Assessment and statistical modeling of the relationship between remotely sensed aerosol optical depth and PM2.5 in the eastern United States.

Research in scientific, public health, and policy disciplines relating to the environment increasingly makes use of high-dimensional remote sensing and the output of numerical models in conjunction with traditional observations. Given the public health and resultant public policy implications of the potential health effects of particulate matter (PM*) air pollution, specifically fine PM with an aerodynamic diameter < or = 2.5 pm (PM2.5), there has been substantial recent interest in the use of remote-sensing information, in particular aerosol optical depth (AOD) retrieved from satellites, to help characterize variability in ground-level PM2.5 concentrations in space and time. While the United States and some other developed countries have extensive PM monitoring networks, gaps in data across space and time necessarily occur; the hope is that remote sensing can help fill these gaps. In this report, we are particularly interested in using remote-sensing data to inform estimates of spatial patterns in ambient PM2.5 concentrations at monthly and longer time scales for use in epidemiologic analyses. However, we also analyzed daily data to better disentangle spatial and temporal relationships. For AOD to be helpful, it needs to add information beyond that available from the monitoring network. For analyses of chronic health effects, it needs to add information about the concentrations of long-term average PM2.5; therefore, filling the spatial gaps is key. Much recent evidence has shown that AOD is correlated with PM2.5 in the eastern United States, but the use of AOD in exposure analysis for epidemiologic work has been rare, in part because discrepancies necessarily exist between satellite-retrieved estimates of AOD, which is an atmospheric-column average, and ground-level PM2.5. In this report, we summarize the results of a number of empirical analyses and of the development of statistical models for the use of proxy information, in particular satellite AOD, in predicting PM2.5 concentrations in the eastern United States. We analyzed the spatiotemporal structure of the relationship between PM2.5 and AOD, first using simple correlations both before and after calibration based on meteorology, as well as large-scale spatial and temporal calibration to account for discrepancies between AOD and PM2.5. We then used both raw and calibrated AOD retrievals in statistical models to predict PM2.5 concentrations, accounting for AOD in two ways: primarily as a separate data source contributing a second likelihood to a Bayesian statistical model, as well as a data source on which we could directly regress. Previous consideration of satellite AOD has largely focused on the National Aeronautics and Space Administration (NASA) moderate resolution imaging spectroradiometer (MODIS) and multiangle imaging spectroradiometer (MISR) instruments. One contribution of our work is more extensive consideration of AOD derived from the Geostationary Operational Environmental Satellite East Aerosol/Smoke Product (GOES GASP) AOD and its relationship with PM2.5. In addition to empirically assessing the spatiotemporal relationship between GASP AOD and PM2.5, we considered new statistical techniques to screen anomalous GOES reflectance measurements and account for background surface reflectance. In our statistical work, we developed a new model structure that allowed for more flexible modeling of the proxy discrepancy than previous statistical efforts have had, with a computationally efficient implementation. We also suggested a diagnostic for assessing the scales of the spatial relationship between the proxy and the spatial process of interest (e.g., PM2.5). In brief, we had little success in improving predictions in our eastern-United States domain for use in epidemiologic applications. We found positive correlations of AOD with PM2.5 over time, but less correlation for long-term averages over space, unless we used calibration that adjusted for large-scale discrepancy between AOD and PM2.5 (see sections 3, 4, and 5). Statistical models that combined AOD, PM2.5 observations, and land-use and meteorologic variables were highly predictive of PM2.5 observations held out of the modeling, but AOD added little information beyond that provided by the other sources (see sections 5 and 6). When we used PM2.5 data estimates from the Community Multiscale Air Quality model (CMAQ) as the proxy instead of using AOD, we similarly found little improvement in predicting held-out observations of PM2.5, but when we regressed on CMAQ PM2.5 estimates, the predictions improved moderately in some cases. These results appeared to be caused in part by the fact that large-scale spatial patterns in PM2.5 could be predicted well by smoothing the monitor values, while small-scale spatial