Dynamics of Airborne Influenza A Viruses Indoors and Dependence on Humidity

There is mounting evidence that the aerosol transmission route plays a significant role in the spread of influenza in temperate regions and that the efficiency of this route depends on humidity. Nevertheless, the precise mechanisms by which humidity might influence transmissibility via the aerosol route have not been elucidated. We hypothesize that airborne concentrations of infectious influenza A viruses (IAVs) vary with humidity through its influence on virus inactivation rate and respiratory droplet size. To gain insight into the mechanisms by which humidity might influence aerosol transmission, we modeled the size distribution and dynamics of IAVs emitted from a cough in typical residential and public settings over a relative humidity (RH) range of 10–90%. The model incorporates the size transformation of virus-containing droplets due to evaporation and then removal by gravitational settling, ventilation, and virus inactivation. The predicted concentration of infectious IAVs in air is 2.4 times higher at 10% RH than at 90% RH after 10 min in a residential setting, and this ratio grows over time. Settling is important for removal of large droplets containing large amounts of IAVs, while ventilation and inactivation are relatively more important for removal of IAVs associated with droplets <5 µm. The inactivation rate increases linearly with RH; at the highest RH, inactivation can remove up to 28% of IAVs in 10 min. Humidity is an important variable in aerosol transmission of IAVs because it both induces droplet size transformation and affects IAV inactivation rates. Our model advances a mechanistic understanding of the aerosol transmission route, and results complement recent studies on the relationship between humidity and influenza's seasonality. Maintaining a high indoor RH and ventilation rate may help reduce chances of IAV infection

Bayesian modeling and inference for asymmetric responses with applications

University of Minnesota Ph.D. dissertation. July 2017. Major: Biostatistics. Advisors: Dipankar Bandyopadhyay, Lynn Eberly. 1 computer file (PDF); x, 129 pages.Analysis of asymmetric data poses several unique challenges. In this thesis, we propose a series of parametric models under the Bayesian hierarchical framework to account for asymmetry (arising from non-Gaussianity, tail behavior, etc) in both continuous and discrete response data. First, we model continuous asymmetric responses assuming normal random errors by using a dynamic linear model discretized from a differential equation which absorbs the asymmetry from the data generation mechanism. We then extend the skew-normal/independent parametric family to accommodate spatial clustering and non-random missingness observed in asymmetric continuous responses, and demonstrate its utility in obtaining precise parameter estimates and prediction in presence of skewness and thick-tails. Finally, under a latent variable formulation, we use a generalized extreme value (GEV) link to model multivariate asymmetric spatially-correlated binary responses that also exhibit non-random missingness, and show how this proposal improves inference over other popular alternative link functions in terms of bias and prediction. We assess our proposed method via simulation studies and two real data analyses on public health. Using simulated data, we investigate the performance of the proposed method to accurately accommodate asymmetry along with other data features such as spatial dependency and non-random missingness simultaneously, leading to precise posterior parameter estimates. Regarding data illustrations, we first validate the efficiency in using differential equations to handle skewed exposure assessment responses derived from an occupational hygiene study. Furthermore, we also conduct efficient risk evaluation of various covariates on periodontal disease responses from a dataset on oral epidemiology. The results from our investigation re-establishes the significance of moving away from the normality assumption and instead consider pragmatic distributional assumptions on the random model terms for efficient Bayesian parameter estimation under a unified framework with a variety of data complexities not earlier considered in the two aforementioned areas of public health research

RISK OF COVID-19 MOVEMENT AND EXPOSURE ON PASSENGER RAILCARS: ASSESSMENT OF AEROSOL TRANSPORT AND RAILCAR VENTILATION SYSTEMS

