Environ. Sci. Technol. 2000, 34, 4214-4220
Downwind Distribution of All-Cancer Relative Risk about a Point Source: Single Source with Reactive and Unreactive Plumes JAMES ARGO* IntrAmericas Centre for Environment and Health, P.O. Box 101, Wolfe Island, Ontario K0H 2Y0, Canada
We examine the association between living from 1967 to 1969 within 5 km of steel furnace operations, petroleum refineries, and kraft and sulfite process pulp mills in Canada and the risk of all-cancer in 1993-1995. We find the risk of all-cancer is significantly associated with the early downwind distance. The adjusted R 2 of the correlation between risk and distance ranges upward from 0.75 to above 0.95. Relative risk increases to a maximum value, corresponding to direct exposure from emissions and then either declines slightly before rising again or continues to rise after a plateau. The RR never returns to 1. We find that additional risk is created within the plume. We find this additional risk in SIC sectors that have sulfur-rich emission plumes, a finding that implicates sulfur aerosols as a potential additional risk factor. Distance cannot be a surrogate for exposure when the source sector has SO2rich plumes. We show that incremental RR can be extracted from cumulative RR to reveal additional sources of risk when residence distance from the sources is accounted for. This is the third in a continuing examination of retrospective exposure assessment (REA) with emission inventories.
Introduction Effective cancer prevention and surveillance depends on the identification and objective evaluation of risk factors, including environmental exposure, smoking status, diet, and occupation. In this work we examine “residence” as a risk factor using all-cancer data collected as described below. Cancer surveillance risk estimates must often be made with poorly or inadequately defined exposure, and it is common practice to use distance from a source as a surrogate for exposure without any certainty of its validity. We investigate this practice and its limits by examining the downwind distribution of risk about several categories of source. We identify conditions previously unknown affecting the use of distance as a surrogate for exposure. The Akwesasne First Nation is on the St. Lawrence River at Cornwall, ON. The director of the Environmental Centre of the Akwesasne First Nation, Henry Likkers, frequently speaks about the impact of pollution in the St. Lawrence River on the health of First Nations people. His chosen example is to discuss the impact of PCBs leaching from a nearby foundry that poisoned the fish his people had lived on for thousands of years. Forbidden to eat the fish by regulators, his people began to eat “a White man’s” diet with * Phone: (613)385-1831; fax: (613)385-1832; e-mail:
[email protected]. 4214
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a heavy reliance on fats and sugars. A genetic susceptibility toward diabetes emerged in a few decades leading to the tertiary effect and title of his talk “PCB's Cause Diabetes” (1). This example illustrates well the profound influence of residence on health. A primary factor in establishing residence as a risk factor is the identification of a source and its distance from the residence. Kaldor et al. (2) 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 incidence of cancer of the 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 (2). Within Canada, an initiative of the Federal Government to illuminate the impact of environmental factors on health was the Action Plan on Health and Environment (APHE). It was designed to study, in part, the impact of exposure to environmental contaminants on the health of Canadians in a variety of residential and occupational settings. The specific health outcome of Environment Related Cancer Surveillance (ERCS) within Health Canada is one of 18 selected cancers (3, 4). The ERCS includes four interacting components of which the two relevant to this work are as follows: (i) An inventory of environmental sources and contaminants that includes all known locations of all commercial manufacturing activity in 61 SIC codes in Canada for about 35+ years, all geolocated, to estimate average daily exposure at a specific residential address. (ii) A case-control study in cooperation with Provincial Cancer Registries to determine lifetime residential and occupational data, all