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.     

Sensitivity of Portuguese forest fires to climatic, human, and landscape variables: subnational differences between fire drivers in extreme fire years and decadal averages

Within the changing fire regimes of Portugal, the relative importance of humans and climatic variability for regional fire statistics remains poorly understood. This work investigates the statistical relationship between temporal dynamics of fire events in Portugal and a set of socioeconomic, landscape, and climatic variables for the time periods of 1980–1990, 1991–2000, and extreme fires years. For 10 of 15 districts, it was possible to observe moderate shifts in the significance of fire drivers for the first two decadal periods. For others, pronounced changes of the significance of fire drivers were found across time. Results point toward a dynamic (perhaps highly non-linear) behavior of socioeconomic and landscape fire drivers, especially during the occurrence of extreme fire years of 2003 and 2005. At country level, population density alone explained 42% of the inter-annual and inter-district deviance in number of fires. At the same temporal and spatial scale, the explanatory power of temperature anomalies proved to explain 43% of area burnt. We highlight the necessity of including a broad set of socioeconomic and landscape fire drivers in order to account for potential significance shifts. In addition, although climate does trigger broad favorable fire conditions across Portugal mainland, socioeconomic and landscape factors proved to determine much of the complex fire patterns at a subnational scale.     

Nonparametric functional data estimation applied to ozone data: prediction and extreme value analysis

The study of extreme values and prediction of ozone data is an important topic of research when dealing with environmental problems. Classical extreme value theory is usually used in air-pollution studies. It consists in fitting a parametric generalised extreme value (GEV) distribution to a data set of extreme values, and using the estimated distribution to compute return levels and other quantities of interest. Here, we propose to estimate these values using nonparametric functional data methods. Functional data analysis is a relatively new statistical methodology that generally deals with data consisting of curves or multi-dimensional variables. In this paper, we use this technique, jointly with nonparametric curve estimation, to provide alternatives to the usual parametric statistical tools. The nonparametric estimators are applied to real samples of maximum ozone values obtained from several monitoring stations belonging to the Automatic Urban and Rural Network (AURN) in the UK. The results show that nonparametric estimators work satisfactorily, outperforming the behaviour of classical parametric estimators. Functional data analysis is also used to predict stratospheric ozone concentrations. We show an application, using the data set of mean monthly ozone concentrations in Arosa, Switzerland, and the results are compared with those obtained by classical time series (ARIMA) analysis.