BACKGROUND: Long-term exposure to fine particulate matter (PM2.5) has been identified as a potential risk factor for developing type 2 diabetes mellitus (T2DM). Given the rising prevalence of T2DM and unhealthy concentrations of PM2.5 in China, our attention is brought to examining the association in this region of the world. Furthermore, rural China, although largely ignored, also finds itself suffering from increased risks of T2DM and high levels of PM2.5. OBJECTIVE: The goal of this study is to characterize the relationship between long-term exposure to PM2.5 and the risk of T2DM in rural China. We do so by confirming that greater long-term exposure to PM2.5 is associated with a higher risk of T2DM incidence, assessing the potential multiplicative and additive interactions with important covariates, and identifying constituents of PM2.5 that may be responsible for the effect PM2.5 on the increased incidence of T2DM. CONCLUSIONS: Greater long-term exposure to PM2.5 is associated with increased risk of developing T2DM in rural Deqing County, Zhejiang, China. Smoking status modifies the relationship between PM2.5 and T2DM incidence on a multiplicative scale. There is no synergism between smoking and PM2.5 in association with T2DM incidence. There is no conclusive evidence on which constituents of PM2.5 play greater roles in the adverse effects of PM2.5 on T2DM incidence.

Air pollution is highly variable in both space and time, which presents many challenges to researchers when they wish to model concentrations. The modelling of air pollution is necessary for a number of reasons, which include the determination of human health effects, providing warning of health risks, and to understand general ecosystem health. In this thesis, modelling of air pollution through both space and time has been explored, with a focus on improving models that can be used to assign air pollution exposure. The techniques presented in this thesis have leveraged the ability of mobile monitoring units to collect air pollution concentration data multiple locations throughout a study period. First, we explore the use of combining mobile air pollution monitoring data with traditional fixed location monitoring. We find that the mobile data is able to provide insight into changes in spatial pattern between two temporal periods that could not be identified solely with the fixed location monitors, which demonstrates value in this monitoring approach that can be built upon with refinement of techniques. Second, we present a method to determine the amount of classical error that will be introduced when a long-term mean concentration is calculated from a discontinuous time-series dataset, which are the type of datasets collected by mobile air pollution monitoring. Third, we merge mobile and stationary air pollution monitoring data, along with meteorological, transportation, and land use information to model the hourly air pollution field using neural network models. The models developed allowed for the assignment of air pollution exposure incorporating human activity patterns. Also, they can be used to provide a spatially refined air quality health index. Lastly, we demonstrate exposure assignment that incorporates human activity patterns to calculate the dose exposure for students during their trips to school. This work commences with a demonstration of the basic utility of mobile air pollution monitoring data, which is to increase the number of monitored locations. Building on that utility of mobile technology, a technique was developed to estimate the error when mobile units are used for long-term estimates, similar to stationary monitoring units; and we were able to provide guiding principles for mobile monitoring data collection. Furthering our objective, to better understand the value of mobile data in a fully integrated monitoring network, we utilized both mobile and stationary data collection techniques together, in a single model, to produce accurate estimates of an air pollution field on an hourly basis. Finally, the research culminates with the demonstration of how mobile monitoring can be used for activity based air pollution exposure estimates, which was shown with a case-study of students’ trips between home and school. Overall, the chapters in this thesis work toward a better understanding of how to incorporate mobile monitoring data into air pollution assessment studies. / Thesis / Doctor of Philosophy (PhD)

<p> Ambient air pollution and pollution emitted from point sources, contribute to the total suspended particulate loadings measured at various monitoring stations, in any given area. Studies have shown that various meteorological variables may influence the concentration of particulates measured at these stations. 1989 data, from monitoring stations throughout Hamilton, Ontario, in conjunction with meteorological data from the Mount Hope airport, have been used to reveal, and to explain the aforementioned relationships. Results from graphical analysis, supports past findinqs from Stewart and Matheson (1967), Rouse and McCutcheon (1970), Dobroff (1990) and others, by showing that winds derived from a northern sector increase mean particulate loadings, and that wind speeds tend to be inversely related to measured particulate concentrations. In contrast to supporting findings from the graphical analysis, statistical ordinary least squares regression showed that for more stations than not, most parameter coefficients were not statistically significant. Results from the coefficient of determination show that none of regressions employed (linear, linear-log and log-log) could explain the relationship between the independent meteorological variables and the dependent variable (particulate concentration at a given monitoring site) with great precision. It follows that a non-linear correlation may well explain the dependence of particulate loading on wind speed, wind direction, mean temperature and total precipitation, and that source, (point and fugitive emissions), and other factors play important roles in this complex relationship.</p> / Thesis / Candidate in Philosophy

No description available.

