Model development and validation of personal exposure to volatile organic compound concentrations

Exploring urinary biomarkers to assess oxidative DNA damage resulting from BTEX exposure in street children

Children are highly susceptible to environmental contaminants as their physiology and some metabolic pathways differ from adults. The present cross-sectional study aimed to assess whether exposure to benzene, toluene, ethylbenzene, o,p-xylene, and m-xylene (BTEX) affects oxidative DNA damage in street children using a biomonitoring approach. Thirty-five boys (7-13 years of age), exposed by working at a busy intersection, and 25 unexposed boys of similar age and living in the neighborhood near the busy intersection were recruited. Urinary un-metabolized BTEX levels were quantified by a headspace gas chromatography-mass spectrometry (GC-MS). Urinary malonaldehyde (MDA) was measured with spectrophotometry. Sociodemographic and lifestyle conditions information was collected by interviews using administered questionnaires. Exposed subjects provided urine before (BE) and after work exposure (AE), while unexposed boys gave a single morning sample. Urinary BTEX concentrations in BE samples were similar to unexposed. Concentrations in AE samples were 2.36-fold higher than observed in BE samples (p < 0.05) and higher than those in the unexposed group (p < 0.05). In addition, urinary MDA levels in AE samples were 3.2 and 3.07-times higher than in BE samples and in the unexposed group (p < 0.05). Environmental tobacco smoke (ETS) increased urinary BTEX and MDA levels in both groups. Our findings confirm that street children working at busy intersections are significantly exposed to BTEX, which is associated with oxidative stress. Implementing protective measures is crucial to reduce exposure and to improve health outcomes in this group.

Biogenic volatile organic compounds from 14 landscape woody species: Tree species selection in the construction of urban greenspace with forest healthcare effects

Biogenic volatile organic compound (BVOC) is an important part of forest healthcare effect, while it has not received enough attention in urban greenspace construction. Consequently, the aim of this study was to analyze BVOC emission rates and compositions released from leaves and flowers of landscape species in urban greenspace and to make suggestions for species selection and planting configuration in urban greenspace construction. BVOC emissions were collected and analyzed using dynamic flow enclosure technique with GC-MS in summer months (for leaf) and spring months (for flower) from 14 woody landscape species in northern China, which are 2 coniferous species (Pinus tabuliformis and Sabina vulgaris) and 12 broad-leaved species (Viburnum opulus, Kolkwitzia amabilis, Philadelphus pekinensis, Lonicera maackii, Cercis chinensis, Deutzia parviflora, Berberis thunbergia, Kerria japonica, Rosa xanthina, Syringa oblata, Syringa reticulata, and Cerasus serrulata). We found leaf emission rates of coniferous species were more than 20 μg∙g^-1Dw∙h^-1 and mainly consisted of monoterpenes, while that of sampled broad-leaved species were less than 10 μg∙g^-1Dw∙h^-1 and mainly consisted of fatty acid derivatives. Most broad-leaved species had relatively high emission rates of flowers, ranging from 15 to 115 μg∙g^-1Dw∙h^-1. Flower emissions of Syringa oblata and Syringa reticulata contained large amounts of aldehydes, and that of other broad-leaved species mainly contained terpenes, alcohols, and esters. We suggest the species with leaves that release large amounts of monoterpenes, and species with flowers that release large amounts of fragrant compounds are classified as healthcare species, while species with a dense crown and low emission rates of pungent compounds are classified as space-creation species. Based on this, planners could design urban greenspace with healthcare effects, and develop multi-functional, innovative, and sustainable urban greenspaces.

The contribution of cooking appliances and residential traffic proximity to aerosol personal exposure

PURPOSE

Indoor and outdoor factors affect personal exposure to air pollutants. Type of cooking appliance (i.e. gas, electricity), and residential location related to traffic are such factors. This research aims to investigate the effect of cooking with gas and electric appliances, as an indoor source of aerosols, and residential traffic as outdoor sources, on personal exposures to particulate matter with an aerodynamic diameter lower than 2.5 μm (PM_2.5), black carbon (BC), and ultrafine particles (UFP).

METHODS

Forty subjects were sampled for four consecutive days measuring personal exposures to three aerosol pollutants, namely PM_2.5, BC, and UFP, which were measured using personal sensors. Subjects were equally distributed into four categories according to the use of gas or electric stoves for cooking, and to residential traffic (i.e. houses located near or away from busy roads).

RESULTS/CONCLUSION

Cooking was identified as an indoor activity affecting exposure to aerosols, with mean concentrations during cooking ranging 24.7-50.0 μg/m³ (PM_2.5), 1.8-4.9 μg/m³ (BC), and 1.4 × 10⁴-4.1 × 10⁴ particles/cm³ (UFP). This study also suggest that traffic is a dominant source of exposure to BC, since people living near busy roads are exposed to higher BC concentrations than those living further away from traffic. In contrast, the contribution of indoor sources to personal exposure to PM_2.5 and UFP seems to be greater than from outdoor traffic sources. This is probably related to a combination of the type of building construction and a varying range of activities conducted indoors. It is recommended to ensure a good ventilation during cooking to minimize exposure to cooking aerosols.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s40201-020-00604-7.

