Environ. Sci. Technol. 2000, 34, 4208-4213
Site-Specific A Priori Cancer Risk Estimator for Mixtures and Multiple Sources: Net Excess Relative Odds JAMES ARGO* IntrAmericas Centre for Environment & Health, P.O. Box 101, Wolfe Island, Ontario, K0H 2Y0 Canada
An essential requirement for effective cancer prevention, cancer surveillance, and cancer “hot-spot” analysis is the ability to evaluate cancer site-specific environmental risk factors associated with the operation of multiple industrial point sources and plumes as mixtures. Here we describe a methodology to make a priori estimates of environmental risk factors for minimal exposure by linking an inventory of releases associated with one of 60 standard industrial codes with an inventory of cancer risks associated with minimal occupational exposure to the same chemical releases. We show that an estimate of the risk of developing one of 10 cancers from living within 5 km of a pulp mill, a refinery, a wood treating plant, a chlorine generator, and a paint factory within a community of 36 000 is easily conducted with net excess relative odds (NERO). While this work is confined to cancer risk, the approach outlined can be expanded to estimate outcomes as diverse from cardiovascular disease to spontaneous abortion. This is the second in a series reexamining environmental risk factors using retrospective exposure assessment with emission inventories.
Introduction Health risk from exposure to complex chemical mixtures under ambient conditions is of great interest to the scientific community, industry, and the public at large. The typical risk assessment process relies on a large number of assumptions regarding target-organ dose and mechanisms of injury all assembled into a complex dose-response model. The model is usually data-sparse and assumption-rich and can lose all connection with the reality that people live. It is our view that the best cancer cure is cancer prevention through identification and elimination of risk factors. The major roles of smoking, diet, occupation, and genetics (1) as causative factors in cancer have been clearly indicated. We will show that the role of environment, specifically residence, as a risk factor for cancer has been overlooked to date, leading to an underestimation of the risk attached to living in vicinity of industrial sources. A major factor in this perceived underestimation is the difficulty of handling mixtures of emissions mathematically and the difficulty of evaluating multiple sources of chemical risk. This work is an attempt to bring increased realism to the discussion and assessment of the risk of chronic disease, specifically cancer, as a consequence of mixture exposure. We will show that a priori estimates of risk from long-term exposure due to living in proximity to multiple sources of * Phone: (613)385-1831; fax: (613)385-1832; e-mail:
[email protected]. 4208
9
ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 34, NO. 19, 2000
chemical emissions with known or estimatable releases can be made with a minimum of assumptions.
Background Environmental risk factors associated with living in proximity to industrial sources are being assessed in Canada in a casecontrol study of 18 cancers and approximately 25 000 subjects (2, 3). The approach links an inventory of cancer cases and controls with a comprehensive inventory of environmental point sources using 61 standard industrial codes (SIC) (4). The linked inventories are capable of situating a residence in relation to a source for any time between ∼1960 and 1995 and identifying all the subjects within a specific distance from the source. A total of 61 U.S. SIC codes are available, which include the sources of more than 225 chemical species. The linked inventories can identify the source types and their distance from a single residence, A summary of the available U.S. SICs that form the inventory is in Table 1. The source inventory and characteristics are compiled from public information only. Mixtures. Real releases from industrial sources are always mixtures and often heterogeneous from a physical-chemical perspective. That is, they contain particles, gases, water, water-soluble