We conducted a series of static and dynamic experiments in a fleet of passenger railcars and locomotives for a large-scale, mass-transit company to measure the: (1) aerosol concentrations corresponding to respirable-sized/viral aerosols; (2) aerosol removal rates; and (3) air changes per hour (ACH) provided by the existing air handling systems. We evaluated the ventilation and air filtration (HVAC) systems effectiveness in a range of representative conditions to assess exposure risk. The risk of exposure to SARS-CoV-2 was assessed: (1) under standard conditions; (2) using minimum reported efficiency value (MERV) filters with increased filtration ratings; and (3) in the presence of a high-efficiency particulate-absorbing (HEPA)-scavenging system. The engineering controls evaluated included: (1) recirculated to fresh air ventilation ratio; (2) MERV filters filtration efficiency; and (3) use of an air purifier. Aerosols were generated in the 0.3–5.0 µm size range using a Collison Nebulizer. Real-time aerosol concentrations were measured at multiple locations using photodetector particle counters. The ACHs and removal rates were calculated using log-linear regression. An analysis of variance was used to compare the particle concentrations under the different experimental conditions while a multiple linear regression was used to identify which engineering control(s) impacted the particle concentrations. The risk of exposure was estimated using an approach developed by Miller et al. The recirculated to fresh air ratio had a minimal effect on particle air concentrations and on particle removal rates. The higher efficiency MERV13 filters significantly reduced particle concentrations (p<0.05) and significantly increased particle removal rates (p<0.01) compared to MERV8 filters. Compared to standard conditions, MERV13 filters reduced the exposure risk by 42%. Use of a HEPA-scavenger with a MERV13 filter causes a further reduction in risk (by 50%). The risk of exposure in the engine locomotives was much lower than in the passenger railcars due to much higher ACH values. These results show that a simple upgrade in the efficiency of the HVAC filters results in reductions of particle concentration and risk of exposure in public-transit vehicles. Widespread upgrading of HVAC filter efficiency in public-transit vehicles could reduce community-spread infectious respiratory diseases, protect transit workers, and slow disease spread

Possible health implications from exposure to formaldehyde emitted from laminate flooring samples tested by the Consumer Product Safety Commission

On March 1, 2015, the CBS news program 60 Minutes reported that an American company, Lumber Liquidators\uc2\uae, was selling a Chinese-produced laminate wood flooring product that released elevated levels of formaldehyde. 60 Minutes tested formaldehyde levels in 31 boxes of commercially available laminate flooring products purchased from Lumber Liquidator\uc2\uae stores in five states (Florida, Illinois, New York, Texas, and Virginia). 60 Minutes reported that some test results were higher than the California Air Resources Board emission standards.Because of concerns raised by the 60 Minutes report, the Consumer Product Safety Commission (CPSC) tested laminate flooring samples manufactured in China during 2012-2014 that were sold at Lumber Liquidators\uc2\uae stores. CPSC subsequently requested that NCEH/ATSDR evaluate the test results for possible health effects.The purpose of this report is to evaluate people\ue2\u20ac\u2122s possible exposures to formaldehyde emitted from laminate flooring tested by the Consumer Product Safety Commission in indoor air and the possible effects on their health. The report also recommends actions that can reduce formaldehyde levels in people\ue2\u20ac\u2122s homes.laminate-flooring-report-3-22-2016_508.pd

An optimization platform based on coupled indoor environment and HVAC simulation and its application in optimal thermostat placement

Model-based optimization can help improve the indoor thermal comfort and energy efficiency of Heating, Ventilation and Air Conditioning (HVAC) systems. The models used in previous optimization studies either omit the dynamic interaction between indoor airflow and HVAC or are too slow for model-based optimization. To address this limitation, we propose an optimization methodology using coupled simulation of the airflow and HVAC that captures the dynamics of both systems. We implement an optimization platform using the coupled models of a coarse grid Fast Fluid Dynamics model for indoor airflow and Modelica models for HVAC which is linked to the GenOpt optimization engine. Then, we demonstrate the new optimization platform by studying the optimal thermostat placement in a typical office room with a VAV terminal box in the design phase. After validating the model, we perform an optimization study, in which the VAV terminal box is dynamically controlled, and find that our optimization platform can determine the optimal location of thermostat to achieve either best thermal comfort or least energy consumption, or the combined. Finally, the time cost for performing such optimization study is about 6.2 h, which is acceptable in the design phase. </div

