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Environmental pollution A multimedia approach to modeling human exposure First part of a three-part series Jeffrey B. Stevens Deborah L. Swackhamer University of Minnesota Minneapolis, MN 55455
Quantitative health risk assessment (HRA) is used today by public health professionals to evaluate the potential impacts of environmental pollutants on human health and the environment. By design, this procedure is closely linked to behavior of chemical substances in 1180
Environ..Scl.Technol., Voi. 23, No. 10. 1989
the environment (Figure I). HRA involves calculations related to the environmental distribution and fate of a chemical substance, calculations that estimate human exposure to a chemical substance from the environment, and calculations related to the toxicological and pharmacokinetics of a chemical substance in humans. This article will focus on those models that specifically describe the distribution and fate of pollutants in the environment and the resulting human exposure to the contamnated media. Municipal solid waste (MSW) incinerator emissions will be
used as an example of a multimedia HRA approach. The first step in an HRA of an incinerator is to select the chemicals of interest and the corresponding emissions data base. As documented by EPA (I), MSW incinerators emit a wide variety of chemical substances to air. Some of the more important compounds from a public health standpoint include the criteria pollutants (SO,, NO,, HC, Pb, particulate matter, CO) and several noncriteria pollutants: p o l y ~ h l ~ r i ~ t e d dibenzo-p-dioxinsldibenzofurans (PCDDIPCDF), toxic metals (cad-
0013~936X/8910923-1180$01.50/0 1989 American Chemical Society
mium, arsenic, chromium, mercury, nickel) and a variety of volatile and semivolatileorganic substances. Unfortunately, no consistent pattern of MSW incinerator emissions has yet been established. Therefore, only a range of reported values can be prese.nted (Table 1) and a data base must be selected from these values for the health risk assessment.
mansport of pouutants Modelmg atmospheric dispersion of the chosen emission substances around the facility is the next step in an HRA. The physical and Chemical state of each pollutant as it is emitted is extremely important in understanding the atmospheric behavior of a chemical in the environment. Pollutants may be emitted as gases, panicles, or both. For example, sulfur dioxide is emitted as a gas, whereas metals are typically emitted as particles (although if emission temperatures are high enough, metal vapors may also be emitted). Some of the higher molecular weight organic compounds like PCBs and dioxins may be emitted partly as gases and partly sorbed to particles. Most modem emission sources have particulate control equipment which efficiently removes large particles. Thus, small particles (up to a few micrometers in aerodynamic diameter) constitute the bulk of particulate emissions from these facilities. Small particles disperse in the atmosphere in a manner similar to gases; the dispersion patterns of both are simulated with currently available dispersion models. Some of these models have been approved by EPA for regulatory purposes, for example, the Industrial Source Complex (ISC) model (3). They do account for certain types of topography, for building wake e f f a , and for other aspects of dispersion near an emission source., but are not recommended for simulation of long-range transport (farther than 50 km from the source), wet or dry deposition, or chemically reactive plumes. Results of the ISC model are usually expressed as ambient air concentration isopleths in the region around the source (Figure 2). Because these isopleth values are the primary input data for an HRA, several of the major uncertainties regarding air dispersion modeling of pollutant emissions require discussion. First, the model-predicted air concentrations are generated using the best available meteorological data, most often data from the nearest National Weather Service site. This lack of site-specific data leads to some uncertainty with respect to the calculated air concentrdons at any parti& site. In cases where the distance between the modeled emission source
mllllon on sdrywlume perwlume basla rams per " e l CUbk meter. per "elcublc meter
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and the site of meteorological data collection is great, or where terrain features affect the representativeness of the meteorological data, this uncertainty can be high. Secondly, the regulatory dispersion models are unable to account for chemical and physical changes that may ocCUT after pollutant emission. Qpically, a constant emission rate is assumed for a particular substance, the dispersion of that substance is simulated, and an ambient air concentration of the substance is predicted. Numerous processes, such as gas-to-particle conversions and chemical and photochemical reactions, are known to OCCUT as pollutant plumes interact with the atmosphere. Also, the input waste streams can vary considerabG in content. Lastly, the current air dispersion models do not adequately treat deposition processes. The model-predicted
