Personal exposure to particles in Banská Bystrica, Slovakia

Real-Time Measurements of Indoor-Outdoor Exchange of Gaseous and Particulate Atmospheric Pollutants in an Urban Area

Air pollution is one of the greatest environmental risks to health, causing millions of deaths and deleterious health effects worldwide, especially in urban areas where citizens are exposed to high ambient levels of pollutants, also influencing indoor air quality (IAQ). Many sources of indoor air are fairly obvious and well known, but the contribution of outside sources to indoor air still leads to significant uncertainties, in particular the influence that environmental variables have on outdoor/indoor pollutant exchange mechanisms. This is a critical aspect to consider in IAQ studies. In this respect, an experimental study was performed at a public site such as a university classroom during a non-academic period in Madrid city. This includes two field campaigns, in summer (2021) and winter (2020), where instruments for measuring gases and particle air pollutants simultaneously measured outdoor and indoor real-time concentrations. This study aimed to investigate the dynamic variations in the indoor/outdoor (I/O) ratios in terms of ambient outdoor conditions (meteorology, turbulence and air quality) and indoor features (human presence or natural ventilation). The results show that the I/O ratio is pollutant-dependent. In this sense, the infiltration capacity is higher for gaseous compounds, and in the case of particles, it depends on the particle size, with a higher infiltration capacity for smaller particles ( Collapse

Key Words

• gaseous and particulate pollutants
• indoor activities
• indoor air quality (IAQ)
• real-time I/O measurements
• urban air pollutants

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MESH Headings

• Humans
• Particulate Matter/analysis
• Environmental Pollutants
• Gases
• Environmental Monitoring/methods
• Air Pollutants/analysis
• Particle Size
• Air Pollution, Indoor/analysis

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Affiliation(s)

Elisabeth Alonso-Blanco Department of Environment, CIEMAT (Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas), 28040 Madrid, Spain; (F.J.G.-M.); (E.D.-R.); (J.F.); (E.C.); (B.A.)
Francisco Javier Gómez-Moreno Department of Environment, CIEMAT (Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas), 28040 Madrid, Spain; (F.J.G.-M.); (E.D.-R.); (J.F.); (E.C.); (B.A.)
Elías Díaz-Ramiro Department of Environment, CIEMAT (Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas), 28040 Madrid, Spain; (F.J.G.-M.); (E.D.-R.); (J.F.); (E.C.); (B.A.)
Javier Fernández Department of Environment, CIEMAT (Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas), 28040 Madrid, Spain; (F.J.G.-M.); (E.D.-R.); (J.F.); (E.C.); (B.A.)
Esther Coz Department of Environment, CIEMAT (Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas), 28040 Madrid, Spain; (F.J.G.-M.); (E.D.-R.); (J.F.); (E.C.); (B.A.)
Carlos Yagüe Department of Earth Physics and Astrophysics, Complutense University of Madrid, 28040 Madrid, Spain;
Carlos Román-Cascón Department of Applied Physics, Marine and Environmental Sciences Faculty, INMAR, CEIMAR, University of Cadiz, 11519 Puerto Real, Cádiz, Spain;
Adolfo Narros Department of Chemical and Environmental Engineering, Technical University of Madrid (UPM), 28006 Madrid, Spain; (A.N.); (R.B.)
Rafael Borge Department of Chemical and Environmental Engineering, Technical University of Madrid (UPM), 28006 Madrid, Spain; (A.N.); (R.B.)
Begoña Artíñano Department of Environment, CIEMAT (Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas), 28040 Madrid, Spain; (F.J.G.-M.); (E.D.-R.); (J.F.); (E.C.); (B.A.)

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Comparing human exposure to fine particulate matter in low and high-income countries: A systematic review of studies measuring personal PM2.5 exposure

BACKGROUND

Due to the adverse health effects of air pollution, researchers have advocated for personal exposure measurements whereby individuals carry portable monitors in order to better characterise and understand the sources of people's pollution exposure.

OBJECTIVES

The aim of this systematic review is to assess the differences in the magnitude and sources of personal PM2.5 exposures experienced between countries at contrasting levels of income.

METHODS

This review summarised studies that measured participants personal exposure by carrying a PM2.5 monitor throughout their typical day. Personal PM2.5 exposures were summarised to indicate the distribution of exposures measured within each country income category (based on low (LIC), lower-middle (LMIC), upper-middle (UMIC), and high (HIC) income countries) and between different groups (i.e. gender, age, urban or rural residents).