A novel living environment exposure matrix of the common organic air pollutants for exposure assessment

Although the most accurate estimations of exposure to organic air pollutants are direct personal measurements, it is prohibitive for large-scale epidemiological studies, both in terms of cost-saving and procedure time. Therefore, indirect exposure assessments offer a potentially feasible approach for estimating population exposures to organic air pollution. The purpose of this study was to develop a novel living environment exposure matrix of the common organic air pollutants, which was used in large-scale epidemiological studies. The common organic air pollutants and a range of potential living environment factors were collected and matched according to the statistics of the Centers for Disease Control and Prevention, the United States Environmental Protection Agency, and the World Health Organization. Paints (dyes), paint removing, and furniture are the most common source of living environment exposure (n = 6). Furthermore, most of the common organic air pollutants are associated with exposure to coal, oil and gasoline burning, smoking, and carpet backing (n ≥ 2). Electricity is considered a clean fuel due to they generate less organic air pollutants compared to other living environment factors in this study. However, whether gas burning is considered as a source of indoor organic air pollutants in large-scale epidemiological studies need to be further investigated. The present study summarizes the living environment exposure matrix of the common organic air pollution, which could be used to estimate exposure to organic air pollutants in large-scale epidemiological studies in the future.

National secular trends in ambient air volatile organic compound levels and biomarkers of exposure in the United States

BACKGROUND

Exposure to air pollution is a leading cause of global mortality. Volatile organic compounds (VOCs) are constituents of ambient air that could exert adverse health effects.

OBJECTIVE

To examine the relationship between VOC levels in ambient air and individual-level exposure to VOCs, as assessed by urinary VOC metabolites.

METHODS

Secular trends in 11 ambient air VOCs (2005-2013) and individual-level metabolites of 14 VOCs (2005-2014) were assessed using National Monitoring Programs (NMP) and National Health and Nutrition Examination Survey (NHANES) data, respectively. To isolate environmental exposure, individuals reporting exposure to tobacco smoke were excluded. Quantile regression models were used to assess secular trends in VOC exposure, and survey-weighted regression models were built to identify factors associated with VOC exposure.

RESULTS

All annual levels of ambient VOCs decreased from 2005 to 2013 (Range: 12.5%-77.2%). However, 11 of the corresponding VOC metabolites increased during the same time (Range: 0.3%-53.6%). There was a proportional change in patterns of VOC exposure across NHANES waves, with the middle quantiles of exposure showing the largest increase. VOC exposures were significantly associated with age, sex, race, education, and physical inactivity, but not with secular VOC trends.

DISCUSSION

In the United States, individual-level exposure to several VOCs increased between 2005 and 2014 despite a decline in ambient air VOC levels. This inverse relationship suggests that ambient VOCs are not the primary source of VOC exposure, therefore, decreasing ambient VOCs alone may not be sufficient to protect against the adverse health effects associated with VOC exposure.

Environmental chronic exposure to metals and effects on attention and executive function in the general population

Heavy metals are neurotoxic, associated with brain dysfunction, and have been linked with cognitive decline in adults. This study was aimed to characterize chronic exposure to metals (Cd, Be, Co, Hg, Sn, V, Al, Ba, Cr, Cu, Fe, Li, Mn, Ni, Pb, and Zn) and metalloids (As, B, Sb) and assess its impact on cognitive performance of Tehran's residents, capital of Iran. Scalp hair samples gathered from 200 volunteered participants (110 men and 90 women), aged 14-70 years and quantified by inductively coupled plasma atomic emission spectroscopy (ICP-OES). Attention and executive function, two measures of cognitive performance, were characterized using the trail making test (TMT) part A and B, respectively. Mental flexibility was characterized as the Delta TMT B-A scores and cognitive efficiency or dissimulation as the ration between TMT B and A scores. A comprehensive questionnaire was used to gather information on demographic and socioeconomic as well as lifestyle and health status. The highest and lowest mean concentrations were observed for B (325 μg/g) and As (0.29 μg/g), respectively. Results indicated that chronic metal exposure measured in hair changed significantly based on gender and age (p < 0.05). The levels of Cr, Fe, Ni, Si, Hg, Pb and B were significantly higher in males' hair, whereas those of Ag and Ba were greater in females' hair (p < 0.05). The results of the cognitive TMT test were significantly different between gender and age groups (p < 0.05). Moreover, results revealed that As, Hg, Mn, and Pb levels in hair were significantly associated with poorer participants' performance scores in the TMT test (p < 0.05). Age, gender, cigarette smoking, water-pipe smoking, traffic density in the area of residence, and dental amalgam filling were significant factors affecting the TMT test scores. The results suggest that chronic exposure to metals has detrimental effects on attention, executive function, mental flexibility and cognitive efficiency.

