Little is known about the main sources of ambient particulate matter (PM) in the 22 Eastern Mediterranean Region (EMR) countries. We designed this study to systematically review all published and unpublished source apportionment (SA), identification and characterization studies as well as emission inventories in the EMR. Of 440 articles identified, 82 (11 emission inventory ones) met our inclusion criteria for final analyses. Of 22 EMR countries, Iran with 30 articles had the highest number of studies on source specific PM followed by Pakistan (n = 15 articles) and Saudi Arabia (n = 8 papers). By contrast, there were no studies in Afghanistan, Bahrain, Djibouti, Libya, Somalia, Sudan, Syria, Tunisia, United Arab Emirates and Yemen. Approximately 72% of studies (51) were published within a span of 2015–2021.48 studies identified the sources of PM₂.₅ and its constituents. Positive matrix factorization (PMF), principal component analysis (PCA) and chemical mass balance (CMB) were the most common approaches to identify the source contributions of ambient PM. Both secondary aerosols and dust, with 12–51% and 8–80% (33% and 30% for all EMR countries, on average) had the greatest contributions in ambient PM₂.₅. The remaining sources for ambient PM₂.₅, including mixed sources (traffic, industry and residential (TIR)), traffic, industries, biomass burning, and sea salt were in the range of approximately 4–69%, 4–49%, 1–53%, 7–25% and 3–29%, respectively. For PM₁₀, the most dominant source was dust with 7–95% (49% for all EMR countries, on average). The limited number of SA studies in the EMR countries (one study per approximately 9.6 million people) in comparison to Europe and North America (1 study per 4.3 and 2.1 million people respectively) can be augmented by future studies that will provide a better understanding of emission sources in the urban environment.

Mangrove sediments are prone to anthropogenic activities that could enrich antibiotics resistance genes (ARGs). The emergence and dissemination of ARGs are of serious concern to public health worldwide. Therefore, a comprehensive resistome analysis of global mangrove sediment is of paramount importance. In this study, we have implemented a deep machine learning approach to analyze the resistome of mangrove sediments from Brazil, China, Saudi Arabia, India, and Malaysia. Geography (RANOSIM = 39.26%; p < 0.005) as well as human intervention (RANOSIM = 16.92%; p < 0.005) influenced the ARG diversity. ARG diversity was also inversely correlated to the human development index (HDI) of the host country (R = −0.53; p < 0.05) rather than antibiotics consumption (p > 0.05). Several genes including multidrug efflux pumps were significantly (p < 0.05) enriched in the sites with human intervention. Resistome was consistently dominated by rpoB2 (19.26 ± 0.01%), multidrug ABC transporter (10.40 ± 0.23%), macB (8.84 ± 0.36n%), tetA (4.13 ± 0.35%), mexF (3.26 ± 0.19%), CpxR (2.93 ± 0.2%), bcrA (2.38 ± 0.24%), acrB (2.37 ± 0.18%), mexW (2.19 ± 0.17%), and vanR (1.99 ± 0.11%). Besides, mobile ARGs such as vanA, tet(48), mcr, and tetX were also detected in the mangrove sediments. Comparative analysis against terrestrial and ocean resistomes showed that the ocean ecosystem harbored the lowest ARG diversity (Chao1 = 71.12) followed by mangroves (Chao1 = 258.07) and terrestrial ecosystem (Chao1 = 294.07). ARG subtypes such as abeS and qacG were detected exclusively in ocean datasets. Likewise, rpoB2, multidrug ABC transporter, and macB, detected in mangrove and terrestrial datasets, were not detected in the ocean datasets. This study shows that the socioeconomic factors strongly determine the antibiotic resistome in the mangrove. Direct anthropogenic intervention in the mangrove environment also enriches antibiotic resistome.

