## 3 Results
### Main Outcomes of the Top 20 Highly Cited Articles from Each Thematic Area
#### 3.2.1 Atmosphere
The sources of dust storms were identified from both the Sahara Desert and the Arabian Desert. Similarly, [PERSON] et al. [39] investigated dust storms over the Arabian Peninsula using the regionally adapted chemistry transport model CHIMEE coupled with the Weather Research and Forecast (WRF) model. They found good agreement (R \(\sim\)0.73 and RMSE \(\sim\)0.1) in the spatiotemporal pattern between simulations and observations (MSG/SEVRI RGB dust, MODIS Deep Blue Aerosol Optical Depth: DB-AOD, Ozone Monitoring Instrument Observed UV Aerosol Absorption Index: OMI-AI and AERONET AOD). [PERSON] et al. [40] used three statistical-based machine learning algorithms (weights of evidence, frequency ratio, and random forest) to produce a dust source susceptibility map (DSSM) in Khorasan Razavi Province (Iran). Land use, lithology, slope, soil, geomorphology, normalized difference vegetation index (NDVI), and distance from river data were included in the models. The random forest model with an area under the curve (AUC) value equal to 88% showed higher performance than frequency ratio (80%) and weights of evidence (81%) when validated with MODIS data (23 images during the 2005-2016 period). Similarly, [PERSON] et al. [41] applied eight algorithms including random forest (RF), support vector machine (SVM), Bayesian additive regression trees (BART), radial basis function (RBF), extreme gradient boosting (XGBoost), regression tree analysis (RTA), Cubist model and boosted regression trees (BRTs), and an ensemble modeling (EM) approach for generating spatial dust distribution maps in Khuzestan Province (Iran) and Iraq. The EM approach showed the highest performance (AUC = 99.8%) for generating the dust storm provenance map. Spaceborne NO\({}_{2}\) column observations from two high-resolution instruments, Tropospheric Monitoring Instrument (TROPOMI, Sentinel-5 Precursor) and Ozone Monitoring Instrument (OMI), were used to assess the impact of the COVID-19 outbreak on NO\({}_{2}\) pollution in five different regions (China, South Korea, western Europe, the USA, and Iran) [43]. The results showed a 20-40% decrease in NO\({}_{2}\) columns over China, Europe, South Korea, and the United States between January and April 2020, due to the lockdown measures implemented by the governments. A likely explanation for the absence of a decrease in NO\({}_{2}\) in Iran is that complete lockdowns were not enforced in Iran. The authors emphasized that further studies are needed to evaluate the effects of the temporary lockdowns on global air quality and climate and the gradual return the values of to prelockdown periods. On the contrary, the study of [PERSON] et al. [44] showed that the COVID-19 lockdown positively affected Iran's air quality, as evident by the reduction in the levels of CO, NO\({}_{2}\), SO\({}_{2}\), and PM\({}_{10}\). Similar trends of NO\({}_{2}\) reduction during the lockdown were observed for other EMMENA countries, such as Egypt and Iraq [45,46]. The remaining studies in the atmosphere thematic area focus on the improvement of CO2 anthropogenic emission measurements [47], climate change [48,49], aerosol pollution [50], drought estimation [51], and preseismic ionospheric anomalies [52]. [PERSON] et al. [47] developed a co-location method with the simultaneous use of CO\({}_{2}\) (OCO-2) and NO\({}_{2}\) (Sentinel-5) data for the identification and quantification of anthropogenic emission plume signals. The authors highlighted that existing satellites present high flux uncertainties which are expected to be reduced by the planned European Copernicus anthropogenic CO\({}_{2}\) monitoring mission (CO2M), due to higher spatial resolution and better imaging capabilities. Long-term trends between vegetation and climate showed apparent climate change effects in Iran [48]. Using 33-year records of NDVI (calculated with data from the Advanced Very High Resolution Radiometer (AVHRR) satellite) and climate variables, [PERSON] et al.