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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

Overview of attention for article published in Environmental Health, April 2016
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2 tweeters

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55 Dimensions

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Title
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
Published in
Environmental Health, April 2016
DOI 10.1186/s12940-016-0137-9
Pubmed ID
Authors

Marloes Eeftens, Reto Meier, Christian Schindler, Inmaculada Aguilera, Harish Phuleria, Alex Ineichen, Mark Davey, Regina Ducret-Stich, Dirk Keidel, Nicole Probst-Hensch, Nino Künzli, Ming-Yi Tsai

Abstract

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. 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. 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. 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.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 105 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 1 <1%
Belgium 1 <1%
Unknown 103 98%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 23 22%
Researcher 17 16%
Student > Master 16 15%
Student > Bachelor 7 7%
Professor > Associate Professor 6 6%
Other 13 12%
Unknown 23 22%
Readers by discipline Count As %
Environmental Science 29 28%
Medicine and Dentistry 12 11%
Engineering 10 10%
Earth and Planetary Sciences 6 6%
Physics and Astronomy 5 5%
Other 12 11%
Unknown 31 30%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 19 April 2016.
All research outputs
#14,616,439
of 22,497,510 outputs
Outputs from Environmental Health
#1,075
of 1,474 outputs
Outputs of similar age
#157,238
of 281,088 outputs
Outputs of similar age from Environmental Health
#1
of 1 outputs
Altmetric has tracked 22,497,510 research outputs across all sources so far. This one is in the 32nd percentile – i.e., 32% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,474 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 31.7. This one is in the 22nd percentile – i.e., 22% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 281,088 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 41st percentile – i.e., 41% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them