Extension of the Advanced REACH Tool (ART) to include welding fume exposure

Introduction: Welding is basic process commonly carried out in the workplace. Robot or automated welding are typically used in welding processes where the weld required is repetitive and quality and speed are crucial. Not every welding operation is suitable for automated welding. If the project is limited to a single non-repetitive process, manual welding may be more suitable. The welding process is applied in various production fields and the demand for welders worldwide is increasing. Welders are exposed to health hazard from inhalation of metal fumes produced as a by-product of the process. The concentrations of welding fumes inhaled by workers can be measured, but it would be advantageous if there were also predictive exposure models to estimate exposure. However, presently, there are few reliable estimation models for welding fume exposure. Objectives: To develop estimation model for welding fume exposure. Methods: This study consisted of five main stages. The first stage comprised a literature review, including an evaluation of relevant generic exposure models, particularly the Advanced REACH Tool (ART), principles of exposure modelling, and various research studies related to welding fumes. The second stage describes an investigation to measure welding fume exposure at a production site. The third stage comprised welding fume exposure model development by adapting the ART model (to be the weldART model), including the identification of key modifying factors (MF) and a suitable computational form to undertake the model calculations. The fourth stage was modelling calibration, which used data obtained from the sampling in stage 2. The last stage was model verification, which applied welding fume measurement data from reports and published papers to test the reliability and uncertainty of the weldART model. Results: The model was developed within a well-mixed mass-balance computational framework. An important MF to be used in model development was fume formation rate (FFR), i.e., the mass emission rate of total metal fume from the welding process. The identified variables that affect fume formation rate were type of welding process, electrical current and input power, shielding gas, and welding consumables. In addition, the model also incorporates other important factors, such as convective dissipation of the welding fume away from the welding area and the welder’s interaction with the fume plume. The review indicated that welding process types with the highest to lowest welding fume particulate emission rates were flux-cored arc welding (FCAW), shielded metal arc welding (SMAW) and gas tungsten arc welding (GTAW). In order to develop effective and probabilistic weldART model, variables, namely welder's head (WH) and localized control (LC) were also taken into consideration. A deterministic four-compartment mass-balance mathematical model, the weldART model, was developed. In the measurement study two types of sample were collected: a Swinnex sampler to collect fume for gravimetric analysis and a MicroPEM direct-reading aerosol monitor. The comparison of fume concentrations between these two samplers showed that the MicroPEM monitors significantly underestimated exposure concentrations and had low correlation with the corresponding data from the Swinnex samplers. It was concluded that it was possible that particles were lost in the sampling tube of the MicroPEM due to the electrostatic deposition before the entering the aerosol sensor, and these data were only used to indicate the duration of welding activity. Meanwhile, estimation of the calibrated four-compartment mass-balance weldART model gave a strong correlation with the welding fume exposure measurements made during this research. To accommodate the uncertainties involved in verifying the model using published exposure data, the weldART was extended to incorporate a probabilistic aspect. This may be due to a positive systematic bias across the whole applicability domain, which becomes dominant at low measured values. Conclusions: The weldART model can produce reliable and accurate estimates of welding fume exposure. Especially, if factors related to distance of welder’s head and localized control were taken into account, along with the presence of additional workplace exposure sources. The weldART could offer an alternative approach to evaluate fume concentration for occupational hygienists. At present the model is available as standalone R-code that is freely available, but it lacks a suitable user-friendly user interface. The weldART is calibrated and has had a limited verification exercise completed, but further development and evaluation is necessary

Inhalation intake fraction of particulate matter from localized indoor emissions

Elevated exposure to airborne particulate matter is linked to deleterious health and well-being outcomes. Exposure assessment can be improved through enhanced understanding of source-receptor relationships, for example as expressed in the inhalation intake fraction metric. This study provides new knowledge about how inhalation intake of airborne particles varies with spatially varying indoor emissions. In a controlled environmental chamber with low background particle levels, we monitored the time- and size-resolved particle concentrations at multiple locations including the subject's breathing zone. We investigated two types of particle emissions: (i) controlled releases from several specific indoor locations; and (ii) natural release from skin and clothing for a range of simulated occupant activities. Findings show that particles released proximate to the human envelope caused a total inhalation intake fraction of 7–10 per thousand, which was 1.5–16 × higher than the intake fraction for other indoor release locations. These outcomes reflect the influence of emissions-receptor proximity combined with the efficient transport of particles by means of the thermal plume to the breathing zone. The results show that the well-mixed representation of an indoor environment could underestimate the inhalation intake by 40–90% for various localized indoor emissions, and by up to 3 × for particles emitted from the human envelope. The post-release exposure period contributed substantially to total inhalation intake. For particles released naturally from the human envelope, inhalation intake fractions varied with activity type and were higher for a subject when seated rather than walking

A generic cross-chemical predictive PBTK model with multiple entry routes running as application in MS Excel; design of the model and comparison of predictions with experimental results.