materials, oil, and oil-soluble materials, all heated and moving turbulently downwind. Plumes include releases and emissions from processes and combustion products from powering the process. Typical fuels are extremely diverse and range from natural gas through coal to waste pulping materials. Mixtures of more than a few components present intractable problems of complexity in evaluating their toxic effect when handled classically. For this reason, the effect of mixtures has been largely ignored in evaluating environmental health risks. Consequently, environmental health risks are often, if not always, underestimated by failing to account for the toxic properties of mixtures. The toxic properties of mixtures are usually estimated, and the most successful mixture estimating methods we have used is the RASH methodology from Jones et al. (5-9). For instance, they were able to successfully model the toxicity of approximately 71 components in environmental tobacco smoke with the RASH method. RASH uses a relative potency approach based on benzo[a]pyrene (BaP). The relative potency (RP) of a chemical species is calculated by dividing the dose of BaP for a test end point by the dose of the chemical for the same test end point, repeated for many tests to obtain statistical validity. Several chemicals in a mixture can be associated with risk for one site, resulting in a distribution of risk. We have shown that sources with sulfur-rich plumes generate additional risk in the form of aerosols or chemical species generated within the plume. The effect of this is to increase the number of persons potentially exposed as the risk is transported away from the source (10). Release Inventory. Our inventory of releases includes those documented under the National Pollutant Release Inventory (NPRI) in Canada for 1995 (11), releases documented in the Factor Information and Retrieval (FIRE) database for the same SIC codes (12), and early EPA emission estimates known as AP-42 from 1972. (13). We augmented the NPRI release inventory for 1995, the FIRE data set, and the AP-42 data set with relevant literature (for example, refs 14-23). Our goal is to identify sources of approximately 250 chemical types from hundreds of references to obtain a more complete picture of releases in a dataset intended to examine issues of health. 10.1021/es990381k CCC: $19.00
2000 American Chemical Society Published on Web 08/25/2000
TABLE 1. Summary of Point Sources Available for NERO Evaluation of U.S. Standard Industrial Code (SIC) SIC
sector
SIC
sector
SIC
sector
1311 2429 2435 2436 2491 2492 2611 2621 2631 2661 2741 2812 2816 2819 2821 2823 2824 2834 2843 2851 2861 2865 9998
upstream oil/gas special wood products hardwood veneer softwood veneer wood preserving particle board pulp mills paper mills paperboard building paper & board misc. printing alkalis & chlorine inorganic pigments ind inorganic chemicals plastic materials synthetic rubber cellulosic MMFc synthetic fibers pharmaceuticals paints & allied products gum & wood chemicals cyclic chemicals waste dumps
2869 2878 2874 2875 2879 2891 2893 2895 2911 2952 2992 2999 3011 3031 3041 3111 3231 3253 3255 3291 3292 3296
ind organic chemicals nitrogenous fertilizers phosphatic fertilizers fertilizer mixing agrochemicals adhesives & sealants printing inks carbon black petroleum refining asphalt felts lubricants petroleum coke tires & tubes reclaimed rubber rubber & plastic hose tanning & finishing brick & structural tile ceramic floor tile clay refractories abrasives asbestos mineral wool
3312 3313 3315 3317 3321 3322 3325 3327 3331 3332 3333 3334 3339 3341 3356 3361 3362 3369 3398 3471 3479 4911 4941
blast furnace & steel mill electrometallurgical prodsa steel reinforcing steel pipe & tube gray iron foundries malleable iron foundries steel investment foundries steel foundries nec copper smelters/refiners lead smelters/refiners zinc smelters/refiners aluminum smelters/refiners nickel smelters/refiners secondary NFMb steel rolling aluminum casting brass/bronze casting NFM casting metal heat treating plating & polishing metal coating thermal power water supply
a
Prods, products.
b
NFM, non-ferrous metals. c MMF, manmade fibers.
