WRF-Chem Simulation for Modeling Seasonal Variations and Distributions of Aerosol Pollutants over the Middle East

Abstract

Atmospheric aerosols and dust have become a challenge for urban air quality. The presented study quantified seasonal spatio-temporal variations of aerosols, tropospheric ozone, and dust over the Middle East (ME) for the year 2012 by using the HTAP emission inventory in the WRF-Chem model. Simulated gaseous pollutants, aerosols and dust were evaluated against satellite measurements and reanalysis datasets. Meteorological parameters, temperature, and wind vector were evaluated against MERRA2. The model showed high spatio-temporal variability in meteorological parameters during summer and low variability in winter. The correlation coefficients for all the parameters are estimated to be 0.92, 0.93, 0.98, and 0.89 for January, April, July, and October respectively, indicating that the WRF-Chem model reproduced results very well. Simulated monthly mean AOD values were maximum in July (1.0–1.5) and minimum in January (0.1–0.4) while April and October were in the range of 0.6–1.0 and 0.3–0.7 respectively. Simulated dust concentrations were high in April and July. The monthly average aerosol concentration was highest over Bahrain, Kuwait, Qatar, and the United Arab Emirates and Jeddah, Makkah. The contributions to urban air pollution were highest over Makkah city with more than 25% from anthropogenic sources.

Study on the Concentration of Top Air Pollutants in Xuzhou City in Winter 2020 Based on the WRF-Chem and ADMS-Urban Models

“…However, it did not match the performance of the other two models in simulating particulate matter concentrations. Shahid et al [15] simulated the seasonal spatial and temporal variations of aerosols, tropospheric ozone, and dust over the Middle East (ME) in 2012 using the WRF-Chem model, and achieved better simulation results. Do et al [16] used the WRF-Chem model to simulate the meteorological variables in winter and summer in northern Vietnam in 2014, along with the local PM 2.5 and PM 10 concentrations, and showed that although the simulation results had a good spatial and temporal consistency with observations, the amount of day-to-day variability at each observation site was difficult to capture, which may be related to either the model itself or the input emissions data.…”

Observed improvement in air quality in Delhi during 2011–2021: Impact of mitigation measures

“…Regional air quality models have been essential tools for scientifically understanding the distribution of emissions sources, transport and transformation (Yarragunta et al, 2020;Shahid et al, 2021;Jena et al, 2021;Du et al, 2022;Kumar et al, 2022). For regional modelling studies, emission inventories are essential for reflecting the emission inputs into the atmosphere.…”

Spatial and Temporal Variations in Spring Dust Concentrations from 2000 to 2020 in China: Simulations with WRF-Chem

“…However, these methods are difficult to use for large areas due to the limited number of observation stations. Therefore, some studies estimated spatial distributions of dust concentrations over a large area by using air quality models, including the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) [37][38][39][40].…”

“…Although air quality models can reproduce the dust concentrations over a large region, most of these studies mainly concentrated on specific dust episodes (e.g., Rizza et al, 2017;Karagulian et al, 2019;Karegar et al, 2019) [41][42][43] or a short-term simulations (e.g., Shahid et al, 2021) [40]. China is one of the major contributors to global dust aerosols [27,44].…”