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Municipal solid waste (MSW) landfills release different gaseous compounds in ambient air, including greenhouse gases and VOCs, with relevant effects in terms of contribution to global warming and odour annoyance. The accurate characterization and monitoring of these emissions is thus of fundamental importance to avoid negative impacts and for identifying and evaluating the effectiveness of mitigation measures. Currently, different and no universally accepted and standardized methods have been validated for the measurement of these emissions from landfill (landfill gas, LFG). The most widely-used techniques for the identification of emission rates from areal sources are known as “hood methods”. However, these methods are not particularly accurate for sampling LFG. Moreover, conventional approaches for LFG sampling frequently neglect the evaluation of fugitive emissions from crucial points of biogas network (i.e.: wells, drains, pipeline), which can be also interested by leakages and can thus result in significant emission of LFG into the atmosphere. For the abovementioned reasons, the development of specific methods for the assessment of the LFG emissions is of great interest to overcome the related issues. In the present work, an integrated and advanced approach for the monitoring and mapping of MSW landfill gas emissions has been presented and validated. The proposed procedure integrates multiple approaches to provide a complex and polyvalent survey of the landfill, also including the evaluation of the landfill terrain morphology and the presence of possible non-homogeneous areas. The experimental activities were carried out at a complex MSW landfill located in Central Italy (Lazio Region) and were organized into four steps: (i) identification and characterization of the fugitive emissions from landfill cover, by Flux Chamber device in different points of a geo-localized grid on landfill body; (ii) characterization of landfill terrain morphology and of possible fugitive emissions from biogas wells and other points considered relevant in the network, by using remote sensing with surveys implemented with drone equipped with RGB, multispectral and thermal cameras together with topographic measurements with GNSS receivers; (iii) integration of the geo-referenced results, including the Digital Terrain Model (DTM), the Normalized Difference Vegetation Index (NDVI), the thermographic mapping and the point monitoring results on landfill cover; (iv) elaboration of maps of the emissions levels for the different investigated compounds and the identification of critical points/areas. The results were elaborated and discussed (i) for the evaluation of the emissions from the investigated landfill, (ii) for proving the accuracy of the proposed procedure with cross-validation approaches and (iii) for extending the proposed procedure to a wider application for different types of landfills.

Population growth is closely linked to the increase in the number of citizens who consume resources and thus to the production of municipal solid waste (MSW). Integrated waste management is a key element for sustainable urban development and environmental protection. Reduction, reuse, recycling, and energy recovery are fundamental actions to implement the principles of community management strategy and circular economy, reducing the quantity of waste to be disposed of in landfills and the consequent impacts on the environment. For this purpose, the separate collection phase represents the most important action. Odour emissions from MSW management are among the main negative pressures perceived by the population. Odour emissions originate mainly from the decomposition of the organic fraction of municipal solid waste (OFMSW), which occurs during the collection, transport, and treatment phases of the waste, and are the cause of many complaints. Therefore, the control of odour emissions in the treatment of municipal waste represents a key action in all the management phases and for all involved actors, which must be carried out to preserve air quality, health, and well-being of the population. The research presents and discusses the development and evaluation of advanced powdered bioactivators aimed at reducing the odorous emissions produced by the separate collection of OFMSW in surface street containers. The bioactivators used consist of a mixture of spores, microorganisms, enzymes, and nutrients. An extensive experimental plan and program have been carried out. Different bioactivators have been studied and analyzed. The concentration of odours emitted at different investigated times were carried out according to UNI EN 13725:2022. The bioactivators investigated showed different behaviors on the odours, including “masking” and abatement, depending on their composition and over time. The research aims to be a useful study for operators in the waste management sector to avoid odour impacts and as a basis for the development of technologies and methods for managing odorous emissions.

