Probabilistic data integration for characterization of spatial distribution of residual LNAPL

The spatial distribution of residual light non-aqueous phase liquid (LNAPL) is an important factor in reactive solute transport modeling studies. There is great uncertainty associated with both the areal limits of LNAPL source zones and smaller scale variability within the areal limits. A statistical approach is proposed to construct a probabilistic model for the spatial distribution of residual NAPL and it is applied to a site characterized by ultra-violet-induced-cone-penetration testing (CPT–UVIF). The uncertainty in areal limits is explicitly addressed by a novel distance function (DF) approach. In modeling the small-scale variability within the areal limits, the CPT–UVIF data are used as primary source of information, while soil texture and distance to water table are treated as secondary data. Two widely used geostatistical techniques are applied for the data integration, namely sequential indicator simulation with locally varying means (SIS–LVM) and Bayesian updating (BU). A close match between the calibrated uncertainty band (UB) and the target probabilities shows the performance of the proposed DF technique in characterization of uncertainty in the areal limits. A cross-validation study also shows that the integration of the secondary data sources substantially improves the prediction of contaminated and uncontaminated locations and that the SIS–LVM algorithm gives a more accurate prediction of residual NAPL contamination. The proposed DF approach is useful in modeling the areal limits of the non-stationary continuous or categorical random variables, and in providing a prior probability map for source zone sizes to be used in Monte Carlo simulations of contaminant transport or Monte Carlo type inverse modeling studies.     

PM10 Source Apportionment in Ahvaz,Iran, Using Positive Matrix Factorization

Source apportionment of particulate matter <10 µm in diameter (PM₁₀), having considerable impacts on human health and the environment, is of high priority in air quality management. The present study, therefore, aimed at identifying the potential sources of PM₁₀ in an arid area of Ahvaz located in southwest of Iran. For this purpose, we collected 24‐h PM₁₀ samples by a high volume air sampler. The samples were then analyzed for their elemental (Al, As, B, Ba, Be, Ca, Cd, Co, Cr, Cu, Fe, Hg, K, Mg, Mn, Na, Ni, Pb, Se, Si, Sn, Sr, Li, Ti, V, Zn, Mo, and Sb) and ionic (NH, Cl^?, NO, and SO) components using inductively coupled plasma optical emission spectrometry and ion chromatography instruments, respectively. Eight factors were identified by positive matrix factorization: crustal dust (41.5%), road dust (5.5%), motor vehicles (11.5%), marine aerosol (8.0%), secondary aerosol (9.5%), metallurgical plants (6.0%), petrochemical industries and fossil fuel combustion (13.0%), and vegetative burning (5.0%). Result of this study suggested that the natural sources contribute most to PM₁₀ particles in the area, followed closely by the anthropogenic sources.     

Comparison of artificial neural network and multivariate linear regression methods for estimation of daily soil temperature in an arid region

Soil temperature (T _S) strongly influences a wide range of biotic and abiotic processes. As an alternative to direct measurement, indirect determination of T _S from meteorological parameters has been the focus of attention of environmental researchers. The main purpose of this study was to estimate daily T _S at six depths (5, 10, 20, 30, 50 and 100?cm) by using a multilayer perceptron (MLP) artificial neural network (ANN) model and a multivariate linear regression (MLR) method in an arid region of Iran. Mean daily meteorological parameters including air temperature (T _a), solar radiation (R _S), relative humidity (RH) and precipitation (P) were used as input data to the ANN and MLR models. The model results of the MLR model were compared to those of ANN. The accuracy of the predictions was evaluated by the correlation coefficient (r), the root mean-square error (RMSE) and the mean absolute error (MAE) between the measured and predicted T _S values. The results showed that the ANN method forecasts were superior to the corresponding values obtained by the MLR model. The regression analysis indicated that T _a, RH, R _S and P were reasonably correlated with T _S at various depths, but the most effective parameters influencing T _S at different depths were T _a and RH.     

Optimal water and waste load allocation in reservoir–river systems: a case study

In this paper, a new methodology is developed for optimization of water and waste load allocation in reservoir–river systems considering the existing uncertainties in reservoir inflow, waste loads and water demands. A stochastic dynamic programming (SDP) model is used to optimize reservoir operation considering the inflow uncertainty, and another model called PSO-SA is developed and linked with the SDP model for optimizing water and waste load allocation in downstream river. In the PSO-SA model, a particle swarm optimization technique with a dynamic penalty function for handling the constraints is used to optimize water and waste load allocation policies. Also, a simulated annealing technique is utilized for determining the upper and lower bounds of constraints and objective function considering the existing uncertainties. As the proposed water and waste load allocation model has a considerable run-time, some powerful soft computing techniques, namely, Regression tree Induction (named M5P), fuzzy K-nearest neighbor, Bayesian network, support vector regression and an adaptive neuro-fuzzy inference system, are trained and validated using the results of the proposed methodology to develop real-time water and waste load allocation rules. To examine the efficiency and applicability of the methodology, it is applied to the Dez reservoir–river system in the south-western part of Iran.     

Simulating urban growth in a megalopolitan area using a patch‐based cellular automata

Although traditional cellular automata (CA)‐based models can effectively simulate urban land‐use changes, they typically ignore the spatial evolution of urban patches, due to their use of cell‐based simulation strategies. This research proposes a new patch‐based CA model to incorporate a spatial constraint based on the growth patterns of urban patches into the conventional CA model for reducing the uncertainty of the distribution of simulated new urban patches. In this model, the growth pattern of urban patches is first estimated using a developed indicator that is based on the local variations in existing urban patches. The urban growth is then simulated by integrating the estimated growth pattern and land suitability using a pattern‐calibrated method. In this method, the pattern of new urban patches is gradually calibrated toward the dominant growth pattern through the steps of the CA model. The proposed model is applied to simulate urban growth in the Tehran megalopolitan area during 2000–2006–2012. The results from this model were compared with two common models: cell‐based CA and logistic‐patch CA. The proposed model yields a degree of patch‐level agreement that is 23.4 and 7.5% higher than those of these pre‐existing models, respectively. This reveals that the patch‐based CA model simulates actual development patterns much better than the two other models.     

