Source Assessment Sampling System

Blake, D. E., Source Assessment Sampling System Design and Development. U. S. Environmental Protection Agency Report, EPA-600/7-78-018, NTIS No. PB 279-757/AS (2/78). 

The emission measurements during this testing included N0X, smoke, particulate and PNA. N0X was determined by a non-disper-sive infrared analyzer, and smoke by the Bacharach test. Both the particulates and PNA were sampled by a source assessment sampling system (SASS). The SASS system isokinetically samples a fraction of the stack gas and traps particulates in a series of cyclones, which classify the particulate by size. Final filtration is through a fiberglass filter mounted in an oven heated to 200°C to prevent condensation of acids. In this program, the cyclones were not used, since previous work (3) had shown the particulate from coal-derived fuel oils to be small, with an average diameter on the order of 0.4 /um. The PNA which is not deposited on the particulate is collected on XAD-2 resin after the gas has been cooled to 15-20°C. PNA analyses were carried out on a combined extract from the particulate, XAD-2 resin, other condensates in the system, and the solvent rinses used to clean the SASS system. 

The principal tool for performance-based quality assessment is the control chart. In a control chart the results from the analysis of quality assessment samples are plotted in the order in which they are collected, providing a continuous record of the statistical state of the analytical system. Quality assessment data collected over time can be summarized by a mean value and a standard deviation. The fundamental assumption behind the use of a control chart is that quality assessment data will show only random variations around the mean value when the analytical system is in statistical control. When an analytical system moves out of statistical control, the quality assessment data is influenced by additional sources of error, increasing the standard deviation or changing the mean value. 

We have defined in-house calibrations of source roek maturity from various petroleum systems, assessed as source roek vitrinite reflectance equivalent as a proxy for maximum source rock temperature. The molecular parameter versus Ro% calibrations referred to below are either based on published source rock parameters we have validated to our satisfaction (e.g. Schaefer Littke 1988) or from in-house work on source rock maturity sequences. Inevitably, as all oils are mixtures we cannot accurately define point maturities for reservoired oils but by judicious choice of calibrant source-rock samples we feel the calibrations referred to are as robust as is possible in dealing with spatially and temporally complex entities such as source basins. The oil maturities we report are those interpreted classically based on correlations between measured oil molecular ratios and the... 

Measurement of Whiteness. The Ciba-Geigy Plastic White Scale is effective in the visual assessment of white effects (79), but the availabihty of this scale is limited. Most evaluations are carried out (ca 1993) by instmmental measurements, utilising the GIF chromaticity coordinates or the Hunter Uniform Color System (see Color). Spectrophotometers and colorimeters designed to measure fluorescent samples must have reversed optics, ie, the sample is illuminated by a polychromatic source and the reflected light passes through the analy2er to the detector. 

Water-sensitive papers are readily available in most countries and provide a convenient system for visually assessing spray drift performance. These papers are coated with bromoethyl blue, which turns from yellow to blue when contacted with water. " However, since any water can cause this change in color, care needs to be taken to prevent accidental exposure to sources of water other than the pesticide. Such cards do not work well under humid conditions, and are not appropriate for sampling droplets with diameter below 15 qm. Quantitative estimates of droplet size distributions must take account of the exponential increase in droplet volume as the droplet diameter increases. As droplets strike the paper, the liquid spreads over the surface and leaves a stain with a size that is dependent on the volume of the droplet. The apparent droplet size will be greater for large droplets than for small droplets, and the size determination must be corrected to avoid bias. 

Finally, the MOS should also take into account the uncertainties in the estimated exposure. For predicted exposure estimates, this requires an uncertainty analysis (Section 8.2.3) involving the determination of the uncertainty in the model output value, based on the collective uncertainty of the model input parameters. General sources of variability and uncertainty in exposure assessments are measurement errors, sampling errors, variability in natural systems and human behavior, limitations in model description, limitations in generic or indirect data, and professional judgment. 

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