Lifestyle and occupational factors affecting exposure to BTEX in municipal solid waste composting facility workers

Composting facilities workers are potentially exposed to different volatile organic compounds (VOCs). This study aims to investigate the potential exposure to benzene, toluene, ethylbenzene and xylenes (BTEX) compounds among workers of composting facilities by measuring un-metabolized BTEX in urine and to investigate the effect that several lifestyle factors (i.e. smoking and residential traffic), using personal protective equipment, and religious practices such as Ramadan fasting can have on the urinary BTEX concentrations. We assessed concentrations of BTEX in the urine of a composting facility workers. Samples were collected in May 2018. Overall, 25 workers chosen as the exposed group and 20 inhabitants living close to the composting facility as a control group. The urine samples were collected from studied subjects. Identification and quantification of un-metabolized BTEX was performed using a headspace gas chromatography-mass spectrometry (GC-MS). Detailed information of participants was gathered by a comprehensive questionnaire. The geometric mean levels of urinary benzene, toluene, ethylbenzene, m‑p xylene, and o‑xylene in the exposed subjects were 1.27, 2.12, 0.54, 1.22 and 1.51 μg/L, respectively; 1.4 to 3.7-time higher than values in control group (p < 0.05). Post-shift levels were significantly higher than pre-shift for all chemicals (p < 0.05). Smoking habits, exposure to environmental tobacco smoke, and Ramadan fasting predicted urinary BTEX levels. Personal protective equipment which included a simple N95 mask did not protected workers from BTEX emissions. Composting facilities represent a significant source BTEX emissions and exposure for staff. More effective protective strategies are required to minimize exposure and related occupational hazards.

Comparison of Machine Learning Approaches with a General Linear Model To Predict Personal Exposure to Benzene

Machine learning techniques (MLTs) offer great power in analyzing complex data sets and have not previously been applied to non-occupational pollutant exposure. MLT models that can predict personal exposure to benzene have been developed and compared with a standard model using a linear regression approach (GLM). The models were tested against independent data sets obtained from three personal exposure measurement campaigns. A correlation-based feature subset (CFS) selection algorithm identified a reduced attribute set, with common attributes grouped under the use of paints in homes, upholstery materials, space heating, and environmental tobacco smoke as the attributes suitable to predict the personal exposure to benzene. Personal exposure was categorized as low, medium, and high, and for big data sets, both the GLM and MLTs show high variability in performance to correctly classify greater than 90 percentile concentrations, but the MLT models have a higher score when accounting for divergence of incorrectly classified cases. Overall, the MLTs perform at least as well as the GLM and avoid the need to input microenvironment concentrations.

Use of urinary biomarkers to characterize occupational exposure to BTEX in healthcare waste autoclave operators

Urinary benzene, toluene, ethylbenzene, and xylenes (BTEX) can be used as a reliable biomarker of exposure to these pollutants. This study was aimed to investigate the urinary BTEX concentration in operators of healthcare waste (HCW) autoclaves. This cross-sectional study was conducted in selected hospitals in Tehran, Iran between April and June 2017. Twenty operators (as the case group) and twenty control subjects were enrolled in the study. Personal urine samples were collected at the beginning and end of the work shift. Urinary BTEX were measured by a headspace gas chromatography-mass spectrometry (GC/MS). A detailed questionnaire was used to gather information from subjects. Results showed that the median of urinary benzene, toluene, ethylbenzene, m-p xylene, and o-xylene levels in the exposed group were 3.26, 3.36, 0.84, 3.94 and 4.48 μg/L, respectively. With the exception of ethylbenzene, subjects in the exposed group had significantly higher urinary BTEX levels than control group (p < 0.05). Urinary BTEX concentrations in the exposed case group were 2.5-fold higher than in the control group. There was a significant relationship between the amount of generated waste per day and the urinary BTEX in the exposed group. Smoking status and type of autoclave used were also identified as predictors of urinary BTEX concentrations. The healthcare waste treatment autoclaves can be considered as a significant BTEX exposure source for operators working with these treatment facilities. The appropriate personal protection equipment and control measures capable in reducing BTEX exposure should be provided to HCW workers to reduce their exposures to BTEX.

Evaluation of the well mixed room and near-field far-field models in occupational settings

Drawing appropriate conclusions about a scenario for which the exposure is truly unacceptable drives appropriate exposure and risk management, and protects the health and safety of those individuals. To ensure the vast majority of these decisions are accurate, these decisions must be based upon proven approaches and tools. When these decisions are based solely on professional judgment guided by subjective inputs, however, they are more than likely wrong, and biased, underestimating the true exposure. Models have been shown anecdotally to be useful in accurately predicting exposure but their use in occupational hygiene has been limited. Possible reasons are a general lack of guidance on model selection and use and scant model input data. The lack of systematic evaluation of the models is also an important factor. This research is the second phase of work building upon the robust evaluation of the Well Mixed Room (WMR) and Near Field Far Field (NF-FF) models under controlled conditions in an exposure chamber, ^[5] in which good concordance between measured and modeled airborne concentrations of three solvents under a range of conditions was observed. In real world environments, the opportunity to control environmental conditions is limited and measuring the model inputs directly can be challenging; in many cases, model inputs must be estimated indirectly without measurement. These circumstances contribute to increased model input uncertainty and consequent uncertainty in the output. Field studies of model performance directly inform us about how well models predict exposures given these practical limitations, and are, therefore, an important component of model evaluation. The evaluation included ten diverse contaminant-exposure scenarios at five workplaces involving six different contaminants. A database of parameter values and measured and modeled exposures was developed and will be useful for modeling similar scenarios in the future.