RESULTS

From the 2259 search results, there were 140 studies that met our criteria. Overall, personal PM2.5 exposures in HICs were lower compared to other countries, with UMICs exposures being slightly lower than exposures measured in LMICs or LICs. 34% of measured groups in HICs reported below the ambient World Health Organisation 24-h PM2.5 guideline of 15 μg/m3, compared to only 1% of UMICs and 0% of LMICs and LICs. There was no difference between rural and urban participant exposures in HICs, but there were noticeably higher exposures recorded in rural areas compared to urban areas in non-HICs, due to significant household sources of PM2.5 in rural locations. In HICs, studies reported that secondhand smoke, ambient pollution infiltrating indoors, and traffic emissions were the dominant contributors to personal exposures. While, in non-HICs, household cooking and heating with biomass and coal were reported as the most important sources.

CONCLUSION

This review revealed a growing literature of personal PM2.5 exposure studies, which highlighted a large variability in exposures recorded and severe inequalities in geographical and social population subgroups.

Difference in ambient-personal exposure to PM2.5 and its inflammatory effect in local residents in urban and peri-urban Beijing, China: results of the AIRLESS project

Measurement of ambient fine particulate matter (PM_2.5) is often used as a proxy of personal exposure in epidemiological studies. However, the difference between personal and ambient exposure, and whether it biases the estimates of health effects remain unknown. Based on an epidemiological study (AIRLESS) and simultaneously launched intensive monitoring campaigns (APHH), we quantified and compared the personal and ambient exposure to PM_2.5 and the related health impact among residents in Beijing, China. In total, 123 urban and 128 peri-urban non-smoking participants were recruited from two well-established cohorts in Beijing. During winter 2016 and summer 2017, each participant was instructed to carry a validated personal air monitor (PAM) to measure PM_2.5 concentration at high spatiotemporal resolution for seven consecutive days in each season. Multiple inflammatory biomarkers were measured, including exhaled NO, blood monocytes counts and C-reactive protein. Linear mixed-effect models were used for the associations between exposure and health outcomes with adjustment for confounders. The average level of daily personal exposure to PM_2.5 was consistently lower than using corresponding ambient concentration, and the difference is greater during the winter. The personal to ambient (P/A) ratio of exposure to PM_2.5 exhibited an exponentially declining trend, and showed larger variations when ambient PM_2.5 levels < 25 μg m^-3. Personal exposure to PM_2.5 was significantly associated with the increase in respiratory and systemic inflammatory biomarkers; however, the associations were weaker or became insignificant when ambient concentrations were used. Exposure to ambient PM_2.5 might not be a good proxy to estimate the health effect of exposure to personal PM_2.5.

Indoor Exposure to Selected Air Pollutants in the Home Environment: A Systematic Review

(1) Background: There is increasing awareness that the quality of the indoor environment affects our health and well-being. Indoor air quality (IAQ) in particular has an impact on multiple health outcomes, including respiratory and cardiovascular illness, allergic symptoms, cancers, and premature mortality. (2) Methods: We carried out a global systematic literature review on indoor exposure to selected air pollutants associated with adverse health effects, and related household characteristics, seasonal influences and occupancy patterns. We screened records from six bibliographic databases: ABI/INFORM, Environment Abstracts, Pollution Abstracts, PubMed, ProQuest Biological and Health Professional, and Scopus. (3) Results: Information on indoor exposure levels and determinants, emission sources, and associated health effects was extracted from 141 studies from 29 countries. The most-studied pollutants were particulate matter (PM_2.5 and PM₁₀); nitrogen dioxide (NO₂); volatile organic compounds (VOCs) including benzene, toluene, xylenes and formaldehyde; and polycyclic aromatic hydrocarbons (PAHs) including naphthalene. Identified indoor PM_2.5 sources include smoking, cooking, heating, use of incense, candles, and insecticides, while cleaning, housework, presence of pets and movement of people were the main sources of coarse particles. Outdoor air is a major PM_2.5 source in rooms with natural ventilation in roadside households. Major sources of NO₂ indoors are unvented gas heaters and cookers. Predictors of indoor NO₂ are ventilation, season, and outdoor NO₂ levels. VOCs are emitted from a wide range of indoor and outdoor sources, including smoking, solvent use, renovations, and household products. Formaldehyde levels are higher in newer houses and in the presence of new furniture, while PAH levels are higher in smoking households. High indoor particulate matter, NO₂ and VOC levels were typically associated with respiratory symptoms, particularly asthma symptoms in children. (4) Conclusions: Household characteristics and occupant activities play a large role in indoor exposure, particularly cigarette smoking for PM_2.5, gas appliances for NO₂, and household products for VOCs and PAHs. Home location near high-traffic-density roads, redecoration, and small house size contribute to high indoor air pollution. In most studies, air exchange rates are negatively associated with indoor air pollution. These findings can inform interventions aiming to improve IAQ in residential properties in a variety of settings.