Releases under the NPRI depend on the scale of the source. In many instances, releases in Canada are not documented because a reporting threshold has not been met. This does not mean the chemical is absent, only that the release does not have to be reported under current regulations. A chemical reported released in one regulatory jurisdiction is considered here to be potentially present in any industry of the same U.S. SIC code in Canada, even when it is not reported under NPRI. We assume that the specific chemicals reported as being released in 1995 or later under these programs do not differ from the chemicals reportedly present in releases dating to at least 1972, except for bring more precisely defined now. We assume that the releases documented today are incomplete both in kind and in amount, and any risk calculated from association with a chemical is therefore a minimal risk. The range of releases from a source type or SIC, using all data possible, is taken as representative of the potential emissions of the SIC irrespective of the regulatory framework where the source is found. This reflects the post-war socioeconomic climate in Canada. Exposure Inventory. Siemiatycki examined the association between occupational exposure to a chemical and the occurrence of one of 12 cancers (24). He used an inventory of 293 chemicals, generic chemical groups, and radiation to assess exposure as “Any” or “Substantial”. Siemiatycki found that in many cases, Any exposure is associated with a statistically significant and elevated odds ratio (OR) for some of the cancers examined. In other words, minimal exposure also carries a real risk of cancer (24). His work is significant in that the odds ratio of developing one of bladder, colon, esophageal, kidney, lung, nonHodgkins lymphoma, pancreas, prostate, rectal, or stomach cancer is associated with exposure to a relevant chemical or generic chemical group. For example, he describes the OR of colon cancer associated with Any exposure to arsenic compounds, chlorinated alkenes, zinc oxide, and aliphatic ketones and carbon tetrachloride. Any exposure to clay dust is not associated with colon cancer but Any exposure to clay dust is associated with elevated ORs for bladder and esophageal cancers. Siemiatycki’s data is corrected for smoking status (24). Emissions to the Ambient Zone: Any Exposure. For Any exposure, the precise concentration of the emission does
not have to be known since it is already defined, as Any ) minimal. All that is needed is an assurance that the chemical is present in the ambient zone, and this assurance comes from the continuing economic activity of the source. By convention, known or potential carcinogens are considered to have no minimum acceptable concentration with zero risk, and exposure is defined in terms of an acceptable risk, presently 1:1 million in Canada. Relative Odds Mixture Model. We use the emissions inventory to identify the chemical releases associated with a given SIC. Then we use the exposure inventory to identify the risk associated with that chemical or generic group. We make the assumption that Siemiatycki’s results for Any exposure can be applied outside the occupational setting and can represent minimal exposure in a residential setting. This process applies to residences within 5-6 km downwind of the sources. Following Jones et al. (7-11), we describe a relative OR for a chemical by dividing the OR of the chemical associated with a site for Any exposure by the OR for BaP, also for Any exposure, yielding the relative OR (rel OR) as
rel{OR} )
OR(chemical)siteAny
(1)
OR(B[a]P)
Siemiatycki found that the OR associated with Any exposure to BaP is from 0.7 to 1.4 with a mean of 1.0 (24). Dividing the OR for a chemical and Any exposure by 1.0 yields what we term the mean relative OR; dividing by 1.4 produces the minimum relative OR; dividing by 0.7 produces the maximum relative OR. If there are k chemicals in a mixture associated with one cancer site, the total relative OR will overestimate the total by counting BaP for each chemical. Since the average value of OR for BaP is 1.0, we subtract this amount for each chemical, i.e., here 1.0 × k chemicals:
net excess relative ORSICsite j = OR(site)Any
∑ k
((
OR(BaP)Any
)
)
SIC
- k × 1.0
(2) site