ambient air concentrations are sufficient if the only exposure route is via inhalation. However, other exposure pathways such as dermal contact, adsorption by soil, and the food web require pollutant fluxes onto terrestrial and aquatic surfaces before exposure can occur (Figure 3). Deposition can occur over a continuum ranging from purely wet to purely dry deposition. Some workers have adapted deposition schemes to the regulatory model (4), but in most cases, simplified deposition assumptions are applied to the predicted ambient air concentrations in order to model pollutant fluxes in the environment. Of
Following the emission, dispersion, and deposition processes, accumulation of chemical substances in specific environmental media is modeled. As should Envlron. ScI.Technol., Vol. 23, No. I O , 1989 1181
Annual average ambient air w " t i 0 n lndushial Source Complex mcdeP
isqjeths generated using the
year), k, is the overall elimination rate constant for the chemical in topsoil (rear'), d is the soil density (g/m'), I is the physical mixing depth of the chemical in topsoil (m), and k is the rate constant of degradation of the chemical in soil. The longevity of a chemical in the topsoil, that is, the volume of soil with which the receptors will most likely come into contact, will depend on processes of degradation (mentioned above) and physical removal (dust dispersion, leaching). At low concentrations of organic chemicals, the degradation and leaching processes, if they are known to occur, can be considered first order and therefore can be expressed in terms of half-lives. For example, 2,3,7,8tetrachlorodibenzo-p-dioxin (TCDD) has been reported to have a topsoil halflife of 12 years (5-7). Dust dispersion has generally not been considered a significant mechanism by which contaminants are lost from topsoil. Modeling the soil contaminant concentrations resulting from chemicals emitted in the vapor phase is difficult because the vapor-soil partition coefficients are not readily available. A first C, = A(l - e-'e') (1) approximation of the level of a gaseous chemical substance in soil can be obd.l.k, tained using a fugacity approach (8, 9) where C, (mglg) is the chemical con- by initially calculating the equilibrium centration in soil at the end of time t of the chemical between air and soil (years), A is the chemical flux to soil water using Henry's law. Then, parti(ambient air concentration of chemical tioning of the chemical from soil water x particle deposition velocity; mg/m2 to soil can be modeled assuming equi-
be evident in Figure 3, the environmental fate of emission substances around a facility is extremely important to quantify-specifically, accumulation in soil, the agricultural food web, and the aquatic food web. Use of the approaches described below requires the input parameters listed in the box. Soil. Chemical accumulation in soil depends on: the deposition velocity and constancy of deposition onto soil; the behavior of the chemical in topsoil (affectedby mixing or tilling, human and animal activities, leaching, runoff); and the longevity of each chemical in topsoil (modeling approaches include identification of the major degradation processes involved, e.g., biological, photochemical, chemical; the chemical concentration dependence; and the influence of various soil and environmental factors on these processes). In general, the following equation has been used to estimate the accumulation of any particulate-bound emission substance in topsoil:
1182 Environ. Sci. Technol., Vol. 23, No. 10, 1989
librium partitioning (IO). It should be pointed out, however, that this approach neglects degradation and physical removal processes in all compartments (air, water, soil) and therefore tends to provide an overestimate of the soil levels of these chemicals. However, degradation rate constants can be incorporated into the modeling equations, if data have been published. The agricultural food web. The environmental fate of emitted substances in the agricultural food web depends on: the deposition of these substances onto and absorption into agricultural crops, such as livestock foods (hay, alfalfa, and corn silage) and garden vegetables; the estimated livestock doses of the contaminants; and the pharmacokinetic behavior of the chemicals in each livestock animal, that is, each chemical's translocation from feed to human foodstuffs (milk, meat, and eggs). A number of assessments of the bioaccumulation of toxic air pollutants into the human food web via common livestock have shown that the steer and dairy cow are the primary agents of exposure (11-13). Calculations related to the accumulation of emission substances in the agricultural food web begin with their deposition and absorption into agricultural crops (Figure 3). Plants can be exposed to chemical contaminants through aerial deposition or impaction, or by absorption from contaminated soil. One common approach to modeling aerial deposition of particulate-bound chemicals onto plant surfaces is that taken by Baes et al. (14). These investigators measured the deposition of radioactive particles onto plants and developed equations correlating fractional deposition (0to crop yield. The general equation that then is used to quantify aerial deposition of pollutants is given below. C, = (A)(f)(l - e-'e') (2) @e)(*)
