PCDD/Fs and PCBs in ambient air in a highly industrialized city in Northern Italy

Ambient air samples were collected in five locations around a polychlorobiphenyl (PCB) production plant in a highly industrialized city in the north of Italy, for the analysis of polychlorinated dibenzo-p-dioxins (PCDDs) and polychlorinated dibenzofurans (PCDFs) and PCBs.PCDD/F atmospheric concentrations were lower than those generally detected in urban areas, while PCB concentrations were higher than in urban and industrialized areas in other countries, especially in the locations closest to the plant. To identify source emissions responsible for air pollution we used principal component analysis (PCA) to compare the “fingerprint” of the air in Brescia with some source-related experimental and published data. This analysis showed that the PCDD/F fingerprints were more similar to steel plant emission data, while none of the PCB air patterns were comparable to emission profiles, indicating that different sources are responsible for the concentration of these two pollutants. The highest PCB concentrations detected in the areas closest to the chemical plant are indicative of its impact on the surrounding environment.     

Magnetic susceptibility measurements as proxy method to monitor soil pollution: development of experimental protocols for field surveys

In the framework of the development of new methods for measuring and monitoring soil pollution, this paper deals with the use of magnetic methodologies to monitor the heavy metals presence in soils. In particular it shows a procedure for collecting magnetic susceptibility measurements in order to interpret them as proxy variable for monitoring heavy metals in soils. Magnetic measurements are carried out using a magnetic susceptibility meter with two different probes for in situ field surveys. The experimental procedure is divided in two parts. In the first part we carry out laboratory tests aimed to evaluate, for both the probes, the effective investigation depth for soil, the measurement reproducibility under different conditions, and the influence of water content. We complete this part comparing in situ measurements obtained by means of two probes with different characteristics. In the second part we carry out tests to evaluate the relationships between heavy metal levels and magnetic susceptibility values of soil samples. We investigate the variability of the magnetic susceptibility measurements contaminating different soil samples with well known concentration of heavy metals. Moreover we study the correlation between magnetic susceptibility values and metal concentrations, determined by means of AAS, in soil samples collected during a field survey. Results suggest that a careful check of the experimental procedure play a crucial role for using magnetic susceptibility measurements for heavy metals in situ monitoring. This is very helpful both for improving the quality of data and for making simpler data interpretation.     

Magnetic susceptibility measurements as proxy method to monitor soil pollution: the case study of S. Nicola di Melfi

The development of in situ, cheep, noninvasive, and fast strategies for soil monitoring is a crucial task for environmental research. In this paper, we present the results of three field surveys carried out in an industrial area of Southern Italy: S. Nicola di Melfi. The monitoring procedure is based on soil magnetic susceptibility measurements carried out by means of experimental protocols that our research group developed during the last years. This field surveys is supported by both geological characterization of the area and analytical determinations of metal concentrations in soils. Magnetic studies were carried out not only in situ but also in laboratory. Results show that, taking into account the influence due to the geomorphologic difference, soil magnetic susceptibility is an optimal indicator of the anthropogenic impact. So, our monitoring strategy discloses that the combined use of magnetic susceptibility measurements and soil geomorphology information may be used as a useful tool for the temporal monitoring of pollution evolution and for a fast screening of polluted zones.     

Soil heavy metal contamination in an industrial area: analysis of the data collected during a decade

Soil contamination by heavy metals has become a serious problem mainly because, above certain concentrations, all metals have adverse effects on human health. In particular, the accumulation of heavy metals in agricultural soils leads to elevated uptake by crops and affects food quality and safety. In this paper, we present the results of a study carried out over a decade for evaluating the impact of a new industrial settlement in an area geared to agriculture and livestock and far from urban sites. We focus our study on the bioavailable fraction of Cd, Co, Cr, Cu, Fe, Mn, Ni, Pb and Zn in soil samples. Heavy metal concentrations in soil are analysed with both univariate and multivariate statistical procedures. The main goal of this paper is the development of a statistical procedure, based on a mix of multivariate analysis, able to compare field surveys carried out during different years and to characterize spatial and temporal changes in soil heavy metals concentrations.     

