A comparison of different approaches to estimate small-scale spatial variation in outdoor NO₂ concentrations

Intra-urban variability of long-term exposure to PM2.5 and NO2 in five cities in Colombia

Rapidly urbanizing cities in Latin America experience high levels of air pollution which are known risk factors for population health. However, the estimates of long-term exposure to air pollution are scarce in the region. We developed intraurban land use regression (LUR) models to map long-term exposure to fine particulate matter (PM2.5) and nitrogen dioxide (NO2) in the five largest cities in Colombia. We conducted air pollution measurement campaigns using gravimetric PM2.5 and passive NO2 sensors for 2 weeks during both the dry and rainy seasons in 2021 in the cities of Barranquilla, Bucaramanga, Bogotá, Cali, and Medellín, and combined these data with geospatial and meteorological variables. Annual models were developed using multivariable spatial regression models. The city annual PM2.5 mean concentrations measured ranged between 12.32 and 15.99 µg/m3 while NO2 concentrations ranged between 24.92 and 49.15 µg/m3. The PM2.5 annual models explained 82% of the variance (R2) in Medellín, 77% in Bucaramanga, 73% in Barranquilla, 70% in Cali, and 44% in Bogotá. The NO2 models explained 65% of the variance in Bucaramanga, 57% in Medellín, 44% in Cali, 40% in Bogotá, and 30% in Barranquilla. Most of the predictor variables included in the models were a combination of specific land use characteristics and roadway variables. Cross-validation suggests that PM2.5 outperformed NO2 models. The developed models can be used as exposure estimate in epidemiological studies, as input in hybrid models to improve personal exposure assessment, and for policy evaluation.

Integrating large-scale stationary and local mobile measurements to estimate hyperlocal long-term air pollution using transfer learning methods

Mobile air quality measurements are collected typically for several seconds per road segment and in specific timeslots (e.g., working hours). These short-term and on-road characteristics of mobile measurements become the ubiquitous shortcomings of applying land use regression (LUR) models to estimate long-term concentrations at residential addresses. This issue was previously found to be mitigated by transferring LUR models to the long-term residential domain using routine long-term measurements in the studied region as the transfer target (local scale). However, long-term measurements are generally sparse in individual cities. For this scenario, we propose an alternative by taking long-term measurements collected over a larger geographical area (global scale) as the transfer target and local mobile measurements as the source (Global2Local model). We empirically tested national, airshed countries (i.e., national plus neighboring countries) and Europe as the global scale in developing Global2Local models to map nitrogen dioxide (NO₂) concentrations in Amsterdam. The airshed countries scale provided the lowest absolute errors, and the Europe-wide scale had the highest R². Compared to a "global" LUR model (trained exclusively with European-wide long-term measurements), and a local mobile LUR model (using mobile data from Amsterdam only), the Global2Local model significantly reduced the absolute error of the local mobile LUR model (root-mean-square error, 6.9 vs 12.6 μg/m³) and improved the percentage explained variances compared to the global model (R², 0.43 vs 0.28, assessed by independent long-term NO₂ measurements in Amsterdam, n = 90). The Global2Local method improves the generalizability of mobile measurements in mapping long-term residential concentrations with a fine spatial resolution, which is preferred in environmental epidemiological studies.

Comparison of associations between mortality and air pollution exposure estimated with a hybrid, a land-use regression and a dispersion model

INTRODUCTION

To characterize air pollution exposure at a fine spatial scale, different exposure assessment methods have been applied. Comparison of associations with health from different exposure methods are scarce. The aim of this study was to evaluate associations of air pollution based on hybrid, land-use regression (LUR) and dispersion models with natural cause and cause-specific mortality.

METHODS

We followed a Dutch national cohort of approximately 10.5 million adults aged 29+ years from 2008 until 2012. We used Cox proportional hazard models with age as underlying time scale and adjusted for several potential individual and area-level socio-economic status confounders to evaluate associations of annual average residential NO2, PM2.5 and BC exposure estimates based on two stochastic models (Dutch LUR, European-wide hybrid) and deterministic Dutch dispersion models.