VOL. 34, NO. 19, 2000 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
9
4209
TABLE 2. Maximum Net Excess Relative or for Selected Industry Types and Cancer Sites NERO Methodology (eq 2) maximum NERO site/SIC
1311
2491
2611
2812
2819
2851
2911
4911
multiplesic
% all-cancer
bladder colon esophagus kidney lung pancreas prostate rectal stomach NHL breast all
6.0 5.0 12.0 9.4 6.7 8.7 9.0 8.0 8.6 4.7 5.3 88.0
7.1 5.6 6.6 11.4 11.9 14.0 9.9 5.7 10.5 11.6 3.9 101.1
11.4 7.4 23.9 16.9 20.9 21.7 15.6 19.1 18.4 13.5 15.4 188.0
2.0 1.6 1.4 1.3 1.0 2.7 1.4 3.9 1.6 1.7 1.1 48.9
9.7 7.9 13.3 14.1 14.1 16.6 11.7 13.7 11.7 10.0 .5 130.0
11.4 8.6 21.4 10.3 15.3 11.1 15.7 17.3 10.1 8.0 4.2 177.0
13.9 10.6 28.0 22.0 20.2 22.0 21.3 22.1 20.3 10.3 14.9 240
7.9 5.1 9.7 10.7 11.3 17.2 13.0 6.9 11.1 7.1 9.2 126.0
63.4 46.8 104.3 86.7 97.7 105.3 88.6 88.7 83.8 62.2 49.0 1011
6.3 4.6 10.3 8.6 9.4 10.4 8.8 8.8 8.3 6.2 4.8
We term this result the net excess relative odds ratio (NERO) a number describing how many times the mixture exceeds the value of OR for BaP. This method is an estimator of risk, specifically for mixtures from industrial sources and, as will be shown, for multiple sources. We consider NERO is a measure of excess risk in the mixture. We did this calculation for a number of single sources, taken from Table 1, and placed the results of the calculation, the net excess relative OR, in Table 2. Table 2 is a twodimensional grid relating the net excess relative OR for the same cancer site and several SICs in the horizontal direction and the net excess relative OR for each cancer site and SIC in the vertical direction. Siemiatycki does not describe a risk from chemical exposure for breast cancer (24). To apply the NERO procedure to estimate a risk for breast cancer, we augmented Siemiatycki with data (25) to describe the association of minimal () Any) chemical exposure and breast cancer and made the NERO calculation for the following SIC codes: 311 ) upstream oil/gas; 2491 ) chemical wood treating; 2611 ) pulp mills; 2812 ) alkalis and chlorine; 2819 ) industrial inorganic chemicals; 2851 ) paints and varnishes; 2911 ) petroleum refineries; 4911 ) thermal power. In Table 2, the number opposite bladder cancer in the column headed SIC ) 1311 is 6.0. We interpret this to mean that the maximum net excess risk associated with exposure to the airborne plume of mixed chemicals (the mixture) from an upstream oil and gas installation (SIC ) 1311) is about 6 times the risk associated with BaP alone. The data in Table 2 gives the maximum excess risk because Table 2 is formed from the maximum relative OR data. Multiple Source Risk Estimate: A NERO Application. To illustrate the utility of the NERO approach, consider a hypothetical community containing the following industrial sources:
a large bleached pulp mill (SIC ) 2611), facilities for making chlorine (SIC ) 2812), a petroleum refinery (SIC ) 2911), a wood treating plant associated with the pulp mill (SIC ) 2491), a small paint formulator (SIC ) 2851), a miscellany of industrial inorganic chemical plants (SIC ) 2819), and thermal power (SIC ) 4911) to model power generation in the two industries all within 5-6 km of a population of approximately 36 000 in the early 1970s. We will use NERO with this combination of sources and emissions to estimate the number of cancer cases in 1991 for persons living in the community with Any exposure as described above. This includes, for example, mothers at home, children, teachers, pharmacists and physicians, priests and clerks, etc. A community in western Canada is the model 4210
9
ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 34, NO. 19, 2000