PM2.5 and NO2 exposure errors using proxy measures, including derived personal exposure from outdoor sources: A systematic review and meta-analysis

BACKGROUND

The use of proxy exposure estimates for PM_2.5 and NO₂ in air pollution studies instead of personal exposures, introduces measurement error, which can produce biased epidemiological effect estimates. Most studies consider total personal exposure as the gold standard. However, when studying the effects of ambient air pollution, personal exposure from outdoor sources is the exposure of interest.

OBJECTIVES

We assessed the magnitude and variability of exposure measurement error by conducting a systematic review of the differences between personal exposures from outdoor sources and the corresponding measurements for ambient concentrations in order to increase understanding of the measurement error structures of the pollutants.

DATA SOURCES AND ELIGIBILITY CRITERIA

We reviewed the literature (ISI Web of Science, Medline, 2000-2016) for English language studies (in any age group in any location (NO₂) or Europe and North America (PM_2.5)) that reported repeated measurements over time both for personal and ambient PM_2.5 or NO₂ concentrations. Only a few studies reported personal exposure from outdoor sources. We also collected data for infiltration factors and time-activity patterns of the individuals in order to estimate personal exposures from outdoor sources in every study.

STUDY APPRAISAL AND SYNTHESIS METHODS

Studies using modelled rather than monitored exposures were excluded. Type of personal exposure monitor was assessed. Random effects meta-analysis was conducted to quantify exposure error as the mean difference between "true" and proxy measures.

RESULTS

Thirty-two papers for PM_2.5 and 24 for NO₂ were identified. Outdoor sources were found to contribute 44% (range: 33-55%) of total personal exposure to PM_2.5 and 74% (range: 57-88%) to NO₂. Overall estimates of personal exposure (24-hour averages) from outdoor sources were 9.3 μg/m³ and 12.0 ppb for PM_2.5 and NO₂ respectively, while the corresponding difference between these exposures and the ambient concentrations (i.e. the measurement error) was 5.72 μg/m³ and 7.17 ppb. Our findings indicated also higher error variability for NO₂ than PM_2.5. Large heterogeneity was observed which was not explained sufficiently by geographical location or age group of the study sample.

LIMITATIONS, CONCLUSIONS AND IMPLICATIONS OF KEY FINDINGS

Relying only on information available in published studies led to some limitations: the contribution of outdoor sources to total personal exposure for NO₂ had to be inferred, individual variation in exposure misclassification was unavailable and instrument error could not be addressed. The larger magnitude and variability of errors for NO₂ compared with PM_2.5 has implications for biases in the health effect estimates of multi-pollutant epidemiological models. Results suggest that further research is needed regarding personal exposure studies and measurement error bias in epidemiological models.

Concentrations, sources and health risk of nitrated- and oxygenated-polycyclic aromatic hydrocarbon in urban indoor air and dust from four cities of Nepal

