TY - JOUR
T1 - Validation and optimization of the ATMO-Street air quality model chain by means of a large-scale citizen-science dataset
AU - Hooyberghs, H.
AU - De Craemer, S.
AU - Lefebvre, W.
AU - Vranckx, S.
AU - Maiheu, B.
AU - Trimpeneers, E.
AU - Vanpoucke, C.
AU - Janssen, S.
AU - Meysman, F. J.R.
AU - Fierens, F.
PY - 2022
Y1 - 2022
N2 - Detailed validation of air quality models is essential, but remains challenging, due to a lack of suitable high-resolution measurement datasets. This is particularly true for pollutants with short-scale spatial variations, such as nitrogen dioxide (NO2). While street-level air quality model chains can predict concentration gradients at high spatial resolution, measurement campaigns lack the coverage and spatial density required to validate these gradients. Citizen science offers a tool to collect large-scale datasets, but it remains unclear to what extent such data can truly increase model performance. Here we use the passive sampler dataset collected within the large-scale citizen science campaign CurieuzeNeuzen to assess the integrated ATMO-Street street-level air quality model chain. The extensiveness of the dataset (20.000 sampling locations across the densely populated region Flanders, ∼1.5 data points per km2) allowed an in-depth model validation and optimization. We illustrate generic techniques and methods to assess and improve street-level air quality models, and show that considerable model improvement can be achieved, in particular with respect to the correct representation of the small-scale spatial variability of the NO2-concentrations. After model optimization, the model skill of the ATMO-Street chain significantly increased, passing the FAIRMODE model quality threshold, and thus substantiating its suitability for policy support. More generally, our results reveal how a “deep validation” based on extensive spatial data can substantially improve model performance, thus demonstrating how air quality modelling can benefit from one-off large-scale monitoring campaigns.
AB - Detailed validation of air quality models is essential, but remains challenging, due to a lack of suitable high-resolution measurement datasets. This is particularly true for pollutants with short-scale spatial variations, such as nitrogen dioxide (NO2). While street-level air quality model chains can predict concentration gradients at high spatial resolution, measurement campaigns lack the coverage and spatial density required to validate these gradients. Citizen science offers a tool to collect large-scale datasets, but it remains unclear to what extent such data can truly increase model performance. Here we use the passive sampler dataset collected within the large-scale citizen science campaign CurieuzeNeuzen to assess the integrated ATMO-Street street-level air quality model chain. The extensiveness of the dataset (20.000 sampling locations across the densely populated region Flanders, ∼1.5 data points per km2) allowed an in-depth model validation and optimization. We illustrate generic techniques and methods to assess and improve street-level air quality models, and show that considerable model improvement can be achieved, in particular with respect to the correct representation of the small-scale spatial variability of the NO2-concentrations. After model optimization, the model skill of the ATMO-Street chain significantly increased, passing the FAIRMODE model quality threshold, and thus substantiating its suitability for policy support. More generally, our results reveal how a “deep validation” based on extensive spatial data can substantially improve model performance, thus demonstrating how air quality modelling can benefit from one-off large-scale monitoring campaigns.
KW - Air pollution
KW - Citizen science
KW - Dispersion modelling
KW - FAIRMODE Model Quality Objective
KW - Model optimization
KW - Model validation
KW - Semi-variogram analysis
KW - Spatial variation
KW - Street level modelling
UR - http://www.scopus.com/inward/record.url?scp=85122751780&partnerID=8YFLogxK
U2 - 10.1016/j.atmosenv.2022.118946
DO - 10.1016/j.atmosenv.2022.118946
M3 - Article
AN - SCOPUS:85122751780
VL - 272
JO - Atmospheric Environment
JF - Atmospheric Environment
SN - 1352-2310
M1 - 118946
ER -