ix
Foreword
Air pollution is inherently linked to human activities and was already mentioned as a
nuisance in antic Roman texts and in the middle ages. The industrial revolution in the
nineteenth century worsened its effects and increasingly turned it to a nonlocal problem. Parallel developments in physical sciences provided new tools to address this
problem. Understanding the rate and patterns of atmospheric dispersion is crucial for
environmental planning (location of industrial plants) and for forecasting high pollution episodes (above legislation thresholds inducing detrimental effects on human
health, ecosystems, and/or materials). Last but not least, local emissions are transported by air motions to create regional environmental problems, and, fi nally, the
accumulation of pollutants in the global atmosphere yield and interfere with climate
change processes. Consequently, there is a strong need for developing ever-better
models and assessment tools for air pollution concentration, dispersion, and effects.
These tools can span from simple analytical models for monitoring and predicting
short-range effects to regional or global three-dimensional models assimilating a
wide range of physical and chemical in situ and satellite observations. The breadth of
the different mathematical, physical, chemical, and biological processes and issues
has generated a lot of basic and applied research that should also take into account
the needs of environmental managers, physicians, and also of process engineers and
lawyers. No book can tackle all these issues in a balanced way; therefore, this book
mainly addresses issues of atmospheric dispersion modelling and their effects on
building surfaces.
To assess spatial and temporal distributions of pollutants and chemical species in
the air and their deposition on the Earth’s surface, atmospheric dispersion and chemical transport models are used at different scales, addressing different applications
from emergency preparedness, ecotoxicology, and air pollution effects on human
health to global atmospheric chemical composition and climate change. During the
last two decades, several basic aspects of air pollution modeling have been substantially developed, thanks to advances in computer technologies and numerical mathematics, as well as in the physics of atmospheric turbulence and the atmospheric
boundary layer (ABL).
Most air quality modeling systems consist of a meteorological model coupled
offl ine or online to emission and air pollution models, and, sometimes, also to a
population-exposure model. The meteorological model calculates three-dimensional
fi elds of wind, temperature, relative humidity, pressure, and, in some cases, turbulent diffusivity, clouds, and precipitation. The emissions model estimates the amount
and chemical composition of primary pollutants based on process information (e.g.,
traffi c intensity) and day-specifi c meteorology (e.g., temperature for biogenic emissions). The outputs of these emission and meteorological models are then inputs to
the air pollution model, which calculates concentrations and deposition rates of gases
and aerosols as a function of space and time. There are various mathematical
models that can be used to simulate meteorology and air pollution in a mesoscale
© 2010 by Taylor and Francis Group, LLC
Foreword
Air pollution is inherently linked to human activities and was already mentioned as a
nuisance in antic Roman texts and in the middle ages. The industrial revolution in the
nineteenth century worsened its effects and increasingly turned it to a nonlocal problem. Parallel developments in physical sciences provided new tools to address this
problem. Understanding the rate and patterns of atmospheric dispersion is crucial for
environmental planning (location of industrial plants) and for forecasting high pollution episodes (above legislation thresholds inducing detrimental effects on human
health, ecosystems, and/or materials). Last but not least, local emissions are transported by air motions to create regional environmental problems, and, fi nally, the
accumulation of pollutants in the global atmosphere yield and interfere with climate
change processes. Consequently, there is a strong need for developing ever-better
models and assessment tools for air pollution concentration, dispersion, and effects.
These tools can span from simple analytical models for monitoring and predicting
short-range effects to regional or global three-dimensional models assimilating a
wide range of physical and chemical in situ and satellite observations. The breadth of
the different mathematical, physical, chemical, and biological processes and issues
has generated a lot of basic and applied research that should also take into account
the needs of environmental managers, physicians, and also of process engineers and
lawyers. No book can tackle all these issues in a balanced way; therefore, this book
mainly addresses issues of atmospheric dispersion modelling and their effects on
building surfaces.
To assess spatial and temporal distributions of pollutants and chemical species in
the air and their deposition on the Earth’s surface, atmospheric dispersion and chemical transport models are used at different scales, addressing different applications
from emergency preparedness, ecotoxicology, and air pollution effects on human
health to global atmospheric chemical composition and climate change. During the
last two decades, several basic aspects of air pollution modeling have been substantially developed, thanks to advances in computer technologies and numerical mathematics, as well as in the physics of atmospheric turbulence and the atmospheric
boundary layer (ABL).
Most air quality modeling systems consist of a meteorological model coupled
offl ine or online to emission and air pollution models, and, sometimes, also to a
population-exposure model. The meteorological model calculates three-dimensional
fi elds of wind, temperature, relative humidity, pressure, and, in some cases, turbulent diffusivity, clouds, and precipitation. The emissions model estimates the amount
and chemical composition of primary pollutants based on process information (e.g.,
traffi c intensity) and day-specifi c meteorology (e.g., temperature for biogenic emissions). The outputs of these emission and meteorological models are then inputs to
the air pollution model, which calculates concentrations and deposition rates of gases
and aerosols as a function of space and time. There are various mathematical
models that can be used to simulate meteorology and air pollution in a mesoscale
© 2010 by Taylor and Francis Group, LLC