Estimation of Dilution of Atmospheric Contaminants From a Paper Factory using the AERMOD Model

• Main Authors: Oscar Andres Vidal Daza, Andrea Perez Vidal
• Format: Article
• Language: Spanish
• Published: Universidad Distrital Francisco José de Caldas 2018-01-01
• Series: Ingeniería
• Subjects: calidad del aire, contaminación atmosférica, dispersión de contaminantes
• Online Access: http://revistas.udistrital.edu.co/ojs/index.php/reving/article/view/12262

Context: modeling of atmospheric pollutants is an useful tool for air quality monitoring and management. In this study the dispersion of three atmospheric pollutants (SO2, NO2 and Total Suspended Particles - TSP) emitted by a boiler from a paper factory at Valle del Cauca department was estimated and its potential impact on the population settlements located at 5Km around the stationary source was identified. Method: the study was structured in three stages: (i) compiling and processing of meteorological, topographic and isokinetic sampling data; (ii) modeling the dispersion of pollutants using AERMOD ViewTM; and (iii) determination of the impact on surrounding populations. Results: the pollution plume is dispersed towards predominant directions of wind and the trajectory is affected by mountainous topography. The maximum concentrations of pollutants found (0,012 μg SO2/m3, 1,42 μg NO2/m3 and 0,109 μgTSP/m3) were lower than the Colombian air quality standards. Conclusions: the emission-control systems used by the studied paper industry ensures compliance with the air quality standard and generates a low impact on exposed populations. In addition, the atmospheric instability conditions favored the dilution of the contaminants discharged to the atmosphere. Although the AERMOD model was useful in the modeling of pollutants under atmospheric instability conditions and short length (<5 km), it has limitations to simulate the chemical transformations of SO2 and NO2, therefore it is recommended to evaluate other models that allow a comparative analysis and select the most appropriate model.