Assessment on personal exposure to particulate compounds using an empirical exposure model in an elderly community in Tianjin, China

Using central site measurement data to predict personal exposure to particulate matter (PM) is challenging, because people spend most of their time indoors and ambient contribution to personal exposure is subject to infiltration conditions affected by many factors. Efforts in assessing and predicting exposure on the basis of associated indoor/outdoor and central site monitoring were limited in China. This study collected daily personal exposure, residential indoor/outdoor and community central site PM filter samples in an elderly community during the non-heating and heating periods in 2009 in Tianjin, China. Based on the chemical analysis results of particulate species, mass concentrations of the particulate compounds were estimated and used to reconstruct the PM mass for mass balance analysis. The infiltration factors (F_inf) of particulate compounds were estimated using both robust regression and mixed effect regression methods, and further estimated the exposure factor (F_pex) according to participants' time-activity patterns. Then an empirical exposure model was developed to predict personal exposure to PM and particulate compounds as the sum of ambient and non-ambient contributions. Results showed that PM mass observed during the heating period could be well represented through chemical mass reconstruction, because unidentified mass was minimal. Excluding the high observations (>300μg/m³), this empirical exposure model performed well for PM and elemental carbon (EC) that had few indoor sources. These results support the use of F_pex as an indicator for ambient contribution predictions, and the use of empirical non-ambient contribution to assess exposure to particulate compounds.

Estimation of chronic personal exposure to airborne polycyclic aromatic hydrocarbons

BACKGROUND

Polycyclic aromatic hydrocarbons (PAH) exposure from solid fuel burning represents an important public health issue for the majority of the global population. Yet, understanding of individual-level exposures remains limited.

OBJECTIVES

To develop regionally adaptable chronic personal exposure model to pro-carcinogenic PAH (c-PAH) for the population in Kraków, Poland.

METHODS

We checked the assumption of spatial uniformity in eight c-PAH using the coefficients of divergence (COD), a marker of absolute concentration differences. Upon successful validation, we developed personal exposure models for eight pro-carcinogenic PAH by integrating individual-level data with area-level meteorological or pollutant data. We checked the resulting model for accuracy and precision against home outdoor monitoring data.

RESULTS

During winter, COD of 0.1 for Kraków suggest overall spatial uniformity in the ambient concentration of the eight c-PAH. The three models that we developed were associated with index of agreement approximately equal to 0.9, root mean square error < 2.6 ng/m(3), and 90th percentile of absolute difference ≤ 4 ng/m(3) for the predicted and the observed concentrations for eight pro-carcinogenic PAH.

CONCLUSIONS

Inexpensive and logistically feasible information could be used to estimate chronic personal exposure to PAH profiles, in lieu of costly and labor-intensive personal air monitoring at wide scale. At the same time, thorough validation through direct personal monitoring and assumption checking are critical for successful model development.

Exposición a COVs en fábricas de muebles de dos poblaciones del norte de Colombia

<p><strong>Objetivos</strong> Evaluar la exposición a compuestos orgánicos volátiles (COVs) en trabajadores de fábricas artesanales de mueblesde dos poblaciones de Sucre (Sincelejo y Sampués), Norte de Colombia. Determinar posibles signos y/o síntomas relacionados con la exposición a estos contaminantes.</p><p><strong>Métodos </strong>Estudio transversal analíticocon aplicación de una encuesta. La población objetivo estuvo conformada por 66 individuos, 41 expuestos y 25 controles. Se tomaron muestras personales para las concentraciones de benceno, tolueno, etilbenceno y xilenos (en ambos grupos) y se contrastó con posibles efectos en la salud atribuidos a estos contaminantes.</p><p><strong>Resultados </strong>Las concentraciones de benceno, tolueno y m/p-xileno fueron mayores en el grupo expuesto (9,5 mg/m³, 8,1 mg/m³ y 12,1 mg/m³) en comparación con el grupo control(0,2 mg/m³, 0,3 mg/m³ y 0,03 mg/m³).Dolor muscular 82,9 % (RP=3,8; IC95%:1,2-11,8) y somnolencia 65,9 % (RP=4,9; IC95%:1,7-14,7)estuvieron asociados a mayor exposición (p&lt; 0,05). Factores como el uso de mezclas solventes (thinner) en el trabajo y el tráfico vehicularpueden contribuir a estos resultados.</p><p><strong>Conclusiones </strong>La contribución de diversas fuentes aumenta la exposición personal a los COVs, de los trabajadores de las fábricas artesanales de muebles en el Norte de Colombia. Adicionalmente, el uso excesivo de estos compuestos puede estar generando efectos adversos en la salud de los trabajadores.</p>

Risk assessments for chronic exposure of children and prospective parents to ethylbenzene (CAS No. 100-41-4)

Potential chronic health risks for children and prospective parents exposed to ethylbenzene were evaluated in response to the Voluntary Children's Chemical Evaluation Program. Ethylbenzene exposure was found to be predominately via inhalation with recent data demonstrating continuing decreases in releases and both outdoor and indoor concentrations over the past several decades. The proportion of ethylbenzene in ambient air that is attributable to the ethylbenzene/styrene chain of commerce appears to be relatively very small, less than 0.1% based on recent relative emission estimates. Toxicity reference values were derived from the available data, with physiologically based pharmacokinetic models and benchmark dose methods used to assess dose-response relationships. An inhalation non-cancer reference concentration or RfC of 0.3 parts per million (ppm) was derived based on ototoxicity. Similarly, an oral non-cancer reference dose or RfD of 0.5 mg/kg body weight/day was derived based on liver effects. For the cancer assessment, emphasis was placed upon mode of action information. Three of four rodent tumor types were determined not to be relevant to human health. A cancer reference value of 0.48 ppm was derived based on mouse lung tumors. The risk characterization for ethylbenzene indicated that even the most highly exposed children and prospective parents are not at risk for non-cancer or cancer effects of ethylbenzene.

