The Self-Forming Dynamic Membrane BioReactor (SFD MBR) as a suitable technology for agro-industrial wastewater treatment.

Two bench-scale Self-Forming Dynamic Membrane BioReactors (SFD MBR), equipped with 50 µm nylon meshes were set up and operated under aerobic conditions in order to treat canning and winery wastewaters. The results showed different behaviors of the two systems, confirming the strong dependence of SFD MBR performance on the type of biomass and, in turn, on the type of stream being treated. Both plants achieved good results in terms of effluent quality, demonstrating the suitability of the proposed technology. Median values of effluent turbidity were 2.7 and 15.4 NTU (Nephelometric Turbidity Units) in the reactors fed with canning wastewater and winery wastewater, respectively. The removal of organic matter (as COD, Chemical Oxygen Demand) was consistently above 90 %, although the retention of suspended solids was variable and somewhat dependent on operating conditions and feed composition. The activated sludge characteristics were observed to affect filtration performance and in particular the capillary suction time (CST) was a possible indicator of efficiency, with a threshold value of 11 s above which filtration performance decreased. This parameter is proposed as an early warning tool for changes in the filtration performance of an SFD MBR, both for effluent quality and cleaning requirements.

Tools based on multivariate statistical analysis for classification of soil and groundwater in Apulian agricultural sites.

In this paper, the results obtained from multivariate statistical techniques such as PCA (Principal component analysis) and LDA (Linear discriminant analysis) applied to a wide soil data set are presented. The results have been compared with those obtained on a groundwater data set, whose samples were collected together with soil ones, within the project "Improvement of the Regional Agro-meteorological Monitoring Network (2004-2007)". LDA, applied to soil data, has allowed to distinguish the geographical origin of the sample from either one of the two macroaeras: Bari and Foggia provinces vs Brindisi, Lecce e Taranto provinces, with a percentage of correct prediction in cross validation of 87%. In the case of the groundwater data set, the best classification was obtained when the samples were grouped into three macroareas: Foggia province, Bari province and Brindisi, Lecce and Taranto provinces, by reaching a percentage of correct predictions in cross validation of 84%. The obtained information can be very useful in supporting soil and water resource management, such as the reduction of water consumption and the reduction of energy and chemical (nutrients and pesticides) inputs in agriculture.

Source apportionment of groundwater pollutants in Apulian agricultural sites using multivariate statistical analyses: case study of Foggia province.

BACKGROUND: Ground waters are an important resource of water supply for human health and activities. Groundwater uses and applications are often related to its composition, which is increasingly influenced by human activities.In fact the water quality of groundwater is affected by many factors including precipitation, surface runoff, groundwater flow, and the characteristics of the catchment area. During the years 2004-2007 the Agricultural and Food Authority of Apulia Region has implemented the project "Expansion of regional agro-meteorological network" in order to assess, monitor and manage of regional groundwater quality. The total wells monitored during this activity amounted to 473, and the water samples analyzed were 1021. This resulted in a huge and complex data matrix comprised of a large number of physical-chemical parameters, which are often difficult to interpret and draw meaningful conclusions. The application of different multivariate statistical techniques such as Cluster Analysis (CA), Principal Component Analysis (PCA), Absolute Principal Component Scores (APCS) for interpretation of the complex databases offers a better understanding of water quality in the study region. RESULTS: Form results obtained by Principal Component and Cluster Analysis applied to data set of Foggia province it's evident that some sampling sites investigated show dissimilarities, mostly due to the location of the site, the land use and management techniques and groundwater overuse. By APCS method it's been possible to identify three pollutant sources: Agricultural pollution 1 due to fertilizer applications, Agricultural pollution 2 due to microelements for agriculture and groundwater overuse and a third source that can be identified as soil run off and rock tracer mining. CONCLUSIONS: Multivariate statistical methods represent a valid tool to understand complex nature of groundwater quality issues, determine priorities in the use of ground waters as irrigation water and suggest interactions between land use and irrigation water quality.