The emission of platinum group elements (PGEs) from automobile catalytic converters has led to rapid increases in Pt, Pd, and Rh concentrations in roadside media. This article represents the first systematic study in Greece to assess PGE levels in road dust and roadside soil and their temporal variation on a seasonal basis over a 12-month period. Road dust and roadside soil samples were analyzed by graphite furnace atomic absorption spectrometry following microwave digestion. Concentrations of PGE in all samples were above the average upper crust values and local background levels, with maximum values of 306.4 ng/g Pt, 18.2 ng/g Pd, and 64.6 ng/g Rh in road dust and 225 ng/g Pt, 14.0 ng/g Pd, and 49.5 ng/g Rh in roadside soil. PGE ratios were not consistent with known catalytic converter composition throughout the sampling period reflecting the high emission of Rh from new catalytic converters. Spatial variations were influenced by various factors like driving style, matrix composition, diurnal change in traffic volume, and climatic conditions. With regard to seasonal variations, systematic changes in the temporal distribution of PGE levels were observed and followed a similar pattern between the two matrices. Except PGEs, Pb levels were determined, since it has been a typical indicator of vehicle-derived environmental lead pollution. The data indicate that despite the use of unleaded gasoline, traffic-related sources of Pb that induce levels above the local background have not been eliminated.

Due to the dry Mediterranean climate in Cyprus, particulate matter is resuspended from soils and other surfaces. From November 2002 to August 2003, gravimetric PM₁₀ measurements were carried out at three characteristic sites (traffic, residential and rural). A significant seasonal trend with high winter concentrations was observed at the traffic site. Special events, e.g. long-range transport of Sahara dust storms, were recorded over traffic, residential and rural areas in the order of six to eight events per year, with a major frequency in summer and spring periods. This contributes to the increase of 24-h EU limit value exceedances for PM₁₀ at the three investigated sites. The origin of the PM₁₀ load was determined by enrichment factors based on analyses of the local soil deposition at the investigated sites. Furthermore, positive matrix factorisation modelling was applied to find the sources of PM₁₀. Results indicate that the major emission sources affecting the PM₁₀ load were mineral soil, sea salt, road dust, oil combustion, secondary pollutants and gasoline vehicles. The natural contribution (local mineral soil and sea salt) at the three sites was in the range of 7-9 μg m⁻³ in PM₁₀. Besides the Sahara dust storms and natural background concentrations, the vehicular pollution was found as the largest contributor (12-14 μg m⁻³) to PM₁₀ load at the traffic site.

Fish and piscivorous bird eggs collected in 2003 from Lake Maggiore (Italy), were analysed for PCB and DDT contamination. Lake Maggiore has been severely polluted by DDT through production of the pesticide within its catchment. Although agricultural application of DDT was banned in Italy in the 1978, industrial production continued until 1996, with enough contamination of water and soil for serious bioaccumulation in the lake biota. PCB and DDT concentrations in a whitefish (Coregonus macrophthalmus Nusslin 1882) were seen to be dependent on season and fish age, but not on sex. The average increase of the lipid-normalised concentration of DDTs and PCBs was two-fold across season and also across age, resulting in an overall increase of four fold. The seasonal variation was related to the eco-physiological cycle of the fish and to the contamination dynamic of the lake, while the effect of the fish age was explained on the base of biomagnification-related mechanisms. A fugacity model was applied to predict the age-dependent bioaccumulation potential of PCBs, whose concentrations were rather stable in recent years in the lake. Predicted values for compounds with negligible biotransformation were in good agreement with experimental data (calculated vs. experimental mean difference of 14%), and a relationship between the increase of experimental age-dependent concentration and K ow was observed. The good correspondence between the predicted and the measured values for most PCB congeners confirmed the general inability of fishes to biotransform these compounds. On the contrary, the importance of biotransformation processes was recognised in birds; eggs of a fish eating bird (Podiceps cristatus) from the same area selectively bioaccumulated p,p'-DDE. For PCBs, congener 149 appears to be completely metabolized by the bird species, and congeners 95, 101, 132, 151 and 174 were reduced as well. The role of the meta-para free position on at least one phenyl ring of PCB congeners in biotransformation processes was confirmed.