Personal exposure to mixtures of volatile organic compounds: modeling and further analysis of the RIOPA data

INTRODUCTION

Emission sources of volatile organic compounds (VOCs*) are numerous and widespread in both indoor and outdoor environments. Concentrations of VOCs indoors typically exceed outdoor levels, and most people spend nearly 90% of their time indoors. Thus, indoor sources generally contribute the majority of VOC exposures for most people. VOC exposure has been associated with a wide range of acute and chronic health effects; for example, asthma, respiratory diseases, liver and kidney dysfunction, neurologic impairment, and cancer. Although exposures to most VOCs for most persons fall below health-based guidelines, and long-term trends show decreases in ambient emissions and concentrations, a subset of individuals experience much higher exposures that exceed guidelines. Thus, exposure to VOCs remains an important environmental health concern. The present understanding of VOC exposures is incomplete. With the exception of a few compounds, concentration and especially exposure data are limited; and like other environmental data, VOC exposure data can show multiple modes, low and high extreme values, and sometimes a large portion of data below method detection limits (MDLs). Field data also show considerable spatial or interpersonal variability, and although evidence is limited, temporal variability seems high. These characteristics can complicate modeling and other analyses aimed at risk assessment, policy actions, and exposure management. In addition to these analytic and statistical issues, exposure typically occurs as a mixture, and mixture components may interact or jointly contribute to adverse effects. However most pollutant regulations, guidelines, and studies remain focused on single compounds, and thus may underestimate cumulative exposures and risks arising from coexposures. In addition, the composition of VOC mixtures has not been thoroughly investigated, and mixture components show varying and complex dependencies. Finally, although many factors are known to affect VOC exposures, many personal, environmental, and socioeconomic determinants remain to be identified, and the significance and applicability of the determinants reported in the literature are uncertain. To help answer these unresolved questions and overcome limitations of previous analyses, this project used several novel and powerful statistical modeling and analysis techniques and two large data sets. The overall objectives of this project were (1) to identify and characterize exposure distributions (including extreme values), (2) evaluate mixtures (including dependencies), and (3) identify determinants of VOC exposure. METHODS VOC data were drawn from two large data sets: the Relationships of Indoor, Outdoor, and Personal Air (RIOPA) study (1999-2001) and the National Health and Nutrition Examination Survey (NHANES; 1999-2000). The RIOPA study used a convenience sample to collect outdoor, indoor, and personal exposure measurements in three cities (Elizabeth, NJ; Houston, TX; Los Angeles, CA). In each city, approximately 100 households with adults and children who did not smoke were sampled twice for 18 VOCs. In addition, information about 500 variables associated with exposure was collected. The NHANES used a nationally representative sample and included personal VOC measurements for 851 participants. NHANES sampled 10 VOCs in common with RIOPA. Both studies used similar sampling methods and study periods. Specific Aim 1. To estimate and model extreme value exposures, extreme value distribution models were fitted to the top 10% and 5% of VOC exposures. Health risks were estimated for individual VOCs and for three VOC mixtures. Simulated extreme value data sets, generated for each VOC and for fitted extreme value and lognormal distributions, were compared with measured concentrations (RIOPA observations) to evaluate each model's goodness of fit. Mixture distributions were fitted with the conventional finite mixture of normal distributions and the semi-parametric Dirichlet process mixture (DPM) of normal distributions for three individual VOCs (chloroform, 1,4-DCB, and styrene). Goodness of fit for these full distribution models was also evaluated using simulated data. Specific Aim 2. Mixtures in the RIOPA VOC data set were identified using positive matrix factorization (PMF) and by toxicologic mode of action. Dependency structures of a mixture's components were examined using mixture fractions and were modeled using copulas, which address correlations of multiple components across their entire distributions. Five candidate copulas (Gaussian, t, Gumbel, Clayton, and Frank) were evaluated, and the performance of fitted models was evaluated using simulation and mixture fractions. Cumulative cancer risks were calculated for mixtures, and results from copulas and multivariate lognormal models were compared with risks based on RIOPA observations. Specific Aim 3. Exposure determinants were identified using stepwise regressions and linear mixed-effects models (LMMs).