An investigation of near surface wind speed trends in arid and semiarid regions of Iran

Wind plays an important role on the ecosystems and hydrological cycles besides other meteorological parameters such as temperature, precipitation, sunshine, and relative humidity. It strongly affects evapotranspiration, especially in arid and semiarid regions where there are serious problems in regard to water resource management. Evaluating the wind speed trend can provide good information for future agricultural planning. This study was conducted in order to investigate the wind speed trends over 24 synoptic meteorological stations located in arid and semiarid regions of Iran from 1975 to 2005. Near-surface wind speed was trended by nonparametric Mann–Kendall test spatially and temporally in three time scales (annual, seasonal, and monthly). Then, Sen’s slope estimator was used to determine the amount of the changes; furthermore, 10-year moving average low-pass filter was applied to show general trends. Finally, the smoothed time series derived from the mentioned filter were classified in three clusters for each time series and then mapped to show their spatial distribution pattern. Results showed insignificant and significant, increasing and decreasing trends during the surveyed time. Wind speeds in less than 50 % of stations changed statistically in all time scales, and in most cases, the frequency of the upward trends was more than that of downward ones. The spatial distribution of significant wind speed showed that the increase mostly occurred in eastern part. Clustering gave us the turning point around 1990. Clearly, when clusters were mapped, they indicated the same pattern as the Z value maps derived from Mann–Kendall test which meant that the outputs of the mentioned method confirmed the other one. As the wind speed trends in different stations likely to follow the previous evapotranspiration (ET₀) trend results in Iran, it confirms that wind speed was an effective parameter on ET₀, even though other parameters should be considered too.     

Chromium Removal through Biosorption and Bioaccumulation by Bacteria from Tannery Effluents Contaminated Soil

Four bacterial isolates (two resistant and two sensitive to chromium) were isolated from soil contaminated with tannery effluents at Jajmau (Kanpur), India, and were identified by 16S rDNA gene sequencing as Stenotrophomonas maltophilia, Exiguobacterium sp., Pantoea sp., and Aeromonas sp. Biosorption of chromium by dried and living biomasses was determined in the resistant and sensitive isolates. The effect of pH, initial metal concentration, and contact time on biosorption was studied. At pH 2.5 the living biomass of chromium resistant isolate Exiguobacterium sp. ZM‐2 biosorbed maximum amount of Cr⁶⁺ (29.8 mg/g) whereas the dried biomass of this isolate biosorbed 20.1 mg/g at an initial concentration of 100 mg/L. In case of chromate sensitive isolates, much difference was not observed in biosorption capacities between their dried and living biomasses. The maximum biosorption of Cr³⁺ was observed at pH 4.5. However, biosorption was identical in resistant and sensitive isolates. The data on chromium biosorption were analyzed using Langmuir and Freundlich isotherm model. The biosorption data of Cr⁶⁺ and Cr³⁺ from aqueous solution were better fitted in Langmuir isotherm model compared to Freundlich isotherm model. Metal recovery through desorption was observed better with dried biomasses compared to the living biomasses for both types of chromium ions. Bioaccumulation of chromate was found higher in chromate resistant isolates compared to the chromate sensitive isolates. Transmission electron microscopy confirmed the accumulation of chromium in cytoplasm in the resistant isolates.     

Seismic vulnerability assessment in the new urban area of Diriyah Governorate,Riyadh, Saudi Arabia

Nine seismic refraction profiles were conducted and processed to study the near-surface sediments in the new urban area of Diriyah. The 2D geoseismic models illustrate two layers: a surface layer of soft sediments and weathered to hard limestone bedrock. Moreover, microtremor measurements were performed at 38 sites for 40 min using three-component seismographs and processed to assess the peak spectral amplitude and the corresponding fundamental resonance frequency. The seismic vulnerability index at each measurement site was estimated. These results correlate well with the geotechnical borehole data. The north-western zone is highly vulnerable due to the great thickness of the soft sediments.     

Assessment of Trace Metal Contamination of Drinking Water in the Pearl Valley,Azad Jammu and Kashmir

The aim of this study was to assess trace metal contamination of drinking water in the Pearl Valley, Azad Jammu and Kashmir (Pakistan). The objectives were to determine physical properties and the dissolved concentration of five trace metals, i. e., lead, copper, nickel, zinc, and manganese, in drinking water samples collected from various sites of municipal water supply, natural water springs and wells in the valley. Concentrations of the metals in the water samples were determined by flame atomic absorption spectrometry. Results showed physical parameters, i. e., appearance, taste and odor within acceptable limits and pH was between 5.5 and 7.0. The observed concentrations of the metals varied between sources of water samples and between sampling sites. Maximum dissolved concentration observed was 4.7 mg/L for Pb and Mn, 4.6 mg/L for Zn, 2.9 mg/L for Ni and 2.8 mg/L for Cu. The observed concentrations of the metals were compared with the World Health Organization's guideline values for drinking water. Overall, the quality of water samples taken from the water springs at Mutyal Mara and Bonjosa was good; however, the water quality was unsuitable for drinking in Kiraki, Kharick, and Pothi Bala localities particularly. Finally, the authors discuss possible causes for increased concentrations of the trace metals in drinking water in the study area.