and perfluorinated alkyl substances (PFASs) are ubiquitously detected all around the world. Herein, for the first time, concentrations of 16 selected legacy and emerging PFASs are reported for sediment and edible fish collected from the Saudi Arabian Red Sea. Mean concentrations varied from 0.57 to 2.6 μg kg⁻¹ dry weight (dw) in sediment, 3.89–7.63 μg kg⁻¹ dw in fish muscle, and 17.9–58.5 μg kg⁻¹ dw in fish liver. Wastewater treatment plant effluents represented the main source of these compounds and contributed to the exposure of PFAS to biota. Perfluorooctane sulfonate (PFOS) was the most abundant compound in sediment and fish tissues analysed, comprising between 42 and 99% of the ∑₁₆PFAS. The short chain perfluorobutanoate (PFBA) was the second most dominant compound in sediment and was detected at a maximum concentration of 0.64 μg kg⁻¹ dw. PFAS levels and patterns differed between tissues of investigated fish species. Across all fish species, ∑₁₆PFAS concentrations in liver were significantly higher than in muscle by a factor ranging from 3 to 7 depending on fish species and size. The PFOS replacements fluorotelomer sulfonate (6:2 FTS) and perfluorobutane sulfonate (PFBS) exhibited a bioaccumulation potential in several fish species and 6:2 FTS, was detected at a maximum concentration of 7.1 ± 3.3 μg kg⁻¹ dw in a doublespotted queenfish (Scomberoides lysan) liver. PFBS was detected at a maximum concentration of 2.65 μg kg⁻¹ dw in strong spine silver-biddy (Gerres longirostris) liver. The calculated dietary intake of PFOS, perfluorooctanoic acid (PFOA), perfluorononanoic acid (PFNA) and perfluorohexane sulfonic acid (PFHxS) exceeded the safety threshold established by the European Food Safety Authority (EFSA) in 2020 in doublespotted queenfish muscle, indicating a potential health risk to humans consuming this fish in Jeddah, Saudi Arabia.

To verify weather mangroves act as sinks for marine litter, we surveyed through visual census 20 forests along the Red Sea and the Arabian Gulf, both in inhabited and remote locations. Anthropogenic debris items were counted and classified along transects, and the influence of main drivers of distribution were considered (i.e. land-based and ocean-based sources, density of the forest and properties of the object). We confirmed that distance to major maritime traffic routes significantly affects the density of anthropogenic debris in Red Sea mangrove forests, while this was independent of land-based activities. This suggests ocean-based activities combined with surface currents as major drivers of litter in this basin. Additionally, litter was more abundant where the mangrove density was higher, and object distribution through the mangrove stand often depended on their shape and dimension. We particularly show that pneumatophores act as a sieve retaining large plastic objects, leading to higher plastic mass estimates in mangroves compared to those of beaches previously surveyed in the Red Sea.

PM2.5 pollution is threatening human health and quality of life, especially in some densely populated regions of Asia and Africa. This paper used remotely sensed annual mean PM2.5 concentrations to explore the spatiotemporal evolution of global continental PM2.5 pollution from 2000 to 2014. The work employed an improved Bayesian space-time hierarchy model combined with a multiscale homogeneous subdivision method. The statistical results quantitatively demonstrated a ‘high-value increasing and low-value decreasing’ trend. Areas with annual PM2.5 concentrations of more than 70μg/m3 and less than 10μg/m3 expanded, while areas with of an annual PM2.5 concentrations of 10–25μg/m3 shrank. The most heavily PM2.5-polluted areas were located in northwest Africa, where the PM2.5 pollution level was 12.0 times higher than the average global continental level; parts of China represented the second most PM2.5-polluted areas, followed by northern India and Saudi Arabia and Iraq in the Middle East region. Nearly all (96.50%) of the highly PM2.5-polluted area (hot spots) had an increasing local trend, while 68.98% of the lightly PM2.5-polluted areas (cold spots) had a decreasing local trend. In contrast, 22.82% of the cold spot areas exhibited an increasing local trend. Moreover, the spatiotemporal variation in the health risk from exposure to PM2.5 over the global continents was also investigated. Four areas, India, eastern and southern China, western Africa and central Europe, had high health risks from PM2.5 exposure. Northern India, northeastern Pakistan, and mid-eastern China had not only the highest risk but also a significant increasing trend; the areas of high PM2.5 pollution risk are thus expanding, and the number of affected people is increasing. Northern and central Africa, the Arabian Peninsula, the Middle East, western Russia and central Europe also exhibited increasing PM2.5 pollution health risks.