Aim: Physiologically based toxicokinetic (PBTK) models are computational tools, which simulate the absorption, distribution, metabolism, and excretion of chemicals. The purpose of this study was to develop a physiologically based pharmacokinetic (PBPK) model with a high level of transparency. The model should be able to predict blood and urine concentrations of environmental chemicals and metabolites, given a certain environmental or occupational exposure scenario. Model: The model refers to a reference human of 70 kg. The partition coefficients of the parent compound and its metabolites (blood:air and tissue:blood partition coefficients of 11 organs) are estimated by means of quantitative structure-property relationship, in which five easily available physicochemical properties of the compound are the independent parameters. The model gives a prediction of the fate of the compound, based on easily available chemical properties; therefore, it can be applied as a generic model applicable to multiple compounds. Three routes of uptake are considered (inhalation, dermal, and/or oral) as well as two built-in exercise levels (at rest and at light work). Dermal uptake is estimated by the use of a dermal diffusion-based module that considers dermal deposition rate and duration of deposition. Moreover, evaporation during skin contact is fully accounted for and related to the volatility of the substance. Saturable metabolism according to Michaelis-Menten kinetics can be modelled in any of 11 organs/tissues or in liver only. Renal tubular resorption is based on a built-in algorithm, dependent on the (log) octanol:water partition coefficient. Enterohepatic circulation is optional at a user-defined rate. The generic PBTK model is available as a spreadsheet application in MS Excel. The differential equations of the model are programmed in Visual Basic. Output is presented as numerical listing over time in tabular form and in graphs. The MS Excel application of the PBTK model is available as freeware. Experimental: The accuracy of the model prediction is illustrated by simulating experimental observations. Published experimental inhalation and dermal exposure studies on a series of different chemicals (pyrene, N-methyl-pyrrolidone, methyl-tert-butylether, heptane, 2-butoxyethanol, and ethanol) were selected to compare the observed data with the model-simulated data. The examples show that the model-predicted concentrations in blood and/or urine after inhalation and/or transdermal uptake have an accuracy of within an order of magnitude. Conclusions: It is advocated that this PBTK model, called IndusChemFate, is suitable for &apos;first tier assessments&apos; and for early explorations of the fate of chemicals and/or metabolites in the human body. The availability of a simple model with a minimum burden of input information on the parent compound and its metabolites might be a stimulation to apply PBTK modelling more often in the field of biomonitoring and exposure science. Keywords: biomarker of exposure; blood; body burden; internal exposure; PBTK-model; prediction, urine *Author to whom correspondence should be addressed. INTRODUCTION A physiologically based toxicokinetic (PBTK) or physiologically based pharmacokinetic (PBPK) model is an structural mathematical model, comprising the tissues and organs of the body with each perfused by, and connected via, the blood circulatory system. Such models are computational tools that can refine the assessment of the fate of chemicals in the body by simulation. In PBTK models, the body is subdivided into anatomical compartments representing individual organs or tissue groups. The transport of chemical in the body is described by mass balance differential equations that incorporate blood flows, partitioning into compartments and tissue volumes. After incorporation of elimination processes like metabolism and excretion, the fate and disposition of the parent chemical and metabolites can be predicted and extrapolated. Most PBPK models are chemical specific. Often, they are built for very specific purposes, for example, the estimation of disposition of a certain drug prior to in vivo studies Several initiatives were taken to develop PBPK models that can be used for industrial compounds Partitioning between blood:air and between tissue:blood is related to easy available physicalchemical properties. The relationships were worked out into QSPRs, which algorithms have been incorporated into the PBTK model. A novel dermal uptake module has been added to the PBTK model. Also, serial metabolism and urinary excretion have been incorporated in the model. The human physiological parameters such as organ volumes, blood flows, cardiac output, and alveolar ventilation are adopted from Technical Guidance Documents of REACH (ECHA, 2008a , b) and are presented in Stepwise numerical integration routine according to Euler can be entered. The integration intervals can be set. Minimum is an integration interval of 1000 steps h -1 , best results are found at 10 000 steps h -1 . Prediction of partitioning of chemicals A partition coefficient is the ratio of the concentration of a chemical between two phases in thermodynamic equilibrium. The tissue:blood partition coefficients are relevant for simulation of the distribution in the body. The blood:air partition coefficient controls the uptake of a compound in the alveoli. A novel QSPR to estimate the blood:air partition coefficient has been derived. A wide range of VOCs with measured blood:-air values for humans from many sources were reported in the paper of In case of substance with Blood : air partition a vapour pressure .4000 coefficient Pa and a dimensionless 5 0:8417=HenryDL Other substances Blood : air partition coefficient 5 0:4445=HenryÀDL þ 0:005189  K oa : ðN 5 57 ; R 2 5 0:99Þ ð2Þ A plot of experimental human partition coefficients blood:air and QSPR estimated values