Bayesian hierarchical modeling of personal exposure to particulate matter

In the US EPA's 1998 Baltimore Epidemiology-Exposure Panel Study, a group of 16 residents of a single building retirement community wore personal monitors recording personal fine particulate air pollution concentrations (PM_2.5) for 27 days, while other monitors recorded concurrent apartment, central indoor, outdoor and ambient site PM_2.5 concentrations. Using the Baltimore panel study data, we develop a Bayesian hierarchical model to characterize the relationship between personal exposure and concentrations of PM_2.5 indoors and outdoors. Personal exposure is expressed as a linear combination of time spent in microenvironments and associated microenvironmental concentrations. The model incorporates all available monitoring data and accounts for missing data and sources of uncertainty such as measurement error and individual differences in exposure. We discuss the implications of using personal versus ambient PM_2.5 measurements in characterization of personal exposure to PM_2.5.     

Modeling the space/time distribution of particulate matter in Thailand and optimizing its monitoring network

The space/time distribution of PM₁₀ in Thailand is modeled using the Bayesian maximum entropy (BME) method of modern spatiotemporal geostatistics. Three kinds of BME spatiotemporal maps over Thailand are sought on the most polluted day for each year of a 6-year period from 1998 to 2003. These three maps are (1) the map of the BME estimate of daily PM₁₀, (2) the map of the associated BME prediction error, and (3) the BME non-attainment map showing areas where the BME estimate does not attain a 68% probability of meeting the ambient standard for PM₁₀. These detailed space/time PM₁₀ maps provide invaluable information for decision-makers in air quality management. Knowing accurately the spatiotemporal distribution of PM₁₀ is necessary to develop and evaluate strategies used to abate PM₁₀ levels. The space/time BME estimate of PM₁₀ on the worst day of the year offers a general picture as to where daily PM₁₀ levels are not in compliance with the air-quality standard. Delineating these areas leads to the BME non-attainment maps, which are useful in identifying unhealthy zones, where sensitive population such as asthmatic children, seniors, or those with cardiopulmonary disease should be advised to avoid outdoor activities. The results of the space/time BME analysis of PM₁₀ are further extended to assess whether the current monitoring network is adequate. The current distribution of monitoring stations can be evaluated by combining the available demographic information with the BME estimation error maps. Administrative districts with large population size and high BME normalized estimation error are suggested as the target for adding new monitoring stations.     

A semi-empirical model for urban PM10 concentrations,and its evaluation against data from an urban measurement network

We have developed a model for evaluating the mass-based concentrations of urban particulate matter. The basic model assumption is that local vehicular traffic is responsible for a substantial fraction of the street-level concentrations of both PM₁₀ and NO_x, either due to primary emissions or resuspension from street surfaces. The modelling system utilises the data from an air quality monitoring network in the Helsinki Metropolitan Area. We have determined linear relationships between the measured urban PM₁₀ data against those of NO_x in various urban surroundings, based on continuously measured hourly concentration values. The data was obtained from two stations in central Helsinki and one suburban station in the Helsinki Metropolitan Area during a period of 3 yr, from 1996 to 1998. The model also includes a treatment of the regional background concentrations, and resuspended particulate matter. The model performance was evaluated against the measured PM₁₀ data from the above-mentioned three stations and from two other stations, using data that was measured in 1999. We used two alternative model versions, one based on separate correlation parameters (PM₁₀ vs. NO_x) for each station, and another based on parameters averaged over the stations considered. We analysed the agreement between the measured and predicted hourly concentration time series, utilising the values of the fractional bias (FB) and the so-called index of agreement (IA). As expected, the model predicts relatively well the yearly mean concentrations of PM₁₀: the FB values range from −0.05 to +0.09. Model performance is also relatively good when predicting the yearly mean values that are classified separately for each hour of the day: the corresponding IA values range from 0.85 to 0.96. However, model performance is substantially worse in predicting the hourly time series of the year: the IA values using the station-specific parameters range from 0.46 to 0.65. The model was applied in evaluating the yearly average spatial concentration distribution of PM₁₀ in central Helsinki, based on the corresponding modelled NO_x concentrations. With re-evaluation of a few parameters that can be determined empirically, the model could be evaluated, and most probably applied, in other urban areas as well.     