RESULTS

Spatial variability of PM2.5 and BC exposure was smaller for LUR compared to hybrid and dispersion models. NO2 exposure variability was similar for the three methods. Pearson correlations between hybrid, LUR and dispersion modeled NO2 and BC ranged from 0.72 to 0.83; correlations for PM2.5 were slightly lower (0.61-0.72). In general, all three models showed stronger associations of air pollutants with respiratory disease and lung cancer mortality than with natural cause and cardiovascular disease mortality. The strength of the associations differed between the three exposure models. Associations of air pollutants estimated by LUR were generally weaker compared to associations of air pollutants estimated by hybrid and dispersion models. For natural cause mortality, we found a hazard ratio (HR) of 1.030 (95% confidence interval (CI): 1.019, 1.041) per 10 µg/m3 for hybrid modeled NO2, a HR of 1.003 (95% CI: 0.993, 1.013) per 10 µg/m3 for LUR modeled NO2 and a HR of 1.015 (95% CI: 1.005, 1.024) per 10 µg/m3 for dispersion modeled NO2.

CONCLUSION

Air pollution was positively associated with natural cause and cause-specific mortality, but the strength of the associations differed between the three exposure models. Our study documents that the selected exposure model may contribute to heterogeneity in effect estimates of associations between air pollution and health.

Land use regression modelling of PM2.5 spatial variations in different seasons in urban areas

As one of the principal components of haze, fine particulate matter (PM_2.5) has potential negative health effects, causing widespread concern. Identification of the pollutant spatial variation is a prerequisite of understanding ambient air pollution exposure and further improving air quality. Seven urban built-up areas in Liaoning central urban agglomeration (LCUA) were used for land use regression (LUR) modelling of PM_2.5 concentrations using small amounts of spatially aggregated data and to assess the model's seasonal consistency. LUR models explained 52-61% of the variation in the PM_2.5 concentrations at urban scales. The average building floor area was the key predictor in each model, and the percent water area was predictor with a negative coefficient. Good seasonal consistency was observed between the heating-seasonal model and annual average model, showing that the annual average PM_2.5 pollution in the LCUA was mainly influenced by pollution during the heating season. Extending the linear LUR model with regression kriging improved the model's explanatory ability and predictive performance. The predicted PM_2.5 concentrations in Shenyang and Anshan were the highest and that in Yingkou was the lowest. The building three-dimensional variables played important roles in the urban spatial modelling of air pollution.

Evaluation of different methods and data sources to optimise modelling of NO2 at a global scale

BACKGROUND

In countries where air pollution stations are unavailable or scarce, station measurements from other countries and atmospheric remote sensing could jointly provide information to estimate ambient air quality at a sufficiently fine resolution to study the relationship between air pollution exposure and health. Predicting NO₂ concentration globally with sufficient spatial and temporal resolution and accuracy for health studies is, however, not a trivial task. Challenges are data deficiency, in terms of NO₂ measurements and NO₂ predictors, and the development of a statistical model that can typify the regional and continental differences, such as traffic regulations, energy sources, and local weather.

OBJECTIVE

We investigated the feasibility of mapping daytime and nighttime NO₂ globally at a high spatial resolution (25 m), by including TROPOMI (TROPOspheric Monitoring Instrument) data and comparing various statistical learning techniques.

METHOD

We separated daytime (7:00 am - 9:59 pm) and nighttime (10:00 pm - 6:59 am) based on the local times. To study if one should build models for each country separately, national models in 4 selected countries (the US, China, Germany, Spain) were developed. We build the models for 2017 and used 3636 stations. Seven statistical learning techniques were applied and the impact of the predictors, model fitting, and predicting accuracy was compared between different techniques, national models, national and global models, and models with and without including the NO₂ vertical column density retrieved from TROPOMI.

RESULT AND CONCLUSION

The ensemble tree-based methods obtained higher accuracy compared to the linear regression-based methods in national and global models. The global tree-based methods obtained similar accuracy to national models. Different spatial prediction patterns are observed even when the prediction accuracy is very similar. Separating between day and night can be important for more accurate air pollution exposure assessment. The TROPOMI variable is ranked as one of the most important variables in the statistical learning techniques but adding it to global models that contain other precedent remote sensing products does not improve the prediction accuracy.

