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Temple, in Air Pollution Control , Vol. Strauss, ed. Schuller, D. Benson, and R. Hirschler and L. Gilbert, Arch. Health , 8 : Chow, Chemistry in Britain , 9 : Hicks, Chem-BioL Interactions , 5 : Daines, H. Motto, and D. Chilko, Envir. Science and Technol.
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Moyers, W. Zoller, R. Duce, and G. Hoffman, Envir. Smith and K. Wildeman, J.
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Educ 51 : Bagg, in Air Pollution Control , Vol. Soltau and R. Engineers, Nov. Hottel and J. Press, Agnew, 34th Mid-year meeting of the A.
Harrison, Soc. Haynes and J.
Weaving, Inst. Engineers, Solihull Conference, Nov.
Jaimee, D. Schneider, A. Rozmanith, and J. Sjoberg, S. Congress, Detroit, Jan. SAE No. Hirschler and F. Marsee, National Petrol. Refiners Assoc. Meeting, April, Yolles and H. Wise, C. Critical Reviews in Environmental Control , 2 Haensel, U. Riekert, Euchem. Butler, Chemistry in Britain , 8 : Sheleff, K. We obtained a national database of U. Cleverly, Personal communication, Facilities included secondary copper smelters, municipal solid waste incinerators, cement kilns burning hazardous waste, iron ore sintering plants, medical waste incinerators, coal-fired electric generating facilities, cement kilns burning non-hazardous waste, sewage sludge incinerators, hazardous waste incinerators, and industrial boilers.
Facility locations and emissions were available in for secondary copper smelters and municipal solid waste incinerators, which had the highest dioxin air emissions in the United States. We checked the accuracy of these facility locations by comparing the coordinates to locations determined through web-based aerial photographs and ancillary information Google Inc. The median distance between the original and corrected location ranged from meters coal-fired electric generating facilities to 23 km hazardous waste incinerators.
For facilities with missing data, we assigned the average for the facility type in For facility types with only data, we assumed the facilities were operating during the entire exposure period. We estimated changes in emissions from — using the average emission levels for each facility type in , , and , which we obtained from an EPA national survey of dioxin-emitting facilities [ 6 ]. We estimated the linear rate of change between and , and between and , by facility type. We assumed constant emission levels from to because air pollution controls were uncommon before A dioxin emission level was available for all of the municipal solid waste incinerators within 5 km of our study population.
When a facility was operating in but not , we assumed it operated from the beginning of the exposure period to midpoint. We assigned the level through assuming that pollution controls were not present in the final years of operation.
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Similarly, when a facility was operating in and not , we assumed that the facility began operating in Since pollution controls were likely to have been installed during construction [ 6 ], we assigned the level from to When facilities were operating in and , we assumed a linear change between and , with stable emission levels before and after In Table 1 , for and , we present average emission levels of all U. In total, and facilities were within 5 and 3 km, respectively, of our study population in the year exposure period. No residences were within 5 km of secondary copper smelters, cement kilns burning hazardous waste, iron ore sintering plants, or industrial boilers.
Emissions were highest for municipal solid waste incinerators. Medical waste incinerators and coal-fired electric generating facilities were the most common dioxin-emitting facilities and accounted for the greatest number of facility-residence pairs. The distances were chosen based on the geographic extent of dioxin pollution plumes estimated by Gaussian models of emissions from municipal solid waste incinerators high concentrations within 3 km and lower concentrations between 3 and 5 km and on soil concentrations determined in other studies.
Briefly, French and two Spanish studies predicted highest ground-level dioxins within about 3 km of municipal solid waste incinerators [ 4 , 5 , 20 , 26 ]. A US study predicted elevated concentrations within about 2 km [ 3 ]. Mathematical models of dispersion and deposition such as the Gaussian models in the French studies [ 6 , 20 ] require facility-specific information such as stack height and local meteorological data.
Since stack height was not available for any of the facilities in our study, we employed a simplified model that weighted the facility-specific emission by the inverse of the squared distance between the residence and the facility. Specifically, we first calculated the inverse distance squared-weighted emission for every facility-residence pair for every year in the exposure period.
For each residence, an annual emission index was then calculated by summing the inverse distance-weighted emissions over all facilities within the specified distance. If a participant lived in more than one residence in a given year, we divided the sum of the residence-specific annual emission indices for that year by the number of residences to calculate a person-specific annual emission index using the following equation:. We then calculated an average AEI for each participant over their year exposure period and used this as an exposure metric in our epidemiological analysis.
All statistical analyses were performed in SAS Version 9. The reference groups were those with no facilities within the specified distance 5 or 3 km.
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We evaluated ever living and duration of living near dioxin emitting facilities in the year exposure period for all facilities combined and separately by facility type. Duration categories for proximity metrics were zero, 1—14, and 15 years. Cut points were based on the approximate median duration of living within 3 km of any facility. We categorized the average AEI into quartiles based on the distribution among controls. We also created metrics that excluded the 5 years before diagnosis for cases reference date for controls because recent exposures may be less likely to be associated with NHL risk.
We used polychotomous regression analysis to evaluate each NHL subtype separately because the etiology of NHL types may be different and previous analyses in this study population have shown varying risk factors by type [ 25 ]. ORs were calculated when there were at least 2 exposed cases.lastsurestart.co.uk/libraries/jailbreaking/76-mobile-phone.php
Other potential confounders that were explored but did not materially change the ORs were family history of NHL, smoking status, body mass index, weekly alcohol consumption, and energy-adjusted monthly servings of green leafy vegetables. We also evaluated occupational dioxin exposure defined as working for 12 months or longer in an industry with exposure to dioxins. To evaluate possible selection bias, we conducted the analyses of the proximity and AEI metrics computed for the diagnosis or reference year among all eligible cases and controls, respectively, which included both respondents and nonrespondents.
For nonrespondents, we geocoded diagnosis address for cases and mailing address for controls and limited our analysis to exact or intersection matches cases, controls. For respondents, we used the home location at the time of the interview. These analyses were adjusted for age and gender only because other information was not available for nonrespondents. Cases and controls included in our analysis were similar to the overall study population except that they had lived for a longer duration within m of a freight route Table 2. Thirty-nine percent of cases and controls had lived within 5 km of one or more dioxin-emitting facility during the year period Table 3.
The percentage of cases and controls residing near other facility types was substantially lower.
We found no association between ever residing within 5 or 3 km of any facility and NHL risk, and no association with duration. We observed no significant associations between residence within 5 and 3 km of any facility and any NHL subtype. Power was limited to evaluate residential proximity to specific facilities by subtype. Lagging the exposure period by 5 years produced results similar to unlagged analyses. There were no notable differences in the associations for the proximity metrics and the AEI across categories of gender, age, and education not shown.
Distributions of the exposure metrics varied by center. Some of the ORs for the proximity metrics varied by center. The inverse association with residence near municipal solid waste incinerators was observed for all centers except Seattle 2 cases, 1 control. However, based on the current residence, the association between proximity to any facility and NHL risk did not differ by participation status.
We observed no association between residence within 5 or 3 km of one or more dioxin-emitting facilities and NHL risk. However, we observed significantly elevated risk of NHL for individuals living within 3 km of cement kilns and an inverse association with proximity to municipal solid waste incinerators.