patterns in AOD appeared to weakly reflect the variation in PM2.5 inferred from the observations. Using a statistical model that allowed for potential proxy discrepancy at both large and small spatial scales was an important component of our modeling. In particular, when our models did not include a component to account for small-scale discrepancy, predictive performance decreased substantially. Even long-term averages of MISR AOD, considered the best, albeit most sparse, of the AOD products, were only weakly correlated with measured PM2.5 (see section 4). This might have been partly related to the fact that our analysis did not account for spatial variation in the vertical profile of the aerosol. Furthermore, we found evidence that some of the correlation between raw AOD and PM2.5 might have been a function of surface brightness related to land use, rather than having been driven by the detection of aerosol in the AOD retrieval algorithms (see sections 4 and 7). Difficulties in estimating the background surface reflectance in the retrieval algorithms likely explain this finding. With regard to GOES, we found moderate correlations of GASP AOD and PM2.5. The higher correlations of monthly and yearly averages after calibration reflected primarily the improved large-scale correlation, a necessary result of the calibration procedure (see section 3). While the results of this study's GOES reflectance screening and surface reflection correction appeared sensible, correlations of our proposed reflectance-based proxy with PM2.5 were no better than GASP AOD correlations with PM2.5 (see section 7). We had difficulty improving spatial prediction of monthly and yearly average PM2.5 using AOD in the eastern United States, which we attribute to the spatial discrepancy between AOD and measured PM2.5, particularly at smaller scales. This points to the importance of paying attention to the discrepancy structure of proxy information, both from remote-sensing and deterministic models. In particular, important statistical challenges arise in accounting for the discrepancy, given the difficulty in the face of sparse observations of distinguishing the discrepancy from the component of the proxy that is informative about the process of interest. Associations between adverse health outcomes and large-scale variation in PM2.5 (e.g., across regions) may be confounded by unmeasured spatial variation in factors such as diet. Therefore, one important goal was to use AOD to improve predictions of PM2.5 for use in epidemiologic analyses at small-to-moderate spatial scales (within urban areas and within regions). In addition, large-scale PM2.5 variation is well estimated from the monitoring data, at least in the United States. We found little evidence that current AOD products are helpful for improving prediction at small-to-moderate scales in the eastern United States and believe more evidence for the reliability of AOD as a proxy at such scales is needed before making use of AOD for PM2.5 prediction in epidemiologic contexts. While our results relied in part on relatively complicated statistical models, which may be sensitive to modeling assumptions, our exploratory correlation analyses (see sections 3 and 5) and relatively simple regression-style modeling of MISR AOD (see section 4) were consistent with the more complicated modeling results. When assessing the usefulness of AOD in the context of studying chronic health effects, we believe efforts need to focus on disentangling the temporal from the spatial correlations of AOD and PM2.5 and on understanding the spatial scale of correlation and of the discrepancy structure. While our results are discouraging, it is important to note that we attempted to make use of smaller-scale spatial variation in AOD to distinguish spatial variations of relatively small magnitude in long-term concentrations of ambient PM2.5. Our efforts pushed the limits of current technology in a spatial domain with relatively low PM2.5 levels and limited spatial variability. AOD may hold more promise in areas with higher aerosol levels, as the AOD signal would be stronger there relative to the background surface reflectance. Furthermore, for developing countries with high aerosol levels, it is difficult to build statistical models based on PM2.5 measurements and land-use covariates, so AOD may add more incremental information in those contexts. More generally, researchers in remote sensing are involved in ongoing efforts to improve AOD products and develop new approaches to using AOD, such as calibration with model-estimated vertical profiles and the use of speciation information in MISR AOD; these efforts warrant continued investigation of the usefulness of remotely sensed AOD for public health research.

Paciorek CJ ，Liu Y ，HEI Health Review Committee 《-》

Evaluating heterogeneity in indoor and outdoor air pollution using land-use regression and constrained factor analysis.