Can dispersion modeling of air pollution be improved by land-use regression? An example from Stockholm, Sweden

Both dispersion modeling (DM) and land-use regression modeling (LUR) are often used for assessment of long-term air pollution exposure in epidemiological studies, but seldom in combination. We developed a hybrid DM-LUR model using 93 biweekly observations of NO_x at 31 sites in greater Stockholm (Sweden). The DM was based on spatially resolved topographic, physiographic and emission data, and hourly meteorological data from a diagnostic wind model. Other data were from land use, meteorology and routine monitoring of NO_x. We built a linear regression model for NO_x, using a stepwise forward selection of covariates. The resulting model predicted observed NO_x (R²=0.89) better than the DM without covariates (R²=0.68, P-interaction <0.001) and with minimal apparent bias. The model included (in descending order of importance) DM, traffic intensity on the nearest street, population (number of inhabitants) within 100 m radius, global radiation (direct sunlight plus diffuse or scattered light) and urban contribution to NO_x levels (routine urban NO_x, less routine rural NO_x). Our results indicate that there is a potential for improving estimates of air pollutant concentrations based on DM, by incorporating further spatial characteristics of the immediate surroundings, possibly accounting for imperfections in the emission data.

Assessing the Suitability of Multiple Dispersion and Land Use Regression Models for Urban Traffic-Related Ultrafine Particles

Comparative evaluations are needed to assess the suitability of near-road air pollution models for traffic-related ultrafine particle number concentration (PNC). Our goal was to evaluate the ability of dispersion (CALINE4, AERMOD, R-LINE, and QUIC) and regression models to predict PNC in a residential neighborhood (Somerville) and an urban center (Chinatown) near highways in and near Boston, Massachusetts. PNC was measured in each area, and models were compared to each other and measurements for hot (>18 °C) and cold (<10 °C) hours with wind directions parallel to and perpendicular downwind from highways. In Somerville, correlation and error statistics were typically acceptable, and all models predicted concentration gradients extending ∼100 m from the highway. In contrast, in Chinatown, PNC trends differed among models, and predictions were poorly correlated with measurements likely due to effects of street canyons and nonhighway particle sources. Our results demonstrate the importance of selecting PNC models that align with study area characteristics (e.g., dominant sources and building geometry). We applied widely available models to typical urban study areas; therefore, our results should be generalizable to models of hourly averaged PNC in similar urban areas.

Independent Validation of National Satellite-Based Land-Use Regression Models for Nitrogen Dioxide Using Passive Samplers

Including satellite observations of nitrogen dioxide (NO₂) in land-use regression (LUR) models can improve their predictive ability, but requires rigorous evaluation. We used 123 passive NO₂ samplers sited to capture within-city and near-road variability in two Australian cities (Sydney and Perth) to assess the validity of annual mean NO₂ estimates from existing national satellite-based LUR models (developed with 68 regulatory monitors). The samplers spanned roadside, urban near traffic (≤100 m to a major road), and urban background (>100 m to a major road) locations. We evaluated model performance using R² (predicted NO₂ regressed on independent measurements of NO₂), mean-square-error R² (MSE-R²), RMSE, and bias. Our models captured up to 69% of spatial variability in NO₂ at urban near-traffic and urban background locations, and up to 58% of variability at all validation sites, including roadside locations. The absolute agreement of measurements and predictions (measured by MSE-R²) was similar to their correlation (measured by R²). Few previous studies have performed independent evaluations of national satellite-based LUR models, and there is little information on the performance of models developed with a small number of NO₂ monitors. We have demonstrated that such models are a valid approach for estimating NO₂ exposures in Australian cities.

Spatial variation in nitrogen dioxide concentrations and cardiopulmonary hospital admissions

BACKGROUND

Air pollution episodes are associated with increased cardiopulmonary hospital admissions. Cohort studies showed associations of spatial variation in traffic-related air pollution with respiratory and cardiovascular mortality. Much less is known in particular about associations with cardiovascular morbidity. We explored the relation between spatial variation in nitrogen dioxide (NO₂) concentrations and cardiopulmonary hospital admissions.

METHODS

This ecological study was based on hospital admissions data (2001-2004) from the National Medical Registration and general population data for the West of the Netherlands (population 4.04 million). At the 4-digit postcode area level (n=683) associations between modeled annual average outdoor NO₂ concentrations and hospital admissions for respiratory and cardiovascular causes were evaluated by linear regression with the log of the postcode-specific percentage of subjects that have been admitted at least once during the study period as the dependent variable. All analyses were adjusted for differences in composition of the population of the postcode areas (age, sex, income).

