PAH air pollution at a Portuguese urban area: carcinogenic risks and sources identification

This study aimed to characterize air pollution and the associated carcinogenic risks of polycyclic aromatic hydrocarbon (PAHs) at an urban site, to identify possible emission sources of PAHs using several statistical methodologies, and to analyze the influence of other air pollutants and meteorological variables on PAH concentrations.The air quality and meteorological data were collected in Oporto, the second largest city of Portugal. Eighteen PAHs (the 16 PAHs considered by United States Environment Protection Agency (USEPA) as priority pollutants, dibenzo[a,l]pyrene, and benzo[j]fluoranthene) were collected daily for 24 h in air (gas phase and in particles) during 40 consecutive days in November and December 2008 by constant low-flow samplers and using polytetrafluoroethylene (PTFE) membrane filters for particulate (PM₁₀ and PM_2.5 bound) PAHs and pre-cleaned polyurethane foam plugs for gaseous compounds. The other monitored air pollutants were SO₂, PM₁₀, NO₂, CO, and O₃; the meteorological variables were temperature, relative humidity, wind speed, total precipitation, and solar radiation. Benzo[a]pyrene reached a mean concentration of 2.02 ng?m^?3, surpassing the EU annual limit value. The target carcinogenic risks were equal than the health-based guideline level set by USEPA (10^?6) at the studied site, with the cancer risks of eight PAHs reaching senior levels of 9.98?×?10^?7 in PM₁₀ and 1.06?×?10^?6 in air. The applied statistical methods, correlation matrix, cluster analysis, and principal component analysis, were in agreement in the grouping of the PAHs. The groups were formed according to their chemical structure (number of rings), phase distribution, and emission sources. PAH diagnostic ratios were also calculated to evaluate the main emission sources. Diesel vehicular emissions were the major source of PAHs at the studied site. Besides that source, emissions from residential heating and oil refinery were identified to contribute to PAH levels at the respective area. Additionally, principal component regression indicated that SO₂, NO₂, PM₁₀, CO, and solar radiation had positive correlation with PAHs concentrations, while O₃, temperature, relative humidity, and wind speed were negatively correlated.     

Author’s Reply

A technique is developed to compute precision requirements for component parts of an emissions inventory to ensure (at a given confidence level) an overall acceptable precision in the estimate of total emissions. Since the emissions inventory is a basic requirement of air quality control implementation plans and provides a valuable management tool for planning air pollution control activities, it isi appropriate to state in quantitative terms the confidence that can be associated with each inventory. The approach reported here uses weighted sensitivity analysis methods to distribute both percentage and physical errors in source class emissions according to their contribution to the total emissions, and utilizes Chebyshev’s inequality to establish confidence levels for total emissions. The analysis has been extended to cover the case where one or more of the error components in a given inventory source class can be fixed by the analyst. The utility of the technique is manifold and several practical applications are reported. In particular, it serves to establish percentage error requirements for source categories to satisfy given error bounds for the overall emissions inventory at a given level of statistical confidence. The weighted sensitivity analysis technique possesses a high degree of generality, being applicable to compute component error requirements for any kind of data inventory which exhibits a hierarchical (tree-like) structure, as exemplified by NEDS Emissions Summary Reports. This work should be of interest to air pollution control planners at all levels of government and to anyone responsible for the air pollution portion of environmental impact statements.     

Turbulent Plume in a Turbulent Cross Flow: Comparison of Wind Tunnel Tests with Field Observations

Plume rise downwind of a large stationary gas turbine was measured in the field and the conditions were then scaled in the laboratory. For the laboratory, the plume exit conditions, wind velocity and temperature profiles, and wind direction were matched. It was found that for high temperature exhaust, the buoyancy is best matched by calculating a dimensionless density difference. With properly calculated buoyancy length scales, the plume trajectories were compared and were found to agree quite well. The probability distributions of the entrainment constant and the average values of the entrapment constant with downwind distance were compared. The field data showed about 15% greater plume rise. The median entrainment constant was about 10% greater for the lab test and the shape of the probability distribution matched very closely.     

A Critical Review

Atmospheric pollution attributed to increased combustion of fossil fuels has been implicated as a likely contributing factor in observed changes to forests in North America and Europe. Uncertainties surrounding the mechanisms and specific causes for these changes have prompted intensified interest in research on air pollution effects on forests. Major objectives of this review are 1) to provide a conceptual and historical perspective against which today's problems, concerns, and approaches can be evaluated; 2) to evaluate evidence of changes in growth and vitality of some tree species in today's forests; 3) to compare levels of principal atmospheric pollutants in Europe and the U.S.; 4) to describe mechanisms, pathways, and concentration thresholds for effects of principal pollutants on tree growth and physiology; 5) to summarize current evidence for the role of pollution in observed forest declines; and 6) to provide some perspectives on future research in this area. Evidence from laboratory and field research is examined to provide a basis for evaluating the role of atmospheric pollution in present and potential future forest responses.