Dust storms have many negative consequences, and affect all kinds of ecosystems, as well as climate and weather conditions. Therefore, classification of dust storm sources into different ...susceptibility categories can help us mitigate its negative effects. This study aimed to classify the susceptibility of dust sources in the Middle East (ME) by developing two novel deep learning (DL) hybrid models based on the convolutional neural network-gated recurrent unit (CNN-GRU) model, and the dense layer deep learning-random forest (DLDL-RF) model. The Dragonfly algorithm (DA) was used to identify the critical features controlling dust sources. Game theory was used for the interpretability of the DL model's output. Predictive DL models were constructed by dividing datasets randomly into train (70%) and test (30%) groups, six statistical indicators being then applied to assess the DL hybrid model performance for both datasets (train and test). Among 13 potential features (or variables) controlling dust sources, seven variables were selected as important and six as non-important by DA, respectively. Based on the DLDL-RF hybrid model - a model with higher accuracy in comparison with CNN-GRU-23.1, 22.8, and 22.2% of the study area were classified as being of very low, low and moderate susceptibility, whereas 20.2 and 11.7% of the area were classified as representing high and very high susceptibility classes, respectively. Among seven important features selected by DA, clay content, silt content, and precipitation were identified as the three most important by game theory through permutation values. Overall, DL hybrid models were found to be efficient methods for prediction purposes on large spatial scales with no or incomplete datasets from ground-based measurements.
Land susceptibility to wind erosion hazard in Isfahan province, Iran, was mapped by testing 16 advanced regression-based machine learning methods: Robust linear regression (RLR), Cforest, Non-convex ...penalized quantile regression (NCPQR), Neural network with feature extraction (NNFE), Monotone multi-layer perception neural network (MMLPNN), Ridge regression (RR), Boosting generalized linear model (BGLM), Negative binomial generalized linear model (NBGLM), Boosting generalized additive model (BGAM), Spline generalized additive model (SGAM), Spike and slab regression (SSR), Stochastic gradient boosting (SGB), support vector machine (SVM), Relevance vector machine (RVM) and the Cubist and Adaptive network-based fuzzy inference system (ANFIS). Thirteen factors controlling wind erosion were mapped, and multicollinearity among these factors was quantified using the tolerance coefficient (TC) and variance inflation factor (VIF). Model performance was assessed by RMSE, MAE, MBE, and a Taylor diagram using both training and validation datasets. The result showed that five models (MMLPNN, SGAM, Cforest, BGAM and SGB) are capable of delivering a high prediction accuracy for land susceptibility to wind erosion hazard. DEM, precipitation, and vegetation (NDVI) are the most critical factors controlling wind erosion in the study area. Overall, regression-based machine learning models are efficient techniques for mapping land susceptibility to wind erosion hazards.
Abstract
This research introduces a new combined modelling approach for mapping soil salinity in the Minab plain in southern Iran. This study assessed the uncertainty (with 95% confidence limits) and ...interpretability of two deep learning (DL) models (deep boltzmann machine—DBM) and a one dimensional convolutional neural networks (1DCNN)—long short-term memory (LSTM) hybrid model (1DCNN-LSTM) for mapping soil salinity by applying DeepQuantreg and game theory (Shapely Additive exPlanations (SHAP) and permutation feature importance measure (PFIM)), respectively. Based on stepwise forward regression (SFR)—a technique for controlling factor selection, 18 of 47 potential controls were selected as effective factors. Inventory maps of soil salinity were generated based on 476 surface soil samples collected for measuring electrical conductivity (ECe). Based on Taylor diagrams, both DL models performed well (RMSE < 20%), but the 1DCNN-LSTM hybrid model performed slightly better than the DBM model. The uncertainty range associated with the ECe values predicted by both models estimated using DeepQuantilreg were similar (0–25 dS/m for the 1DCNN-LSTM hybrid model and 2–27 dS/m for DBM model). Based on the SFR and PFIM (permutation feature importance measure)—a measure in game theory, four controls (evaporation, sand content, precipitation and vertical distance to channel) were selected as the most important factors for soil salinity in the study area. The results of SHAP (Shapely Additive exPlanations)—the second measure used in game theory—suggested that five factors (evaporation, vertical distance to channel, sand content, cation exchange capacity (CEC) and digital elevation model (DEM)) have the strongest impact on model outputs. Overall, the methodology used in this study is recommend for applications in other regions for mapping environmental problems.
