QSPR for prediction of subcooled vapor pressures (log PL) of polychlorinated trans-azobenzenes

In this study the values of subcooled vapor pressures (log P_L) were estimated for 209 trans chloroazobenzenes (Ct-ABs) that fill some gaps in analytical and experimental data on these compounds. There are 209 chloro derivatives of trans azobenzenes that are relatively stable and more environmentally relevant than 209 chloro cis congeners. The calculations models were based on the Quantitative Structure-Property Relationship (QSPR) scheme using the semi-empirical method (PM6) in molecular package (MOPAC) software and density functional theory (DFT) method using B3LYP functional and 6-311++G** basis set) in Gaussian 03 software method and the artificial neural networks (ANNs) prediction. The values of log P_L predicted by models used varied between ?3.94 to ?2.66 for Mono-; ?4.85 to ?2.97 for Di-; ?5.18 to ?3.17 for Tri-; ?6.02 to ?3.77 for Tetra-; ?6.64 to ?4.64 for Penta-; ?7.36 to ?4.76 for Hexa-; ?7.54 to ?5.79 for Hepta-; ?7.75 to ?6.64 for Octa-; ?7.89 to ?7.44 for Nona-Ct-Abs; and ?8.09 and ?8.13 for Deca-Ct-AB. Based on these values Ct-ABs can be grouped localized among relatively low (log P_L ?4 to ?2) and low (log P_L < ?4) mobile Persistent Organic Pollutants (POPs). Both the calculation methods employed were characterized by similar prediction ability of subcooled vapor pressure values of Ct-ABs, while those of PM6 are much more efficient due to a cheaper hardware used and around 300-fold less time spent on calculations.     

Patterns and concentration levels of polybrominated diphenyl ethers (PBDEs) in placental tissue of women in Denmark

The levels and congener patterns of PBDEs were investigated in human placental samples in Denmark. The median concentrations of ∑PBDE_tri-hepta and BDE-209 in the 50 samples were 1.22 and 1.14 ng g⁻¹ lw, respectively, with the total sum ranging from 0.51 to 17.1 ng g⁻¹ lw, which is similar to previous placental studies. The PBDE content in placental tissue was dominated by BDE-209, which accounted for approximately 50% of the total amount of PBDEs. BDE-47, -99, and -153 were detected in all samples. Approximately equal amounts of BDE-47 and BDE-153 were observed in the placental tissue, which is in agreement with previous European studies of human serum. Principal Component Analysis (PCA) was performed to analyze congener patterns within and between mothers. The loading plot showed groupings of the measured PBDE variables in three groups, representative of Penta-, Octa- and Deca-BDE technical mixtures. Congeners representing the individual technical mixtures were close to orthogonal or inversely correlated, indicating variation in the congener patterns of internal exposure corresponding to the patterns of technical mixtures used in products. Visualisation of the participant objects according to body mass index (BMI), revealed inherent congener patterns (19% X-variance) showing increased frequency for participants within the highest BMI group to have elevated concentrations of BDE-209 in the placental tissue.     

Prediction of ozone concentration in ambient air using multivariate methods

Multivariate statistical methods including pattern recognition (Principal Component Analysis--PCA) and modeling (Multiple Linear Regression--MLR, Partial Least Squares--PLS, as well as Principal Component Regression--PCR) methods were carried out to evaluate the state of ambient air in Miskolc (second largest city in Hungary). Samples were taken from near the ground at a place with an extremely heavy traffic. Although PCA is not able to determine the significance of variables, it can uncover their similarities and classify the cases. PCA revealed that it is worth to separate day and night data because different factors influence the ozone concentrations during all day. Ozone concentration was modeled by MLR and PCR with the same efficiency if the conditions of meteorological parameters were not changed (i.e. morning and afternoon). Without night data, PCR and PLS suggest that the main process is not a photochemical but a chemical one.     

