Optimization of Classification Prediction Performances of an Instrumental Odour Monitoring System by Using Temperature Correction Approach

Advanced treatment of toluene emissions with a cutting-edge algal bacterial photo-bioreactor: Performance assessment in a circular economy perspective

A novel approach for the treatment of VOCs (by using toluene used as a model compound) and the simultaneous conversion of carbon dioxide into valuable biomass has been investigated by using a combination of an activated sludge moving bed bioreactor (MBBR) and an algal photo-bioreactor (PBR). The first unit (MBBR, R1) promoted toluene removal up to 99.9 % for inlet load (IL) of 119.91 g m^-3 d^-1. The CO₂ resulting from the degradation of toluene was then fixed in PBR (R2), with a fixation rate up to 95.8 %. The CO₂ uptake was promoted by algae, with average production of algal biomass in Stage VI of 1.3 g L^-1 d^-1. In the contest of the circular economy, alternative sources of nutrients have been assessed, using synthetic urban wastewater (UWW) and dairy wastewater (DWW) for liquid renewal. The produced biomass with DWW showed a high lipid content, with a maximum productivity of 450.25 mg of lipids L^-1 d^-1. The solution proposed may be thus regarded as a sustainable and profitable strategy for VOCs treatment in a circular economy perspective.

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.

Classification of Three Volatiles Using a Single-Type eNose with Detailed Class-Map Visualization

The use of electronic noses (eNoses) as analysis tools are growing in popularity; however, the lack of a comprehensive, visual representation of how the different classes are organized and distributed largely complicates the interpretation of the classification results, thus reducing their practicality. The new contributions of this paper are the assessment of the multivariate classification performance of a custom, low-cost eNose composed of 16 single-type (identical) MOX gas sensors for the classification of three volatiles, along with a proposal to improve the visual interpretation of the classification results by means of generating a detailed 2D class-map representation based on the inverse of the orthogonal linear transformation obtained from a PCA and LDA analysis. The results showed that this single-type eNose implementation was able to perform multivariate classification, while the class-map visualization summarized the learned features and how these features may affect the performance of the classification, simplifying the interpretation and understanding of the eNose results.

Degradation of gaseous volatile organic compounds (VOCs) by a novel UV-ozone technology

In this study, a UV-assisted ozonation (UV/O₃) process for the degradation of VOCs emissions with a final scrubbing phase was implemented to evaluate the removal efficiency of toluene and to prevent the release of polluting intermediates of the single-step process. Inlet toluene concentration and applied voltage were varied in order to investigate several operating conditions. The results highlighted that at higher inlet concentration the abatement of toluene was lower, while increase in ozone concentration led to an increase of the degradation efficiencies. The additional water scrubbing step enhanced the abatement of UV/O₃ up to 98.5%, due to the solubilisation of ozone and by-products in the process water and, thus, the further oxidation of the contaminants within this phase. A maximum Elimination Capacity (EC_max) of 22.6 g m⁻³ h⁻¹ was achieved with the UV/O₃ + Scrubbing. The combined system boosted higher performance and stability compared to the stand-alone (UV/O₃) process along with a more economical and environmental sustainability.

RHINOS: A lightweight portable electronic nose for real-time odor quantification in wastewater treatment plants

Quantification of odor emissions in wastewater treatment plants (WWTPs) is key to minimize odor impact to surrounding communities. Odor measurements in WWTPs are usually performed via either expensive and discontinuous olfactometry hydrogen sulfide detectors or via fixed electronic noses. We propose a portable lightweight electronic nose specially designed for real-time odor monitoring in WWTPs using small drones. The so-called RHINOS e-nose allows odor measurements with high spatial resolution, and its accuracy is only slightly worse than that of dynamic olfactometry. The device has been calibrated using odor samples collected in a WWTP in Spain over a period of six months and validated in the same WWTP three weeks after calibration. The promising results obtained support the suitability of the proposed instrument to identify the odor sources having the highest emissions, which may give a useful indication to the plant managers as regards odor control and abatement.

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A portable e-nose for real time odor quantification according to EN13725 is described

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The e-nose is installed on a small drone for dense spatial measurements

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The e-nose is demonstrated and validated in a wastewater treatment plant

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Errors in odor quantification are only slightly worse than dynamic olfactometry

Full-Scale Odor Abatement Technologies in Wastewater Treatment Plants (WWTPs): A Review

The release of air pollutants from the operation of wastewater treatment plants (WWTPs) is often a cause of odor annoyance for the people living in the surrounding area. Odors have been indeed recently classified as atmospheric pollutants and are the main cause of complaints to local authorities. In this context, the implementation of effective treatment solutions is of key importance for urban water cycle management. This work presents a critical review of the state of the art of odor treatment technologies (OTTs) applied in full-scale WWTPs to address this issue. An overview of these technologies is given by discussing their strengths and weaknesses. A sensitivity analysis is presented, by considering land requirements, operational parameters and efficiencies, based on data of full-scale applications. The investment and operating costs have been reviewed with reference to the different OTTs. Biofilters and biotrickling filters represent the two most applied technologies for odor abatement at full-scale plants, due to lower costs and high removal efficiencies. An analysis of the odors emitted by the different wastewater treatment units is reported, with the aim of identifying the principal odor sources. Innovative and sustainable technologies are also presented and discussed, evaluating their potential for full-scale applicability.

Application of Machine Learning for Fenceline Monitoring of Odor Classes and Concentrations at a Wastewater Treatment Plant

The development of low-cost sensors, the introduction of technical performance specifications, and increasingly effective machine learning algorithms for managing big data have led to a growing interest in the use of instrumental odor monitoring systems (IOMS) for odor measurements from industrial plants. The classification and quantification of odor concentration are the main goals of IOMS installed inside industrial plants in order to identify the most important odor sources and to assess whether the regulatory thresholds have been exceeded. This paper illustrates the use of two machine learning algorithms applied to the concurrent classification and quantification of odors. Random Forest was employed, which is a machine learning algorithm that thus far has not been used in the field of odor quantification and classification for complex industrial situations. Furthermore, the results were compared with commonly used algorithms in this field, such as artificial neural network (ANN), which was here employed in the form of a deep neural network. Both techniques were applied to the data collected from an IOMS installed for fenceline monitoring at a wastewater treatment plant. Cohen’s kappa and Normalized RMSE are used as specifical performance indicators for classification and regression: the indicators were calculated for the test dataset, and the results were compared with data in the literature obtained in contexts of similar complexity. A Cohen’s kappa of 97% was reached for the classification task, while the best Normalized RMSE, namely 4%, for the interval 20–2435 ouE/m³ was obtained with Random Forest.