Abstract
According to World Health Organization, 9 out of 10 people breathe polluted air and the ambient air pollution accounts for nearly 4.2 million early deaths worldwide. There is an urgent need for scientific management of urban air systems. Mathematical modeling of air quality helps the researchers and urban authorities in devising scientific management plans for mitigation of the associated impacts. We present an organized review of the broad aspects related to urban air quality modeling such as – urban microclimate, geospatial data, chemical transport models, computational fluid dynamics (CFD) models and integration of CFD and mesoscale models. The paper also discusses about the influence of urban land scape features on air quality, accuracy of emission inventory and model validation methods. The present review provides a vantage point to the researchers in the emerging field of high resolution urban air quality modeling for devising the location specific mitigation plans for the scientific management of the clean air.
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Abbreviations
- ABL:
-
Atmospheric boundary layer
- ACCMIP:
-
Atmospheric chemistry & climate model intercomparison project
- AIRS:
-
Atmospheric infrared sounder
- AOD:
-
Aerosol optical depth
- AppEEARS:
-
Application for extracting and exploring analysis ready samples
- CALPUFF:
-
California puff model
- CAMS-GLOB-BIO:
-
CAMS (Copernicus atmosphere monitoring service)-Global-Biogenic emissions
- CAMx:
-
Comprehensive air quality model with extensions
- CB-5:
-
Carbon bond −5
- CBM-Z:
-
Carbon bond mechanism version -Z
- CFD:
-
Computational fluid dynamics
- CMAQ:
-
Community multi-scale air quality model
- CTM :
-
Chemical transport model
- DEM:
-
Digital elevation model
- DNS:
-
Direct numerical simulation
- DSM:
-
Digital surface model
- EDGAR:
-
Emission database for global atmospheric research
- F-TUV:
-
Fast troposphere ultraviolet visible photolysis scheme
- GEIA:
-
Global emissions initiative
- GOCART:
-
Global ozone chemistry aerosol radiation and transport
- IASI:
-
Infrared atmospheric sounding interferometer
- ISL:
-
Inertial sub-layer
- LAADS:
-
The Level-1 and atmosphere archive & distribution system
- LES:
-
Large eddy simulation
- LiDAR:
-
Light detection and ranging
- LOD:
-
Level of detail
- LPDAAC:
-
Land processes distributed active archive center
- MADE:
-
Modal aerosol dynamics model for europe
- MAM:
-
Modal AEROSOL MODule
- MEGAN:
-
Model of emissions of gases and aerosols from nature
- MISR:
-
Multi-angle imaging spectroradiometer
- MM5:
-
Mesoscale model 5th generation
- MODIS:
-
Moderate resolution imaging spectroradiometer
- MOPITT:
-
Measurement of pollution in the troposphere
- MOSAIC:
-
Model for simulating aerosol interactions and chemistry
- NASA:
-
The National aeronautics and space administration
- NMVOC:
-
Non-methane volatile organic compound
- OMI:
-
Ozone monitoring instrument
- OpenFOAM:
-
Open field operation and manipulation
- OSM:
-
Open street maps
- PBL:
-
Planetary boundary layer
- POET:
-
Precursors of ozone and their effects in the troposphere
- RACM:
-
Regional atmospheric chemistry mechanism
- RADM2:
-
Regional acid deposition model-2nd version
- RANS:
-
Reynolds averaged navier-stokes
- RETRO:
-
Reanalysis of the TROpospheric chemical composition
- RSL:
-
Roughness sub-layer
- SIMPLE:
-
Semi-implicit method for pressure linked eqs.
- SL:
-
Surface layer
- SORGAM:
-
Secondary organic aerosol model
- SUMO:
-
Simulation of URBAN Mobility
- TES:
-
Tropospheric emission spectrometer
- TKE:
-
Turbulent kinetic energy
- UBL:
-
Urban boundary layer
- UCL:
-
Urban canopy layer
- UCM:
-
Urban canopy model
- UHI:
-
Urban heat island
- VBS:
-
Volatility basis set
- WHO:
-
World health organization
- WRF-Chem:
-
Weather research and forecast – chemistry
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Acknowledgments
Authors wish to thank Director of CSIR-National Environmental Engineering Research Institute, Nagpur and Director of National Institute of Technology, Warangal for the support. Authors also acknowledge the NEERI’s KRC No.CSIR-NEERI/KRC/2018/JULY/CTMD/1.
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Highlights
1. The study provides an organized review on topics associated with the high-resolution urban air quality modeling.
2. Provides the present scenario of the urban air quality modeling methods.
3. Identifies the challenges for further development of the urban air quality modeling.
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Kadaverugu, R., Sharma, A., Matli, C. et al. High Resolution Urban Air Quality Modeling by Coupling CFD and Mesoscale Models: a Review. Asia-Pacific J Atmos Sci 55, 539–556 (2019). https://doi.org/10.1007/s13143-019-00110-3
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DOI: https://doi.org/10.1007/s13143-019-00110-3