Relationship between DOC photochemistry and mercury redox transformations in temperate lakes and wetlands

We compared diurnal variations in dissolved gaseous mercury (DGM) concentration and in losses of dissolved organic carbon fluorescence (DOCF) in four boreal Canadian Shield lakes, in one beaver pond, and in filtered and unfiltered water from a wetland in Lake St. Pierre, a fluvial lake of the St. Lawrence River. These systems were chosen to represent a spectrum of DOC. We also determined the contribution of UVB, UVA and visible light on DGM photo-induced production in the four lakes. Our results showed a strong relationship between DGM concentrations and light intensity and between DGM production and losses in DOCF, in all study sites. We also observed higher rates of DGM formation and of DOCF bleaching in the presence of UV radiation. Under UVB light, production of DGM was higher in clear lakes than in the humic ones. Inversely, in the UVA range, DGM production tended to be higher in humic lakes. We suggest that DOCF bleaching can be used as a proxy for the rate of formation of reactive species that may alter the redox state of mercury in surface waters. We also have indications that DGM production is more important in clear than in humic waters.     

Impact of the North Atlantic Oscillation on streamflow in Western Iran

The Middle East region, where arid and semi‐arid regions occupy most of the land, is extremely vulnerable to any natural or anthropogenic reductions in available water resources. Much of the observed interannual‐decadal variability in Middle Eastern streamflow is physically linked to a large‐scale atmospheric circulation patterns such as the North Atlantic Oscillation (NAO). In this work, the relationship between the NAO index and the seasonal and annual streamflows in the west of Iran was statistically examined during the last four decades. The correlations were constructed for two scenarios (with and without time lag). The associations between the annual and seasonal streamflows and the simultaneous NAO index were found to be poor and insignificant. The possibility of streamflow forecasting was also explored, and the results of lag correlations revealed that streamflow responses at the NAO signal with two and three seasons delays. The highest Spearman correlation coefficient of 0.379 was found between the spring NAO index and the autumn streamflow series at Taghsimab station, indicating that roughly 14% of the variance in the streamflow series is associated with NAO forcing. Copyright © 2013 John Wiley & Sons, Ltd.     

Monitoring and prediction of land use/land cover changes using CA-Markov model: a case study of Ravansar County in Iran

Human‐induced land use/land cover (LULC) changes are among the most important processes that shape the dynamics of the earth’s surface. This phenomenon, which is occurring at an astonishing rate, and its consequential environmental impacts have become an important area of research for scientists.Therefore, a wide range of methods and models have been developed to detect and predict these alterations, among which cellular automata (CA) models such as the CA‐Markov model, due to their affinity to geographic information system (GIS) and remote sensing (RS), are appropriate for detailed resolution modelling and simulating dynamic spatial processes. In Iran, the district of Ravansar has undergone severe LULC changes recently, thus to take the necessary precautions, decision‐makers need to predict and determine the extent of these changes. In this study, using spatial analysis methods the LULC changes in Ravansar were investigated from 1992 to 2015. Subsequently, the CA‐Markov model was applied to simulate the spatial pattern changes of LULC until 2030. Our results indicated that from 1992 to 2015, this region has witnessed a noticeable increase in the areas of the built‐up and agricultural lands (both aquatic and non‐aquatic), resulting in the decrease of the gardens, range, and bare lands. The simulated LULC map showed that this trend will continue due to more urbanization and development of agricultural areas.     

Numerical ANFIS-Based Formulation for Prediction of the Ultimate Axial Load Bearing Capacity of Piles Through CPT Data

This study explores the potential of adaptive neuro-fuzzy inference systems (ANFIS) for prediction of the ultimate axial load bearing capacity of piles (P_u) using cone penetration test (CPT) data. In this regard, a reliable previously published database composed of 108 datasets was selected to develop ANFIS models. The collected database contains information regarding pile geometry, material, installation, full-scale static pile load test and CPT results for each sample. Reviewing the literature, several common and uncommon variables have been considered for direct or indirect estimation of P_u based on static pile load test, cone penetration test data or other in situ or laboratory testing methods. In present study, the pile shaft and tip area, the average cone tip resistance along the embedded length of the pile, the average cone tip resistance over influence zone and the average sleeve friction along the embedded length of the pile which are obtained from CPT data are considered as independent input variables where the output variable is P_u for the ANFIS model development. Besides, a notable criticism about ANFIS as a prediction tool is that it does not provide practical prediction equations. To tackle this issue, the obtained optimal ANFIS model is represented as a tractable equation which can be used via spread sheet software or hand calculations to provide precise predictions of P_u with the calculated correlation coefficient of 0.96 between predicted and experimental values for all of the data in this study. Considering several criteria, it is represented that the proposed model is able to estimate the output with a high degree of accuracy as compared to those results obtained by some direct CPT-based methods in the literature. Furthermore, in order to assess the capability of the proposed model from geotechnical engineering viewpoints, sensitivity and parametric analyses are done.     

Application of Geochemistry and VNIR Spectroscopy in Mapping Heavy Metal Pollution of Stream Sediments in the Takab Mining Area, NW of Iran

This study considered the possibility of using visible and near infrared (VNIR) spectral absorption feature parameters (SAFPs) in predicting the concentration and mapping the distribution of heavy metals in sediments of the Takab area. In total, 60 sediment samples were collected along main streams draining from the mining districts and tailing sites, in order to measure the concentration of As, Co, V, Cu, Cr, Ni, Hg, Ti, Pb and Zn and the reflectance spectra (350–2500 nm). The quantitative relationship between SAFPs (Depth500nm, R610/500nm, R1344/778nm, Area500nm, Depth2200nm, Area2200nm, Asym2200nm) and geochemical data were assessed using stepwise multiple linear regression (SMLR) and enter multiple linear regression (EMLR) methods. The results showed a strong negative correlation between Ni and Cr with Area2200nm, a significant positive correlation between As and Asym2200nm, Ni and Co with Depth2200nm, as well as Co, V and total values with Depth500nm. The EMLR method eventuated in a significant prediction result for Ni, Cr, Co and As concentrations based on spectral parameters, whereas the prediction for Zn, V and total value was relatively weak. The spatial distribution pattern of geochemical data showed that mining activities, along with the natural weathering of base metal occurrences and rock units, has caused high concentrations of heavy metals in sediments of the Sarough River tributaries.     

