Although dust storms have been associated with adverse health outcomes, studies on the burden of dust storms on deaths are limited. As global warming has induced significant climate changes in recent decades, which have accelerated desertification worldwide, it is necessary to evaluate the burden of dust storm-induced premature mortality using a critical measure of disease burden, such as the years of life lost (YLL). The YLL attributable to dust storms have not been examined to date. This study investigated the association between Asian dust storms (ADS) and the YLL in Seoul, South Korea, during 2002–2013. We conducted a time-series study using a generalized additive model assuming a Gaussian distribution and applied a distributed lag model with a maximum lag of 5 days to investigate the delayed and cumulative effects of ADS on the YLL. We also conducted stratified analyses using the cause of death (respiratory and cardiovascular diseases) and sociodemographic status (sex, age, education level, occupation, and marital status). During the study period, 108 ADS events occurred, and the average daily YLL was 1511 years due to non-accidental causes. The cumulative ADS exposure over the 6-day lag period was associated with a significant increase of 104.7 (95% CI, 31.0–178.5 years) and 34.4 years (4.0–64.7 years) in the YLL due to non-accidental causes and cardiovascular mortality, respectively. Sociodemographic analyses revealed associations between ADS exposure and the YLL in males, both <65 and ≥ 65 years old, those with middle-level education, and the unemployed, unmarried, and widowed (26.5–83.8 years). This study provides new evidence suggesting that exposure to dust storms significantly increases the YLL. Our findings suggest that dust storms are a critical environmental risk affecting premature mortality. These results could contribute to the establishment of public health policies aimed at managing dust storm exposure and reducing premature deaths.

China continues to suffer from severe acid deposition, despite the government implying a series of policies to control air pollution. In this study, rainwater samples were collected from 2011 to 2016 in Sichuan province to measure the pH values and the concentrations of nine inorganic ions (SO₄²⁻, NO₃⁻, NH₄⁺, Cl⁻, Na⁺, Ca²⁺, K⁺, Mg²⁺, and F⁻), and then to investigate their spatiotemporal variations. Besides, the dominant sources for the acidic ions in the precipitation were also revealed by statistical model. The results showed that the rainwater continued to be highly acidic, and the Volume-Weighted Mean (VWM) pH value was calculated to be 5.18 during 2011 and 2016. NH₄⁺, Ca²⁺, NO₃⁻, and SO₄²⁻ were the dominant water-soluble inorganic ions, accounting for 79.2% of the total ions on average. The remarkable decrease in NO₃⁻ and SO₄²⁻ concentrations (from 75.9 to 54.3 μeq L⁻¹ and from 285 to 145 μeq L⁻¹, respectively) resulted in an increase in the pH value of rainwater from 5.24 in 2011 to 5.70 in 2016. The concentrations of SO₄²⁻, NO₃⁻, F⁻, Na⁺, and K⁺ showed remarkably seasonal variation, with the highest value observed in winter, followed by spring and autumn, and the lowest value observed in summer. High VWM concentration of these ions in winter were mainly due to adverse meteorological conditions (e.g., rare rainfall, lower planetary boundary height, and stagnant air) and intensive anthropogenic emissions. SO₄²⁻, NO₃⁻, and F⁻ ions peaked in the southeastern Sichuan province, which is a typical industrial region. NH₄⁺ concentrations decreased from 268 μeq L⁻¹ in the east to 10.4 μeq L⁻¹ in the western Sichuan province, which could be related to the development of agriculture in the eastern Sichuan province. Ca²⁺ peaked in southeastern Sichuan province due to intensive construction activities and severe stone desertification. On the basis of Positive Matrix Factorization (PMF) analysis, four sources of inorganic ions in rainwater were identified, including anthropogenic source, crust, biomass burning, and aging sea salt aerosol. Geographically Weighted Regression (GWR) was used to find the spatial correlations between the socio-economic factors and ions in the rainwater. At the regional scale, the influence of fertilizer consumption and Gross Agricultural Production (GAP) on NH₄⁺ increased from east to west; moreover the influence of Gross Industrial Production (GIP) on SO₄²⁻ and NO₃⁻ also increased.

