Estimation of concentration and exposure doses due to radon by using CR-39 plastic track detectors in the residences of Sudhnuti, Azad Kashmir, Pakistan

This paper presents the results of indoor radon concentration measurements in 120 dwellings of district Sudhnuti of Azad Kashmir. Measurements were taken with CR-39 passive alpha track detector. CR-39 based box type radon detectors were installed in a bedroom and living rooms of each house. The detectors were retrieved after exposing to indoor radon for period of 6 months and then etched in 6 M NaOH at 80°C for 16 h, the observed track densities were converted in to the indoor radon concentration. Indoor radon concentration varied from 20 ± 12 to 170 ± 4 Bq m⁻³ for the houses of the district Sudhnuti. Arithmetic mean (AM), geometric mean (GM) and geometric standard deviations (GSD) were found to be 82 ± 6, 77 ± 6 and 1.51, respectively. The minimum value of weighted average radon concentration was recorded in one of the house of Mang town, whereas the maximum value was found in the Pattan Sher Khan region. Doses due to indoor radon exposure vary from 0.50 ± 0.31 to 4.28 ± 0.11 mSv year⁻¹ AM, GM and GSD. of mean effective doses were found to be 2.06 ± 0.13, 1.95 ± 0.18 and 1.51, respectively. According to the recommendations made by the Health Protection Agency, UK (200 Bq m⁻³) all the houses surveyed are within the safe limits.     

Comparison of SDSM and LARS-WG for simulation and downscaling of extreme precipitation events in a watershed

Future climate projections of Global Climate Models (GCMs) under different emission scenarios are usually used for developing climate change mitigation and adaptation strategies. However, the existing GCMs have only limited ability to simulate the complex and local climate features, such as precipitation. Furthermore, the outputs provided by GCMs are too coarse to be useful in hydrologic impact assessment models, as these models require information at much finer scales. Therefore, downscaling of GCM outputs is usually employed to provide fine-resolution information required for impact models. Among the downscaling techniques based on statistical principles, multiple regression and weather generator are considered to be more popular, as they are computationally less demanding than the other downscaling techniques. In the present study, the performances of a multiple regression model (called SDSM) and a weather generator (called LARS-WG) are evaluated in terms of their ability to simulate the frequency of extreme precipitation events of current climate and downscaling of future extreme events. Areal average daily precipitation data of the Clutha watershed located in South Island, New Zealand, are used as baseline data in the analysis. Precipitation frequency analysis is performed by fitting the Generalized Extreme Value (GEV) distribution to the observed, the SDSM simulated/downscaled, and the LARS-WG simulated/downscaled annual maximum (AM) series. The computations are performed for five return periods: 10-, 20-, 40-, 50- and 100-year. The present results illustrate that both models have similar and good ability to simulate the extreme precipitation events and, thus, can be adopted with confidence for climate change impact studies of this nature.     

Household vulnerability and resilience in flood hazards from disaster-prone areas of Punjab,Pakistan

Natural Hazards - An environmental variation has caused Pakistan an alarming portrait of vulnerability in flood disasters. The government has focused on a number of realistic actions, heartening...     

Combining AHP and genetic algorithms approaches to modify DRASTIC model to assess groundwater vulnerability: a case study from Jianghan Plain,China

Accurate identification of vulnerability areas is critical for groundwater resources protection and management. The present study employed the modified DRASTIC model to assess the groundwater vulnerability of Jianghan Plain, a major farming area in central China. DRASTICL model was developed by incorporating the land use factor to the original model. The ratings and weightings of the selected parameters were optimized by analytic hierarchy process (AHP) method and genetic algorithms (GAs) method, respectively. A combined AHP–GAs method was proposed to further develop this methodology. The unity-based normalization process was employed to categorize the vulnerability maps into four types, such as very high (>0.75), high (0.5–0.75), low (0.25–0.5), and very low (<0.25). The accuracy of vulnerability mapping was validated by Pearson’s correlation coefficient between vulnerability index and the nitrate concentration in groundwater and analysis of variance F statistic. The results revealed that the modified DRASTIC model had a large improvement over the conventional model. The correlation coefficient increased significantly from 41.07 to 75.31% after modification. Sensitivity analysis indicated that the depth to groundwater with 39.28% of mean effective weight was the most critical factor affecting the groundwater vulnerability. The developed vulnerability model proposed in this study could provide important objective information for groundwater and environmental management at local level and innovation for international researchers.     

Felsic dykes in the Neoproterozoic Nagar Parkar Igneous Complex,SE Sindh,Pakistan: geochemistry and tectonic settings

The Nagar Parkar Igneous Complex consists of Neoproterozoic igneous and metamorphic rocks dissected by mafic, felsic, and rhyolitic dykes. The latter can be classified broadly into porphyritic felsic dykes intruding gray and pink granites at Nagar Parkar and the surrounding areas, and the orthophyric felsic dykes intruding amphibolites, deformed pink granites, and the alkaline mafic dykes in the Dhedvero area, north of Nagar Parkar. The porphyritic felsic dykes are composed of perthites, quartz, and albitic plagioclase whereas the orthopheric felsic dykes contain K-feldspar (dominant), plagioclase, and minor quartz. Geochemically, the porphyritic and orthophyric felsic dykes are subalkaline and alkaline demonstrating post-orogenic A2- and OIB-A1-type characteristic on Nb–Y–Ce and Nb–Y–3Ga ternary plots, respectively. One orthophyric felsic dyke contains normative acmite and sodium metasilicate. This study suggests two distinct tectonic regimes for the origin of the felsic dykes of the area. The porphyritic felsic dykes show similarities with the ~800–700 Ma granites of the area, the rhyolite dykes of the Mount Abu, western Rajasthan in India, and the granites of the Seychelles microcontinent. The orthophyric felsic dykes show chemical resemblance with the Tavidar volcanic suite of western Rajasthan and the Silhouette and North islands of the Seychelles microcontinent. This study confirms spatial and temporal links among the Rodinian fragments exposed in the Nagar Parkar area of Pakistan, western Rajasthan of India, and the Seychelles microcontinent.     