Previous studies have identified associations between traffic exposures and a variety of adverse health effects, but many of these studies relied on proximity measures rather than measured or modeled concentrations of specific air pollutants, complicating interpretability of the findings. An increasing number of studies have used land-use regression (LUR) or other techniques to model small-scale variability in concentrations of specific air pollutants. However, these studies have generally considered a limited number of pollutants, focused on outdoor concentrations (or indoor concentrations of ambient origin) when indoor concentrations are better proxies for personal exposures, and have not taken full advantage of statistical methods for source apportionment that may have provided insight about the structure of the LUR models and the interpretability of model results. Given these issues, the primary objective of our study was to determine predictors of indoor and outdoor residential concentrations of multiple traffic-related air pollutants within an urban area, based on a combination of central site monitoring data; geographic information system (GIS) covariates reflecting traffic and other outdoor sources; questionnaire data reflecting indoor sources and activities that affect ventilation rates; and factor-analytic methods to better infer source contributions. As part of a prospective birth cohort study assessing asthma etiology in urban Boston, we collected indoor and/or outdoor 3-to-4 day samples of nitrogen dioxide (NO2) and fine particulate matter with an aerodynamic diameter or = 2.5 pm (PM2.5) at 44 residences during multiple seasons of the year from 2003 through 2005. We performed reflectance analysis, x-ray fluorescence spectroscopy (XRF), and high-resolution inductively coupled plasma-mass spectrometry (ICP-MS) on particle filters to estimate the concentrations of elemental carbon (EC), trace elements, and water-soluble metals, respectively. We derived multiple indicators of traffic using Massachusetts Highway Department (MHD) data and traffic counts collected outside the residences where the air monitoring was conducted. We used a standardized questionnaire to collect data on home characteristics and occupant behaviors. Additional housing information was collected through property tax records. Ambient concentrations of pollutants as well as meteorological data were collected from centrally located ambient monitors. We used GIS-based LUR models to explain spatial and temporal variability in residential outdoor concentrations of PM2.5, EC, and NO2. We subsequently derived latent-source factors for residential outdoor concentrations using confirmatory factor analysis constrained to nonnegative loadings. We developed LUR models to determine whether GIS covariates and other predictors explain factor variability and thereby support initial factor interpretations. To evaluate indoor concentrations, we developed physically interpretable regression models that explored the relationship between measured indoor and outdoor concentrations, relying on questionnaire data to characterize indoor sources and activities. Because outdoor pollutant concentrations measured directly outside of homes are unlikely to be available for most large epidemiologic studies, we developed regression models to explain indoor concentrations of PM2.5, EC, and NO2 as a function of other, more readily available data: GIS covariates, questionnaire data reflecting both sources and ventilation, and central site monitoring data. As we did for outdoor concentrations, we then derived latent-source factors for residential indoor concentrations and developed regression models explaining variability in these indoor latent-source factors. Finally, to provide insight about the effects of improved characterization of exposures for the results of subsequent epidemiologic investigations, we developed a simulation framework to quantitatively compare the implications of using exposure models derived from validation studies with the use of other surrogate models with varying amounts of measurement error. The concentrations of outdoor PM2.5 were strongly associated with the central site monitor data, whereas EC concentrations showed greater spatial variability, especially during colder months, and were predicted by the length of roadway within 200 m of the home. Outdoor NO2 also showed significant spatial variability, predicted in part by population density and roadway length within 50 m of the home. Our constrained factor analysis of outdoor concentrations produced loadings indicating long-range transport, brake wear and traffic exhaust, diesel exhaust, fuel oil combustion, and resuspended road dust as sources; corresponding LUR models largely corroborated these factor interpretations through covariate significance. For example, long-range transport was predicted by central site PM2.5, and season, brake wear and traffic exhaust and resuspended road dust by traffic and residential density, diesel exhaust by the percentage of diesel traffic on the nearest major road, and fuel oil combustion by population density. Our modeling of the concentrations of indoor pollutants demonstrated substantial variability in indoor-outdoor relationships across constituents, helping to separate constituents dominated by outdoor sources (e.g., S, Se, and V) from those dominated by indoor sources (e.g., Ca and Si). Regression models indicated that indoor PM2.5 was not influenced substantially by local traffic but had significant indoor sources (cooking activity and occupant density), while EC was associated with distance to the nearest designated truck route, and NO2 was associated with both traffic density within 50 m of the home and gas stove