Meteorological data factors

The results of the PCA from each subset are similar except that the data subsets which did not either Include the meteorological data or normalize the data to reduce meteorological variability (subsets 2 and 3) were not able to separate several of the components probably due to the atmospheric masltlng effect. Information on the wind direction and rainfall quantity dependence of seasalt and metals Is obtained when meteorological data are Included In the analysis. From the standpoint of separation of chemical factors the fourth subset (normalization to fractional composition) provided the best resolution of the data. Using deposition or concentrations, a component that Indicated a combined Influence of sulfate, nitrate, lead and calcium emission sources was resolved Into separate components when the fractional composition data were analyzed by PCA. 

Cluster analysis Is used to determine the particle types that occur in an aerosol. These types are used to classify the particles in samples collected from various locations and sampling periods. The results of the sample classifications, together with meteorological data and bulk analytical data from methods such as instrunental neutron activation analysis (INAA). are used to study emission patterns and to screen samples for further study. The classification results are used in factor analysis to characterize spatial and temporal structure and to aid in source attribution. The classification results are also used in mass balance comparisons between ASEM and bulk chemical analyses. Such comparisons allow the combined use of the detailed characterizations of the individual-particle analyses and the trace-element capability of bulk analytical methods. 

Meteorological Data. Ambient temperature, humidity, rainfall, and wind velocity at the plant locatkm are important factors. 

Once the emission factors and their variability are estimated, dispersion models can be used in order to enable point data to be interpreted in terms of geographical distribution of source contributions, as suggested by the Air Quality Directive (2008/50/EC). This could serve as a basis for calculating the collective exposure of the population living in the area and for assessing air quality with respect to the limit values. Dispersion models are based on the use of meteorological data, modules to account with physico-chemical processes occurring in the atmosphere and EFs. 

A thorough analysis of atmospheric transport and deposition to the Great Lakes has been carried out using the HYSPLIT model developed by the US National Atmospheric and Oceanic Administration (NOAA) [28,29]. An emissions inventory of PCDD/Fs for North America in 1996 was used as input to the model. Factors considered in the fate and distribution were meteorological data, vapor-particle partitioning, aerosol characteristics, reaction with hydroxyl radicals, photolysis, and dry and wet deposition. The model was generally satisfactory at estimating fluxes, except for HpCDD and OCDD, which appeared to be underestimated by about a factor of four. The model output was summarized as 2378-TeCDD toxic equivalent concentrations (TEQs) based on the WHO mammalian 2378-TeCDD toxic equivalent factors (TEFs) [30]. Since HpCDD and OCDD were estimated to contribute only 2% of TEQs, the model was considered to be valid for the purpose intended. 

Climate and Environmental Factors. The biomass species selected for energy appHcations and the climate must be compatible to faciUtate operation of fuel farms. The three primary climatic parameters that have the most influence on the productivity of an iadigenous or transplanted species are iasolation, rainfall, and temperature. Natural fluctuations ia these factors remove them from human control, but the information compiled over the years ia meteorological records and from agricultural practice suppHes a valuable data bank from which to develop biomass energy appHcations. Ambient carbon dioxide concentration and the availabiHty of nutrients are also important factors ia biomass production. 

FIGURE 2.3 The ability to detect trends in atmospheric emissions can be strongly affected by the distance from the source (top) and meteorological factors such as wind direction (bottom). These measurements were made near a Hg source in southeastern Idaho. (Source Abbott 2003, unpublished data, with permission.)... 

The Half-life in the Environment data reflect observations of the rate of disappearance of the chemical from a medium, without necessarily identifying the cause of mechanism of loss. For example, loss from water may be a combination of evaporation, biodegradation and photolysis. Clearly these times are highly variable and depend on factors such as temperature, meteorology and the nature of the media. Again, the reader is urged to consult the original references. 

Although the measurement uncertainties limit the conclusions which can be drawn from these results, the data set proved useful for the determination of general Influences on rainwater composition In the Seattle area and for the demonstration of the application of these exploratory data analysis techniques. Current efforts to collect and analyze aerosol and rainwater samples over meteorologically appropriate time scales with precise analytical techniques are expected to provide better resolution of the factors controlling the composition of rainwater. 

The data were analyzed first by factor analysis. Depending upon the number of variables included in the analysis, three to five factors were obtained which could be associated with emission sources or with meteorology. The source tracers V, Pb, Cu and... 

Uncontrolled open fires in tyre dumps may burn for several months (e.g., Rhinehard tyre fire in Winchester, Virginia), generating many hazardous products of incomplete combustion, which are released directly into the atmosphere (EPA 1997). Owing to safety concerns and other factors (e.g., meteorological conditions, fire-fighting activities), it is difficult to collect air samples from the smoke plumes of these fires. The available field data revealed potentially hazardous levels of several PAHs and CO, as well as relatively high concentrations of Pb and Zn in such plumes (Ohio Air Quality Development Authority 1991 EPA 1997). 

Start with all the potential predictors In a statistical package, the data are entered into five columns and then you will have to indicate that toxin is the dependent variable/response and the four meteorological factors the independent variables/ predictors. Generic output is shown in Table 14.7. 

Miller, J. M. Tellus. in press) have examined the transport of North American sulfur emissions across the north Atlantic Ocean to Europe. In a review of available precipitation-sulfate data from the north Atlantic and adjacent coastal regions, they report a concentration field consistent with known source distributions and meteorological factors. The excess sulfate concentration of marine background... 

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