1. Title, Journal, and Authors
Title: Identifying and Estimating the Relative Importance of Sources of Airborne Particulates
Journal: Environmental Science & Technology
Authors: Michael T. Kleinman, Bernard S. Pasternack, Merril Eisenbud, and Theo. J. Kneip*
2. Summary
Background
Environmental protection and cost-effective control strategies require knowledge of emission sources and emission generation and emissions volumes.
Purpose of research
The author conducted a study to develop a regression model for pollutants and pollutional loading amounts of total suspended solids (TSP) in the atmosphere.
Research method
Using the concentration of trace substances (metal, sulfur -ion) in urban aerosol samples, it was revealed how much influence each pollutant has on TSP.
In addition, chemical tracers were selected/ used, citing the previous study, and the method of connecting particles emitted from specific emission sources to aerosol burden in the ambient atmosphere was used.
Statistical analysis procedures for the University of Michigan's OSIRIS III program package were used to predict total floating particle concentrations as samples vary.
The "statistical analysis procedure of the University of Michigan's OSIRIS III program package" was used to predict the total suspended particle concentration according to the change in trace substances in the sample.
Result
Analysis of fly ash samples from an incinerator in New York confirmed the presence of copper, a larger residual value occurred in multiple regression tests using zinc instead of copper, and that copper was more likely to be a useful predictor in previous studies. Looking ahead, it can be seen that copper is a better choice than zinc as an independent predictor of incineration in New York City.
It seems that between 1969 and 1972, regulation of coal, control of lead concentration in gasoline, and control of sulfur concentration in petroleum, led to the reduction of Cu, Mg, Pb, and V in the air. There is a possibility that the concentration of lead and vanadium may change depending on the change in fuel quality, and lead seems to be closely related to traffic volume. Manganese was chosen as a soil chemical tracer or a soil-like substance.
The difference between the measured value and the predicted value was found to be within about 10%, but in the worst case, the TSP concentration was overestimated by 13 μg/m3.
The model does not cover all pollutant sources in the city, and unassigned pollutant sources are expressed in constant terms that vary by region. Nevertheless, the contribution of TSP to the modeled pollutant sources was approximately 75%.
In the non-urban areas where there is little commercial or industrial activity, the constant term was treated as 0, and the accuracy of the model in predicting the TSP value and the pollutant source was improved by comparing the data for two cities and one non-city.
Conclusion
1. Prediction using the model may result in errors due to atmospheric dispersion and seasonal temperature changes.
2. The model has been successful in classifying sources from multiple sites where similar long-term data may be available.
3. If there is a change in the pollutant source, the regression coefficient can be adjusted, and in cities with similar factors, a new model can be derived by
appropriately modifying the existing regression coefficient.
4. For cities with either more or less industrialization, some adjustment of the constant term (in Mg/m3) or addition of suitable tracers might be necessary in order to
apply the model.
3. Contact
Yeong Gyu Gu / Intern student
Environmental Systems Engineering Lab.
School of Earth Sciences and Environmental Engineering Gwangju Institute of Science and Technology
1 Oryong-dong Buk-gu Gwangju, 500-712, Korea
Phone : +82-10-6589-6653
E-mail : kududrb1@naver.com