Although the fate and behavior of parent polycyclic aromatic hydrocarbon (PAHs) have been documented worldwide, the information about PAH-derivatives (NPAHs and OPAHs) is limited, especially in developing countries, including Nepal. Moreover, the greater parts of the investigations concentrating on NPAHs/OPAHs are on the air (borne) particulate phase only; and are primarily based on a limited number of compounds analyzed. Little is known about the environmental concentration, fate, and behavior of NPAHs and OPAHs in air gas phase and dust. In this study, the concentration, fate, spatial distributions of 26 NPAHs and 3 OPAHs in the air (n = 34) and dust (n = 24) were investigated in suspected source area/more densely populated areas of Nepal. Four critical source areas in Nepal were considered as it was conjectured that the urban areas are more prone to NPAH/OPAH contamination due to the high density of automobiles and industrial activities. Overall, the measured ∑₁₉NPAHs in air and dust were 5 and 2 times lower than their parent-PAHs, respectively. Highest levels of NPAHs/OPAHs were measured in Birgunj, followed by Kathmandu, Biratnagar, and Pokhara, respectively, while Biratnagar showed the highest level of ∑OPAHs. 3-Nitrodibenzofuran (3-NDBF) was the most abundant NPAHs measured both in air and dust, whereas 9-Fluorenone (9-FLUONE) prevailing OPAHs. The molecular diagnostic ratio (MDR) of 2-Nitrofluoranthene/1-Nitropyrene indicated the contribution from secondary emission via photochemical reaction as the primary source of NPAHs, while solid fuel combustion and crop residue burning were identified as the essential sources of OPAHs. The human exposure to NPAH/OPAH through the different route of intake suggested dermal contact via dust as the primary pathway of NPAH/OPAH exposure for both adult and children. However, other routes of exposure, for instance, dietary intake or dermal absorption via soil may still be prominent in case of Nepal.

Chemical composition of outdoor and indoor PM2.5 collected during haze events: Transformations and modified source contributions resulting from outdoor-to-indoor transport

Changes in the chemical constitution and sources of ambient PM_2.5 following the infiltration of air into indoor environments were investigated. We collected PM_2.5 samples from air inside and outside 31 rooms in Beijing residences during hazy episodes. We calculated the indoor-to-outdoor ratios and the correction (kⁱ ) of each infiltration factor for each chemical component of PM_2.5 to determine the effects of infiltrative behavior. The outdoor and indoor mass concentrations of PM_2.5 during the sampling period were 70-460 and 10-315 μg/m³ , respectively. Differences in the average indoor-to-outdoor ratios of PM_2.5 mass and each component (mean value ± standard deviation: PM_2.5 mass = 0.53 ± 0.26, organic matter = 0.75 ± 0.34, elemental carbon = 0.62 ± 0.31, trace elements = 0.62 ± 0.26, SO 4 2 - = 0.67 ± 0.32 , NH 4 + = 0.53 ± 0.54 , NO 3 - = 0.45 ± 0.36 , Cl^- = 0.37 ± 0.35, and crustal dust = 0.30 ± 0.19) may be attributed to size distribution, chemical properties, temperature, and humidity. The positive matrix factorization model was applied to calculate the source contributions to equivalent population exposure (Indoor concentration·Indoor time fraction + Outdoor concentration·Outdoor time fraction). The contributions of fossil fuel combustion, secondary source, vehicle exhaust, and mixed dust to the equivalent PM_2.5 population source exposure were 37%, 24%, 22%, and 17%, respectively.

Environmental impacts of commuting modes in Lisbon: a life-cycle assessment addressing particulate matter impacts on health

A life-cycle assessment of commuting alternatives is conducted that compares six transportation modes (car, bus, train, subway, motorcycle and bicycle) for eight impact indicators. Fine particulate matter (PM_2.5) emissions and health impacts are incorporated in the assessment using intake fractions that differentiate between urban and non-urban emissions, combined with an effect factor. The potential benefits of different strategies for reducing environmental impacts are illustrated. The results demonstrate the need for comprehensive approaches that avoid problem-shifting among transportation-related strategies. Policies aiming to improve the environmental performance of urban transportation should target strategies that decrease local emissions, life-cycle impacts and health effects.

Reduction in population exposure to PM2.5 and cancer risk due to PM2.5-bound PAHs exposure in Beijing, China during the APEC meeting

