The quality of stationary source emission factors is typically described using data quality ratings, as in AP-42, "Compilation of Air Pollutant Emission Factors." Such ratings are qualitative and provide no indication of the precision of the emission factor for an average emission source, nor of the variability in emissions from one source to another within a category. Advances in methodology and computing power enable the application of a quantitative approach to characterizing both variability and uncertainty in emission factors. Variability refers to actual differences in emissions from one source to another due to differences in feedstock composition, design, maintenance, and operation. Uncertainty refers to lack of knowledge regarding the true emissions because of measurement errors (both random and systematic), limited sample sizes (statistical random sampling error), and non-representativeness (which can introduce additional errors, including systematic errors). The set of numerical methods generically known as bootstrap simulation are a powerful tool for characterization of both variability and random sampling error. In this paper, we demonstrate the use of bootstrap simulation and related techniques for the quantification of variability and uncertainty for a selected example of NOx emissions from coal-fired power plants. We have developed a prototype software tool that enables a user to display data sets for emission factors and activity factors for selected power plant technology groups. The user can select a parametric distribution to fit to the data. The user enters information regarding the number of power plant units in the inventory, and can display a variety of results regarding both variability and uncertainty in the inputs to the inventory, as well as uncertainty in various outputs of the inventory. While our example is focused upon emission factors for a selected criteria pollutant, the same methodology can be applied to other pollutants (e.g., hazardous air pollutants, greenhouse gases). The policy relevance of probabilistic inventories will be discussed.
[1]
Allison K Pollak.
INVESTIGATION OF EMISSION FACTORS IN THE CALIFORNIA EMFAC7G MODEL.
,
1999
.
[2]
H. Christopher Frey,et al.
Probabilistic Techniques in Exposure Assessment: A Handbook for Dealing with Variability and Uncertainty in Models and Inputs
,
1999
.
[3]
H. Christopher Frey,et al.
Quantitative Analysis of Variability and Uncertainty in Environmental Data and Models Volume 2. Performance, Emissions, and Cost of Combustion-Based NOx Controls for Wall and Tangential Furnace Coal-Fired Power Plants
,
1999
.
[4]
H. Frey,et al.
Characterizing, simulating, and analyzing variability and uncertainty: An illustration of methods using an air toxics emissions example
,
1996
.
[5]
U. Epa,et al.
Guiding Principles for Monte Carlo Analysis
,
1997
.
[6]
H. Christopher Frey,et al.
Quantification of Variability and Uncertainty in Stationary Natural Gas-fueled Internal Combustion Engine NOx and Total Organic Compounds Emission Factors
,
2001
.
[7]
H C Frey,et al.
PROBABILISTIC EVALUATION OF MOBILE SOURCE AIR POLLUTION: VOLUME 1, PROBABILISTIC MODELING OF EXHAUST EMISSIONS FROM LIGHT DUTY GASOLINE VEHICLES
,
1997
.
[8]
H. Christopher Frey,et al.
Characterization and Simulation of Uncertain Frequency Distributions: Effects of Distribution Choice, Variability, Uncertainty, and Parameter Dependence
,
1998
.
[9]
H Christopher Frey,et al.
Quantification of Variability and Uncertainty in Lawn and Garden Equipment NOx and Total Hydrocarbon Emission Factors
,
2002,
Journal of the Air & Waste Management Association.
[10]
J. Seinfeld.
RETHINKING THE OZONE PROBLEM IN URBAN AND REGIONAL AIR POLLUTION
,
1991
.
[11]
H. Christopher Frey,et al.
Quantitative Analysis of Variability and Uncertainty in Emissions Estimation
,
1999
.