To understand the characteristics of particulate matter (PM) and other air pollutants in Xinjiang, a region with one of the largest sand-shifting deserts in the world and significant natural dust emissions, the concentrations of six air pollutants monitored in 16 cities were analyzed for the period January 2013–June 2019. The annual mean PM₂.₅, PM₁₀, SO₂, NO₂, CO, and O₃ concentrations ranged from 51.44 to 59.54 μg m⁻³, 128.43–155.28 μg m⁻³, 10.99–17.99 μg m⁻³, 26.27–31.71 μg m⁻³, 1.04–1.32 mg m⁻³, and 55.27–65.26 μg m⁻³, respectively. The highest PM concentrations were recorded in cities surrounding the Taklimakan Desert during the spring season and caused by higher amounts of wind-blown dust from the desert. Coarse PM (PM₁₀₋₂.₅) was predominant, particularly during the spring and summer seasons. The highest PM₂.₅/PM₁₀ ratio was recorded in most cities during the winter months, indicating the influence of anthropogenic emissions in winters. The annual mean PM₂.₅ (PM₁₀) concentrations in the study area exceeded the annual mean guidelines recommended by the World Health Organization (WHO) by a factor of ca. ∼5–6 (∼7–8). Very high ambient PM concentrations were recorded during March 19–22, 2019, that gradually influenced the air quality across four different cities, with daily mean PM₂.₅ (PM₁₀) concentrations ∼8–54 (∼26–115) times higher than the WHO guidelines for daily mean concentrations, and the daily mean coarse PM concentration reaching 4.4 mg m⁻³. Such high PM₂.₅ and PM₁₀ concentrations pose a significant risk to public health. These findings call for the formulation of various policies and action plans, including controlling the land degradation and desertification and reducing the concentrations of PM and other air pollutants in the region.

Natural sources, such as soil and wind-erosion dust (SWD), biomass open burning (BOB), sea salt spray (SSAS) and biogenic source (BIO), are major contributors to atmospheric emissions of trace elements (TEs) globally. In this study, we used a comprehensive approach to account for area-, production- and biofuel consumption-based emission factor calculation methods, and thus developed an integrated high-resolution emission inventory for 15 types of TEs (As, B, Cd, Co, Cr, Cu, Hg, Mn, Mo, Ni, Pb, Sb, Se, V and Zn) originated from natural sources in China for the year 2015. The results show that national emissions of TEs in 2015 range from 7.45 tons (Hg) to 1, 400 tons (Zn) except for the extremely high emissions of Mn (10, 677 tons). SWD and BIO are identified as the top two source contributors, accounting for approximately 67.7% and 26.1% of the total emissions, respectively. Absolute emissions of TEs from natural sources are high in the Xinjiang, Inner Mongolia and Tibet autonomous regions with large areas of bare soil and desert. However, emission intensity of TEs per unit area in the Southern provinces of China is higher than those in Northern China and Southwestern China, with the Yunnan and Sichuan provinces displaying the highest emission intensity. Our results suggest that controlling SWD can play a significant role in reducing fugitive particulate matter and the associated emissions of TEs from natural sources in China; and desertification control is particularly critical in the Northwest provinces where the majority of deserts are located.

Reductions of zooplankton biomasses and grazing pressures were observed during overfishing-induced trophic cascades and concurrent oil spills at global scales. Recent phytoplankton increments followed, once Fe-, P-, and N-nutrient limitations of commensal diazotrophs and dinoflagellates were also eliminated by respective human desertification, deforestation, and eutrophication during climate changes. Si-limitation of diatoms instead ensued during these last anthropogenic perturbations of agricultural effluents and sewage loadings. Consequently, ~15% of total world-wide annual asthma trigger responses, i.e. amounting to ~45 million adjacent humans during 2004, resulted from brevetoxin and palytoxin poisons in aerosol forms of western boundary current origins. They were denoted by greater global harmful algal bloom [HAB] abundances and breathing attacks among sea-side children during prior decadal surveys of asthma prevalence, compiled here in ten paired shelf ecosystems of western and eutrophied boundary currents. Since 1965, such inferred onshore fluxes of aerosolized DOC poisons of HABs may have served as additional wind-borne organic carriers of toxic marine MeHg, phthalate, and DDT/DDE vectors, traced by radio-iodine isotopes to potentially elicit carcinomas. During these exchanges, as much as 40% of mercury poisonings may instead have been effected by inhalation of collateral HAB-carried marine neurotoxic aerosols of MeHg, not just from eating marine fish. Health impacts in some areas were additional asthma and pneumonia episodes, as well as endocrine disruptions among the same adjacent humans, with known large local rates of thyroid cancers, physician-diagnosed pulmonary problems, and ubiquitous high indices of mercury in hair, pesticides in breast milk, and phthalates in urine.