Evaluation and application of biomagnetic monitoring of traffic-derived particulate pollution

Inhalation of particulate pollutants below 10 μm in size (PM₁₀) is associated with adverse health effects. Here we use magnetic remanence measurements of roadside tree leaves to examine levels of vehicle-derived PM around Lancaster, UK. Leaf saturation remanence (SIRM) values exhibit strong correlation with both the SIRM and particulate mass of co-located, pumped-air samples, indicating that these leaf magnetic values are an effective proxy for ambient PM₁₀ concentrations. Biomagnetic monitoring using tree leaves can thus provide high spatial resolution data sets for assessment of particulate pollution levels at pedestrian-relevant heights. Leaf SIRM values not only increase with proximity to roads with higher traffic volumes, but are also ～100% higher at 0.3 m than at ～1.5–2 m height. Magnetic and SEM data indicate that the particle populations are dominated by spherical, iron-rich particles ～0.1–1 μm in diameter, with fewer larger, more angular, silica-rich particles. Comparison of the roadside leaf-calculated PM₁₀ concentrations with PM₁₀ concentrations predicted by a widely-used atmospheric dispersion model indicates some agreement between them. However, the model under-predicts PM₁₀ concentrations at ‘urban hotspots’ such as major–minor road junctions and traffic lights. Conversely, the model over-predicts PM₁₀ concentrations with distance from the road wherever one tree is screened by another, indicating the filtering/protective effect of roadside trees in leaf.     

Personal exposure to particulate air pollution in transport microenvironments

Personal measurements of exposure to particulate air pollution (PM₁₀, PM_2.5, PM₁) were simultaneously made during walking and in-car journeys on two suburban routes in Northampton, UK, during the winter of 1999/2000. Comparisons were made between concentrations found in each transport mode by particle fraction, between different particle fractions by transport mode, and between transport microenvironments and a fixed-site monitor located within the study area. High levels of correlation were seen between walking and in-car concentrations for each of the particle fractions (PM₁₀: r²=0.82; PM_2.5: r²=0.98; PM₁: r²=0.99). On an average, PM₁₀ concentrations were 16% higher inside the car than for the walker, but there were no difference in average PM_2.5 and PM₁ concentrations between the two modes. High PM_2.5:PM₁₀ ratios (0.6–0.73) were found to be associated with elevated sulphate levels. The PM_2.5:PM₁₀ and PM₁:PM_2.5 ratios were shown to be similar between walking and in-car concentrations. Concentrations of PM₁₀ were found to be more closely related between transport mode than either mode was with concentrations recorded at the fixed-site (roadside) monitor. The fixed-site monitor was shown to be a poor marker for PM₁₀ concentrations recorded during walking and in-car on a route over 1 km away.     

Retrospective assessment of air quality management practices in Taiwan

In 1995, Taiwan's Environmental Protection Administration (EPA/TW) instituted a policy of levying emission taxes on polluters in order to combat the rampant national issue of pollution. Since that time, pollution control strategies, tightening exhaust emission standards for industry, improvements in fuel quality, and new stricter vehicle emission standards, etc., have been implemented. This study evaluates the effectiveness of these measures and examines the improvement of Taiwan's air quality. In this paper, we conduct a detailed analysis of change in the concentrations of pollutants (SO₂, NO_x and particulate matter [PM]) between two three-year periods (from 1996 to1998 and from 2000 to 2002). The pollution levels were generally lower in the latter period. Concentrations at 14 EPA/TW stations in central Taiwan were simulated and source apportionment analyses in three of Central Taiwan's largest cities were conducted using a trajectory transfer-coefficient air quality model. Correlation coefficients (r) between simulations and observations for the monthly means of the concentrations of SO₂, NO_x, PM_2.5 and PM₁₀ during the study periods at the 14 stations are 0.56, 0.63, 0.70 and 0.31, respectively. The sulfur control policy greatly reduced SO₂ concentration island-wide, a stringent emission standard put into place for gasoline vehicles reduced NO_x concentration along highways, and an emissions tax placed on construction sites, as well as a regular program for road-dust sweeping, reduced primary particulate matter. Among all of the pollution abatement policies implemented, the most effective method for reducing PM_2.5 concentrations in the three largest cities involved the reduction of fine ammonium sulfate aerosols from point sources (56–63% of net PM_2.5 reduction). The next largest reduction was attributed to a diminishment in primary PM_2.5 emanating from point sources (27–56% of net PM_2.5 reduction). Secondary particulate matter, especially sulfate, was reduced from distances up to 150 km leeward of major pollution point sources such as Taichung Power Plant.     