on which the calculation is based and existed in this configuration before the refinery closed in 1978-1980. We assume a latency of 20 years, lifetime residence in this community, and residence within 5-6 km of the sources that we group together, near the center of a circle. We show in Table 2 that all of the sources release emissions are associated with each of the cancer sites we are discussing and that each site has a net excess relative OR > 1.0, indicating that there is more risk from the mixture than with BaP alone. Each of the industries contributes to the total risk for each cancer site, so that an exposed subject will have a compound risk and not the simple risk of one source-one chemical frequently described. The total net excess relative OR for each cancer site and source type is in the row total in Table 2 under the heading “multiple SIC”. This includes the NERO for each SIC except 1311. The total is a descriptor for the total risk for that site in this hypothetical environment. We express this as a fraction of the all-cancer risk in Table 2, under the heading “fraction all-cancer”. The net excess relative odds calculated for an industry type with no cancer site specified is the NERO for all-cancer for that SIC. For example, the row total for bladder cancer is 63.4, the row total for all-cancer is 1011, and the fraction of the identified risk of the mixture directed to bladder cancer is about 6.3% of all cancers. The relatively low amount of risk associated with breast cancer in this work is an artifact of the small set of chemicals available to be examined and the poor state of knowledge of the interaction of industrial chemical exposure on the etiology of breast cancer. Single Source Type: NERO. A dominant industry in western North America and any area in the world where oil and gas exploration and exploitation takes place is the upstream oil and gas industry, described with the SIC ) 1311. Releases are imperfectly known and reported but certainly include hydrogen sulfide, benzene, toluene, xylene, ethylbenzene, ethylene glycol, process related chemicals (ethanolamines), and when flaring is employed, sulfur dioxide, soot, oil, and polyaromatic hydrocarbons (PAHs) (11). The sources are frequently located in rural areas, and their immediate impact is on local farmers and their herds. Conventional exposure assessment methods fail for a multitude of reasons (26). However, the inventory of known releases is sufficient to prepare a NERO assessment of SIC ) 1311. In Table 2, we calculated the net excess relative OR for SIC ) 1311. In western Canada, a section of land is 1 mi2, so any SIC )1311 source within approximately three sections will still be close enough to give Any ) minimal exposure as discussed here. The realistic exposure scenario, multiple sources of the same source type (SIC) distributed around an individual, will be addressed in a later work. Our objective here is simply
TABLE 3. Validation of NERO Multiple Sites, Multiple Sources, Multiple Chemicals Estimated Distribution of Cancer Sites, ASIR, and New Cases in 1991 NERO estd cases
Canadian cancer statistics 1991
site
% all cancer
cases per 18 875
rateper 100 000
ASIR per 100 000
cases per 18 825
% all cancer
cases per 18 825
bladder colorectal colon esophagus kidney lung pancreas prostate rectal stomach NHL breast total sites all-cancer totala
0.063 0.132 0.046 0.103 0.086 0.094 0.104 0.088 0.088 0.083 0.062 0.048
5 12 4 8 7 7 8 7 7 6 5 3 74 88
25.4
27.1 62.7
5 9 4 0 3 12 2 23 7 2 3 19 86 89
0.0404
3
0.0893 0.0097 0.0285 0.1556 0.0248 0.1215 0.0438 0.0269 30.038 0.1338
7 1 2 12 2 9 3 2 3 12 64 89
a
21.2 47.3 39.3 42.9 47.7 40.2 40.2 38.0 28.2 16.2
6.1 14.9 90.7 10.9 112.5 15.7 17.5 100.2 440
Calculated with Female all-cancer rates.
TABLE 4. Chemical Releases For Pulp Mills (SIC ) 2611) AP42 (1972)
NPRI (1995) FIRE (1995)
kraft process chemical emissions (from NCASI)
sulfite process chemical emissions (from NCASI)
Pulp process chemicals (sulfite and sulfate), dusts, etc. for both acidic and basic processes; pulping chemicals including ammonia, chlorine, hydrogen chloride, defoamers, hydrogen peroxide, lime, magnesium hydroxide, organic pitch dispersants, sodium borohydride, carbonate, chlorate, hydroxide, hydrosulfite, sulfate, sulfur, sulfur dioxide, sulfuric acid, talc; wax emulsions: paraffin wax, zinc dust, zinc hydrosulfite acetaldehyde, acetone, chlorine, chlorine dioxide, chloroform, chromium, ethylene glycol, hydrogen chloride methanol, methyl ethyl ketone, phenol, sulfuric acid, zinc compounds acetaldehyde, arsenic, benzene, beryllium, cadmium, carbon tetrachloride, chlorine, chlorine dioxide, chloroform, chromium, copper, formaldehyde, manganese, mercury, methanol, 1,1,1-trichloroethane, methyl ethyl ketone, methylene chloride, selenium, trichloroethylene, 2,3,7,8-TCDD, fluoranthene, chrysene, nickel, 1,2-dibromoethane 1,1,1-trichloroethane, 1,2-dibromoethane, 