Modern-Era Retrospective analysis for Research and Applications v.2 (MERRA-2), Copernicus Atmosphere Monitoring Service Operational Analysis (CAMS-OA), and a high-resolution regional Weather Research and Forecasting model coupled with chemistry (WRF-Chem) were used to evaluate natural and anthropogenic particulate matter (PM) air pollution in the Middle East (ME) during 2015–2016. Two Moderate Resolution Imaging Spectrometer (MODIS) retrievals – combined product Deep Blue and Deep Target (MODIS-DB&DT) and Multi-Angle Implementation of Atmospheric Correction (MAIAC) – and Aerosol Robotic Network (AERONET) aerosol optical depth (AOD) observations as well as in situ PM measurements for 2016 were used for validation of the WRF-Chem output and both assimilation products. MERRA-2 and CAMS-OA assimilate AOD observations. WRF-Chem is a free-running model, but dust emission in WRF-Chem is tuned to fit AOD and aerosol volume size distributions obtained from AERONET. MERRA-2 was used to construct WRF-Chem initial and boundary conditions both for meteorology and chemical and aerosol species. SO₂ emissions in WRF-Chem are based on the novel OMI-HTAP SO₂ emission dataset. The correlation with the AERONET AOD is highest for MERRA-2 (0.72–0.91), MAIAC (0.63–0.96), and CAMS-OA (0.65–0.87), followed by MODIS-DB&DT (0.56–0.84) and WRF-Chem (0.43–0.85). However, CAMS-OA has a relatively high positive mean bias with respect to AERONET AOD. The spatial distributions of seasonally averaged AODs from WRF-Chem, assimilation products, and MAIAC are well correlated with MODIS-DB&DT AOD product. MAIAC has the highest correlation (R=0.8), followed by MERRA-2 (R=0.66), CAMS-OA (R=0.65), and WRF-Chem (R=0.61). WRF-Chem, MERRA-2, and MAIAC underestimate and CAMS-OA overestimates MODIS-DB&DT AOD. The simulated and observed PM concentrations might differ by a factor of 2 because it is more challenging for the model and the assimilation products to reproduce PM concentration measured within the city. Although aerosol fields in WRF-Chem and assimilation products are entirely consistent, WRF-Chem is preferable for analysis of regional air quality over the ME due to its higher spatial resolution and better SO₂ emissions. The WRF-Chem’s PM background concentrations exceed the World Health Organization (WHO) guidelines over the entire ME. Mineral dust is the major contributor to PM (≈75 %–95 %) compared to other aerosol types. Near and downwind from the SO₂ emission sources, nondust aerosols (primarily sulfate) contribute up to 30 % to PM_2.5. The contribution of sea salt to PM in coastal regions can reach 5 %. The contributions of organic matter, black carbon and organic carbon to PM over the Middle East are insignificant. In the major cities over the Arabian Peninsula, the 90th percentile of PM₁₀ and PM_2.5 (particles with diameters less than 10 and 2.5 µm, respectively) daily mean surface concentrations exceed the corresponding Kingdom of Saudi Arabia air quality limits. The contribution of the nondust component to PM_2.5 is <25 %, which limits the emission control effect on air quality. The mitigation of the dust effect on air quality requires the development of environment-based approaches like growing tree belts around the cities and enhancing in-city vegetation cover. The WRF-Chem configuration presented in this study could be a prototype of a future air quality forecast system that warns the population against air pollution hazards.

