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.     

Estimating the concentration of indoor particles of outdoor origin: A review

Recent toxicological results highlight the importance of separating exposure to indoor- and outdoor-generated particles, due to their different physicochemical and toxicological properties. In this framework, a number of studies have attempted to estimate the relative contribution of particles of indoor and outdoor origins to indoor concentrations, using either statistical analysis of indoor and outdoor concentration time-series or mass balance equations. The aim of this work is to review and compare the methodologies developed in order to determine the ambient particle infiltration factor (F _INF) (i.e., the fraction of ambient particles that enter indoors and remains suspended). The different approaches are grouped into four categories according to their methodological principles: (1) steady-state assumption using the steady-state form of the mass balance equation; (2) dynamic solution of the mass balance equation using complex statistical techniques; (3) experimental studies using conditions that simplify model calculations (e.g., decreasing the number of unknowns); and (4) infiltration surrogates using a particulate matter (PM) constituent with no indoor sources to act as surrogate of indoor PM of outdoor origin. Examination of the various methodologies and results reveals that estimating infiltration parameters is still challenging. The main difficulty lies in the separate calculation of penetration efficiency (P) and deposition rate (k). The values for these two parameters that are reported in the literature vary significantly. Deposition rate presents the widest range of values, both between studies and size fractions. Penetration efficiency seems to be more accurately calculated through the application of dynamic models. Overall, estimates of the infiltration factor generated using dynamic models and infiltration surrogates show good agreement. This is a strong argument in favor of the latter methodology, which is simple and easy to apply when chemical speciation data are available.

Implications: ?Taking into account that increased health risks may be related with ambient particles, a reliable estimation of the main parameters governing ambient particle infiltration indoors may assist towards the development of appropriate regulation and control measures, targeted to specific sources/factors contributing to increased exposures. The overall study of the methodological approaches estimating particle infiltration indoors suggests that dynamic models provide a more complete and realistic picture of ambient particle infiltration indoors, whereas the use of specific PM constituents to act as surrogates of indoor particles of outdoor origin seems also a promising new methodology.     

Indoor and outdoor PM mass and number concentrations at schools in the Athens area

Simultaneous indoor and outdoor PM₁₀ and PM_2.5 concentration measurements were conducted in seven primary schools in the Athens area. Both gravimetric samplers and continuous monitors were used. Filters were subsequently analyzed for anion species. Moreover ultrafine particles number concentration was monitored continuously indoors and outdoors. Mean 8-hr PM₁₀ concentration was measured equal to 229 ± 182 μg/m³ indoors and 166 ± 133 μg/m³ outdoors. The respective PM_2.5 concentrations were 82 ± 56 μg/m³ indoors and 56 ± 26 μg/m³ outdoors. Ultrafine particles 8-h mean number concentration was measured equal to 24,000 ± 17,900 particles/cm³ indoors and 32,000 ± 14,200 particles/cm³ outdoors. PM₁₀ outdoor concentrations exhibited a greater spatial variability than the corresponding PM_2.5 ones. I/O ratios were close or above 1.00 for PM₁₀ and PM_2.5 and smaller than 1.00 for ultrafine particles. Very high I/O ratios were observed when intense activities took place. The initial results of the chemical analysis showed that accounts for the 6.6 ± 3.5% of the PM₁₀ and for the 3.1 ± 1.4%.The corresponding results for PM_2.5 are 12.0 ± 7.7% for and 3.1 ± 1.9% for . PM_2.5 indoor concentrations were highly correlated with outdoor ones and the regression line had the largest slope and a very low intercept, indicative of no indoor sources of fine particulate . The results of the statistical analysis of indoor and outdoor concentration data support the use of as a proper surrogate for indoor PM of outdoor origin.     

