Articles | Volume 13, issue 3
https://doi.org/10.5194/gmd-13-1623-2020
https://doi.org/10.5194/gmd-13-1623-2020
Development and technical paper
 | 
30 Mar 2020
Development and technical paper |  | 30 Mar 2020

Local fractions – a method for the calculation of local source contributions to air pollution, illustrated by examples using the EMEP MSC-W model (rv4_33)

Peter Wind, Bruce Rolstad Denby, and Michael Gauss

Related authors

Global sensitivities of reactive N and S gas and particle concentrations and deposition to precursor emissions reductions
Yao Ge, Massimo Vieno, David S. Stevenson, Peter Wind, and Mathew R. Heal
Atmos. Chem. Phys., 23, 6083–6112, https://doi.org/10.5194/acp-23-6083-2023,https://doi.org/10.5194/acp-23-6083-2023, 2023
Short summary
A new assessment of global and regional budgets, fluxes, and lifetimes of atmospheric reactive N and S gases and aerosols
Yao Ge, Massimo Vieno, David S. Stevenson, Peter Wind, and Mathew R. Heal
Atmos. Chem. Phys., 22, 8343–8368, https://doi.org/10.5194/acp-22-8343-2022,https://doi.org/10.5194/acp-22-8343-2022, 2022
Short summary
Eurodelta multi-model simulated and observed particulate matter trends in Europe in the period of 1990–2010
Svetlana Tsyro, Wenche Aas, Augustin Colette, Camilla Andersson, Bertrand Bessagnet, Giancarlo Ciarelli, Florian Couvidat, Kees Cuvelier, Astrid Manders, Kathleen Mar, Mihaela Mircea, Noelia Otero, Maria-Teresa Pay, Valentin Raffort, Yelva Roustan, Mark R. Theobald, Marta G. Vivanco, Hilde Fagerli, Peter Wind, Gino Briganti, Andrea Cappelletti, Massimo D'Isidoro, and Mario Adani
Atmos. Chem. Phys., 22, 7207–7257, https://doi.org/10.5194/acp-22-7207-2022,https://doi.org/10.5194/acp-22-7207-2022, 2022
Short summary
Evaluation of global EMEP MSC-W (rv4.34) WRF (v3.9.1.1) model surface concentrations and wet deposition of reactive N and S with measurements
Yao Ge, Mathew R. Heal, David S. Stevenson, Peter Wind, and Massimo Vieno
Geosci. Model Dev., 14, 7021–7046, https://doi.org/10.5194/gmd-14-7021-2021,https://doi.org/10.5194/gmd-14-7021-2021, 2021
Short summary
Description of the uEMEP_v5 downscaling approach for the EMEP MSC-W chemistry transport model
Bruce Rolstad Denby, Michael Gauss, Peter Wind, Qing Mu, Eivind Grøtting Wærsted, Hilde Fagerli, Alvaro Valdebenito, and Heiko Klein
Geosci. Model Dev., 13, 6303–6323, https://doi.org/10.5194/gmd-13-6303-2020,https://doi.org/10.5194/gmd-13-6303-2020, 2020
Short summary

Related subject area

Atmospheric sciences
Modeling of polycyclic aromatic hydrocarbons (PAHs) from global to regional scales: model development (IAP-AACM_PAH v1.0) and investigation of health risks in 2013 and 2018 in China
Zichen Wu, Xueshun Chen, Zifa Wang, Huansheng Chen, Zhe Wang, Qing Mu, Lin Wu, Wending Wang, Xiao Tang, Jie Li, Ying Li, Qizhong Wu, Yang Wang, Zhiyin Zou, and Zijian Jiang
Geosci. Model Dev., 17, 8885–8907, https://doi.org/10.5194/gmd-17-8885-2024,https://doi.org/10.5194/gmd-17-8885-2024, 2024
Short summary
LIMA (v2.0): A full two-moment cloud microphysical scheme for the mesoscale non-hydrostatic model Meso-NH v5-6
Marie Taufour, Jean-Pierre Pinty, Christelle Barthe, Benoît Vié, and Chien Wang
Geosci. Model Dev., 17, 8773–8798, https://doi.org/10.5194/gmd-17-8773-2024,https://doi.org/10.5194/gmd-17-8773-2024, 2024
Short summary
SLUCM+BEM (v1.0): a simple parameterisation for dynamic anthropogenic heat and electricity consumption in WRF-Urban (v4.3.2)
Yuya Takane, Yukihiro Kikegawa, Ko Nakajima, and Hiroyuki Kusaka
Geosci. Model Dev., 17, 8639–8664, https://doi.org/10.5194/gmd-17-8639-2024,https://doi.org/10.5194/gmd-17-8639-2024, 2024
Short summary
NAQPMS-PDAF v2.0: a novel hybrid nonlinear data assimilation system for improved simulation of PM2.5 chemical components
Hongyi Li, Ting Yang, Lars Nerger, Dawei Zhang, Di Zhang, Guigang Tang, Haibo Wang, Yele Sun, Pingqing Fu, Hang Su, and Zifa Wang
Geosci. Model Dev., 17, 8495–8519, https://doi.org/10.5194/gmd-17-8495-2024,https://doi.org/10.5194/gmd-17-8495-2024, 2024
Short summary
Source-specific bias correction of US background and anthropogenic ozone modeled in CMAQ
T. Nash Skipper, Christian Hogrefe, Barron H. Henderson, Rohit Mathur, Kristen M. Foley, and Armistead G. Russell
Geosci. Model Dev., 17, 8373–8397, https://doi.org/10.5194/gmd-17-8373-2024,https://doi.org/10.5194/gmd-17-8373-2024, 2024
Short summary

Cited articles

Butler, T., Lupascu, A., Coates, J., and Zhu, S.: TOAST 1.0: Tropospheric Ozone Attribution of Sources with Tagging for CESM 1.2.2, Geosci. Model Dev., 11, 2825–2840, https://doi.org/10.5194/gmd-11-2825-2018, 2018. a
Clappier, A., Belis, C. A., Pernigotti, D., and Thunis, P.: Source apportionment and sensitivity analysis: two methodologies with two different purposes, Geosci. Model Dev., 10, 4245–4256, https://doi.org/10.5194/gmd-10-4245-2017, 2017. a
Denby, B. R., et al.: Geosci. Model Dev., in preparation, 2020. a
Dunker, A. M., Yarwood, G., Ortmann, J. P., and Wilson, G. M.: Comparison of Source Apportionment and Source Sensitivity of Ozone in a Three-Dimensional Air Quality Model, Environ. Sci. Tech., 36, 2953–2964, https://doi.org/10.1021/es011418f, 2002. a
Elbern, H. and Schmidt, H.: A four-dimensional variational chemistry data assimilations scheme for Eulerian chemistry transport modeling, J. Geophys. Res., 104, 18583–18598, 1999. a
Download
Short summary
We present a new method for individually quantifying the contributions from different sources to local air pollution. The method can be used to distinguish the sources of local air pollution for any position in one single model simulation and thus to provide detailed maps of the origin of the pollutants. Hence, it can be used for time-critical operational services by providing scientific information as input for local policy decisions on air pollution abatement.