RESULTS

At the postcode level, positive associations were found between outdoor NO₂ concentrations and hospital admission rates for asthma, chronic obstructive pulmonary disease (COPD), all cardiovascular causes, ischemic heart disease and stroke (e.g. adjusted relative risk (95% confidence interval) for the second to fourth quartile relative to the first quartile of exposure were 1.87 (1.46-2.40), 2.34 (1.83-3.01) and 2.81 (2.16-3.65) for asthma; 1.44 (1.19-1.74), 1.50 (1.24-1.82) and 1.60 (1.31-1.96) for COPD). Associations remained after additional (indirect) adjustment for smoking (COPD admission rate) and degree of urbanization.

CONCLUSIONS

Our study suggests an increased risk of hospitalization for respiratory and cardiovascular causes in areas with higher levels of NO₂. Our findings add to the currently limited evidence of a long-term effect of air pollution on hospitalization. The ecological design of our study is a limitation and more studies with individual data are needed to confirm our findings.

Development, Evaluation, and Comparison of Land Use Regression Modeling Methods to Estimate Residential Exposure to Nitrogen Dioxide in a Cohort Study

We used a network of 135 NO₂ passive diffusion tube sites to develop land use regression (LUR) models in a UK conurbation. Network sites were divided into four groups (32-35 sites per group) and models developed using combinations of 1-3 groups of "training" sites to evaluate how the number of training sites influenced model performance and residential NO₂ exposure estimates for a cohort of 13 679 participants. All models explained moderate to high variance in training and independent "hold-out" data (Training adj. R²: 62-89%; Hold-out R²: 44-85%). Average hold-out R² increased by 9.5%, while average training adj. R² decreased by 7.2% when the number of training groups was increased from 1 to 3. Exposure estimate precision improved with increasing number of training sites (median intralocation relative standard deviations of 19.2, 10.3, and 7.7% for 1-group, 2-group and 3-group models respectively). Independent 1-group models gave highly variable exposure estimates suggesting that variations in LUR sampling networks with relatively low numbers of sites (≤35) may substantially alter exposure estimates. Collectively, our analyses suggest that use of more than 60 training sites has quantifiable benefits in epidemiological application of LUR models.

A Novel Approach in Quantifying the Effect of Urban Design Features on Local-Scale Air Pollution in Central Urban Areas

Differences in urban design features may affect emission and dispersion patterns of air pollution at local-scales within cities. However, the complexity of urban forms, interdependence of variables, and temporal and spatial variability of processes make it difficult to quantify determinants of local-scale air pollution. This paper uses a combination of dense measurements and a novel approach to land-use regression (LUR) modeling to identify key controls on concentrations of ambient nitrogen dioxide (NO2) at a local-scale within a central business district (CBD). Sixty-two locations were measured over 44 days in Auckland, New Zealand at high density (study area 0.15 km(2)). A local-scale LUR model was developed, with seven variables identified as determinants based on standard model criteria. A novel method for improving standard LUR design was developed using two independent data sets (at local and "city" scales) to generate improved accuracy in predictions and greater confidence in results. This revised multiscale LUR model identified three urban design variables (intersection, proximity to a bus stop, and street width) as having the more significant determination on local-scale air quality, and had improved adaptability between data sets.

Air Pollution and Lung Function in Dutch Children: A Comparison of Exposure Estimates and Associations Based on Land Use Regression and Dispersion Exposure Modeling Approaches

BACKGROUND

There is limited knowledge about the extent to which estimates of air pollution effects on health are affected by the choice for a specific exposure model.

OBJECTIVES

We aimed to evaluate the correlation between long-term air pollution exposure estimates using two commonly used exposure modeling techniques [dispersion and land use regression (LUR) models] and, in addition, to compare the estimates of the association between long-term exposure to air pollution and lung function in children using these exposure modeling techniques.

METHODS

We used data of 1,058 participants of a Dutch birth cohort study with measured forced expiratory volume in 1 sec (FEV1), forced vital capacity (FVC), and peak expiratory flow (PEF) measurements at 8 years of age. For each child, annual average outdoor air pollution exposure [nitrogen dioxide (NO2), mass concentration of particulate matter with diameters ≤ 2.5 and ≤ 10 μm (PM2.5, PM10), and PM2.5 soot] was estimated for the current addresses of the participants by a dispersion and a LUR model. Associations between exposures to air pollution and lung function parameters were estimated using linear regression analysis with confounder adjustment.

RESULTS

Correlations between LUR- and dispersion-modeled pollution concentrations were high for NO2, PM2.5, and PM2.5 soot (R = 0.86-0.90) but low for PM10 (R = 0.57). Associations with lung function were similar for air pollutant exposures estimated using LUR and dispersion modeling, except for associations of PM2.5 with FEV1 and FVC, which were stronger but less precise for exposures based on LUR compared with dispersion model.