Spatial variability of air pollutants in the city of Taranto, Italy and its potential impact on exposure assessment

Epidemiological studies typically use monitored air pollution data from a single station or as averaged data from several stations to estimate population exposure. In industrialized urban areas, this approach may present critical issues due to the spatial complexities of air pollutants which are emitted by different sources. This study focused on the city of Taranto, which is one of the most highly industrialized cities in southern Italy. Epidemiological studies have revealed several critical situations in this area, in terms of mortality excess and short-term health effects of air pollution. The aims of this paper are to study the variability of air pollutants in the city of Taranto and to interpret the results in relation to the applicability of the data in assessing population exposure. Meteorological and pollution data (SO2, NO2, PM10), measured simultaneously and continuously during the period 2006–2010 in five air quality stations, were analyzed. Relative and absolute spatial concentration variations were investigated by means of statistical indexes. Results show significant differences among stations. The highest correlation between stations was observed for PM10 concentrations, while critical values were found for NO2. The worst values were observed for the SO2 series. The high values of 90th percentile of differences between pairs of monitoring sites for the three pollutants index suggest that mean concentrations differ by large amounts from site to site. The overall analysis supports the hypothesis that various parts of the city are differently affected by the different emission sources, depending on meteorological conditions. In particular, analysis revealed that the influence of the industrial site may be primarily identified with the series of SO2 data which exhibit higher mean concentration values and positive correlations with wind intensity when the monitoring station is downwind from the industrial site. Results suggest evaluating the population exposure to air pollutants in industrialized cities by taking into account the possible zones of influence of different emission sources. More research is needed to identify an indicator, which ought to be a synthesis of several pollutants, and take into account the meteorological variables.     

Metabolic and bacterial diversity in soils historically contaminated by heavy metals and hydrocarbons

The aim of this study was to characterize soils contaminated by different levels of heavy metals and hydrocarbons (Madonna Dell'Acqua, Pisa, Italy). The soils were chemically and biochemically analysed by measuring the standard chemical properties and some enzyme activities related to microbial activity (dehydrogenase activity) and the soil carbon cycle (total and extracellular beta-glucosidase activities). The metabolic capacities of soil microorganisms to degrade hydrocarbons through catechol 2,3-dioxygenase were also described. The microbial diversity of contaminated and uncontaminated soils was estimated by denaturing gradient gel electrophoresis (DGGE) of amplified 16S rDNA sequences. The PCR/single-strand conformation polymorphism (PCR/SSCP) method was used to estimate the genetic diversity of PAH-degrading genes in both contaminated and uncontaminated soils. A greater bacterial diversity and lower catechol 2,3-dioxygenase activity was detected in unpolluted soils. The complexity of the microbial community (Shannon and Simpson indices) as well as the dehydrogenase soil activity negatively correlated with contamination levels. The greatest PAH-degrading gene diversity and the most intense catechol 2,3-dioxygenase activity were found in the soils with the highest levels of hydrocarbons. Heavy metals and hydrocarbon pollution has caused a genetic and metabolic alteration in microbial communities, corresponding to a reduction in microbial activity. A multi-technique approach combining traditional biochemical methods with molecular-based techniques, along with some methodological improvements, may represent an important tool to expand our knowledge of the role of microbial diversity in contaminated soil.     

Bioconversion of wastes from olive oil industries by vermicomposting process using the epigeic earthworm Eisenia andrei

The present work evaluates the possible bioconversion of wet olive cake by low-cost biostabilization (vermicomposting process). Wet olive cake fresh (WOC), precomposted (WOCP), or mixed with biosolids (WOCB), were vermicomposted for 6 months to obtain organic amendments for agricultural and remediation purposes. The results showed initial differences depending on previous treatment. WOCP was initially more stable, presented a low C:N ratio, and showed more dehydrogenase and urease activity. By contrast, there was no dehydrogenase activity initially in WOC and WOCB, due to the presence of some different types of polyphenols. Finally, the end product showed relatively higher amounts of total nitrogen and humic acid and met the standard of quality for composts and vermicomposts for use both in conventional and organic agriculture and soil-restoration programs.     