usage. Our constrained factor analysis of indoor concentrations helped to separate outdoor-dominated factors from indoor-dominated factors, though some factors appeared to be influenced by both indoor and outdoor sources. Subsequent factor analyses of the indoor-attributable fractions from indoor-outdoor regression models provided generally consistent interpretations of indoor-dominated factors. The use of regression models on indoor factors demonstrated the limited predictive power of questionnaire data related to indoor sources, but reinforced the viability of modeling indoor concentrations of pollutants of ambient origin. In spite of the relatively weak predictive power of some of the indoor-concentration regression models, our epidemiologic simulations illustrated that exposure models with fairly modest R2 values (in the range of 0.3 through 0.4, corresponding with the regression models for PM2.5 and NO2) yielded substantial improvements in epidemiologic study performance relative to the use of exposure proxies that could be applied in the absence of validation studies. In spite of limitations related to sample size and available covariate data, our study demonstrated significant outdoor spatial variability within an urban area in NO2 and in several constituents of airborne particles. LUR techniques combined with constrained factor analysis helped to disentangle the contributions to temporal variability of local, long-range transport, and other sources, ultimately allowing exposures from defined source categories to be investigated in epidemiologic studies. For the indoor residential environment, we demonstrated substantial variability in indoor-outdoor relationships among particle constituents; then, using information from public databases and focused questionnaire data, we were able to predict indoor concentrations for a subset of key pollutants. Constrained factor analysis methods applied to the indoor environment helped to separate indoor sources from outdoor sources. The corresponding indoor regression models had limited predictive power, reinforcing the complexity of characterizing the indoor environment when only limited information about key predictors is available. This finding also underscores the likelihood that these regression models might characterize indoor concentrations of pollutants with ambient origins better than they can the indoor concentrations from all sources. Our findings provide direction for future studies characterizing indoor exposure sources and patterns, and our epidemiologic simulation reinforced the importance of reducing measurement error in a context where many traffic-related air pollutants are influenced by both indoor and outdoor sources. The combination of analytical techniques used in our study could ultimately allow for more refined exposure characterization and evaluation of the relative contributions of various sources to health outcomes in epidemiologic studies.

Levy JI ，Clougherty JE ，Baxter LK ，Houseman EA ，Paciorek CJ ，HEI Health Review Committee ... -  《-》

The London low emission zone baseline study.

On February 4, 2008, the world's largest low emission zone (LEZ) was established. At 2644 km2, the zone encompasses most of Greater London. It restricts the entry of the oldest and most polluting diesel vehicles, including heavy-goods vehicles (haulage trucks), buses and coaches, larger vans, and minibuses. It does not apply to cars or motorcycles. The LEZ scheme will introduce increasingly stringent Euro emissions standards over time. The creation of this zone presented a unique opportunity to estimate the effects of a stepwise reduction in vehicle emissions on air quality and health. Before undertaking such an investigation, robust baseline data were gathered on air quality and the oxidative activity and metal content of particulate matter (PM) from air pollution monitors located in Greater London. In addition, methods were developed for using databases of electronic primary-care records in order to evaluate the zone's health effects. Our study began in 2007, using information about the planned restrictions in an agreed-upon LEZ scenario and year-on-year changes in the vehicle fleet in models to predict air pollution concentrations in London for the years 2005, 2008, and 2010. Based on this detailed emissions and air pollution modeling, the areas in London were then identified that were expected to show the greatest changes in air pollution concentrations and population exposures after the implementation of the LEZ. Using these predictions, the best placement of a pollution monitoring network was determined and the feasibility of evaluating the health effects using electronic primary-care records was assessed. To measure baseline pollutant concentrations before the implementation of the LEZ, a comprehensive monitoring network was established close to major roadways and intersections. Output-difference plots from statistical modeling for 2010 indicated seven key areas likely to experience the greatest change in concentrations of nitrogen dioxide (NO2) (at least 3 microg/m3) and of PM with an aerodynamic diameter < or = 10 microm (PM10) (at least 0.75 microg/m3) as a result of the LEZ; these suggested that the clearest signals of change were most likely to be measured near roadsides. The seven key areas were also likely to be of importance in carrying out a study to assess the health outcomes of an air quality intervention like the LEZ. Of the seven key areas, two already had monitoring sites with a full complement of equipment, four had monitoring sites that required upgrades of existing equipment, and one required a completely new installation. With the upgrades and new installations in place, fully ratified (verified) pollutant