geolocated, diet, occupation, smoking, and other confounding information. The linked inventories are called the Environmental Quality Database (EQDB) system. A prominent public source of recent release data for the EQDB is the National Pollutant Release Inventory (NPRI) published annually by Environment Canada (5). Data from the 1995 data set are included in the EQDB. This is supplemented with emission data from FIRE, the 1995 data set from the United States Environmental Protection Agency (6), and data from many of the approximately 10 000 references addressed to identify the sources of approximately 220 separate chemical species. Releases of chemicals are from 61 standard industrial codes (SIC), using the U.S. SIC system. A query will conform to one of two options: (i) a source-centric search will return the distribution of subjects (case and control) around a selected point source(s). For example, all the subjects living within a specified distance of a single pulp mill in a specified time range can be identified; or all the subjects living within a specified distance of all the pulp mills extant at some time range can be identified. This is the origin of the data used in the sectoral analysis described in this paper. (ii) a subject-centric search will identify the industries selected within a specific time range and specific distance of a subject. There are a total of 61 separate SIC codes available. Industrial point sources operating in Canada between 1960 and 1990 defined by one of 61 SIC codes are included (7). A few sources have been updated to 1995. 10.1021/es990382c CCC: $19.00
2000 American Chemical Society Published on Web 08/31/2000
FIGURE 1. Subject residences, Regina, 1967-1969. Cases ) X; controls ) O, all-cancer; ST ) location of steel mill. The linked inventories situate a residence in relation to a source type for any time between ∼1960 and 1995. We collected residential data on a total of 24 908 subjects including 5073 controls. This represents in excess of 126 000 residences, all geolocated. The case:control ratio is (24 9085073)/5073 ) 3.91. Cases and controls were collected during the period 1993-1996. The system allows approximately 30 years latency. It is an axiom of environmental epidemiology that “distance is protective”; as the subject is removed from the source, all other things being equal, the risk associated with exposure to the source is expected to drop. This infers a uniform, perhaps monotonic, decline in risk without actually demonstrating it. This expression should be considered as more intuitive than demonstrated (8) at least for distances similar to those used here, although the use of distance as a surrogate for exposure seems well established (9-11). If distance is truly protective then we expect an association between the relative risk (RR) of some disease, say all-cancer, RRAll, observed today for a population who lived near a point source at some time in the past and the known sourceresidence distance then. This statement can be tested if some method exists to identify the exposure surrogate, i.e., distance, at the time in the past for the population exposed then for specific source types. The EQDB system was explicitly designed to conduct this analysis by solving three problems: retrospective exposure assessment, latency, and mobility. We see these three factors as the principal impediment to current efforts to evaluate residence/environment as a risk factor in cancer. Latency is an epidemiological confounder and is the delay in years between first-exposure and first-identification of a cancer. Mobility is also a confounder and refers to the tendency of a population to change address and, therefore, exposure in a fairly regular manner. Retrospective exposure assessment (REA) is a procedure to identify and evaluate chemical exposures in the past. We consider only airborne exposure pathways here. It can be shown that the EQDB has solved the problem of mobility. This work addresses some of the problems associated with REA. Hypothesis. From the generality of the Gaussian plume model, any expression of risk associated with a source should show a downwind distribution of disease that compares with the profile of contaminant emissions downwind from the source of any plume. Expressions of risk include incremental RR () observed/expected), excess risk () (O - E)/E), and cumulative RR (cf. Appendix 1), and the outcome is cancer.
