Limits...
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.

Eeftens M, Meier R, Schindler C, Aguilera I, Phuleria H, Ineichen A, Davey M, Ducret-Stich R, Keidel D, Probst-Hensch N, Künzli N, Tsai MY - Environ Health (2016)

Bottom Line: LUR 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology and Public Health, Swiss Tropical & Public Health Institute, Socinstrasse 57, P.O. Box 4002, Basel, Switzerland. marloes.eeftens@unibas.ch.

ABSTRACT

Background: Land 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.

Methods: Air 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.

Results: Model explained variance (R(2)) 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 (R(2) range 0.52-0.89) outperformed combined-area alpine (R (2)  = 0.53) and non-alpine (R (2)  = 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.

Conclusions: LUR 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.

No MeSH data available.


Related in: MedlinePlus

The boundaries of the 10 km and 20 km buffer areas, drawn around the measurement sites which were used to develop the area-specific NO2 LUR models. Black dots represent resident locations of SAPALDIA participants
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
getmorefigures.php?uid=PMC4835865&req=5

Fig1: The boundaries of the 10 km and 20 km buffer areas, drawn around the measurement sites which were used to develop the area-specific NO2 LUR models. Black dots represent resident locations of SAPALDIA participants

Mentions: We observed substantial under- and over-prediction by study area, and significant spatial autocorrelation in the residuals for the eight-area NO2 model and the four-area PM2.5 absorbance, PNC and LDSA models (Additional file 5, Table 2). These models also showed dependence of the model residuals on study area, and substantial over- or under-prediction bias in some of the areas (Additional file 5, Table 3). We therefore ultimately fitted area-specific NO2 LUR models which could adequately capture local variability. Additionally, we fitted NO2 LUR models for alpine (Davos and Montana) and non-alpine (Aarau, Basel, Geneva, Lugano, Payerne, Wald) areas, which could be applied to predict NO2 exposures for addresses outside of the eight SAPALDIA areas, above and below 1000 m, respectively (Fig. 1). For PM2.5 absorbance, neither the PM2.5 nor the NO2 dispersion-model estimates were selected since neither explained the between area variability. For the novel markers of ultrafines (PNC and LDSA), allowing dispersion model estimates for PM10 and PM2.5 to enter the models resulted in no systematic under- of overprediction by area in the models (Additional file 5, Table 3). However, the inclusion of these dispersion-model estimates did not allow us to capture the spatial variation of ultrafines independently from the mass. To better explain between-area variability in these models, we introduced area-indicators for PM2.5 absorbance, PNC and LDSA.Fig. 1


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.

Eeftens M, Meier R, Schindler C, Aguilera I, Phuleria H, Ineichen A, Davey M, Ducret-Stich R, Keidel D, Probst-Hensch N, Künzli N, Tsai MY - Environ Health (2016)

The boundaries of the 10 km and 20 km buffer areas, drawn around the measurement sites which were used to develop the area-specific NO2 LUR models. Black dots represent resident locations of SAPALDIA participants
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4835865&req=5

Fig1: The boundaries of the 10 km and 20 km buffer areas, drawn around the measurement sites which were used to develop the area-specific NO2 LUR models. Black dots represent resident locations of SAPALDIA participants
Mentions: We observed substantial under- and over-prediction by study area, and significant spatial autocorrelation in the residuals for the eight-area NO2 model and the four-area PM2.5 absorbance, PNC and LDSA models (Additional file 5, Table 2). These models also showed dependence of the model residuals on study area, and substantial over- or under-prediction bias in some of the areas (Additional file 5, Table 3). We therefore ultimately fitted area-specific NO2 LUR models which could adequately capture local variability. Additionally, we fitted NO2 LUR models for alpine (Davos and Montana) and non-alpine (Aarau, Basel, Geneva, Lugano, Payerne, Wald) areas, which could be applied to predict NO2 exposures for addresses outside of the eight SAPALDIA areas, above and below 1000 m, respectively (Fig. 1). For PM2.5 absorbance, neither the PM2.5 nor the NO2 dispersion-model estimates were selected since neither explained the between area variability. For the novel markers of ultrafines (PNC and LDSA), allowing dispersion model estimates for PM10 and PM2.5 to enter the models resulted in no systematic under- of overprediction by area in the models (Additional file 5, Table 3). However, the inclusion of these dispersion-model estimates did not allow us to capture the spatial variation of ultrafines independently from the mass. To better explain between-area variability in these models, we introduced area-indicators for PM2.5 absorbance, PNC and LDSA.Fig. 1

Bottom Line: LUR 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.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology and Public Health, Swiss Tropical & Public Health Institute, Socinstrasse 57, P.O. Box 4002, Basel, Switzerland. marloes.eeftens@unibas.ch.

ABSTRACT

Background: Land 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.

Methods: Air 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.

Results: Model explained variance (R(2)) 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 (R(2) range 0.52-0.89) outperformed combined-area alpine (R (2)  = 0.53) and non-alpine (R (2)  = 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.

Conclusions: LUR 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.

No MeSH data available.


Related in: MedlinePlus