Determinants of perceived air pollution annoyance and association between annoyance scores and air pollution (PM2.5, NO2) concentrations in the European EXPOLIS study

Apart from its traditionally considered objective impacts on health, air pollution can also have perceived effects, such as annoyance. The psychological effects of air pollution may often be more important to well-being than the biophysical effects. Health effects of perceived annoyance from air pollution are so far unknown. More knowledge of air pollution annoyance levels, determinants and also associations with different air pollution components is needed. In the European air pollution exposure study, EXPOLIS, the air pollution annoyance as perceived at home, workplace and in traffic were surveyed among other study objectives. Overall 1736 randomly drawn 25–55-yr-old subjects participated in six cities (Athens, Basel, Milan, Oxford, Prague and Helsinki). Levels and predictors of individual perceived annoyances from air pollution were assessed. Instead of the usual air pollution concentrations at fixed monitoring sites, this paper compares the measured microenvironment concentrations and personal exposures of PM_2.5 and NO₂ to the perceived annoyance levels. A considerable proportion of the adults surveyed was annoyed by air pollution. Female gender, self-reported respiratory symptoms, downtown living and self-reported sensitivity to air pollution were directly associated with high air pollution annoyance score while in traffic, but smoking status, age or education level were not significantly associated. Population level annoyance averages correlated with the city average exposure levels of PM_2.5 and NO₂. A high correlation was observed between the personal 48-h PM_2.5 exposure and perceived annoyance at home as well as between the mean annoyance at work and both the average work indoor PM_2.5 and the personal work time PM_2.5 exposure. With the other significant determinants (gender, city code, home location) and home outdoor levels the model explained 14% (PM_2.5) and 19% (NO₂) of the variation in perceived air pollution annoyance in traffic. Compared to Helsinki, in Basel and Prague the adult participants were more annoyed by air pollution while in traffic even after taking the current home outdoor PM_2.5 and NO₂ levels into account.     

An empirical approach for the prediction of daily mean PM10 concentrations

An empirical model has been devised to predict concentrations of PM₁₀ at background and roadside locations in London. Factors to calculate primary PM₁₀ and PM_2.5 concentrations are derived from annual mean NO_X, PM_2.5 and PM₁₀ measurements across London and south east England. These factors are used to calculate daily means for the primary and non-primary PM₁₀ fractions for the London area. The model accurately predicts daily mean PM₁₀ and EU Directive Limit values across a range of sites from kerbside to rural. Predictions of future PM₁₀ can be made using the expected reductions in secondary PM₁₀ and site specific annual mean NO_X predicted from emission inventories and dispersion modelling. The model suggests that the EU Directive Limit values will be exceeded close to many of London's busiest roads, and perhaps at central background sites should there be a repeat of 1996 meteorological conditions during 2005. A repeat of 1997 meteorology conditions during 2005 would lead to the EU Limit Value being exceeded alongside the busiest central London roads only. The model is applicable for London and south east England but the methodology could be applied elsewhere at a city or regional level. The model relies on the currently observed ratio between NO_X and PM₁₀. This ratio has remained constant over the last 4 years but might change in the future. The NO_X:PM₁₀ ratio derived from measurements and used in this model, implies that emission inventories might over estimate primary PM₁₀ by more than 50%.     

冬季沈阳市典型源排放PM_(10)浓度分布模拟分析

选取沈阳市7个典型的大气污染源2006年12月~2007年2月的PM10排放浓度资料,利用CALPUFF对PM10浓度月平均分布做模拟分析。模拟结果分析表明:冬季月平均PM10浓度分布的范围与风场、地形有直接的关系。地势平坦、风速大时,污染物扩散范围大,污染物浓度小;地势不平、风速小时,污染物扩散范围小,污染物浓度大。1月份是沈阳市冬季月平均大气污染最严重的月份,污染物分布主要集中在市区的北部、东部和南部地区,东部地区大气污染最为严重。     

The dispersion of the Buncefield oil fire plume: An extreme accident without air quality consequences