The unsaturated zone of an aquifer serves as a water reservoir which discharges water and eventual pollution to the saturated zone for a relatively long period after the cessation of surface input. Effective protection of a water resource requires detailed knowledge of transport mechanisms through the unsaturated zone with regard to its protective function. The article presents the application of isotope methods in the study of groundwater transport processes in the unsaturated zone of Selniška Dobrava coarse gravel aquifer. Emphasis is given to the use of environmental isotopes as natural tracers in the study of groundwater dynamics in the unsaturated zone. The estimation of groundwater flow characteristics was based on experimental work in lysimeter. Based on long-time isotope investigations with the use of lumped parameter models, some water flow parameters (mean residence time, mean matrix flow velocity) in the unsaturated zone were calculated. The results were compared with tracing experiment results in the same lysimeter.

Nonpoint source pollution is the leading remaining cause of water quality problems. The extent of NPS pollution is often more difficult or expensive to monitor at the point(s) of origin, as compared to monitoring of point sources. This study evaluated the hypothesis that animal manure (chicken, cow, horse, and pig) applied to pasture contribute fecal sterols and bile acids to runoff. The study also assessed the potential benefit of fecal sterols and bile acids as biomarkers in distinguishing fecal pollution and its sources. Fecal sterol and bile acid concentrations were determined in flow-weighted composite runoff samples collected from 2.4 x 6.1 m plots (n = 3) amended with manure. Runoff was generated from simulated rainfall (152 mm.h⁻¹). Runoff samples from manure-amended plots showed high concentrations of fecal sterol (ranged from 13 ± 1 to 1,287 ± 183) and bile acid (ranged from 24 ± 1 to 2,251 ± 248) biomarkers. The profiles of fecal sterols and bile acids in runoff samples were similar to those of fresh manure for all selected animals. For runoff and fresh manure, chenodeoxycholic acid, deoxycholic acid, epicoprostanol, and hyodeoxycholic acid were consistent biomarkers for chicken, cow, horse, and pig, respectively, suggesting that sterols and bile acids can be used to identify sources and occurrence of fecal matter in water and sediments.

The problem of air pollution is a frequently recurring situation and its management has social and economic considerable effects. Given the interaction of the numerous factors involved in the raising of the atmospheric pollution rates, it should be considered that the relation between the intensity of emission produced by the polluting source and the resulting pollution is not immediate. The aim of this study was to realise and to compare two support decision system (neural networks and multivariate regression model) that, correlating the air quality data with the meteorological information, are able to predict the critical pollution events. The development of a back-propagation neural network is presented to predict the daily PM₁₀ concentration 1, 2 and 3 days early. The measurements obtained by the territorial monitoring stations are one of the primary data sources; the forecasting of the major weather parameters available on the website and the forecasting of the Saharan dust obtained by the “Centro Nacional de Supercomputaciòn” website, satellite images and back trajectories analysis are used for the weather input data. The results obtained with the neural network were compared with those obtained by a multivariate linear regression model for 1 and 2 days forecasting. The relative root mean square error for both methods shows that the artificial neural networks (ANN) gives more accurate results than the multivariate linear regression model mostly for 1 day forecasting; moreover, the regression model used, in spite of ANN, failed when it had to fit spiked high values of PM₁₀ concentration.

