Suchergebnisse

Workers' compensation (WC) insurers collect large amounts of industrial hygiene (IH) data in the United States. The data collected is not easily accessible for research or surveillance purposes. Individual WC insurers are using computerized systems to standardize and store the IH data, leaving a gap in standardization among the different WC insurers. This study sought to standardize IH data collection among WC insurers and to determine the feasibility of pooling collected IH data. IH air and noise survey forms were collected from WC insurers. Data fields on the forms were evaluated for importance and a study list of core fields was developed. The core study list was presented to an IH review panel for review before finalization. The final core study list was compared to recommendations published by the American Conference of Governmental Industrial Hygienists (ACGIH) and the American Industrial Hygiene Association (AIHA). Fifty-nine forms from 10 organizations were collected. Industrial hygienists from research organizations, state-based WC insurers, and private WC insurers participated in the data field evaluation and on the review panel. For both air and noise survey forms, more than half the data fields (55% and 54%, respectively) were ranked as "essential." Three of the four fields in the worker and control observations category ranked "essential" were found less than half of the time on both types of survey forms. The study list of core data elements consisted of more than half of the data fields from both the air and noise survey forms. Three additional fields were added based on the comparison to the ACGIH-AIHA recommendations. Data fields essential to standardizing IH data collection were identified and verified. The "essential" data fields will be made available and have the potential to be incorporated into WC insurers electronic IH data management systems. Future research should focus on other IH survey forms, such as those used in ergonomic assessments and specific chemical exposures, and methods to ...

Abstract
The American Industrial Hygiene Association Emerging Economies Microgrants Program was created in 2018 as a means to provide a fair, structured, transparent, and consistent mechanism for funding education and research projects that promote occupational hygiene in economically developing nations. The vision of the AG is to expand occupational hygiene awareness and capacity globally to reduce the risk of injury, illness, and fatality to workers in nations with the fewest resources. Over the past six years AIHA and corporate sponsors such as Amazon have funded nearly $200,000 USD for training, development, and research projects. This session will provide attendees with an introduction to the program and describe the microgrant proposal submission process and selection criteria. The session will present some of the remarkable projects that have been awarded and completed around the world over the past several years. Answers to frequently asked questions will be provided in addition to questions provided by the audience.

"This book provides industrial hygienists with a reference containing the equations, conversions, and formulas they encounter in their day-to-day duties. Containing 725 unique problems and solutions, the book is a study aid to those taking the certification exams (CIH, CSP, CHMM, and DABT). It also includes 154 business economic case studies demonstrating how to preserve your clients' financial resources, promote industrial hygiene, foster worksite safety, learn the financial ropes of business economics, and help control your clients' potential adverse environmental impact and, in so doing, greatly enhance career progress"--

Cover -- Title Page -- Copyright Page -- Dedication -- The Author -- Author Disclaimer -- Preface -- Table of Contents -- Introduction -- American Board of Industrial Hygiene Certification Examination Hints -- At Least Six Months Before the Examinations -- Immediately Before and During the Examinations -- Equations, Constants, Conversions, Formulae to Memorize -- Gas and Vapor Air Contaminant Molecular Weights -- The Not So Mysterious Mole -- Conversion Factors and Constants -- Ideal Gas (and Vapor) Laws -- Dilution Ventilation and Tank and Room Purging -- Density (Specific Gravity) Calculations -- Vapor Pressure Problems and Calculations -- ABIH Certification Examination Homework: Calculations and Ventilation -- Handbook Problems -- Index to Problems

Abstract
Occupational exposure assessments are dominated by small sample sizes and low spatial and temporal resolution with a focus on conducting Occupational Safety and Health Administration regulatory compliance sampling. However, this style of exposure assessment is likely to underestimate true exposures and their variability in sampled areas, and entirely fail to characterize exposures in unsampled areas. The American Industrial Hygiene Association (AIHA) has developed a more realistic system of exposure ratings based on estimating the 95th percentiles of the exposures that can be used to better represent exposure uncertainty and exposure variability for decision-making; however, the ratings can still fail to capture realistic exposure with small sample sizes. Therefore, low-cost sensor networks consisting of numerous lower-quality sensors have been used to measure occupational exposures at a high spatiotemporal scale. However, the sensors must be calibrated in the laboratory or field to a reference standard. Using data from carbon monoxide (CO) sensors deployed in a heavy equipment manufacturing facility for eight months from August 2017 to March 2018, we demonstrate that machine learning with probabilistic gradient boosted decision trees (GBDT) can model raw sensor readings to reference data highly accurately, entirely removing the need for laboratory calibration. Further, we indicate how the machine learning models can produce probabilistic hazard maps of the manufacturing floor, creating a visual tool for assessing facility-wide exposures. Additionally, the ability to have a fully modeled prediction distribution for each measurement enables the use of the AIHA exposure ratings, which provide an enhanced industrial decision-making framework as opposed to simply determining if a small number of measurements were above or below a pertinent occupational exposure limit. Lastly, we show how a probabilistic modeling exposure assessment with high spatiotemporal resolution data can prevent exposure misclassifications associated with traditional models that rely exclusively on mean or point predictions.