The continuous increase in the generation of municipal solid waste (MSW) worldwide represents a major challenge for global sustainability efforts. In accordance with the Sustainable Development Goals and the principles of the circular economy, it is essential to manage MSW involving environmental aspects that require careful control to prevent potential environmental impacts. Among the MSW, the organic fraction stands out as a highly fermentable mixed matrix that demands special attention due to the quantities produced and the different management phases it undergoes, including collection, transportation, and treatment. In particular, the collection phase emerges as a linchpin for the overall success of waste management assessment. Various methodologies for the collection of the organic fraction of municipal solid waste (OFMSW) are currently employed, with door-to-door collection being a prevalent approach in many European countries. This method includes the specific collection of the organic fraction, strategically placed in designated surface or underground containers. The time and modalities of organizing the collection of the organic fraction are not universally defined but rather determined by the collection service operator. In Italian regulations, the only indication is that the collected waste must be delivered to the treatment plant within 72 hours of collection, emphasizing the urgency and efficiency required in waste management operations. Notably, one of the primary environmental pressures linked to the management of the OFMSW is the production of odorous emissions, which can significantly impact the surrounding environment and community well-being. The research presents and discusses the variation in time of the odour emissions of the OFMSW, aiming to quantify the potential odour production occurring between the collection phase of the OFMSW and their delivery to the plant and therefore provides a useful tool to reduce potential odour impacts and direct towards suitable management strategies. Intensive experimental activities were conducted, considering different samples of OFMSW. Characterization of the principal chemical-physical parameters and odour concentration at different times of the investigated OFMSW were performed. Statistical studies, applying multivariate analysis techniques, were conducted to identify potential correlations between the investigated parameters. Preliminary results indicate a notable increasing trend in odour concentration over time, with an inverse correlation between odour concentration and humidity and a direct correlation with temperature. The research provides valuable insights into the factors influencing odour production in the management of OFMSW.

Wastewater treatment plants (WWTPs) play a crucial role in the water cycle. These facilities ensure the balance of the ecosystem and avoid significant negative effects on the environment and human health. However, they also exert negative pressures. Among these, odours and odorous compound emissions, principally hydrogen sulphide (H2S), require more attention. Prolonged exposure to odour emissions produces negative effects on exposed populations, such as nausea, headaches, and other related respiratory problems. The perception of odours is also considered a sign of a polluted environment and therefore the first cause of complaints. In this context, there is a need to characterize and control odour emissions. Currently, among the odour treatment processes, biological ones show the greatest potential for future development, as they are cheaper and more sustainable for the environment. However, decomposition by microorganisms with oxidative properties produces carbon dioxide, which is one of the main greenhouse gases and contributes to global warming. The research presents and discusses the application and validation of an advanced sustainable biological-based system aimed at treating odours and avoiding the release of GHG into the ambient air, thus ensuring clean air and mitigating climate change according to the Sustainable Development Goals (SDGs) of the Agenda 2030. In-depth and extensive experimental activities are carried out and reported to validate the proposed system, consisting of an integration of a Moving Bed Biofilm Reactor (MBBR) and an algal Photobioreactor (aPBR). System performance has been evaluated under different operating conditions, with reference to H2S removal efficiencies and CO2 abatement rates and subsequent conversion to usable biomass. The analyses also included the determination of the concentration of odours according to EN 13725:2022. The results obtained confirm the efficiency of the proposed system in reducing odours and biofixation of CO2, promoting a novel advanced and environmentally friendly solution in odour treatment.

Odour annoyance in urban contexts is of particular concerns and requires effective monitoring in order to avoid complaints from citizens.The research presents an advanced and integrated odour monitoring system, which combines analytical, senso-instrumental and citizen science tools, for the continuous and proactive characterization of the odours in ambient air to prevent odour annoyance. The on-site validation of the system was implemented integrating data collected by IOMSs and air quality station with 768 reports collected by a newly designed Citizen Science APP. A maximum density of 329 signals per 0.01 km2 was recorded at south-west of the odour sources investigated.

This study aims to present an overview of incorporating the Water Framework Directive into Italian law through Law 152/06. Subsequently, it will focus on a case study involving 34 rivers in the Salerno Province, utilizing data obtained from ARPA Campania. This work analyses the macro pollution indicators of the observed rivers using two aggregated indexes, namely PLM and PLMeco. These indexes were introduced by Italian legislation in 1999 and 2006, respectively. The results show the quality of the river and the variations observed over the years in the Province of Salerno, utilizing non-parametric tests.

The cultivation of microalgae for carbon capture and utilization (CCU) emerged as sustainable and effective platform to reduce GHGS and produce valuable biomass. In the study, systematic comparison of two identical algal photo-bioreactors (PBRw and PBRp), with white and purple led lights respectively, has been implemented. Carbon removals up to 98% has been obtained, with PBRp supporting enhanced cultivation conditions, higher CO2 removals and increased biomass production (up to 855 mg d−1 of dry algal biomass). The results demonstrate the potential of the proposed solutions as sustainable strategy to increase the applicability of algal photo-bioreactors for carbon capture and utilization.