The dispersion of pollutants from the huge Buncefield oil depot fire that occurred on 11 December 2005 is simulated using a regional Eulerian chemistry-transport model. We analyse the transport and mixing of the fire plume. We show that the hot plume never reached the ground. Instead, it pierced the thin wintertime boundary layer and was injected into the free troposphere at higher altitudes. This is in agreement with data from many air quality stations. This high injection was fortunate because the fine aerosol particles (PM₁₀) mass column generated by the fire smoke exceeded that of ordinary pollution by an order of magnitude. Our regional chemistry-transport modelling is able to predict the early development of the plume dispersion, as shown by a qualitative comparison between simulated PM₁₀ columns and a satellite image obtained by the EOS-TERRA-MODIS sensor.If the accident had occurred in summer when boundary layers are much deeper and convective, a severe degradation in air quality due to PM₁₀ could have occurred, as shown by a sensitivity simulation assuming a similar fire during one of the hottest days of August 2003. The modelled impact of the fire on regional and European air quality levels strongly depends on the altitude reached by the buoyant plume, as shown by a set of sensitivity simulations with variable injection heights. However, in all cases we found that the fire only affected surface aerosol concentrations without increasing photochemical pollution.     

Determination and analysis of PM10 source apportionment during episodes of air pollution in Central Eastern European urban areas: The case of wintertime 2006

Source apportionment of air pollution due to particulate matter with an aerodynamic diameter <10 μm (PM₁₀) was investigated in Central Eastern European urban areas. A combination of four methods was developed to distinguish long-range transport (LRT) and regional transport (RT) from local pollution (LP) sources as well as to discern the involvement of traffic or residential sources in LP. Sources of PM₁₀ events of pollution were determined in January 2006 in representative Polish cities using monitored air quality and meteorological data, backward air mass trajectories, correlation and principal component analysis (PCA). Daily patterns of PM₁₀ levels show that several peak episodes were registered in Poland; January 21–30th being the most polluted days. Air mass back-trajectory analysis shows that all cities were under the influence of LRT from North-eastern origins (Russia–Belarus–Ukraine), most were also under LRT from Southern origin (Slovakia, Czech Republic), and northern cities were under national RT influence. PCA analysis shows that ion-sums of secondary inorganic aerosols account for LRT pollution while arsenic and chromium represents markers of RT (industrial) and LP (residential) sources of PM₁₀, respectively. Determination of several ratios (REG/UB, REG/TRAF, TRAF/UB) calculated between PM₁₀ levels measured at regional background (REG); urban background (UB) and traffic (TRAF) monitoring sites shows that, with ratios REG/UB ≥ 0.57, PM₁₀ episodes in both Szczecin and Warsaw bore a marked RT origin. The lower REG/UB ≤ 0.35 in the Southern cities of Cracow and Zabrze indicates that LP was the main contributor to the observed episodes. Only PM₁₀ episodes in Southern-western Poland (Jelenia Góra) were clearly of LP origin as characterized, by the lowest REG/UB ratio (<0.2). The high TRAF/UB ratios obtained for all cities (close to 1) indicate that there was a great uniformity of PM levels on an urban scale owing to the meteorologically stagnant conditions. A high correlation between PM₁₀, NO₂ and CO confirms that traffic emission represented a common and an important LP source of urban pollution in most Polish cities during January 2006. On the other hand PM₁₀ which is also highly correlated with SO₂ in 4 cities out of 6, indicates that coal combustion through domestic heating or industrial activities was also an important LP source of PM₁₀. Finally, extremely unfavourable meteorological conditions caused by the influence of a Siberian high-pressure system were found to be associated with the occurrence of severe PM₁₀ episodes of pollution.     

Air quality at Santiago,Chile: a box modeling approach II. PM2.5, coarse and PM10 particulate matter fractions

