Optimal estimators for ambient air quality levels

Procedures to estimate missing data, determine extrema, and derive uncertainties for data collected in ambient air monitoring networks are presented. The optimal linear estimators used obtain unbiased, minimum variance results based on the temporal and spatial correlation of the data and estimates of sample uncertainty. The first estimator interpolates missing data. The second estimator derives extrema, e.g. minimum and maximum concentrations, from the completed data set. Together the estimators can be used to check the validity of monitored observations, identify outliers, and estimate regional and local components of pollutant levels. The estimators are evaluated using data collected in urban air quality monitoring networks in Houston, Philadelphia and St Louis.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/30301/1/0000703.pd

Time-resolved cutaneous absorption and permeation rates of methanol in human volunteers

This paper reports on an experimental study of dermal exposure to neat methanol in human volunteers for the purposes of estimating percutaneous absorption rates, permeation kinetics, baseline (pre-exposure) levels of methanol in blood, and inter- and intrasubject variability. A total of 12 volunteers (seven men and five women) were exposed to methanol via one hand for durations of 0 to 16 min in a total of 65 sessions, making this the largest controlled study of percutaneous absorption for this common solvent. In each session, 14 blood samples were collected sequentially and analyzed for methanol. These data were used to derive absorption rates and delivery kinetics using a two compartment model that accounts for elimination and pre-exposure levels. The pre-exposure methanol concentration in blood was 1.7 ± 0.9 mg l −1 , and subjects had statistically different mean concentrations. The maximum methanol concentration in blood was reached 1.9 ± 1.0 h after exposure. Delivery rates from skin into blood lagged exposure by 0.5 h, and methanol continued to enter the systemic circulation for 4 h following exposure. While in vitro studies have reported comparable lag times, the prolonged permeation or epidermal reservoir effect for such miscible solvents has not been previously measured. The mean derived absorption rate, 8.1 ± 3.7 mg cm −2  h −1 , is compatible with that found in the other in vivo study of methanol absorption. Both in vivo absorption rate estimates considerably exceed in vitro estimates. The maximum concentration of methanol in blood following an exposure to one hand lasting ∼20 min is comparable to that reached following inhalational exposures at a methanol concentration of 200 ppm, the threshold limit value-time weighted average (TLV-TWA). While variability in blood concentrations and absorption rates approached a factor of two, differences between individuals were not statistically significant. The derived absorption and permeation rates provide information regarding kinetics and absorbed dose that can help to interpret biological monitoring data and confirm mathematical models of chemical permeation.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/42237/1/420-70-5-341_70700341.pd

Environmental Reporting by the Fortune 50 Firms

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/42398/1/267-21-6-865_21n6p865.pd

Enhancing models and measurements of traffic-related air pollutants for health studies using dispersion modeling and Bayesian data fusion

Research Report 202 describes a study led by Dr. Stuart Batterman at the University of Michigan, Ann Arbor and colleagues. The investigators evaluated the ability to predict traffic-related air pollution using a variety of methods and models, including a line source air pollution dispersion model and sophisticated spatiotemporal Bayesian data fusion methods. Exposure assessment for traffic-related air pollution is challenging because the pollutants are a complex mixture and vary greatly over space and time. Because extensive direct monitoring is difficult and expensive, a number of modeling approaches have been developed, but each model has its own limitations and errors. Dr. Batterman and colleagues sought to improve model estimations by applying and systematically comparing the performance of different statistical models. The study made extensive use of data collected in the Near-road EXposures and effects of Urban air pollutants Study (NEXUS), a cohort study designed to examine the relationship between near-roadway pollutant exposures and respiratory outcomes in children with asthma who live close to major roadways in Detroit, Michigan

Statistical strategies for constructing health risk models with multiple pollutants and their interactions: possible choices and comparisons

