Improving Estimation of Specific Surface Area by Artificial Neural Network Ensembles Using Fractal and Particle Size Distribution Curve Parameters as Predictors

Specific surface area (SSA) is one of the principal soil properties used in modeling soil processes. In this study, artificial neural network (ANN) ensembles were evaluated to predict SSA. Complete soil particle-size distribution was estimated from sand, silt, and clay fractions using the model by Skaggs et al. and then the particle-size distribution curve parameters (PSDCPs) and fractal parameters were calculated. The PSDCPs were used to predict 20 particle-size classes for a soil sample’s particle size distribution. Fractal parameters were calculated by the model of Bird et al. In addition, total soil-specific surface area (TSS) was calculated using the above 20 size classes. Pedotransfer functions were developed for SSA and TSS using ANN ensembles from 63 pieces of SSA data taken from the literature. Fractal parameters, PSDCPs, and some other soil properties were used to predict SSA and TSS. Introducing fractal parameters and PSDCPs improved the SSA estimations by 12.5 and 11.1 %, respectively. The improvements were even better for TSS estimations (27.7 and 27.0 %, respectively). The use of fractal parameters as estimators described 44 and 92.8 % of the variation in SSA and TSS, respectively, while PSDCPs explained 42 and 6.6 % of the variation in SSA and TSS, respectively. The results suggested that fractal parameters and PSDCPs could be successfully used as predictors in ANN ensembles to predict SSA and TSS.     

Health risk assessment of heavy metal intake due to fish consumption in the Sistan region,Iran

The heavy metal (Pb, Cd, Cr, and Ni) content of a fish species consumed by the Sistan population and its associated health risk factors were investigated. The mean concentrations of Pb, Cd, and Cr were slightly higher than the standard levels. The Ni content of fish was below the maximum guideline proposed by the US Food and Drug Administration (USFDA). The average estimated weekly intake was significantly below the provisional tolerable intake based on the FAO and WHO standards for all studied metals. The target hazard quotients (THQ) of all metals were below 1, showing an absence of health hazard for the population of Sistan. The combined target hazard quotient for the considered metals was 26.94 × 10^?3. The cancer risk factor for Pb (1.57 × 10^?7) was below the acceptable lifetime carcinogenic risk (10^?5). The results of this study reveal an almost safe level of Pb, Cd, Cr, and Ni contents in the fish consumed by the Sistan population.

Open image in new window

Graphical abstract ?

     

Assessment of particulate matter in the urban atmosphere: size distribution, metal composition and source characterization using principal component analysis

In this study, the size distribution of airborne particles and related heavy metals Co, Cd, Sn, Cu, Ni, Cr, Pb and V in two urban areas in Istanbul: Yenibosna and Goztepe, were examined. The different inhalable particles were collected by using a cascade impactor in eight size fractions (<0.4 μm, 0.4-0.7 μm, 1.1-2.1 μm, 2.1-3.3 μm, 3.3-4.7 μm, 4.7-5.8 μm, 5.8-9 μm and >9 μm) for six months at each station. Samples were collected on glass fiber filters and filters were extracted and analyzed using ICP-MS. Log-normal distributions showed that the particles collected at the Yenibosna site have a smaller size compared to the Goztepe samples and the size distribution of PM was represented the best by the tri-modal. The average total particle concentrations and standard deviations were obtained as 67.7 ± 17.0 μg m(-3) and 82.1 ± 21.2 μg m(-3), at the Yenibosna and G?ztepe sites, respectively. The higher metal rate in fine and medium coarse PM showed that the anthropogenic sources were the most significant pollutant source. Principal component analysis identified five components for PM namely traffic, road dust, coal and fuel oil combustion, and industrial.     

Batch and continuous biosorption of Cu by immobilized biomass of Arthrobacter sp.

The ability of free and polysulphone immobilized biomass of Arthrobacter sp. to remove Cu²⁺ ions from aqueous solution was studied in batch and continuous systems. The Langmuir and Freundlich isotherm models were applied to the data. The Langmuir isotherm model was found to fit the sorption data indicating that sorption was monolayer and uptake capacity (Q^o) was 175.87 and 158.7 mg/g for free and immobilized biomass respectively at pH 5.0 and 30 °C temperature, which was also confirmed by a high correlation coefficient, a low RMSE and a low Chi-square value. A kinetic study was carried out with pseudo-first-order reaction and pseudo-second-order reaction equations and it was found that the Cu²⁺ uptake process followed the pseudo-second-order rate expression. The diffusivity of Cu²⁺ on immobilized beads increased (0.402 × 10⁻⁴ to 0.435 × 10⁻⁴ cm²/s) with increasing concentration from 50 to 150 mg/L. The maximum percentage Cu²⁺ removal (89.56%) and uptake (32.64 mg/g) were found at 3.5 mL/min and 20 cm bed height. In addition to this the Bed Depth Service Time (BDST) model was in good agreement with the experimental data with a high correlation coefficient (>0.995). Furthermore, sorption and desorption studies were also carried out which showed that polysulphone immobilized biomass could be reused for up to six sorption–desorption cycles.     