Method In Appendix 1, we show that the downwind profile of emissions from a source can be approximated with an inverse j-half power series of the downwind distance. This duplicates to a very high degree the values obtained for the ground level concentration (GLC) of the plume at 100-m intervals from the source to beyond 10 km. We will examine the downwind distribution of risk of all-cancer using the inverse j-half power series of the downwind distance, first for specific locations and later for four industrial sectors. We initially tried to calculate the RR ( ) O/E) for each increment of distance of 100 m. We immediately found that the variance was large because the number of new controls was often small or zero. We found that the statistical fit was bad to impossible. Much more satisfactory regression results were obtained using the cumulative RR. This necessitated a method to extract an expression for incremental RR, the RR in an increment of 100 m, from the cumulative RR. This is described in Appendix 1. Validation of Cumulative RR for Specific Locations. Initially we examine residence as a risk factor in cancer for four specific locations with the same industry type. We queried the EQDB to identify cases and controls residing in proximity to three steel furnace operations in Calgary, AB, and one in Regina, SK. The search criteria are anyone living within 5 km of any of the above sites during the period 19671969. Cases and controls were collected in the period 19931995. Each residence with a complete postal code is geolocated. Distance in kilometers separating the residence and that source are provided as output by the EQDB. Regina, SK, Canada, is a prairie city of about 100 000 population with few topographic disturbances. Calgary, AB, Canada, was of a comparable size for the period considered here (1967-1969) and lies in the valley of the Bow River close to the foothills of the Rocky Mountains with maximal topographic perturbation. Steel furnace operations (SIC ) 3312) were present in both communities at this time producing a similar spectrum of products and, therefore, a comparable ambient chemical environment. We initially assume that the topographic perturbation can be ignored over short distances In Figure 1, we show the distribution of residences (cases ) X, controls ) O) within 5 km of the steel furnace operation in Regina (1967-1969), and in Figure 2, we show the residences in the ambient zones of the three mills in Calgary. Each mill was accessed individually for the analysis and is identified with an alphanumeric postal code locator, the FSA VOL. 34, NO. 19, 2000 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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FIGURE 2. Subject residences, Calgary, 1967-1969. Cases ) X; controls ) O, all-cancer; ST ) location of steel mill.
TABLE 1. Correlation of Relative Risk of All-Cancer with Downwind Distance Steel Furnace Operationsa location Regina
no. of cases
no. of controls
eq A4 + R 2 or eq A7
264
24
[A4] + 0.754 [A7] [A4] + 0.825 [A7] [A4] + 0.831
Calgary T2B
314
34
Calgary T2C
160
19
Calgary T2H
251
25
a
[A7] [A4] + 0.923 [A7]
SD
range of significance
max RR
1.1-4.0
3.17 2.01 4.69 1.65 3.69
0.76 0.7-5 0.74
0.38 0.44
1.4-2.9 4.0-5.0 3.2-5.0 1.4-5.0 1.3-5.0
2.07 2.45 1.72
min RR 0.98 0.95
0.92 0.98
R ) 5-km resident from 1967 to 1969. [A4] or [A7] is eq A4 or A7of Appendix 1.
code. In both Figures 1 and 2, the letters “ST” mark the approximate geographic center of the steel mill. All sourceresidence distances are measured from this point. We selected to ensure that no person was counted more than once. The X indicates the residence of an individual identified in 19931995 with one of 18 primary cancers and who, during the period 1967-1969, resided at this distance from this source, while the O indicates the corresponding data for controls. We regressed the cumulative RR for all-cancer, for each successive increment of 100 m against distance, using the inverse j-half power series (eq A4). The results of this regression for the Regina mill and the three Calgary mills are in Table 1. The adjusted R 2 values range from 0.754 to 0.923 and show that the cumulative RR of all-cancer today (i.e., 1993-1995) is strongly correlated with distance from the source in the past (i.e., 1967-1969). The data in Table 1 are uncorrected for smoking. The similar values of adjusted R 2 in Table 1 for both Calgary and Regina show that, for the relatively short distances considered in this work, the assumption to ignore topographic perturbations in formulating eq A4 is supported. We repeated the analysis using the procedure in Appendix 1 to extract the incremental RR for these specific locations from the cumulative RR. With an annulus of 100 m, inadequate numbers of controls are expected, and this must of necessity increase the variance. However, we are not seeking statistical significance in these data since it has already been shown that the underlying data are highly correlated with radius from the source. Rather, we are seeking sufficient correlational power to display the incremental RR 4216
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function and from this indicate additional underlying associated risks. Validation of Cumulative RR within a Sectoral Examination. We queried the EQDB to identify all subjects living in proximity to kraft and/or sulfite process pulp mills (SIC ) 2611), steel furnace operations (SIC ) 3312), and petroleum refineries (SIC ) 2911). The search criteria are identical except that all subjects residing within 5 km of all the above sources operating in Canada in 1967-1969 are considered. The inventory of pulp mills was identified from the industry annual directory (12), the inventory of steel furnace operations was compiled from government publications (13), and the inventory of petroleum refineries was taken from the industry weekly newsletter (14). Sites were geolocated with Canadian postal codes and cross-referenced to contemporaneous topographic maps and/or to NPRI data locating sources. We repeated the analysis using the cumulative RR against downwind distance in the pooled samples to examine the generality of eq A4 applied to an entire sector, defined by an SIC or process. We consider all subjects who lived within 5 km of steel furnace plants (SIC ) 3312), kraft process pulp mills (SIC ) 2611), sulfite process pulp mills (SIC ) 2611), or petroleum refineries (SIC ) 2911) operating in Canada in 1967-1969. We have 8944 cases and 747 controls for steel mills, 2874 cases and 346 controls for petroleum refineries, 412 cases and 36 controls for kraft mills, and 610 cases and 47 controls for sulfite pulp mills. We calculated the cumulative RR and regressed this against distance using eq A4. The results appear in Table 2.
