| Title | IDENTIFICATION OF POLLUTION SOURCES AND CLASSIFICATION OF APULIA REGION GROUNDWATERS BY MULTIVARIATE STATISTICAL METHODS AND NEURAL NETWORKS |
| Abstract | Multivariate statistical techniques, including discriminant function analysis (DFA), cluster analysis (CA),
principal component analysis (PCA), absolute principal component score (APCS), and radial basis function neural network
(RBF-NN), were applied to a data set formed by 905 samples and 15 parameters, including pH, electrical conductivity
(EC), total dissolved solids (TDS), dissolved oxygen (O2), chemical oxygen demand (COD), Na+, Ca2+, Mg2+, K+, Cl-,
NO3
-, SO4
2-, HCO3, and vital organism at 22°C and 36°C, of groundwater samples collected in the Apulia region (southern
Italy). Among all collected samples, only samples showing values for all parameters were used to compose the data set on
which the multivariate statistical techniques were applied. PCA and APCS allowed us to identify, for each province as well
as the sites diverging from the main cluster, the pollution sources pressuring the sampling sites investigated: they were
identified as fertilizer applications, the use of unpurified irrigation water, marine water intrusion, and calcareous characteristics
of the soil. We found that the groundwater pollution sources pressuring the sites were similar among the five Apulian
provinces (Foggia, Bari, Brindisi, Lecce, and Taranto). Moreover, for each province, marine water intrusion showed
the highest contribution. The application of DFA to the data set allowed us to obtain good results in discriminating among
four provinces, with the exception of Taranto. The model also gave good performance results in forecasting. However,
RBF-NN provided more accurate results than DFA and confirmed that EC had the greatest relative importance. This is
probably due to the different salinity among the sites (Na+ also showed good discriminant importance). In fact, with PCA
and APCS, it was possible to observe that EC, together with Na+, Cl-, and TDS, was the parameter that most often showed
high loading values, and the scattered samples with these loading values were collected at sites in which marine water
intrusion had been hypothesized. The results obtained by multivariate statistical methods can be useful both in guiding
stakeholders and in providing a valid tool to authorities for assessing and managing groundwater resources. |
| Source | Transactions of the ASABE (Print) 56 (6), pp. 1377–1386 |
| Keywords | ClassificationCluster analysisDiscriminant function analysisForecastingGroundwatersNeural networksPrincipal component analysisSource apportionment. |
| Journal | Transactions of the ASABE (Print) |
| Editor | American Society of Agricultural and Biological Engineers,, St. Joseph, MI, Stati Uniti d'America |
| Year | 2013 |
| Type | Articolo in rivista |
| DOI | 10.13031/trans.56.9976 |
| Authors | P. Ielpo, D. Cassano, V. F. Uricchio, A. Lopez, G. Pappagallo, L. Trizio, G. de Gennaro |
| Text | 277152 2013 10.13031/trans.56.9976 Classification Cluster analysis Discriminant function analysis Forecasting Groundwaters Neural networks Principal component analysis Source apportionment. IDENTIFICATION OF POLLUTION SOURCES AND CLASSIFICATION OF APULIA REGION GROUNDWATERS BY MULTIVARIATE STATISTICAL METHODS AND NEURAL NETWORKS P. Ielpo, D. Cassano, V. F. Uricchio, A. Lopez, G. Pappagallo, L. Trizio, G. de Gennaro IRSA CNR sede di Bari; ISAC CNR sede di Lecce ARPA Puglia direzione scientifica, Universita degli studi di Bari dipartimento di chimica Multivariate statistical techniques, including discriminant function analysis DFA , cluster analysis CA , principal component analysis PCA , absolute principal component score APCS , and radial basis function neural network RBF NN , were applied to a data set formed by 905 samples and 15 parameters, including pH, electrical conductivity EC , total dissolved solids TDS , dissolved oxygen O2 , chemical oxygen demand COD , Na , Ca2 , Mg2 , K , Cl , NO3 , SO4 2 , HCO3, and vital organism at 22°C and 36°C, of groundwater samples collected in the Apulia region southern Italy . Among all collected samples, only samples showing values for all parameters were used to compose the data set on which the multivariate statistical techniques were applied. PCA and APCS allowed us to identify, for each province as well as the sites diverging from the main cluster, the pollution sources pressuring the sampling sites investigated they were identified as fertilizer applications, the use of unpurified irrigation water, marine water intrusion, and calcareous characteristics of the soil. We found that the groundwater pollution sources pressuring the sites were similar among the five Apulian provinces Foggia, Bari, Brindisi, Lecce, and Taranto . Moreover, for each province, marine water intrusion showed the highest contribution. The application of DFA to the data set allowed us to obtain good results in discriminating among four provinces, with the exception of Taranto. The model also gave good performance results in forecasting. However, RBF NN provided more accurate results than DFA and confirmed that EC had the greatest relative importance. This is probably due to the different salinity among the sites Na also showed good discriminant importance . In fact, with PCA and APCS, it was possible to observe that EC, together with Na , Cl , and TDS, was the parameter that most often showed high loading values, and the scattered samples with these loading values were collected at sites in which marine water intrusion had been hypothesized. The results obtained by multivariate statistical methods can be useful both in guiding stakeholders and in providing a valid tool to authorities for assessing and managing groundwater resources. 56 IDENTIFICATION OF POLLUTION SOURCES AND CLASSIFICATION OF APULIA REGION GROUNDWATERS BY MULTIVARIATE STATISTICAL METHODS AND NEURAL NETWORKS Transactions_of_the_ASABE_56_6_Ielpo_et_al_asp.pdf Articolo in rivista American Society of Agricultural and Biological Engineers, 2151 0032 Transactions of the ASABE Print Transactions of the ASABE Print Trans. ASABE Print Transactions of the ASABE. Print Transactions of the American Society of Agricultural and Biological Engineers Print CASSANO DANIELA vitofelice.uricchio URICCHIO VITO FELICE pierina.ielpo IELPO PIERINA antonio.lopez LOPEZ ANTONIO TA.P04.005.005 Gestione integrata delle informazioni ambientali |