Two computer models, namely MOBILE2 and PAL were utilized to estimate the air pollution emissions and concentrations due to traffic to and in the Central Florida Research Park. Since the Park is under construction, there are no actual existing data. Generally accepted engineering techniques were adopted to estimate traffic flow used for the study. Sensitivity analyses were conducted on the results, and it was found that changing the number of lanes for the roads - with different volumes of traffic on each lane - does not change the results significantly . But changing the dimensions of area sources (the parking lots in this study) did change the results. Therefore, accurate dimensions of those area sources are important to an accurate model. For areas with prevailing wind directions, long rectangular parking lots can be designed with their lengths perpendicular to the wind direction to get low pollutant concentrations. The results indicate that the carbon monoxide concentrations generated by vehicles in the Research Park will be within the Ambient Air Quality Standards for all cases studied.

This report is an examination of the practical design of a phosphoric acid production facility wet scrubber. It includes a brief background of the Florida Phosphate Industry, in addition to the rules and regulations affecting the emission of fluoride contaminates. The theoretical aspects of a packed absorption tower is discussed prior to the actual design of a cross-flow wet scrubber of the type typically found in the Florida Phosphate Industry.

This paper presents a review of the basic thermal, chemical and radiation balances existing in the atmosphere and discusses the mechanisms by which jet engine exhaust products can disturb these balances. Possible effects of stratospheric pollution on plant and animal life are discussed. Methods for reducing harmful emissions through engine design modifications are outlined and current successful research programs are surveyed. The SST type aircraft is shown to be a greater threat to the environment than conventional jets because of differences in cruise altitudes. It is concluded that due to the existence of several potential environmental problems associated with contamination of the stratosphere, large scale deployment of the SST should be discouraged, at least until current studies are complete and more data is available.

<p> In this report we explore how the effect of meteorological factors, allergens, and air pollution on respiratory conditions in children using longitudinal data. Our analysis makes use of a dataset from the DAVIS study in southern Ontario. The response variables are children's lower respiratory tract (URT) and upper respiratory tract (URT) scores. The explanatory variables are readings of various meteorological, allergen, and air pollution factors. First we make use of generalized estimating equations to find the main factors that are associated with certain respiratory conditions in children as measured by LRT and URT scores. Then we determine whether there are any interactions between the significant factors associated with LRT /URT scores. Comparisons between case and control groups are made to determine whether children with asthma are more sensitive to any of the changes in meteorological, allergen, and air pollution factors. The analysis results show that the significant factor that is associated with LRT scores for children with asthma is the two-day lag daily average changes in air pressure. On average an increase in air pressure will result in an increase in children's LRT scores. The interaction terms that remained in the final model show some degree of significance but without strong evidence to support it. Children in the case groups are more sensitive to meteorological factors, allergens, and air pollution than the children in control groups. </p> / Thesis / Master of Science (MSc)

The purpose of this research is to provide an extensive evaluation of neural network models for the prediction and the simulation of some key air pollutants in Hamilton, Ontario, Canada. Hamilton experiences one of Canada's highest air pollution exposures because of the dual problem associated with continuing industrial emission and gradual increase of traffic related emissions along with the transboundary air pollutions from heavily industrialized neighboring north-eastern and mid-western US cities. These factors combined with meteorology, cause large degradation of Hamilton's air quality. Hence an appropriate and robust method is of most importance in order to get an early notification of the future air quality situation. Data driven methods such as neural networks (NNs) are becoming very popular due to their inherent capability to capture the complex non-linear relationships between pollutants, climatic and other non-climatic variables such as traffic variables, emission factors, etc. This study investigates dynamic neural networks, namely time lagged feed-forward neural network (TLFN), Bayesian neural network (BNN) and recurrent neural network (RNN) for short term forecasting. The results are being compared with the benchmark static multilayer perceptron (MLP) models. The analysis shows that TLFN model with its time delay memory and RNN with its adaptive memory has outperformed the static MLP models in ground level ozone (O_3) forecasting for up to 12 hours ahead. Furthermore the model developed using the annual database is able to map the variations in the seasonal concentrations. On the other hand, MLP model was quite competitive for nitrogen dioxide (NO_2) prediction when compared to the dynamic NN based models. The study further assesses the ability of the neural network models to generate pollutant concentrations at sites where sampling has not been done. Using these neural network models, data values were generated for total suspended particulate (TSP) and inhalable particulates (PM_10) concentrations. The obtained results show promising potential. Although there were under-predictions and over-predictions on some occasions, the neural network models, in general were able to generate the missing information and to obtain air quality situation in the study area. / Thesis / Master of Applied Science (MASc)

No description available.