In the Kingdom of Saudi Arabia (KSA), meteorological drought leads to significant water scarcity and triggers profound social, economic, and environmental challenges. The main aim of this paper is to quantify spatiotemporal variability in drought using precipitation data from 28 meteorological stations (1985–2023) and produce future projections. Drought is expressed in terms of the amount of precipitations and a drought index known as Standardized Precipitation Index (SPI), which is used globally for quantifying drought intensity. Theil-Sen technique showed that during 1985–2023 at eleven stations and during 2010–2023 at 19 stations either drought decreased or remained unchanged. When the amount of precipitation in 1985 and in 2010 was deducted from the amount of precipitation in 2023, the difference was positive, showing an increase in the amount of precipitation in 2023. Since 2010, 25 sites and since 1985, 21 sites demonstrated an increase in the amount of precipitations. Gassim, Taif and Madinah demonstrated the highest increase in the amount of precipitation. When SPI values were averaged over the 39 years period, 5 out of the last 8 years had positive SPI values, showing a reduction in drought throughout the KSA. In 2023 drought increased only at 2 sites, all the remaining 26 sites showed a reduction in drought. A multi-model mean of 4 CMIP6 models estimated more drought in the near future, which will turn wet at the end of this century. The study will help the decision makers to finetune their strategies and implement interventions for addressing drought issue in KSA.

https://link.springer.com/article/10.1007/s41748-025-00570-w

This research aims to assess the spatial potential of solar energy in Saudi Arabia by estimating the total sum and analyzing the spatial variability of solar radiation to determine the best sites for solar energy generation that are least affected by sandstorms in the country. It also explores the effects of sandstorms on solar panels, identifies preventive measures, analyzes their impact on productivity, and recommends best practices for future development. The study utilizes time series analysis to estimate the impact of dust on productivity and identify effective ways to mitigate it. The spatial suitability of solar cell placement in different regions of the country is also analyzed. The study reveals that the Al-Ahsa region is significantly affected by sandstorms, with an average of around 25 sandstorms annually, indicating the region’s vulnerability to these environmental phenomena. The western region, specifically the Tabuk station, experiences a lower frequency of about 5.5 sandstorms per year. The Al-Ahsa region shows the highest daily average rate of sandstorms, ranging from 0.4 to 0.7 sandstorms per day, with the highest rates occurring from March to May. In terms of solar energy potential, the northwestern region in Tabuk exhibits the highest average potential of about 6 watts per square meter per day, followed by the southwestern region in Asir with approximately 5.5 watts per square meter per day. These findings provide valuable insights into understanding sandstorm patterns and identifying optimal locations for solar energy production, contributing to sustainable development efforts and climate change mitigation.

https://www.ajrsp.com/en/Archive/issue-58/1/

Communities play a crucial role in protecting the health of vulnerable populations such as the elderly, low-income groups, and high-risk individuals during cold spells. However, current strategies for responding to cold spells primarily consist of programmatic policies that lack practicality, specificity, and detailed implementation guidelines for community workers. Therefore, this study aims to identify and analyze the challenges faced by communities in responding to cold spells, review international experiences, and develop a set of practical checklists for community-level health protection. These checklists will assist community workers and volunteers in effectively preparing for, responding to, and recovering from cold spells.

https://doi.org/10.46234/ccdcw2024.018

Non-optimal temperatures significantly influence public health. However, the role of socio-economic factors in modulating health risks associated with non-optimal temperatures varies geographically and among different populations. Thus, the meteorological, air quality, health data, and socio-economic indicators were obtained from 23 districts in North and 48 districts in East China, respectively. Employing a two-stage meta-analysis, the exposure-response relationship was constructed for temperature against mortality from non-accidental causes, cardiovascular and cerebrovascular diseases, and respiratory illnesses. Furthermore, a non-linear spline regression was applied to assess the impact of socio-economic indicators on the exposure-response relationship and predicted future risks under various Shared Socioeconomic Pathways. The results revealed that the influence of socio-economic factors on the exposure-response curve showed heterogeneity in East China and North China. In North China, the shape of the exposure-response curve changed greatly under different socio-economic levels, while it remained similar in East China. In East China, the relative risk of heat and cold exposure was reduced in regions with high GDP, high levels of public finance, good medical services, and a low proportion of the elderly population. Specifically, the risk of non-accidental deaths due to heat shows a nearly linear negative correlation with per capita GDP in East China, with a decrease of the relative risk by 0.075 for every 10 thousand yuan increase in per capita GDP. Future projections indicate that population aging plays a decisive role in shaping the exposure-response curves. Although economic growth can reduce the risk of heat-related mortality, the combined effect of population aging and economic increase results in steeper exposure-response curves in both hot and cold temperature ranges in the future. In conclusion, although spatial variations in relative risk changes still exist, enhancing the adaptive capacity of populations can mitigate health risks associated with future climate change.

