Mark Davey

Adopting Clean Fuels and Technologies on School Buses. Pollution and Health Impacts in Children

RATIONALE More than 25 million American children breathe polluted air on diesel school buses. Emission reduction policies exist, but the health impacts to individual children have not been evaluated. METHODS Using a natural experiment, we characterized the exposures and health of 275 school bus riders before, during, and after the adoption of clean technologies and fuels between 2005 and 2009. Air pollution was measured during 597 trips on 188 school buses. Repeated measures of exhaled nitric oxide (FeNO), lung function (FEV1, FVC), and absenteeism were also collected monthly (1,768 visits). Mixed-effects models longitudinally related the adoption of diesel oxidation catalysts (DOCs), closed crankcase ventilation systems (CCVs), ultralow-sulfur diesel (ULSD), or biodiesel with exposures and health. MEASUREMENTS AND MAIN RESULTS Fine and ultrafine particle concentrations were 10-50% lower on buses using ULSD, DOCs, and/or CCVs. ULSD adoption was also associated with reduced FeNO (-16% [95% confidence interval (CI), -21 to -10%]), greater changes in FVC and FEV1 (0.02 [95% CI, 0.003 to 0.05] and 0.01 [95% CI, -0.006 to 0.03] L/yr, respectively), and lower absenteeism (-8% [95% CI, -16.0 to -0.7%]), with stronger associations among patients with asthma. DOCs, and to a lesser extent CCVs, also were associated with improved FeNO, FVC growth, and absenteeism, but these findings were primarily restricted to patients with persistent asthma and were often sensitive to control for ULSD. No health benefits were noted for biodiesel. Extrapolating to the U.S. population, changed fuel/technologies likely reduced absenteeism by more than 14 million/yr. CONCLUSIONS National and local diesel policies appear to have reduced childrens exposures and improved health.

Development of land use regression models for nitrogen dioxide, ultrafine particles, lung deposited surface area, and four other markers of particulate matter pollution in the Swiss SAPALDIA regions

BackgroundLand Use Regression (LUR) is a popular method to explain and predict spatial contrasts in air pollution concentrations, but LUR models for ultrafine particles, such as particle number concentration (PNC) are especially scarce. Moreover, no models have been previously presented for the lung deposited surface area (LDSA) of ultrafine particles. The additional value of ultrafine particle metrics has not been well investigated due to lack of exposure measurements and models.MethodsAir pollution measurements were performed in 2011 and 2012 in the eight areas of the Swiss SAPALDIA study at up to 40 sites per area for NO2 and at 20 sites in four areas for markers of particulate air pollution. We developed multi-area LUR models for biannual average concentrations of PM2.5, PM2.5 absorbance, PM10, PMcoarse, PNC and LDSA, as well as alpine, non-alpine and study area specific models for NO2, using predictor variables which were available at a national level. Models were validated using leave-one-out cross-validation, as well as independent external validation with routine monitoring data.ResultsModel explained variance (R2) was moderate for the various PM mass fractions PM2.5 (0.57), PM10 (0.63) and PMcoarse (0.45), and was high for PM2.5 absorbance (0.81), PNC (0.87) and LDSA (0.91). Study-area specific LUR models for NO2 (R2 range 0.52–0.89) outperformed combined-area alpine (R2 = 0.53) and non-alpine (R2 = 0.65) models in terms of both cross-validation and independent external validation, and were better able to account for between-area variability. Predictor variables related to traffic and national dispersion model estimates were important predictors.ConclusionsLUR models for all pollutants captured spatial variability of long-term average concentrations, performed adequately in validation, and could be successfully applied to the SAPALDIA cohort. Dispersion model predictions or area indicators served well to capture the between area variance. For NO2, applying study-area specific models was preferable over applying combined-area alpine/non-alpine models. Correlations between pollutants were higher in the model predictions than in the measurements, so it will remain challenging to disentangle their health effects.

Land Use Regression Models for Ultrafine Particles in Six European Areas

Long-term ultrafine particle (UFP) exposure estimates at a fine spatial scale are needed for epidemiological studies. Land use regression (LUR) models were developed and evaluated for six European areas based on repeated 30 min monitoring following standardized protocols. In each area; Basel (Switzerland), Heraklion (Greece), Amsterdam, Maastricht, and Utrecht (“The Netherlands”), Norwich (United Kingdom), Sabadell (Spain), and Turin (Italy), 160–240 sites were monitored to develop LUR models by supervised stepwise selection of GIS predictors. For each area and all areas combined, 10 models were developed in stratified random selections of 90% of sites. UFP prediction robustness was evaluated with the intraclass correlation coefficient (ICC) at 31–50 external sites per area. Models from Basel and The Netherlands were validated against repeated 24 h outdoor measurements. Structure and model R2 of local models were similar within, but varied between areas (e.g., 38–43% Turin; 25–31% Sabadell). Robustness of predictions within areas was high (ICC 0.73–0.98). External validation R2 was 53% in Basel and 50% in The Netherlands. Combined area models were robust (ICC 0.93–1.00) and explained UFP variation almost equally well as local models. In conclusion, robust UFP LUR models could be developed on short-term monitoring, explaining around 50% of spatial variance in longer-term measurements.