Accurate information on the sources of suspended sediment in riverine systems is essential to target mitigation. Accordingly, we applied a generalized likelihood uncertainty estimation (GLUE) ...framework for quantifying contributions from three sub-basin spatial sediment sources in the Mehran River catchment draining into the Persian Gulf, Hormozgan province, southern Iran. A total of 28 sediment samples were collected from the three sub-basin sources and six from the overall outlet. 43 geochemical elements (e.g., major, trace and rare earth elements) were measured in the samples. Four different combinations of statistical tests comprising: (1) traditional range test (TRT), Kruskal-Wallis (KW) H-test and stepwise discriminant function analysis (DFA) (TRT + KW + DFA); (2) traditional range test using mean values (RTM) and two additional tests (RTM + KW + DFA); (3) TRT + KW + PCA (principle component analysis), and; 4) RTM + KW + PCA, were used to the spatial sediment source discrimination. Tracer bi-plots were used as an additional step to assess the tracers selected in the different final composite signatures for source discrimination. The predictions of spatial source contributions generated by GLUE were assessed using statistical tests and virtual sample mixtures. On this basis, TRT + KW + DFA and RTM + KW + DFA yielded the best source discrimination and the tracers in these composite signatures were shown by the biplots to be broadly conservative during transportation from source to sink. Using these final two composite signatures, the estimated mean contributions for the western, central and eastern sub-basins, respectively, ranged between 10-60% (overall mean contribution 36%), 0.3-16% (overall mean contribution 6%) and 38-77% (overall mean contribution 58%). In comparison, the final tracers selected using TRT + KW + PCA generated respective corresponding contributions of 1-42% (overall mean 20%), 0.5-30% (overall mean 12%) and 55-84% (overall mean 68%) compared with 17-69% (overall mean 41%), 0.2-12% (overall mean 5%) and 29-76% (overall mean 54%) using the final tracers selected by RTM + KW + PCA. Based on the mean absolute fit (MAF; ≥ 95% for all target sediment samples) and goodness-of-fit (GOF; ≥ 99% for all samples), GLUE with the final tracers selected using TRT + KW + PCA performed slightly better than GLUE with the final signatures selected by the three other combinations of statistical tests. Based on the virtual mixture tests, however, predictions provided by GLUE with the final tracers selected using TRT + KW + DFA and RTM + KW + DFA (mean MAE = 11% and mean RMSE = 13%) performed marginally better than GLUE with RTM + KW + PCA (mean MAE = 14% and mean RMSE = 16%) and GLUE with TRT + KW + PCA (mean MAE = 17% and mean RMSE = 19%). The estimated source proportions can help watershed engineers plan the targeting of conservation programmes for soil and water resources.
