Abstract

Ambient monitored data at Santiago, Chile, are analyzed using box models with the goal of assessing contributions of different economic activities to air pollution levels. The box modeling approach was applied to PM10, PM2.5 and coarse (PM10-PM2.5) particulate matter (PM) fractions; the period analyzed is 1989-1999. A linear model for each PM fraction was obtained, having as independent variables CO and SO2 concentrations, plus a term proportional to (wind speed)-1 that lumps together non-combustion emissions and secondary generation terms; wet scavenging is included as another independent variable. Model identification results show good agreement for the different parameters across monitoring stations. The washout ratios and scavenging coefficients agree with data published in the literature, being higher for the coarse PM fraction. The CO and SO2 coefficients fitted for 1989-1995 agree with a priori estimates for the same period. Background estimates for the PM fractions are in agreement with measurement campaigns in upwind sites. Results show that transportation sources have become the dominant contributors to ambient PM levels, while stationary sources have decreased their contributions in the last years. The relative importance of mobile sources to PM2.5 ambient concentrations has doubled in the last 10 years, whereas stationary sources have reduced their relative contributions to half the value in the early 1990s. Model estimates of regional background of PM2.5 and PM10 have decreased 50% and 22% in the last decade, respectively; coarse background has shown no significant change. The final conclusion is that there is room and need for a more intensive emission reduction strategy for Santiago, focusing on mobile sources. The approach pursued in this work is feasible for cities or regions where comprehensive, transport and chemistry models are not available yet, but estimates of air quality contributions are needed for policy purposes. The methodology requires data on ambient air quality measurements and surface meteorology. © 2002 Elsevier Science Ltd. All rights reserved.