Available at: https://digitalcommons.calpoly.edu/theses/626
Date of Award
MS in Civil and Environmental Engineering
PM2.5 variability within the neighborhood scale has not been thoroughly studied for wood burning communities. High variability in near-field PM2.5 concentration may lead to harmful public exposure since monitoring does not occur on that scale. This study measures near-field PM2.5 variability by measuring black carbon (BC), a component of PM2.5, in a 1 km2 area located in Cambria, California. BC and meteorological data (when meteorological instruments were available) were measured over thirteen 12-hour intensive operation periods (IOPs) occurring over the winters of 2009 and 2010. Near-field BC variability was measured to understand the type of exposures found in communities where many homes are burning wood simultaneously within a small area. In addition, relationships between meteorological, geographical, and burning source characteristics and BC were observed as tools for understanding BC concentration. The computer air dispersion modeling programs, ISC-PRIME and ISCST3, were also evaluated for applicability to the near field.
BC concentrations were measured using 1- to 2-minute resolution aethalometers and 12 hour resolution Personal Environmental Monitors (PEMs). On average, over all IOPs and sites, aethalometer and PEM BC averages were very similar, ranging between 200 and 250 ng/m3, or 4 and 5 µg/m3 for PM2.5, and standard deviations were often high. Averaging all BC measurements, aethalometer BC standard deviation values were 360 percent of the average BC concentration and PEM BC standard deviations were 120 percent the average BC concentration. The average standard deviation detected during each IOP was 190 percent of the average BC concentration for aethalometers and 79 percent of the average BC concentration for PEMs. The average standard deviation detected at each site was 220 percent of the average BC concentration for aethalometers and 76 percent of the average BC concentration for PEMs. The larger standard deviations measured by higher resolution aethalometers demonstrated that low resolution instruments, such as PEMs, are unable to detect high concentrations that may occur.
In addition to examining BC variability, multiple linear regression analyses were conducted to determine the impact of meteorological variables and geographic and burning source characteristics on BC concentration and a weighted BC deviation function (BC standard deviation divided by average BC concentration). Time impacts, humidity, and wind speed, accounted for about 50 percent of variability in aethalometer average BC and BC deviation. However, because all model assumptions were not satisfied, improvements are needed. Regression models based on PEM BC found wind speed and direction to account for about 80 percent of average PEM BC variability and number of burning sources to account for about 30 percent of PEM BC deviation. Although PEM BC models accounted for a high percentage of BC variability, few data points were available for the PEM analyses and more IOPs are needed to determine their accuracy.
When evaluating correlations between geographic and burning source characteristics and PEM BC concentrations, specific IOP and PEM sampling location explained almost 70 percent of variability in BC concentration, though model residuals suggested model bias. IOP likely explained variation in burning patterns and meteorology over each night while sampling location was likely a proxy for housing density, tree coverage, and/or elevation. Because all regression model assumptions could not be satisfied, the predictors were also observed graphically. Plotting BC concentration versus the number of burning sources suggested that number of burning sources may affect BC concentration in areas of low tree coverage and high housing density and in the case that the level of surrounding vegetation and structures are minimal. More data points will be needed to determine whether or not these relationships are significant.
ISC-PRIME and ISCST3 modeling overall tended to under predict BC concentrations with average modeled-to-measured ratios averaging 0.25 and 0.15, for ISC-PRIME and ISCST3, respectively. Correction factors of 9.75 and 18.2 for ISC-PRIME and ISCST3, respectively, were determined to bring modeled BC concentrations closer to unity, but the range of ratios was still high. Both programs were unable to consistently capture BC variability in the area and more investigation will be needed to improve models.
The results of the study indicate high BC variability exists on the near-field scale, but that the variability is not clearly explained by existing regression and air dispersion models. To prevent public exposure to harmful concentrations, more investigation will be needed to determine factors that largely influence pollutant variability on the neighborhood scale.