Wasatch Front Wood Smoke: Modeling To Consider The Impact Of Emissions From Solid Fuel Burning Devices

• Principal Investigator: Nancy Daher ([email protected]), Ph. D., Utah Division of Air Quality

Background

The 2015 Utah Legislature provided $70,000 to the Utah Division of Air Quality (DAQ) to undertake a modeling study to address two specific questions. What would be the effect of a two-stage program that would reduce emissions from wood-burning stoves while at the same time maximizing the number of days that EPA-certified stoves and other devices could be used for home heating. Second, what would be the impact of a program that would encourage individuals and families to replace older stoves and fireplaces with new, clean burning devices.

To address these questions DAQ decided to take a three-pronged approach.

• Gather an extended set of PM_2.5 measurements and analyze these for a specific marker to distinguish wood smoke from other pollutants.
• Use the insights gained from the analysis in step 1 as input to a statistical model to compare wood smoke contributions to PM_2.5 to the contribution from other sources of this pollution.
• Run a series of “sensitivity” tests in an air quality model to quantify the difference in PM_2.5 pollution when the current stock of wood stoves and fireplaces are replaced with the most efficient and clean burning devices.

1. Isolating the Wood Smoke Contribution

Concentrations of PM_2.5 are measured by capturing the pollutants on a Teflon filter and then weighed to determine how much of this type of pollution is in the air on a given day. When necessary, DAQ can send these filters to a laboratory for more detailed analysis that provides information about the sources that contribute to this pollution. A specialized test is available that measures the amount of “levoglucosan” which is directly related to wood smoke and is a unique chemical marker of the PM_2.5 that comes from wood burning. Summary results of this analysis (188 KB) indicate that emissions from wood burning do, indeed, contribute an appreciable amount of pollution during winter inversions in the PM_2.5 non-attainment areas of northern Utah, even during our mandatory no-burn periods.

2. Positive Matrix Factorization (PMF) Statistical Model

Staff at DAQ recognized that it would be challenging to definitively answer the questions posed by the legislature since emissions from wood burning are only one part of a wide array of different source contributions. To that end, DAQ approached the problem by using two different models. The first of these is the PMF model which provides a technique to differentiate the main sources that contribute to measured PM_2.5. By dividing sources into major groups and then estimating the amount that each group contributes to the total PM_2.5 load in the air, 2016 (732 KB) found similar contributions from wood burning. These studies contained more uncertainty than DAQ’s approach since they did not have the benefit of the chemical marker for wood smoke.

3. Air Quality Model with Source Apportionment

While not providing a definitive answer, Steps 1 and 2 provide insight into the first question asked in the legislative appropriation: What is the effect of a two-stage burning program on emissions during the buildup period of an inversion? Answering the second question asked by the legislature on the impact of replacing older stoves with newer technology requires a different approach.

The CAMx air quality model is used to simulate the reduction in PM_2.5 when the current mix of wood stoves and fireplaces are replaced by those using the cleanest, most efficient technology available. A second model variation that estimates the effect of converting all wood burning to natural gas in the non-attainment areas is also analyzed. This model, which is used by DAQ to estimate air quality benefits of particular regulations, is used in this study with a technique that can look specifically at the effect of wood smoke reductions (480 KB) on total PM_2.5 during an inversion.

A report of the final results will be completed by March 2017. This report will be submitted to the project stakeholders along with a presentation of results. The report will be available to the public through this project website.