where C, (mg/g) is the chemical concentration on the plant at time of harvest t (days), A is the total chemical flux on the crop field (ambient air concentration x particle deposition rate; mg/m2/day), f is the fractional deposition of the particulate flux onto the plant surface, k, is the elimination rate constant for the chemical substance on the plant surface (day-'), and -# is the crop yield (g/mz). Crop yields can be obtained either from national, state, or local data bases. The elimination rate constants for chemicals on plant surfaces generally are not known. Therefore, most studies
"tal fate pathways and exposure mutes i ~ultimedihealth risk assessment
input data for environm fate analyses Particle deposition velocity Overall elimination rate constant, k, Length of time of chenucal input, t
cOr'-'r~iion
Soil density, d physical mixing depth, I constant, H
a m partition coefficient, kW A g l h h u d food web Particle deposition velmty Fraction of particle deposition to plant surface, f Elimination rate constant from dant surfwe. k. Crop yield, T Soil-@plant translocation factors Feed-ro&dk (or meat) translocation factors Rate of food ingestion by livestock Octanol-water partition coefficient, 16. Aquatic food web Panicle deposition velocity Suspended paniculate maner concentration, SPM Water wtaow rate Scdtmentation rate. s Sediment organic carbon fr Sediment mixed depth, Sediment dry density, d Mcan water dcph, h Len@ of time of chemical Input, t Major fish species consumed by poplati Fish species lipid hctmn, L Water-particle partition weficient, K, or K, (K Air-water mass transfer coefficiuu, Kn, Onanol-water partition anfficicnt, Henry's law cacfficient, H
resort to modeling particulate loss caused by weathering. Several studies have shown that an average weathering half-life for small particles on a variety of plants is 14 days (1416). It is also important to note that Equation 2 calculates the concentration of a particulate-adsorbed chemical on the plant surface, not the amount of chemical absorbed into the plant. Models designed to calculate chemical absorption into plants from deposited particles have not yet been developed. In general, the approach has been to conservatively assume that all of the deposited material remains associated with the plant through its processing and therefore is ingested by the animal (agricultural crops) or by humans (garden vegetables). Absorption of gaseous materials into plants is still an emerging area of study. Very little information is currently available on most chemicals, but models are being developed (17, Z8). Absorption of chemicals from contaminated soils is generally modeled by the use of soil-to-plant translocation factors. No single source of these factors is currently available. One approach is
to analyze the scientific literature on a case-by-case basis. Alternatively, a partitionha amroach wherein uotake of an organic chemical is estima& from its octanol-water partition coefficient KW) can be done (19,ZO). Once estimates are made of the chemical levels in agricultural crops, the next step in the model is to estimate chemical doses to the farm animals of concern. Both the dairy cow and the steer are assumed to be exposed to air pollutants via inhalation,. ingestion of contaminated soil, and ingestion of contaminated foodstuffs. Of these three mutes of exposure, food ingestion always has been shown to be most significant (11-13). Food ingestion rates by livestock are a highly sitespecific input parameter. Either feed-to-milk (or meat) or daily dose-to-milk (or meat) translocation factors are most often used to translate feed levels (or daily dose estimates) to tissue levels. These translocation factors are expected to be chemical-specific and thus should be obtained from pharmacokineticanalysis of the current literature of each chemical substance (11).Alternatively, when the l i t e r a m
-