Vibration analysis of damaged and undamaged steel structure systems: cantilever column and frame

This paper presents the experimental and numerical studies conducted on a steel column and a steel frame structure using free vibration analysis. The effects of damages on structures were investigated, which were simulated by introducing multiple cracks at different locations in the experimental and numerical models. The acceleration responses of the test models, were recorded through an accelerometer, and were used to calibrate the numerical models developed in finite element based software. Modal frequencies of damaged and undamaged structures were compared and analyzed, to derive relationships for damaged and undamaged structures' frequencies in terms of crack depth. It was found that, due to the presence of cracks, the mechanical properties of a structure changes, whereby, the modal frequencies decrease. An approximately linear trend was observed for the frequency decrease with the increase in crack depth, which was also confirmed by the numerical models. The derived relationships were extended to further develop a mechanics-based damage scale for steel structures, to help facilitate structural health monitoring and screening of vulnerable structures.     

Poverty and environmental nexus in rural Pakistan: a multidimensional approach

The study examines the relationship between poverty and forest cover degradation in rural areas of Pakistan. The area selected for the study District Upper Dir is a rural and relatively backward region located in northwestern Pakistan, in Khyber-Pakhtunkhwa province. The study area is undergoing severe deforestation and natural disasters in the recent past. The study consists of two stages, in first stage the traditional Geographical information system image was used to analyze the spatial–temporal situation of the surroundings. In the second stage, well-designed questionnaire was used to collect the primary information from 420 randomly selected households of research areas. A multidimensional poverty index has been used to measure the poverty profile of the population. It has been found that 55% households were below the poverty line. Almost, 95% households are using wood for cooking purposes. High dependence on natural resources causes forest cover degradation while burning off too much wood causes CO₂ emission and leads to environmental degradation. A major portion of population is living on steeply sloped areas with certain risks. It is found that frequency of flash flood is 53% and agricultural land (54%) is at high risk and often flows with flash floods. It is concluded that there is strong correlation between multidimensional poverty and forest cover degradation which leads to climate and environmental risks.

     

GIS-based high-resolution spatial interpolation of precipitation in mountain–plain areas of Upper Pakistan for regional climate change impact studies

In this study, the baseline period (1960–1990) precipitation simulation of regional climate model PRECIS is evaluated and downscaled on a monthly basis for northwestern Himalayan mountains and upper Indus plains of Pakistan. Different interpolation models in GIS environment are used to generate fine scale (250?×?250 m²) precipitation surfaces from PRECIS precipitation data. Results show that the multivariate extension model of ordinary kriging that uses elevation as secondary data is the best model especially for monsoon months. Model results are further compared with observations from 25 meteorological stations in the study area. Modeled data show overall good correlation with observations confirming the ability of PRECIS to capture major precipitation features in the region. Results for low and erratic precipitation months, September and October, are however showing poor correlation with observations. During monsoon months (June, July, August) precipitation pattern is different from the rest of the months. It increases from south to north, but during monsoon maximum precipitation is in the southern regions of the Himalayas, and extreme northern areas receive very less precipitation. Modeled precipitation toward the end of the twenty-first century under A2 and B2 scenarios show overall decrease during winter and increase in spring and monsoon in the study area. Spatially, both scenarios show similar pattern but with varying magnitude. In monsoon, the Himalayan southern regions will have more precipitation, whereas northern areas and southern plains will face decrease in precipitation. Western and south western areas will suffer from less precipitation throughout the year except peak monsoon months. T test results also show that changes in monthly precipitation over the study area are significant except for July, August, and December. Result of this study provide reliable basis for further climate change impact studies on various resources.     

Evaluation of Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR) for Water Quality Monitoring: A Case Study for the Estimation of Salinity

Landsat-5 Thematic Mapper (TM) dataset have been used to estimate salinity in the coastal area of Hong Kong. Four adjacent Landsat TM images were used in this study, which was atmospherically corrected using the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) radiative transfer code. The atmospherically corrected images were further used to develop models for salinity using Ordinary Least Square (OLS) regression and Geographically Weighted Regression (GWR) based on in situ data of October 2009. Results show that the coefficient of determination (R²) of 0.42 between the OLS estimated and in situ measured salinity is much lower than that of the GWR model, which is two times higher (R² = 0.86). It indicates that the GWR model has more ability than the OLS regression model to predict salinity and show its spatial heterogeneity better. It was observed that the salinity was high in Deep Bay (north-western part of Hong Kong) which might be due to the industrial waste disposal, whereas the salinity was estimated to be constant (32 practical salinity units) towards the open sea.