Differences in indoor versus outdoor concentrations of ultrafine particles, PM2.5, PMabsorbance and NO2 in Swiss homes

Indoor air quality is a growing concern as we spend the majority of time indoors and as new buildings are increasingly airtight for energy saving purposes. For a better understanding of residential indoor air pollution in Switzerland we conducted repeated 1–2-week-long indoor and outdoor measurements of particle number concentrations (PNC), particulate matter (PM), light absorbance of PM2.5 (PMabsorbance) and nitrogen dioxide (NO2). Residents of all homes were enrolled in the Swiss Cohort Study on Air Pollution and Lung and Heart Diseases in Adults (SAPALDIA). Indoor levels were comparable in urban areas and generally low in rural homes. Average indoor levels were 7800 particles/cm3 (interquartile range=7200); 8.7 μg/m3 (6.5) PM2.5 and 10.2 μg/m3 (11.2) NO2. All pollutants showed large variability of indoor/outdoor ratios between sites. We observed similar diurnal patterns for indoor and outdoor PNC. Nevertheless, the correlation of average indoor and outdoor PNC between sites as well as longitudinal indoor/outdoor correlations within sites were low. Our results show that a careful evaluation of home characteristics is needed when estimating indoor exposure to pollutants with outdoor origin.

Integrating data from multiple time-location measurement methods for use in exposure assessment: the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air)

Tools to assess time-location patterns related to environmental exposures have expanded from reliance on time-location diaries (TLDs) and questionnaires to use of geospatial location devices such as data-logging Global Positioning System (GPS) equipment. The Multi-Ethnic Study of Atherosclerosis and Air Pollution obtained typical time-location patterns via questionnaire for 6424 adults in six US cities. At a later time (mean 4.6 years after questionnaire), a subset (n=128) participated in high-resolution data collection for specific 2-week periods resulting in concurrent GPS and detailed TLD data, which were aggregated to estimate time spent in various microenvironments. During these 2-week periods, participants were observed to spend the most time at home indoors (mean of 78%) and a small proportion of time in-vehicle (mean of 4%). Similar overall patterns were reported by these participants on the prior questionnaire (mean home indoors: 75%; mean in-vehicle: 4%). However, individual micro-environmental time estimates measured over specific 2-week periods were not highly correlated with an individual’s questionnaire report of typical behavior (Spearman’s ρ of 0.43 for home indoors and 0.39 for in-vehicle). Although questionnaire data about typical time-location patterns can inform interpretation of long-term epidemiological analyses and risk assessment, they may not reliably represent an individual’s short-term experience.

Modelling the vertical gradient of nitrogen dioxide in an urban area

INTRODUCTION Land use regression models environmental predictors to estimate ground-floor air pollution concentration surfaces of a study area. While many cities are expanding vertically, such models typically ignore the vertical dimension. METHODS We took integrated measurements of NO2 at up to three different floors on the facades of 25 buildings in the mid-sized European city of Basel, Switzerland. We quantified the decrease in NO2 concentration with increasing height at each facade over two 14-day periods in different seasons. Using predictors of traffic load, population density and street configuration, we built conventional land use regression (LUR) models which predicted ground floor concentrations. We further evaluated which predictors best explained the vertical decay rate. Ultimately, we combined ground floor and decay models to explain the measured concentrations at all heights. RESULTS We found a clear decrease in mean nitrogen dioxide concentrations between measurements at ground level and those at higher floors for both seasons. The median concentration decrease was 8.1% at 10 m above street level in winter and 10.4% in summer. The decrease with height was sharper at buildings where high concentrations were measured on the ground and in canyon-like street configurations. While the conventional ground floor model was able to explain ground floor concentrations with a model R2 of 0.84 (RMSE 4.1 μg/m3), it predicted measured concentrations at all heights with an R2 of 0.79 (RMSE 4.5 μg/m3), systematically overpredicting concentrations at higher floors. The LUR model considering vertical decay was able to predict ground floor and higher floor concentrations with a model R2 of 0.84 (RMSE 3.8 μg/m3) and without systematic bias. DISCUSSION Height above the ground is a relevant determinant of outdoor residential exposure, even in medium-sized European cities without much high-rise. It is likely that conventional LUR models overestimate exposure for residences at higher floors near major roads. This overestimation can be minimized by considering decay with height.

