Approximations to Discrepancies Between Visual and Instrument Opacity Readings for Submicron Particulate Material C. Dean Wolbach
Texas Air Control Board, Austin, Tex. 78758 Control agencies are beginning to encounter numerous conflicts between instrument and visual opacity readings. Theoretical approximation to the magnitude of such discrepancies can be made. A mathematical formula for approximating discrepancies between visual and instrument opacity readings is derived. Some values for these discrepancies are plotted and a specific case is calculated.
State agencies and industries are beginning to come into conflict over discrepancies between visual opacity readings and instrumental readings. These conflicts invariably arise when the particle size is primarily submicron. By combining the efforts of earlier workers (Ensor and Pilat, 1970; Halow and Zeek, 1970), approximations to these discrepancies can be obtained. Ensor and Pilat have derived a simple formula for calculating transmissivity of plumes from first principals. This equation is: In ( I / I O l t= -WD/kp
represents the theoretical discrepancy between visual and instrument opacity readings. For a particle size geometric standard deviation ( u g ) of 2, the specific particulate volume/extinction coefficient ratio, k, in the region 0.1-1 p can be approximated by the hyperbolic curve function, (In rgu.
+ 1.39y = 2.07 (In k + 1.83)
while the concomitant plume to background brightness ratio ( B s s / B , ) , a t 90" by Halow and Zeek can be approximated by (In 2 rgW -I-0.69)' = -1.87 (In
(These equations are for white particulate material with a refractive index of 1.5.) Solving for the dependent variables in terms of rgw (geometric mass mean radius), we get:
OJ6 e -0.48
where W = mass concentration in g/m3 D = plume diameter in meters p = particle density in g/m3 Zo = incident radiation intensity Z = transmitted radiation intensity and k is a complex function of the particle radius, frequency distribution, refractive index of the material, and color of the material (white or black). Ensor and Pilat have calculated some curves fork. On the other hand, Halow and Zeek have combined theoretical calculations for plume brightness and empirical observations of plume opacities to generate a curve relationship. The theoretical sky to plume brightness ratio is given by the formula:
where W, D, and p are the same as given above but in units of pounds and feet. The term ( B s S / B o ) is , a different complex function of many of the same variables as k, B,, is the function for plume brightness from scattered light, and Bo represents the background brightness of the sky. When we determined a curve fit equation for Halow and Zeek's empirical curve relating (Bss/Bo)oto opacity, the following relationship was obtained:
+ 2.9 X
From Equation 1, instrument opacity readings will be:
[1 - ( I / I o ) l z= 1 - exp(-WD/kp)
Environmental Science & Technology
opac (instr) ( 5 )
Figure 1. Discrepancies between instrument and visual opacity readings as a function of geometric weight mean particle size for various W D / p ratios
-0lo-? ft, 4- 2
- [l - (I/Z0)l/ = opac (visual)
Discrepancies are given in percent opacity of visual reading above instrument reading
25 e -0.53
(In 2 rpu
When we allow the prelog function in Equation 3 to approximate 0.94, substitute Equations 1, 3, 8, and 9 into Equation 5, and rearrange, the following difference function is obtained:
riving the ( B s s / B o ) to o visual opacity relationship would probably negate the effort involved. Experimental verification of this discrepancy was observed on a glass furnace stack. The observed parameters were:
E = 1.96 x 10-7ft 4 rgw = 0.25 p = -2 [I - (Z/Zo)]i = 9 - 12% (obsd) [l - (Z/Zo)]u = 35% i 10% (obsd) [l - (Z/Io)lU= 30 - 33% (predic)
where 6, - represents the fraction difference between visual and instrumental readings. Equation 2 has been converted to read in fractions rather than percent; ( W D / p ) and ( W D / p ) ’ are in units compatible with their respective parameters. An example of the discrepancies obtained is plotted for various ( W D / p ) ratios in Figure 1. By varying the constants in Equations 8 and 9, 6 functions for different refractive indices and viewing angles can be determined. Closer approximations could be obtained with computer solutions of the theoretical equations for k and ( B s s / B o ) o as functions r g W . However, the experimental error in de-
The implications of this type of discrepancy will become quite serious in light of the increasing use of instruments vs. human observers for the determination of opacities. A detailed examination of the philosophies behind the use of opacity measurements is indicated.
