Suchergebnisse

Ben shu jie shao le er shi shi ji jiu shi nian dai " xin zuo pai " he " xin zi you zhu yi " liang da zhen ying de si xiang jiao feng. nei rong bao gua le jiu shi nian dai zhong guo zhi shi jie zai jiao feng zhong fen hua he zheng he de qu shi

AbstractThis study uses several years of effluent quality data from 43 sites where online monitoring has been operational for many years resulting in the accumulation of high‐resolution data sets. The data show a highly repeatable, seasonal dependence of effluent turbidity on temperature. This has been modelled with simple techniques producing a generic model that is neither data hungry nor requires site calibration. Viscosity‐based mechanisms are proposed and discussed. These mechanisms differ from conventional assumptions of suspended solids peaks being solely flow related and or ascribed to 'spring sloughing'. The proposal is that the organic loading of the filter and the physical temperature effects on settlement velocity are both as influential as hydraulic loading in determining effluent suspended solids concentration. Application of the models enables improved design for new/upgraded units and more accurate capacity assessment of trickling filters and humus tanks enabling improved operational risk management of compliance failure.

Abstract

Background:
Generic job-exposure matrices (JEMs) are often used in population-based epidemiologic studies to assess occupational risk factors when only the job and industry information of each subject is available. JEM ratings are often based on professional judgment, are usually ordinal or semi-quantitative, and often do not account for changes in exposure over time. We present an empirical Bayesian framework that combines ordinal subjective JEM ratings with benzene measurements. Our aim was to better discriminate between job, industry, and time differences in exposure levels compared to using a JEM alone.


Methods:
We combined 63 221 short-term area air measurements of benzene exposure (1954–2000) collected during routine health and safety inspections in Shanghai, China, with independently developed JEM intensity ratings for each job and industry using a mixed-effects model. The fixed-effects terms included the JEM intensity ratings for job and industry (both ordinal, 0–3) and a time trend that we incorporated as a b-spline. The random-effects terms included job (n = 33) and industry nested within job (n = 399). We predicted the benzene concentration in two ways: (i) a calibrated JEM estimate was calculated using the fixed-effects model parameters for calendar year and JEM intensity ratings; (ii) a job-/industry-specific estimate was calculated using the fixed-effects model parameters and the best linear unbiased predictors from the random effects for job and industry using an empirical Bayes estimation procedure. Finally, we applied the predicted benzene exposures to a prospective population-based cohort of women in Shanghai, China (n = 74 942).


Results:
Exposure levels were 13 times higher in 1965 than in 2000 and declined at a rate that varied from 4 to 15% per year from 1965 to 1985, followed by a small peak in the mid-1990s. The job-/industry-specific estimates had greater differences between exposure levels than the calibrated JEM estimates (97.5th percentile/2.5th percentile exposure level, BGR95B: 20.4 versus 3.0, respectively). The calibrated JEM and job-/industry-specific estimates were moderately correlated in any given year (Pearson correlation, rp = 0.58). We classified only those jobs and industries with a job or industry JEM exposure probability rating of 3 (>50% of workers exposed) as exposed. As a result, 14.8% of the subjects and 8.7% of the employed person-years in the study population were classified as benzene exposed. The cumulative exposure metrics based on the calibrated JEM and job-/industry-specific estimates were highly correlated (rp = 0.88).


Conclusions:
We provide a useful framework for combining quantitative exposure data with expert-based exposure ratings in population-based studies that maximized the information from both sources. Our framework calibrated the ratings to a concentration scale between ratings and across time and provided a mechanism to estimate exposure when a job/industry group reported by a subject was not represented in the exposure database. It also allowed the job/industry groups' exposure levels to deviate from the pooled average for their respective JEM intensity ratings.