A yearlong (December 2003 to February 2005) monitoring program was undertaken for urban roadside measurement of benzene, toluene, ethyl benzene, m- and p-xylene, and o-xylene (BTEX) at three different sites of Kolkata, India. The concentrations of monoaromatic hydrocarbons were found to be sufficiently high. Chemical mass balance model was applied to identify the sources and estimate their percentage contribution. Vehicular exhaust emission was found to be the dominant source of the target compounds and contributed 38.8-44.8% toward total volatile organic compound (VOC) level. Assuming that the vehicular exhaust fraction of the ambient BTEX level was due to the vehicular activity in the adjacent road of the monitoring site, vehicular emission factors for individual VOCs were estimated by running CALINE4 dispersion model in an inverse way. The total emission factor, average for all vehicles, was found to be in the range of 9.1 to 43.1 mg vehicle⁻¹ km⁻¹ for BTEX. From the measured vehicular composition during sampling, the category-wise emission factors for light-duty vehicles (LDVs), medium-duty (MDVs), and heavy-duty vehicles (HDVs), were also estimated by constrained nonlinear regression analysis. The emission factor of benzene for heavy, medium, and light vehicles was found to be 13.4, 21.0, and 31.2 mg vehicle⁻¹ km⁻¹ respectively.

Comprehensive survey of major rivers in the Tokyo metropolitan area was conducted for clarifying the emission sources of perfluorooctane sulfonate (PFOS) in Tokyo. PFOS was found at all sampling sites at concentrations ranging from 0.5 to 58 ng L⁻¹; in addition to this, it was also indicated that unknown PFOS emission sources are present in the midstream of the Tama River basin. The relationship between PFOS and perfluorooctanoic acid (PFOA) was constant at a ratio of 10:3 (PFOS/PFOA) throughout the Tama River basin. The sum of daily load amounts of PFOS from Tokyo's major rivers to Tokyo Bay reached 215 g day⁻¹. This value corresponds to 12.8 μg day⁻¹ per person using the sum of wastewater treatment district populations. In contrast, an estimation of PFOS contribution of domestic wastewater was also attempted, and the contribution was 1.6 μg day⁻¹ per person. We took samples up trunk sewers in the Tama River and finally found at the highest PFOS concentration (58,000 ng L⁻¹) from one of the wastewater of the electronic parts manufacturing facilities.

A simple algorithm for merging aerosol number particle size distribution (AMANpsd) data from multiple instruments is presented. The code uses a multilognormal model for the evaluation of merged aerosol particle size distribution data. AMANpsd is able to recalculate the aerodynamic diameter to the mobility diameter in the cases when, e.g., the Aerodynamic Particle Sizer data need to be connected to the data obtained by the Scanning Mobility Particle Sizer. The only assumption is the lognormality of the particle size distribution. As a result, the algorithm gives the parameters of the multilognormal model (particle number concentration, geometric mean diameter, and geometric standard deviation of each single lognormal mode). The algorithm was tested on a set of data from intensive indoor/outdoor measurement campaigns performed in Prague (Czech Republic), Oslo (Norway), and Milan (Italy). The particle size distribution data set covers the range of particle mobility diameters from 2 nm up to 8 μm. The agreement between the multilognormal model and the available experimental data from multiple instruments was satisfactory.

This paper presents experimental data on particulate matter (PM₁₀ and PM₂.₅) in the atmosphere of Candiota--South Brazil. Samples were collected using stacked filter units equipped with polycarbonate filters, which separate particles into two fractions: coarse 10-2.5 μm and fine <2.5 μm. The particulate matter was collected from January 2003 to September 2004 at three sampling sites: Acegua, 8 de Agosto, and Três Lagoas. The collected material was analyzed for Na, Mg, Al, Si, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Se, Br, Mo, Hg, and Pb, using particle-induced X-ray emission technique. Seasonal variation of the chemical elements with the size fraction was assessed. The elements K, Ca, Si, Ti, Fe, and Mn showed similar seasonal variability in the fine and coarse fractions with a low enrichment factor (<1.0) in both fractions, except for Mn and K, indicating common sources (soil dust, fly ash). Chemical species showing seasonal variability and high values of enrichment factor in fine fractions may indicate an anthropogenic origin.