Literature Cited Ensor, D. S., Pilat, M. J., “The Relationship Betweep, the Visibility and Aerosol Properties of Smoke Stack Plumes, Second International Clean Air Congress of the IUAPPA, 1970. Halow, John S., Zeek, Susan J., “Predicting Ringelmann Number and Optical Characteristics of Plumes,” J. Air Pollut Contr. ASS.,23 ( 8 ) ,676-84 (1973). Received for review July 30, 1973. Accepted N o v e m b e r 30, 1973.
Mercury-Organic Matter Associations in Estuarine Sediments and Interstitial Water Steven E. Lindberg’ and Robert C. Harriss Department of Oceanography, Florida State University, Tallahassee, Fla. 32306
Sediment from the Florida Everglades and Mobile Bay estuary reveal significant associations between sediment Hg and sediment organic matter and between dissolved interstitial Hg and dissolved organic carbon. The bulk of dissolved Hg and dissolved organic carbon exists in the <500 molecular weight fraction in Everglades pore water and in the >100,000 molecular weight fraction in Mobile Bay pore water. Mercury in sediments and interstitial water occurs a t higher concentrations in the Everglades than in Mobile Bay, which receives anthropogenic mercury effluents. When normalized to organic content of the sediment or dissolved organic carbon concentration of the pore water, higher relative mercury concentrations occur in Mobile Bay. Interstitial dissolved mercury is enriched from 2.6 to 36 times over the associated surface water values, and in sulfide-rich pore waters far exceeds the thermodynamic solubility of HgS. Enrichment may be due to formation of organic and polysulfide complexes with mercury. The quantitative importance and potential ecological effects of anthropogenic additions of mercury to natural aquatic environments have been well documented in the recent scientific literature (Harriss, 1971; Wallace et al., 1971). As a result of several cases of acute mercury contamination in ,Japan and Sweden, most large industrial sources have been identified and the discharge of mercury to natural waters reduced to negligible flux rates relative to natural sources. However, much of the mercury released prior to the implementation of pollution control To whom correspondence should be addressed
regulations has accumulated in the sediments of watersheds receiving discharges. For example, the combination of high reactivity with particulates (Cranston and Buckley, 1972) and physical conditions conducive to high sedimentation rates has trapped large quantities of mercury in near shore sediments. The mobility and ultimate fate of mercury in the sedimentary environment are controlled to a large extent by its interstitial water chemistry. The present investigation is a study of sediment mercury and its interstitial water chemistry in an undisturbed estuarine environment, the western section of the Florida Everglades, and in an estuary known to receive industrially derived mercury effluents, Mobile Bay, Ala.
Materials and Methods Sediment cores were obtained manually from near-shore marsh areas with a piston-fitted polycarbonate tube and immediately extruded into polyethylene Whirlpak bags kept near ambient temperature until squeezing (2-6 hr). Interstitial water was extracted using a nitrogen-operated, Teflon-lined squeezer similar to that described by Presley et al. (1967). Salinity was determined in the field laboratory on freshly extracted pore water with a Goldberg refractometer (precision +0.5 O/OO); 25 ml of pore water samples for dissolved Hg analysis were stored in Pyrex containers after acidification with “ 0 3 to pH < 1 and addition of 6% KMn04; 5-ml samples for determination of dissolved organic carbon (DOC) were placed in glass ampules containing 100 mg of K ~ S 2 0 8and 0.1 ml of H3P04, and were then purged of inorganic COz and sealed; sediment samples were kept a t 0°C until laboratory analysis. Volume 8, Number 5, May 1974