CONCLUSIONS

Predictions from LUR and dispersion models correlated very well for PM2.5, NO2, and PM2.5 soot but not for PM10. Health effect estimates did not depend on the type of model used to estimate exposure in a population of Dutch children.

Transferability and generalizability of regression models of ultrafine particles in urban neighborhoods in the Boston area

Land use regression (LUR) models have been used to assess air pollutant exposure, but limited evidence exists on whether location-specific LUR models are applicable to other locations (transferability) or general models are applicable to smaller areas (generalizability). We tested transferability and generalizability of spatial-temporal LUR models of hourly particle number concentration (PNC) for Boston-area (MA, U.S.A.) urban neighborhoods near Interstate 93. Four neighborhood-specific regression models and one Boston-area model were developed from mobile monitoring measurements (34-46 days/neighborhood over one year each). Transferability was tested by applying each neighborhood-specific model to the other neighborhoods; generalizability was tested by applying the Boston-area model to each neighborhood. Both the transferability and generalizability of models were tested with and without neighborhood-specific calibration. Important PNC predictors (adjusted-R(2) = 0.24-0.43) included wind speed and direction, temperature, highway traffic volume, and distance from the highway edge. Direct model transferability was poor (R(2) < 0.17). Locally-calibrated transferred models (R(2) = 0.19-0.40) and the Boston-area model (adjusted-R(2) = 0.26, range: 0.13-0.30) performed similarly to neighborhood-specific models; however, some coefficients of locally calibrated transferred models were uninterpretable. Our results show that transferability of neighborhood-specific LUR models of hourly PNC was limited, but that a general model performed acceptably in multiple areas when calibrated with local data.

Environmental, dietary, maternal, and fetal predictors of bulky DNA adducts in cord blood: a European mother-child study (NewGeneris)

BACKGROUND

Bulky DNA adducts reflect genotoxic exposures, have been associated with lower birth weight, and may predict cancer risk.

OBJECTIVE

We selected factors known or hypothesized to affect in utero adduct formation and repair and examined their associations with adduct levels in neonates.

METHODS

Pregnant women from Greece, Spain, England, Denmark, and Norway were recruited in 2006-2010. Cord blood bulky DNA adduct levels were measured by the 32P-postlabeling technique (n = 511). Diet and maternal characteristics were assessed via questionnaires. Modeled exposures to air pollutants and drinking-water disinfection by-products, mainly trihalomethanes (THMs), were available for a large proportion of the study population.

RESULTS

Greek and Spanish neonates had higher adduct levels than the northern European neonates [median, 12.1 (n = 179) vs. 6.8 (n = 332) adducts per 108 nucleotides, p < 0.001]. Residence in southern European countries, higher maternal body mass index, delivery by cesarean section, male infant sex, low maternal intake of fruits rich in vitamin C, high intake of dairy products, and low adherence to healthy diet score were statistically significantly associated with higher adduct levels in adjusted models. Exposure to fine particulate matter and nitrogen dioxide was associated with significantly higher adducts in the Danish subsample only. Overall, the pooled results for THMs in water show no evidence of association with adduct levels; however, there are country-specific differences in results with a suggestion of an association in England.

CONCLUSION

These findings suggest that a combination of factors, including unknown country-specific factors, influence the bulky DNA adduct levels in neonates.

Comparing land use regression and dispersion modelling to assess residential exposure to ambient air pollution for epidemiological studies

BACKGROUND

Land-use regression (LUR) and dispersion models (DM) are commonly used for estimating individual air pollution exposure in population studies. Few comparisons have however been made of the performance of these methods.

OBJECTIVES

Within the European Study of Cohorts for Air Pollution Effects (ESCAPE) we explored the differences between LUR and DM estimates for NO2, PM10 and PM2.5.

METHODS

The ESCAPE study developed LUR models for outdoor air pollution levels based on a harmonised monitoring campaign. In thirteen ESCAPE study areas we further applied dispersion models. We compared LUR and DM estimates at the residential addresses of participants in 13 cohorts for NO2; 7 for PM10 and 4 for PM2.5. Additionally, we compared the DM estimates with measured concentrations at the 20-40 ESCAPE monitoring sites in each area.