https://iopscience.iop.org/article/10.1088/1748-9326/ad57d4/meta

The complexity of monitoring is compounded by the environmental and health impacts linked to air pollution. The elevated expenses and intricate execution involved in measurements prompt the integration of modeling as a complementary approach alongside monitoring and surveillance efforts. Transport chemistry models like CHIMERE operate deterministically, utilizing meteorological factors, emissions data, boundary conditions, and various physical processes such as transport and Horizontal Mesh-Grid to influence inputs and outputs. The findings are validated using monitoring data over different periods of 2010, 2016, and 2021 and compared with results from prior research. The initial aspect reveals: (1) Enhanced resolution increases the probability of accurate forecasts, particularly for ozone, with PM10 displaying less distinct patterns. (2) Spatial resolution has minimal impact on temperature and wind speed. (3) Planetary Boundary Layer Height (PBLH) exhibits higher sensitivity, influencing Land Use and Land Cover (LULC), primarily due to emissions, advocating for higher resolution. The second aspect demonstrates: (4) CHIMERE-Artificial Neural Network (CHIMERE-ANN) demonstrates high accuracy in predicting ozone levels for Agadir and Casablanca, achieving improved correlation coefficients of 80% and 94%, respectively, accompanied by a notable decrease in Root Mean Square Error (RMSE) to 7.5 µg m–3 and 7.4 µg m–3. (5) Implementing CHIMERE-ANN with high spatial resolution concentrations (RA3 and RC3) enhances the accuracy of pollutant concentration forecasts. The proposed model enables rapid and detailed simulation of air pollution scenarios alongside flexibility for continuous updates.

https://link.springer.com/article/10.4209/aaqr.230309

While air pollution due to fine particulate matter (PM2.5) has been effectively controlled in China; the photochemical pollution characterized by elevated ozone (O3) has emerged as a major concern for air quality improvement. Except for ozone, Lhasa is one of the cleanest cities in China with the lowest annual PM2.5 concentration in 2017. The levels of major air pollutants in Lhasa are much lower than those of other cities in the eastern region of China, especially in May, when the O3 concentration peaks. This study was based on multi-source observations in combination with the Goddard Earth Observing System coupled with chemistry (GEOS-Chem) and the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) models to explore the causes of O3 pollution in Lhasa in May 2023. The results indicated that during the high O3 episodes, the concentrations of O3 precursors were low in Lhasa. Surrounding cities in other parts of the Tibetan Plateau also experienced high ozone concentrations despite being in different airsheds, suggesting that the O3 pollution in Lhasa was caused by regional transport rather than purely local emissions. Stratospheric intrusion events modulated by the westerly jet led to elevated ozone in the troposphere above Lhasa City. The results of the GEOS-Chem model indicated that horizontal advection, turbulence, diffusion, and other effects led to high concentrations of ozone in the near-surface above Lhasa. Vertical transport was the dominant factor leading to ozone concentration increases during high ozone days, with a contribution of 6.33 Gg/day. In addition, high-altitude air masses with a maximum altitude of over 8000 m, were observed arriving in Lhasa during the high ozone days. This study revealed that stratospheric intrusions have a greater contribution to the high O3 concentration in Lhasa in spring and provided a scientific basis for mitigating O3 pollution in the plateau cities.

https://doi.org/10.1016/j.atmosres.2024.107687

Desert dust and sand storms from the Sahara can sometimes be transported thousands of kilometers, causing social and economic damages throughout their path, deteriorating the air quality, and affecting human health. The seasonal climatology and transport pathways of dust aerosols are, therefore, essential to a deeper understanding of the impact of dust on the affected region. This study investigated the three-dimensional climatology of dust aerosol distribution and its transportation characteristics over the Sahara, Mediterranean, and Europe using multiple satellites and reanalysis data. The results showed a strong spatial variation of dust aerosols, with the highest loading over the Sahara Desert and a gradual latitudinal decrease toward the north with the lowest dust load over Europe. Temporally, higher dust activities were observed during summer and spring in terms of dust occurrence frequency and contribution to the total aerosol loading. Dust aerosols significantly increased over Central Sahara and decreased over Central Europe. Saharan dust was efficiently transported toward Europe during summer and spring, mainly at higher altitudes of 2–6 km. It could reach as north as the latitude of ∼42° N in the transect of 5° – 15° E in the layer 2–4 km during summer, while in winter, the occurrence of dust was at lower altitudes (< 2 km) in some areas in Europe, north of 45° N. The dust transport pathway toward Europe showed a long-standing dust layer mainly appearing over the Mediterranean from the near-surface up to 3 to 4 km during summer and spring, contributing to the meridional transport of dust. The dust layer characteristics and the planetary boundary layer (PBL) height above Europe showed the likelihood of dust intrusion into Europe’s PBL during summer. This work also highlighted the main elements affecting dust distribution and transport toward Europe: wind, topography, and the PBL height.