Emissions into the Atmosphere Due to the Ceramic Salt Glazing Process

The techniques of Principal Component Analysis (PCA) and subsequent regression analysis were used in an attempt to describe local and upwind chemical and physical factors which affect the variability of SO₄ ^–2 concentrations observed in a rural area of the northeastern U.S. The data used in the analyses included upwind and local O₃ concentrations, temperature, relative humidity and other climatological information, SO₂, and meteorological information associated with backward trajectories. The investigation identified five principal components, three major (eigenvalues >1) and two minor (eigenvalues < one), which accounted for 52% (r = 0.72) of the variability in the SO₄ ^–2 regression model. These components can be described as representing local and upwind photochemistry, droplet growth, SO₂ emissions, and air mass characteristics. The study also indicated that in future studies it will be necessary to a priori select air pollution and meteorological variables for measurement to potentially increase the sensitivity of this type of receptor model.     

A neural network-based approach for the prediction of urban SO2 concentrations in the Istanbul metropolitan area

A three-layer Artificial Neural Network (ANN) model was developed to forecast air pollution levels. The subsequent SO₂ concentration (24-hour averaged) being the output parameter of this study was estimated by seven input parameters such as preceding SO₂ concentrations (24-hour averaged), average daily temperature, sea-level pressure, relative humidity, cloudiness, average daily wind speed and daily dominant wind direction. After Backpropagation training combined with Principal Component Analysis (PCA), the proposed model predicted subsequent SO₂ values based on measured data. ANN testing outputs were proven to be satisfactory with correlation coefficients of about 0.770, 0.744 and 0.751 for the winter, summer and overall data, respectively.     

Polychlorinated biphenyls in two mollusc species from the coast of Tenerife (Canary Islands, Spain)

Levels of PCBs were determined in two marine organisms, limpets (Patella ullisiponensis aspera) and winkles (Ossilinus atratus), sampled in three stations in the south east coast of Tenerife during 1993. Winkles and limpets exhibited different PCB patterns. Principal Component Analysis (PCA) was used to study the patterns and the relationships among PCBs.     

Regression modelling of hourly NOx and NO2 concentrations in urban air in London

Based on hourly measurements of NO_x NO₂ and O₃ and meteorological data, an ordinary least squares (OLS) model and a first-order autocorrelation (AR) model were developed to analyse the regression and prediction of NO_x and NO₂ concentrations in London. Primary emissions and wind speed are the most important factors influencing NO_x concentrations; in addition to these two, reaction of NO with O₃ is also a major factor influencing NO₂ concentrations. The AR model resulted in high correlation coefficients (R > 0.95) for the NO_x and NO₂ regression based on a whole year's data, and is capable of predicting NO₂ (R = 0.83) and NO_x (R = 0.65) concentrations when the explanatory variables were available. The analysis of the structure of regression models by Principal Component Analysis (PCA) indicates that the regression models are stable. The results of the OLS model indicate that there was an exceptional NO₂ source, other than primary emission and reaction of NO with O₃, in the air pollution episode in London in December 1991.     

Earthworms to assess the innocuousness of spent biomixtures employed for glyphosate degradation

In this study, the innocuousness of different biomixtures employed for glyphosate degradation was tested through Eisenia fetida earthworms. Eight biomixtures were prepared with local materials: alfalfa straw (AS), wheat stubble (WS), river waste (RW) and two different soils (A and B). Each biomixture was divided into two equal portions: one without glyphosate application (control substrate) and the other was sprayed with a commercial glyphosate formulation of 1,000 mg glyphosate a.i. kg^?1 biomixture (applied substrate). The bioassay started when all sprayed biomixtures reached high percentages of glyphosate degradation (spent biomixtures). Three parameters were studied: survival, adults and juveniles biomass and reproduction. The results allowed the identification of three biomixtures (AWS, BWS and BWSRW) for good maintenance and development of E. fetida. In addition, at the end of the bioassay two of the viable biomixtures (AWS and BWS) showed the highest performance of juvenile earthworms compared to a reference soil. The Principal Component Analysis (PCA) indicated that the biomixtures containing high silt and clay percentages and minor density renders higher values of earthworm growth and reproduction. Therefore, these innocuous biomixtures can be used as organic amendments or recycled materials for new treatments on biobeds.     