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data base is insufficient, translocation factors have been derived from the Kw of the chemical 120).The final result of this food web analysis is an estimate of the amount of chemical contamination in common human foodstuffs (mg/g). The aquatic food web. The environmental fate of emission substances in the aquatic food web depends on: the deposition onto and behavior of the chemical substances in surface water; the hioavailabilitv of the chemical substances to bigta from the various aauatic commrtments (i.e.. . ,the water column, skdiments, suspended solids); and the bioaccumulation potential of each substance in aquatic biota. Human exposure to the aquatic food web involves two pathways: drinking contaminated water and consuming aquatic life such as fish and molluscs that live in contaminated water. It is the latter pathway that will be addressed in this paper. There are several approaches to calculating fish tissue contaminant concentrations. It is necessary to know the water or sediment concentrations of the Environ. Sci.Technoi.. Vol. 23,No. 10,1989 1183
contaminants to make these estimates. Calculations for estimating water concentrations depend on the &, of the compound and the deposition process that delivers them to the water surface. For compounds that enter the water associated with particles, such as most semivolatile and nonvolatile organic compounds and metals (mercury excepted), the concentration in the water particulate phase, C,,p in ng/g, is estimated from the total particle flux (both wet and dry), A (nglm2 year); the suspended particulate matter concentration, SPM (glm’); the mean depth of the water body, h (m); and the overall contaminant removal rate constant, k @ear’)(includes all removal processes from the water column, including outflow, sedimentation, degradation, and volatilization):
fish contaminant concentrations depends on the chemical under consideration. For metals, bioconcentration factors have been determined in controlled laboratory studies that are defined as the ratio of the metal in fish tissue to that dissolved in water (23).These BCF values from the literature are typically used to estimate fish tissue concentrations of the metal, but they represent only exposure from the dissolved phase. The exposure of particulatebound metal, and the differences in bioavailability between dissolved and particulate phase metal, are generally unknown and thus ignored in most assessment models. For organic compounds with &, < 6, a first approximation of the concentration of an organic pollutant in fish resulting from bioconcentration can be estimated using the equilibrium, or fuGP= A (3) gacity, approach (9, IO). For comh-k-SPM pounds mostly in the dissolved phase, the contaminant concentration in fish For compounds with log %, > 6, it tissue can be estimated by assuming is reasonable to assume that most of the that the log hW approximates the concompound remains associated with par- centration in fish, normalized to lipid ticles in the water column. For com- content (24). pounds with log %, < 6, partitioning Another approach is to assume that between the dissolved and particulate the concentration in fish lipids is in phases should be considered. The con- equilibrium with the organic carbon taminant concentration in the dissolved (OC) in the aquatic system, that is, the phase, Cw.d,can be determined from OC associated with SPM. Because the water-particle partition coefficient, between lipid and OC is asKp, which is the ratio of C,,p in the nglg equilibrium sumed, the chemical concentration in to in ng/mL. Many Kp values can fish lipids is independent of its source he found in the literature, or they can to the fish. It has been shown that the be estimated from %, and the organic ratio of organic pollutant concentracarbon content of the sediment (21). tions in fish lipid to that in sediment For volatile organic compounds (VOCs) and vapor phase metals such as organic carbon ranges from 1.5 (21, 24) to 4 (25). Thus, the pollutant conmercury that enter water by gas exchange across the air-water interface, centration in whole fish (nglg) can be estimated from the fish lipid fraction, the dissolved phase concentration can L; the pollutant concentration in sedibe estimated from the thin-film model ment, C, (ng/g); and the organic carbon applied by Liss and Slater (22): fraction in sediment, OC: cw,d -& (4)
published by Connelly and Pederson (30). This calculation requires knowledge of the major components of the food web; the growth rates of the species being modeled; and the appropriate uptake, assimilation, and elimination rate constants. Often these parameters are not known for a specific site, and fish tissue concentrations must be approximated by the fugacity approach. This can lead to an underestimation for very planar molecules such as DDT or 2,3,7,8-tetrachlorodibenzodioxin, and overestimation of nonplanar molecules (26,27, 31). Clearly, the contaminant distribution in aquatic systems is more complex than described by the above approaches. Usually assessment models ignore degradation processes and metabolism. However, practical limitations in our knowledge of the necessary distribution coefficients or rate constants prevent assessment models from being more sophisticated.