Particulate matter and black smoke concentration levels in central Athens, Greece

This study presents the statistical analysis of PM(10) and PM(2.5) concentrations (measured at a central site, in the Athens area), along with black smoke (BS) data, for a 2-year period. The biennial average concentrations of PM(10) and PM(2.5) were 75 and 40 microg m(-3). The respective average concentration of BS, as estimated by the OECD method, was 108 microg m(-3). Severe exceedances of the PM(10) air quality standards were recorded. The seasonal variation of PM(10) and BS was less pronounced than the variation of PM(2.5), which concentration was elevated by 14.2% during the cold period. Concentrations of all three pollutants were significantly lower during weekends; however, PM(2.5) and BS displayed a more uniform weekly distribution pattern. PM(10) particles were found to be almost equally comprised by PM(2.5) and PM(10-2.5) particles (PM(2.5)/PM(10) ratio=0.53+/-0.09 microg/m(3)). The average PM(10)/BS value was found lower than unity revealing the inappropriateness of the used reflectance conversion method, for the estimation of mass-equivalent BS concentrations, in the study area, where diesel-powered vehicles mainly control emissions of light-absorbing substances. Important reductions in concentrations were observed during days when drivers of diesel-powered taxies and transportation buses went on strike (reaching 40% for BS). Calm wind conditions were found to have an incremental effect on particle concentrations and were also associated with the appearance of persistent episodic events. Increased PM levels were also observed during southern-southwestern wind flows while significantly lower-than-average concentrations were measured during precipitation events. Separate regression analyses were performed for PM(10), PM(2.5) with BS and NO(x) as independent variables, in an attempt to estimate the relative contribution of specific source types (diesel-powered vehicles) to measured particle levels. The contribution of the diesel-exhaust component to PM(10) mass was estimated at 49.9%, while the corresponding contributions to PM(2.5) mass concentrations was 53.8%. These results may have important implications with the oncoming decision of national authorities to allow the purchase of diesel-powered private cars to the residents of the Greater Athens Area, which was forbidden up to this day.     

PM10-bound polycyclic aromatic hydrocarbons (PAHs) in the Greater Area of Athens, Greece

Polycyclic aromatic hydrocarbons (PAHs) associated to ambient PM10 were determined at four sites within the Greater Athens Area (GAA), Greece, during the period May 2001-June 2002. Daily average PM10 samples were collected using reference samplers installed in two urban locations (Maroussi, MAR and Aristotelous, ARI), a mixed urban-industrial location (Elefsina, ELE), and a background location (Thracomacedones, THR). Spatial and temporal variation of ambient PAH levels and possible relationships with conventional air pollutants and meteorological parameters were investigated. Moreover, source identification was attempted using diagnostic ratios. The PAH concentrations observed in this study, even in downtown Athens, were towards the lowest end of the range of values reported for other European urban locations. The mean concentration of the well known carcinogen B[alpha]Py was at all four sites below the proposed value of 1 ng m-3 (annual average assessment threshold). Automobile traffic was identified as the major PAH source in the GAA exhibiting greatest contribution in the urban sites.     

Toxic metal content of particulate matter (PM10), within the Greater Area of Athens

This study presents results from a yearlong particulate matter measurement campaign, conducted across the Greater Athens Area, at four locations, between 1st June 2001 and 31st May 2002. The collected PM(10) 24-h samples were analyzed for nine toxic metals and metalloids (Pb, As, Cd, Ni, Cr, Mn, V, Cu, Hg). Concerning the five elements regulated by the European Union, annual average concentrations of Pb were found below the limit values at all sites, Cd and Ni concentrations were lower than the prospective assessment thresholds at all sites, concentrations of As exceeded the assessment threshold at two sites, while concentrations of Hg were found below detection limits in all samples. Concentration levels of Mn and V were in compliance with the values proposed by the World Health Organization. The seasonal and spatial variability of metal concentrations was examined and site-specific correlation analysis was conducted for the identification of metals with similar origin. The association between trace metals and NO(x) concentrations was explored to account for the impact of automotive sources, at two traffic-impacted sites. Cu was the metal most closely linked with the road transport sector. The relation of concentration levels with the prevalence of winds from different sectors was studied in an effort to investigate the transport of metal particles from various zones of the city. Finally, factor analysis was carried out to extract the main components responsible for the variance of the dataset and to attribute them to specific source categories, with vehicle-related sources being important in all cases.     