1,2,4-trichlorobenzene, 2-methylphenol, 2-propanol, 3-carene, acetaldehyde, acetone, acetophenone, acrolein, R-pinene, R-terpineol, benzaldehyde, benzene, β-pinene, bromodichloromethane, bromomethane, carbon disulfide, carbon tetrachloride, chloromethane, chloroform, cumene, dibromomethane, dimethyl disulfide, dimethyl sulfide, ethanol, ethyl benzene, formaldehyde, hydrochloric acid, hydrogen sulfide, iodomethane, isooctane, 2-propanol, methanol, methyl ethyl ketone, methyl mercaptan, methylene chloride, naphthalene, PAH's phenol, styrene, toluene, trichloroethylene, vinyl chloride, o-,m-,p-xylene, n-hexane, p-cymene metals including antimony, arsenic, barium, cadmium, chromium, copper, lead, manganese, mercury, nickel, silver, thallium, zinc 1,1,1,-trichloroetheane, 2-methyl-2-pentanone, acetaldehyde, acetone, benzene, bromomethane, carbon disulfide, chloromethane, diethylphthalate, ethanol, ethyl benzene, formaldehyde, hydrochloric acid, methanol, methyl ethyl ketone, naphthalene, styrene, toluene, vinyl acetate. metals including arsenic, barium, cadmium, chromium, copper, lead, manganese, zinc
to show that a risk assessment can be completed with NERO, for any source type, provided only that some estimate of the nature of the releases can be made. Validation. We validate the multiple source model by examining the predictions of NERO and compare with the Canadian Cancer Statistics (CCS). We used the agestandardized incidence rates in Table 3 under the heading “ASIR” from this source (Table 4 in ref 23). We used the unadjusted number of new cases for the relevant sites (Appendix II in ref 23) expressed as a fraction of all-cancer appearing in Table 3 under the heading “fraction all-cancer”. The 1991 age-standardized incidence rate of all-cancer was 469.4 per 100 000 for males and 337.4 per 100 000 for females. In a population of 18 000, we expect 74 cancers in the sites described by NERO and a total of 88 cancers. We expect 86 cancers in the same sites using the ASIR and a total of 89 cancers. Using the unadjusted new cancers as a fraction of the all-cancers, we expect a total of 64 cancers and 89 allcancers. We find that the projections with NERO for esophageal, pancreatic, and kidney cancers are substantially greater than the number predicted with CCS. The potential source of this inaccuracy can be seen in Table 2, where the values of NERO for SIC ) 2611, 2911, and 2851 representing pulp mills, petroleum refineries, and paint formulators, respectively, are 2-3 times greater than the remaining SICs. These three SICs are the source of many potential emissions, and NERO for these sources is correspondingly higher. This suggests that
a model considering multiple sources and multiple chemicals in the plumes providing minimal exposure can account for 1/2 to nearly 8/10 of the cancers expected. We combined the colon and rectal cases in NERO into colorectal, which has a rate reported in CCS.
Discussion In Table 3, comparison between NERO and CCS is generally good for such a simple method, and for several sites it is quite good. This is largely due to both the very high quality of the data in Siemiatycki (24) and the strength of the RASH model upon which NERO is based. The predictions from NERO for lung, prostate, colon, and breast underestimate the risk as assessed from the lower number of cases estimated. For several sites, including esophagus, kidney, and pancreas cancers, NERO has overestimated the risks as compared to the 1991 data. For other sites, NERO has accurately predicted the number of cases. We consider the results in Table 3 to validate the model and the method and to demonstrate acceptable agreement given the simplicity of the model and the complexity of the exposure scenario. Kaldor et al. (21) examined the association between cancer incidence and exposure to air emissions from petroleum and chemical plants. Exposure was described in terms of distance between the source and the subject’s residence. They identified an increased incidence of cancer of the buccal cavity and pharynx for both men and women and increased VOL. 34, NO. 19, 2000 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
9
4211
TABLE 5. Chemical Releases for Petroleum Refining, SIC ) 2911 AP42 (1972)