Abstract An advanced concept of aerobic membrane bioreactors (MBRs) for highly efficient wastewater treatment has been disclosed by introduction of an electro and encapsulated self-forming dynamic biomembrane (e-ESFDM). The biological filtering membrane is intercalated between two woven polyester fabrics as supports that assist the formation and protect the biomembrane. The innovative architecture of the e-ESFDM in combination with electrocoagulation processes resulted in efficient and cost-effective wastewater treatment and control of the membrane fouling. The performance of the e-ESFDMBR was compared to a yet highly efficient ESFDMBR, where the electric field was not present. The ESFDM-based reactors both showed comparable results in the removal of organic matter, in terms of COD and DOC. On the other hand, e-ESFDMBR exceeded the performance of the ESFDMBR in the reduction of nitrogen- and phosphorous-containing pollutants, responsible for eutrophication processes in the environment, and recalcitrant molecules, such as humic-like substances. In addition, an extremely low fouling rate was observed for the e-ESFDM bioreactor. Insights on the biological processes involved in the developed MBR were provided by investigations on the microbiological diversity found in reactor mixed liquor, ESFDM layer and treated wastewater.

Wastewater-based epidemiology (WBE) is a powerful tool that has the potential to reveal the extent of an ongoing disease outbreak or to predict an emerging one. Recent studies have shown that SARS-CoV-2 concentration in wastewater may be correlated with the number of COVID-19 cases in the corresponding population. Most of the recent studies and applications of wastewater-based surveillance of SARS-CoV-2 applied the “gold standard” real-time quantitative reverse transcription-polymerase chain reaction (RT-qPCR) detection method. However, this method also has its limitations. The paper aimed to present recent improvements and applications of the PCR-based methods for SARS-CoV-2 monitoring in wastewater. Furthermore, it aimed to review alternative methods utilized and/or proposed for the detection of the virus in wastewater matrices. From the review, it was found that several studies have investigated the use of reverse-transcription digital polymerase reaction (RT-dPCR), which was generally shown to have a lower limit of detection (LOD) over the RT-qPCR. Aside from this, non-PCR-based and non-RNA based methods have also been explored for the detection of SARS-CoV-2 in wastewater, with detailed attention given to the detection of SARS-CoV-2 proteins. The potential methods for protein detection include mass spectrometry, the use of immunosensors, and nanotechnological applications. In addition, the review of recent studies also revealed two types of emerging methods related to the detection of SARS-CoV-2 in wastewater: i) capsid-integrity assays to infer about the infectivity of SARS-CoV-2 present in wastewater, and ii) alternative methods for detection of SARS-CoV-2 variants of concern (VOCs) in wastewater. The recent studies on proposed methods of SARS-CoV-2 detection in wastewater have considered improving this approach in one or more of the following aspects: rapidity, simplicity, cost, sensitivity, and specificity. However, further studies are needed in order to realize the full application of these methods for WBE in the field.

The paper presents and discusses the development and application of different odour monitoring models (OMMs) for the classification and quantification of odour emissions with Instrumental Odour Monitoring Systems (IOMSs). Feed-forward neural network and linear discriminant analysis were considered for the classification of different type of odours, while feed-forward neural network and partial least square were investigated for the odour quantification. The prediction accuracy of the models was examined by analyzing different data extracted from the sensors’ response curve (at rise, intermediate and peak period). The application has been carried out in a complex petroleum refinery plant. A total of 44 potential odour sources were monitored and grouped into 7 different classes. Results highlight that the feed-forward neural network prevails in terms of high prediction capability having an architecture with three layers (input, hidden and output) of respectively 14-8-7 for odour classification, and of 14-8-1 for odour quantification, at =0.982 R2. Meanwhile, the most useful data were found using the peak period. The research contributes to the understanding of IOMS applications, providing data on refinery plant odour emissions and applicable mathematical models to ensure great data reliability. The study highlights the influence of the pattern recognition algorithms in the Odour Montoring Model (OMM) elaboration and suggests the utility of promoting the implementation of flexible and adaptable IOMS.

The research presents the microalgal lipids production from odour treatment emissions as innovative platform for resource recovery. The study investigated the sequential treatment of xylene and the production of microalgal lipids that are of interest for biodiesel production. The effect of inlet gas flowrate was assessed in a moving bed bioreactor treating xylene, a mean maximum ECxyl of 0.32±0.25 g xyl m-3 h-1, corresponding to a RExyl of 93% and PCO2 of 4±2.6 g CO2 m-3 h-1, when working with an inlet gas flowrate of 1.0 L min-1. On the other hand, the PBR recorded a maximum mean ECCO2 was 4.86±1.42 g CO2 m-3 h-1 corresponding to a RE CO2 of 33.8%. Simultaneously lipids were produced at a maximum mean calculated rate of 5.5 g lipids m-3 d-1, as biomass showed a lipid concentration of 12.7%. The results show the feasibility of producing in a circular economy perspective lipid from odour treatment emissions.

