Issue 4 [December]

Odour emissions from liquid waste treatment plants (LWTPs) generally cause significant effects on the environment in terms of nuisance to exposed population. The particular and complex nature of the mixture of the volatile substances, its variability in time and the strong influence of the atmospheric conditions, are the elements that delayed their regulation and relative management.

Limited data are available in the technical and scientific literature, regarding the odour emissions characterization from liquid waste treatment plants. Moreover there isn’t a common strategy from the different European Countries in the regulation of their emissions.

Different methods can be used to measure odour emissions from environmental engineering plants, and currently, in Europe, the most used techniques for odour emissions characterization and quantification is the dynamic olfactometry, according to EN 13725:2003.

The aim of this study is the characterization of the odour emissions from different liquid waste treatment plants (LWTPs), trough a case study of two large real LWTPs, in order to identify the principal odour sources and to define their related odour emissions.

Odour Concentration Index (OCI) is proposed as a useful and simply odour management tool for the identification of the priority actions necessary to identify and control the main odorous sources. Relationship between the measured odour emissions and the types of treated liquid waste (identified in terms of EWC code, COD and NH4+) is also discussed.

Results show that the influent collection tank is the source with the highest detected odour emissions. OCI results are useful for the definition of a clear priority action for odour control, similar for both investigated plants. Between the characterized types of liquid waste treated by LWTPs the leachate (EWC 190703) show the maximum odour emissions.

Different land uses, infrastructures, industrial activities and residential patterns of developed cities expose simultaneously people to several annoying sources.

Over recent years, the European Union has provided several tools to harmonize noise mapping methodologies and relative Noise Action Plans through directives. Unfortunately, the same effort has not been made for the harmonization of approaches of other annoying sources such as odours. As a consequence, each European Member State has defined its own direct or indirect approach to limit odour impacts.

The most common approach to deal with noise impact is the use of priority indices to highlight areas that are sensitive to both noise and, generally, odour impacts. The aim of the present research is to provide a brief review of the most widely used European strategies in noise action plans as well as try to extend the approaches to the definition of a nuisance action plan, capable of controlling both odour and noise.

The analysis underlines that is possible to define the degree of sensitivity of areas according to population, land uses, levels of exposures and/or distance from the annoying sources. Nuisance acceptability levels are then definable according to the sensitivity degree of the locations.

Two different methodologies for hydraulic head simulation were compared in this study. The first methodology is a classic numerical groundwater flow simulation model, Princeton Transport Code (PTC), while the second one is a black-box approach that uses Artificial Neural Networks (ANNs). Both methodologies were implemented in the Bavaria region in Germany at thirty observation wells. When using PTC, meteorological and geological data are used in order to compute the simulated hydraulic head following the calibration of the appropriate model parameters. The ANNs use meteorological and hydrological data as input parameters. Different input parameters and ANN architectures were tested and the ANN with the best performance was compared with the PTC model simulation results. One ANN was trained for every observation well and the hydraulic head change was simulated on a daily time step. The performance of the two models was then compared based on the real field data from the study area. The cases in which one model outperforms the other were summarized, while the use of one instead of the other depends on the application and further use of the model.