Health risk assessments due to nitrate levels in drinking water in villages of Azadshahr,northeastern Iran

Groundwater is the main water source used for drinking and cooking purposes globally. Nitrate level in most groundwater resources in arid and semi-arid areas has increased in the past several decades as a result of human activities and natural processes. This may exert a great impact on human health. To learn the contamination circumstances of groundwater nitrate in villages of Azadshahr, Iran and assess its probable risk to the health of adults, children and infants, fifty-eight groundwater samples were collected from wells and springs in 2018. Nitrate concentrations had a wide spatial variability in wells and springs of the studied villages, with values going from 1 up to 51 mg/L. Exceedances of the EPA standard value were limited to two village springs (villages Nili and Narab, with nitrate level of 51 and 46 mg/L, respectively). The hazard quotients (HQ) values for 41% of children and infants were above the safety level (i.e., HQ?>?1), suggesting that groundwater nitrate would have significant health effects on these age groups. Therefore, appropriate control measures and sanitation improvement programs should be put in place to protect the health of the residents in the contaminated villages.     

Prediction of river flow using hybrid neuro-fuzzy models

The complex nature of hydrological phenomena, like rainfall and river flow, causes some limitations for some admired soft computing models in order to predict the phenomenon. Evolutionary algorithms (EA) are novel methods that used to cover the weaknesses of the classic training algorithms, such as trapping in local optima, poor performance in networks with large parameters, over-fitting, and etc. In this study, some evolutionary algorithms, including genetic algorithm (GA), ant colony optimization for continuous domain (ACO_R), and particle swarm optimization (PSO), have been used to train adaptive neuro-fuzzy inference system (ANFIS) in order to predict river flow. For this purpose, classic and hybrid ANFIS models were trained using river flow data obtained from upstream stations to predict 1-, 3-, 5-, and 7-day ahead river flow of downstream station. The best inputs were selected using correlation coefficient and a sensitivity analysis test (cosine amplitude). The results showed that PSO improved the performance of classic ANFIS in all the periods such that the averages of coefficient of determination, R², root mean square error, RMSE (m³/s), mean absolute relative error, MARE, and Nash-Sutcliffe efficiency coefficient (NSE) were improved up to 0.19, 0.30, 43.8, and 0.13%, respectively. Classic ANFIS was only capable to predict river flow in 1-day ahead while EA improved this ability to 5-day ahead. Cosine amplitude method was recognized as an appropriate sensitivity analysis method in order to select the best inputs.     

Peri-Gondwana Late Early–Middle Cambrian trilobites from the Kuhbanan Formation in Dahu section,Kerman Province,Iran

Outcropped of the Kuhbanan Formation at Dahu, near Zarand, about 63 km north of Kerman, Iran contains peri-Gondwana trilobites. In this study, 185 trilobite samples including six species and genera were identified and described from Dahu section. This trilobite’s assemblage including Redlichia noetlingi, Redlichia sp., Kermanella kuhbananensis, Kermanella lata lata, Kermanella lata minuta, Iranoleesia pisiformis, and Iranaspis sp. based on occurrence of the trilobite fauna a late Early to Middle Cambrian (Series 2–3) is suggested for this strata. These trilobite fauna help confirm conclusions from recent geological studies that place the Kerman Basin of Iran during the Cambrian.     

Integration of cadastral survey data into building information models

Cadastral surveying plays an important role in defining legal boundaries of land and property. The current practice for recording cadastral survey data mainly relies on 2D digital or analog documents. This practice is efficient for simple land parcels but can be challenged in complex building developments. To address the issues stemmed from 2D methods of representing cadastral survey data, 3D spatial information models can be considered as a viable solution for managing cadastral survey data. Building Information Modeling (BIM) enables colsslaborative 3D management of the design, construction, and operation of buildings. There have been extensive studies conducted to investigate the connectivity between BIM and 3D cadaster. Most of these studies focus on managing legal information, such as ownership boundaries and attributes, in BIM-based environments. However, there is limited investigation on how survey- ing measurements can be mapped into BIM. In this study, the proposed method for integrating the cadastral survey data into the BIM environment includes identifying cadastral survey requirements, using BIM entities relevant to cadastral survey data, enrichment of a BIM proto- type, and evaluation of the prototype. The major contribution of this study is to demonstrate the storage of cadastral survey data such as survey marks and traverse lines in the BIM environment. Therefore, this research contributes to the further enrichment of BIM with incorporating data elements related to cadastral surveying practices. It is confirmed that current BIM-based tools provide restricted capabilities for explicit management and visualiza- tion of cadastral survey data. This limitation can be addressed in the future enhancements of BIM in terms of supporting important elements for cadastral survey data.     

Evaluating scale effects and bearing portions in centrifuge modeling of helical anchors: sand

Acta Geotechnica - Helical anchors are bearing elements that can resist uplift loads by a combination of shaft and helical plate bearing. The application of helical piles as offshore wind turbine...     