One-Year Study of the Elemental Composition and Source Apportionment of PM10 Aerosols in Florence,Italy

Abstract

An extensive investigation was carried out for the characterisation of the air particulate composition in Florence. The aim was to determine the aerosol elemental concentrations, as well as to identify pollution sources. For our investigation, the external Particle-Induced X-Ray Emission–Particle-Induced γ-Ray Emission beam facility of the Istituto Nazionale di Fisica Nucleare, Van de Graaff accelerator at the Physics Department of the Florence University was used. We report the results of the analysis of a long temporal series (approximately 1 yr) of PM₁₀ particulate samples, collected on Millipore filters on a daily basis in three different sites (characterised by different urban settings). Daily concentrations of more than 20 elements were detected. The long sampling period (approximately 1 yr) allowed a comparison with the air quality recommended values and the identification of seasonal variations. Four main sources (traffic, oil-combustion, soil-dust, and wind transported sea-salt) were extracted with the help of Principal Component Analysis (PCA). An absolute PCA showed traffic to be the major source both in the high traffic site and in the urban background site.     

Pattern recognition and classification of sediments according to their metal content using chemometric tools. A case study: the estuary of Nerbioi-Ibaizabal River (Bilbao, Basque Country)

Chemometrics are increasingly used in environmental monitoring studies, but are still far from being accepted as routine tools by field specialists. The multivariate character of usually highly correlated environmental data recommends the use of advanced chemometrics as part of the analytical methodology in order to get information on the basic structure of data. In this work, we have applied a battery of non-supervised (Principal Component Analysis (PCA)) and supervised (k-Nearest Neighbour (k-NN), Soft Independent Modelling of Class Analogies (SIMCA), Linear Discriminant Analysis (LDA) and Artificial Neural Networks (ANNs)) multivariate techniques on a specific environmental dataset. The dataset consists on the concentration of 14 elements (Al, As, Cd, Co, Cr, Cu, Fe, Mg, Mn, Ni, Pb, Sn, V and Zn) in 95 sediments collected at eight different locations of the estuary of the Nerbioi-Ibaizabal River (Bilbao, Basque Country) during 12 sampling campaigns conducted every 3 months between 2005 and 2008. The study aims to present a simple methodology of general applicability which may result in a flexible and practical tool to assess chemical pollution in sediments of a given specific site.     

Enhancement of source traceability of atmospheric PM by elemental chemical fractionation

During a 1-year study (“Fine dust” Project) funded by the Lazio regional government (Italy), about 1000 daily PM₁₀ and PM_2.5 samples collected from six sites in the region were subjected to chemical fractionation based on differences in elemental solubility. In this way, it was possible to achieve meaningful characterization of the elemental composition of individual samples. For most of the investigated elements, we found significant differences between the extracted and the mineralized residual fraction. In general, fine particulate was best characterized by the composition of the extracted fraction, while coarse particles from traffic-related sources were best characterized using residues. For several metals (Cd, Pb, Sn, Sb and V) having a critical environmental impact, this result was particularly clear.The application of Principal Component Analysis (PCA) and receptor modelling (PCR) to the data set allowed us to evidence the enhancement of selectivity towards different emission sources that is obtained when chemical fractionated data are considered instead of total element content. Chemical fractionation seems to generate very selective markers for specific emission sources and in particular for re-suspended road dusts, one of the main factors responsible for the increase of elemental concentrations in urban areas.     

Influence of benomyl and prometryn on the soil microbial activities and community structures in pasture grasslands of Slovakia

The effects of pesticides (a herbicide and a fungicide) on the microbial community structure and their activity were analyzed in soil from four alpine pasture grasslands in Slovakia. Specifically, the effects of the herbicide, Gesagard (prometryn active ingredient), and fungicide, Fundazol 50 WP (benomyl active ingredient), on the microbial respiration activity (CO₂ production), the numbers of selective microbial physiological groups (CFU.g^?1) and the structure (relative abundance) of soil microbial communities [(phospholipid fatty acid (PLFA)] were analyzed under controlled laboratory conditions. All treatments including the treatments with pesticides increased (statistically significantly) the production of CO₂ in all fields during 21 days of incubation and posed a statistically insignificant negative influence on the numbers of the observed physiological groups of microorganisms. The significantly negative influence was evaluated only in the numbers of two physiological groups; spores of bacteria utilizing organic nitrogen and bacteria, and their spores utilizing inorganic nitrogen. A shift in the microbial composition was evident when the PLFA patterns of samples from different sites and treatments were compared by the Principal Component Analysis (PCA). According to the second component PCA 2 (15.95 %) the locations were grouped into two clusters. The first one involved the Donovaly and Dubakovo sites and the second one contained the Velka Fatra and Mala Fatra locations. The PLFA composition of the soils showed important changes after the treatment with pesticides according to PCA 1 (66.06 %). Other treatments had not had a significant effect on the soil microbial community with the exception of the population of fungi. The lower relative abundance (significant effect) of Gram-positive bacteria, actinomycetes and general group of bacteria were determined in samples treated by the herbicide Gesagard. The application of fungicide Fundazol decreased (statistically significantly) the relative abundance of actinomycetes and general group of bacteria and paradoxically increased the population of fungi.     

