Final Report: Coarse PM Emissions Model Development and Inventory Validation

EPA Grant Number: R834552
Title: Coarse PM Emissions Model Development and Inventory Validation
Investigators: Hannigan, Michael P. , Fierer, Noah , Wiedinmyer, Christine
Institution: University of Colorado at Boulder , National Center for Atmospheric Research
EPA Project Officer: Chung, Serena
Project Period: June 1, 2010 through May 31, 2013
Project Amount: $500,000
RFA: Novel Approaches to Improving Air Pollution Emissions Information (2009) RFA Text |  Recipients Lists
Research Category: Air Quality and Air Toxics , Air

Objective:

This research project was designed to ultimately provide new information about emissions from PM10-2.5 and PM10. Existing measurements of PM10-2.5 would be used to evaluate existing inventories and their use in CMAQ. This evaluation process would result in a set of recommendations for updating existing emissions modules to more accurately simulate PM10-2.5 emissions. Further, a new emissions model that would simulate the emissions of primary biological particles would be developed.

Summary/Accomplishments (Outputs/Outcomes):

Below are some highlights of each of the study sub-components, but we wanted to lead with a brief synthesis of our findings. Emissions of PM10-2.5 are rarely linked with emissions of PM2.5, even when the general source (for example, roadways) is the same. As such, any emissions estimations that are tied to PM2.5 emissions are not wise. Emissions of PM10-2.5 are intimately linked with meteorological/environmental conditions (for example, wind speed and soil moisture) in addition to human activity (for example, vehicle braking). Thus, emissions estimates for PM10- 2.5 need to be generated ‘on-line’ in an atmospheric model, similar to biogenic VOC emissions. This link also impacts spatial variability. Ambient PM10-2.5 has a shorter atmospheric lifetime than PM2.5 so it exhibits more temporal variability than PM2.5 at any given site and it can have a large impact with significant spatial variability near to big sources. However, we often observe relatively spatially homogeneous PM10-2.5 in a region, when we are not monitoring near to and downwind of a source. This lack of spatial variability can be attributed to the broad reach of weather systems; in other words, when it is dry and windy at one location in the Front Range, it is dry and windy at most locations in the Front Range. We hypothesize that this also is the reason that we saw little variability in PM10-2.5 with height at the BOA tower, while observing a decreasing gradient for PM2.5 going up the tower. Emissions of PM10-2.5 contain significant organic content (~15-50%), and the make-up of those organics is different than the organics in the PM2.5. The PM10-2.5 organic material is less water soluble than PM2.5; similarly, when the high pH water is used, PM2.5 water soluble components increase more than PM10-2.5. Ambient concentrations of carbonaceous PM10-2.5 and PM2.5 exhibit seasonal differences but not the same seasonality patterns. Summer increases in carbonaceous PM2.5, with increasing water solubility, are likely due to increased atmospheric processing of gas-phase organics and not an increase in a primary PM source. Spring/summer/fall increases in carbonaceous PM10-2.5 are likely due to increases in primary sources, potentially biological particles but also likely are meteorological/environmental conditions that are more conducive to emissions.

Model-Measurement Comparison

We characterized coarse particulate matter (PM10-2.5) spanning the western United States based on the analysis of measurements from 50 sites reporting in the U.S. EPA Air Quality System (AQS) and two state agencies. We found that the observed PM10-2.5 concentrations show significant spatial variability and distinct spatial patterns, associated with the distributions of land use/land cover and soil moisture. The highest concentrations were observed in the southwestern United States, where sparse vegetation, barren or shrublands dominate with lower soil moistures, whereas the lowest concentrations were observed in areas dominated by grasslands, forest, or croplands with higher surface soil moistures. The observed PM10-2.5 concentrations also show variable seasonal, weekly, and diurnal patterns, indicating a variety of sources and their relative importance at different locations. To obtain insights for regional PM10-2.5 modeling, the observed results also were compared to modeled PM10-2.5 concentrations from an annual simulation using the Community Multiscale Air Quality (CMAQ) modeling system that has been designed for regulatory or policy assessments of a variety of pollutants including PM10, which consists of PM10-2.5 and fine particulate matter (PM2.5). The model under-predicts PM10-2.5 observations at 49 of 50 sites, among which 14 sites have annual observation means that are at least 5 times greater than model means. Below, we show this comparison for two sites with different characteristics, Seattle and Fargo.