Prediction of odor complaints at a large composite reservoir in a highly urbanized area: A machine learning approach

Addressing the challenges of combined sewer overflows

Europe's ageing wastewater system often combines domestic sewage with surface runoff and industrial wastewaters. To reduce the associated risk of overloading wastewater treatment works during storms, and to prevent wastewater backing-up into properties, Combined Sewer Overflows (CSOs) are designed into wastewater networks to release excess discharge into rivers or coastal waters without treatment. In view of growing regulatory scrutiny and increasing public concern about their excessive discharge frequencies and potential impacts on environments and people, there is a need to better understand these impacts to allow prioritisation of cost-effective solutions.We review: i) the chemical, physical and biological composition of CSOs discharges; ii) spatio-temporal variations in the quantity, quality and load of overflows spilling into receiving waters; iii) the potential impacts on people, ecosystems and economies. Despite investigations illustrating the discharge frequency of CSOs, data on spill composition and loading of pollutants are too few to reach representative conclusions, particularly for emerging contaminants. Studies appraising impacts are also scarce, especially in contexts where there are multiple stressors affecting receiving waters. Given the costs of addressing CSOs problems, but also the likely long-term gains (e.g. economic stimulation as well as improvements to biodiversity, ecosystem services, public health and wellbeing), we highlight here the need to bolster these evidence gaps. We also advocate no-regrets options to alleviate CSO problems taking into consideration economic costs, carbon neutrality, ecosystem benefit and community well-being. Besides pragmatic, risk-based investment by utilities and local authorities to modernise wastewater systems, these include i) more systemic thinking, linking policy makers, consumers, utilities and regulators, to shift from local CSO issues to integrated catchment solutions with the aim of reducing contributions to wastewater from surface drainage and water consumption; ii) broader societal responsibilities for CSOs, for example through improved regulation, behavioural changes in water consumption and disposal of waste into wastewater networks, and iii) greater cost-sharing of wastewater use.

Comparison of random forest and multiple linear regression to model the mass balance of biosolids from a complex biosolids management area

The use of biosolids as a soil amendment provides an important alternative to disposal and can improve soil health; however, distribution for water resource recovery facilities (WRRFs) in the United States can be challenging due to decreasing cropland, increased precipitation, variable plant operations, and financial constraints. Although statistical modeling is commonly used in the water sector, machine learning is still an emerging tool and can provide insights to optimize operations. Random forest (RF), a machine learning model, and multiple linear regression (MLR) were used in this study to model the mass balance of biosolids from a complex biosolids management area. The RF model outperformed (R²  = 0.89) the MLR model (R²  = 0.49) and showed that rainfall was a major factor impacting distribution. Storage for dried biosolids would help decouple drying operations from wet weather and increase distribution. This study demonstrated how machine learning can assist in decision-making processes for long-term planning at WRRFs. PRACTITIONER POINTS: Random forest predicted the 7-day average mass balance of biosolids from a complex biosolids management area. Decoupling biosolids drying operations from wet weather was identified as the highest operational priority. Machine learning outperformed multiple linear regression and can be an important tool for the water sector.

Advanced machine learning application for odor and corrosion control at a water resource recovery facility

The objective of this study was to develop a machine learning (ML) application to determine the optimal dosage of sodium hypochlorite (NaOCl) to curtail corrosion and odor by H₂ S in the headworks of a water resource recovery facility (WRRF) without overly consuming volatile fatty acids (VFAs) that are essential for the enhanced biological phosphorus removal. Given the highly diverse datasets available, three subproblems were formulated, and three cascaded ML modules were developed accordingly. The final ML models, chosen based on performance, were able to predict various targeted variables. More specifically, in Module 1, a recurrent neural network (RNN) was designed to predict wastewater characteristics. In Module 2, a random forest (RF) classifier and a support vector machine (SVM) classifier were built with the information from Module 1 along with other datasets to predict the concentrations of VFAs and H₂ S, respectively. Finally, in Module 3, with the information obtained from Module 2, another RF classifier was developed to predict NaOCl dosage to reduce H₂ S but keeping VFAs within the target range. These efforts are relevant and informative for WRRFs that are considering developing Intelligent Water Systems to predict the wastewater characteristics to make operational improvements. PRACTITIONER POINTS: A recurrent neural network (RNN) using long short-term memory (LSTM) successfully predicted influent wastewater parameters. A support vector machine classifier predicted hydrogen sulfide (H₂ S) with 97.6% accuracy. The concentration of VFAs, an important parameter in EBPR, was predicted using a random forest classifier with 93.4% accuracy. The optimal NaOCl dosage for H₂ S control can be predicted with a random forest classifier using H₂ S, VFAs, and flow.

Gaseous emissions from wastewater facilities

A review of the literature published in 2019 on topics relating to gaseous emissions from wastewater facilities is presented. This review is divided into the following sections: odorant emissions from Water Resource Recovery Facilities (WRRFs); greenhouse gas (GHG) emissions; gaseous emissions from wastewater collection systems; physiochemical odor/emissions control methods; biological odor/emissions control methods; odor/GHG characterization and monitoring; and odor impacts/risk assessments. © 2020 Water Environment Federation PRACTITIONER POINTS: Provide a quick reference list for readers who do not have time to go through the 2019 published articles. This prescreening of relevant literatures will save them time and effort. Utilities, engineers, and researchers can identify knowledge gaps, which help them to plan for future testing and R&D needs. Designers can make use of the lit review findings to support their design.