Oil recovery, power generation, water desalination, gas flaring, and traffic are the main contributors to SO emissions in the Middle East (ME). Satellite observations suggest that the traditional emission inventories do not account for multiple SO emission sources in the ME. This study aims to evaluate the most frequently used SO emission data sets over the ME by comparing high‐resolution regional model simulations and meteorology/chemistry assimilation products, MERRA‐2 and CAMS, with satellite and available ground‐based air‐quality observations. Here, we employ the WRF‐Chem‐3.7.1 regional meteorology‐chemistry model and conduct simulations for the period 2015–2016 with 10 km grid spacing using HTAP‐2.2 emission data sets and the new OMI‐HTAP data, which is based on the combination of the near‐surface SO emissions taken from the HTAP‐2.2 inventory with strong (>30 kt/year) SO point sources obtained from the satellite Ozone Monitoring Instrument (OMI) observations. We find that conventional emission inventories (EDGAR‐4.2, MACCity, and HTAP‐2.2) have uncertainties in the location and magnitude of SO sources in the ME and significantly underestimate SO emissions in the Arabian Gulf. The WRF‐Chem, run in conjunction with the new OMI‐HTAP emissions, improves comparisons between the satellite and ground‐based SO observations. Our simulations show that SO surface concentrations in Jeddah and Riyadh frequently exceed European air‐quality limits. The ME generates about 10% of global anthropogenic SO emissions, on par with India. Therefore, the development of effective emission controls and improvement of air‐quality monitoring in the ME are urgently needed.

Modern-Era Retrospective analysis for Research and Applications v.2 (MERRA-2), Copernicus Atmosphere Monitoring Service Operational Analysis (CAMS-OA) data assimilation products, and a regional Weather Research and Forecasting model (10 km resolution) coupled with Chemistry (WRF-Chem) were used to evaluate natural and anthropogenic aerosol air pollution in the ME during 2015–2016. Satellite and ground-based AOD observations, as well as in-situ Particulate Matter (PM) measurements for 2016, were used for validation. WRF-Chem code was modified to correct the calculation of dust gravitational settling and aerosol optical properties. The dust emission in WRF-Chem is calibrated to fit Aerosol Optical Depth (AOD) and aerosol volume size distributions obtained from Aerosol Robotic Network (AERONET) observations. MERRA-2 was used to construct WRF-Chem initial and boundary conditions both for meteorology and chemical/aerosol species. SO₂ emissions in WRF-Chem are based on the novel NASA SO₂ emission dataset that reveals unaccounted sources over the ME. Although aerosol fields in WRF-Chem and assimilation products are quite consistent, WRF-Chem, due to its higher spatial resolution and better SO₂ emissions, is preferable for analysis of regional air-quality over the ME. The WRF-Chem's PM background concentrations exceed the World Health Organization (WHO) guidelines over the entire ME. The major contributor to PM (~ 75–95 %) is mineral dust. In the ME urban centers and near oil recovery fields, non-dust aerosols (primarily sulfate) contribute up to 26 % into PM_2.5. The contribution of sea salt into PM can rich up to 5 %. The contribution of organic matter into PM prevails over black carbon.

The Middle East is notorious for high air pollution that affects both air-quality and regional climate. The Middle East generates about 30% of world dust annually and emits about 10% of anthropogenic SO2. In this study we use Modern-Era Retrospective analysis for Research and Applications v.2 (MERRA-2), Copernicus Atmosphere Monitoring Service Operational Analysis (CAMS-OA) data assimilation products, and a regional Weather Research and Forecasting model (10 km resolution) coupled with Chemistry (WRF-Chem) to evaluate natural and anthropogenic air pollution in the ME. The SO2 anthropogenic emissions used in WRF-Chem are updated using the independent satellite SO2 emission dataset obtained from the Ozone Monitoring Instrument (OMI) observations onboard NASA EOS Aura satellite. Satellite and ground-based aerosol optical depth (AOD) observations, as well as Particulate Matter (PM) and SO2 in situ measurements for 2015-2016, were used for validation and model evaluation.  Although aerosol fields in regional WRF-Chem and global assimilation products are quite consistent, WRF-Chem, due to its higher spatial resolution and novel OMI SO2 emissions, is preferable for analysis of regional air-quality over the ME. We found that conventional emission inventories (EDGAR-4.2, MACCity, and HTAP-2.2) have uncertainties in the location and magnitude of SO2 sources in the ME and significantly underestimate SO2 emissions in the Arabian Gulf. CAMS reanalysis tends to overestimate PM2.5 and underestimate PM10 concentrations. In the coastal areas, MERRA2 underestimates sulfate and tends to overestimate sea salt concentrations. The WRF-Chem’s PM background concentrations exceed the World Health Organization (WHO) guidelines over the entire ME. The major contributor to PM (~75–95%) is mineral dust. In the ME urban centers and near oil recovery fields, non-dust aerosols (primarily sulfate) contribute up to 26% into PM2.5. The contribution of sea salt into PM can rich up to 5%. The contribution of organic matter into PM prevails over black carbon. SO2 surface concentrations in major ME cities frequently exceed European air-quality limits.

