Date of Award


Degree Name

MS in Civil and Environmental Engineering


Civil and Environmental Engineering


Tracy Thatcher


ABSTRACT Assessing Near-Field Outdoor Concentration Variability from Residential Wood Smoke Combustion Sources Courtney Ward The primary goal of this research was to determine whether near field effects from residential wood smoke emissions have a significant impact on acute and/or average PM2.5 concentrations, and therefore health risks. To this end, three primary research objectives were addressed: (1) measurement of the variability of wood smoke concentrations within a residential neighborhood with wood smoke sources, (2) establishment of whether the magnitude of near-source contributions to exposures can be estimated using typically available data, and (3) prediction of wood smoke concentrations using linear regression techniques on meteorological parameters.

This project was divided into 4 primary tasks. Neighborhood Selection (Task 1), Detailed Sample Plan and Method Validation (Task 2), Field Study to deploy equipment and personnel to the study area (Task 3), and Data Analysis (Task 4). The data analysis was divided into three sections: (1) evaluation of aethalometer black carbon (BC) variability, (2) regression and correlation analyses between meteorological factors and aethalometer BC, and (3) evaluation of the Personal Environmental Monitor (PEM) BC variability.

BC concentrations, as measured by aethalometers, showed that near-source contributions to average concentrations varied widely within the 1 km2 study area, with measured BC differences up to 3.27 &#;g/m3 which corresponds to an estimated PM2.5 concentration ranging from 54.5 to 81.75 &#;g/m3. Consequently, BC concentrations, and therefore exposures, are dependent upon the location within a residential area and cannot be estimated well using measurements from a single location. Based on the results from this study, it is recommended that the standard method of measuring PM2.5 should be updated by either placing additional monitors throughout the region or estimating the variation of PM2.5 using meteorological data and an understanding of the factors leading to near source variability.

PEMs’ BC measurements also showed that near-source contributions to average wood smoke concentrations vary widely over relatively short distances. Based on the BC variations between the eight to twelve monitored locations, it is unreasonable to assume that the BC measurements, and hence wood smoke PM2.5, are constant over a residential area. The maximum PEM BC difference of 0.76 &#;g/m3, or a PM2.5 concentration of 12.7 to 19.0 &#;g/m3 (depending on the BC/ PM2.5 fraction), could result in inappropriate measures being taken to protect the health of local residents. This research showed significant concentration variability associated with wood smoke burning within a residential neighborhood, with an average standard deviation of 0.10 &#;g/m3 and a relative standard deviation of 77.2%. Since these average standard deviations and ranges of PEM BC concentration variability were calculated in Cambria, these BC/PM2.5 fractions can only be applied to Cambria, in particular, the 1 km2 study area. Given the BC concentration differences between each PEM measured in the residential study area and the Cambria Fire Station, ranging from 0.09 to 0.45 &#;g/m3, it is evident that the central monitoring station is not a reasonable proxy for the average wood smoke concentrations to which people are exposed.

Using meteorological data to estimate PM2.5 concentrations from residential wood smoke is difficult because it requires knowledge the number of homes burning, distance from burners, wind speed, inversion conditions and other parameters that have spatiotemporal variability. The multiple regression analysis between the meteorological predictors and BC concentration did not detect a significant correlation for any of the meteorological factors or burning conditions. The correlation between meteorological factors and BC concentration was weak because the meteorological data was unlikely representative of the true conditions within the study area, and a lack of repeatable meteorological conditions between IOPs. Based on the meteorological data collected for the IOPs, wind directions and speeds varied considerably from 6 to 9 p.m., between IOPs, with wind directions coming from all directions and surface inversions occurring during half of the IOPs.