Evaluation of the implementation of best available techniques in IPPC context: an environmental performance indicators approach

In this paper an integrated methodological approach is presented for the evaluation of the implementation of the Best Available Techniques (BAT) in facilities operated under Integrated Prevention Pollution and Control (IPPC) Directive, based on the development and application of a set of sub-indices. An aggregated form of sub-indices that could be used as a composite IPPC facility index is proposed. A sector specific index can be derived from facilities composite indices.This approach uses environmental performance data from European Polluting Emissions Register (EPER) reports, environmental permits and BAT Reference Documents, and gives a coherent and interesting picture of the degree of BAT implementation and an indication of IPPC obligations fulfilment.A specific application is demonstrated concerning the paper manufacturing sector in Greece. It is concluded for this sector that there is a measurable discrepancy from the BAT fulfilment obligations and that the most problematic sub-indices are the “Releases to Water” and “Releases to Air”.     

Neural Network and Multiple Regression Models for PM10 Prediction in Athens: A Comparative Assessment

Abstract

Particulate atmospheric pollution in urban areas is considered to have significant impact on human health. Therefore, the ability to make accurate predictions of particulate ambient concentrations is important to improve public awareness and air quality management. This study examines the possibility of using neural network methods as tools for daily average particulate matter with aerodynamic diameter <10 µm (PM₁₀) concentration forecasting, providing an alternative to statistical models widely used up to this day. Based on a data inventory, in a fixed central site in Athens, Greece, ranging over a two-year period, and using mainly meteorological variables as inputs, neural network models and multiple linear regression models were developed and evaluated. Comparison statistics used indicate that the neural network approach has an edge over regression models, expressed both in terms of prediction error (root mean square error values lower by 8.2–9.4%) and of episodic prediction ability (false alarm rate values lower by 7–13%). The results demonstrate that artificial neural networks (ANNs), if properly trained and formed, can provide adequate solutions to particulate pollution prognostic demands.     

Neural network and multiple regression models for PM10 prediction in Athens: a comparative assessment

Particulate atmospheric pollution in urban areas is considered to have significant impact on human health. Therefore, the ability to make accurate predictions of particulate ambient concentrations is important to improve public awareness and air quality management. This study examines the possibility of using neural network methods as tools for daily average particulate matter with aerodynamic diameter <10 microm (PM10) concentration forecasting, providing an alternative to statistical models widely used up to this day. Based on a data inventory, in a fixed central site in Athens, Greece, ranging over a two-year period, and using mainly meteorological variables as inputs, neural network models and multiple linear regression models were developed and evaluated. Comparison statistics used indicate that the neural network approach has an edge over regression models, expressed both in terms of prediction error (root mean square error values lower by 8.2-9.4%) and of episodic prediction ability (false alarm rate values lower by 7-13%). The results demonstrate that artificial neural networks (ANNs), if properly trained and formed, can provide adequate solutions to particulate pollution prognostic demands.     

Forecasting Daily Maximum Ozone Concentrations in the Athens Basin

In the work ozone data from the Liossion monitoring station of the Athens/PERPA network are analysed. Data cover the months May to September for the period 1987–93. Four statistical models, three multiple regression and one ARIMA (0,1,2), for the prediction of the daily maximum 1-hour ozone concentrations are developed. All models together, with a persistence forecast, are evaluated and compared with the 1993's data, not used in the models development. Validation statistics were used to assess the relative accuracy of models. Analysis, concerning the models' ability to forecast real ozone episodes, was also carried out. Two of the three regression models provide the most accurate forecasts. The ARIMA model had the worst performance, even lower than the persistence one. The forecast skill of a bivariate wind speed and persistence based regression model for ozone episode days was found to be quite satisfactory, with a detection rate of 73% and 60% for O3 >180 g m-3 and O3 >200 g m-3, respectively.