Key Words:Air Quality; Air Pollution Monitoring; Low-Cost Sensors; Reference Methods, Microsensors, Experimental Campaign  Air quality in the Middle East (ME) is strongly affected by desert dust besides anthropogenic pollutants. The health hazards associated with particulate matter (PM) are the most severe in this desert region. The enhancement of Air quality monitoring is needed to implement abatement strategies and stimulate environmental awareness among citizens. Several techniques are used to monitor PM concentration. The air quality monitoring stations (AQMS) equipped with certified instrumentation is the most reliable option. However, AQMSs are quite expensive and require regular maintenance. Another option is low-cost sensors (LCS) that seen as innovative tools for future smart cities. They are cost-effective and allow to increase the spatial coverage of air-quality measurements as the number of conventional AQMS is generally quite small, so the current density of the monitoring stations in the Middle East is low. In this work, we evaluated the PM air-quality climatology in the major cities in Saudi Arabia (Jeddah, Riyadh, and Dammam) for four years between 2016 and continued until 2020. We used the measurement data that were conducted by the Saudi Authority for Industrial Cities and Technology Zones (MODON) using certified reference AQMS installed inside the suburban areas of the three major cities in Saudi Arabia. Also, we tested the performance of the five LCS systems for eight months, starting in May 2019 and continued until January 2020. For this purpose, we set AQMS with the PM reference instrumentation (based on beta-ray absorption) side-by-side with five different LCS systems (based on light scattering) in the industrial part of Jeddah city. We collected, filtered, validated PM data, and applied standard measurement and calibration procedures.The AQMS measurements show that in Summer, the daily mean PM concentrations exceed the World Health Organization (WHO) limits for PM2.5 and PM10 almost every day in Jeddah, Riyadh, and Dammam. The WHO limits are also frequently violated in the winter months. The AQMS measurements reliably show dust storm spikes when PM pollution is extremely high while all the LCSs fail to capture these severe events. We found that LCS and AQMS PM measurements are poorly correlated in Summer, but show slightly better results in fair-weather Winter days when humidity and temperature are low. But they still cannot capture severe dust events.

The dust emission simulated within the up-to-date global and regional models differs by almost an order of magnitude. The models are tuned to reproduce the observed aerosol optical depth (AOD) that, with some caveats, reflects the dust mass retained in the atmosphere. However, the amount of dust suspended in the atmosphere is controlled independently by the dust emission and deposition; therefore, only AOD observations are insufficient to constrain both these processes. To calculate the dust emission over the Middle East (ME), in this study, we employ dust deposition observations, AERONET AOD, micro-pulse lidar, and satellite observations to constrain the WRF-Chem simulations. The dust deposition is measured on a monthly bases for 2015-2019 using passive samplers over six sites over land and the sea. We compare the WRF-Chem simulations, conducted with 10-km grid spacing, with the recent MERRA-2 and CAMS reanalysis. WRF-Chem is configured with the GOCART dust scheme. We calculate the meteorological and aerosol initial and boundary conditions using the MERRA-2 reanalysis.  We evaluated the dust regional mass balance controlled by emission, deposition, and cross-boundary transport. The smallest dust particles are transported at vast distances while the heaviest ones deposit inside of the domain. Since the model accounts for dust particles with radii<10 um, we process the deposition samples to extract the weight of particles smaller than 10 um. WRF-Chem was tuned to reproduce the observed AOD and monthly deposition of dust particles with radii < 10 um. We found that the ME dust emission comprises about 30% of the global annual dust emission. MERRA-2 underestimates the ME dust emission by about 15%.