Radical measures for controlling ambient air pollution sources were employed by the Chinese government during the Asia-Pacific Economic Cooperation (APEC) meeting in 2014, providing a unique case to evaluate the health effect benefits from such measures. To examine the cancer risk reduction from the source control measures during the APEC meeting, we estimated the reduction in population exposure to PM_2.5 and PAHs and the reduction in PAHs-associated cancer risk if the control measures were sustained over time. We determined the population exposure to PM_2.5 and PM_2.5-bound PAHs for the 21.52 million Beijing residents using a Land Use Regression model to determine the spatial distribution of PM_2.5 and a Monte Carlo approach to revise indoor/outdoor infiltration factor and time activity patterns. Into the model and approach, we incorporated the spatial variance and indoor/outdoor differences in the PM_2.5 and PM_2.5-bound PAHs concentrations, based on measurements. We then estimated lung cancer risk using the population attributable fraction (PAF), assuming the control measures were sustained over time. The mean PM_2.5 exposure concentration decreased from 37.5 μg/m3 (CI:17.1-74.9 μg/m3) to 24.0 μg/m3 (CI:10.2-47.7 μg/m3), whereas the mean PM_2.5-bound equivalent benzo[a]pyrene (BaP_eq) exposure concentration decreased from 7.1 ng/m³ (CI:3.3-14.2 ng/m³) to 4.2 ng/m³ (CI:1.8-7.7 ng/m³), resulting in a reduction in the lung cancer PAF from 0.75% to 0.45%, if the measures were sustained over time.

Estimating the concentration of indoor particles of outdoor origin: a review

Recent toxicological results highlight the importance of separating exposure to indoor- and outdoor-generated particles, due to their different physicochemical and toxicological properties. In this framework, a number of studies have attempted to estimate the relative contribution of particles of indoor and outdoor origins to indoor concentrations, using either statistical analysis of indoor and outdoor concentration time-series or mass balance equations. The aim of this work is to review and compare the methodologies developed in order to determine the ambient particle infiltration factor (F(INF)) (i.e., the fraction of ambient particles that enter indoors and remains suspended). The different approaches are grouped into four categories according to their methodological principles: (1) steady-state assumption using the steady-state form of the mass balance equation; (2) dynamic solution of the mass balance equation using complex statistical techniques; (3) experimental studies using conditions that simplify model calculations (e.g., decreasing the number of unknowns); and (4) infiltration surrogates using a particulate matter (PM) constituent with no indoor sources to act as surrogate of indoor PM of outdoor origin. Examination of the various methodologies and results reveals that estimating infiltration parameters is still challenging. The main difficulty lies in the separate calculation of penetration efficiency (P) and deposition rate (k). The values for these two parameters that are reported in the literature vary significantly. Deposition rate presents the widest range of values, both between studies and size fractions. Penetration efficiency seems to be more accurately calculated through the application of dynamic models. Overall, estimates of the infiltration factor generated using dynamic models and infiltration surrogates show good agreement. This is a strong argument in favor of the latter methodology, which is simple and easy to apply when chemical speciation data are available.

Predictors of indoor fine particulate matter in infants' bedrooms in Denmark

BACKGROUND

Particulate matter (PM) in ambient air is responsible for adverse health effects in adults and children. Relatively little is known about the concentrations, sources and health effects of PM in indoor air.

OBJECTIVE

To identify sources of fine PM in infants' bedrooms.

METHODS

We conducted 1122 measurements of fine PM (PM(2.5) and black smoke) in the bedrooms of 389 infants and registered indoor activities and characteristics of the house. We used mixed models to identify and quantify associations between predictors and concentrations.

RESULTS

The concentration of PM(2.5) was 2.8 times (95% confidence interval [CI], 1.4-5.5 times) higher in houses where people smoked; the concentration increased by 19% (95% CI, 15-23%) per doubling of the amount of tobacco smoked and decreased by 16% (95% CI, 9-27%) per 5-m increase in the distance between the smoking area and the infant's bedroom. Frying without a range hood was associated with a 32% (95% CI, 12-54%) higher PM(2.5) concentration per time per day, whereas frying with use of a range hood did not increase the concentration in the infant's bedroom. Use of a fireplace, stove, candles or vacuum-cleaner, interior rebuilding or renovation, local traffic, inner city residence and cold season increased the fine PM concentration. Open windows decreased the PM(2.5) concentration in homes with smokers but increased the concentration in non-smoking homes.

CONCLUSIONS

We identified several sources of fine PM in infants' bedrooms. The concentrations can be reduced by use of a range hood for frying, by not using candles, a fireplace or a stove, by increasing the distance between the bedroom and the smoking area and by opening windows in houses of smokers. Smoking is a strong predictor of fine PM in infants' bedrooms and should be avoided.

