Monthly meteorological data from 27 observation stations provided by the Presidency of Meteorology and Environment (PME) of Saudi Arabia were used to analyze the spatial and temporal distribution of atmospheric dust in Saudi Arabia between 2000 and 2016. These data were used to analyze the effects of environmental forcing on the occurrence of dust storms across Saudi Arabia by considering the relationships between dust storm frequency and temperature, precipitation, and wind variables. We reveal a clear seasonality in the reported incidence of dust storms, with the highest frequency of events during the spring. Our results show significant positive relationships (p < 0.005) between dust storm occurrence and wind speed, wind direction, and precipitation. However, we did not detect a significant relationship with temperature. Our results reveal important spatial patterns, as well as seasonal and inter-annual variations, in the occurrence of dust storms in Saudi Arabia. For instance, the eastern part of the study area experienced an increase in dust storm events over time, especially in the region near Al-Ahsa. Similarly, an increasing trend in dust storms was also observed in the west of the study area near Jeddah. However, the occurrence of dust storm events is decreasing over time in the north, in areas such as Hail and Qaisumah. Overall, the eastern part of Saudi Arabia experiences the highest number of dust storms per year (i.e., 10 to 60 events), followed by the northern region, with the south and the west having fewer dust storm events (i.e., five to 15 events per year). In addition, our results showed that the wind speeds during a dust storm are 15–20 m/s and above, while, on a non-dust day, the wind speeds are approximately 10–15 m/s or lower. Findings of this study provide insight into the relationship between environmental conditions and dust storm occurrence across Saudi Arabia, and a basis for future research into the drivers behind these observed spatio-temporal trends.

Sand and dust storm events (SDEs), which result from strong surface winds in arid and semi-arid areas, exhibiting loose dry soil surfaces are detrimental to human health, agricultural land, infrastructure, and transport. The accurate detection of near-surface dust is crucial for quantifying the spatial and temporal occurrence of SDEs globally. The Arabian Peninsula is an important source region for global dust due to the presence of extensive deserts. This paper evaluates the suitability of five different MODIS-based methods for detecting airborne dust over the Arabian Peninsula: (a) Normalized Difference Dust Index (NDDI); (b) Brightness Temperature Difference (BTD) (31–32); (c) BTD (20–31); (d) Middle East Dust Index (MEDI) and (e) Reflective Solar Band (RSB). We derive detection thresholds for each index by comparing observed values for ‘dust-present’ versus ‘dust-free’ conditions, taking into account various land cover settings and analyzing associated temporal trends. Our results suggest that the BTD (31–32) method and the RSB index are the most suitable indices for detecting dust storms over different land-cover types across the Arabian Peninsula. The NDDI and BTD (20–31) methods have limitations in identifying dust over multiple land-cover types. Furthermore, the MEDI has been found to be unsuitable for detecting dust in the study area across all land-cover types.

Background/Objectives: Pediatric asthma is a leading cause of emergency department visits, and air pollution is a known primary environmental trigger. Although worldwide air pollutants have been associated with asthma exacerbations, limited data have been reported in the Eastern Province of Saudi Arabia. This study aimed to investigate the relationship between air pollution and pediatric asthma admissions among children aged 2 to 14 years old at King Fahd Hospital of the University Hospital (KFHU). Methods: This is a retrospective cohort study, over 366 days, including 1750 pediatric asthma-related ER visits and daily concentrations of air pollutants (PM_2.5, PM₁₀, NO₂, SO₂, CO, and O₃) and meteorological factors (temperature and humidity). Various statistical models, such as Poisson regression and ARIMA, were applied to determine the association between pollutants levels and hospital ER visits. The data were visit-based in nature, and it was not possible to follow up with repeat visits or for admission status for individual patients. Results: Elevated levels of PM_2.5, NO₂, and CO were significantly associated with more pediatric asthma ER visits, mainly on the same day and with short lags. PM_2.5 displayed the strongest association, consistent with its deeper pulmonary penetration and greater toxicity. Also, PM₁₀ levels were inversely associated with ER visits, possibly due to particle size and deposition location differences. Significantly correlated with increased ER visits are lower ambient temperature and higher humidity. Conclusions: This study offers strong evidence on the relationship between air pollution and pediatric asthma events, in turn highlighting the vital importance of air quality regulation, public health policies, and clinical vigilance for environmental exposures.

Dust storms are a major environmental hazard across the Arabian Peninsula, affecting air quality, human health, transportation, and infrastructure. Despite their frequency in Riyadh, event-based studies integrating aerosol optical observations, atmospheric transport analyses, potential source-region identification, and surface validation remain limited. This study characterized dust events over Riyadh during 2024–2025 using AERONET aerosol observations, NOAA Integrated Surface Database (ISD) meteorological records, HYSPLIT backward trajectories, and potential source contribution function and concentration-weighted trajectory analyses. Dust events were identified using a dual optical criterion based on aerosol optical depth (AOD) at 500 nm (AOD₅₀₀ ≥ 0.50) and the 440–870 nm Ångström exponent (≤0.50) and were compared with co-located NOAA ISD present-weather and visibility observations. Among the 34 identified dust events, 82% occurred between March and May. The events were dominated by coarse-mode aerosols, with a mean Ångström exponent of 0.28 and a mean fine-mode fraction of 0.26. Same-day dust-related present-weather reports were recorded for 26 events (76%), while the remaining eight events (24%) were classified as elevated because no dust-related present-weather code was reported at the co-located ISD station during the corresponding event day. The trajectory and source–receptor analyses showed a consistent spatial association with the Mesopotamian Basin as a major potential source region and the northern Rub’ al-Khali as a secondary potential source region. Surface wind speed was significantly and positively associated with coarse-mode aerosol loading and dust-event severity, whereas sea-level pressure showed no statistically significant association with severity. These findings provide an updated observational characterization of dust events affecting Riyadh during 2024–2025 and demonstrate the complementary value of aerosol optical observations, co-located surface records, atmospheric transport analysis, and source–receptor methods for examining dust-event characteristics and transport over central Saudi Arabia.

Dust storms are a major environmental hazard in arid regions, significantly affecting air quality, public health, and infrastructure. Accurate forecasting is essential, particularly in the Middle East, where dust activity is driven by regional meteorological and land surface conditions. While machine learning has been increasingly used for dust storm prediction, the combined use of surface meteorological observations and high-resolution satellite-derived aerosol products remains underexplored. The main objective of this study is to evaluate whether integrating MODIS MAIAC Aerosol Optical Depth (AOD, Optical_Depth_047) with surface meteorological data from the NOAA Integrated Surface Dataset improves the predictive accuracy and calibration of machine learning models for daily dust storm detection in Al-Ahsa, Saudi Arabia (2015–2024). To achieve this, two modelling frameworks were developed: Model 1, which used meteorological variables only (temperature, wind speed, and visibility), and Model 2, which incorporated AOD. Five algorithms Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and Multilayer Perceptron were assessed using temporal validation, k-fold cross-validation, and probabilistic calibration. Random Forest achieved the best performance, with high predictive accuracy and improved calibration when AOD was included (Brier score reduced from 0.087 to 0.080). These findings confirm the dominant role of meteorological variables while showing that AOD enhances probabilistic reliability, providing a robust framework for operational dust forecasting in arid regions. Graphical Abstract The alternative text for this image may have been generated using AI.