Next Article in Journal
Variations in the Simulation of Climate Change Impact Indices due to Different Land Surface Schemes over the Mediterranean, Middle East and Northern Africa
Next Article in Special Issue
Major Source Contributions to Ambient PM2.5 and Exposures within the New South Wales Greater Metropolitan Region
Previous Article in Journal
Ensemble Sensitivity Analysis-Based Ensemble Transform with 3D Rescaling Initialization Method for Storm-Scale Ensemble Forecast
Previous Article in Special Issue
Urban Air Quality in a Coastal City: Wollongong during the MUMBA Campaign
Article

Skill-Testing Chemical Transport Models across Contrasting Atmospheric Mixing States Using Radon-222

1
ANSTO, Environmental Research, Locked Bag 2001, Kirrawee DC, NSW 2232, Australia
2
Centre for Atmospheric Chemistry, Chemistry, Science Medicine and Health, University of Wollongong, Wollongong, NSW 2500, Australia
3
New South Wales Office of Environment and Heritage, Sydney, NSW 2000, Australia
4
Department of Marine, Earth and Atmospheric Sciences, North Carolina State University, Raleigh, NC 27695, USA
5
Climate Science Centre, CSIRO Oceans and Atmosphere, Aspendale, VIC 3195, Australia
6
School of Earth Sciences, University of Melbourne, Melbourne, VIC 3052, Australia
*
Author to whom correspondence should be addressed.
Atmosphere 2019, 10(1), 25; https://doi.org/10.3390/atmos10010025
Received: 27 November 2018 / Revised: 2 January 2019 / Accepted: 3 January 2019 / Published: 11 January 2019
(This article belongs to the Special Issue Air Quality in New South Wales, Australia)
We propose a new technique to prepare statistically-robust benchmarking data for evaluating chemical transport model meteorology and air quality parameters within the urban boundary layer. The approach employs atmospheric class-typing, using nocturnal radon measurements to assign atmospheric mixing classes, and can be applied temporally (across the diurnal cycle), or spatially (to create angular distributions of pollutants as a top-down constraint on emissions inventories). In this study only a short (<1-month) campaign is used, but grouping of the relative mixing classes based on nocturnal mean radon concentrations can be adjusted according to dataset length (i.e., number of days per category), or desired range of within-class variability. Calculating hourly distributions of observed and simulated values across diurnal composites of each class-type helps to: (i) bridge the gap between scales of simulation and observation, (ii) represent the variability associated with spatial and temporal heterogeneity of sources and meteorology without being confused by it, and (iii) provide an objective way to group results over whole diurnal cycles that separates ‘natural complicating factors’ (synoptic non-stationarity, rainfall, mesoscale motions, extreme stability, etc.) from problems related to parameterizations, or between-model differences. We demonstrate the utility of this technique using output from a suite of seven contemporary regional forecast and chemical transport models. Meteorological model skill varied across the diurnal cycle for all models, with an additional dependence on the atmospheric mixing class that varied between models. From an air quality perspective, model skill regarding the duration and magnitude of morning and evening “rush hour” pollution events varied strongly as a function of mixing class. Model skill was typically the lowest when public exposure would have been the highest, which has important implications for assessing potential health risks in new and rapidly evolving urban regions, and also for prioritizing the areas of model improvement for future applications. View Full-Text
Keywords: radon; atmospheric stability; air quality; model evaluation; modelling; SNBL; ABL; urban pollution radon; atmospheric stability; air quality; model evaluation; modelling; SNBL; ABL; urban pollution
Show Figures

Figure 1

MDPI and ACS Style

Chambers, S.D.; Guérette, E.-A.; Monk, K.; Griffiths, A.D.; Zhang, Y.; Duc, H.; Cope, M.; Emmerson, K.M.; Chang, L.T.; Silver, J.D.; Utembe, S.; Crawford, J.; Williams, A.G.; Keywood, M. Skill-Testing Chemical Transport Models across Contrasting Atmospheric Mixing States Using Radon-222. Atmosphere 2019, 10, 25. https://doi.org/10.3390/atmos10010025

AMA Style

Chambers SD, Guérette E-A, Monk K, Griffiths AD, Zhang Y, Duc H, Cope M, Emmerson KM, Chang LT, Silver JD, Utembe S, Crawford J, Williams AG, Keywood M. Skill-Testing Chemical Transport Models across Contrasting Atmospheric Mixing States Using Radon-222. Atmosphere. 2019; 10(1):25. https://doi.org/10.3390/atmos10010025

Chicago/Turabian Style

Chambers, Scott D., Elise-Andree Guérette, Khalia Monk, Alan D. Griffiths, Yang Zhang, Hiep Duc, Martin Cope, Kathryn M. Emmerson, Lisa T. Chang, Jeremy D. Silver, Steven Utembe, Jagoda Crawford, Alastair G. Williams, and Melita Keywood. 2019. "Skill-Testing Chemical Transport Models across Contrasting Atmospheric Mixing States Using Radon-222" Atmosphere 10, no. 1: 25. https://doi.org/10.3390/atmos10010025

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop