Using geographical information systems to assess groundwater contamination from arsenic and related diseases based on survey data in Lahore,Pakistan

The present study was aimed at assessing the groundwater contamination from arsenic (As) and its impact on health from survey data in Lahore, Pakistan. OK (ordinary kriging) technique was used to create As and pH surfaces for samples from 380 groundwater wells at different locations and depths in the study area. Geographic information systems (GIS) was applied to delineate areas for safe, risk, and dangerous zones of As in drinking water from groundwater wells at 640 to 850 ft depths. To analyze effects of high As on public health, GIS-based field surveys were conducted to link health data along location of respondents to As contamination levels in the delineated safe, risk, and dangerous zones. Moreover, various pH levels and their effectiveness were studied to suggest cost-effective As treatment in the study area. Our results show that As contaminated strata varies with depth of groundwater wells, i.e., 44.50% areas comprising safe zone of drinking water from deep wells at 850 ft depth compared to 26% areas of shallow water at 640 ft depth. About 35% area with pH range (7 to 7.5) is marked in the risk and danger zones of As that can be initially targeted for treatment. Surveys confirm that people living in the risk and danger zones have some kind of As-related diseases.     

Applications of variogram modeling to electrical resistivity data for the occurrence and distribution of saline groundwater in Domail Plain,northwestern Himalayan fold and thrust belt,Pakistan

This paper studies electrical resistivity dataset acquired for a groundwater study in the Domail Plain in the northwestern Himalayan section of Pakistan. Through a combination of geostatistical analysis, geophysical inversion and visualization techniques, it is possible to re-model and visualize the single dimension resistivity data into 2D and 3D space. The variogram models are utilized to extend the interpretation of the data and to distinguish individual lithologic units and the occurrence of saline water within the subsurface. The resistivity data has been calibrated with the lithological logs taken from the available boreholes. As such the alluvial system of the Domail Plain has formed during episodes of local tectonic activity with fluvial erosion and deposition yielding coarse sediments with high electrical resistivities near to the mountain ranges and finer sediments with medium to low electrical resistivities which tend to settle in the basin center. Thus a change is depositional setting happened from basin lacustrine environment to flash flooding during the Himalayan orogeny. The occurrence of rock salt in the northern mountains has imparted a great influence on the groundwater quality of the study area. The salt is dissolved by water which infiltrates into the subsurface through the water channels. Variogram aided gridding of resistivity data helps to identify the occurrence and distribution of saline water in the subsurface.     

Spatiotemporal Analysis of Extracted Groundwater Volumes Estimated from Electricity Consumption

Land subsidence caused by groundwater overexploitation is a serious global problem. The acquisition of spatiotemporal pumping rates and volumes is a first step for water managers to develop a strategic plan for mitigating land subsidence. This study investigates an empirical formulation to estimate the monthly maximum pumped volume over a 10-year period based on electric power consumption data. A spatiotemporal variability analysis of monthly pumped volume is developed to provide an improved understanding of seasonal pumping patterns and the role of irrigation type. The analysis of regional pumped volume provides an approximation of the spatiotemporal patterns of the variations in pumped volume. Results show the effects of climate, seasonal changes in pumping from irrigation, and the local differences in pumping caused to crop types. A seasonal pumped volume peak occurs annually, with the highest and least pumped volumes occurring in March (highest peak) and September (lowest peak), respectively. However, the majority of the historical maximum pumped volumes have occurred during the last few years. Extracted volumes continue to increase in some locations. The analysis reveals increasing trends in pumping, thereby possibly providing the locations where increased effective stresses may lead to land subsidence.     

Risk assessment of extreme air pollution based on partial duration series: IDF approach

The occurrences of extreme pollution events have serious effects on human health, environmental ecosystems, and the national economy. To gain a better understanding of this issue, risk assessments on the behavior of these events must be effectively designed to anticipate the likelihood of their occurrence. In this study, we propose using the intensity–duration–frequency (IDF) technique to describe the relationship of pollution intensity (i) to its duration (d) and return period (T). As a case study, we used data from the city of Klang, Malaysia. The construction of IDF curves involves a process of determining a partial duration series of an extreme pollution event. Based on PDS data, a generalized Pareto distribution (GPD) is used to represent its probabilistic behaviors. The estimated return period and IDF curves for pollution intensities corresponding to various return periods are determined based on the fitted GPD model. The results reveal that pollution intensities in Klang tend to increase with increases in the length of time between return periods. Although the IDF curves show different magnitudes for different return periods, all the curves show similar increasing trends. In fact, longer return periods are associated with higher estimates of pollution intensity. Based on the study results, we can conclude that the IDF approach provides a good basis for decision-makers to evaluate the expected risk of future extreme pollution events.     