RESULTS

Specific Aim 1. Extreme value exposures in RIOPA typically were best fitted by three-parameter generalized extreme value (GEV) distributions, and sometimes by the two-parameter Gumbel distribution. In contrast, lognormal distributions significantly underestimated both the level and likelihood of extreme values. Among the VOCs measured in RIOPA, 1,4-dichlorobenzene (1,4-DCB) was associated with the greatest cancer risks; for example, for the highest 10% of measurements of 1,4-DCB, all individuals had risk levels above 10(-4), and 13% of all participants had risk levels above 10(-2). Of the full-distribution models, the finite mixture of normal distributions with two to four clusters and the DPM of normal distributions had superior performance in comparison with the lognormal models. DPM distributions provided slightly better fit than the finite mixture distributions; the advantages of the DPM model were avoiding certain convergence issues associated with the finite mixture distributions, adaptively selecting the number of needed clusters, and providing uncertainty estimates. Although the results apply to the RIOPA data set, GEV distributions and mixture models appear more broadly applicable. These models can be used to simulate VOC distributions, which are neither normally nor lognormally distributed, and they accurately represent the highest exposures, which may have the greatest health significance. Specific Aim 2. Four VOC mixtures were identified and apportioned by PMF; they represented gasoline vapor, vehicle exhaust, chlorinated solvents and disinfection byproducts, and cleaning products and odorants. The last mixture (cleaning products and odorants) accounted for the largest fraction of an individual's total exposure (average of 42% across RIOPA participants). Often, a single compound dominated a mixture but the mixture fractions were heterogeneous; that is, the fractions of the compounds changed with the concentration of the mixture. Three VOC mixtures were identified by toxicologic mode of action and represented VOCs associated with hematopoietic, liver, and renal tumors. Estimated lifetime cumulative cancer risks exceeded 10(-3) for about 10% of RIOPA participants. The dependency structures of the VOC mixtures in the RIOPA data set fitted Gumbel (two mixtures) and t copulas (four mixtures). These copula types emphasize dependencies found in the upper and lower tails of a distribution. The copulas reproduced both risk predictions and exposure fractions with a high degree of accuracy and performed better than multivariate lognormal distributions. Specific Aim 3. In an analysis focused on the home environment and the outdoor (close to home) environment, home VOC concentrations dominated personal exposures (66% to 78% of the total exposure, depending on VOC); this was largely the result of the amount of time participants spent at home and the fact that indoor concentrations were much higher than outdoor concentrations for most VOCs. In a different analysis focused on the sources inside the home and outside (but close to the home), it was assumed that 100% of VOCs from outside sources would penetrate the home. Outdoor VOC sources accounted for 5% (d-limonene) to 81% (carbon tetrachloride [CTC]) of the total exposure. Personal exposure and indoor measurements had similar determinants depending on the VOC. Gasoline-related VOCs (e.g., benzene and methyl tert-butyl ether [MTBE]) were associated with city, residences with attached garages, pumping gas, wind speed, and home air exchange rate (AER). Odorant and cleaning-related VOCs (e.g., 1,4-DCB and chloroform) also were associated with city, and a residence's AER, size, and family members showering. Dry-cleaning and industry-related VOCs (e.g., tetrachloroethylene [or perchloroethylene, PERC] and trichloroethylene [TCE]) were associated with city, type of water supply to the home, and visits to the dry cleaner. These and other relationships were significant, they explained from 10% to 40% of the variance in the measurements, and are consistent with known emission sources and those reported in the literature. Outdoor concentrations of VOCs had only two determinants in common: city and wind speed. Overall, personal exposure was dominated by the home setting, although a large fraction of indoor VOC concentrations were due to outdoor sources. City of residence, personal activities, household characteristics, and meteorology were significant determinants. Concentrations in RIOPA were considerably lower than levels in the nationally representative NHANES for all VOCs except MTBE and 1,4-DCB. Differences between RIOPA and NHANES results can be explained by contrasts between the sampling designs and staging in the two studies, and by differences in the demographics, smoking, employment, occupations, and home locations. (ABSTRACT TRUNCATED)

Determinants of personal, indoor and outdoor VOC concentrations: an analysis of the RIOPA data

Community and environmental exposure to volatile organic compounds (VOCs) has been associated with a number of emission sources and activities, e.g., environmental tobacco smoke and pumping gasoline. Such factors have been identified from mostly small studies with relatively limited information regarding influences on VOC levels. This study uses data from the Relationship of Indoor Outdoor and Personal Air (RIOPA) study to investigate environmental, individual and social determinants of VOC concentrations. RIOPA included outdoor, indoor and personal measurements of 18 VOCs from 310 non-smoking households and adults in three cities and two seasons, and collected a wide range of information pertaining to participants, family members, households, and neighborhoods. Exposure determinants were identified using stepwise regressions and linear mixed-effect models. Most VOC exposure (66 to 78% of the total exposure, depending on VOC) occurred indoors, and outdoor VOC sources accounted for 5 (d-limonene) to 81% (carbon tetrachloride) of the total exposure. Personal exposure and indoor measurements had similar determinants, which depended on the VOC. Gasoline-related VOCs (e.g., benzene, methyl tertiary butyl ether) were associated with city, residences with attached garages, self-pumping of gas, wind speed, and house air exchange rate (AER). Odorant and cleaning-related VOCs (e.g., 1,4-dichlorobenzene and chloroform) also were associated with city and AER, and with house size and family members showering. Dry-cleaning and industry-related VOCs (e.g., tetrachloroethylene and trichloroethylene) were associated with city, residence water supply type, and dry-cleaner visits. These and other relationships were significant, explained from 10 to 40% of the variation, and are consistent with known emission sources and the literature. Outdoor concentrations had only two common determinants: city and wind speed. Overall, personal exposure was dominated by the home setting, although a large fraction of VOC concentrations were due to outdoor sources. City, personal activities, household characteristics and meteorology were significant determinants.