Abstract Background As public awareness of consequences of environmental exposures has grown, estimating the adverse health effects due to simultaneous exposure to multiple pollutants is an important topic to explore. The challenges of evaluating the health impacts of environmental factors in a multipollutant model include, but are not limited to: identification of the most critical components of the pollutant mixture, examination of potential interaction effects, and attribution of health effects to individual pollutants in the presence of multicollinearity. Methods In this paper, we reviewed five methods available in the statistical literature that are potentially helpful for constructing multipollutant models. We conducted a simulation study and presented two data examples to assess the performance of these methods on feature selection, effect estimation and interaction identification using both cross-sectional and time-series designs. We also proposed and evaluated a two-step strategy employing an initial screening by a tree-based method followed by further dimension reduction/variable selection by the aforementioned five approaches at the second step. Results Among the five methods, least absolute shrinkage and selection operator regression performs well in general for identifying important exposures, but will yield biased estimates and slightly larger model dimension given many correlated candidate exposures and modest sample size. Bayesian model averaging, and supervised principal component analysis are also useful in variable selection when there is a moderately strong exposure-response association. Substantial improvements on reducing model dimension and identifying important variables have been observed for all the five statistical methods using the two-step modeling strategy when the number of candidate variables is large. Conclusions There is no uniform dominance of one method across all simulation scenarios and all criteria. The performances differ according to the nature of the response variable, the sample size, the number of pollutants involved, and the strength of exposure-response association/interaction. However, the two-step modeling strategy proposed here is potentially applicable under a multipollutant framework with many covariates by taking advantage of both the screening feature of an initial tree-based method and dimension reduction/variable selection property of the subsequent method. The choice of the method should also depend on the goal of the study: risk prediction, effect estimation or screening for important predictors and their interactions.http://deepblue.lib.umich.edu/bitstream/2027.42/112386/1/12940_2013_Article_691.pd

Extended disjoint principal-components regression analysis of SAW vapor sensor-array responses

The application of a disjoint principal-components regression method to the analysis of sensor-array response patterns is demonstrated using published data from ten polymer-coated surface-acoustic-wave (SAW) sensors exposed to each of nine vapors. Use of the method for the identification and quantitation of the components of vapor mixtures is shown by simulating the 36 possible binary mixtures and 84 possible ternary mixtures under the assumption of additive responses. Retaining information on vapor concentrations in the classification models allows vapors to be accurately identified, while facilitating prediction of the concentrations of individual vapors and the vapors comprising the mixtures. The effects on the rates of correct classification of placing constraints on the maximum and minimum vapor concentrations and superimposing error on the sensor responses are investigated.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/30857/1/0000520.pd

Breath, urine, and blood measurements as biological exposure indices of short-term inhalation exposure to methanol

Due to their transient nature, short-term exposures can be difficult to detect and quantify using conventional monitoring techniques. Biological monitoring may be capable of registering such exposures and may also be used to estimate important toxicological parameters. This paper investigates relationships between methanol concentrations in the blood, urine, and breath of volunteers exposed to methanol vapor at 800 ppm for periods of 0.5, 1, 2, and 8 h. The results indicate factors that must be considered for interpretation of the results of biological monitoring. For methanol, concentrations are not proportional to the exposure duration due to metabolic and other elimination processes that occur concurrently with the exposure. First-order clearance models can be used with blood, breath, or urine concentrations to estimate exposures if the time that has elapsed since the exposure and the model parameters are known. The 0.5 to 2-h periods of exposure were used to estimate the half-life of methanol. Blood data gave a half-life of 1.44±0.33 h. Comparable but slightly more variable results were obtained using urine data corrected for voiding time (1.55±0.67 h) and breath data corrected for mucous membrane desorption (1.40±0.38 h). Methanol concentrations in blood lagged some 15–30 min behind the termination of exposure, and concentrations in urine were further delayed. Although breath sampling may be convenient, breath concentrations reflect end-expired or alveolar air only if subjects are in a methanol-free environment for 30 min or more after the exposure. At earlier times, breath concentrations included contributions from airway desorption or diffusion processes. As based on multicompartmental models, the desorption processes have half-lives ranging between 0.6 and 5 min. Preliminary estimates of the mucous membrane reservoir indicate contributions of under 10% for a 0.5-h exposure and smaller effects for longer periods of exposure.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/42238/1/420-71-5-325_80710325.pd

