Every year millions of construction workers are exposed to dust in levels that create a hazard to them (Fundukian, 2011). Their environment is contaminated by activities such as cutting, chipping, grinding and sanding building materials. The Occupational Safety and Health Administration (OSHA) refers to this general collection of building materials debris and fine particulates as nuisance dust. Some of the particles in nuisance dust possess properties that make them especially hazardous, such as their shape or specific gravity. It has been found by the Center for Disease Control (CDC) that inhalation of quantities of silica dust above the permissible exposure limit (15.0 mg/m3) causes a deterioration of the outside lining of the lung.This research seeks to limit this exposure by a pretreatment process using acid application and then absorbed moisture content that reduces airborne particulate during the removal of cement stucco surfacing materials. Successful pretreatment would allow removal of CSSM from substrates such that the release of airborne particulates does not exceed the permissible exposure limits (PEL) found in the 29 CFR-Table Z-3 for mining applications (15-mg/m3). / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2015 / FAU Electronic Theses and Dissertations Collection

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On-road remote sensing has the ability to operate in real-time, and under real world conditions, making it an ideal candidate for detecting gross polluters on major freeways and thoroughfares. In this study, remote sensing was employed to detect carbon monoxide (CO), hydrocarbons (HC), and nitrogen oxide (NO). On-road remote sensing data taken from measurements performed in six states, (Arizona, California, Colorado, Illinois, Texas, and Utah) were cleaned and analyzed. Data mining and exploration were first undertaken in order to search for relationships among variables such as make, year, engine type, vehicle weight, and location. Descriptive statistics were obtained for the three pollutants of interest. The data were found to have non-normal distributions. Applied transformations were ineffective, and nonparametric tests were applied. Due to the extremely large sample size of the dataset (508,617 records), nonparametric tests resulted in "p" values that demonstrated "significance." The general linear model was selected due to its ability to handle data with non-normal distributions. The general linear model was run on each pollutant with output producing descriptive statistics, profile plots, between-subjects effects, and estimated marginal means. Due to insufficient data within certain cells, results were not obtained for gross vehicle weight and engine type. The "year" variable was not directly analyzed in the GLM because "year" was employed in a weighted least squares transformation. "Year" was found to be a source of heteroscedasticity; and therefore, the basis of a least-squares transformation. Grouped-years were analyzed using medians, and the results were displayed graphically. Based on the GLM results and descriptives, Japanese vehicles typically had the lowest CO, HC, and NO emissions, while American vehicles ranked high for the three. Illinois, ranked lowest for CO, while Texas ranked highest. Illinois and Colorado were lowest for HC emissions, while Utah and California were highest. For NO, Colorado ranked highest with Texas and Arizona, lowest.