OP III – 5 Land use regression modelling of outdoor no2 and pm2.5 concentrations in three low-income areas of the urban western cape, south africa

Background/aim Intra-urban air pollution has been associated with adverse health effects, such as cardiovascular or respiratory disorders. Land Use Regression (LUR) modelling is one method to describe small-scale spatial variation in air pollution levels based on several measurements and geographical predictors. Methods The main goal of the study is to characterise and model the spatial distribution of air pollutants in three neighbourhoods in the Western Cape, South Africa. Weekly measurements of NO2 and PM2.5 were performed in these areas (Khayelitsha, Marconi-Beam and Masiphumulele) during 2015–2016. They were temporally adjusted to obtain seasonal means using routinely monitored pollution data in Cape Town region. We developed six LUR models (four seasonal and two annual averages) using supervised forward stepwise regression for NO2 and PM2.5. Predictor variables, like road, land use and emission data were either obtained or collected on site. The models were validated using leave-one-out-cross-validation (LOOCV) and were tested for spatial autocorrelation. Results Measured air pollution levels were generally low. The annual mean NO2 levels were 21.5 µg/m3 and 10.0 µg/m3 for PM2.5. The NO2 annual model explained 45% of the variance (R2) in the study areas and was found to have a satisfactory internal validity (LOOCV R2=70%). The PM2.5 annual model presented lower explanatory power (R2=25%, LOOCV R2=13%). The best predictors for NO2 modelling were traffic-related variables (major roads and bus routes) and proximity to some land-use features. Smaller local sources such as open grills and waste burning sites were good predictors for PM2.5 spatial variability, together with population density. NO2 and PM2.5 mean exposure will be predicted for home and school locations of about 400 pupils at primary schools involved in an epidemiological health study. Conclusion This research shows that land use regression modelling can be successfully applied to informal urban settings in South Africa using similar predictor variables to those performed in European and North American studies. We could also provide NO2 and PM2.5 seasonal exposure estimates and maps for the selected study areas.

Contribution of the in-vehicle microenvironment to individual ambient-source nitrogen dioxide exposure: the Multi-Ethnic Study of Atherosclerosis and Air Pollution

Exposure estimates that do not account for time in-transit may underestimate exposure to traffic-related air pollution, but exact contributions have not been studied directly. We conducted a 2-week monitoring, including novel in-vehicle sampling, in a subset of the Multi-Ethnic Study of Atherosclerosis and Air Pollution cohort in two cities. Participants spent the majority of their time indoors and only 4.4% of their time (63 min/day) in-vehicle, on average. The mean ambient-source NO2 concentration was 5.1 ppb indoors and 32.3 ppb in-vehicle during drives. On average, indoor exposure contributed 69% and in-vehicle exposure contributed 24% of participants’ ambient-source NO2 exposure. For participants in the highest quartile of time in-vehicle (≥1.3 h/day), indoor and in-vehicle contributions were 60 and 31%, respectively. Incorporating infiltrated indoor and measured in-vehicle NO2 produced exposure estimates 5.6 ppb lower, on average, than using only outdoor concentrations. The indoor microenvironment accounted for the largest proportion of ambient-source exposure in this older population, despite higher concentrations of NO2 outdoors and in vehicles than indoors. In-vehicle exposure was more influential among participants who drove the most and for participants residing in areas with lower outdoor air pollution. Failure to characterize exposures in these microenvironments may contribute to exposure misclassification in epidemiologic studies.

Land use regression modelling of outdoor NO2 and PM2.5 concentrations in three low income areas in the Western Cape Province, South Africa

Air pollution can cause many adverse health outcomes, including cardiovascular and respiratory disorders. Land use regression (LUR) models are frequently used to describe small-scale spatial variation in air pollution levels based on measurements and geographical predictors. They are particularly suitable in resource limited settings and can help to inform communities, industries, and policy makers. Weekly measurements of NO2 and PM2.5 were performed in three informal areas of the Western Cape in the warm and cold seasons 2015–2016. Seasonal means were calculated using routinely monitored pollution data. Six LUR models were developed (four seasonal and two annual) using a supervised stepwise land-use-regression method. The models were validated using leave-one-out-cross-validation and tested for spatial autocorrelation. Annual measured mean NO2 and PM2.5 were 22.1 μg/m3 and 10.2 μg/m3, respectively. The NO2 models for the warm season, cold season, and overall year explained 62%, 77%, and 76% of the variance (R2). The PM2.5 annual models had lower explanatory power (R2 = 0.36, 0.29, and 0.29). The best predictors for NO2 were traffic related variables (major roads, bus routes). Local sources such as grills and waste burning sites appeared to be good predictors for PM2.5, together with population density. This study demonstrates that land-use-regression modelling for NO2 can be successfully applied to informal peri-urban settlements in South Africa using similar predictor variables to those performed in Europe and North America. Explanatory power for PM2.5 models is lower due to lower spatial variability and the possible impact of local transient sources. The study was able to provide NO2 and PM2.5 seasonal exposure estimates and maps for further health studies.