RESULTS

The median Pearson R (range) correlation coefficients between LUR and DM estimates for the annual average concentrations of NO2, PM10 and PM2.5 were 0.75 (0.19-0.89), 0.39 (0.23-0.66) and 0.29 (0.22-0.81) for 112,971 (13 study areas), 69,591 (7) and 28,519 (4) addresses respectively. The median Pearson R correlation coefficients (range) between DM estimates and ESCAPE measurements were of 0.74 (0.09-0.86) for NO2; 0.58 (0.36-0.88) for PM10 and 0.58 (0.39-0.66) for PM2.5.

CONCLUSIONS

LUR and dispersion model estimates correlated on average well for NO2 but only moderately for PM10 and PM2.5, with large variability across areas. DM predicted a moderate to large proportion of the measured variation for NO2 but less for PM10 and PM2.5.

Assessing the relationship among urban trees, nitrogen dioxide, and respiratory health

Modeled atmospheric pollution removal by trees based on eddy flux, leaf, and chamber studies of relatively few species may not scale up to adequately assess landscape-level air pollution effects of the urban forest. A land use regression (LUR) model (R(2) = 0.70) based on NO2 measured at 144 sites in Portland, Oregon (USA), after controlling for roads, railroads, and elevation, estimated every 10 ha (20%) of tree canopy within 400 m of a site was associated with a 0.57 ppb decrease in NO2. Using BenMAP and a 200 m resolution NO2 model, we estimated that the NO2 reduction associated with trees in Portland could result in significantly fewer incidences of respiratory problems, providing a $7 million USD benefit annually. These in-situ urban measurements predict a significantly higher reduction of NO2 by urban trees than do existing models. Further studies are needed to maximize the potential of urban trees in improving air quality.

Health effects of ambient air pollution: do different methods for estimating exposure lead to different results?

BACKGROUND

Spatially resolved exposure models are increasingly used in epidemiology. We previously reported that, although exhibiting a moderate correlation, pregnancy nitrogen dioxide (NO2) levels estimated by the nearest air quality monitoring station (AQMS) model and a geostatistical model, showed similar associations with infant birth weight.

OBJECTIVES

We extended this study by comparing a total of four exposure models, including two highly spatially resolved models: a land-use regression (LUR) model and a dispersion model. Comparisons were made in terms of predicted NO2 and particle (aerodynamic diameter<10 μm, PM10) exposure and adjusted association with birth weight.

METHODS

The four exposure models were implemented in two French metropolitan areas where 1026 pregnant women were followed as part of the EDEN mother-child cohort.

RESULTS

Correlations between model predictions were high (≥ 0.70), except for NO2 between the AQMS and both the LUR (r = 0.54) and dispersion models (r = 0.63). Spatial variations as estimated by the AQMS model were greater for NO2 (95%) than for PM10 (22%). The direction of effect estimates of NO2 on birth weight varied according to the exposure model, while PM10 effect estimates were more consistent across exposure models.

CONCLUSIONS

For PM10, highly spatially resolved exposure model agreed with the poor spatial resolution AQMS model in terms of estimated pollutant levels and health effects. For more spatially heterogeneous pollutants like NO2, although predicted levels from spatially resolved models (all but AQMS) agreed with each other, our results suggest that some may disagree with each other as well as with the AQMS regarding the direction of the estimated health effects.

A new exposure metric for traffic-related air pollution? An analysis of determinants of hopanes in settled indoor house dust

BACKGROUND

Exposure to traffic-related air pollution (TRAP) can adversely impact health but epidemiologic studies are limited in their abilities to assess long-term exposures and incorporate variability in indoor pollutant infiltration.

METHODS

In order to examine settled house dust levels of hopanes, engine lubricating oil byproducts found in vehicle exhaust, as a novel TRAP exposure measure, dust samples were collected from 171 homes in five Canadian cities and analyzed by gas chromatography-mass spectrometry. To evaluate source contributions, the relative abundance of the highest concentration hopane monomer in house dust was compared to that in outdoor air. Geographic variables related to TRAP emissions and outdoor NO2 concentrations from city-specific TRAP land use regression (LUR) models were calculated at each georeferenced residence location and assessed as predictors of variability in dust hopanes.

RESULTS

Hopanes relative abundance in house dust and ambient air were significantly correlated (Pearson's r=0.48, p<0.05), suggesting that dust hopanes likely result from traffic emissions. The proportion of variance in dust hopanes concentrations explained by LUR NO2 was less than 10% in Vancouver, Winnipeg and Toronto while the correlations in Edmonton and Windsor explained 20 to 40% of the variance. Modeling with household factors such as air conditioning and shoe removal along with geographic predictors related to TRAP generally increased the proportion of explained variability (10-80%) in measured indoor hopanes dust levels.

