On the Methods for the Delimitation of Seasons

It is well known that air pollution is one of the major factors affecting human health and the activities in a town. Among the various air pollutants an interesting one, especially for sunny climates, is the reactive O₃. O₃ varies significantly through the year; it is therefore very important to know the onset and the duration of the warm period of the year since at that time O₃ concentrations increase significantly. In this work, an attempt was made to determine the onset of warm season of the year as well as its duration in various sites located in Northeastern Mediterranean. For this purpose, a fuzzy clustering method is used. The method was applied on the isobaric thickness between 1000 and 500 hPa for the period from 1974 to 1997. The results were compared to conventional methods based on synoptic classification and were found to be in good agreement.     

Development of an aggregate Air Quality Index for an urban Mediterranean agglomeration: relation to potential health effects

It is very useful for the authorities and the people to have daily easy understandable information about the levels of air pollution and the proper measures to be taken for the protection of human health. In this paper we develop an aggregate Air Quality Index (AQI) based on the combined effects of five criteria pollutants (CO, SO2, NO2, O3 and PM10) taking into account the European standards. We evaluate it for each monitoring station and for the whole area of Athens, Greece, an area with serious air pollution problems. A comparison was made with a modified version of Environmental Protection Agency/USA (USEPA) maximum value AQI model adjusted for European conditions. Hourly data of air pollutants from 4 monitoring stations, available during 1983-1999, were analysed for the development of the proposed index. The analysis reveals the Athenian population exposure reaches high levels and during last years a gradual increase of days with unhealthy conditions was detected. The proposed aggregate model estimates more effectively the exposure of citizens comparing with the modified USEPA maximum value model, because counts the impact of all the pollutants measured. Towards the informing and protection of the citizens in an urban agglomeration this model advantages as a political and administrative tool for the design of abatement strategies and effective measures of intervention.     

The sensitivity of OMI-derived nitrogen dioxide to boundary layer temperature inversions

We assess the sensitivity of tropospheric nitrogen dioxide (NO₂) derived from the Ozone Monitoring Instrument (OMI), to episodes of temperature inversion in the lower boundary layer. Vertical temperature data were obtained from a 91-m meteorological tower located in the study area, which is centered on the Hamilton Census Metropolitan Area, Ontario, Canada. Hamilton is an industrial city with high traffic volumes, and is therefore subjected to high levels of pollution. Pollution buildup is amplified by frequent temperature inversions which are commonly radiative, but are also induced by local physiography, proximity to Lake Ontario, and regional meteorology. The four-year period from January 2005 to December 2008 was investigated. Ground-level data for validation were obtained from in situ air quality monitors located in the study area. The results indicate that OMI is sensitive to changes in the NO₂ levels during temperature inversions, and exhibits changes which roughly parallel those of in situ monitors. Overall, an 11% increase in NO₂ was identified by OMI on inversion days, compared to a 44% increase measured by in situ monitors. The weekend effect was clearly exhibited under both normal and inversion scenarios with OMI. Seasonal and wind direction patterns also correlated fairly well with ground-level data. Temperature inversions have resulted in poor air quality episodes which have severely compromised the health of susceptible populations, sometime leading to premature death. The rationale for this study is to further assess the usefulness of OMI for population exposure studies in areas with sparse resources for ground-level monitoring.     

A land use regression model for predicting ambient volatile organic compound concentrations in Toronto,Canada

More than 25 studies have employed land use regression (LUR) models to estimate nitrogen oxides and to a lesser extent particulate matter indicators, but these methods have been less commonly applied to ambient concentrations of volatile organic compounds (VOCs). Some VOCs have high plausibility as sources of health effects and others are specific indicators of motor vehicle exhaust. We used LUR models to estimate spatial variability of VOCs in Toronto, Canada. Benzene, n-hexane and total hydrocarbons (THC) were measured from July 25 to August 9, 2006 at 50 locations using the TraceAir organic vapor monitors. Nitrogen dioxide (NO₂) was also sampled to assess its spatial pattern agreement with VOC exposures. Buffers for land use, population density, traffic density, physical geography, and remote sensing measures of greenness and surface brightness were also tested. The remote sensing measures have the highest correlations with VOCs and NO₂ levels (i.e., explains >36% of the variance). Our regression models explain 66–68% of the variance in the spatial distribution of VOCs, compared to 81% for the NO₂ model. The ranks of agreement between various VOCs range from 48 to 63% and increases substantially – up to 75% – for the top and bottom quartile groups. Agreements between NO₂ and VOCs are much smaller with an average rank of 36%. Future epidemiologic studies may therefore benefit from using VOCs as potential toxic agents for traffic-related pollutants.     