is presented in HenryÀDL 5 Vapour pressure  molecular weight=ðwater solubility  gas constant  temperature°KÞ ð3Þ logðK oa Þ 5 logðK ow Þ À logðHenryÀDLÞ ð4Þ For the blood:tissue partitioning, the QSPR algorithm as described by As an example, the algorithm for the brain:blood partition coefficient is shown as formula 5. Application of this equation to adipose tissue results into negative partition coefficients in case of log(K ow ) , 0.4. This has no scientific meaning. So if the partition coefficient adipose tissue:blood is estimated to be ,0.1, the adipose tissue:blood partition coefficient is fixed to 0.1. Modelling of uptake of chemicals Tissue concentrations for each of the chemicals and metabolites can be simulated for either acute, occupational, or environmental exposure regimes with its typical duration, routes, concentrations, or dose rate. The impact of exercise that may influence uptake, distribution, metabolism, and excretion is accounted for by two levels of exercise (at rest and at light exercise, with a heart rate of, respectively, 78 and 114 beats min -1 ) with corresponding physiology parameters (cardiac output and pulmonary ventilation) according to Inhalation in the IndusChemFate PBTK model is controlled by the concentration of the compound in the inhaled air, the alveolar ventilation, and the blood:air partition coefficient. In the model, the maximum concentration in inhaled air is limited at the level of saturated vapour pressure. The actual concentration in inhaled air can be lower than the environmental air concentration due to wearing of respiratory protective equipment (5 RPE). The reduction factor of the RPE can be entered. The default respiratory reduction factor 1 (5 no RPE). In the last two decades, the awareness has grown that dermal absorption of chemicals after environmental and/or occupational exposure can be very significant. This has lead to the development of PBPK models with an integrated dermal compartment (1) Dermal deposition of a substance (liquid) on the skin, (2) Diffusion to the stratum corneum (SC), and (3) Absorption to the dermis/blood flow. After or during deposition of a liquid or solid substance on the skin, evaporation of the substance and dermal absorption will start simultaneously (see scheme in The dermal absorption from the vapour phase is also considered (see Oral intake of compounds is considered as a bolus dose that is applied to the intestinal lumen (via the stomach) and then absorbed into the intestinal tissue at a first order rate. From the intestines, the compound is released to the blood stream towards the liver (portal vein). The first order absorption rate is defined as the velocity at which the oral dose is absorbed by the intestinal tissue (as a fraction of the dose in the lumen per hour). Stomach and intestines are lumped in the model. The oral dose [in milligrams per kilogram body weight (BW)] and the absorption rate are the required input parameters for oral uptake in the model. Enterohepatic circulation Phase II metabolism with conjugation of metabolites generally increases the solubility. Enzymes produced by intestinal bacteria-such as b-glucuronidase, sulfatase, and various glycosidases-deconjugate these compounds in the intestines, releasing the parent compounds after which these are readily reabsorbed across the intestinal wall to the blood. This results in enterohepatic circulation (of conjugated phase II metabolites). Few published PBPK models consider enterohepatic circulation If, for example, the removal ratio of a nonconjugated metabolite from the liver by enterohepatic circulation is set 0, there is no enterohepatic circulation. If in the case of a conjugated metabolite, the removal ratio is set to 1, 50% of the total amount that leaves the liver per unit of time is excreted to blood, and 50% to the intestinal lumen via bile, available for reabsorption with a fixed rate of 0.3 h -1 . Elimination The chemical in the human body is eliminated in the model by two processes: metabolism (or biotransformation) and direct excretion in air or urine. Biotransformation is described by Michaelis-Menten saturable metabolism following the mathematical algorithms as described by Contrary to many PBPK models, the occurrence of metabolism is not limited to the liver compartment but can be considered in any of the 11 model compartments. However, the default setting is metabolism in the liver only. Metabolic kinetic parameters are the maximum velocity of metabolism [5 V max in lmol/(kg tissue  h)] and the MichaelisMenten constant (5k M in lM). Preferably, these values are taken form experimental data with human tissue. Conversion of reported experimental V max to the proper units is given in the addendum. When parallel metabolic pathways are involved, the V max and k M values for a specific metabolite production can be set as different from those the parent compound. That is possible because the model considers both removal of the parent compound and production of metabolite as separate steps. That means the biotransformation of the parent compound occurs for only x% into the metabolite of interest and for (100Àx)% into other (unknown) metabolites. V max and k M metabolism constants of a series of VOC have recently been summarized (Aylward et al., 2010). Substances can be excreted via urine, either unchanged as parent compound or as a metabolite. DeWoskin and Thompson (2008) published a paper in which renal clearance is modelled in great detail; however, the required input data transcends application in a generic model. Urinary excretion is mainly based on the lipophilicity of substances, assuming that lipophilic substances are less water soluble and therefore excreted via urine to a lower extent. The QSPR as developed by The total volume of excreted urine in 24 h is set to 1.44 l. Generic PBTK-model in MS Excel 847 by guest on October 14, 2011 annhyg.oxfordjournals.org