Geostatistics-Based Method for Irregular Mineral Resource Estimation,in Ouenza Iron Mine,Northeastern Algeria

Geotechnical and Geological Engineering - Geostatistical techniques are usually practical in the development and production stages of mining projects. The Ouenza deposit is the main iron ore...     

Biomass energy consumption and economic growth: panel data evidence from ASEAN member countries

The present study investigates the dynamic impact of biomass energy consumption on economic growth across nine (9) ASEAN economic union member countries for the period of 1980–2011. We applied heterogeneous panel cointegration techniques. The result based on Pedroni panel cointegration test shows that, variables have long-run relationship as the null hypothesis of no cointegration was rejected at 1% and 5% respectively. Kao residual cointegration test also shows the same result as null hypothesis of no cointegration is rejected at 1% level of significance. The main empirical finding based on dynamic OLS, fully modified OLS and panel OLS reveals that; there is a positive and significant relationship between biomass energy consumptions and economic growth in the region. Moreover, the result based on dynamic ordinary least square (DOLS) also shows that; capital stock and human capital have a positive and significant impact on economic growth. Same result is also obtained from fully modified OLS (FMOLS) with the exception of human capital which is insignificant on economic growth. Panel ordinary least square also reconfirmed the finding of DOLS as all the three variables significantly influences economic growth. The policy suggestion remains that, authorities in ASEAN economic union should focus more on encouraging the use of renewable sources of energy, particularly biomass source of energy considering its positive impact on enhancing economic growth with little or no environmental degradation.     

Measuring the relationships between landscape aesthetics suitability and spatial patterns of urbanized lands: an informed modelling framework for developing urban growth scenarios

This study develops an informed modelling approach that follows a bottom-up planning strategy to define plausible urban growth scenarios. In this case, landscape aesthetics suitability of the area was first generated using multi-criteria evaluation method. Then, a buffer zone of 1 km was considered to extract the average values of aesthetics suitability scores surrounding urban patches with medium physical size (10–30 hectares). The averaged values were considered as the dependent variable. In the next step, landscape metrics of these urban patches, as explanatory variables, were also computed to measure compositional and configuration-based attributes of urban clusters. Bivariate associations (Pearson correlation analysis) and statistical relationships (linear regression algorithm) between landscape metrics and their associated aesthetics values were measured and modelled. According to the results, both composition and configuration values are significantly correlated to the dependent variable in which configuration-based attributes depicted a stronger explanatory power.     

Trends of precipitation extreme indices over a subtropical semi-arid area using PERSIANN-CDR

In this study, satellite-based daily precipitation estimation data from precipitation estimation from remotely sensed information using artificial neural networks (PERSIANN)-climate data record (CDR) are being evaluated in Iran. This dataset (0.25°, daily), which covers over three decades of continuous observation beginning in 1983, is evaluated using rain-gauge data for the period of 1998–2007. In addition to categorical statistics and mean annual amount and number of rainy days, ten standard extreme indices were calculated to observe the behavior of daily extremes. The results show that PERSIANN-CDR exhibits reasonable performance associated with the probability of detection and false-alarm ratio, but it overestimates precipitation in the area. Although PERSIANN-CDR mostly underestimates extreme indices, it shows relatively high correlations (between 0.6316–0.7797) for intensity indices. PERSIANN-CDR data are also used to calculate the trend in annual amounts of precipitation, the number of rainy days, and precipitation extremes over Iran covering the period of 1983–2012. Our analysis shows that, although annual precipitation decreased in the western and eastern regions of Iran, the annual number of rainy days increased in the northern and northwestern areas. Statistically significant negative trends are identified in the 90th percentile daily precipitation, as well as the mean daily precipitation from wet days in the northern part of the study area. The positive trends of the maximum annual number of consecutive dry days in the eastern regions indicate that the dry periods became longer in these arid areas.     

Effectiveness of BIOECODS for peak flow attenuation: an appraisal using InfoWorks SD

The Bio-Ecological Drainage System (BIOECODS) is a sustainable drainage system, which adopts a “control at source” approach for urban storm water management in Malaysia. This study attempts to model a small-scale BIOECODS using InfoWorks SD. New modelling techniques are used to fully integrate the surface and on-line subsurface conveyance system, in which overland flow routing is described by a storm water management model that uses a nonlinear reservoir method and the kinematic wave approximation of the St Venant equation, and subsurface flow is described by the Horton method in conjunction with the Soil Conservation Service (SCS) curve number (CN) method. The observed water levels at primary outlets are compared with those obtained from model simulation. The modelling approach has been proven successful as the hydrographs (predicted and observed) match each other closely, with a mean error in the range of 4.58–7.32%. Results from the model showed that the BIOECODS is able to attenuate peak flow by 60–75%, and increase the lag time by 20 min within an area of <28?300 m² when compared with a traditional drainage system.