Rapid urbanization with an increasing rate of urban built-up area is decreasing urban green space resulting in changing urban microclimate conditions showing increasing land surface temperature. A better understanding of these effects is important to formulate effective strategies in addressing the impact of increasing built-up area. Land surface temperature patterns in an urbanized city in Bangladesh (Mymensingh district) were investigated using Landsat satellite sensor data from 1988 to 2016. A total of nineteen Landsat satellite images were used to retrieve land surface temperature (LST), normalized difference vegetation index (NDVI), and normalized difference built-up index (NDBI). The radiative transfer equation (RTE) model was applied to derive LST for the years 1988, 1992, 1999, 2004, 2008, 2012, and 2016. Further, the Landsat-derived LST results were compared with MODIS Terra satellite outputs (MOD11A1) for the validation of our study results. Our results showed NDVI higher in 2008 and lower in 2004, LST maximum in 1988 and minimum in 2008, and NDBI higher in 2004 and lower in 2012. Seasonally, summer was characterized by higher LST and winter by lower LST, while NDVI was higher in autumn and lower in winter, however, NDBI was higher in winter and lower in autumn. Spatially, a relatively higher LST and NDBI was observed in the southwest, followed by central, and northern regions, whereas the trend was opposite for NDVI. Using Pearson’s correlation, results showed a strong significant negative correlation between LST and NDVI and a positive significant correlation between LST and NDBI. Further, simple linear regression analysis revealed that LST decreased with increasing NDVI most quickly in 2012, followed by the years 2016, 2008, 1992, 1988, 1999, and 2004. On the other hand, LST increased with increasing NDBI most quickly in 1999, followed by the years 2016, 1988, 1992, 2012, 2004, and 2008. Thus, long-term observation suggested that urbanization had driven a decrease in green space while simultaneously increasing the land surface temperature within an urbanized area. This study has concluded that the protection of urban green spaces is needed as an effective step toward addressing adverse effects of regional climate change and desertification.

Understanding the spatial distribution of soil salinity is required to conserve land against degradation and desertification. Against this background, this study is the first attempt to predict soil salinity in the Jaghin basin, in southern Iran, by applying and comparing the performance of four deep learning (DL) models (deep convolutional neural networks—DCNNs, dense connected deep neural networks—DenseDNNs, recurrent neural networks-long short-term memory—RNN-LSTM and recurrent neural networks-gated recurrent unit—RNN-GRU) and six shallow machine learning (ML) models (bagged classification and regression tree—BCART, cforest, cubist, quantile regression with LASSO penalty—QR-LASSO, ridge regression—RR and support vectore machine—SVM). To do this, 49 environmental landsat8-derived variables including digital elevation model (DEM)-extracted covariates, soil-salinity indices, and other variables (e.g., soil order, lithology, land use) were mapped spatially. For assessing the relationships between soil salinity (EC) and factors controlling EC, we collected 319 surficial (0–5 cm depth) soil samples for measuring soil salinity on the basis of electrical conductivity (EC). We then selected the most important features (covariates) controlling soil salinity by applying a MARS model. The performance of the DL and shallow ML models for generating soil salinity spatial maps (SSSMs) was assessed using a Taylor diagram and the Nash Sutcliff coefficient (NSE). Among all 10 predictive models, DL models with NSE ≥ 0.9 (DCNNs was the most accurate model with NSE = 0.96) were selected as the four best models, and performed better than the six shallow ML models with NSE ≤ 0.83 (QR-LASSO was the weakest predictive model with NSE = 0.50). Based on DCNNs-, the values of the EC ranged between 0.67 and 14.73 dS/m, whereas for QR-LASSO the corresponding EC values were 0.37 to 19.6 dS/m. Overall, DL models performed better than shallow ML models for production of the SSSMs and therefore we recommend applying DL models for prediction purposes in environmental sciences.