TABLE 2. Correlation of Relative Risk of All-Cancer with Downwind Distance Steel, Kraft Pulp, Sulfite Pulp, and Petroleum Refinersa no. of cases
no. of controls
crude O/E
steel furnaces
8944
747
3.1
petroleum refineries
2874
346
2.1
kraft pulp
412
36
2.9
sulfite pulp
610
47
3.3
sector
no. of controls
steel furnaces
5184
433
3.86
petroleum refineries petroleum refineries kraft pulp
1666 1666 239
200 200 21
2.13 2.13 2.9
353
27
3.3
sulfite pulp a
SD
range of SS (km)b
max RR
[A4] + 0.903 [A7] [A4] + 0.761 [A7] [A4] + 0.891 [A7] [A4] + 0.941 [A7]
0.093 1.64 0.11 0.77 0.35 1.11 0.22 0.87
0.1-5.0 0.5-1.5 0.4-5.0 0.4-5.0 0.8-4.0 0.9-1.6 0.8-5.0 0.9-5.0
3.24 5.42 2.52 2.47 4.29 4.12 4.01 3.91
1.01
SD
range of SS (km)b
max RR
min RR
0.4-3.6
3.40
1.60
0.9-2.2
3.49
1.44
3.5-5.0
3.52
0.36
Corrected for Smoking crude O/E analysis + R 2 or analysis
no. of cases
sector
analysis + R 2 or analysis
[A4] + 0.903 [A7] [A4] + 0.760 [A4] + 0.760 [A4] + 0.804 [A7] [A4] + 0.941 [A7]
0.051 0.95 0.065 0.065 0.217 0.99 0.130 0.89
min RR 2.23 0.93 1.21
R ) 5-km resident 1967-1969. b SS ) statistically significant (p > 1.0) range.
The adjusted R 2 values range from 0.761 to 0.941, showing that the cumulative RR of all-cancer is strongly associated with downwind distance from any of the four industrial sectors examined here. The data for each sector is again subdivided according to the analysis, and both are given in Table 2. The procedure in Appendix 1 is used to extract the incremental RR for each sector from the cumulative RR function. Maximum values of the cumulative RR function are given. Maximum and minimum values of the incremental RR are slightly higher as compared to the cumulative RR. We have made a correction for smoking in Table 2 (labeled Corrected for Smoking) by adjusting for the nonsmokers fraction of the population in 1967-1969. This adjustment corrects the population and the risk ratios to a value that corresponds with considering only nonsmokers:
NS ) (1 - [fmale × 0.51 + ffemale × 0.49]) as exposed. The nonsmoking proportion of the population, NS, is calculated from the estimated number of male and female smokers over age 15 in 1967-69 where fmale is the proportion of male smokers and ffemale is the proportion of female smokers in Canada (15). The correction is 0.5797 for 1967-1969.