Human exposure assessment
Once the modeling of chemical emissions into the various environmental media has been completed, hu? exposure modeling can be undertaken. Human exposure modeling primarily involves human behavioral and physiological assumptions. The types of questions that must be addressed in a human exposure analysis are: How many subgroups in the population require modeling? How many age groups in each receptor population require modeling? What is the frequency and extent (amount) of contact between the receptor and the contaminated media? How does exposure to each chemical from the various routes relate to the target tissue levels of the chemical (route-specific bioavailability factors)? Because an in-depth discussion of n KO~ these questions is beyond the scope of where C, is the contaminant concentraThe concentration in sediment, if not this article, two examples of exposure tion in vapor (ng/m3), H is the dimen(ng/ pathway equations are provided as ilsionless Henry’s law constant, F, is the known, can be estimated from G,p g); the sedimentation rate, s (g/an2/ lustrations. Inhalation of air will be contaminant vapor flux to the air-water modeled as an example of a direct route interface (ng/m2/year), and is the year); the time period of deposition, t of contaminant exposure, and ingestion overall liquid mass transfer coefficient (year); the mixed depth, md (cm); and of fish will be modeled as an example (m/year). If the concentration of the dry density, d (g/cm’): of an indirect route of exposure. pollutant in air and water is assumed to Inhalation pathway. The general c, = (CW.D)(~)(t) (6) be at equilibrium, then Cw.dis equal to equation that is used to quantify an in(md)(d) CJH. h.-lation exposure dr- :-given below. The fugacity approach is not as sucThe concentration in biota is a result of direct uptake from the water cessful at describing bioaccumulation Ambienl ai ..nOation (bioconcentration) as well as uptake of hydrophobic organic compounds Mation CollCEntrstion rate Deposition from consumption of lower trophic lev- with log %, greater than approxidose = ofchemical (m’lday) hction els (biomagnification). The latter may mately 6 (26-29).This biomagnifica(WWW)(wW consist of numerous pathways that tion is thought to be a result of unequal make up the organism’s fnod web, in- uptake and elimination rate constants. [Humanbody weif cluding pelagic, benthic, and microbial The concentration of such compounds The ambient air concentra.-.~- of in fish is best estimated using a bioenerpathways. The approach that is taken to estimate getics approach, such as that recently each air contaminant is obtained from
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1184 Environ. Sci.Technol., Vol. 23, No. 10,1989
the air dispersion model isopleth maps. Standard ventilation rates for humans as a function of both age group and sex have been published (32) and are used in most assessments. An average value for a resting adult is 20 m3/day. The alveolar deposition fraction is the fraction of inhaled particulate material that deposits onto the surface of the alveolar region of the respiratory system. This fraction is a function of particle size (33, 34). For example, an alveolar deposition fraction of particles < 1 I” in diameter is 0.60,indicating that 60% of the inhaled particles deposit into the deep lung. This deposition h t i o n generally does not apply to gaseous pollutants or to those chemicals such as arseNc that elicit adverse health effects on the lung. Most assessments also utilize the EPA standard body weight estimates (32) for exposure dose modeling. Estimates can be found for specific age groups and sexes. Fish ingestion pathway. The general equation that is used to quanti% exposure doses. from ingestion of contamin given below: Fish &mid Amount! Fraclionc Ingestion eoncentralion frequeneyof fish dwe = mfA !ish ingestion caught in
(“day)
(mgg)
@day)
impactraoel
[Human body weight @)I
The chemical wncentrations in variare calculated in the environmental distribution and fate section of the assessment. The amount and frequency of fish ingestion can vary considerably depending upon the type of individual modeled in the assessment. We have provided risk managers with a range of exposure doses by modeling both average ingestion rates for the general population and sport-fisher ingestion raiesaS a worstcasi scenario. Data bases on the amount of fish wnsumed by individuals in the general population and hy sport fishers have been published (35, 36). The amount, as well as the frequency, of local fish consumption is usually a highly sitespecific value. Creel census data (Le., data on the distribution of fish species in traps) for a particular site are the best to use in an assessment, because these data not only provide an estimate of the amount of fish ingested but also provide the species of fish ingested. The final step in the exposure assessment is the summation of all the relevant route-specific exposure doses in order to obtain an overall exposure dose for each population age group. Our assessments typically model exposure to infants, toddlers, adolescents, and adults so that both subchronic ex-
ous fish species
posure doses for any particular age group (e.g., toddlers and lead exposure) and chronic exposure doses can be assessed.