Remote sensing of specific climatic and biogeographical parameters is an effective means of evaluating the large-scale desertification status of drylands affected by negative human impacts. Here, we ...identify and analyze desertification trends in Iran for the period 2001-2015 via a combination of three indices for vegetation (NPP-net primary production, NDVI-normalized difference vegetation index, LAI-leaf area index) and two climate indices (LST-land surface temperature, P-precipitation). We combine these indices to identify and map areas of Iran that are susceptible to land degradation. We then apply a simple linear regression method, the Mann-Kendall non-parametric test, and the Theil-Sen estimator to identify long-term temporal and spatial trends within the data. Based on desertification map, we find that 68% of Iran shows a high to very high susceptibility to desertification, representing an area of 1.1 million km
(excluding 0.42 million km
classified as unvegetated). Our results highlight the importance of scale in assessments of desertification, and the value of high-resolution data, in particular. Annually, no significant change is evident within any of the five indices, but significant changes (some positive, some negative) become apparent on a seasonal basis. Some observations follow expectations; for instance, NDVI is strongly associated with cooler, wet spring and summer seasons, and milder winters. Others require more explanation; for instance, vegetation appears decoupled from climatic forcing during autumn. Spatially, too, there is much local and regional variation, which is lost when the data are considered only at the largest nationwide scale. We identify a northwest-southeast belt spanning central Iran, which has experienced significant vegetation decline (2001-2015). We tentatively link this belt of land degradation with intensified agriculture in the hinterlands of Iran's major cities. The spatial and temporal trends identified with the three vegetation and two climate indices afford a cost-effective framework for the prediction and management of future environmental trends in developing regions at risk of desertification.
Nickel is a heavy metal that has a variety of negative impacts on living organisms and causes different health disorders. Probiotic bacteria have been recently utilized for nickel detoxification ...through bioremediation strategies. We inspected that the role of probiotic lactic acid bacteria in reducing nickel toxicity has been investigated using two biological methods, including biosorption and bioaccumulation. Seventeen strains of nickel-resistant probiotic lactic acid bacteria isolated from the human microbiome were selected out of 88 strains by three different screening stages comprising disc diffusion, MIC, and biosorption/bioaccumulation tests culminated in four of the most powerful strains in reducing nickel from their culture medium. They were L. brevis 205, L. mocusae 226, L. casei 375, and B. infantis 1001 with the mean nickel biosorption rate of 82%, 66%, 70%, and 74%, respectively. The bioaccumulation test resulted in an approximate rate of accumulated nickel inside the strains cells. L. casei, L. brevis, and L. mocusae had the best rate of about 43% nickel accumulation, followed by B. infantis with a 42% bioaccumulation rate. This study supports that the theory of applying probiotic lactic acid bacteria to food and water detoxification could be a safe, bio-friendly alternative for gut remediation and in edible industries.
This research developed a more efficient integrated model (IM) based on combining the Nash-Sutcliffe efficiency coefficient (NSEC) and individual data mining (DM) algorithms for the spatial mapping ...of dust provenance in the Hamoun-e-Hirmand Basin, southeastern Iran. This region experiences severe wind erosion and includes the Sistan plain which is one of the most PM
2.5
-polluted regions in the world. Due to a prolonged drought over the last two decades, the frequency of dust storms in the study area is increasing remarkably. Herein, 14 factors controlling dust emissions (FCDEs) including soil characteristics, climatic variables, digital elevation map, normalized difference vegetation index, land use and geology were mapped. Correlation and collinearity among the FCDEs were examined by the Pearson test, tolerance coefficient (TC) and variance inflation factor (VIF), with the results suggesting a lack of collinearity between FCDEs. A tree-based genetic algorithm was applied to prioritize and quantify the importance weights of the FCDEs. Thirteen individual data mining models were applied for mapping dust provenance. The model performance was assessed using root mean square error, mean absolute error and NSEC. Based on clustering analysis, the 13 DM models were grouped into five clusters and then the cluster with the highest NSEC values used in an integrated modelling process. Based on the results, the IM (NSEC = 93%) outperformed the individual DM models (the NSEC values range between 51 and 92%). Using the IM, 11, 5, 7 and 77% of the total study area were classified into low, moderate, high and very high susceptibility classes for dust provenance, respectively. Overall, the results illustrate the benefits of an IM for mapping spatial variation in the susceptibility of catchment areas to act as dust sources.