Ambient monitored data at Santiago, Chile, are analyzed using box models with the goal of assessing contributions of different economic activities to air pollution levels. The box modeling approach was applied to PM₁₀, PM_2.5 and coarse (PM₁₀–PM_2.5) particulate matter (PM) fractions; the period analyzed is 1989–1999. A linear model for each PM fraction was obtained, having as independent variables CO and SO₂ concentrations, plus a term proportional to (wind speed)⁻¹ that lumps together non-combustion emissions and secondary generation terms; wet scavenging is included as another independent variable. Model identification results show good agreement for the different parameters across monitoring stations. The washout ratios and scavenging coefficients agree with data published in the literature, being higher for the coarse PM fraction. The CO and SO₂ coefficients fitted for 1989–1995 agree with a priori estimates for the same period. Background estimates for the PM fractions are in agreement with measurement campaigns in upwind sites. Results show that transportation sources have become the dominant contributors to ambient PM levels, while stationary sources have decreased their contributions in the last years. The relative importance of mobile sources to PM_2.5 ambient concentrations has doubled in the last 10 years, whereas stationary sources have reduced their relative contributions to half the value in the early 1990s. Model estimates of regional background of PM_2.5 and PM₁₀ have decreased 50% and 22% in the last decade, respectively; coarse background has shown no significant change. The final conclusion is that there is room and need for a more intensive emission reduction strategy for Santiago, focusing on mobile sources. The approach pursued in this work is feasible for cities or regions where comprehensive, transport and chemistry models are not available yet, but estimates of air quality contributions are needed for policy purposes. The methodology requires data on ambient air quality measurements and surface meteorology.     

Exploring relationships between outdoor air particulate-associated polycyclic aromatic hydrocarbon and PM2.5: A case study of benzo(a)pyrene in California metropolitan regions

Polycyclic aromatic hydrocarbons (PAHs) and particulate matter (PM) are co-pollutants emitted as by-products of combustion processes. Convincing evidence exists for PAHs as a primary toxic component of fine PM (PM_2.5). Because PM_2.5 is listed by the US EPA as a “Criteria Pollutant”, it is monitored regularly at sites nationwide. In contrast, very limited data is available on measured ambient air concentrations of PAHs. However, between 1999 and 2001, ambient air concentrations of PM_2.5 and benzo(a)pyrene (BaP) are available for California locations. We use multivariate linear regression models (MLRMs) to predict ambient air levels of BaP in four air basins based on reported PM_2.5 concentrations and spatial, temporal and meteorological variables as variates. We obtain an R² ranging from 0.57 to 0.72 among these basins. Significant variables (p<0.05) include the average daily PM_2.5 concentration, wind speed, temperature and relative humidity, and the coastal distance as well as season, and holiday or weekend. Combining the data from all sites and using only these variables to estimate ambient BaP levels, we obtain an R² of 0.55. These R²-values, combined with analysis of the residual error and cross validation using the PRESS-statistic, demonstrate the potential of our method to estimate reported outdoor air PAH exposure levels in metropolitan regions. These MLRMs provide a first step towards relating outdoor ambient PM_2.5 and PAH concentrations for epidemiological studies when PAH measurements are unavailable, or limited in spatial coverage, based on publicly available meteorological and PM_2.5 data.     

Evaluation of PM10 forecasting based on the artificial neural network model and intake fraction in an urban area: A case study in Taiyuan City,China

Primary fine particulate matters with a diameter of less than 10 µm (PM₁₀) are important air emissions causing human health damage. PM₁₀ concentration forecast is important and necessary to perform in order to assess the impact of air on the health of living beings. To better understand the PM₁₀ pollution health risk in Taiyuan City, China, this paper forecasted the temporal and spatial distribution of PM₁₀ yearly average concentration, using Back Propagation Artificial Neural Network (BPANN) model with various air quality parameters. The predicted results of the models were consistent with the observations with a correlation coefficient of 0.72. The PM₁₀ yearly average concentrations combined with the population data from 2002 to 2008 were given into the Intake Fraction (IF) model to calculate the IFs, which are defined as the integrated incremental intake of a pollutant released from a source category or a region over all exposed individuals. The results in this study are only for main stationary sources of the research area, and the traffic sources have not been included. The computed IFs results are therefore under-estimations. The IFs of PM₁₀ from Taiyuan with a mean of 8.5 per million were relatively high compared with other IFs of the United States, Northern Europe and other cities in China. The results of this study indicate that the artificial neural network is an effective method for PM₁₀ pollution modeling, and the Intake Fraction model provides a rapid population risk estimate for pollutant emission reduction strategies and policies.

Implications The PM₁₀ (particulate matter with an aerodynamic diameter ≤10 μm) yearly average concentration of Taiyuan, with a mean of 0.176 mg/m³, was higher than the 65 μg/m³ recommended by the U.S. Environmental Protection Agency (EPA). The spatial distribution of PM₁₀ yearly average concentrations showed that wind direction and wind speed played an important role, whereas temperature and humidity had a lower effect than expected. Intake fraction estimates of Taiyuan were relatively high compared with those observed in other cities. Population density was the major factor influencing PM₁₀ spatial distribution. The results indicated that the artificial neural network was an effective method for PM₁₀ pollution modeling.     