The aim of this study is to investigate the variability of aerosol over The Kingdom of Saudi Arabia. For this analysis, Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue (DB) Aerosol Optical Depth (AOD) product from Terra and Aqua satellites for the years 2000–2013 is used. The product is validated using AERONET data from ground stations, which are situated at Solar Village Riyadh and King Abdullah University of Science and Technology (KAUST) Jeddah. The results show that both Terra and Aqua satellites exhibit a tendency to show the spatial variation of AOD with Aqua being better than Terra to represent the ground based AOD measurements over the study region. The results also show that the eastern, central, and southern regions of the country have a high concentration of AOD during the study period. The validation results show the highest correlation coefficient between Aqua and KAUST data with a value of 0.79, whilst the Aqua and Solar Village based AOD indicates the lowest Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values which are, 0.17 and 0.12 respectively. Furthermore, the Relative Mean Bias (RMB) based analysis show that the DB algorithm overestimates the AOD when using Terra and Solar Village data, while it underestimates the AOD when using Aqua with Solar Village and KAUST data. The RMB value for Aqua and Solar Village data indicates that the DB algorithm is close to normal in the study region.

A large-scale assessment of polycyclic aromatic hydrocarbons (PAHs) from the 1991 Gulf War oil spill was performed for 2002-2003 sediment samples (n = 1679) collected from habitats along the shoreline of Saudi Arabia. Benthic sediment toxicity was characterized using the Equilibrium Partitioning Sediment Benchmark Toxic Unit approach for 43 PAHs (ESBTUFCV,43). Samples were assigned to risk categories according to ESBTUFCV,43 values: no-risk (≤1), low (>1–≤2), low-medium (>2–≤3), medium (>3–≤5) and high-risk (>5). Sixty seven percent of samples had ESBTUFCV,43 > 1 indicating potential adverse ecological effects. Sediments from the 0–30 cm layer from tidal flats, and the >30–<60 cm layer from heavily oiled halophytes and mangroves had high frequency of high-risk samples. No-risk samples were characterized by chrysene enrichment and depletion of lighter molecular weight PAHs, while high-risk samples showed little oil weathering and PAH patterns similar to 1993 samples. North of Safaniya sediments were not likely to pose adverse ecological effects contrary to sediments south of Tanaqib. Landscape and geomorphology has played a role on the distribution and persistence in sediments of oil from the Gulf War.

Air pollution remains a major global public health and environmental issue. We assessed the levels of PM₂.₅ and delineated the major sources in Makkah, Saudi Arabia. Fine particulate matter (PM₂.₅) sampling was performed from February 26, 2014–January 27, 2015 in four cycles/seasons. Samples were analyzed for black carbon (BC) and trace elements (TEs). PM₂.₅ source apportionment was performed by computing enrichment factors (EFs) and positive matrix factorization (PMF). Backward-in time trajectories were used to assess the long-range transport. Significant seasonal variations in PM₂.₅ were observed, Spring: 113 ± 67.1, Summer: 88.3 ± 36.4, Fall: 67.8 ± 24, and Winter: 67.6 ± 36.9 μg m⁻³. The 24-h PM₂.₅ exceeded the WHO (25 μg m⁻³) and Saudi Arabia's (35 μg m⁻³) guidelines, with an air quality index (AQI) of “unhealthy to hazardous” to human health. Most delta–C computations were below zero, indicating minor contributions from bio-mass burning. TEs were primarily Si, Ca, Fe, Al, S, K and Mg, suggesting major contributions from soil (Si, Ca, Fe, Al, Mg), and industrial and vehicular emissions (S, Ca, Al, Fe, K). EF defined two broad categories of TEs as: anthropogenic (Cu, Zn, Eu, Cl, Pb, S, Br and Lu), and earth-crust derived (Al, Si, Na, Mg, Rb, K, Zr, Ti, Fe, Mn, Sr, Y, Cr, Ga, Ca, Ni and Ce). Notably, all the anthropogenic TEs can be linked to industrial and vehicular emissions. PMF analysis defined four major sources as: vehicular emissions, 30.1%; industrial-mixed dust, 28.9%; soil/earth-crust, 24.7%; and fossil-fuels/oil combustion, 16.3%. Plots of wind trajectories indicated wind direction and regional transport as major influences on air pollution levels in Makkah. In collusion, anthropogenic emissions contributed >75% of the observed air pollution in Makkah. Developing strategies for reducing anthropogenic emissions are paramount to controlling particulate air pollution in this region.

