Improving Emission Inventories Using Direct Flux Measurements and ModelingEPA Grant Number: R834556
Title: Improving Emission Inventories Using Direct Flux Measurements and Modeling
Investigators: Schade, Gunnar W. , Collins, Don , Ying, Qi
Institution: Texas A & M University
EPA Project Officer: Chung, Serena
Project Period: April 1, 2010 through March 31, 2014
Project Amount: $499,992
RFA: Novel Approaches to Improving Air Pollution Emissions Information (2009) RFA Text | Recipients Lists
Research Category: Air Quality and Air Toxics , Air
This project uses a novel approach to measure real-world pollutant fluxes on an extended spatial and temporal scale, and to infer from those the source-specific pollutant emissions needed for a comparison to and an improvement of current emissions inventories. Air pollutants addressed include EPA criteria pollutants except SO2 and Pb, and volatile organics that are either air toxics (e.g. benzene) and/or photochemical ozone formation precursors (e.g. isoprene). We will:
- commence and extend our micrometeorological measurements of pollutant fluxes in urban Houston, Texas, for two years,
- compare measured to modeled fluxes in a top-down-bottom-up approach, and
- use the comparison to adjust the inventory and test its effects on air quality modeling.
Analyses will use existing and newly acquired air pollutant data, particularly flux data, geographic information systems, and modern statistical methods to study typical and extraordinary emission situations, then compare mapped emissions with expected emissions. Ultimately, identified shortcomings in expected emissions will be used to update emission inventories used in current air quality modeling to study whether the modifications improve forecasts.
Our objectives are to use tall tower micrometeorological flux measurements of air pollutants in an urban area to derive direct information on emissions sources, which commonly are only indirectly inferred from ambient measurements and extrapolations from data obtained under controlled conditions. We hypothesize that our data from this typical urban region of Houston, Texas, can be used to identify inaccuracies in and adjust current emissions inventories, and can inform emissions on larger scales creating improved inventories that lead to improved air quality modeling.
The experimental approach uses well-established micrometeorological methods to measure fluxes, and novel means, such as flux footprint analysis, positive matrix factorization, and advanced air quality modeling, to analyze the results. Volatile organic compound (VOC) fluxes are measured with a relaxed eddy accumulation (REA) GC-FID method, criteria air pollutant (CO, NOx, and O3) fluxes are assessed with a flux gradient method. Particulate Matter (PM) number density fluxes will be measured with a novel REA method. Measured fluxes (top-down) will be compared to expected fluxes using footprint modeling by overlaying the footprint density function with various GIS data sets upgraded by onsite surveys. Detailed traffic surveys will be conducted as input, particularly into MOBILE6 and the new MOVES model to create bottom-up estimates of pollutant emissions from roads in the tower’s flux footprint. Additional ground surveys (land use) are conducted to inform an evaporative (organics) and mechanical (PM) sources geographic information system to assess non-road emissions. Top-down-bottom-up comparisons will be used to inform necessary changes to the inventory, which will be applied to larger areas for selected air pollution episodes to compare detailed CMAQ air quality modeling outputs using altered inventory input.
Current air quality models are used to forecast air quality for public awareness and benefit, but also for planning purposes such as the State Implementation Plans (SIPs), which are used to demonstrate NAAQS achievement. Input to these models consists of emission inventories (air pollutant sources), a transport model, and an air chemistry model (chemical transport models, CTMs). Each of these introduces uncertainty, and a mismatch of model output with ambient measurements can usually not be traced to errors in a single input. To eliminate one unknown in the equation, the emission inventory is commonly assumed to be without error. This can lead to results that are correct for the wrong reasons, and can subsequently misinform policymakers delaying the proper actions needed to protect public health. The expected outcomes of our project include:
- a characterization of short-falls in current emission inventories,
- an explanation of spatial and temporal issues of emissions not currently parameterized adequately,
- a description of emissions, such as individual VOCs or PM, currently missing from inventories or otherwise poorly parameterized due to lack of knowledge, and
- improved emission inventories for improved air quality modeling.
Reducing uncertainties in the current emission inventories through this project alongside recent improvements in chemical transport modeling go a long way toward increasing accuracy and reliability of air quality forecasting for public health and policy.