Influence of viral infection on the relationships between airway cytokines and lung function in asthmatic children

Abstract Background Few longitudinal studies examine inflammation and lung function in asthma. We sought to determine the cytokines that reduce airflow, and the influence of respiratory viral infections on these relationships. Methods Children underwent home collections of nasal lavage during scheduled surveillance periods and self-reported respiratory illnesses. We studied 53 children for one year, analyzing 392 surveillance samples and 203 samples from 85 respiratory illnesses. Generalized estimated equations were used to evaluate associations between nasal lavage biomarkers (7 mRNAs, 10 proteins), lung function and viral infection. Results As anticipated, viral infection was associated with increased cytokines and reduced FVC and FEV1. However, we found frequent and strong interactions between biomarkers and virus on lung function. For example, in the absence of viral infection, CXCL10 mRNA, MDA5 mRNA, CXCL10, IL-4, IL-13, CCL4, CCL5, CCL20 and CCL24 were negatively associated with FVC. In contrast, during infection, the opposite relationship was frequently found, with IL-4, IL-13, CCL5, CCL20 and CCL24 levels associated with less severe reductions in both FVC and FEV1. Conclusions In asthmatic children, airflow obstruction is driven by specific pro-inflammatory cytokines. In the absence of viral infection, higher cytokine levels are associated with decreasing lung function. However, with infection, there is a reversal in this relationship, with cytokine abundance associated with reduced lung function decline. While nasal samples may not reflect lower airway responses, these data suggest that some aspects of the inflammatory response may be protective against viral infection. This study may have ramifications for the treatment of viral-induced asthma exacerbations.https://deepblue.lib.umich.edu/bitstream/2027.42/146519/1/12931_2018_Article_922.pd

Prediction and analysis of near-road concentrations using a reduced-form emission/dispersion model

<p>Abstract</p> <p>Background</p> <p>Near-road exposures of traffic-related air pollutants have been receiving increased attention due to evidence linking emissions from high-traffic roadways to adverse health outcomes. To date, most epidemiological and risk analyses have utilized simple but crude exposure indicators, most typically proximity measures, such as the distance between freeways and residences, to represent air quality impacts from traffic. This paper derives and analyzes a simplified microscale simulation model designed to predict short- (hourly) to long-term (annual average) pollutant concentrations near roads. Sensitivity analyses and case studies are used to highlight issues in predicting near-road exposures.</p> <p>Methods</p> <p>Process-based simulation models using a computationally efficient reduced-form response surface structure and a minimum number of inputs integrate the major determinants of air pollution exposures: traffic volume and vehicle emissions, meteorology, and receptor location. We identify the most influential variables and then derive a set of multiplicative submodels that match predictions from "parent" models MOBILE6.2 and CALINE4. The assembled model is applied to two case studies in the Detroit, Michigan area. The first predicts carbon monoxide (CO) concentrations at a monitoring site near a freeway. The second predicts CO and PM_2.5concentrations in a dense receptor grid over a 1 km²area around the intersection of two major roads. We analyze the spatial and temporal patterns of pollutant concentration predictions.</p> <p>Results</p> <p>Predicted CO concentrations showed reasonable agreement with annual average and 24-hour measurements, e.g., 59% of the 24-hr predictions were within a factor of two of observations in the warmer months when CO emissions are more consistent. The highest concentrations of both CO and PM_2.5were predicted to occur near intersections and downwind of major roads during periods of unfavorable meteorology (e.g., low wind speeds) and high emissions (e.g., weekday rush hour). The spatial and temporal variation among predicted concentrations was significant, and resulted in unusual distributional and correlation characteristics, including strong negative correlation for receptors on opposite sides of a road and the highest short-term concentrations on the "upwind" side of the road.</p> <p>Conclusions</p> <p>The case study findings can likely be generalized to many other locations, and they have important implications for epidemiological and other studies. The reduced-form model is intended for exposure assessment, risk assessment, epidemiological, geographical information systems, and other applications.</p