Personal PM10 exposure in asthmatic adults in Padova, Italy: seasonal variability and factors affecting individual concentrations of particulate matter

Personal exposure to PM(10) measured in different seasons in a sample of asthmatic subjects living in Padova (Northern Italy) was compared with simultaneously measured outdoor PM(10) concentrations. The specific contribution of ambient PM(10) and other factors to individual exposure was evaluated in one of the areas of Europe with the worst air pollution. Thirty-one asthmatic subjects (21 non-smokers and 10 smokers) carried personal PM(10) monitors for six 24-hr sessions, in different seasons of the year. Concomitant daily 24-hr ambient PM(10) concentrations were measured by air quality monitoring networks. A multivariate analysis was performed to identify factors explaining personal exposure to PM(10), using a random effect model. The analysis on the 31 subjects referred to a total of 155 observations. The mean personal PM(10) exposure was higher (range 79.3-126.1microg/m(3)) than the outdoor concentrations (range 37.3-85.4microg/m(3)) in all seasons; and personal exposures varied less than outdoor PM(10) levels from one season to another. Smokers had significantly higher personal PM(10) concentrations than non-smokers (127.99 vs 78.8microg/m(3); T=-5.70; p<0.001). Moderate correlations emerged between outdoor and personal PM(10) concentrations. The correlation improved after excluding subjects exposed to active or passive smoking (median Pearson's R 0.41 vs 0.26). Considering all the subjects, smoking was the main factor affecting personal exposure, contributing to 41% of the variability. Outdoor PM(10) concentrations (25%), temperature (12%) and season (15%) also contributed to personal PM(10) exposure. Outdoor PM(10) (46%), temperature (20%), season (19%) and time spent indoors (6%) were significantly associated with personal exposure in non-smokers. We concluded that it is crucial to perform personal monitoring and to evaluate the complexity of factors that contribute to individual PM exposure. While tobacco smoke was the primary source of PM(10) in all subjects, the contribution of ambient components was particularly relevant for the personal exposure levels of our non-smokers living in a highly-polluted environment.

A systematic review of the relation between long-term exposure to ambient air pollution and chronic diseases

We conducted a systematic review of all studies published between 1950 and 2007 of associations between long-term exposure to ambient air pollution and the risks in adults of nonaccidental mortality and the incidence and mortality from cancer and cardiovascular and respiratory diseases. We searched bibliographic databases for cohort and case-control studies, abstracted characteristics of their design and conduct, and synthesized the quantitative findings in tabular and graphic form. We assessed heterogeneity, estimated pooled effects for specific pollutants, and conducted sensitivity analyses according to selected characteristics of the studies. Our analysis showed that long-term exposure to PM2.5 increases the risk of nonaccidental mortality by 6% per a 10 microg/m3 increase, independent of age, gender, and geographic region. Exposure to PM2.5 was also associated with an increased risk of mortality from lung cancer (range: 15% to 21% per a 10 microg/m3 increase) and total cardiovascular mortality (range: 12% to 14% per a 10 microg/m3 increase). In addition, living close to busy traffic appears to be associated with elevated risks of these three outcomes. Suggestive evidence was found that exposure to PM2.5 is positively associated with mortality from coronary heart diseases and exposure to SO2 increases mortality from lung cancer. For the other pollutants and health outcomes, the data were insufficient data to make solid conclusions.

Personal exposure to PM2.5, black smoke and NO2 in Copenhagen: relationship to bedroom and outdoor concentrations covering seasonal variation

