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High Resolution Urban Air Quality Modeling by Coupling CFD and Mesoscale Models: a Review

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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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Correspondence to Rakesh Kadaverugu or Chandrasekhar Matli.

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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

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