Stability of waterfront retaining wall subjected to pseudo-static earthquake forces

Waterfront retaining walls supporting dry backfill are subjected to hydrostatic pressure on upstream face and earth pressure on the downstream face. Under seismic conditions, if such a wall retains a submerged backfill, additional hydrodynamic pressures are generated. This paper pertains to a study in which the effect of earthquakes along with the hydrodynamic pressure including inertial forces on such a retaining wall is observed. The hydrodynamic pressure is calculated using Westergaard's approach, while the earth pressure is calculated using Mononobe-Okabe's pseudo-static analysis. It is observed that when the horizontal seismic acceleration coefficient is increased from 0 to 0.2, there is a 57% decrease in the factor of safety of the retaining wall in sliding mode. For investigating the effect of different parameters, a parametric study is also done. It is observed that if φ is increased from 30° to 35°, there is an increase in the factor of safety in the sliding mode by 20.4%. Similar observations were made for other parameters as well. Comparison of results obtained from the present approach with [Ebeling, R.M., Morrison Jr, E.E., 1992. The seismic design of waterfront retaining structures. US Army Technical Report ITL-92-11. Washington DC] reveal that the factor of safety for static condition (k_h=0), calculated by both the approaches, is 1.60 while for an earthquake with k_h=0.2, they differ by 22.5% due to the consideration of wall inertia in the present study.     

Applications of AnnAGNPS model for soil loss estimation and nutrient loading for Malaysian conditions

The study evaluated the performance and suitability of AnnAGNPS model in assessing runoff, sediment loading and nutrient loading under Malaysian conditions. The watershed of River Kuala Tasik in Malaysia, a combination of two sub-watersheds, was selected as the area of study. The data for the year 2004 was used to calibrate the model and the data for the year 2005 was used for validation purposes. Several input parameters were computed using methods suggested by other researchers and studies carried out in Malaysia. The study shows that runoff was predicted well with an overall R² value of 0.90 and E value of 0.70. Sediment loading was able to produce a moderate result of R² = 0.66 and E = 0.49, nitrogen loading predictions were slightly better with R² = 0.68 and E = 0.53, and phosphorus loading performance was slightly poor with an R² = 0.63 and E = 0.33. The erosion map developed was in agreement with the erosion risk map produced by the Department of Agriculture, Malaysia. Rubber estates and urban areas were found to be the main contributors to soil erosion. The simulation results showed that AnnAGNPS has the potential to be used as a valuable tool for planning and management of watersheds under Malaysian conditions.     

Assessment of spatial variations in water quality of Garra River at Shahjahanpur,Ganga Basin,India

Arabian Journal of Geosciences - A comprehensive study on the chemical considerations of thermal waters (springs and geothermal wells) on the performance of solute geothermometers in predicting the...     

Estimation of air-overpressure produced by blasting operation through a neuro-genetic technique

Blasting operations usually produce significant environmental problems which may cause severe damage to the nearby areas. Air-overpressure (AOp) is one of the most important environmental impacts of blasting operations which needs to be predicted and subsequently controlled to minimize the potential risk of damage. In order to solve AOp problem in Hulu Langat granite quarry site, Malaysia, three non-linear methods namely empirical, artificial neural network (ANN) and a hybrid model of genetic algorithm (GA)–ANN were developed in this study. To do this, 76 blasting operations were investigated and relevant blasting parameters were measured in the site. The most influential parameters on AOp namely maximum charge per delay and the distance from the blast-face were considered as model inputs or predictors. Using the five randomly selected datasets and considering the modeling procedure of each method, 15 models were constructed for all predictive techniques. Several performance indices including coefficient of determination (R ²), root mean square error and variance account for were utilized to check the performance capacity of the predictive methods. Considering these performance indices and using simple ranking method, the best models for AOp prediction were selected. It was found that the GA–ANN technique can provide higher performance capacity in predicting AOp compared to other predictive methods. This is due to the fact that the GA–ANN model can optimize the weights and biases of the network connection for training by ANN. In this study, GA–ANN is introduced as superior model for solving AOp problem in Hulu Langat site.     

Heavy metal exposure from co-processing of hazardous wastes for cement production and associated human risk assessment

This study was carried out to determine the concentration of heavy metals (Cd, Ni, Pb, Cr, Ni and Zn) in ordinary Portland cement (OPC) produced from the co-processing with hazardous waste in comparison with OPC produced using natural raw materials. The results showed that the concentration of heavy metals in cement produced from natural raw material was in the order of Zn > Pb > Cr > Ni > Cu > Cd. Zn and Cd were the highest and the lowest concentrations, respectively, in cements produced from the co-processing activity. The difference between heavy metals concentrations in OPC produced with and without co-processing was found to be statistically significant. The concentration of heavy metals in the cement produced is generally factory dependent. The human risk assessment associated with the heavy metals for non-carcinogenic and carcinogenic risks has been evaluated. The calculated hazard index (HI) and total lifetime cancer risks (LCR) were lower than the acceptable threshold reference values, HI < 1 and LCR < 1 × 10^?4, respectively. Thus, it is anticipated that there is no potential of non-carcinogenic and carcinogenic risks for both adult and children. However, the findings indicated that there is a need for regulatory monitoring. The exposure pathway for both non-carcinogenic and carcinogenic risks are both in the order of ingestion > dermal > inhalation.     