Odours emissions sources are always closer to residential areas since their increasing expansion towards industrial contexts. The need of an effective solution to characterize the real odour annoyance perceived by the exposed population is thus of fundamental importance to avoid complaints and increase the quality of life in the areas surrounding industrial centres. The characterization of the odour emissions can be implemented with analytical, sensorial and senso-instrumental techniques. However, in order to have a complete and effective characterization, the implementation of integrated approaches is rising as preferred method since the need to take into account simultaneously emissive, sociological and morphological aspects. The present study aims to report the development and application of a novel integrated method for the proactive characterization of odour annoyance in urban areas. The system has been implemented with a tailor-made design with regards to a sensitive municipality in which are located significant odour emissions plants. The proposed approach integrates analytical, sensorial and senso-instrumental techniques. The main feature is related to the validation of odour annoyance data collected from citizens with the data collected from Instrumental Odour Monitoring Systems (IOMSs), Air Quality and meteorological stations. Data from citizens are collected by a novel mobile APP. All the collected data are continuously elaborated for the creation and validation of interactive maps able to proactively identify possible undesirable conditions before the occurrence of odour annoyance events. The results of the real application of the proposed method demonstrated the importance of applying the citizen science approach to increasing the social engagement and of validating sensorial data with meteorological data by applying near-real-time dispersion models.

This work provides a brief overview of the application of Sewage Sludge-Derived Biochar (SSBC) for the removal of micropollutants from aqueous solutions and wastewater. A particular emphasis is placed on the adsorption efficiency of SSBC regarding the Priority Substances defined under the scope of the EU Water Framework Directive.

The adverse effects of the accumulation of plastic on our planet are no longer sustainable; plastic is a major threat to all forms of life in all environments in addition to contributing to global warming. The academic world has been focusing on registering the damages caused by plastic pollution and finding solutions to refrain from and substitute plastic and its usages, which our consumer society is so heavily dependent on. A pathway towards limiting the use of plastic comes from the European Union 2019/904 Directive for limiting the production of single-use and oxo-degradable plastics. Currently, bioplastics are one of the major alternatives in substituting fossil-based plastics, but question remain about its use. as too what extent could bioplastics be a long-term solution to plastic pollution? Is it a misconception to consider bioplastics completely harmless to the environment? This short review article aims to draw attention to the counter effects connected to the limitations and mismanagement of bioplastics through their life cycle by collecting data not published until now. A review of several cradle-to-Grave Life Cycle Assessments has been made to analyse bioplastics from production to end-of-life options. The result produced from this review article shows that bioplastics do not represent a long-term solution to plastic pollution and, on the contrary, may seem to contribute to overall environmental endangerment. The novelty of this work lies in pointing out the misconception of bioplastics’ healthy effects on the environment by thoroughly analysing all environmental impacts of current production and disposal of bioplastics and by providing a more sustainable production of bioplastic through wastewater treatment plants.

The characterization and quantification of the odour emissions in wastewater treatment plant (WWTP) represents a fundamental step in order to identify potential odour problems and establish the best and most appropriate odour management tools. Nowadays odour emissions in WWTP are characterized by the use of sampling techniques which take samples directly from the gaseous phase: i.e. when the emission and consequently the potential problem are already present. The definition of proactive tools for the assessment of potential odour emissions from the emitting sources can prevent the occurrence of potential significant odours events and as consequence complaints. The research presents and describes a novel system, called H2Odour, useful to measure the potential Odour Emission Capacity (OEC) directly from liquids. The system has been designed and developed to standardize the stripping phase of gaseous samples, ensuring the maximization of the odour compounds transfer from the liquid to the gaseous phase. The application on a real full scale WWTP is highlighted. H2Odour represents a novel, proactive device to prevent potential critical situations, allowing a preliminary characterization of the maximum potential odour emissions directly from the liquid phase and through this control activity, leading to a greater environmental and social acceptability of the WWTP.