Discussion We find the present day (i.e., 1993-1995) cumulative RR of all-cancer strongly associated with where a person lived in relation to a source almost 30 years in the past. The strength of the association between the source type and the downwind distance is in the order:
sulfite process pulp > steel furnace > kraft process pulp mills > petroleum refiners The graph of incremental RR against distance shows either a narrow or broadly defined maximum or both within the first 5 km from the source. The narrow maximum is within the first 2 km, and the broadly defined maximum is around 3-4 km from the source. That is there are at least two foci of incremental RR, one within 2 km of the source and another farther from the source.
We have summarized these observations and include an indication of the likely trend at distances greater than the 5 km limit we arbitrarily selected: sector
narrow max
broad max
greater than 5 km
kraft pulp mills sulfite pulp mills steel petroleum refiners Calgary T2B Calgary T2H Calgary T2C Regina
1.1 km RR >4.0 1.1 km RR ∼3 0.7 km RR >5 1.2 km RR ∼2.5 0.6 km RR ∼1.1 0.5 km RR >1.5 1.6 km RR ∼24.2 0.8 km RR ∼1.4
3.5 km RR ∼3.0 5 km RR ∼4 min >3.5 min ∼2.25 1.8 km RR ∼1.8 2.6 km RR 1.75 4.2 km RR >2 3.5 km RR >2
RR falling RR rising RR rising RR flat ? RR rising RR falling RR falling RR flat ?
There is a demonstrable increase of incremental RR at approximately the same downwind distance as the location of the maximum in the GLC. This is represented by the narrow maximum in RR above. We feel this increase in RR at this location is more than coincidentally related to the maximum in the GLC of the plume emissions. We feel this increase in risk is directly associated with emissions from the source. In Table 2, we show the range of statistical significance of the incremental RR. It always includes the first maximum. The broad maximum in risk for subjects residing more than 2.5 km from the source represents a second and unexpected focus of risk. Since there is no corresponding increase in emissions in this location and no increase in GLC of the plume, we consider that this demonstrates additional risk being created within the plume as the contents are transported away from the source. In our approximation, the RR never declines to 1, that is, there is always a residual risk downwind of the sources In the case of sulfite pulp mills, both the cumulative and incremental RR increase to a plateau value before 2 km then continue to increase after 3 km. There is no maximum in either function at less than 5 km. Petroleum refiners show a slight decline after the maximum RR as indicated above and the suggestion that the risk may continue flat or increase. Steel furnace operations show a definite minimum at about 3 km and a definite increase in risk after the minimum. All the sectors show an increase in the incremental RR as distance approaches 5 km. VOL. 34, NO. 19, 2000 / ENVIRONMENTAL SCIENCE & TECHNOLOGY
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Conventional dispersion models, including the Gaussian plume model, describe a process where the plume contents are diluted through turbulent interactions with atmospheric moisture. For simplicity, they assume that no chemical reactions occur in the plume and that dispersion is a physical process. We observe that the generation of additional risk becomes a plausible outcome when sulfur aerosols or plume chemistry reactions with SO2 are considered. Elliot et al. (9) have observed a periodicity in RR of lung cancer as a function of distance from the source, a steel mill. They examined risk at distances of several tens of kilometers. They were unable to attribute a cause to the periodic rise and fall of risk. We propose that their observation (9) and our own represent the action of a reactive plume as compared to a passive plume. We suggest that both chemical and physical reactions transform the plume contents into a source of additional risk. Certain plumes will be more susceptible to this action, and our work suggests that industrial sectors that generated sulfur-rich plumes in 1967-1969 appear to have also generated additional risk for nearby residents. Plumes from sulfite pulp mills, steel mills, refineries, and thermal generating stations are known to be rich in sulfur dioxide. Thermal generating stations will be dealt with in later work. The dynamics of aerosol formation depend critically on the presence of SO2, NO, and hydrocarbon. When SO2 is present, SO2 to aerosol conversion dominates the Aitken