2019年马六甲地区红色精灵观测及母体雷暴分析

红色精灵是一种发生于闪电放电活跃的雷暴云上空的中高层大气瞬态发光现象,它们通常由中尺度对流系统层状云降水区内的强地闪回击产生,是对流层和中间层之间的一种能量耦合过程。目前,有关中国南海及东南亚地区的红色精灵观测鲜有报道。为了进一步了解热带地区产生红色精灵事件的沿海性雷暴特征,于2019年利用低光度光学观测系统和低频磁场天线在马来西亚马六甲地区开展了地基观测。实验于11月9日、12月11日和12月15日三次在沿海雷暴上空共捕捉到7例红色精灵事件,其中包括4例圆柱型、2例胡萝卜型和1例舞蹈型。结合闪电定位、云顶亮温和低频磁场信号等同步数据,分析表明所有事件均由正极性地闪回击产生,且母体闪电回击位于雷暴对流区附近（云顶亮温≤ 210 K处）,这可能是该地区产生红色精灵的沿海性雷暴的共同特征。此外,红色精灵生成期并不是闪电活动最强期,而是发生于闪电频数短暂降低后,这表明红色精灵的发生可能是该地区成熟雷暴中对流减弱的一个信号。     

Bioenergy consumption and economic growth in the EU-28 region: evidence from a panel cointegration model

GeoJournal - The purpose of this research is to examine the correlation between the bioenergy industry and economic outgrowth. This study investigates the growth hypothesis within the setting of...     

Changes to the Surface Level Solar Ultraviolet-B Radiation Due to Haze Perturbation

The South-East Asian region experienced a haze episode in 1994 which was widely believed to be due to widespread forest fires in Sumatra and Kalimantan (Indonesia). Broadband measurements of the surface level solar ultraviolet-B, UV-A and Global radiation at Penang (Malaysia) are used to study the effect of the 1994 haze on effective UV-B irradiance. We find that during the haze episode, there is enhanced absorption of surface level UV-B radiation. The effect of haze on UV-A and Global radiation is much less. The reduction in absolute noon time UV-B irradiance (mostly cloud free) during the 1994 haze period was 23% relative to the UV-B irradiance during thecorresponding haze-free period in 1995. Even though the noon time radiation data minimizes the cloud effect in the results presented some cloud effect is still present.     

Downscaling and projection of precipitation from general circulation model predictors in an equatorial climate region by the automated regression-based statistical method

The authors have applied an automated regression-based statistical method, namely, the automated statistical downscaling (ASD) model, to downscale and project the precipitation climatology in an equatorial climate region (Peninsular Malaysia). Five precipitation indices are, principally, downscaled and projected: mean monthly values of precipitation (Mean), standard deviation (STD), 90th percentile of rain day amount, percentage of wet days (Wet-day), and maximum number of consecutive dry days (CDD). The predictors, National Centers for Environmental Prediction (NCEP) products, are taken from the daily series reanalysis data, while the global climate model (GCM) outputs are from the Hadley Centre Coupled Model, version 3 (HadCM3) in A2/B2 emission scenarios and Third-Generation Coupled Global Climate Model (CGCM3) in A2 emission scenario. Meanwhile, the predictand data are taken from the arithmetically averaged rain gauge information and used as a baseline data for the evaluation. The results reveal, from the calibration and validation periods spanning a period of 40 years (1961–2000), the ASD model is capable to downscale the precipitation with reasonable accuracy. Overall, during the validation period, the model simulations with the NCEP predictors produce mean monthly precipitation of 6.18–6.20 mm/day (root mean squared error 0.78 and 0.82 mm/day), interpolated, respectively, on HadCM3 and CGCM3 grids, in contrast to 6.00 mm/day as observation. Nevertheless, the model suffers to perform reasonably well at the time of extreme precipitation and summer time, more specifically to generate the CDD and STD indices. The future projections of precipitation (2011–2099) exhibit that there would be an increase in the precipitation amount and frequency in most of the months. Taking the 1961–2000 timeline as the base period, overall, the annual mean precipitation would indicate a surplus projection by nearly 14~18 % under both GCM output cases (HadCM3 A2/B2 scenarios and CGCM3 A2 scenario). According to the model simulation, the September–November periods might be the more significant months projecting the increment of the precipitation amount around over 50 %, while the precipitation deficit would be seen in March–May periods.     

Estimating groundwater vulnerability to pollution using a modified DRASTIC model in the Kerman agricultural area,Iran

Groundwater contamination from intensive fertilizer application affects conservation areas in a plain. The DRASTIC model can be applied in the evaluation of groundwater vulnerability to such pollution. The main purpose of using the DRASTIC model is to map groundwater susceptibility to pollution in different areas. However, this method has been used in various areas without modification, thereby disregarding the effects of pollution types and their characteristics. Thus, this technique must be standardized and be approved for applications in aquifers and particular types of pollution. In this study, the potential for the more accurate assessment of vulnerability to pollution is achieved by correcting the rates of the DRASTIC parameters. The new rates were calculated by identifying the relationships among the parameters with respect to the nitrate concentration in groundwater. The methodology was implemented in the Kerman plain in the southeastern region of Iran. The nitrate concentration in water from underground wells was tested and analyzed in 27 different locations. The measured nitrate concentrations were used to associate and correlate the pollution in the aquifer to the DRASTIC index. The Wilcoxon rank-sum nonparametric statistical test was applied to determine the relationship between the index and the measured pollution in Kerman plain. Also, the weights of the DRASTIC parameters were modified through the sensitivity analysis. Subsequently, the rates and weights were computed. The results of the study revealed that the modified DRASTIC model performs more efficiently than the traditional method for nonpoint source pollution, particularly in agricultural areas. The regression coefficients showed that the relationship between the vulnerability index and the nitrate concentration was 82 % after modification and 44 % before modification. This comparison indicated that the results of the modified DRASTIC of this region are better than those of the original method.