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Estimating air chemical emissions from research activities using stack measurement data.

Marcel Y Ballinger¹, Cheryl J Duchsherer, Rodger K Woodruff

¹Pacific Northwest National Laboratory, Battelle Seattle Research Center Seattle, Washington, USA. marcel.ballinger@pnnl.gov

Journal of the Air & Waste Management Association (1995)

|April 6, 2013

Summary

This summary is machine-generated.

Estimating air emissions from research and development (R&D) is challenging. A Monte Carlo technique using stack measurements quantified low emissions and release fractions, revealing complexities in R&D emission estimations.

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Area of Science:

Environmental Science
Chemical Engineering
Occupational Health

Background:

Current methods for estimating air emissions from research and development (R&D) activities rely on diverse release fractions or emission factors.
Existing methods lack reported uncertainties and confidence levels, despite being considered conservative.
Chemical emissions from R&D facilities are difficult to quantify due to the dynamic nature of research and the wide variety of chemicals used in small quantities.

Purpose of the Study:

To develop and apply a robust method for estimating chemical air emissions from R&D facilities.
To quantify low-level emissions and estimate release fractions using empirical data.
To assess the correlation between release fractions and chemical properties or inventory.

Main Methods:

Utilized a Monte Carlo technique to analyze 10 years of sampling data from four research facilities.
Created annual emission estimate distributions for target compounds, accounting for detection frequency.
Calculated release fractions using chemical usage and standing inventory, applying filters for realistic values (0-1).

Main Results:

Monte Carlo analysis without filtering showed emission distributions often spanning zero, with 40% having negative means, indicating emissions below background levels.
Applying a filter for positive emission values increased the mean estimate by an average of 16%.
Release fractions varied widely across chemicals and facilities; regressions against molecular weight, vapor pressure, and inventory showed weak correlations.

Conclusions:

Air emissions from R&D operations are inherently difficult to estimate accurately.
The Monte Carlo technique provides a viable method for quantifying low emissions and estimating release fractions from R&D activities.
The lack of strong correlations confirms the complexity and variability in estimating R&D emissions, highlighting the need for refined methodologies.