Assessment of odour emissions by the use of a dispersion model in the context of the proposed new law in Chile

Odour impact simulation of a large urban wastewater treatment plant through the numerical solution of a Eulerian model

This study aims to develop a solver to calculate the dispersion of emitted odour from the main sources located in a large urban wastewater treatment plant (WWTP). Its seasonal odour impact on surrounding areas, including nearby populations, was also evaluated. Different seasons of the year were studied using the prevailing meteorological conditions in each case, within the framework of a Eulerian model. Dynamic olfactometry was used to measure the odour concentration (OC) of the main emission sources of the WWTP, with such data being input parameters of the model. The calculations were carried out by a robust and precise fully-implicit-temporal-discretisation scheme and an exponential spatial scheme (in the control volume formulation), which was solved using the Modified Strongly Implicit (MSI) method. A code in the programming language Fortran90 was developed to calculate the odour immission concentration (OIC). Odour emissions from the WWTP were found to derive mainly from the wastewater line, which contributed with 98.86% of the total emission of the facility, with odour emission rates (OERs) as high as 62,100 ouE/s, 55,800 ouE/s, 88,400 ouE/s and 11,300 ouE/s in the pre-treatment header, sand and fat removal, primary settling and biological treatment, respectively. Such values corresponded to summer, which is the season that registered the most intense emissions. The first two odour sources and the units for gravity thickening, flotation thickening and sludge dehydration consisted of odour treatment systems based on adsorption by granular activated carbon (GAC). Gravity thickening achieved the highest OER value (1500 ouE/s) in the sludge line, but this only contributed with 1.14% to the total emission of the WWTP. Similar OER values were observed in other seasons of the year, although somewhat lower. The highest odour impact (538 ou/m3) was predicted in the south direction in autumn, which corresponded to the lowest wind speed in the main direction (1.23 m/s).

Uncovering the features of industrial odors-derived environmental complaints and proactive countermeasures by using machine-learning

Industrial odor-derived environmental complaints pose an emerging and far-reaching challenge in cities worldwide with intensive industries. Developing effective odor complaint management strategies is essential for mitigating the public impact of industrial odors. Based on a typical case of persistent tire manufacturing odors affecting local communities, we proposed an environmental complaint risks (ECR) prediction model using machine-learning (ML) approaches, which combined complaints with temporal-resolution manufacturing-meteorology-environment data. Through intensive match-making between ML algorithms and multi-source parameters, Random Forest models can achieve a reliable ECR-prediction model performance with an average ROC-AUC of 0.79 at a monthly timescale, indicating the effectiveness of ML-based ECR prediction models. The interpretable ML model quantitively depicted the underlying mechanisms of ECR prediction, driven by process emission behaviors, local wind direction, and historical high-risk period. Furthermore, to mitigate predictable ECR within a future period, we designed a model framework that integrated ECR prediction models with an adaptive optimization genetic algorithm. This enabled the proactive management by precisely and dynamically allocating limited resources of emission regulatory to high-ECR periods in advance. The strategy was proven effective, achieving a significant 24.7% average reduction in the overall ECR forecast during a period with intensive complaints. Overall, this study proposed a data-driven model framework that efficiently helps the multi-stakeholders shift from passive response to proactive ECR management, thereby enhancing the environmental and social sustainability.

Smart instrumental Odour Monitoring Station for the efficient odour emission management and control in wastewater treatment plants

Odour emission assessment in wastewater treatment plants (WWTP) is a key aspect that needs to be improved in the plant management to avoid complaints and guarantee a sustainable environment. The research presents a smart instrumental odour monitoring station (SiOMS) composed of an advanced instrumental odour monitoring system (IOMS) integrated with other measurement units, for the continuous characterization and measurement of the odour emissions, with the aim of managing the potential odour annoyance causes in real time, in order to avoid negative effects. The application and on-site validation procedure of the trained IOMS is discussed. Experimental studies have been conducted at a large-scale WWTP. Fingerprint analysis has been applied to analyze and identify the principal gaseous compounds responsible for the odour annoyance. The artificial neural network has been adopted to elaborate and dynamically update the odour monitoring classification and quantification models (OMMs) of the IOMS. The results highlight the usefulness of a real-time measurement and control system to provide continuous and different information to the plant operators, thus allowing the identification of the odour sources and the most appropriate mitigation actions to be implemented. The paper provides important information for WWTP operators, as well as for the regulating bodies, authorities, manufacturers and end-users of odour monitoring systems involved in environmental odour impact management.

Odors Emitted from Biological Waste and Wastewater Treatment Plants: A Mini-Review

In recent decades, a new generation of waste treatment plants based on biological treatments (mainly anaerobic digestion and/or composting) has arisen all over the world. These plants have been progressively substituted for incineration facilities and landfills. Although these plants have evident benefits in terms of their environmental impact and higher recovery of material and energy, the release into atmosphere of malodorous compounds and its mitigation is one of the main challenges that these plants face. In this review, the methodology to determine odors, the main causes of having undesirable gaseous emissions, and the characterization of odors are reviewed. Finally, another important topic of odor abatement technologies is treated, especially those related to biological low-impact processes. In conclusion, odor control is the main challenge for a sustainable implementation of modern waste treatment plants.