Downloaded from When the volatility is high, chemicals (and in a few cases a metabolite) will be exhaled. The exhaled concentration is a mixture of the inhaled air concentration (air that has not reached the alveoli) and alveolar air. The concentration of a compound in the alveolar space of the lungs is controlled by the blood concentration in the (arterial) lung blood and the blood: air partition coefficient. The amount of a compound that is exhaled is calculated by multiplying the alveolar concentration by the alveolar ventilation rate. Mass balance After every model simulation, a mass balance is calculated. Absorbed amounts per route are summarized and compared with the total of excreted amounts, amounts in tissues, and amounts to undefined metabolites, not assigned to the metabolic route considered. The Excel application of the PBTK model The generic PBTK model is available as a spreadsheet application in MS Excel. The differential equations of the model are programmed in Visual Basic. The spreadsheet template can be operated after a few instructions. The numerical integration is fast. A typical simulation of 24 h after exposure takes a few seconds on a standard personal computer, including the plotting of the results. The full mathematical description of the PBTK model is presented in Supplementary 1 (available at Annals of Occupational Hygiene online) in the online edition. The mathematical description of dermal absorption of chemicals is presented in supplement 2 in the online edition. Output is presented as numerical listing over time in tabular form and in graphs. The program does provide the amounts in micromoles and concentrations in micromoles per litre. After each run, amounts and concentrations in compartments and fluids are listed together with the estimated partition coefficients of the chemical and metabolites under study and the data of the mass balance. Also, graphs of the concentrations in alveolar air, blood, and urine are presented. The IndusChemFate PBTK model is available free of charge from the CEFIC-LRI website as a Visual Basic application in Microsoft Excel (available at http://www.cefic-lri.org/lri-toolbox/induschemfate last accessed on 7 September, 2011). EXPERIMENTAL Studies with exposure to chemicals (inhaled concentration or dermal dose rate) and with repeated measurements of concentration of the chemical and metabolites in blood and/or urine were searched for. Six experimental or observational studies with six different compounds were selected, e.g. the compounds pyrene The time course of the blood and urine concentrations of the parent compound and/or metabolites were simulated with the PBTK model IndusChemFate following the reported exposure scenario of the selected study. The physical--chemical input parameters of the compounds-molecular weight, density, vapour pressure, log(octanol:water) partition coefficient at pH 5.5 and at 7.4, and water solubility-were taken from the EPI suite database of US-EPA (2009) , the Chemspider database RESULTS Comparison 1: excretion of hydroxylated metabolite of pyrene in creosote impregnating worker The concentration of a 1-hydroxypyrene (1-OHP, a hydroxylated metabolite of pyrene) was measured in urine of an operator of a creosote impregnating site The excretion of 1-OHP was simulated with the PBTK model IndusChemFate. Reported exposure data of creosote plant operators of other studies were used to make an estimate of the representative exposure scenario. Airborne exposure of creosote plant operators to pyrene might be up to 3 lg m measurements of pyrene ranges from ND-90 ng cm -2 skin in 8-h work shift; the exposed dermal surface area is large: neck, wrist, and jaw/neck of creosoting workers were clearly exposed The PBTK model requires input data of three compounds: the parent compound pyrene, 1-OHP, and 1-OHP-gluc. The physical-chemical input data and the kinetic input data are presented in The simulation showed that 1-OHP-gluc is the dominant metabolite in urine. Free 1-OHP was predicted to be is ,0.1% of 1-OHP-gluc. The simulated excretion pattern of 1-OHP-gluc of the last 4 days of a working week is shown in Comparison 2: blood and urine concentrations of MTBE and metabolites after inhalation Six volunteers (three males and three females) were exposed to a concentration of 40 p.p.m. MTBE (methyl-tertiary-butylether) (5 144 mg m -3 ) for 4 h in a dynamic exposure chamber In the body, MTBE is metabolized to tert-butanol (Metabolite 1). This metabolite is further metabolized, mainly to MPD (Metabolite 2), which is further oxidized to HiBA (Metabolite 3). The fate of MTBE and three metabolites in blood and urine was simulated with PBPK model IndusChemFate with the given scenario of exposure: 4-h exposure to a concentration of 140 mg m -3 . The entry data of MTBE and the three metabolites that were used for the simulation are presented in Furthermore, blood levels were reported. The concentration of MTBE and t-butanol in blood was determined at the end of the 4-h exposure period. The mass balance showed that exhalation of MTBE was the preferred route of elimination; the exhaled amount of excreted MTBE and metabolites in 48 h was 2-3 fold larger than the excreted amount in urine (respectively, 34 lmol kg -1 BW and 13.2 lmol kg -1 BW). Comparison 3: urine concentrations of NMP after transdermal vapour absorption Dermal vapour phase absorption is an important route of uptake of the solvent N-methyl-2-pyrrolidone (NMP). This particular aspect was investigated in an experimental study with 16 volunteers exposed to 80 mg m -3 NMP for 8 h under either whole body, i.e. inhalation plus dermal vapour exposure, or dermal only conditions The fate of NMP and metabolites after 8-h exposure to a concentration of 80 mg m À3 was simulated with PBPK model IndusChemFate. The chemicalspecific data of NMP and metabolites that were used for the simulation are presented in I