https://doi.org/10.1016/j.atmosres.2023.106658

Biomass fuel burning is a significant contributor of household fine particulate matter (PM_2.5) in the low to middle income countries (LMIC) and assessing PM_2.5 levels is essential to investigate exposure-related health effects such as pregnancy outcomes and acute lower respiratory infection in infants. However, measuring household PM_2.5 requires significant investments of labor, resources, and time, which limits the ability to conduct health effects studies. It is therefore imperative to leverage lower-cost measurement techniques to develop exposure models coupled with survey information about housing characteristics. Between April 2017 and March 2018, we continuously sampled PM_2.5 in three seasonal waves for approximately 48-h (range 46 to 52-h) in 74 rural and semi-urban households among the participants of the Bangladesh Cook Stove Pregnancy Cohort Study (CSPCS). Measurements were taken simultaneously in the kitchen, bedroom, and open space within the household. Structured questionnaires captured household-level information related to the sources of air pollution. With data from two waves, we fit multivariate mixed effect models to estimate 24-h average, cooking time average, daytime and nighttime average PM_2.5 in each of the household locations. Households using biomass cookstoves had significantly higher PM_2.5 concentrations than those using electricity/liquefied petroleum gas (626 μg/m³ vs. 213 μg/m³). Exposure model performances showed 10-fold cross validated R² ranging from 0.52 to 0.76 with excellent agreement in independent tests against measured PM_2.5 from the third wave of monitoring and ambient PM_2.5 from a separate satellite-based model (correlation coefficient, r = 0.82). Significant predictors of household PM_2.5 included ambient PM_2.5, season, and types of fuel used for cooking. This study demonstrates that we can predict household PM_2.5 with moderate to high confidence using ambient PM_2.5 and household characteristics. Our results present a framework for estimating household PM_2.5 exposures in LMICs, which are often understudied and underrepresented due to resource limitations.

https://doi.org/10.1016/j.envpol.2023.122568

Fine particulate matter, with an aerodynamic diameter ≤ 2.5 μm (PM_2.5), is a severe problem in China. The lack of ground-based measurements and its sparse distribution obstruct long-term air pollution impact studies over China. Therefore, the present study used newly updated Global Estimates (V5. GL.02) of monthly PM_2.5 data from 2001 to 2020 based on Geographically Weighted Regression (GWR) by Washington University. The GWR PM_2.5 data were validated against ground-based measurements from 2014 to 2020, and the validation results demonstrated a good agreement between GWR and ground-based PM_2.5 with a higher correlation (r = 0.95), lower error (8.14), and lower bias (−3.10 %). The long-term (2001−2020) PM_2.5 data were used to identify pollution hotspots and sources across China using the potential source contribution function (PSCF). The results showed highly significant PM_2.5 pollution hotspots in central (Henan, Hubei), North China Plain (NCP), northwest (Taklimakan), and Sichuan Basin (Chongqing, Sichuan) in China, with the most severe pollution occurring in winter compared to other seasons. During the winter, PM_2.5 was in the range from 6.08 to 93.05 μg/m³ in 33 provinces, which is 1.22 to 18.61 times higher than the World Health Organization (WHO) Air Quality Guidelines (AQG-2021; annual mean: 5 μg/m³). In 26 provinces, the reported PM_2.5 was 1.07 to 2.66 times higher than the Chinese Ambient Air Quality Standard (AAQS; annual mean: 35 μg/m³). Furthermore, provincial-level trend analysis shows that in most Chinese provinces, PM_2.5 increased significantly (3–43 %) from 2001 to 2012, whereas it decreased by 12–94 % from 2013 to 2020 due to the implementation of air pollution control policies. Finally, the PSCF analysis demonstrates that China’s air quality is mainly affected by local PM_2.5 sources rather than by pollutants imported from outside China.

https://doi.org/10.1016/j.scitotenv.2023.164871