Abstract. 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&amp;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&amp;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&amp;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&amp;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 PM2.5. 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 PM2.5 (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 PM2.5 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.

&lt;p&gt;&lt;strong&gt;Abstract.&lt;/strong&gt; 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&amp;#8201;km resolution) coupled with Chemistry (WRF-Chem) were used to evaluate natural and anthropogenic aerosol air pollution in the ME during 2015&amp;#8211;2016. Satellite and ground-based AOD observations, as well as in-situ Particulate Matter (PM) measurements for 2016, were used for validation.&lt;/p&gt; &lt;p&gt;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. SO&lt;sub&gt;2&lt;/sub&gt; emissions in WRF-Chem are based on the novel NASA SO&lt;sub&gt;2&lt;/sub&gt; emission dataset that reveals unaccounted sources over the ME.&lt;/p&gt; &lt;p&gt;Although aerosol fields in WRF-Chem and assimilation products are quite consistent, WRF-Chem, due to its higher spatial resolution and better SO&lt;sub&gt;2&lt;/sub&gt; 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 (~&amp;#8201;75&amp;#8211;95&amp;#8201;%) is mineral dust. In the ME urban centers and near oil recovery fields, non-dust aerosols (primarily sulfate) contribute up to 26&amp;#8201;% into PM&lt;sub&gt;2.5&lt;/sub&gt;. The contribution of sea salt into PM can rich up to 5&amp;#8201;%. The contribution of organic matter into PM prevails over black carbon.&lt;/p&gt;

Oil recovery, power generation, water desalination, gas flaring, and traffic are the main contributors to SO2 emissions in the Middle East (ME). Satellite observations suggest that the traditional emission inventories do not account for multiple SO2 emission sources in the ME. This study aims to evaluate the most frequently used SO2 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 SO2 emissions taken from the HTAP‐2.2 inventory with strong (>30 kt/year) SO2 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 SO2 sources in the ME and significantly underestimate SO2 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 SO2 observations. Our simulations show that SO2 surface concentrations in Jeddah and Riyadh frequently exceed European air‐quality limits. The ME generates about 10% of global anthropogenic SO2 emissions, on par with India. Therefore, the development of effective emission controls and improvement of air‐quality monitoring in the ME are urgently needed.

As part of Saudi Vision 2030, a major strategic framework developed by the Council of Economic and Development Affairs of Saudi Arabia, the country aims to reduce its dependency on oil and promote renewable energy for domestic power generation. Among the sustainable energy resources, solar energy is one of the leading resources because of the endowment of Saudi Arabia with plentiful sunlight exposure and year-round clear skies. This essentializes to forecast and simulate solar irradiance, in particular global horizontal irradiance (GHI), as accurately as possible, mainly to be utilized by the power system operators among many others. Motivated by a dataset of hourly solar GHIs, this article proposes a model for short-term point forecast and simulation of GHIs. Two key points, that make our model competent, are: (1) the consideration of the strong dependency of GHIs on aerosol optical depths and (2) the identification of the periodic correlation structure or cyclostationarity of GHIs. The proposed model is shown to produce better forecasts and more realistic simulations than a classical model, which fails to recognize the GHI data as cyclostationary. Further, simulated samples from both the models as well as the original GHI data are used to calculate the corresponding photovoltaic power outputs to provide a comprehensive comparison among them.

&lt;p&gt;&lt;strong&gt;Key Words:&lt;/strong&gt;&lt;br /&gt;Air Quality; Air Pollution Monitoring; Low-Cost Sensors; Reference Methods, Microsensors, Experimental Campaign&lt;br /&gt;&amp;#160;&lt;br /&gt;&lt;strong&gt;Abstract:&lt;/strong&gt;&lt;/p&gt; &lt;p&gt;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&amp;#160;innovative&amp;#160;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.&lt;/p&gt; &lt;p&gt;&lt;br /&gt;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&amp;#160;the Saudi Authority for Industrial Cities and Technology Zones (MODON)&amp;#160;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.&lt;br /&gt;&lt;br /&gt;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.&lt;/p&gt;