Application of geographically weighted regression model to analysis of spatiotemporal varying relationships between groundwater quantity and land use changes (case study: Khanmirza Plain,Iran)

Understanding the spatiotemporal relationships between land use/cover changes (LUCC) and groundwater resources is necessary for effective and efficient land use management. In this paper, geographically weighted regression (GWR) and ordinary least squares (OLS) models have been expanded to analyze varying spatial relationships between groundwater quantity changes and LUCC for three periods: 1987–2000, 2000–2010, and 1987–2010 in the Khanmirza Plain of southwestern Iran. For this purpose, TM images were used to generate LUCC (rainfed, irrigated, meadow, and bare lands). Groundwater quantity variables, including groundwater level changes (GLC) and groundwater withdrawal differences (GWD), were gathered from piezometric and agricultural wells data. The analysis of spatial autocorrelation (Moran’s I and local indicators of spatial association ) demonstrated that GWR has a better ability to model spatially varying data with very minimal clustering of residuals. The results R ² and corrected Akaike’s Information Criterion parameters revealed that the GWR has the lowest similarity in space and time in neighboring situations and it has the high ability to explain more variance in the LUCC as a function of the groundwater quantity changes. All results of the distribution of local R ² values from GWR confirm our assertion that there is a spatiotemporal relationship between types of land use and each of groundwater quantity variables within the region. According to the t test results from GWR, there are significant differences between the GLC and GWD and the land use types in different places of region in each of the three time series. The GWR results can help decision-makers to make appropriate decisions for future planning.     

Plant growth regulators and EDTA improve phytoremediation potential and antioxidant response of Dysphania ambrosioides (L.) Mosyakin & Clemants in a Cd-spiked soil

Environmental Science and Pollution Research - Soil pollution due to potentially toxic elements is a worldwide challenge for health and food security. Chelate-assisted phytoextraction along with...     

Comparison the effects of bioaugmentation versus biostimulation on marine microbial community by PCR–DGGE: A mesocosm scale

In order to better understand the effects of biostimulation and bioaugmentation processes on a marine microbial community, three different mesocosm experiments were planned. Natural seawater(10.000 L) was artificially polluted with crude oil(1 L) and(1) inorganic nutrients(Biostimulating Mesocosm, BM),(2) inorganic nutrients and an inoculum of Alcanivorax borkumensis SK2(Single Bioaugmentation Mesocosm, SBM),(3) inorganic nutrients and inoculums of A. borkumensis SK2 and Thalassolituus oleivorans MIL-1(Consortium Bioaugmentation Mesocosm, CBM). During the experimental period(20 days), samples were taken from each mesocosm and the community structure was analyzed by PCR–DGGE. The 16 S r RNA gene DGGE banding patterns and sequence analysis demonstrated that biostimulation had the lowest effect on microbial biodiversity in the mesocosms; however, the biodiversity of the marine microbial community dramatically decreased in the CBM(Shannon index was 0.6 in T3). The community structures among the three mesocosms were also markedly different,and major bacteria derived from DGGE bands were related to uncultured Gamma Proteobacteria. The biodegradation results show that the Single Bioaugmentation Mesocosm(SBM) system had the highest percentage of degradation(95%) in comparison to the BM mesocosm(80%) and CBM(70%).     

Particulate and dissolved organic carbon in cloud water in southern Scotland

Total particulate carbon (TPC), which includes both elemental carbon and particulate organic carbon, total suspended particulate matter (TSP) and dissolved organic carbon (DOC) were measured in 53 cloud water samples collected using a passive 'Harp-wire' cloud collector at weekly intervals at a hill-top site in southern Scotland (Dunslair Heights, 602 m above sea level) between December 1990 and April 1992. The concentrations of TPC, TSP and DOC were in the range 0.03-6.9 mg 1(-1) (median 1.05 mg l(-1)), 2.6-51.6 mg l(-1) (median 13.6 mg l(-1)) and 0.-14 mg l(-1) (median 3.6 mg l(-1)), respectively. The concentrations of TPC, TSP and DOC were greatest in winter (December-February), up to 6.9, 42 and 4.6 mg l(-1) respectively in 1990-1991 and up to 6.0, 51 and 14 mg l(-1), respectively, in 1991-1992. Particulate carbon in cloud water samples comprised 1-47% of the TSP. Concentrations of major anions (Cl(-), NO(-)(3), SO(2-)(4)) and pH were measured on the same water samples. Estimates of cloud liquid water content from January to April 1992 were derived from measured wind speeds and volumes of water collected. These estimates suggested that the air contained up to 1.2 microg TPC m(-3), 16 microg TSP m(-3) and 2.3 microg DOC m(-3), which are typical of concentrations to be expected in rural air. There was no correlation between concentrations of DOC in cloud water and either TPC or TSP, indicating that the sources and partitioning of DOC and TPC in the atmosphere are different. The largest concentrations of TPC coincided with the largest concentrations of non-marine sulphate, and although there was a significant linear correlation between the two sets of data, the log-transformed data were not correlated. Concentrations of TPC were significantly correlated with concentrations of other particulate matter (TSP-TPC), suggesting that similar sources and/or partitioning processes were involved in determining concentrations in cloud. Concentrations of DOC in cloud were significantly correlated (p < 0.02) with concentrations of nitrate, suggesting that sources of DOC were related to the emission and chemistry of nitrogen oxides. The very large concentrations of particulate carbon, especially in winter, indicate that carbon-catalysed oxidation of sulphur dioxide by molecular oxygen in cloud water may be a significant pathway when concentrations of hydrogen peroxide are small.