Addition of Phosphorus and Nitrogen Support the Invasiveness of Alternanthera Philoxeroides Under Water Stress

Coordination of plant functional traits with changes in the environment is helpful to understand the mechanisms underlying both invasiveness and adaptation of plants. Thus, to investigate the performance and functional traits in invasive Alternanthera philoxeroides (Mart.), an experiment of water stress is conducted with different nutrient concentrations. Alternanthera philoxeroides plants are grown under natural and nutrient soils and subjected to three levels of nitrogen (N) and phosphorus (P) solution: ambient P and N concentration (P?N?), P addition with an ambient N concentration (P+N?), and P addition with high N concentration (P+N+) in combination with three different irrigation water levels as 1) 100% irrigation, 2) 50% irrigation, and 3) 25% deficit irrigation. Based on results, A. philoxeroides produces significantly higher biomass in both soils under 100% irrigation with P+N? treatment and exhibits higher values of leaf area and root length. However, 25% irrigation with P?N? treatment in both soils exerts a significant negative effect on relative growth rate and root/shoot ratio of A. philoxeroides plants. Under 50% irrigation in soils with both P+N? and P+N+ treatments, high values of leaf nitrogen are recorded. Moreover, nutrient soil is more supportive to A. philoxeroides than natural soil.     

Hydrological appraisal of rainfall estimates from radar,satellite, raingauge and satellite–gauge combination on the Qinhuai River Basin,China

ABSTRACT

Multisource rainfall products can be used to overcome the absence of gauged precipitation data for hydrological applications. This study aims to evaluate rainfall estimates from the Chinese S-band weather radar (CINRAD-SA), operational raingauges, multiple satellites (CMORPH, ERA-Interim, GPM, TRMM-3B42RT) and the merged satellite–gauge rainfall products, CMORPH-GC, as inputs to a calibrated probability distribution model (PDM) on the Qinhuai River Basin in Nanjing, China. The Qinhuai is a middle-sized catchment with an area of 799 km². All sources used in this study are capable of recording rainfall at high spatial and temporal resolution (3 h). The discrepancies between satellite and radar data are analysed by statistical comparison with raingauge data. The streamflow simulation results from three flood events suggest that rainfall estimates using CMORPH-GC, TRMM-3B42RT and S-band radar are more accurate than those using the other rainfall sources. These findings indicate the potential to use satellite and radar data as alternatives to raingauge data in hydrological applications for ungauged or poorly gauged basins.     

Spatial disaggregation of ASCAT soil moisture under all sky condition using support vector machine

With recent advances in downscaling methodologies, soil moisture (SM) estimation using microwave remote sensing has become feasible for local application. However, disaggregation of SM under all sky conditions remains challenging. This study suggests a new downscaling approach under all sky conditions based on support vector regression (SVR) using microwave and optical/infrared data and geolocation information. Optically derived estimates of land surface temperature and normalized difference vegetation index from MODerate Resolution Imaging Spectroradiometer land and atmosphere products were utilized to obtain a continuous spatio-temporal input datasets to disaggregate SM observation from Advanced SCATterometer in South Korea during 2015 growing season. SVR model was compared to synergistic downscaling approach (SDA), which is based on physical relationship between SM and hydrometeorological factors. Evaluation against in situ observations showed that the SVR model under all sky conditions (R: 0.57 to 0.81, ubRMSE: 0.0292 m³ m^?3 to 0.0398 m³ m^?3) outperformed coarse ASCAT SM (R: 0.55 to 0.77, ubRMSE: 0.0300 m³ m^?3 to 0.0408 m³ m^?3) and SDA model (mean R: 0.56 to 0.78, ubRMSE: 0.0324 m³ m^?3 to 0.0436 m³ m^?3) in terms of statistical results as well as sensitivity with precipitation. This study suggests that the spatial downscaling technique based on remote sensing has the potential to derive high resolution SM regardless of weather conditions without relying on data from other sources. It offers an insight for analyzing hydrological, climate, and agricultural conditions at regional to local scale.     

