Giuseppe Pappagallo

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

BackgroundGround 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.ResultsForm 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.ConclusionsMultivariate 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.

Assessing flow regime alterations in a temporary river – the River Celone case study

Abstract In this paper, we present an approach to evaluate the hydrological alterations of a temporary river. In these rivers, it is expected that anthropogenic pressures largely modify low-flow components of the flow regime with consequences for aquatic habitat and diversity in invertebrate species. First, by using a simple hydrological index (IARI) river segments of the Celone stream (southern Italy) whose hydrological regime is significantly influenced by anthropogenic activities have been identified. Hydrological alteration has been further classified through the analysis of two metrics: the degree (Mf) and the predictability of dry flow conditions (Sd6). Measured streamflow data were used to calculate the metrics in present conditions (impacted). Given the lack of data from pristine conditions, simulated streamflow time series were used to calculate the metrics in reference conditions. The Soil and Water Assessment Tool (SWAT) model was used to estimate daily natural streamflow. Hydrological alterations associated with water abstractions, point discharges and the presence of a reservoir were assessed by comparing the metrics (Mf, Sd6) before and after the impacts. The results show that the hydrological regime of the river segment located in the upper part of the basin is slightly altered, while the regime of the river segment downstream of the reservoir is heavily altered. This approach is intended for use with ecological metrics in defining the water quality status and in planning streamflow management activities.

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.

Multivariate Statistical Methods and Neural Networks for the Identification of Pollution Sources and Classification of the Apulia Region Ground Waters

Multivariate statistical techniques such as Discriminant Function Analysis (DFA), Cluster Analysis (CA), Principal Component Analysis (PCA), Absolute Principal Component Score (APCS) and Neural Networks (NN) have been applied to a data set, of Apulian ground waters, formed by 1009 samples and 15 parameters: pH, Electrical Conductivity, Total Dissolved Solids, Dissolved Oxygen, Chemical Oxygen Demand, Na+, Ca2+, Mg2+, K+, Cl-, NO3-, SO42- and HCO3-, vital organism at 22 °C and 36 °C. Principal Component Analysis and Absolute Principal Component Scores allowed to identify, for each province, as well the sites diverging from the mean cluster, as the pollution sources (due to fertilizer applications, marine water intrusion, etc…) pressurizing the sampling sites investigated. Discriminant Function Analysis allowed on the hand to identify variables with bigger discriminatory power, on the other to obtain good results in discriminating among the considered provinces and in forecasting. The application of Radial Basis Function Neural Networks gives results with bigger accuracy than DFA and confirms the electrical conductivity has the bigger relative importance.