Biomass smoke in Burkina Faso: what is the relationship between particulate matter,carbon monoxide,and kitchen characteristics?

In Burkina Faso where cooking with biomass is very common, little information exists regarding kitchen characteristics and their impact on air pollutant levels. The measurement of air pollutants such as respirable particulate matter (PM₁₀), an important component of biomass smoke that has been linked to adverse health outcomes, can also pose challenges in terms of cost and the type of equipment needed. Carbon monoxide could potentially be a more economical and simpler measure of air pollution. The focus of this study was to first assess the association of kitchen characteristics with measured PM₁₀ and CO levels and second, the relationship of PM₁₀ with CO concentrations, across these different kitchen characteristics in households in Nouna, Burkina Faso. Twenty-four-hour concentrations of PM₁₀ (area) were measured with portable monitors and CO (area and personal) estimated using color dosimeter tubes. Data on kitchen characteristics were collected through surveys. Most households used both wood and charcoal burned in three-stone and charcoal stoves. Mean outdoor kitchen PM₁₀ levels were relatively high (774 μg/m³, 95 % CI 329–1,218 μg/m³), but lower than indoor concentrations (Satterthwaite t value, ?6.14; p?<?0.0001). In multivariable analyses, outdoor kitchens were negatively associated with PM₁₀ (OR?=?0.06, 95 % CI 0.02–0.16, p value <0.0001) and CO (OR?=?0.03, 95 % CI 0.01–0.11, p value <0.0001) concentrations. Strong area PM₁₀ and area CO correlations were found with indoor kitchens (Spearman’s r?=?0.82, p?<?0.0001), indoor stove use (Spearman’s r?=?0.82, p?<?0.0001), and the presence of a smoker in the household (Spearman’s r?=?0.83, p?<?0.0001). Weak correlations between area PM₁₀ and personal CO levels were observed with three-stone (Spearman’s r?=?0.23, p?=?0.008) and improved stoves (Spearman’s r?=?0.34, p?=?0.003). This indicates that the extensive use of biomass fuels and multiple stove types for cooking still produce relatively high levels of exposure, even outdoors, suggesting that both fuel subsidies and stove improvement programs are likely necessary to address this problem. These findings also indicate that area CO color dosimeter tubes could be a useful measure of area PM₁₀ concentrations when levels are influenced by strong emission sources or when used in indoors. The weaker correlation observed between area PM₁₀ and personal CO levels suggests that area exposures are not as useful as proxies for personal exposures, which can vary widely from those recorded by stationary monitors.     

PM10 concentration levels at an urban and background site in Cyprus: The impact of urban sources and dust storms

Air quality in Cyprus is influenced by both local and transported pollution, including desert dust storms. We examined PM₁₀ concentration data collected in Nicosia (urban representative) from April 1, 1993, through December 11, 2008, and in Ayia Marina (rural background representative) from January 1, 1999, through December 31, 2008. Measurements were conducted using a Tapered Element Oscillating Micro-balance (TEOM). PM₁₀ concentrations, meteorological records, and satellite data were used to identify dust storm days. We investigated long-term trends using a Generalized Additive Model (GAM) after controlling for day of week, month, temperature, wind speed, and relative humidity. In Nicosia, annual PM₁₀ concentrations ranged from 50.4 to 63.8 μg/m³ and exceeded the EU annual standard limit enacted in 2005 of 40 μg/m³ every year. A large, statistically significant impact of urban sources (defined as the difference between urban and background levels) was seen in Nicosia over the period 2000–2008, and was highest during traffic hours, weekdays, cold months, and low wind conditions. Our estimate of the mean (standard error) contribution of urban sources to the daily ambient PM₁₀ was 24.0 (0.4) μg/m³. The study of yearly trends showed that PM₁₀ levels in Nicosia decreased from 59.4 μg/m³ in 1993 to 49.0 μg/m³ in 2008, probably in part as a result of traffic emission control policies in Cyprus. In Ayia Marina, annual concentrations ranged from 27.3 to 35.6 μg/m³, and no obvious time trends were observed. The levels measured at the Cyprus background site are comparable to background concentrations reported in other Eastern Mediterranean countries. Average daily PM₁₀ concentrations during desert dust storms were around 100 μg/m³ since 2000 and much higher in earlier years. Despite the large impact of dust storms and their increasing frequency over time, dust storms were responsible for a small fraction of the exceedances of the daily PM₁₀ limit.