Quantifying climate internal variability using an hourly ensemble generator over South Korea

Identifying climate internal variability (CIV) generated by non-linear interactions and feedbacks among many components of the climate system is essential and challenging because of its irreducible and unpredictable characteristics. A range of studies have addressed this issue; however, these studies focused on the first order moments of few representative climate variables at relatively larger spatial and temporal scales. To investigate the magnitude and the spatial pattern of CIV relevant at finer spatial (point) and temporal (hourly) scales, CIV is assessed over a 30-year period in South Korea by analyzing 100-member ensemble generated using an hourly weather generator and a bootstrapping approach. Statistics addressing the first and second order moments, occurrences, and extremes are successfully verified at various temporal scales. The CIV is then estimated by the ‘detrended’ and ‘differenced’ methods for the four metrics proposed at different scales that signify rainfall volume, maxima, and occurrence. Consequently, the implications of this study are the following: (1) the estimation of CIV using bootstrapped ensembles often fails to represent the proper uncertainty range, resulting in high chances of underestimating extreme statistics, such as the maximum rainfall depth; (2) regardless of which of the two methods is used, no significant difference in the CIV estimation is observed; and (3) a temporal scale-dependency is observed for the proposed metrics used to identify the magnitude and the seasonal pattern of the CIV—the utility of an hourly time series and its associated extreme properties deserves significant attention. Ultimately, the spatial mapping and grouping of CIV will provide valuable information to identify which regions have high variability compared to climatological norms and thus are more vulnerable to extremes, and will serve as a guide for planning adaptation and mitigation measures against future extreme events.     

Traffic ‐and Industry‐Related Air Pollution Exposure Assessment in an Asian Megacity

Like other Asian countries, Pakistan is facing the issue of air pollution due to rapid urbanization, enormous transportation increases, and other related human activities. Moreover, continuously increasing emission sources have not only raised pollutant concentrations but also their types, thus damaging both human health and the environment. Faisalabad is the third largest megacity of Pakistan and its state of air quality is getting worse due to factors such as industrialization, high traffic volumes, and extensive fossil‐fuel‐burning activities. This review article aims to highlight the present status of air pollution in this city with special reference to particulate matter, elemental profiles, gaseous pollutants, organic–inorganic particulate contents as well as their sources. The concentration levels of these entities were also compared with other national and international cities, and related environmental standards. It is found that current levels of these pollutants are beyond safety limits as specified by various environment protection agencies and organizations. Several weak aspects and gaps are also identified along with suggestions for improvements of the present situation and directions for future research.     

Look-ahead vertical seismic profiling inversion approach for density and compressional wave velocity in Bayesian framework

To reduce drilling uncertainties, zero-offset vertical seismic profiles can be inverted to quantify acoustic properties ahead of the bit. In this work, we propose an approach to invert vertical seismic profile corridor stacks in Bayesian framework for look-ahead prediction. The implemented approach helps to successfully predict density and compressional wave velocity using prior knowledge from drilled interval. Hence, this information can be used to monitor reservoir depth as well as quantifying high-pressure zones, which enables taking the correct decision during drilling. The inversion algorithm uses Gauss–Newton as an optimization tool, which requires the calculation of the sensitivity matrix of trace samples with respect to model parameters. Gauss–Newton has quadratic rate of convergence, which can speed up the inversion process. Moreover, geo-statistical analysis has been used to efficiently utilize prior information supplied to the inversion process. The algorithm has been tested on synthetic and field cases. For the field case, a zero-offset vertical seismic profile data taken from an offshore well were used as input to the inversion algorithm. Well logs acquired after drilling the prediction section was used to validate the inversion results. The results from the synthetic case applications were encouraging to accurately predict compressional wave velocity and density from just a constant prior model. The field case application shows the strength of our proposed approach in inverting vertical seismic profile data to obtain density and compressional wave velocity ahead of a bit with reasonable accuracy. Unlike the commonly used vertical seismic profile inversion approach for acoustic impedance using simple error to represent the prior covariance matrix, this work shows the importance of inverting for both density and compressional wave velocity using geo-statistical knowledge of density and compressional wave velocity from the drilled section to quantify the prior covariance matrix required during Bayesian inversion.