Epidemiological studies have found negative associations between human health and particulate matter in urban air. In most studies outdoor monitoring of urban background has been used to assess exposure. In a field study, personal exposure as well as bedroom, front door and background concentrations of PM(2.5), black smoke (BS), and nitrogen dioxide (NO(2)) were measured during 2-day periods in 30 subjects (20-33 years old) living and studying in central parts of Copenhagen. The measurements were repeated in the four seasons. Information on indoor exposure sources such as environmental tobacco smoke (ETS) and burning of candles was collected by questionnaires. The personal exposure, the bedroom concentration and the front door concentration was set as outcome variable in separate models and analysed by mixed effect model regression methodology, regarding subject levels as a random factor. Seasons were defined as a dichotomised grouping of outdoor temperature (above and below 8 degrees C). For NO(2) there was a significant association between personal exposure and both the bedroom, the front door and the background concentrations, whereas for PM(2.5) and BS only the bedroom and the front door concentrations, and not the background concentration, were significantly associated to the personal exposure. The bedroom concentration was the strongest predictor of all three pollution measurements. The association between the bedroom and front door concentrations was significant for all three measurements, and the association between the front door and the background concentrations was significant for PM(2.5) and NO(2), but not for BS, indicating greater spatial variation for BS than for PM(2.5) and NO(2). For NO(2), the relationship between the personal exposure and the front door concentration was dependent upon the "season", with a stronger association in the warm season compared with the cold season, and for PM(2.5) and BS the same tendency was seen. Time exposed to burning of candles was a significant predictor of personal PM(2.5), BS and NO(2) exposure, and time exposed to ETS only associated with personal PM(2.5) exposure. These findings imply that the personal exposure to PM(2.5), BS and NO(2) depends on many factors besides the outdoor levels, and that information on, for example, time of season or outdoor temperature and residence exposure, could improve the accuracy of the personal exposure estimation.

Development of the PEARLS model (Particulate Exposure from Ambient to Regional Lung by Subgroup) and use of Monte Carlo simulation to predict internal exposure to 2.5 in Toronto

Air pollution is a current and growing concern for Canadians, and there is evidence that ambient levels that meet current exposure standards may be associated with mortality and morbidity in Toronto, Canada. Evaluating exposure is an important step in understanding the relationship between particulate matter (PM) exposure and health outcomes. This report describes the PEARLS model (Particulate Exposure from Ambient to Regional Lung by Subgroup), which predicts exposure distributions for 11 age-gender population subgroups in Toronto to PM2.5 (PM with a median aerodynamic diameter of 2.5 microm or less) using Monte Carlo simulation techniques. The model uses physiological and activity pattern characteristics of each subgroup to determine region-specific lung exposure to PM2.5, which is defined as the mass of PM2.5 deposited per unit time to each of five lung regions (two extrathoracic, bronchial, bronchiolar, and alveolar). The modeling results predict that children, toddlers, and infants have the broadest distributions of exposure, and the greatest chance of experiencing extreme exposures in the alveolar region of the lung. Importance analysis indicates that the most influential model variables are air exchange rate into indoor environments, time spent outdoors, and time spent at high activity levels. Additionally, a "critical point" was defined and introduced to the PEARLS to investigate the effects of possible threshold-pathogenic phenomena on subgroup exposure patterns. The analysis indicates that the subgroups initially predicted to be most highly exposed were likely to have the highest proportion of their population exposed above the critical point. Substantial exposures above the critical point were predicted in all subgroups for ambient concentrations of PM2.5 commonly observed in Toronto after continuous exposure of 24 hours or more.

Personal exposure to particulate matter less than 2.5 microm in Mexico City: a pilot study

This study was aimed to describe the personal exposure of permanent residents in Mexico City's Metropolitan Area (MCMA) to particulate matter of less than 2.5 microm diameter (PM(2.5)) during their daily activities. A total of 40 healthy volunteers (30 women and 10 men) with sedentary activities were included. All of them carried a PM(2.5) personal monitor during 13 h and registered their activities in a written diary that classified them in indoor and outdoor microenvironments in each 30 min period. All sample collections started at 0900 hours, and even though measurements were obtained during the rainy season (April-August 2002), the relative humidity was less than 70%. The data were categorized and evaluated under the following criteria: morning and afternoon exposure, indoor and outdoor activities, and geographical location. The descriptive analysis showed that the overall outdoor median concentration of PM(2.5) (89.50 microg/m(3)) was higher than the indoor one (67.55 microg/m(3)). PM(2.5) concentrations in the morning to early afternoon were more elevated than in the late afternoon, suggesting a circadian-like behavior. In the indoor microenvironment, the highest concentration occurred in the subway (106.2 microg/m(3)) followed by school (93.27 microg/m(3)), and the lowest at home (53.1 microg/m(3)). The outdoor microenvironment with the highest concentrations was the public transportation (bus) (99.95 microg/m(3)), while the automobile had the lowest (64.9 microg/m(3)). The geographical zone with the highest concentration was the Center city area (87.87 microg/m(3)), and the one with the lowest concentration was the northeast area of the city (50 microg/m(3)). All the differences were statistically significant (P<0.05). Multivariate analysis corroborated that PM(2.5) concentrations are mainly determined by geographical locations and hour of the day, but not by the type of microenvironment. The inclusion of covariables in the multivariable analysis ensures a more accurate estimation and prediction of the real PM(2.5) concentrations. In conclusion, PM(2.5) personal exposure of healthy adult permanent residents of MCMA is usually higher than recommended by the international standards in outdoor and even in indoor microenvironments. Particulate matter personal exposure varies in relation to hour of the day, daily activities and microenvironments.