Environmental and lifestyle factors affect benzene uptake biomonitoring of residents near a petrochemical plant

BACKGROUND

We monitored urinary benzene excretion to examine factors affecting benzene uptake in a sample of the general population living near a petrochemical plant.

METHODS

Our study population included 143 subjects: 33 petrochemical plant workers (W) with low level occupational benzene exposure; 30 residents in a small town 2 km from the plant (2kmR); 26 residents in a second small town located 2 to 4 km from the plant (4kmR); and 54 urban residents 25km from the plant (25kmR). Exposure to benzene was evaluated by personal air sampling during one work-shift for the W group, and from 8.00 to 20:00 for general population subgroups, and by urinary benzene (BEN-U).

RESULTS

Median airborne benzene exposure was 25, 9, 7 and 6 μg/m(3) benzene among the W, 2kmR, 4kmR, and 25kmR subgroups, respectively; the highest level was found among the workers, while there was no significant difference among the other groups. Median BEN-U was 2 to 14-fold higher in smokers compared to non-smokers; among non-smokers BEN-U was the highest in W (median 236 ng/L), and lower in the 2kmR (48 ng/L) and 4kmR (63 ng/L) subgroups than in the 25kmR (120 ng/L) subgroup. A multiple linear regression analysis, explaining up to 73% of BEN-U variability, confirmed that active smoking and airborne benzene most strongly affected BEN-U. Among the non-smoking, non-occupationally exposed study subjects, a positive association was found between BEN-U and the distance of residence from the plant. This association was explained by increased exposure to urban traffic emissions in the study group residing at a greater distance from the plant. Environmental tobacco smoke had a marginally positive role.

CONCLUSION

Among factors affecting benzene uptake in non-occupationally exposed individuals, urban residence contributes to benzene exposure more than residing in close proximity to a petrochemical plant.

Modelling inhalation exposure to combustion-related air pollutants in residential buildings: Application to health impact assessment

Buildings in developed countries are becoming increasingly airtight as a response to stricter energy efficiency requirements. At the same time, changes are occurring to the ways in which household energy is supplied, distributed and used. These changes are having important impacts on exposure to indoor air pollutants in residential buildings and present new challenges for professionals interested in assessing the effects of housing on public health. In many circumstances, models are the most appropriate way with which to examine the potential outcomes of future environmental and/or building interventions and policies. As such, there is a need to consider the current state of indoor air pollution exposure modelling. Various indoor exposure modelling techniques are available, ranging from simple statistical regression and mass-balance approaches, to more complex multizone and computational fluid dynamics tools that have correspondingly large input data requirements. This review demonstrates that there remain challenges which limit the applicability of current models to health impact assessment. However, these issues also present opportunities for better integration of indoor exposure modelling and epidemiology in the future. The final part of the review describes the application of indoor exposure models to health impact assessments, given current knowledge and data, and makes recommendations aimed at improving model predictions in the future.

Relationship of personal exposure to volatile organic compounds to home, work and fixed site outdoor concentrations

Personal exposures of 100 adult non-smokers living in the UK, as well as home and workplace microenvironment concentrations of 15 volatile organic compounds were investigated. The strength of the association between personal exposure and indoor home and workplace concentrations as well as with central site ambient air concentrations in medium to low pollution areas was assessed. Home microenvironment concentrations were strongly associated with personal exposures indicating that the home is the driving factor determining personal exposures to VOCs, explaining between 11 and 75% of the total variability. Workplace and central site ambient concentrations were less correlated with the corresponding personal concentrations, explaining up to 11-22% of the variability only at the low exposure end of the concentration range (e.g. benzene concentrations <2.5 μg m(-3)). One of the reasons for the discrepancies between personal exposures and central site data was that the latter does not account for exposure due to personal activities (e.g. commuting, painting). A moderate effect of season on the strength of the association between personal exposure and ambient concentrations was found. This needs to be taken into account when using fixed site measurements to infer exposures.

Comparative modeling approaches for personal exposure to particle-associated PAH

Several models for simulation of personal exposure (PE) to particle-associated polycyclic aromatic hydrocarbons (PAH) have been developed and tested. The modeling approaches include linear regression models (Model 1), time activity weighted models (Models 2 and 3), a hybrid model (Model 4), a univariate linear model (Model 5), and machine learning technique models (Model 6 and 7). The hybrid model (Model 4), which utilizes microenvironment data derived from time-activity diaries (TAD) with the implementation of add-on variables to account for external factors that might affect PE, proved to be the best regression model (R(2) for B(a)P = 0.346, p < 0.01; N = 68). This model was compared with results from two machine learning techniques, namely decision trees (Model 6) and neural networks (Model 7), which represent an innovative approach to PE modeling. The neural network model was promising in giving higher correlation coefficient results for all PAH (R(2) for B(a)P = 0.567, p < 0.01; N = 68) and good performance with the smaller test data set (R(2) for B(a)P = 0.640, p < 0.01; N = 23). Decision tree accuracies (Model 6) which assess how precisely the algorithm can determine the correct classification of a PE concentration range indicate good performance, but this is not comparable to the other models through R(2) values. Using neural networks (Model 7) showed significant improvements over the performance of hybrid Model 4 and the univariate general linear Model 5 for test samples (not used in developing the models). The worst performance was given by linear regression Models 1 to 3 based solely on home and workplace concentrations and time-activity data.