Rapid economic expansion poses serious problems for groundwater resources in arid areas, which typically have high rates of groundwater depletion. In this study, integration of hydrochemical investigations involving chemical and statistical analyses are conducted to assess the factors controlling hydrochemistry and potential pollution in an arid region. Fifty-four groundwater samples were collected from the Dhurma aquifer in Saudi Arabia, and twenty-one physicochemical variables were examined for each sample. Spatial patterns of salinity and nitrate were mapped using fitted variograms. The nitrate spatial distribution shows that nitrate pollution is a persistent problem affecting a wide area of the aquifer. The hydrochemical investigations and cluster analysis reveal four significant clusters of groundwater zones. Five main factors were extracted, which explain >77% of the total data variance. These factors indicated that the chemical characteristics of the groundwater were influenced by rock–water interactions and anthropogenic factors. The identified clusters and factors were validated with hydrochemical investigations. The geogenic factors include the dissolution of various minerals (calcite, aragonite, gypsum, anhydrite, halite and fluorite) and ion exchange processes. The anthropogenic factors include the impact of irrigation return flows and the application of potassium, nitrate, and phosphate fertilizers. Over time, these anthropogenic factors will most likely contribute to further declines in groundwater quality.

Heavy metals are the most commonly encountered toxic substances that increase susceptibility to various diseases after prolonged exposure. We have previously shown that healthy volunteers living near a mining area had significant contamination with heavy metals associated with significant changes in the expression of some detoxifying genes, xenobiotic metabolizing enzymes, and DNA repair genes. However, alterations of most of the molecular target genes associated with diseases are still unknown. Thus, the aims of this study were to (a) evaluate the gene expression profile and (b) identify the toxicities and potentially relevant human disease outcomes associated with long-term human exposure to environmental heavy metals in mining area using microarray analysis. For this purpose, 40 healthy male volunteers who were residents of a heavy metal-polluted area (Mahd Al-Dhahab city, Saudi Arabia) and 20 healthy male volunteers who were residents of a non-heavy metal-polluted area were included in the study. Total RNA was isolated from whole blood using PAXgene Blood RNA tubes and then reversed transcribed and hybridized to the gene array using the Affymetrix U219 GeneChip. Microarray analysis showed about 2129 genes were identified and differentially altered, among which a shared set of 425 genes was differentially expressed in the heavy metal-exposed groups. Ingenuity pathway analysis revealed that the most altered gene-regulated diseases in heavy metal-exposed groups included hematological and developmental disorders and mostly renal and urological diseases. Quantitative real-time polymerase chain reaction closely matched the microarray data for some genes tested. Importantly, changes in gene-related diseases were attributed to alterations in the genes encoded for protein synthesis. Renal and urological diseases were the diseases that were most frequently associated with the heavy metal-exposed group. Therefore, there is a need for further studies to validate these genes, which could be used as early biomarkers to prevent renal injury.