reports potential exposure to particulates, sulfur oxides, carbon monoxide, hydrocarbons, nitrogen oxides, aldehydes and ammonia NPRI reports releases of 1,2-4 trimethylbenzene, 1,3-butadiene, acetone, anthracene, antimony compounds, arsenic compounds, benzene, biphenyl, butylbenzyl phthalate, carbon tetrachloride, chlorine, cobalt compounds, copper compounds, cumene, cyclohexane, diethanolamine, ethylbenzene, ethylene, ethylene glycol, hydrochloric acid, hydrofluoric acid, 2-propanol, lead compounds, manganese compounds, mercury compounds, methanol, methyl ethyl ketone, methyl isobutyl ketone, methyl-tert-butyl ether, molybdenum trioxide, naphthalene, nickel compounds, nitric acid, phenol, phosphoric acids, propylene, styrene, sulfuric acid, tetrachloroethylene, toluene, vanadium fume or dust, vinyl chloride, xylene isomers, zinc compounds, o-,m-,p-xylene, o-dichlorobenzene FIRE reports emission factors for acetaldehyde, benzene, benzo[a]pyrene, cadmium, chromium, copper, formaldehyde, HCl, manganese, mercury, toluene, chromium(VI), phenol, benz[a]anthracene, chrysene, fluoranthene, mixed xylene isomers and nickel associated with this SIC
stomach, lung, prostate, kidney, and urinary tract in men. In both sexes they found a strong positive association between the degree of residential exposure and death rates from cancer and cardiovascular disease and a less strong positive association between exposure and cerebrovascular disease (23). NERO is a method to estimate risk from complex exposures, either multiple chemicals in a plume or multiple sources or both. By complex exposures, we include situations such as shown in Table 4 for pulp mills, Table 5 for releases from petroleum refineries, or Table 2 for multiple sources. In this work, we have shown that complex exposure scenarios can account for about 1/3-1/2 of the cases in proximity to industrial sources and that NERO is a simple, efficient, rapid method to complete the estimate. The NERO assessment can be applied to any ambient zone for cancer surveillance or to identify environmental hot-spots or to suggest potential public health issues for examination. The cancer sites and/or chemicals can be expanded using the minimal exposure in any study that associates chemical exposure and risk. All the SICs in Table 1 have been evaluated with NERO for cancer. There were no identified releases with NPRI, FIRE, or AP-42 in only two SICs, 3031 and 3041. We have been unable to identify any cases within the corresponding ambient zones of these sources at this time in the EQDB. The NERO assessment is a rapid estimation method to aid in identifying and evaluating environmental risk factors posed by residing in the ambient zone of a source. NERO, will support cancer surveillance and public health issues and hot-spot analysis as an initial rationale for conventional epidemiological studies. The calculation needs an estimate of the chemical nature of the releases and the risk that exposure to this chemical or chemicals is associated to a cancer site or other appropriate health outcome. (27). There are several caveats that must be reinforced at this time. This method we propose to describe as the net excess relative odds (NERO) is intended to be an estimator of complex risk, specifically for mixtures from industrial sources and for multiple sources. The risk is that arising from living in proximity to sources say within 5-6 km, with Any ) minimal exposure, and in the model presented here, mobility is ignored. However, when the fraction of a person’s lifetime spent in proximity to multiple different source types is known, then the description of complex risk is quite simple using NERO. The association is for one of the cancers sites identified by Siemiatycki (23). We assume our subjects are exposed for a lifetime, giving a latency of 20+ years. Finally we have ignored mobility, the tendency of people to move on a predictable manner thereby changing their exposure. The application of NERO to include mobility will be the subject of later work. A summary of the available U.S. SICs that form the inventory is in Table 1. The source inventory and characteristics are compiled from public information only. The 4212
9
ENVIRONMENTAL SCIENCE & TECHNOLOGY / VOL. 34, NO. 19, 2000
Inventory of Releases report is available for $40 (U.S.) from the author.
