University of Macau / Faculty of Science and Technology / Department of Computer and Information Science

published_or_final_version / abstract / toc / Mechanical Engineering / Master / Master of Philosophy

One of the important factors in developing an emission control strategy for a coal fired steam generator is the characterization of the insitu variability of the coal being used in the furnace. Development of a model to correctly capture the insitu variability of the coal is thus fundamental to the analyses of emission control strategies. A simulation model of a portion of the Upper Freeport coal seam in Western Pennsylvania was developed using the recently developed technique called conditional simulation. This model was constructed so that it has the same mean, variance, and distribution of values as the real deposit, and most importantly, has the same spatial correlations as the real deposit. Validation of the model confirmed that the statistical characteristics of the model closely matched the characteristics of the real deposit. A second validation of the model showed that when the model is "mined" according to an actual daily mining sequence, the resulting daily variability corresponded extremely well to what was observed during the actual mining. This second verification served not only to validate the model but also served as a practical demonstration that the model can be successfully used to predict day by day variation in the quality of run of mine coal. One potential use of conditional simulation to "test" how well a mine plan works in actual mining was illustrated by an example where four mine plans were tested on their ability to correctly estimate coal production and sulfur content on a yearly basis. In each case, the simulated deposit was mined out according to the mine plan. The resulting comparison of "actual" production and estimated production clearly shows the adequacy or inadequacy of each one.

published_or_final_version / Environmental Management / Master / Master of Science in Environmental Management

Open burning of biomass and anthropogenic waste is a major source of aerosols at the biosphere-atmosphere interface, yet its impact on Earth’s climate and air quality is not fully understood due to the intricate feedbacks between the natural environment and human activities. Earth system models (ESMs) are a vital tool in the study of these aerosol-biosphere-atmosphere interactions. ESMs allow the estimation of radiative forcing and climate impacts in terms of changes to temperature and precipitation as well as the attribution to natural or anthropogenic drivers. To provide coherent results, however, ESMs require rigorous development and evaluation against observations. In my work I use the NASA-GISS ESM: ModelE. One of its strengths lie in its detailed aerosol schemes that include microphysics and thermodynamic partitioning, both necessary for the simulation of secondary inorganic aerosols. To overcome one of ModelE’s weaknesses, namely its lack of interactive biomass burning (BB) emissions, I developed pyre: ModelE’s interactive fire emissions module. pyrE is driven by flammability and cloud-to-ground lightning, both of which are calculated in ModelE, and anthropogenic ignition and regional suppression parameterizations, based on population density data. Notably, the interactive fire emissions are generated from the flaming phase in pyrE (fire count), rather than the scar left behind (burned area), which is commonly used in other interactive fire modules. The performance of pyrE was evaluated against MODIS satellite retrievals and GFED4s inventory, as well as simulations with prescribed emissions. Although the simulated fire count is bias-high compared to MODIS, simulated fire emissions are bias-low compared to GFED4s. However, the bias in total emissions does not propagate to atmospheric composition, as pyrE simulates aerosol optical depth just as well as a simulation with GFED4s prescribed emissions. Upon the development and evaluation of the fire-aerosol capabilities of ModelE, I have utilized it, with the EVA health model, to study the health impacts of outdoor smoke in 1950, 2015, and 2050. I find that chronic exposure to aerosols (PM2.5) is the main driver of premature deaths from smoke exposure, yet by 2050, acute exposure to ozone, formed downwind of BB smoke plumes, is projected to cause more premature deaths than exposure to PM2.5. I estimate the annual premature deaths from BB and waste burning (WB) smoke in 1950 to be ~41,000 and ~19,000, respectively, and in 2015 to be ~310,000 and ~840,000, respectively. By 2050 I project 390,000 and 1.5 million premature deaths from BB and WB respectively. In light of the growing impact of WB smoke exposure I identify the need to scale up viable waste management practices in regions of rapid population growth.

published_or_final_version / Mechanical Engineering / Master / Master of Philosophy

No abstract available. / Thesis (Ph.D.)-University of Natal, Durban, 1986.

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Meteorology and Physical Oceanography, 1982. / Microfiche copy available in Archives and Science / Bibliography: leaves 194-201. / by Amram Golombek. / Ph.D.

No description available.

Ambient and indoor air pollution is a major cause of premature mortality, and has been associated with more than three million preventative deaths per year worldwide. Most of these health impacts are from the effects from fine particulate matter. It is suspected that PM2.5 health effects vary by composition, which depends on the mixture of pollutants emitted by sources. This has led to efforts to estimate relationships between sources of PM2.5 and health effects. The health effects of PM2.5 may be preferentially dependent on specific species; however, recent work has suggested that health impacts may actually be caused by the net effect of the mixture of pollutants which make up PM2.5. Recently, there have been efforts to use source impacts from source apportionment (SA) studies as a proxy for these multipollutant effects. Source impacts can be quantified using both receptor and chemical transport models (RMs and CTMs), and have both advantages and limitations for their use in health studies. In this work, a technique is developed that reconciles differences between source apportionment (SA) models by ensemble-averaging source impacts results from several SA models. This method uses a two-step process to calculate the ensemble average. An initial ensemble average is used calculate new estimates of uncertainties for the individual SA methods that are used in the ensemble. Next, an updated ensemble average is calculated using the SA method uncertainties as weights. Finally, uncertainties of the ensemble average are calculated using propagation of errors that includes covariance terms. The ensemble technique is extended to include a Bayesian formulation of weights used in ensemble-averaging source impacts. In a Bayesian approach, probabilistic distributions of the parameters of interest are estimated using prior distributions, along with information from observed data. Ensemble averaging results in updated estimates of source impacts with lower uncertainties than individual SA methods. Overall uncertainties for ensemble-averaged source impacts were ~45 - 74%. The Bayesian approach also captures the expected seasonal variation of biomass burning and secondary impacts. Sensitivity analysis found that using non-informative prior weighting performed better than using weighting based on method-derived uncertainties. The Bayesian-based source impacts for biomass burning correlate better with observed levoglucosan (R2=0.66) and water soluble potassium (R2=0.63) than source impacts estimated using more traditional methods, and more closely agreed with observed total mass. Power spectra of the time series of biomass burning source impacts suggest that profiles/factors associated with this source have the greatest variability across methods and locations. A secondary focus of this work is to examine the impacts of biomass burning. First a field campaign was undertaken to measure emissions from prescribed fires. An emissions factor of 14±17 g PM2.5/kg fuel burned was determined. Water soluble organic carbon (WSOC) was highly correlated with potassium (K) (R2=.93) and levoglucosan (R2=0.98). Results using a biomass burning source profile derived from this work further indicate that source apportionment is sensitive to levels of potassium in biomass burning source profiles, underscoring the importance of quantifying local biomass burning source profiles. Second, the sensitivity of ambient PM2.5 to various fire and meteorological parameters in was examined using the method of principle components regression (PCR) to estimate sensitivity of PM2.5 to fire data and, observed and forecast meteorological parameters. PM2.5 showed significant sensitivity to PB, with a unit-based sensitivity of 3.2±1 µg m-3 PM2.5 per 1000 acres burned. PM2.5 had a negative sensitivity to dispersive parameters such as wind speed.