data (for PM10, PM with an aerodynamic diameter < or = 2.5 microm [PM2.5], nitrogen oxides [NOx], and ozone [O3] at all sites as well as for particle number, black smoke [BS], carbon monoxide [CO], and sulfur dioxide [SO2] at selected sites) were then collected for analysis. In addition, the seven key monitoring sites were supported by other sites in the London Air Quality Network (LAQN). From these, a robust set of baseline air quality data was produced. Data from automatic and manual traffic counters as well as automatic license-plate recognition cameras were used to compile detailed vehicle profiles. This enabled us to establish more precise associations between ambient pollutant concentrations and vehicle emissions. An additional goal of the study was to collect baseline PM data in order to test the hypothesis that changes in traffic densities and vehicle mixes caused by the LEZ would affect the oxidative potential and metal content of ambient PM10 and PM2.5. The resulting baseline PM data set was the first to describe, in detail, the oxidative potential and metal content of the PM10 and PM2.5 of a major city's airshed. PM in London has considerable oxidative potential; clear differences in this measure were found from site to site, with evidence that the oxidative potential of both PM10 and PM2.5 at roadside monitoring sites was higher than at urban background locations. In the PM10 samples this increased oxidative activity appeared to be associated with increased concentrations of copper (Cu), barium (Ba), and bathophenanthroline disulfonate-mobilized iron (BPS Fe) in the roadside samples. In the PM2.5 samples, no simple association could be seen, suggesting that other unmeasured components were driving the increased oxidative potential in this fraction of the roadside samples. These data suggest that two components were contributing to the oxidative potential of roadside PM, namely Cu and BPS Fe in the coarse fraction of PM (PM with an aerodynamic diameter of 2.5 microm to 10 microm; PM(2.5-10)) and an unidentified redox catalyst in PM2.5. The data derived for this baseline study confirmed key observations from a more limited spatial mapping exercise published in our earlier HEI report on the introduction of the London's Congestion Charging Scheme (CCS) in 2003 (Kelly et al. 2011a,b). In addition, the data set in the current report provided robust baseline information on the oxidative potential and metal content of PM found in the London airshed in the period before implementation of the LEZ; the finding that a proportion of the oxidative potential appears in the PM coarse mode and is apparently related to brake wear raises important issues regarding the nature of traffic management schemes. The final goal of this baseline study was to establish the feasibility, in ethical and operational terms, of using the U.K.'s electronic primary-care records to evaluate the effects of the LEZ on human health outcomes. Data on consultations and prescriptions were compiled from a pilot group of general practices (13 distributed across London, with 100,000 patients; 29 situated in the inner London Borough of Lambeth, with 200,000 patients). Ethics approvals were obtained to link individual primary-care records to modeled NOx concentrations by means of post-codes. (To preserve anonymity, the postcodes were removed before delivery to the research team.) A wide range of NOx exposures was found across London as well as within and between the practices examined. Although we observed little association between NOx exposure and smoking status, a positive relationship was found between exposure and increased socioeconomic deprivation. The health outcomes we chose to study were asthma, chronic obstructive pulmonary disease, wheeze, hay fever, upper and lower respiratory tract infections, ischemic heart disease, heart failure, and atrial fibrillation. These outcomes were measured as prevalence or incidence. Their distributions by age, sex, socioeconomic deprivation, ethnicity, and smoking were found to accord with those reported in the epidemiology literature. No cross-sectional positive associations were found between exposure to NOx and any of the studied health outcomes; some associations were significantly negative. After the pilot study, a suitable primary-care database of London patients was identified, the General Practice Research Database responsible for giving us access to these data agreed to collaborate in the evaluation of the LEZ, and an acceptable method of ensuring privacy of the records was agreed upon. The database included about 350,000 patients who had remained at the same address over the four-year period of the study. Power calculations for a controlled longitudinal analysis were then performed, indicating that for outcomes such as consultations for respiratory illnesses or prescriptions for asthma there was sufficient power to identify a 5% to 10% reduction in consultations for patients most exposed to the intervention compared with patients presumed to not be exposed to it. In conclusion, the work undertaken in this study provides a good foundation for future LEZ evaluations. Our extensive monitoring network, measuring a comprehensive set of pollutants (and a range of particle metrics), will continue to provide a valuable tool both for assessing the impact of LEZ regulations on air quality in London and for furthering understanding of the link between PM's composition and toxicity. Finally, we believe that in combination with our modeling of the predicted population-based changes in pollution exposure in London, the use of primary-care databases forms a sound basis and has sufficient statistical power for the evaluation of the potential impact of the LEZ on human health.