CONCLUSIONS

Hopanes can consistently be detected in house dust and may be a useful tracer of TRAP exposure if determinants of their spatiotemporal variability are well-characterized, and when home-specific factors are considered.

Systematic evaluation of land use regression models for NO₂

Land use regression (LUR) models have become popular to explain the spatial variation of air pollution concentrations. Independent evaluation is important. We developed LUR models for nitrogen dioxide (NO(2)) using measurements conducted at 144 sampling sites in The Netherlands. Sites were randomly divided into training data sets with a size of 24, 36, 48, 72, 96, 108, and 120 sites. LUR models were evaluated using (1) internal "leave-one-out-cross-validation (LOOCV)" within the training data sets and (2) external "hold-out" validation (HV) against independent test data sets. In addition, we calculated Mean Square Error based validation R(2)s. The mean adjusted model and LOOCV R(2) slightly decreased from 0.87 to 0.82 and 0.83 to 0.79, respectively, with an increasing number of training sites. In contrast, the mean HV R(2) was lowest (0.60) with the smallest training sets and increased to 0.74 with the largest training sets. Predicted concentrations were more accurate in sites with out of range values for prediction variables after changing these values to the minimum or maximum of the range observed in the corresponding training data set. LUR models for NO(2) perform less well, when evaluated against independent measurements, when they are based on relatively small training sets. In our specific application, models based on as few as 24 training sites, however, achieved acceptable hold out validation R(2)s of, on average, 0.60.

Predictors of intra-community variation in air quality

Air quality has emerged as a key determinant of important health outcomes in children and adults. This study aims to identify factors that influence local, within-community air quality, and to build a model for traffic-related air pollution (TRP).We utilized concentrations of NO(2), NO, and total oxides of nitrogen (NO(x)), which were measured at 942 locations in 12 southern California communities. For each location, population density, elevation, land-use, and several indicators of traffic were calculated. A spatial random effects model was used to study the relationship of these predictors to each TRP.Variation in TRP was strongly correlated with traffic on nearby freeways and other major roads, and also with population density and elevation. After accounting for traffic, categories of land-use were not associated with the pollutants. Traffic had a larger relative impact in small urban (low regional pollution) communities than in large urban (high regional pollution) communities. For example, our best fitting model explained 70% of the variation in NO(x) in large urban areas and 76% in small urban areas. Compared with living at least 1,500 m from a freeway, living within 250 m of a freeway was associated with up to a 41% increase in TRP in a large urban area, and up to a 75% increase in small urban areas.Thus, traffic strongly affects local air quality in large and small urban areas, which has implications for exposure assessment and estimation of health risks.

Long-term exposure to traffic-related air pollution and type 2 diabetes prevalence in a cross-sectional screening-study in the Netherlands

BACKGROUND

Air pollution may promote type 2 diabetes by increasing adipose inflammation and insulin resistance. This study examined the relation between long-term exposure to traffic-related air pollution and type 2 diabetes prevalence among 50- to 75-year-old subjects living in Westfriesland, the Netherlands.

METHODS

Participants were recruited in a cross-sectional diabetes screening-study conducted between 1998 and 2000. Exposure to traffic-related air pollution was characterized at the participants' home-address. Indicators of exposure were land use regression modeled nitrogen dioxide (NO2) concentration, distance to the nearest main road, traffic flow at the nearest main road and traffic in a 250 m circular buffer. Crude and age-, gender- and neighborhood income adjusted associations were examined by logistic regression.

RESULTS

8,018 participants were included, of whom 619 (8%) subjects had type 2 diabetes. Smoothed plots of exposure versus type 2 diabetes supported some association with traffic in a 250 m buffer (the highest three quartiles compared to the lowest also showed increased prevalence, though non-significant and not increasing with increasing quartile), but not with the other exposure metrics. Modeled NO2-concentration, distance to the nearest main road and traffic flow at the nearest main road were not associated with diabetes. Exposure-response relations seemed somewhat more pronounced for women than for men (non-significant).

CONCLUSIONS

We did not find consistent associations between type 2 diabetes prevalence and exposure to traffic-related air pollution, though there were some indications for a relation with traffic in a 250 m buffer.