nuclei count. Physical size distributions of sulfur-containing aerosols are characterized by a trimodal model consisting of (Aitken) nuclei mode (geometric mean size 0.015-0.04 µm.), an accumulation mode (geometric mean size 0.15-0.5 µm), and a coarse particle mode (geometric mean size 5-30 µm). Fine particles (nuclei and accumulation mode) are essentially independent of coarse particles. The size range of the fine particles places them into the range of inhalable particulates. The presence of SO2 in a mixture causes the peak number to be at least 2 orders of magnitude higher when the mixture is photoxidized with NO and hydrocarbon than in the absence of SO2 (16). When sulfate-containing aerosols system are first irradiated, new particles form at a rate that depends on the concentration of the preexisting aerosol. If the concentration is low, particles form rapidly and in high concentration. Increasing the initial concentration suppresses new particle concentration because of scavenging. The aerosol that forms by irradiation is primarily sulfuric acid. The new particle formation that takes place in the polluted urban atmosphere is primarily a photochemical process, reliant on sunshine. Particles are present in parcels of gas in which new particle formation is initiated. Condensable molecules formed by gas-phase reactions may either form small clusters or deposit on preexisting particles. In experiments with similar rates of SO2 oxidation but different initial aerosol loadings, the number concentrations of aerosol nuclei peak between 20 and 25 min (17, 18). The time available to create additional risk in the plume is related to the average wind speed. In Canada, the longterm, 30-year average annual wind speeds in communities with sulfite process mills is 15.1 ( 1.6 kph or 4.2 m/s (19). This corresponds to an average transport time for the 5 km from source to subject residence of ∼20 min ( about 16-25 min. The sulfur-containing aerosols described above are in a size range 20 kph winds], and the statistical fit of the total VOC discharge data to the terms of eq A2 is given in Table A1. From the strong statistical association between the GLC profile and the inverse j-halfs power of the radial distance, we conclude that eq A2 can be used as an approximation to evaluate the effects in terms of radial distance, as an expression of surrogate exposure. Cumulative Relative Risk. The EQDB query identifies persons whose residence was within 5 km of the source and who resided at that location between 1967 and 1969 and who today (i.e., 1993-1995) are identified as either a case or a control. We analyze these data by first ensuring that a subject
The correlation is in excess of 0.7 for all sites and all selected sectors. We show below that it can be used to extract incremental RR, when the incremental RR cannot be evaluated because the variance is too large. Incrremental RR is the ratio () O/E) of risk in an increment of distance here 0.1 km. Incremental RR. Health scientists prefer an incremental RR of All-cancer, incremental RRall. This is the ratio of O cases to E controls in the annulus of distance, from r to r + δ, where δ in this case is 100 m. We calculated incremental RRall initially and found the raw data poorly correlated with distance, principally because the numbers of new controls in δ were often small to nil and the variance in incremental RRall large. We propose the following procedure to extract the incremental RRall from the statistically significant and welldefined cummulative RRall in order to reveal additional sources of risk.
incremental RRall = d(cumRRall)/dr
(A5)
Substitute eq A4 into the derivative on the right side of eq A5 and expand the summation for the first 7 terms:
) d(β1r-1/2 + β2r-1 + β3r-3/2 + β4r-2 +
β5r-5/2 + β6r-3) dr (A6)
Differentiate term by term
) -1/2β1r-3/2 - β2r-2 - β3r-5/2 - 2β4r-3 -
5/2β5r-7/2 - 3β6r-4 (A7)
The coefficients, β1 in eqs A6 and A7 are the coefficients of the correlation of cumulative RR with radius. We regress the raw data for incremental RR calculated for each annulus of 100 m against the function (eq A7). The regression results in Tables 1 and 2 are for both single sources and sectors and yield R 2 values about 0.2-0.3. This suggests that considering downwind distance from the source alone may account for 45-55% of the variance in the incremental risk estimate.
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Received for review April 5, 1999. Revised manuscript received June 28, 2000. Accepted July 6, 2000. ES990382C