Polyaniline was synthesized chemically in an acidic medium in the presence of Ammonium Peroxydisulphate (APS) as an oxidizing agent. PANI(Polyaniline) nanocomposites were prepared in the presence of ...various amount of carbon nanotube and zinc oxide (from 1 to 5wt%) by solution casting method. The free-standing film of polyaniline and its nanocomposites were obtained by vaporization of solvent content. The composition, morphology and structure of the polymer and the nanocomposites were characterized by Fourier transform infrared spectroscopy FT-IR spectra, scanning electron microscopy (SEM) image and XRD pattern. In addition, thermal stability was studied by TGA analysis, electrical conductivity was measured by four-point probe technique and mechanical properties were studied by tensile strength test. The characteristic FTIR peaks of PANI were found to shift to lower wave number in nanocomposites due to the formation of H-bonding. XRD results revealed that the crystallinity of PANI was more noticeable after addition of nano-ZnO, while the intensity of the peaks increased by the addition of ZnO nanoparticles. Furthermore, TGA results showed that the decomposition of the nanocomposite was less than that of pure polyaniline which confirms the successful fabrication of products. Young's modulus and strength at break point were increased in the case of the nanocomposite, in addition, the electrical conductivity of the PANI/ZnO nanocomposite film was found to be smaller than that of the PANI film while CNTs increase the conductivity of polyaniline.
Dust storms in arid and desert areas affect radiation budget, air quality, visibility, enzymatic activities, agricultural products and human health. Due to increased drought and land use changes in ...recent years, the frequency of dust storms occurrence in Iran has been increased. This study aims to identify dust source areas in the Sistan watershed (Iran-Afghanistan borders)—an important regional source for dust storms in southwestern Asia, using remote sensing (RS) and bivariate statistical models. Furthermore, this study determines the relative importance of factors controlling dust emissions using frequency ratio (FR) and weights of evidence (WOE) models and interpretability of predictive models using game theory. For this purpose, we identified 211 dust sources in the study area and generated a dust source distribution map—inventory map—by dust source potential index based on RS data. In addition, spatial maps of topographic factors affecting dust source areas including soil, lithology, slope, Normalized difference vegetation index (NDVI), geomorphology and land use were prepared. The performance of two models (WOE and FR) was evaluated using the area under curve (AUC) of the receiver operating characteristic curve. The results showed that soil, geomorphology and slope exhibited the greatest influence in the dust source areas. The 55.3% (according to FR) and 62.6% (according to WOE) of the total area were classified as high and very high potential dust sources, while both models displayed acceptable accuracy with subsurface levels of 0.704 for FR and 0.751 for WOE, although they predict different fractions of dust potential classes. Based on Shapley additive explanations (SHAP), three factors, i.e., soil, slope and NDVI have the highest impact on the model’s output. Overall, combination of statistic-based predictive models (or data mining models), RS and game theory techniques can provide accurate maps of dust source areas in arid and semi-arid regions, which can be helpful for mitigation of negative effects of dust storms.
Atmospheric dust is one of the most recent environmental pollutions in Iran. This study examines the concentration of heavy metals and the assessment of environmental and human health risk in the ...dust samples of Hendijan region as one of the most important centers of wind erosion in the southwestern of Iran. ICP-MSS analysis was performed on 18 samples of fine dust to specify the concentration of heavy metals. Studies showed that the highest concentrations of metals in these fine dust samples belong to Cr, Ni, Zn, Cu, As, Pb and Cd, respectively. Examining fine dust’s pollution assessment showed that the highest enrichment and geo-accumulation index belong to As, Ni and Cr metals. Environmental risk assessment shows the low environmental risk of these fine dusts. The hazard quotient in children and adults belongs to Cr, As and Ni, respectively. Human health risk assessment also showed that the highest absorption of metals in both children and adults is through ingestion. The non-carcinogenic risk of heavy metals of dust samples in children is about 9 times more than adults. The highest risk of cancer in the adult group belongs to Ni metal and in the group of children belongs to As and Ni metal. PCA analysis showed that As, Cu, Cd, Cr and Ni are of anthropogenic origin and Zn and Pb are of geogenic origin. The source of the dust phenomenon with the HYSPLIT model and the backward method indicates the tracking of this dust mass through Iraq, and its probable origin was assessed in the centers of northern Iraq and southeastern Syria.
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