Oil refinery and petrochemical plants are of crucial importance to enhance world economy. Besides the related considerable economic impact of this industries, the population and governments pose towards a great attention which boosted the necessity of developing effective tools for the control of the associated environmental pressures, without hindering economy growth. Among the environmental aspects of key relevance in the refinery sector, air quality and atmospheric pollution in terms of odour emissions, especially in the last years, have become a priority concern in the management of the plants. The monitoring and control of the odour emissions are thus needed in order to prevent potential impact and avoid complaints from the exposed population. Nowadays, senso-instrumental methods by using Instrumental Odour Emissions Systems (IOMSs) represents the most attractive tool for the monitoring of environmental odours, allowing the possibility to obtain real-time and continuous information’s, to support the decision-making process and for the implementation of proactive approach. No universally accepted regulation or standardized procedure exist at present and limited data are available in the scientific and technical literature with reference to these systems for the odour monitoring control in the oil refinery plants. In the CEN/TC264 ‘Air Quality’ standardization committee, a specific working group (WG41) is currently working to produce a validation procedure for the IOMSs. The research presents and discusses an advanced instrumental odour monitoring system device useful for the continuous characterization of the odour emissions in complex oil refinery and petrochemical plants. The influence of the use of different feature extraction methods in the odour monitoring model elaboration, in terms of classification, is analysed. Results demonstrate the existence of considerable differences in terms of correct classification percentage, in the use of different feature extraction methods. The importance and usefulness of having a fully-developed flexible system that allows to select and compare automatically different settings options, as the different feature extraction methods, in order to guarantee the best training model, is highlighted. Results demonstrated the high reliability of the system in recognizing artificial gas samples, with characteristics similar to the emissions from refinery plants.

Odours emissions from various facilities such as wastewater treatment plants (WWTPs), are becoming one of the main issues to face down for the plant operators to avoid complaints and potential impact, especially for the plants located nearby to the residential areas. The need to implement odour control strategies, for instance by using treatment technologies, is therefore a key point in the management of the plant. Moreover, in a world increasingly concerned about sustainability and environmental preservation, the identification of environmentally friendly odour treatment technologies is to be recommended and prescribed. The research presents the development of an advanced sustainable biotechnology-based control system able to treat odour and greenhouse gas emissions (GHG), with the aim to guarantee a sustainable process. The performance of the advanced biotechnology system is highlighted in terms of CO2 capture and reduction, by analysing different operating conditions (e.g. CO2 inlet load, L/G liquid:gas ratio and light intensity). The result shows that the proposed technology can capture up to 80% of CO2 concentration, produce microalgae biomass (742 mg l-1) and potentially biodegrade aromatic substances such as VOCs. The proposed advanced biotechnology system proves to be effective sustainable treatment process for the fixation of CO2, but further studies are necessary for evaluate the treatment efficiency of VOCs. The information presented in this research will be of interest to anyone involved with carbon capture and utilization (CCU) technology.

Using an environmentally friendly approach for eliminating methylene blue from an aqueous solution, the authors developed a unique electrospun nanofiber membrane made of a combination of polyethersulfone and hydroxypropyl cellulose (PES/HPC). SEM results confirmed the formation of a uniformly sized nanofiber membrane with an ultrathin diameter of 168.5 nm (for PES/HPC) and 261.5 nm (for pristine PES), which can be correlated by observing the absorption peaks in FTIR spectra and their amorphous/crystalline phases in the XRD pattern. Additionally, TGA analysis indicated that the addition of HPC plays a role in modulating their thermal stability. Moreover, the blended nanofiber membrane exhibited better mechanical strength and good hydrophilicity (measured by the contact angle). The highest adsorption capacity was achieved at a neutral pH under room temperature (259.74 mg/g), and the pseudo-second-order model was found to be accurate. In accordance with the Langmuir fitted model and MB adsorption data, it was revealed that the adsorption process occurred in a monolayer form on the membrane surface. The adsorption capacity of the MB was affected by the presence of various concentrations of NaCl (0.1–0.5 M). The satisfactory reusability of the PES/HPC nanofiber membrane was revealed for up to five cycles. According to the mechanism given for the adsorption process, the electrostatic attraction was shown to be the most dominant in increasing the adsorption capacity. Based on these findings, it can be concluded that this unique membrane may be used for wastewater treatment operations with high efficiency and performance.