Kelly F ，Armstrong B ，Atkinson R ，Anderson HR ，Barratt B ，Beevers S ，Cook D ，Green D ，Derwent D ，Mudway I ，Wilkinson P ，HEI Health Review Committee ... -  《-》

Extended follow-up and spatial analysis of the American Cancer Society study linking particulate air pollution and mortality.

We conducted an extended follow-up and spatial analysis of the American Cancer Society (ACS) Cancer Prevention Study II (CPS-II) cohort in order to further examine associations between long-term exposure to particulate air pollution and mortality in large U.S. cities. The current study sought to clarify outstanding scientific issues that arose from our earlier HEI-sponsored Reanalysis of the original ACS study data (the Particle Epidemiology Reanalysis Project). Specifically, we examined (1) how ecologic covariates at the community and neighborhood levels might confound and modify the air pollution-mortality association; (2) how spatial autocorrelation and multiple levels of data (e.g., individual and neighborhood) can be taken into account within the random effects Cox model; (3) how using land-use regression to refine measurements of air pollution exposure to the within-city (or intra-urban) scale might affect the size and significance of health effects in the Los Angeles and New York City regions; and (4) what exposure time windows may be most critical to the air pollution-mortality association. The 18 years of follow-up (extended from 7 years in the original study [Pope et al. 1995]) included vital status data for the CPS-II cohort (approximately 1.2 million participants) with multiple cause-of-death codes through December 31, 2000 and more recent exposure data from air pollution monitoring sites for the metropolitan areas. In the Nationwide Analysis, the influence of ecologic covariate data (such as education attainment, housing characteristics, and level of income; data obtained from the 1980 U.S. Census; see Ecologic Covariates sidebar on page 14) on the air pollution-mortality association were examined at the Zip Code area (ZCA) scale, the metropolitan statistical area (MSA) scale, and by the difference between each ZCA value and the MSA value (DIFF). In contrast to previous analyses that did not directly include ecologic covariates at the ZCA scale, risk estimates increased when ecologic covariates were included at all scales. The ecologic covariates exerted their greatest effect on mortality from ischemic heart disease (IHD), which was also the health outcome most strongly related with exposure to PM2.5 (particles 2.5 microm or smaller in aerodynamic diameter), sulfate (SO4(2-)), and sulfur dioxide (SO2), and the only outcome significantly associated with exposure to nitrogen dioxide (NO2). When ecologic covariates were simultaneously included at both the MSA and DIFF levels, the hazard ratio (HR) for mortality from IHD associated with PM2.5 exposure (average concentration for 1999-2000) increased by 7.5% and that associated with SO4(2-) exposure (average concentration for 1990) increased by 12.8%. The two covariates found to exert the greatest confounding influence on the PM2.5-mortality association were the percentage of the population with a grade 12 education and the median household income. Also in the Nationwide Analysis, complex spatial patterns in the CPS-II data were explored with an extended random effects Cox model (see Glossary of Statistical Terms at end of report) that is capable of clustering up to two geographic levels of data. Using this model tended to increase the HR estimate for exposure to air pollution and also to inflate the uncertainty in the estimates. Including ecologic covariates decreased the variance of the results at both the MSA and ZCA scales; the largest decrease was in residual variation based on models in which the MSA and DIFF levels of data were included together, which suggests that partitioning the ecologic covariates into between-MSA and within-MSA values more completely captures the sources of variation in the relationship between air pollution, ecologic covariates, and mortality. Intra-Urban Analyses were conducted for the New York City and Los Angeles regions. The results of the Los Angeles spatial analysis, where we found high exposure contrasts within the Los Angeles region, showed that air pollution-mortality risks were nearly 3 times greater than those reported from earlier analyses. This suggests that chronic health effects associated with intra-urban gradients in exposure to PM2.5 may be even larger between ZCAs within an MSA than the associations between MSAs that have been previously reported. However, in the New York City spatial analysis, where we found very little exposure contrast between ZCAs within the New York region, mortality from all causes, cardiopulmonary disease (CPD), and lung cancer was not elevated. A positive association was seen for PM2.5 exposure and IHD, which provides evidence of a specific association with a cause of death that has high biologic plausibility. These results were robust when analyses controlled (1) the 44 individual-level covariates (from the ACS enrollment questionnaire in 1982; see 44 Individual-Level