Literature Cited (1) Doll, R.; Peto, R. The Causes of Cancer: Quantitative Estimates of Avoidable Risks of Cancer in the United States Today; Oxford University Press: London, 1981. (2) Argo, J. Environmetrics 1998, 9, 505 (3) Argo, J.; Mao, Y. In Emission Inventory: Planning for the Future, Proceedings of a Specialty Conference, October 28-30, 1997, Research Triangle Park, NC; Cosponsored by Air & Waste Management Association and the U.S. Environmental Protection Agency. (4) Johnson, K. C.; Mao, Y.; Argo, J.; Dubois, S. Enhanced Cancer Surveillance Case-Control Component: Proposal for a Collaborative Project; Health Canada and Canadian Cancer Registries Research Group: Ottawa, 1994. (5) Jones, T. D.; Walsh, P. J.; Zeighami, E. A. Toxicol. Ind. Health 1985, 1, 213. (6) Glass, L. R.; Jones, T. D.; Easterly, C. E.; Walsh, P. J. Environ. Auditor 1983, 2, 103. (7) Owen, B. A.; Jones, T. D. Regul. Toxicol. Pharmacol. 1990, 11, 132. (8) Jones, T. D.; Walsh, P. J.; Watson, A. P.; Owen, B. A.; Barnthouse, L. W.; Sanders, D. A.; Risk Anal. 1988, 8, 99. (9) Glass, L. R.; Easterly, C. E.; Jones, T. D.; Walsh, P. J.; Arch. Environ. Contam. Toxicol. 1991, 21, 169. (10) Argo, J. Environ. Sci. Technol. 2000, 34, 4214-4220. (11) Environment Canada. Summary Report National Pollutant Releases Inventory; Ottawa, 1995. (12) U.S. Environmental Protection Agency. Factor Information and Release Inventory (FIRE); Office of Air Quality: Research Triangle Park, NC, 1995. (13) U.S. Environmental Protection Agency. Compilation of air pollutant emission factors, 2nd ed.; United States Environmental Protection Agency, Office of Air and Waste Management, Office of Air Quality, Planning and Standards: Research Triangle Park, NC, 1975. (14) National Council of the Paper Industry for Air and Stream Improvement. Compilation of "air toxic" emission data for boilers, pulp mills, and bleach plants; NCASI Technical Bulletin 650; June 1993. (15) Lafoux, R. Practical Toxicology of Plastics; CRC Press: Boca Raton, FL, 1968. (16) Lai, K.-Y., Ed. Manufacture of Liquid Detergents; Marcel Dekker: New York, 1997. (17) Environmental Inventory of the Canadian Organic Chemical Industry 1974; Prepared by J. F. MacLaren Ltd. for the Water Pollution Control Directorate: Toronto, 1974. (18) Al-Dabbagh, S.; Forman, D.; Bryson, D.; Stratton, I.; Doll, R. Br. J. Ind. Med. 1986, 43, 507. (19) de Tonnancour, C. G.; PACE Air Quality Committee. Canadian petroleum refineries atmospheric emission survey; Petroleum Association for the Canadian Environment Report 80-5; PACE: December 1980. (20) Brown, L. M.; Pottern, L. M.; Blott, W. J. Environ. Res. 1984, 34, 250. (21) Kaldor, J.; Harris, J. A.; Glazer, E.; Glazer, S.; Neutra, R.; Mayberry, R.; Nelson, V.; Robinson, L.; Reed, D. Environ. Health Perspect. 1984, 54, 319. (22) Wong, O.; Bailey, W. J. J. Environ. Health 1993, 56, 11. (23) Pulp & Paper Canada Annual & Directory; Southam Business Publications: 1968, 1973, 1978, 1983, 1988, 1993.
(24) Siemiatycki, J., Ed. Risk Factors for Cancer in the Workplace; CRC Press: Boca Raton, 1991. (25) Cantor, K. P.; Stewart, P. A.; Brinton, L. A.; Dosemeci, M. J. Occup. Environ. Med. 1995, 37, 336. (26) Argo, J. Exposure Assessment and Risk Analysis for Emissions from Ranger Oil Heavy Bitumen Extraction Sites, Cold Lake AB; A Report prepared for The Alberta Energy Utilities Board Hearing into the Expansion request by Ranger Oil, Cold Lake AB; ICEH: Wolfe Island, ON, 1999.
(27) Canadian Cancer Statistics 1996; National Cancer Institute of Canada.
Received for review April 5, 1999. Revised manuscript received June 28, 2000. Accepted July 6, 2000. ES990381K
VOL. 34, NO. 19, 2000 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
9
4213