s&
“p The quality of any HRA depends on both the number of routes of exposure considered and the approaches taken to model those exposures. However, the accuracy of the final risk assessment is only as good as the accuracy of the input parameter values. Thus, it is imperative to have access to as many of the input parameters (listed in the box) possible. Although many of these parameters can be taken from the literature, others are site-specific, and local data should be used rather than average literamre values. Local data include meteorological data needed for the dispersion modeling; soil density and physical mixing depth, specific crop yields and time to harvest; food ingestion rates by local livestock; specific l i o l o g i c a l data for modeling local aquatic systems, such as the water depth, outnow rate, SPM concentration, sedimentation rate and mixed depth, sediment OC fraction and dry density; and amount and frequency of fish species wnsumed locally. Actual measurements of contaminant wnccntrations in air, soil, water, sediment, and fish are much preferable and would preclude modeling these concentrations as shown by the above equations. However, accurate input data require sufficient numbers of measurements to account for spatial and temporal variability, and so are usuallv bevond the SCOE of most assessments. In closine. this article has nrovided an overview of a multimedia risk assessment process and pointed out some of the current shortcomings or uncertainties inherent in this type of modeling. The accuracy of these models is not known, but they do serve three. very important purposes: to provide risk managers with a best estimate of the fate of emission substances around a particular facility; to provide guidance as to which environmental media might serve as sentinels for emissions from a particular facility, and, because these fate and exposure models tend to be conservative; and to provide the toxicologist with reasonable upper bound exposure doses for the emission substances that may affect humans.
. .
Acknowledgment The authors would like to acknowledge the contribution of Gregory F’ralt, Minnesota Pollution Control Agency, for his input to the air dispersion section of this paper.
Referenees (1) Fed. Regist. 1987.52, 25399-408. (*) Midwest Research Institute;
Solid Waste Combustion SNdy, Emission Data Base for Municipal Waste Combusters”; U.S. Environmental Protection Agency. Omce of Solid Waste and Emergency Response. US. Government Printing Oflice: Washington, DC, 1987; EPA530-SW-87-02], (3) “Industrial Source Complex (ISC) Dispersion Model User’s Guide; Second Edilion”; US. Environmental Protection Aeencv. Office of Air Oualih, Plannine a d Siandards. US. GGernGent Pin< ing Oflice: Research Triangle Park, NC, 1987; EPA-450/4-88-002a. (4) Croes, B. E. Presented at the 81st Annual Meeting of the Air Pollution Control Association, Dallas, TX, lune 1988; p a p 88-62.8. (5) Dihmenico, A.; Viviano, G.;Zapponi, G. In Chlorinated Dioxins and Related Compounds: Impact on tka Environment; Hutsinger, O . , Ed.; Pergamoa: New York, 1982, pp. 104-14. (6) Young, A. L.; Kang, H. K.;Shepard, B. M. Environ. Sci. Technol. 1984, 17,
__”.. 52nA-A
(7) Kimbmu h R D el al. J Toxicol. EnviIO”. Heafth’1984; 14,47193. (8) Msckay, D. Environ. Sci. Technol. 1!7l9, 13, 1218-23. (9) Clark, T. ef al. Environ. Sci. Technol.
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1988.22. 120-9. (10) , , Brises. 0 . G . Proc. BE Insecfic. Funnic. ” CoGf 1973, 11, 475-78. (11) Stevens, 1. B.;Gerbec, E. N. RiskAnalyv i r ____, 19110. R 17’4-15 _._ (12) Stevens, 1. B. et al. “Health Risk Assess~
_.__ ~
ment for the Praposed Resource Recovery Facility in Winona, MN”; Minnesota Pollution Control Agency: St. Paul, MN, 19uu.