Linking exposure to environmental pollutants with biological effects

Exposure to ambient air pollution has been associated with cancer. Ambient air contains a complex mixture of toxics, including particulate matter (PM) and benzene. Carcinogenic effects of PM may relate both to the content of PAH and to oxidative DNA damage generated by transition metals, benzene, metabolism and inflammation. By means of personal monitoring and biomarkers of internal dose, biologically effective dose and susceptibility, it should be possible to characterize individual exposure and identify air pollution sources with relevant biological effects. In a series of studies, individual exposure to PM(2.5), nitrogen dioxide (NO(2)) and benzene has been measured in groups of 40-50 subjects. Measured biomarkers included 1-hydroxypyrene, benzene metabolites (phenylmercapturic acid (PMA) and trans-trans-muconic acid (ttMA)), 8-oxo-7,8-dihydro-2'-deoxyguanosine (8-oxodG) in urine, DNA strand breaks, base oxidation, 8-oxodG and PAH bulky adducts in lymphocytes, markers of oxidative stress in plasma and genotypes of glutathione transferases (GSTs) and NADPH:quinone reductase (NQO1). With respect to benzene, the main result indicates that DNA base oxidation is correlated with PMA excretion. With respect to exposure to PM, biomarkers of oxidative damage showed significant positive association with the individual exposure. Thus, 8-oxodG in lymphocyte DNA and markers of oxidative damage to lipids and protein in plasma associated with PM(2.5) exposure. Several types of DNA damage showed seasonal variation. PAH adduct levels, DNA strand breaks and 8-oxodG in lymphocytes increased significantly in the summer period, requiring control of confounders. Similar seasonal effects on strand breaks and expression of the relevant DNA repair genes ERCC1 and OGG1 have been reported. In the present setting, biological effects of air pollutants appear mainly related to oxidative stress via personal exposure and not to urban background levels. Future developments include personal time-resolved monitors for exposure to ultrafine PM and PM(2.5,) use of GPS, as well as genomics and proteomics based biomarkers.

Estimating long-term average particulate air pollution concentrations: application of traffic indicators and geographic information systems

BACKGROUND

As part of a multicenter study relating traffic-related air pollution with incidence of asthma in three birth cohort studies (TRAPCA), we used a measurement and modelling procedure to estimate long-term average exposure to traffic-related particulate air pollution in communities throughout the Netherlands; in Munich, Germany; and in Stockholm County, Sweden.

METHODS

In each of the three locations, 40-42 measurement sites were selected to represent rural, urban background and urban traffic locations. At each site and fine particles and filter absorbance (a marker for diesel exhaust particles) were measured for four 2-week periods distributed over approximately 1-year periods between February 1999 and July 2000. We used these measurements to calculate annual average concentrations after adjustment for temporal variation. Traffic-related variables (eg, population density and traffic intensity) were collected using Geographic Information Systems and used in regression models predicting annual average concentrations. From these models we estimated ambient air concentrations at the home addresses of the cohort members.

RESULTS

Regression models using traffic-related variables explained 73%, 56% and 50% of the variability in annual average fine particle concentrations for the Netherlands, Munich and Stockholm County, respectively. For filter absorbance, the regression models explained 81%, 67% and 66% of the variability in the annual average concentrations. Cross-validation to estimate the model prediction errors indicated root mean squared errors of 1.1-1.6 microg/m for PM(2.5) and 0.22-0.31 *10(-5) m for absorbance.

CONCLUSIONS

A substantial fraction of the variability in annual average concentrations for all locations was explained by traffic-related variables. This approach can be used to estimate individual exposures for epidemiologic studies and offers advantages over alternative techniques relying on surrogate variables or traditional approaches that utilize ambient monitoring data alone.