ImplicationsThis paper examines PM₁₀ concentrations in Nicosia, Cyprus, from 1993 to 2008. The decrease in PM₁₀ levels in Nicosia suggests that the implementation of traffic emission control policies in Cyprus has been effective. However, particle levels still exceeded the European Union annual standard, and dust storms were responsible for a small fraction of the daily PM₁₀ limit exceedances. Other natural particles that are not assessed in this study, such as resuspended soil and sea salt, may be responsible in part for the high particle levels.     

Cumulative effects and threshold levels in air pollution mortality: Data analysis of nine large US cities using the NMMAPS dataset

We examined the existence of thresholds, cumulative effects and the homogeneity of five air pollutants on the relative risk of three mortality outcomes using data from nine major US cities using data from NMMAPS. Overall, PM₁₀ (usually 200-day accumulation) and ozone (3-day accumulation) were the two important predictors of outcome but their effect was not uniform across the nine cities. Many models exhibited thresholds (25–45 μm g/m³ for PM₁₀, and 10–45 ppb for O₃). Our preliminary exploratory analyses suggest that the use of a linear, no threshold, model for pollution studies is not consistent with the observed data. The heterogeneity in the risk estimates across the nine cities suggests combining the local risk estimates to obtain a national risk estimate may not be justifiable and the estimate is likely to be confounded.     

Predicting residential indoor concentrations of nitrogen dioxide,fine particulate matter,and elemental carbon using questionnaire and geographic information system based data

Previous studies have identified associations between traffic-related air pollution and adverse health effects. Most have used measurements from a few central ambient monitors and/or some measure of traffic as indicators of exposure, disregarding spatial variability and factors influencing personal exposure-ambient concentration relationships. This study seeks to utilize publicly available data (i.e., central site monitors, geographic information system, and property assessment data) and questionnaire responses to predict residential indoor concentrations of traffic-related air pollutants for lower socioeconomic status (SES) urban households.As part of a prospective birth cohort study in urban Boston, we collected indoor and outdoor 3–4 day samples of nitrogen dioxide (NO₂) and fine particulate matter (PM_2.5) in 43 low SES residences across multiple seasons from 2003 to 2005. Elemental carbon (EC) concentrations were determined via reflectance analysis. Multiple traffic indicators were derived using Massachusetts Highway Department data and traffic counts collected outside sampling homes. Home characteristics and occupant behaviors were collected via a standardized questionnaire. Additional housing information was collected through property tax records, and ambient concentrations were collected from a centrally located ambient monitor.The contributions of ambient concentrations, local traffic and indoor sources to indoor concentrations were quantified with regression analyses. PM_2.5 was influenced less by local traffic but had significant indoor sources, while EC was associated with traffic and NO₂ with both traffic and indoor sources. Comparing models based on covariate selection using p-values or a Bayesian approach yielded similar results, with traffic density within a 50 m buffer of a home and distance from a truck route as important contributors to indoor levels of NO₂ and EC, respectively. The Bayesian approach also highlighted the uncertanity in the models. We conclude that by utilizing public databases and focused questionnaire data we can identify important predictors of indoor concentrations for multiple air pollutants in a high-risk population.     

Errors in coarse particulate matter mass concentrations and spatiotemporal characteristics when using subtraction estimation methods