Measurements of benzene and formaldehyde in a medium sized urban environment. Indoor/outdoor health risk implications on special population groups

In the present study, the results of a measurement campaign aiming to assess cancer risk among two special groups of population: policemen and laboratory technicians exposed to the toxic substances, benzene and formaldehyde are presented. The exposure is compared to general population risk. The results show that policemen working outdoor (traffic regulation, patrol on foot or in vehicles, etc.) are exposed at a significantly higher benzene concentration (3-5 times) than the general population, while the exposure to carbonyls is in general lower. The laboratory technicians appear to be highly exposed to formaldehyde while no significant variation of benzene exposure in comparison to the general population is recorded. The assessment revealed that laboratory technicians and policemen run a 20% and 1% higher cancer risk respectively compared to the general population. Indoor working place air quality is more significant in assessing cancer risk in these two categories of professionals, due to the higher Inhalation Unit Risk (IUR) of formaldehyde compared to benzene. Since the origin of the danger to laboratory technicians is clear (use of chemicals necessary for the experiments), in policemen the presence of carbonyls in indoor air concentrations due to smoking or used materials constitute a danger equal to the exposure to traffic originated air pollutants.     

Behavior study of trace elements in pulverized lignite, bottom ash, and fly ash of Amyntaio power station, Greece

The Kozani–Ptolemais–Amyntaio basin constitutes the principal coal field of Greece. Approximately 50 % of the total power production of Greece is generated by five power stations operating in the area. Lignite samples, together with the corresponding fly ash and bottom ash were collected, over a period of 3 months, from the power plant of Amyntaio and analyzed for their content in 16 trace elements. The results indicate that Y, Nb, U, Rb, Zr, Ni, Pb, Ba, Zn, Sr, Cu, and Th demonstrate an organic affinity during the combustion of lignite, while V has an inorganic affinity. Three elements (Co, Cr, and Sc) show an intermediate affinity.     

Forecasting hourly PM<Subscript>10</Subscript> concentration in Cyprus through artificial neural networks and multiple regression models: implications to local environmental management

In the present work, two types of artificial neural network (NN) models using the multilayer perceptron (MLP) and the radial basis function (RBF) techniques, as well as a model based on principal component regression analysis (PCRA), are employed to forecast hourly PM₁₀ concentrations in four urban areas (Larnaca, Limassol, Nicosia and Paphos) in Cyprus. The model development is based on a variety of meteorological and pollutant parameters corresponding to the 2-year period between July 2006 and June 2008, and the model evaluation is achieved through the use of a series of well-established evaluation instruments and methodologies. The evaluation reveals that the MLP NN models display the best forecasting performance with R ² values ranging between 0.65 and 0.76, whereas the RBF NNs and the PCRA models reveal a rather weak performance with R ² values between 0.37-0.43 and 0.33-0.38, respectively. The derived MLP models are also used to forecast Saharan dust episodes with remarkable success (probability of detection ranging between 0.68 and 0.71). On the whole, the analysis shows that the models introduced here could provide local authorities with reliable and precise predictions and alarms about air quality if used on an operational basis.     

Assessment and prediction of exposure to benzene of filling station employees

In the present study, the exposure to benzene of employees working in two filling stations (one urban and one rural) was estimated, through the method of passive sampling. Additional data (30′ measurements of benzene exposure through active sampling to employees dealing with different activities, meteorological and traffic data) were collected. The measurements campaign was performed in both summer and wintertime to determine the seasonal variation of the exposure pattern.In addition, a set of artificial neural networks (ANNs) was developed to predict benzene exposure pattern for the filling station employees based on active sampling data and the parameters related to the employees’ exposure. The quantification of the contribution of each parameter to the overall exposure pattern was also attempted.The results showed that although vapour recovery technologies are installed in the refuelling systems and benzene emissions are significantly reduced compared to the past, filling station employees are still highly exposed to benzene (52–15 μg m⁻³). Benzene exposure is strongly correlated to car refuelling (exposure levels up to 85 μg m⁻³), while activities like car washing or working in cash machine inside an office contribute to lower exposure levels (up to 44 and 24 μg m⁻³ respectively). In rural filling station, exposure levels were in general lower compared to the urban ones, due to the smaller amount of gasoline that was traded and the absence of any significant traffic effect or urban background concentration. The developed ANN seemed to be a promising technique in the prediction of the exposure pattern giving very good results, and the quantification of the parameters affirmed the importance of the refueling procedure to the exposure levels.     

Employee judgments of and behaviors toward corporate social responsibility: A multi‐study investigation of direct,cascading, and moderating effects

Do employee judgments of their organization's corporate social responsibility (CSR) programs relate to CSR‐specific performance and in‐role job performance? Can middle managers influence the formation of such judgments and what factors might moderate such cascading influences? To answer these yet unaddressed questions, we conduct three studies. Study 1 takes an organizational justice perspective and tests our baseline model. Results show that employees' CSR judgments trigger their affective commitment and performance on extra‐role CSR‐specific behaviors; however, extra‐role CSR‐specific performance is unrelated to in‐role job performance. Study 2 replicates Study 1's findings while, in addition, applies a social information processing approach and offers novel insights by demonstrating the cascading effects of managers' CSR judgments on employee CSR judgments. Investments made in CSR programs in order to improve employee judgments and behaviors may be unsuccessful if employees' CSR judgments are based on social information that remains unchanged. In addition to replicating the findings from studies 1 and 2, study 3 draws from middle management involvement and leadership theories to show that leadership styles and managers' involvement in implementing deliberate strategy can strengthen or weaken these cascading effects. This highlights the important role of middle managers as “linking pins” in the CSR strategy implementation process. Copyright © 2014 John Wiley & Sons, Ltd.