Atmospheric particulate matter is known to have significant effects on human health, visibility, and global climate. The magnitudes of these effects, however, depend in complex ways on chemical composition, relative humidity, temperature, phase state, and other parameters. Current regional air quality models such as CMAQ (Community Multiscale Air Quality model) ignore many of these considerations, and consider that the formation of secondary organic aerosol (SOA) can be calculated by assuming thermodynamic ideality in the organic particulate matter (OPM) phase as well as negligible uptake of water into the OPM phase. Theoretical predictions and model simulations considering non-ideality and water uptake show that the standard model assumptions can lead to large errors in predicted SOA mass, and that the magnitude of these errors is sensitive to the composition of the OPM phase. The SOA module in CMAQ v4.7.1 has been revised in this work to allow consideration of the effects of both non-ideality and water uptake. First, a reasonable specific surrogate structure was assigned to each of the lumped products assumed to be produced by reaction of the different precursor hydrocarbons considered in CMAQ (e.g., isoprene, benzene, and toluene). Second, the CMAQ code was modified to allow iterative calculation (at each point in space and time) of the gas/particle partitioning coefficient for each of the SOA-forming products and for water. Third, model simulations were performed for the Eastern US at a resolution of 36-km x 36-km for late summer 2006, under a range of relative humidity conditions. When compared with an appropriate base case, the modified code produced increases in SOA ranging from 0.17 to 0.51 micrograms per cubic meter. The average change was 0.30 micrograms per cubic meter, corresponding to a 37% increase in SOA formation. Incorporation of phase separation effects would likely lead to further increases in predicted SOA levels.

The Chehalis River Basin is located in the southwest region of Washington State, originating in the Olympic Mountains and flowing to Grays Harbor and the Pacific Ocean. The Chehalis River is over 125 miles, exists within five counties, and flows through agricultural, residential, industrial, and forest land areas. Four major rivers discharge to the Chehalis River, as well as many smaller creeks, five wastewater treatment plants, and groundwater flows. Flooding is a major problem in the relatively flat areas surrounding the cities of Chehalis and Centralia, with severe consequences for property, safety and transportation. As a result, construction of a flood-control dam in the upper basin has been proposed. One major concern of constructing a dam is the potentially severe impacts to fish health and habitat. The Chehalis River has routinely violated water quality standards for primarily temperature and dissolved oxygen, and has had multiple water quality and Total Maximum Daily Load studies beginning in 1990. CE-QUAL-W2, a two-dimensional (longitudinal and vertical) hydrodynamic and water quality model, was used to simulate the Chehalis River, including free flowing river stretches and stratified (in summer) lake-like stretches. The goals of this research were to assess the flood retention structure's impacts to water quality, as well as river responses to potential climate change scenarios. In order to use the model to achieve these goals, calibration to field data for flow, temperature, and water quality constituents was performed. This involved developing meteorological data, riparian shading data, and flow, temperature, water quality records for all tributaries during the calibration period of January 1, 2013 to December 31, 2014. System cross-sectional geometry data were also required for the model grid. Because of the short travel time in the river, the model was sensitive to boundary condition data, wind speed, bathymetry, nutrient kinetics, and algae, epiphyton, and zooplankton kinetics. Future conditions showed predictions of warmer water temperatures and slight changes to water quality conditions on the river. As fish in the area prefer cooler water temperatures, this could pose a threat to fish health and habitat. Flood retention structures also showed impacts to river temperature and water quality. Structures with the purpose of flood retention only (only operating during times of flooding) gave model predictions for daily maximum temperature higher than structures that employed flood retention and flow augmentation (operating during all times of the year). This suggested the management of flow passage or retention by the dam is important for water quality on the river. As this research continues improvements will be made, particularly to temperature and water quality constituents. Additional data for the system would be beneficial to this process. Model predictions of temperature were sensitive to meteorological data, including cloud cover, which were largely estimated based on solar radiation. Additional meteorological data throughout the basin would be useful to temperature results. Temperature results were also sensitive to the model bathymetry, and additional investigations into segments widths and water depths may improve temperature predictions. Water quality constituent data were largely lacking for the system. Many estimation techniques and approximations were used for input water quality constituents for the model upstream boundary and tributaries when little or no data were available, introducing uncertainty to the model. It was not possible to calibrate pH to field data because alkalinity data were essentially unavailable. However, other constituents had good agreement between model predictions and field data, including dissolved oxygen, nitrates, total phosphorus, and total suspended solids.

The current approach to assessing the educational quality applicable to assessing objects and processes formed and realized in producing spheres is widely spread. However, as education is a much more complicated anthropological, social and cultural object in comparison to that of production, the above mentioned approach is least effective. In education both \"strong\" and \"weak\" models are used. There do not exist measurement instruments for accurate assessing mild results. Self control, expert assessing method and portfolio are being put forward.