to which monitoring objectives could be achieved with different sampling frequencies, with a view to providing recommendations and best practices for water quality monitoring frequency in places with limited resources with which to implement a high-frequency monitoring plan. Water quality data from two watersheds (Maumee River and Raisin River) located in the Western Lake Erie Basin (WLEB) were used since these watersheds have consistent records over substantial periods of time, and the water quality data available have a high resolution (at least daily). The water quality constituents analyzed included suspended solids (SS), total phosphorus (TP), soluble reactive phosphorus (SRP), and nitrate + nitrite (NO₂₊₃). Sources of pollutants for watersheds located in the WLEB include contributions from point sources like discharges from sewage treatment plants and non-point sources such as agricultural and urban storm runoff. Weekly, bi-weekly, monthly, and seasonal datasets were created from the original datasets, following different sampling rules based on the day of the week, week of the month, and month of the year. The resulting datasets were then compared to the original dataset to determine how the sampling frequency would affect the results obtained in a water quality assessment when different monitoring objectives are considered. Results indicated that constituents easily transported by water (such as sediments and nutrients) require more than 50 samples/year to provide a small error (< 10%) with a confidence interval of 95%. Monthly and seasonal sampling were found appropriate to report a stream's prevailing water quality status and statistical properties. However, these resolutions might not be sufficient to capture long-term trends, in which case bi-weekly samples would be preferable. Limitations of low-resolution sampling frequency could be overcome by including rainfall events and random sampling during specific time windows as part of the monitoring plan.

An Integrated Water Quality Model to Support Multiscale Decisions in a Highly Altered Catchment

Decision-making in highly altered catchments occurs at different temporal and spatial scales, requiring integration of various datasets and models. This paper introduces two of the components of an environmental multiscale decision support system (EMDSS) for highly altered catchments, designed to make decisions at different time scales. First, an integrated dynamic flow and water quality model is proposed to analyze the river system, including wastewater discharges and water intakes. This integrated model is capable of representing unsteady flow conditions, allowing analysis at different time scales. Second, three postprocessing tools are presented to support short- (hours to days), medium- (days to months), and long- (years to decades) term operational, management, and planning decisions. The water quality component of the model can represent conventional and toxic determinands to simultaneously analyze domestic and industrial pollution throughout a river system. The first postprocessing tool of the EMDSS is useful in defining concentration limits for wastewater discharges for different water users downstream. The second tool allows the assessment of river water quantity and quality to determine water availability for intake extensions and medium-term wastewater flow augmentation. The third makes it possible to simulate and perform effective operational reservoir releases to improve water quality in the river during short-term pollution incidents. The proposed integrated model and postprocessing tools are applied in the upper Bogotá River stretch in Colombia, one of the most altered catchments and polluted rivers in the world. The results obtained illustrate the utility of the proposed EMDSS for river management and decision making regarding water quality at different time scales.

Water quality modeling in sewer networks: Review and future research directions

Urban sewer networks (SNs) are increasingly facing water quality issues as a result of many challenges, such as population growth, urbanization and climate change. A promising way to addressing these issues is by developing and using water quality models. Many of these models have been developed in recent years to facilitate the management of SNs. Given the proliferation of different water quality models and the promise they have shown, it is timely to assess the state-of-the-art in this field, to identify potential challenges and suggest future research directions. In this review, model types, modeled quality parameters, modeling purpose, data availability, type of case studies and model performance evaluation are critically analyzed and discussed based on a review of 110 papers published between 2010 and 2019. The review identified that applications of empirical and kinetic models dominate those of data-driven models for addressing water quality issues. The majority of models are developed for prediction and process understanding using experimental or field sampled data. While many models have been applied to real problems, the corresponding prediction accuracies are overall moderate or, in some cases, low, especially when dealing with larger SNs. The review also identified the most common issues associated with water quality modeling of SNs and based on these proposed several future research directions. These include the identification of appropriate data resolutions for the development of different SN models, the need and opportunity to develop hybrid SN models and the improvement of SN model transferability.

A multi-disciplinary approach to understanding the impacts of mines on traditional uses of water in Northern Mongolia

Mongolia is an example of a nation where the rapidity of mining development is outpacing capacity to manage the potential land and water resources impacts. Further, Mongolia has a particular social and economic reliance on traditional uses of land and water, principally livestock herding. While some mining operations are setting high standards in protecting the natural resources surrounding the mine site, others have less incentive and capacity to do so and therefore are having adverse effects on surrounding communities. The paper describes a case study of the Sharyn Gol Soum in northern Mongolia where a range of mining types, from artisanal, small-scale mining to a large coal mine, operate alongside traditional herding lifestyles. A multi-disciplinary approach is taken to observe and attribute causes to the water resources impacts in the area. Surveys of the herding household community, land use mapping, and monitoring the spatial variations in water quality indicate deterioration of water resources. Collectively, the different sources of evidence suggest that the deterioration is mainly due to small-scale gold mining. The evidence included the perception of 78% of the interviewed herders that water quality had changed due to mining; a change in the footprint of small-scale gold mining from 2.8 to 15.2km(2) during the period 1999 to 2015; and pH and sulphate values in 2015 consistently outside the ranges observed at a baseline site in the same region. It is concluded that the lack of baseline data and effective governance mechanisms are fundamental challenges that need to be addressed if Mongolia's transition to a mining economy is to be managed alongside sustainability of herder lifestyles.

Can direct conversion of used nitrogen to new feed and protein help feed the world?

The increase in the world population, vulnerability of conventional crop production to climate change, and population shifts to megacities justify a re-examination of current methods of converting reactive nitrogen to dinitrogen gas in sewage and waste treatment plants. Indeed, by up-grading treatment plants to factories in which the incoming materials are first deconstructed to units such as ammonia, carbon dioxide and clean minerals, one can implement a highly intensive and efficient microbial resynthesis process in which the used nitrogen is harvested as microbial protein (at efficiencies close to 100%). This can be used for animal feed and food purposes. The technology for recovery of reactive nitrogen as microbial protein is available but a change of mindset needs to be achieved to make such recovery acceptable.