Indication of airborne pollution by birch and spruce in the vicinity of copper smelter

The aim of the study was determination of air pollution impact of the copper smelter in Bor and its surroundings (Serbia) by assessing the suitability of birch (Betula pendula Roth.) and spruce (Picea abies L.) for the purposes of biomonitoring and comparing it with previously published data from the same study area. The concentrations of Cu, Zn, Pb and Mn in leaves/needles, branches, roots and soil were determined. Sampling was performed during 2009 in two zones with high load of air pollution due to copper mining and smelting activities, and one background zone. Metal accumulation and translocation was evaluated in terms of biological factors. In addition, plant enrichment factor was calculated. According to the results, plant foliage was not enriched through soil, which indicates absorption from the air, with both species acting as excluders of Cu, Pb, Zn and Mn. Leaves were more enriched with all the metals than needles, indicating a better response of birch to airborne pollution than spruce. Cluster analysis showed different level of pollution at the sites, while correlations between Cu and Pb obtained by Principal Component Analysis indicated their anthropogenic origin. Regarding previously published results, beside birch leaves, pine needles (which showed higher level of response to pollution compared to linden leaves) could be applied in air biomonitoring surveys near copper smelters.     

Assessing spatial variability of SO2 field as detected by an air quality network using Self-Organizing Maps,cluster, and Principal Component Analysis

In Bilbao (Spain), an air quality network measures sulphur dioxide levels at 4 locations. The objective of this paper is to develop a practical methodology to identify redundant sensors and evaluate a network's capability to correctly follow and represent SO₂ fields in Bilbao, in the frame of a continuous network optimization process.The methodology is developed and tested at this particular location, but it is general enough to be useable at other places as well, since it is not tied neither to the particular geographical characteristics of the place nor to the phenomenology of the air quality over the area.To assess the spatial variability of SO₂ measured at 4 locations in the area, three different techniques have been used: Self-Organizing Maps (SOMs), cluster analysis (CA) and Principal Component Analysis (PCA). The results show that the three techniques yield the same results, but the information obtained via PCA can be helpful not only for that purpose but also to throw light on the major mechanisms involved. This might be used in future network optimization stages. The main advantage of cluster analysis and SOMs is that they provide readily interpretable results. All the calculations have been carried out using the freely available software R.     

The use of real-time monitoring data to evaluate major sources of airborne particulate matter

Real-time chemical measurements have been made as part of a field study of air quality in the city and harbour of Cork, Ireland. The data relate to the year 2008, with particular attention paid to the period between May and August. Eight air quality parameters were measured: NO, O₃, NO₂, SO₂, EC, OC, particulate SO₄^2? and PM_2.5. The data have been used in a novel way involving wind and temporal averaging, along with Principal Component Analysis (PCA) and Positive Matrix Factorisation (PMF) methodologies to extrapolate major source contributions for PM_2.5. It is demonstrated that continuous monitoring of standard air quality parameters, such as NO, NO₂, SO₂, along with EC, OC and particulate SO₄^2?, can be used to provide relevant, cost-effective initial estimates of source contributions to ambient PM_2.5 levels. It is also shown that the benefit of including OC and particulate SO₄^2? in the monitoring protocol is considerable. Three major source groups of ambient PM_2.5 mass in Cork were identified and quantified using this combined monitoring and modelling approach; road transport (19%), domestic solid fuel burning (14%) and oil-fired domestic and industrial boilers, including power generation plants (31%).