Cultivation of desert cyanobacteria in wastewater can lead to the optimal redistribution of regional resources and is likely to solve two global problems, i.e., wastewater pollution and desertification. However, the potential of using wastewater instead of traditional artificial culture media to cultivate sand-consolidating cyanobacteria for desert management is not well understood. This study compares undistilled and distilled wastewater with an artificial culture medium (BG11₀) to explore the potential of wastewater as a replacement culture medium for Scytonema javanicum. The results show that the photosynthetic activity (Fᵥ/Fₘ) of S. javanicum was inhibited in the undistilled wastewater and was lower than that in distilled water and the culture medium. The lowest Chl-a concentration and the highest concentration in BG11₀ were found in distilled wastewater. However, there was no difference in the biomass (dry weight) between the undistilled wastewater and BG11₀ at the end of the experiment. After long-term dry storage of the biomass collected after cultivation, there was no difference in the photosynthetic recovery between S. javanicum cultivated in undistilled wastewater and that cultivated in BG11₀. Accordingly, although wastewater depressed the Chl-a content, it did not affect the biomass accumulation and subsequent photosynthetic recovery after long-term storage. The results reveal the significant potential of cultivating sand-consolidating cyanobacterium in wastewater and using this technology as a new nutrient redistribution method in human settlements and desert areas.

Soil microbes play key roles in ecosystem functions, especially in the recovery of ecosystems from disturbance, and exploring community assembly under changing environments has long been a central theme in microbial ecology. The response of abundant and rare bacteria in desertified land to restoration is still unclear. Here, we investigated the effects of vegetation restoration on the assemblage patterns of abundant and rare bacteria in soil across the four sandy lands (Hulunbeir, Horqin, Otindag, and Mu Us) in northern China. Our results revealed that abundant bacteria maintained a relatively stable state under restoration, whereas rare taxa were more responsive, indicating the higher resilience of the rare community to change. Our network analysis also showed that restoration promoted destabilizing properties in rare, but not in abundant, bacterial co-occurrence networks in soil. Environmental selection played a key role in abundant and rare community assembly under restoration. Of the two, the rare subcommunity was mainly affected by environmental filtering. The variations in the abundant and rare communities at the sampling sites under restoration were controlled mainly by plant species richness, and stronger effects were observed in the rare taxa. Overall, these results provide new insight into the mechanisms controlling bacterial community assembly in response to vegetation restoration.

The drastic desiccation of the Aral Sea has led to severe desertification of the former lake areas. Dust storms occur frequently, causing regional environmental degradation of the Aral basin and a serious ecological disaster. Knowledge of the temporal variability in dust emissions and the potential diffusion characteristics of dust aerosol originating from the Aral Sea basin in recent years are, however, lacking. To address this knowledge gap, we studied the interannual and intraannual changes in dust aerosol from the Aral Sea basin and its potentially seasonal diffusion characteristics from 2005 to 2013 using Ozone Monitoring Instrument (OMI) aerosol data (2005–2013) and the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. Results show that the OMI aerosol index (AI) annual mean, standard deviation, median, and maximum values exhibit a strong increasing trend because of the continuous decrease in the water level since 2005. The annually mean OMI AI increases to 1.47 by 2013. Peak AI values are recorded in spring (March–May) and early winter (November–January of the following year), indicating notifying seasonal differences. The potential distance and height of air parcel trajectories to the northeast are greater than those to the west and south, whereas the air parcel trajectory proportion of the former is lower than that of the latter. The potential transport distance of dust aerosol to the northeast is greatest in spring and winter. This transport distance is less in autumn, with the minimum observed in summer. Dust transport distance to the west and south in different seasons is not significantly different. The present results may help in further understanding the emission, long-range transport, and deposition of dust from the dry lake bed of the Aral Sea as well as providing a motivation for the sensible use and protection of these tail-end lakes.