Estudo de caso de eficiência energética e controle de qualidade do ar interior em ambiente condicionado

Trabalho de Conclusão de Curso (graduação)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Mecânica, 2018.O objetivo deste trabalho é realizar um estudo de caso sobre eficiência energética e controle de qualidade do ar interior (QAI) de sistema de ar condicionado com tecnologias de vazão de ar exterior variável (DCV), recuperação de energia (ERV) e free cooling no plenário Ulysses Guimarães, Brasília - DF. Uma simulação computacional com auxílio dos softwares OpenStudio e Energyplus acerca do sistema de climatização em estudo foi realizada para analisar o potencial de economia na utilização de recursos como ERV, DCV e free cooling. Para tal, foram consideradas condições climáticas locais, características arquitetônicas do plenário, bem como as eficiências dos equipamentos utilizados. Paralelamente, foi realizada uma avaliação da qualidade do ar interior e possíveis soluções de monitoramento. Os resultados mostraram que a combinação das tecnologias avaliadas foi capaz de proporcionar economias de até 20% nos custos operacionais do sistema de ar condicionado em ambientes que apresentam perfil de ocupação variável.The objective of this work is to perform a case study on energy efficiency and indoor air quality control (IAQ) of air conditioning system with variable outdoor airflow (DCV), energy recovery (ERV) and free cooling technologies in plenary Ulysses Guimarães, Brasilia DF. A computer simulation with the help of the OpenStudio and Energyplus software on the air conditioning system under study was performed to analyze the potential savings in the use of resources such as ERV, DCV and free cooling. For this, local climatic conditions, architecture characteristics of the plenary, as well as the efficiencies of the equipment used were considered. At the same time, an indoor air quality assessment and possible monitoring solutions were carried out. The results showed that the combination of the technologies evaluated was able to provide savings up to 20% in the operating costs of the air conditioning system in environments with variable occupancy profiles