Unwanted odour emissions are considered air pollutants that may cause detrimental impacts to the environment as well as an indicator of unhealthy air to the affected individuals resulting in annoyance and health related issues. These pollutants are challenging to handle due to their invisibility to the naked eye and can only be felt by the human olfactory stimuli. A strategy to address this issue is by introducing an intelligent processing system to odour monitoring instrument such as artificial neural network to achieve a robust result. In this paper, a review on the application of artificial neural network for the management of environmental odours is presented. The principal factors in developing an optimum artificial neural network were identified as elements, structure and learning algorithms. The management of environmental odour has been distinguished into four aspects such as measurement, characterization, control and treatment and continuous monitoring. For each aspect, the performance of the neural network is critically evaluated emphasizing the strengths and weaknesses. This work aims to address the scarcity of information by addressing the gaps from existing studies in terms of the selection of the most suitable configuration, the benefits and consequences. Adopting this technique could provide a new avenue in the management of environmental odours through the use of a powerful mathematical computing tool for a more efficient and reliable outcome. Keywords: Electronic nose, Environmental pollution, Human health, Odour emission, Public concern

Water is needed for food processing facilities to carry out a number of tasks, including moving goods, washing, processing, and cleaning operations. This causes them to produce wastewater effluent, and they are typically undesirable since it contains a high volume of suspended solids, bacteria, dyestuffs, salts, oils, fats, chemical oxygen demand and biological oxygen demand. Therefore, treatment of food industry wastewater effluent is critical in improving process conditions, socio-economic benefits and our environmental. This short review summarizes the role of available membrane technologies that have been employed for food wastewater treatment and analyse their performance. Particularly, electrospun nanofiber membrane technology is revealed as an emerging membrane science and technology area producing materials of increasing performance and effectiveness in treating wastewater. This review reveals the challenges and perspectives that will assist in treating the food industry wastewater by developing novel membrane technologies.

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.

Odour emissions are a global issue that needs to be controlled to prevent negative impacts. Instrumental odour monitoring systems (IOMS) are an intelligent technology that can be applied to continuously assess annoyance and thus avoid complaints. However, gaps to be improved in terms of accuracy in deciphering information, especially in the implementation of the mathematical model, are still being researched, especially in environmental odour monitoring applications. This research presents and discusses the implementation of traditional and innovative parametric and non-parametric prediction techniques for the elaboration of an effective odour quantification monitoring model (OQMM), with the aim of optimizing the accuracy of the measurements. Artificial neural network (ANN), multivariate adaptive regression splines (MARSpline), partial least square (PLS), multiple linear regression (MLR) and response surface regression (RSR) are implemented and compared for prediction of odour concentrations using an advanced IOMS. Experimental analyses are carried out by using real environmental odour samples collected from a municipal solid waste treatment plant. Results highlight the strengths and weaknesses of the analysed models and their accuracy in terms of environmental odour concentration prediction. The ANN application allows us to obtain the most accurate results among the investigated techniques. This paper provides useful information to select the appropriate computational tool to process the signals from sensors, in order to improve the reliability and stability of the measurements and create a robust prediction model.

Odour emissions generated by industrial and environmental protection plants are often a cause of nuisances and consequent conflicts in exposed populations. Their control is a key action to avoid complaints. Among the odour measurement techniques, the sensory-instrumental method with the application of Instrumental Odour Monitoring Systems (IOMSs) currently represents an effective solution to allow a continuous classification and quantification of odours in real time, combining the advantages of conventional analytical and sensorial techniques. However, some aspects still need to be improved. The study presents and discusses the investigation and optimization of the operational phases of an advanced IOMS, applied for monitoring of environmental odours, with the aim of increasing their performances and reliability of the measures. Accuracy rates of over 98% were reached in terms of classification performances. The implementation of automatic correction systems for the resistance values of the measurement sensors, by considering the influence of the temperature, has been proven to be a solution to further improve the reliability of IOMS. The proposed approach was based on the application of corrective coefficients experimentally determined by analyzing the correlation between resistance values and operating conditions. The paper provides useful information for the implementation of real-time management activities by using a tailor-made software, able to increase and enlarge the IOMS fields of application.

Prolonged exposure to odour emissions causes annoyance which leads to nuisance and consequently to complaints. Different methodologies exist in the literature to evaluate odour impacts, but not all are suitable to assess environmental odour nuisance. Information about their applicability criteria and comparison, is scarce and referred to short time analysis. The research presents and discusses the application of different methods to characterize and assess odour nuisance around an industrial plant localized in a sensitive area. Experimental activities are carried out through a long-time analysis programme. Field inspections and predictive methods are investigated and compared. A modification of the traditional dispersion modelling approach is proposed in order to adapt its application for the prediction of the odour nuisance. The offensiveness and location factors are identified as key parameters in the quantification of the perceived nuisance. The integrated dispersion modelling multi-level approach is highlighted as the most suitable for defining the plant strategies. The paper provides useful information to characterize environmental odour problems and identify appropriate solutions for an effective management of odorous sources, with the aim of reducing complaints, restoring the proper relationship between odorous plants and the surrounding communities and increasing the overall quality of the environment.