Covariates sidebar on page 22) and (2) spatial clustering using the random effects Cox model. Effects were mildly lower when unemployment at the ZCA scale was included. To examine whether there is a critical exposure time window that is primarily responsible for the increased mortality associated with ambient air pollution, we constructed individual time-dependent exposure profiles for particulate and gaseous air pollutants (PM2.5 and SO2) for a subset of the ACS CPS-II participants for whom residence histories were available. The relevance of the three exposure time windows we considered was gauged using the magnitude of the relative risk (HR) of mortality as well as the Akaike information criterion (AIC), which measures the goodness of fit of the model to the data. For PM2.5, no one exposure time window stood out as demonstrating the greatest HR; nor was there any clear pattern of a trend in HR going from recent to more distant windows or vice versa. Differences in AIC values among the three exposure time windows were also small. The HRs for mortality associated with exposure to SO2 were highest in the most recent time window (1 to 5 years), although none of these HRs were significantly elevated. Identifying critical exposure time windows remains a challenge that warrants further work with other relevant data sets. This study provides additional support toward developing cost-effective air quality management policies and strategies. The epidemiologic results reported here are consistent with those from other population-based studies, which collectively have strongly supported the hypothesis that long-term exposure to PM2.5 increases mortality in the general population. Future research using the extended Cox-Poisson random effects methods, advanced geostatistical modeling techniques, and newer exposure assessment techniques will provide additional insight.

Krewski D ，Jerrett M ，Burnett RT ，Ma R ，Hughes E ，Shi Y ，Turner MC ，Pope CA 3rd ，Thurston G ，Calle EE ，Thun MJ ，Beckerman B ，DeLuca P ，Finkelstein N ，Ito K ，Moore DK ，Newbold KB ，Ramsay T ，Ross Z ，Shin H ，Tempalski B ... -  《-》

Development and application of an aerosol screening model for size-resolved urban aerosols.

Predictive models of vehicular ultrafine particles less than 0.1 microm in diameter (UFPs*) and other urban pollutants with high spatial and temporal variation are useful and important in applications such as (1) decision support for infrastructure projects, emissions controls, and transportation-mode shifts; (2) the interpretation and enhancement of observations (e.g., source apportionment, extrapolation, interpolation, and gap-filling in space and time); and (3) the generation of spatially and temporally resolved exposure estimates where monitoring is unfeasible. The objective of the current study was to develop, test, and apply the Aerosol Screening Model (ASM), a new physically based vehicular UFP model for use in near-road environments. The ASM simulates hourly average outdoor concentrations of roadway-derived aerosols and gases. Its distinguishing features include user-specified spatial resolution; use of the Weather Research and Forecasting (WRF) meteorologic model for winds estimates; use of a database of more than 100,000 road segments in the Los Angeles, California, region, including freeway ramps and local streets; and extensive testing against more than 9000 hours of observed particle concentrations at 11 sites. After initialization of air parcels at an upwind boundary, the model solves for vehicle emissions, dispersion, coagulation, and deposition using a Lagrangian modeling framework. The Lagrangian parcel of air is subdivided vertically (into 11 levels) and in the crosswind direction (into 3 parcels). It has overall dimensions of 10 m (downwind), 300 m (vertically), and 2.1 km (crosswind). The simulation is typically started 4 km upwind from the receptor, that is, the location at which the exposure is to be estimated. As parcels approach the receptor, depending on the user-specified resolution, step size is decreased, and crosswind resolution is enhanced through subdivision of parcels in the crosswind direction. Hourly concentrations and size distributions of aerosols were simulated for 11 sites in the Los Angeles area with large variations in proximal traffic and particle number concentrations (ranging from 6000 to 41,000/cm3). Observed data were from the 2005-2007 Harbor Community Monitoring Study (HCMS; Moore et al. 2009), in Long Beach, California, and the Coronary Health and Air Pollution Study (CHAPS; Delfino et al. 2008), in the Los Angeles area. Meteorologic fields were extracted from 1-km-resolution meteorologic simulations, and observed wind direction and speed were incorporated. Using on-road and tunnel measurements, size-resolved emission factors ranging from 1.4 x 10(15) to 16 x 10(15) particles/kg fuel were developed specifically for the ASM. Four separate size-resolved emissions were used. Traffic and emission factors were separately estimated for heavy-duty diesel and light-duty vehicles (LDV), and both cruise and acceleration emission factors were used. The light-duty