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A national satellite-based land-use regression model for air pollution exposure assessment in Australia.

Overview of attention for article published in Environmental Research, October 2014
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1 tweeter

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38 Mendeley
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Title
A national satellite-based land-use regression model for air pollution exposure assessment in Australia.
Published in
Environmental Research, October 2014
DOI 10.1016/j.envres.2014.09.011
Pubmed ID
Authors

Knibbs LD, Hewson MG, Bechle MJ, Marshall JD, Barnett AG, Luke D. Knibbs, Michael G. Hewson, Matthew J. Bechle, Julian D. Marshall, Adrian G. Barnett

Abstract

Land-use regression (LUR) is a technique that can improve the accuracy of air pollution exposure assessment in epidemiological studies. Most LUR models are developed for single cities, which places limitations on their applicability to other locations. We sought to develop a model to predict nitrogen dioxide (NO2) concentrations with national coverage of Australia by using satellite observations of tropospheric NO2 columns combined with other predictor variables. We used a generalised estimating equation (GEE) model to predict annual and monthly average ambient NO2 concentrations measured by a national monitoring network from 2006 through 2011. The best annual model explained 81% of spatial variation in NO2 (absolute RMS error=1.4 ppb), while the best monthly model explained 76% (absolute RMS error=1.9 ppb). We applied our models to predict NO2 concentrations at the ~350,000 census mesh blocks across the country (a mesh block is the smallest spatial unit in the Australian census). National population-weighted average concentrations ranged from 7.3 ppb (2006) to 6.3 ppb (2011). We found that a simple approach using tropospheric NO2 column data yielded models with slightly better predictive ability than those produced using a more involved approach that required simulation of surface-to-column ratios. The models were capable of capturing within-urban variability in NO2, and offer the ability to estimate ambient NO2 concentrations at monthly and annual time scales across Australia from 2006-2011. We are making our model predictions freely available for research.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 38 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 5%
India 1 3%
France 1 3%
Canada 1 3%
Unknown 33 87%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 26%
Researcher 10 26%
Student > Bachelor 5 13%
Student > Master 4 11%
Professor 2 5%
Other 7 18%
Readers by discipline Count As %
Environmental Science 20 53%
Earth and Planetary Sciences 5 13%
Engineering 4 11%
Unspecified 3 8%
Computer Science 3 8%
Other 3 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 12 February 2015.
All research outputs
#2,524,003
of 4,751,697 outputs
Outputs from Environmental Research
#480
of 788 outputs
Outputs of similar age
#90,231
of 170,077 outputs
Outputs of similar age from Environmental Research
#27
of 45 outputs
Altmetric has tracked 4,751,697 research outputs across all sources so far. This one is in the 33rd percentile – i.e., 33% of other outputs scored the same or lower than it.
So far Altmetric has tracked 788 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.3. This one is in the 23rd percentile – i.e., 23% 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 170,077 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 34th percentile – i.e., 34% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 45 others from the same source and published within six weeks on either side of this one. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.