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.     

Comparative analysis of bacterial contamination in tap and groundwater: A case study on water quality of Quetta City,an arid zone in Pakistan

Water is an essential element on earth, which provides human a variety of services in domestic use, agriculture, or industries. However, some serious health risks of drinking water are associated with microbial contamination, particularly with fecal matter. Therefore, microbial quality assessment is considered to be a necessary component of water quality assessment. This study investigates microbial contamination of water distributary system around the city by comparing groundwater (GW) and tap water (TW) quality in Quetta city. 31 GW samples and 31 TW samples were collected in the study area during the months of September, October, and November. Fecal coliform test was carried out in laboratory and their average total coliform contamination was computed. Results showed that the TW sample were all contaminated by coliform except for Chiltan town, hence are not considered suitable for drinking without any treatment according to WHO drinking water quality standards. The average coliform concentrations were 12 in Quetta main city, 11.6 in Jinnah town, 5.3 in Satallite town, 10 in Shahbaz town and 5 in Brewery town (0/100 mL CFU) and the TW samples from the three towns were even more contaminated with E.coli. Whereas among the GW, average microbial concentrations were 1.8 in Quetta main city, 2 in Satallite town, 1.4 in Shahbaz town, and 0.4 in Chiltan town (0/100 mL CFU), respectively, which shows that the contamination is occurring within the water distributary pipeline system when the water flows through the pipelines. Moreover, this research will be valuable for researchers and administrative authorities to conduct elaborative studies, and develop new policies to prevent further deterioration of drinking water in the water distribution system by pathogenic microorganisms and ensure safe drinking water to the public of Quetta city.     

Spatial assessment of forest cover and land-use changes in the Hindu-Kush mountain ranges of northern Pakistan

Anthropogenic activities and natural processes are continuously altering the mountainous environment through deforestation, forest degradation and other land-use changes. It is highly important to assess, monitor and forecast forest cover and other land-use changes for the protection and conservation of mountainous environment. The present study deals with the assessment of forest cover and other land-use changes in the mountain ranges of Dir Kohistan in northern Pakistan, using high resolution multi-temporal SPOT-5 satellite images. The SPOT-5 satellite images of years 2004, 2007, 2010 and 2013 were acquired and classified into land-cover units. In addition, forest cover and land-use change detection map was developed using the classified maps of 2004 and 2013. The classified maps were verified through random field samples and Google Earth imagery (Quick birds and SPOT-5). The results showed that during the period 2004 to 2013 the area of forest land decreased by 6.4%, however, area of range land and agriculture land have increased by 22.1% and 2.9%, respectively. Similarly, barren land increased by 1.1%, whereas, area of snow cover/glacier is significantly decreased by 21.3%. The findings from the study will be useful for forestry and landscape planning and can be utilized by the local, provincial and national forest departments; and REDD+ policy makers in Pakistan.     

Assessing the impacts of rainfall intensity and urbanization on storm runoff in an arid catchment

In the past few decades, rapid urbanization has occurred in many regions of the Kingdom of Saudi Arabia due to increasing population and urban development. Additionally, the effects of global warming on rainfall characteristics have been observed. This rapid change in urbanization and climate change has cause significant changes in the nature of land surfaces and rainfall patterns, which affect the runoff process and the amount of surface runoff during floods. This study investigated the effect of urbanization and rainfall intensity for Hafr Al-Batin watershed located in Saudi Arabia. For this purpose, a hydrologic model, HEC-HMS, was adopted to simulate the flow of different rainfall intesities and urbanization levels. Simulated results showed that for a 100-year storm, a 24-h duration, and an urbanization level of 80%, the peak flow was 213% higher than the estimated current peak and the runoff volume was 112% higher than the current runoff volume. These results show a strong linear correlation between the level of urbanization and both peak discharge and runoff volume. Furthermore, the results indicate that for short return periods, the peak flow is more sensitive to the level of urbanization compared to long periods.     

Yield Forecasting of Spring Maize Using Remote Sensing and Crop Modeling in Faisalabad-Punjab Pakistan

Real time, accurate and reliable estimation of maize yield is valuable to policy makers in decision making. The current study was planned for yield estimation of spring maize using remote sensing and crop modeling. In crop modeling, the CERES-Maize model was calibrated and evaluated with the field experiment data and after calibration and evaluation, this model was used to forecast maize yield. A Field survey of 64 farm was also conducted in Faisalabad to collect data on initial field conditions and crop management data. These data were used to forecast maize yield using crop model at farmers’ field. While in remote sensing, peak season Landsat 8 images were classified for landcover classification using machine learning algorithm. After classification, time series normalized difference vegetation index (NDVI) and land surface temperature (LST) of the surveyed 64 farms were calculated. Principle component analysis were run to correlate the indicators with maize yield. The selected LSTs and NDVIs were used to develop yield forecasting equations using least absolute shrinkage and selection operator (LASSO) regression. Calibrated and evaluated results of CERES-Maize showed the mean absolute % error (MAPE) of 0.35–6.71% for all recorded variables. In remote sensing all machine learning algorithms showed the accuracy greater the 90%, however support vector machine (SVM-radial basis) showed the higher accuracy of 97%, that was used for classification of maize area. The accuracy of area estimated through SVM-radial basis was 91%, when validated with crop reporting service. Yield forecasting results of crop model were precise with RMSE of 255 kg ha^?1, while remote sensing showed the RMSE of 397 kg ha^?1. Overall strength of relationship between estimated and actual grain yields were good with R² of 0.94 in both techniques. For regional yield forecasting remote sensing could be used due greater advantages of less input dataset and if focus is to assess specific stress, and interaction of plant genetics to soil and environmental conditions than crop model is very useful tool.     

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.     

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...     

Crustal attenuation characteristics of S-waves beneath the Eastern Tohoku region,Japan

An inversion method was applied to crustal earthquakes dataset to find S-wave attenuation characteristics beneath the Eastern Tohoku region of Japan.Accelerograms from 85 shallow crustal earthquakes up to25 km depth and magnitude range between 3.5 and 5.5were analyzed to estimate the seismic quality factor Q_s. A homogeneous attenuation model Q_s for the wave propagation path was evaluated from spectral amplitudes, at 24 different frequencies between 0.5 and 20 Hz by using generalized inversion technique. To do this, non-parametric attenuation functions were calculated to observe spectral amplitude decay with hypocentral distance. Then, these functions were parameterized to estimate Q_s. It was found that in Eastern Tohoku region, the Q_s frequency dependence can be approximated with the function 33 f~(1.22) within a frequency range between 0.5 and 20 Hz. However,the frequency dependence of Q_s in the frequency range between 0.5 and 6 Hz is best approximated by Q_s(f) = 36f~(0.94) showing relatively weaker frequency dependence as compared to the relation Q_s(f) = 6 f~(2.09) for the frequency range between 6 and 15 Hz. These results could be used to estimate source and site parameters for seismic hazard assessment in the region.