cruise size-resolved number emission factor had a single prominent mode at 12 nm. The diesel cruise size-resolved number emission factor was bimodal, with a large mode at 16 nm and a secondary mode at around 100 nm. Emitted particles were assumed to be nonvolatile. Data on traffic activity came from a 2008 travel-demand model, supplemented by data on diurnal patterns. Simulated ambient number size distributions and number concentrations were compared to observations taking into account estimated losses from particle transmission efficiency in instrument inlet tubing. The skill of the model in predicting number concentrations and size distributions was mixed, with some promising prediction features and some other areas in need of substantial improvement. For long-term (-15-day) average concentrations, the variability from site to site could be modeled with a coefficient of determination (r2) of 0.76. Model underprediction was more common than overprediction. The average of the absolute normalized bias was 0.30; in other words, long-term mean particle concentrations at each site were on average predicted to within 30% of the measured values. Observed 24-hour number concentrations were simulated to within a factor of 1.6 on 48% of days at HCMS sites and 81% at CHAPS sites, lower than the original design goal of 90%. Extensive evaluation of hourly concentrations, diurnal patterns, sizedistributions, and directional patterns was performed. At two sites with heavy freeway and heavy-duty-vehicle (HDV) influences and extensive size-resolved measurements, the ASM made significant errors in the diurnal pattern, concentration, and mode position of the aerosol size distribution. Observations indicated a shift in concentrations and size distributions corresponding to the afternoon development of offshore wind at the HCMS sites. The model did not reproduce the changes in particles associated with this wind shift and suffered from overprediction for particles of less than 15 nm and underprediction for particles of between 15 and 500 nm, raising doubt about the applicability of the HDV emission factors and the model's assumptions that particles were nonvolatile. The model's temporal prediction skill at individual monitoring sites was variable; the index of agreement (IOA) for hourly values at single sites ranged from 0.30 to 0.56. The model's ability to reproduce diurnal patterns in aerosol concentrations was site dependent; midday underprediction as well as underprediction for particle sizes greater than 15 nm were typical errors. Despite some problems in model skill, the number of time periods and locations evaluated as well as the extent of our qualitative and quantitative evaluations versus physical measurements well exceeded other published size-resolved modeling efforts. As a trial of a typical application, the sensitivity of the concentrations at each receptor site to LDV traffic, HDV traffic, and various road classes was evaluated. The sensitivity of overall particle numbers to all types of traffic ranged from 0.87 at the site with the heaviest traffic to 0.28 at the site with the lightest traffic, meaning that a 1% reduction in traffic could yield a reduction in particle number of 0.87% to 0.28%. Key conclusions and implications of the study are the following: 1. That variable-resolution (down to 10 m) modeling in a relatively simple framework is feasible and can support most of the applications mentioned above; 2. That model improvements will be required for some applications, especially in the areas of the HDV emission factor and the parameterization of meteorologic dispersion; 3. That particle loss from instrument transmission efficiency can be significant for particles smaller than 50 nm, and especially significant for particles smaller than 20 nm. In cases where loss corrections are not accounted for, or are inaccurate, this loss can cause disagreements in observation-model and observation-observation comparisons. 4. That LDV traffic exposures likely exceed HDV traffic exposures in some locations; 5. That variable step size and adaptive parcel width are critical to balancing computational efficiency and resolution; and 6. That the effects of roadways on air quality depend on both traffic volume and distance--in other words, low traffic volumes at close proximity need to be considered in health and planning studies just as much as do high traffic volumes at distances up to several kilometers. Future improvements to the model have been identified. They include improved emission factors; integration with the U.S. Environmental Protection Agency (EPA) Motor Vehicle Emission Simulator (MOVES) model; nesting with three-dimensional (3D) Eulerian models such as the Community Multi-scale Air Quality (CMAQ) model; increased emission dependence on acceleration, load, grade, and speed as well as evaporation and condensation of semivolatile aerosol species; and modeling of carbon dioxide (CO2) as an on-road and near-road dilution tracer. In addition, comparison with other statistically and physically based models would be highly beneficial.

Stanier CO ，Lee SR ，HEI Health Review Committee 《-》