Instrumental odour monitoring systems (IOMS) are intelligent electronic sensing tools for which the primary application is the generation of odour metrics that are indicators of odour as perceived by human observers. The quality of the odour sensor signal, the mathematical treatment of the acquired data, and the validation of the correlation of the odour metric are key topics to control in order to ensure a robust and reliable measurement. The research presents and discusses the use of different pattern recognition and feature extraction techniques in the elaboration and effectiveness of the odour classification monitoring model (OCMM). The effect of the rise, intermediate, and peak period from the original response curve, in collaboration with Linear Discriminant Analysis (LDA) and Artificial Neural Networks (ANN) as a pattern recognition algorithm, were investigated. Laboratory analyses were performed with real odour samples collected in a complex industrial plant, using an advanced smart IOMS. The results demonstrate the influence of the choice of method on the quality of the OCMM produced. The peak period in combination with the Artificial Neural Network (ANN) highlighted the best combination on the basis of high classification rates. The paper provides information to develop a solution to optimize the performance of IOMS.

Persulfate (PS)-activated, iron-based heterogeneous catalysts have attracted significant attention as a potential advanced and sustainable water purification system. Herein, a novel Fe3O4 impregnated graphene oxide (Fe3O4@GO)-activated persulfate system (Fe3O4@GO+K2S2O8) was synthesized by following a sustainable protocol and was tested on real wastewater containing dye pollutants. In the presence of the PS-activated system, the degradation efficiency of Rhodamine B (RhB) was significantly increased to a level of ≈95% compared with that of Fe3O4 (≈25%). The influences of different operational parameters, including solution pH, persulfate dosage, and RhB concentration, were systemically evaluated. This system maintained its catalytic activity and durability with a negligible amount of iron leached during successive recirculation experiments. The degradation intermediates were further identified through reactive oxygen species (ROS) studies, where surface-bound SO4− was found to be dominant radical for RhB degradation. Moreover, the degradation mechanism of RhB in the Fe3O4@GO+K2S2O8 system was discussed. Finally, the results indicate that the persulfate-activated Fe3O4@GO catalyst provided an effective pathway for the degradation of dye pollutants in real wastewater treatment.

Volatile Organic Compounds (VOCs) are among the gas pollutants with a higher detrimental impact on human health and the environment. The increasing awareness of the population on the importance of air quality and the enforcement of stricter environmental regulations regarding the anthropogenic emissions of these hazardous pollutants have triggered the development of cost-efficient, environmentally friendly off-gas treatment technologies. Biological technologies such as biotrickling filters have been consistently proven as well stablished technologies for the treatment of VOC emissions, although the low solubility of oxygen might limit the aerobic degradation when treating high concentrations of VOCs. In this context, the synergic effects between microalgae and bacteria represent a sustainable platform to promote the simultaneous abatement of VOCs and CO2 by increasing the dissolved oxygen concentration as a result of the photosynthetic process. In this study, a conventional biotrickling filter (BTF) and an innovative tubular photobioreactor (TPBR) inoculated with a microalgal-bacterial consortium, and interconnected to an external absorption, column were comparatively evaluated for toluene removal. Operating parameters such as the gas residence time (GRT) and the mineral salt medium renewal rate were optimized in order to boost toluene removal. In this sense, both bioreactors were capable of achieving removals > 85 % at a GRT of 45 s and a medium renewal rate of 800 mL d-1. Maximum toluene elimination capacities of ~21 g m-3 h-1 were recorded in both the BTF and the TPBR. Despite the satisfactory performance of both systems, the photobioreactor presented a competitive advantage due to the capacity of algal biomass to fix the produced CO2, considerably reducing the emissions of this greenhouse gas, while increasing the concentration of dissolved oxygen in the cultivation broth up to 7.7 mg O2 L-1.