Environmental and biological monitoring of exposures to PAHs and ETS in the general population

The objective of this study was to analyse environmental tobacco smoke (ETS) and PAH metabolites in urine samples of non-occupationally exposed non-smoker adult subjects and to establish relationships between airborne exposures and urinary concentrations in order to (a) assess the suitability of the studied metabolites as biomarkers of PAH and ETS, (b) study the use of 3-ethenypyridine as ETS tracer and (c) link ETS scenarios with exposures to carcinogenic PAH and VOC. Urine samples from 100 subjects were collected and concentrations of monophenolic metabolites of naphthalene, fluorene, phenanthrene, and pyrene and the nicotine metabolites cotinine and trans-3'-hydroxycotinine were measured using liquid chromatography-tandem mass spectrometry (LC-MS/MS) to assess PAH and ETS exposures. Airborne exposures were measured using personal exposure samplers and analysed using GC-MS. These included 1,3-butadiene (BUT), 3-ethenylpyridine (3-EP) (a tobacco-specific tracer derived from nicotine pyrolysis) and PAHs. ETS was reported by the subjects in 30-min time-activity questionnaires and specific comments were collected in an ETS questionnaire each time ETS exposure occurred. The values of 3-EP (>0.25 microg/m(3) for ETS) were used to confirm the ETS exposure status of the subject. Concentrations as geometric mean, GM, and standard deviation (GSD) of personal exposures were 0.16 (5.50)microg/m(3) for 3-EP, 0.22 (4.28)microg/m(3) for BUT and 0.09 (3.03)ng/m(3) for benzo(a)pyrene. Concentrations of urinary metabolites were 0.44 (1.70)ng/mL for 1-hydroxypyrene and 0.88 (5.28)ng/mL for cotinine. Concentrations of urinary metabolites of nicotine were lower than in most previous studies, suggesting very low exposures in the ETS-exposed group. Nonetheless, concentrations were higher in the ETS population for cotinine, trans-3'hydroxycotinine, 3-EP, BUT and most high molecular weight PAH, whilst 2-hydroxyphenanthrene, 3+4-hydroxyphenanthrene and 1-hydroxyphenanthrene were only higher in the high-ETS subpopulation. There were not many significant correlations between either personal exposures to PAH and their urinary metabolites, or of the latter with ETS markers. However, it was found that the urinary log cotinine concentration showed significant correlation with log concentrations of 3-EP (R=0.75), BUT (R=0.47), and high molecular weight PAHs (MW>200), especially chrysene (R=0.55) at the p=0.01 level. On the other hand, low correlation was observed between the PAH metabolite 2-naphthol and the parent PAH, gas-phase naphthalene. These results suggest that (1) ETS is a significant source of inhalation exposure to the carcinogen 1,3-butadiene and high molecular weight PAHs, many of which are carcinogenic, and (2) that for lower molecular weight PAHs such as naphthalene, exposure by routes other than inhalation predominate, since metabolite levels correlated poorly with personal exposure air sampling.

Health risk assessment of exposure to selected volatile organic compounds emitted from an integrated iron and steel plant

Workplace air samples from sintering, cokemaking, and hot and cold forming processes in the integrated iron and steel industry were analyzed to determine their volatile organic compound (VOC) concentration. Sixteen VOC species including three paraffins (cyclohexane, n-hexane, methylcyclohexane), five chlorinated VOC species (trichloroethylene, 1,1,1-trichloroethane, tetrachloroethylene, chlorobenzene, 1,4-dichlorobenzene), and eight aromatics (benzene, ethylbenzene, styrene, toluene, m,p-xylene, o-xylene, 1,2,4-trimethylbenzene, 1,3,5-trimethylbenzene) were selected to measure their noncancer risk for workers. Concentrations of toluene, xylene, 1,2,4-trimethylbenzene, 1,3,5-trimethylbenzene, dichlorobenzene, and trichloroethylene were high in all four processes. Carbon tetrachloride and tetrachloroethylene concentrations were high in the hot and cold forming processes. The noncancer risk followed the increasing order: cokemaking > sintering > hot forming > cold forming. 1,2,4-trimethylbenzene and 1,3,5-trimethylbenzene contributed 44% to 65% and 13% to 20% of noncancer risk, respectively, for the four processes. Benzene accounted for a high portion of the noncancer risk in cokemaking. The hazard index (HI: 17-108) of the average VOC concentrations suggests that health risks can be reduced by improving workplace air quality and protecting workers.

Application of femtosecond laser mass spectrometry to the analysis of volatile organic compounds

Femtosecond (fs) lasers have high intensity and ultrashort pulse duration. Tunneling ionization occurs for molecules subject to such intense laser fields. We have studied the mass spectra of a variety of molecules irradiated by intense fs laser pulses. These molecules include some typical volatile organic compounds contained in human breath and in the atmosphere. The results demonstrate that all of these molecules can be ionized by intense fs laser pulses. Dominant parent ion and some characteristic ionic fragments are observed for each molecule. The degree of fragmentation can be controlled by adjusting the laser intensity. Moreover, saturation ionization can occur for each molecule by increasing the laser intensity. These features indicate that fs laser mass spectrometry can be a sensitive tool to identify and quantify volatile organic compounds in human breath.