Spatial distribution and temporal variation of reference evapotranspiration in arid and semi‐arid regions of Iran

Analysis of spatial and temporal variations of reference evapotranspiration (ET_o) is important in arid and semi‐arid regions where water resources are limited. The main aim of this study was to analyse the spatial distribution and the annual, seasonal and monthly trends of the Penman–Monteith ET_o for 21 stations in the arid and semi‐arid regions of Iran. Three statistical tests the Mann‐Kendall, Sen's slope estimator and linear regression were used for the analysis. The analysis revealed that ET_o increased from January to July and deceased from July to December at almost all stations. Additionally, higher annual ET_o values were found in the southeast of the study region and lower values in the northwest of the region. Although the results showed both positive and negative trends in annual ET_o series, ET_o generally increased, significantly so in six (～30%) of the stations. Analysis of the impacts of meteorological variables on the temporal trends of ET_o indicated that the increasing trend of ET_o was most likely due to a significant increase in minimum air temperature, while decreasing trend of ET_o was mainly caused by a significant decrease in wind speed. At the sites where increasing ET_o trends were statistically significant, the rate of increase varied from (+)8·36 mm/year at Mashhad station to (+)31·68 mm/year at Iranshahr station. On average, an increasing trend of (+)4·42 mm/year was obtained for the whole study area during the last four decades. Seasonal and monthly ET_o have also tended to increase at the majority of the stations. The greatest numbers of significant trends were observed in winter on the seasonal time‐scale and in September on the monthly time‐scale. Copyright © 2011 John Wiley & Sons, Ltd.     

Daily soil temperature modeling using neuro-fuzzy approach

Soil temperature is an important meteorological parameter which influences a number of processes in agriculture, hydrology, and environment. However, soil temperature records are not routinely available from meteorological stations. This work aimed to estimate daily soil temperature using the coactive neuro-fuzzy inference system (CANFIS) in arid and semiarid regions. For this purpose, daily soil temperatures were recorded at six depths of 5, 10, 20, 30, 50, and 100 cm below the surface at two synoptic stations in Iran. According to correlation analysis, mean, maximum, and minimum air temperatures, relative humidity, sunshine hours, and solar radiation were selected as the inputs of the CANFIS models. It was concluded that, in most cases, the best soil temperature estimates with a CANFIS model can be provided with the Takagi–Sugeno–Kang (TSK) fuzzy model and the Gaussian membership function. Comparison of the models’ performances at arid and semiarid locations showed that the CANFIS models’ performances in arid site were slightly better than those in semiarid site. Overall, the obtained results indicated the capabilities of the CANFIS model in estimating soil temperature in arid and semiarid regions.     

Observed changes in relative humidity and dew point temperature in coastal regions of Iran

The analysis of trends in hydroclimatic parameters and assessment of their statistical significance have recently received a great concern to clarify whether or not there is an obvious climate change. In the current study, parametric linear regression and nonparametric Mann?CKendall tests were applied for detecting annual and seasonal trends in the relative humidity (RH) and dew point temperature (T _dew) time series at ten coastal weather stations in Iran during 1966?C2005. The serial structure of the data was considered, and the significant serial correlations were eliminated using the trend-free pre-whitening method. The results showed that annual RH increased by 1.03 and 0.28?%/decade at the northern and southern coastal regions of the country, respectively, while annual T _dew increased by 0.29 and 0.15°C per decade at the northern and southern regions, respectively. The significant trends were frequent in the T _dew series, but they were observed only at 2 out of the 50 RH series. The results showed that the difference between the results of the parametric and nonparametric tests was small, although the parametric test detected larger significant trends in the RH and T _dew time series. Furthermore, the differences between the results of the trend tests were not related to the normality of the statistical distribution.     

Modeling Vapor Migration for Estimating the Time to Reach Steady State Conditions

The design and planning of soil vapor sampling for vapor intrusion assessment require an estimate of the time for vapor migration from the contamination source to reach steady state prior to vapor sampling and analysis for volatile organic compounds (VOCs). This study presents the model derivation, analytical solutions, as well as the assumptions and limitations of a one-dimensional VOC vapor transport model based on diffusion in porous media and equilibrium partitioning of VOCs in solid, aqueous, and vapor phases. The model assumes a finite domain with boundary conditions that represent the scenarios of vapor migration in the real environment. The derivation of the conceptual model is presented along with its practical use and implications as illustrated through case examples. Consideration of the upper (or exit) boundary condition along with the distance between the source and the applicable boundary, rather than the distance from the source to the measurement point, are shown to be critical in the time estimates as compared to an expression typically used and cited in guidance documents. The study reveals the importance of defining a conceptual model and relevant boundaries in assessing near steady state conditions, and suggests a tiered approach in refining the estimate with increasing level of effort for practical applications in vapor assessment.     

Evaluation of Tailings from a Porphyry Copper Mine based on Joint Simulation of Contaminants

Natural Resources Research - Tailings from porphyry copper mines contain environmentally harmful amounts of elements such as copper, molybdenum, lead and cobalt. Geostatistical simulation of...