Volatile Organic Compounds (VOCs) emissions from a wide range of industrial plants have become a major issue in the framework of atmospheric pollution, due to their negative effects on human and environmental health. VOC-laden emissions are also responsible for odour annoyance. To comply with the stringent regulations and to avoid complaints from the population living near these plants, the most suitable treatment technologies should be identified and implemented. Chemical-physical off-gas treatments such as adsorption and scrubbing, are proven and tested technologies; however, they only promote the transfer of the contaminants from the gaseous to solid or liquid phase, and further treatments are thus required. Biological processes and advanced oxidation processes (AOPs), instead, are able to support the degradation and mineralization of organic compounds, resulting in more effective solutions. This study presents and discusses a comparative analysis of the biological processes and AOPs for the removal of VOCs, focusing on assessing their potential application for industrial waste gas treatment. A numerical procedure, based on the quantification of a set of parameters classified into clusters, was proposed to evaluate the most suitable process for the treatment of the VOC-laden emissions in the different industrial sectors. The results, based on a semi-quantitative ranking of the different identified parameters, pointed out the weaknesses and strengths of the investigated processes. AOPs entailed high elimination capacities, but the emissions of hazardous by-products should be controlled and reduced. Biotechnologies have emerged as cost-effective and environmental friendly processes; however, the efficiencies of these processes are often limited by the presence of recalcitrant and toxic secondary metabolites.

Odour measurement plays a crucial role in environmental odour management. Continuous odour measurement systems are promoted to keep the situation always under control, such as being able to adopt the most suitable mitigation measures in real time to avoid odour complaints and impacts. Electronic Nose (eNose) represents currently the instrument of having the highest future developing potential to guarantee continuous odour measurements. To use an eNose, a training phase is however mandatory, which has the scope to create the Odour Monitoring Model (OMM) that is able to identify the presence of odour, the different odour classes and the quantification of the odorous stimuly. Statistical or biological inspired measurement techniques are applied to create the optimum OMM. The study presents and discusses the elaboration of an Artificial Neural Network (ANN) technique to recognize environmental odour with eNose. The proposed system was architected on a feed-forward neural network with Bayesian Regularization algorithm using Matlab R2017a software. The elaborated ANN was tested and validated using the seedOA eNose, realized by the Sanitary Environmental Engineering Division (SEED) of the Department of Civil Engineering of the University of Salerno (Italy). Tests were carried out analyzing odour samples collected at a large Wastewater Treatment Plant (WWTP). The comparison between the Odour Monitoring Model (OMM) elaborated through the proposed ANN system and the traditional statistical techniques, such as the Partial Least Square (PLS) and the Linear Discriminant Analysis (LDA), is also discussed. Results shown the efficiency of the elaborated ANN to identify the different odour classes and predict the odour concentration in terms of OUm-3. The artificial neural network shows smaller Root Mean Squared Errors (RMSE) and greater coefficient of determination (R2) as compared to the traditional statistical methods. The main advantages of neural networks are their adaptability in terms of learning, self-organization, training and noise-tolerance.

Odour concentration expressed in terms of OUE/m3 by using the dynamic olfactometry analysis, standardized by the European Norm (EN) 13725 are by now the most commonly and worldwide accepted method to measure odours. The EN, first edition published in the year 2003, is actually under review process by the WG2 of the CEN/TC246 and the end of the revision work is expected for the year 2018. Related to the odour panel the standard fixes some parameters (e.g: panel size). Nothing is, however, performed about the composition of the panel members in terms of their gender, the age of the involved persons and their origin and nationality. Also the current literature is limited to discussing how the measurement of emission rates derived from the same odour source could be related to the panel composition. The scope of this study is to investigate the influence on the determination of odour concentration with dynamic olfactometry, according to EN 13725 standards, applying different panel conditions, with the objective to define the optimal criteria to evaluate the odour concentration and obtain the highest repeatability and accuracy of the sensorial measure. A critical evaluation was carried out in relation to the same odour source with the comparison of the different panel composition (in terms of gender, age and nationality) to measure the odour concentration. In the experimental studies the measurements were conducted within 5 h after sampling to reduce the variability of the mixture and increase the reliability. All assessors who participate in odour measurements were qualified and tested according the quality criteria assurance foreseen by the EN13725. Studies were taken considering real environmental odour emissions collected at a wastewater treatment plant (WWTP). The results obtained show a not significantly influence of the investigated characteristics of the single panel member in the repeatability assurance of the measure of the odour concentration by dynamic olfactometry. While a variation of the results in terms of OU/m3 is demonstrated related to the different nationality and biological factors (age and gender) of the assessors. There is therefore the need to consider these factors in the current odour determination practices, for example in calculating the uncertainty factor of the measurement.