Source composition data, chemical

Chemical Composition Data Sources for Plants, Feeds, Blood, Urine and Soils... 

Chemical Composition. Chemical compositional data iaclude proximate and ultimate analyses, measures of aromaticity and reactivity, elemental composition of ash, and trace metal compositions of fuel and ash. All of these characteristics impact the combustion processes associated with wastes as fuels. Table 4 presents an analysis of a variety of wood-waste fuels these energy sources have modest energy contents. 

The major purpose of ambient particulate sampling is to obtain mass concentration and chemical composition data, preferably as a function of particle diameter. This information is valuable for a variety of problems effects on human health, identification of particulate matter sources, understanding of atmospheric haze, and particle removal processes. 

Although the release of large numbers of these highly chemically enriched particles may have profound health and environmental consequences, the published literature contains virtually no data characterizing the distribution, physical properties, chemical composition, and source terms of a broad range of important primary pollutants emitted in submicrometer particles. 

Mohr C, Richter R, DeCarlo FF, Prevot ASH, Baltensperger U (2011) Spatial variation of chemical composition and sources of submicron aerosol in Zurich during wintertime using mobile aerosol mass spectrometer data. Atmos Chem Phys 11 7465-7482... 

Gu J, Pitz M, Schnelle-Kreis J, Diemer J, Reller A, Zimmermann R, Soentgen J, Stoelzel M, Wichmann H-E, Peters A, Cyrys J (2011) Source apportionment of ambient particles comparison of positive matrix factorization analysis applied to particle size distribution and chemical composition data. Atmos Environ 45(10) 1849-1857... 

The problem mentioned before has led to the development of statistical receptor models (Fig. 3) which nowadays are the most widely used tools for PM source apportionment. They can be applied even to a single site and need a time series of PM mass concentrations and corresponding chemical composition data. Depending on the method, these analyses are based only on the receptor data or additionally use information on the chemical composition from the relevant emission sources (emission profiles). 

Tables III-XIV are organized geographically and list chemical composition data for woods from various countries. The data as published originally were of interest to the local pulp and paper industries. This compilation provides a worldwide view of wood composition. Most of the data were obtained using similar test methods (Tappi Standards). When it is known that other test methods were used, the method is footnoted in the tables. Most of the values reported from all sources had one or two figures beyond the decimal point. Except for the ether solubility and ash values (usually less than 1%), values have been rounded off to the nearest percent because this reflects the precision of the sampling and assay methods.

A three year study monitoring acid precipitation across Central Indiana has just been completed by the authors ( ) and the local International Rotary Club who donated the manpower for sample collection. Over 3000 individual samples were collected during approximately 300 individual precipitation events. Using acceptible analytical and statistical protocol, the samples were characterized by meteorological, physical, and trace chemical composition data to determine possible sources of the acidity. The study yielded pertinent environmental data that set the parameters for this work. 

Obtaining of data concerning the chemical composition of water is critical significance for monitoring water reservoirs and forecasting the quality of drinking water from different water supply sources. A dry residue is commonly used with the methods AAS, ICP-AES, ICP-MS (analysis of liquid) widely applied for determination of water composition. So it is vital to create a standard sample of the composition of dry residue of ultra-fresh Lake Baikal water, its development launched since 1992 at the Institute of Geochemistry SB RAS. 

More recently, attempts have been made to correlate mathematically the chemical composition of natural waters and their aggressivity to iron by direct measurements on corrosion coupons or pipe samples removed from distribution systemsThis work has been of limited success, either producing a mathematical best fit only for the particular data set examined or very general trends. The particular interest to the water supply industry of the corrosivity of natural waters to cast iron has led to the development of a simple corrosion rig for the direct measurement of corrosion ratesThe results obtained using this rig has suggested an aggressivity classification of waters by source type i.e. 

A practical problem faced by tanneries is that many chemical products are proprietary products. Many suppliers do not specify the chemical composition of the products, so tanneries may have to seek additional information from the chemical suppliers in order to determine the environmental impact of the products they use. Sometimes it is also difficult for tanneries to receive accurate information also from the suppliers of the chemicals. Material safety data sheets generally provide some data on the toxicity of the products to humans and environment, and many tanneries use these as the sole source of information to determine the environmental impact of a certain substance. It can be expected in the EU in the future that tanneries will receive more detailed information about the substances used because of the REACH legislation. 

As some necessary meteorological data were unavailable, we employed two different techniques to estimate the element abundance in air. Reverse calculations, in the framework of the American program MEPAS, allowed us to find the concentration fields based on experimental and especially adapted meteorological data. The second technique included direct calculations in the framework of the Russian standardized program Ecologist, which took into account the actual chemical composition of copper-smelting production contaminants. Both techniques had some restrictions, mainly insufficient initial information on the sources of contaminants and limited possibilities of the analytical equipment used. 

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. 

Multivariate methods, on the other hand, resolve the major sources by analyzing the entire ambient data matrix. Factor analysis, for example, examines elemental and sample correlations in the ambient data matrix. This analysis yields the minimum number of factors required to reproduce the ambient data matrix, their relative chemical composition and their contribution to the mass variability. A major limitation in common and principal component factor analysis is the abstract nature of the factors and the difficulty these methods have in relating these factors to real world sources. Hopke, et al. (13.14) have improved the methods ability to associate these abstract factors with controllable sources by combining source data from the F matrix, with Malinowski s target transformation factor analysis program. (15) Hopke, et al. (13,14) as well as Klelnman, et al. (10) have used the results of factor analysis along with multiple regression to quantify the source contributions. Their approach is similar to the chemical mass balance approach except they use a least squares fit of the total mass on different filters Instead of a least squares fit of the chemicals on an individual filter. 

Source characterization results are not located in a centralized facility which is constantly updated. The Environmental Protection Agency has established the Environmental Assessment Data System (EADS) (31) which contains chemical compositions of particulate matter emissions tests. This existing computerized structure can provide the centralized location for receptor model source characterization information. Procedures such as those described for ambient data in the previous section need to be developed in order to allow receptor model users access to this data base over telephone lines. The data required of receptor model source tests should be incorporated into the EADS, and source characterization results should report this information in an EADS compatible format. 

Drug (IND) must be filed with the FDA (Figure 5-1). The IND includes (1) information on the composition and source of the drug, (2) chemical and manufacturing information, (3) all data from animal studies, (4) proposed clinical plans and protocols, (5) the names and credentials of physicians who will conduct the clinical trials, and (6) a compilation of the key data relevant to study the drug in man made available to investigators and their institutional review boards. 

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