S&ns, 1. B. et A. ”Health Risk Assessment far the 3M Chemolite Hazardous Waste Incinerator Located in Conage Grove, M N ; Minnesota Pollution Control Agency: St. Paul, MN, 1989. B B ~ sC. , P.,m.et al. Oak Ridge National Laboratory Report No. 5786; U.S. Government Printing Oflice:Oak Ridge, TN, 1984. Highland, I. “Generic Risk Assessment. Volume 2.” Ontario Waste Management Corporation: Toronto, Canada, 1986. Moghissi, A. A. et al. In Dynamics, Exposure ond Hazard Assessmcnrfor Toxic Chemicals; Hague, R., Ed.; Ana Arbor Science: Ann Arbor, MI, 1980. MacFariane, C. Proceedings of Ihe Internarionnl Workshop on the Enviromntal Fate of Ckem’cals; Mackay. D., Ed.: Toronto, Canada, December, 1988. Mackay, D.; Paterson, S. Proceedings of the International Hbrkshop on the Enviromntol Fate of Chemicals: Mackay, D., Ed.; Tomnto, Canada, December, 1988. Briggs, 0 . G.; Bmmilow, R. H.;Evans. A. A. Pestic. Sci. 1982,13,495-504. ltavis, C. C.; Arms, A. D. Environ. Sci. Technol. 1988.22, 271-4. Karickhoff, S. W.; Brown, D. S.; Scott, T A. H ~ I w R u . 1979.13.241-48. Lis. P. A,; Slater, P. D. Nature 1979, 247, 181-84. “Ambient Water Quality Criteria Documents.’; Office of Water Regulations and Standards. US. 6ovironmental Profcction Agency. US. Government Printing Ofice: Washington, DC, 1980, 1984. , Lake, J.L.; Rubinstein, N.; Pavignano, S. “Fate and Effects of Sediment-bound Chemicals in Aauatic Systems”; Proceedings of the 6th Pcllsbn Hbhkop: Florissant, CO, 1984. (25) Mackay, D.Envimn. Sci. Tecknol. lW, 16.214-78. (26) Swackhamer, D. L.; Hites, R. A. EnviEnviron. Sci. Technol., Vol. 23,No. 10, 198Q 1185
ron. Sci. Echnol. 1988.22, 543-48. (27) Opprhuizcn. A. et al. Chemosphere 1985.14. 1871-%. (28) Oliver. B. G.: Niimi. A. J . Environ. Sci. Ttrhnol 1983.17. 287-91 (29) Hsukrr. D W , Connell. D W Chrmor
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(30) Cannclly. J . P.; Pedersen, C. 1. Environ. Sci. Gchnol. 1988.22, 99-103. (31) Kuehl. D. W. et al. Chemosphere 1%. 14. 427-37. (32) GCA Corporalion; “Developmen! of Statistical Distribution or Ranges of Standard Factors used in Exposure Assessments”: U.S. Environmental Protection Agency. Office of Health and Environmental Assessment. U.S. Governmenl Printing Ofice: Washington. DC. 1985. EPAIW018-85-010. (33) Hinds. W. C. In Aerosol Ethnology. Properrirs. Behavior. and Mensurcmenrr of Airborne Parriclcs; Wiley: New York. 1982: pp. 211-32. (34) PEI Associates. Inc. “Characteristics. Deposition and Fale of Inhaled Particulme Matter”: US. Environmental Protection Agency. Onice of Health and Env i r o n m e i t a i Assessment. U.S. Government Printing Office: WarhingIon. DC. 1987. EPA-60018-871W2.36. (35) “Food and Nilrient Intakes of Individuals in I Day in the United States. Spring 1977. Nationwide Food Consumption Survey, 1977-78“: U S Department of Aerieulture. U.S. Government Printine O h : Washington. DC. 1980. (36) “The National Health and Nutrition Examination Survev from ~~, -, I N H A N F 11) 1976-1980; National Ccntcr for Health Statistics. U.S. Government Printing Office: Washington. DC. 1982.
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Jejjhy B. Stevens is an assisranr professor in rhe Division of Environmenral and Occuparional Healrh, School of Public Healrh. Universiry of Minnesora. He has a B.S. and M.S.in biochemisrryfrom Michigan Srare Universiry and a Ph.D. in biochemisrryfrom Corncll Universiry. H e also is rhe presidenr of J . B. Stevens and Associares, a privare consulringfirm in Minneapolis. MN. rhar specializes in healrh risk as-
sessmenrs.
Lkbomh .L Swackhamer is an assistant professor in rhe Environmenral and Occuparional Healrh Division in rhe School af Public Healrh, Universiry of Minnesora in Minneapolis. She has an A B . in chemisrry from Grinnell CoUege and an M.S. in water chemisrry and Ph.D in limnology and oreanographyfrom rhe University of Wisconsin-Madison. Her research inreresrs are in rhe chemical and biological fare af hydrophobic organicpolluranrs in rhe environmenr.