In studies of coarse particulate matter (PM_10-2.5), mass concentrations are often estimated through the subtraction of PM_2.5 from collocated PM₁₀ tapered element oscillating microbalance (TEOM) measurements. Though all field instruments have yet to be updated, the Filter Dynamic Measurement System (FDMS) was introduced to account for the loss of semivolatile material from heated TEOM filters. To assess errors in PM_10-2.5 estimation when using the possible combinations of PM₁₀ and PM_2.5 TEOM units with and without FDMS, data from three monitoring sites of the Colorado Coarse Rural–Urban Sources and Health (CCRUSH) study were used to simulate four possible subtraction methods for estimating PM_10-2.5 mass concentrations. Assuming all mass is accounted for using collocated TEOMs with FDMS, the three other subtraction methods were assessed for biases in absolute mass concentration, temporal variability, spatial correlation, and homogeneity. Results show collocated units without FDMS closely estimate actual PM_10-2.5 mass and spatial characteristics due to the very low semivolatile PM_10-2.5 concentrations in Colorado. Estimation using either a PM_2.5 or PM₁₀ monitor without FDMS introduced absolute biases of 2.4 µg/m³ (25%) to –2.3 µg/m³ (–24%), respectively. Such errors are directly related to the unmeasured semivolatile mass and alter measures of spatiotemporal variability and homogeneity, all of which have implications for the regulatory and epidemiology communities concerned about PM_10-2.5. Two monitoring sites operated by the state of Colorado were considered for inclusion in the CCRUSH acute health effects study, but concentrations were biased due to sampling with an FDMS-equipped PM_2.5 TEOM and PM₁₀ TEOM not corrected for semivolatile mass loss. A regression-based model was developed for removing the error in these measurements by estimating the semivolatile concentration of PM_2.5 from total PM_2.5 concentrations. By estimating nonvolatile PM_2.5 concentrations from this relationship, PM_10-2.5 was calculated as the difference between nonvolatile PM₁₀ and PM_2.5 concentrations.

Implications: Errors in the estimation of PM_10-2.5 concentrations using subtraction methods were shown to be related to the unmeasured semivolatile mass when using certain combinations of TEOM instruments. For the northeastern Colorado region, the absolute bias associated with this error significantly affects mean and 95th percentile values, which would affect assessment of compliance if PM_10-2.5 is regulated in the future. Estimating PM_10-2.5 mass concentrations using nonvolatile mass concentrations from collocated PM₁₀ and PM_2.5 TEOM monitors closely estimates the total PM_10-2.5 mass concentrations. A corrective model that removes the described error was developed and applied to data from two sites in Denver.

Supplemental Materials: Supplemental materials are available for this paper. Go to the publisher's online edition of the Journal of the Air & Waste Management Association.     

Roadside Particulate Air Pollution in Bangkok

Abstract

Airborne fine particles of PM_2.5-10 and PM_2.5 in Bangkok, Nonthaburi, and Ayutthaya were measured from December 22, 1998, to March 26, 1999, and from November 30, 1999, to December 2, 1999. Almost all the PM₁₀ values in the high-polluted (H) area exceeded the Thailand National Ambient Air Quality Standards (NAAQS) of 120 μg/m³. The low-polluted (L) area showed low PM₁₀ (34–74 μg/m³ in the daytime and 54–89 μg/m³ at night). PM_2.5 in the H area varied between 82 and 143 μg/m³ in the daytime and between 45 and 146 μg/m³ at night. In the L area, PM_2.5 was quite low both day and night and varied between 24 and 54 μg/m³, lower than the U.S. Environmental Protection Agency (EPA) standard (65 μg/m³). The personal exposure results showed a significantly higher proportion of PM_2.5 to PM₁₀ in the H area than in the L area (H = 0.80 ± 0.08 and L = 0.65 ± 0.04).

Roadside PM₁₀ was measured simultaneously with the Thailand Pollution Control Department (PCD) monitoring station at the same site and at the intersections where police work. The result from dual simultaneous measurements of PM₁₀ showed a good correlation (correlation coefficient: r = 0.93); however, PM levels near the roadside at the intersections were higher than the concentrations at the monitoring station. The relationship between ambient PM level and actual personal exposures was examined. Correlation coefficients between the general ambient outdoors and personal exposure levels were 0.92 for both PM_2.5 and PM₁₀.

Bangkok air quality data for 1997–2000, including 24-hr average PM₁₀, NO₂, SO₂, and O₃ from eight PCD monitoring stations, were analyzed and validated. The annual arithmetic mean PM₁₀ of the PCD data at the roadside monitoring stations for the last 3 years decreased from 130 to 73 μg/m³, whereas the corresponding levels at the general monitoring stations decreased from 90 to 49 μg/m³. The proportion of days when the level of the 24-hr average PM₁₀ exceeded the NAAQS was between 13 and 26% at roadside stations. PCD data showed PM₁₀ was well correlated with NO₂ but not with SO₂, suggesting that automobile exhaust is the main source of the particulate air pollution. The results obtained from the simultaneous measurement of PM_2.5 and PM₁₀ indicate the potential environmental health hazard of fine particles. In conclusion, Bangkok traffic police were exposed to high levels of automobile-derived particulate air pollution.