ABSTRACT
Air pollution models are powerful tools for policy makers as they can be used to relate emissions and concentrations. Also, considering that observations are often sparse, models can be used to make inferences on concentrations where there is no information. Combined with the information on population, models can readily be used to estimate exposure and, ultimately, health effects. In the correct framework, the estimation of costs of emission reductions and the economic benets of reduced health effects and environmental effects can be used as a basis for costbenet analysis of emission standards, pollution prevention plans, or day-to-day mitigation strategies. Air pollution modeling has been used for a long time in support of long-term and short-term policies. In the short term, forecast models, for instance, are used to inform the public about possible bad air quality in the coming days (Zeldin and Cassmassi, 1979) and have even been used to decide whether real-time pollution abatement strategies are being put into place, in the case of Santiago and Temuco in Chile, for example (Ulriksen and Merino, 2003; DiazRobles et al., 2008). In the long term, air quality models are used to evaluate the effect of pollution abatement strategies in support of studies on emission standards (Mediavilla-Sahagun and ApSimon, 2006) and on attainment of air quality standards in both developed (Chock, 1999; Seigneur, 1999; USEPA, 2007) and developing countries (Hao et al., 2007). In Chile, when nonattainment of particulate matter (PM10) is detected through monitoring stations, dispersion models are used to delimit the area under which specic emission standards will be put into place (Jorquera, 2008). However, it is important to note that for models to be used in support of policy decisions, it is necessary for them to correctly represent “reality.”
