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This paper presents the development of a 3D computational fluid dynamics (CFD) model of a covered deep anaerobic pond treating raw sewage. The model was based on an unsteady-state laminar flow which was solved in accordance with the finite volume method and simulated the hydrodynamic pattern and thermal energy balance of the anaerobic pond throughout a year of operation. The model input included hourly ambient air temperature, monthly soil temperature profile, daily influent wastewater temperature and velocity, which were incorporated through an external routine in C ++ . Numerical simulation was validated by temperature measurements from the experimental pond. The mean relative error and correlation factor out of 164 temperature values of the simulated pond, in comparison with the experimental one, was 9.34% and 0.91, respectively. The average temperature of the simulated pond throughout the experimental period was 18.9 • C. The validated 3D thermal model can be used as a tool for assessing and evaluating the impact of various design modifications (changes in construction material, adding insulation, installing a heat exchanger, etc.) on the thermal behaviour of an anaerobic pond, aiming at its average temperature increase which will, in turn, positively affect its organic removal performance. The thermal model presented, is the first stage of a complete anaerobic pond model which will include wastewater quality transport and basic biochemical reaction mechanisms of the anaerobic decomposition process present in an anaerobic pond. The complete anaerobic pond model will be able to predict the effluent COD of the pond.

The performance of three waste stabilization ponds (WSP) treatment units (lines A, B, C) for a period of two years is presented. The above units were part of a research and demonstration project in North Greece concerning wastewater treatment with natural systems and effluent reuse for irrigation. Line A consisted of a deep anaerobic pond followed by a facultative and two maturation ponds. The anaerobic pond supplied with 120 m3/d was also feeding line B at a ratio 1:3 (line A: line B). Line B had the same design features with line A but with a recirculation of effluent. The third unit fed with primary treated effluent at a rate of 50m3/d from the WWTP of Thessaloniki was consisted of a facultative and two maturation ponds. In the anaerobic pond the BOD5, COD, SS, TC and FC mean reduction was 28%, 42%, 58%, 31% and 36% for the cold period (T<10°C) and 45%, 53%, 72%, 67% and 71% for the warm period (T>10°C) respectively. In line A, the BOD5, COD, SS, TC and FC total reduction was 98%, 89%, 93%, 99.99% and 99.99% for the cold and 98%, 85%, 88%, 99.99% and 99.99% for the warm period. In line B the reduction was 92%, 86%, 88%, 98.5% and 98.82% for the cold and 96%, 88%, 83%, 99.95% and 99.97% for the warm period. In line C the reduction of the same parameters was 92%, 64%, 40%, 99.88% and 99.96% for the cold and 92%, 59%, 22%, 99.99% and 99.99% for the warm period. The effluent quality was further improved with the use of a storage reservoir and a slow sand filter and reused successfully for crop irrigation.

Air quality (AQ) in urban areas is deteriorating, thus having negative effects on people’s everyday lives. Official air quality monitoring stations provide the most reliable information, but do not always depict air pollution levels at scales reflecting human activities. They also have a high cost and therefore are limited in number. This issue can be addressed by deploying low cost AQ monitoring devices (LCAQMD), though their measurements are of far lower quality. In this paper we study the correlation of air pollution levels reported by such a device and by a reference station for particulate matter, ozone and nitrogen dioxide in Thessaloniki, Greece. On this basis, a corrective factor is modeled via seven machine learning algorithms in order to improve the quality of measurements for the LCAQMD against reference stations, thus leading to its on-field computational improvement. We show that our computational intelligence approach can improve the performance of such a device for PM10 under operational conditions.