Strategic environmental assessment of mining activities: A methodology for quantification of cumulative impacts on the air quality

Environmental impact assessments in Brazil have usually focused solely on project-related issues without considering the regional context. Although required by current environmental legislation, cumulative impact assessments have not been included in the overall environmental assessment of projects. However, in recent Strategic Environmental Assessment (SEA) studies of policies, plans, and programs undertaken on a voluntary basis in support of the decision-making process, this kind of assessment has been performed especially with respect to air quality. This paper presents the application of a methodology for the quantification of cumulative impacts on air quality under high uncertainty caused by various mining activities in a single region that is recommended for SEA studies. In this way, the methodology presented here is suitable for areas lacking detailed modeling information. The developed approach uses a relatively simplified mathematical model, lowering information gathering costs and requiring little processing time. The application of the methodology is illustrated in the case of a SEA of the Corumbá Mining and Industrial Complex Development Program. Despite the lack of data needed for a minimum characterization of conditions of the area surrounding the region modeled, the quantification of impact cumulativeness on air quality has played an important role in the context of the SEA.     

Estrogenicity profile and estrogenic compounds determined in river sediments by chemical analysis, ELISA and yeast assays

An effects-directed strategy was applied to bed sediments of a polluted tributary in order to isolate and identify the major estrogenic chemicals it discharges into the River Po, the principal Italian watercourse. Sediment extract was concentrated by solid phase extraction and then fractioned into 10 fractions by reversed phase high performance liquid chromatography (RP-HPLC). Estrogenic activity of whole extract and fractions were determined using a recombinant yeast assay containing the human estrogen receptor (YES). The 10 fractions and whole extract were analysed for target compounds, e.g. estrone (E1), 17beta-estradiol (E2), estriol (E3), 4-nonylphenol (NP), 4-tert-octylphenol (t-OP), bisphenol A (BPA), using both liquid chromatography-tandem mass spectrometry (LC-MS/MS) and non-competitive enzyme-linked immunosorbent assays (ELISA). The YES assay determined high estrogenic activity in whole sediment (15.6 ng/g EE2 equivalents), and positive results for fractions nr 1, 2, 6, 7 and 8. E1, E3 and NP were the main estrogenic chemicals, however, other unidentified compounds contributed to sediment estrogenicity, particularly for polar fractions nr 1 and 2. A GC-MS screening performed in scan mode identified other potential contributors such as phthalates (DBP, BBP), and OP isomers. A next sampling campaign extended to other tributaries and receiving stretches of the River Po confirmed E1, E3 and NP as major estrogenic chemicals potentially threatening other sites of the main river. In general, target compound ELISAs have been shown to be suitable tools for a rapid screening of wide areas or large numbers of environmental samples for estrogenic risk. The potential for interferences suggests however to use cautiously the concentration values obtained from some of the immunoassays.     

A new hybrid system of QSAR models for predicting bioconcentration factors (BCF)

The aim was to develop a reliable and practical quantitative structure-activity relationship (QSAR) model validated by strict conditions for predicting bioconcentration factors (BCF). We built up several QSAR models starting from a large data set of 473 heterogeneous chemicals, based on multiple linear regression (MLR), radial basis function neural network (RBFNN) and support vector machine (SVM) methods. To improve the results, we also applied a hybrid model, which gave better prediction than single models. All models were statistically analysed using strict criteria, including an external test set. The outliers were also examined to understand better in which cases large errors were to be expected and to improve the predictive models. The models offer more robust tools for regulatory purposes, on the basis of the statistical results and the quality check on the input data.