• Energy Research

Copper (Cu(2+)) containing wastewaters are extensively released from different industries and its excessive entry into food chains results in serious health impairments, carcinogenicity and mutagenesis in various living systems. An array of technologies is in use to remediate Cu(2+) from wastewaters. Adsorption is the most attractive option due to the availability of cost effective, sustainable and eco-friendly bioadsorbents. The current review is dedicated to presenting state of the art knowledge on various bioadsorbents and physico-chemical conditions used to remediate Cu(2+) from waste streams. The advantages and constraints of various adsorbents were also discussed. The literature revealed the maximum Cu adsorption capacities of various bioadsorbents in the order of algae>agricultural and forest>fungal>bacterial>activated carbon>yeast. However, based on the average Cu adsorption capacity, the arrangement can be: activated carbon>algal>bacterial>agriculture and forest-derived>fungal>yeast biomass. The data of Cu removal using these bioadsorbents were found best fit both Freundlich and Langmuir models. Agriculture and forest derived bioadsorbents have greater potential for Cu removal because of higher uptake, cheaper nature, bulk availability and mono to multilayer adsorption behavior. Higher costs at the biomass transformation stage and decreasing efficiency with desorption cycles are the major constraints to implement this technology.

Projecting future hydrology for the mountainous, highly glaciated upper Indus basin (UIB) is a challenging task because of uncertainties in future climate projections and issues with the coverage and quality of available reference climatic data and hydrological modelling approaches. This study attempts to address these issues by utilizing the semi-distributed hydrological model “Soil and water assessment tool” (SWAT) with new climate datasets and better spatial and altitudinal representation as well as a wider range of future climate forcing models (general circulation model/regional climate model combinations (GCMs_RCMs) from the “Coordinated Regional Climate Downscaling Experiment-South Asia (CORDEX-SA) project to assess different aspects of future hydrology (mean flows, extremes and seasonal changes). Contour maps for the mean annual flow and actual evapotranspiration as a function of the downscaled projected mean annual precipitation and temperatures are produced and can serve as a “hands-on” forecast tool of future hydrology. The overall results of these future SWAT hydrological projections indicate similar trends of changes in magnitudes, seasonal patterns and extremes of the UIB—stream flows for almost all climate scenarios/models/periods—combinations analyzed. In particular, all but one GCM_RCM model—the one predicting a very high future temperature rise—indicated mean annual flow increases throughout the 21st century, wherefore, interestingly, these are stronger for the middle years (2041–2070) than at its end (2071–2100). The seasonal shifts as well as the extremes follow also similar trends for all climate scenario/model/period combinations, e.g., an earlier future arrival (in May–June instead of July–August) of high flows and increased spring and winter flows, with upper flow extremes (peaks) projected to drastically increase by 50 to >100%, and with significantly decreased annual recurrence intervals, i.e., a tremendously increased future flood hazard for the UIB. The future low flows projections also show more extreme values, with lower-than-nowadays-experienced minimal flows occurring more frequently and with much longer annual total duration.

Population growth and population inflow from other regions has caused urbanization which altered land use land cover (LULC) in the lower Himalayan regions of Pakistan. This LULC change increased the land surface temperature (LST) in the region. LULC and LST changes were assessed for the period of 1990–2017 using Landsat data and the support vector machine (SVM) method. A combined cellular automata and artificial neural network (CA-ANN) prediction model was used for simulation of LULC changes for the period of 2032 and 2047 using transition potential matrix obtained from the data years of 2002 and 2017. The accuracy of the CA-ANN model was validated using simulated and classified images of 2017 with correctness value of 70% using validation modules in QGIS. The thermal bands of Landsat images from the years 1990, 2002 and 2017 were used for LST derivation. LST acquired for this period was then modeled for 2032 and 2047 using urban indices (UI) and linear regression analysis. The SVM land cover classification results showed a 5.75% and 4.22% increase in built-up area and bare soil respectively, while vegetation declined by 9.88% during 1990–2017. The results of LST for LULC classes showed that the built-up area had the highest mean LST as compared to other classes. The future projection of LULC and LST showed that the built-up area may increase by 12.48% and 14.65% in 2032 and 2047, respectively, of the total LULC area which was ~11% in 2017. Similarly, the area with temperature above 30 °C could be 44.01% and 58.02% in 2032 and 2047, respectively, of the total study area which was 18.64% in 2017. This study identified major challenges for urban planners to mitigate the urban heat island (UHI) phenomenon. In order to address the UHI in the study area, an urban planner might focus on urban plantation and decentralization of urban areas.

AbstractGlaciers in the northern Pakistan are a distinctive source of freshwater for the irrigation, drinking and industrial water supplies of the people living in those regions and downstream. These glaciers are under a direct global warming impact as indicated in many previous studies. In this study, we estimated the glacier dynamics in terms of Equilibrium Line Altitude (ELA), mass balance and the snout position variation using remote sensing data between 2001 and 2018. Six glaciers, having area ≥ 20 km2 each, situated in the Chitral region (Hindukush Mountains) were investigated in this study. Digital Elevation Model (DEM) and available cloud-free continuous series of Landsat and Sentinel the entire study area was a retreat of -231 ± 140 m. No obvious relationship was found between the glacier variation trends and the available gauged climatic data possibly due to the presence of debris cover in ablation zones of all the studied glaciers which provides insulation and reduces the immediate climatic effects.

The Weather Research and Forecasting (WRF) model is used to simulate the spatiotemporal distribution of precipitation over central Asia over the year April 2005 through March 2006. Experiments are performed at 6.7 km horizontal grid spacing, with an emphasis on winter and summer precipitation over the Himalaya. The model and the Tropical Rainfall Measuring Mission show a similar inter-seasonal cycle of precipitation, from extratropical cyclones to monsoon precipitation, with agreement also in the diurnal cycle of monsoon precipitation. In winter months, WRF compares better in timeseries of daily precipitation to stations below than above 3-km elevation, likely due to inferior measurement of snow than rain by the stations, highlighting the need for reliable snowfall measurements at high elevations in winter. In summer months, the nocturnal precipitation cycle in the foothills and valleys of the Himalaya is captured by this 6.7-km WRF simulation, while coarser simulations with convective parameterization show near zero nocturnal precipitation. In winter months, higher resolution is less important, serving only to slightly increase precipitation magnitudes due to steeper slopes. However, even in the 6.7-km simulation, afternoon precipitation is overestimated at high elevations, which can be reduced by even higher-resolution (2.2-km) simulations. These results indicate that WRF provides skillful simulations of precipitation relevant for studies of water resources over the complex terrain in the Himalaya.

Pakistan is an agriculture-based economy and major proportion of irrigation water for its cultivated lands is abstracted from the Upper Indus Basin (UIB). UIB water supplies are mostly contributed from the high-altitude snow and glacier fields situated in the Hindukush–Karakoram–Himalayan ranges. Any change in the flows of these river catchments due to climate variability may result in the form of catastrophic events like floods and droughts and hence will adversely affect the economy of Pakistan. This study aims to simulate snowmelt runoff in a mountainous sub-catchment (Shyok River basin) of the UIB under climate change scenarios. Snowmelt Runoff Model (SRM) coupled with remotely sensed snow cover product (MOD10A2) is used to simulate the snowmelt runoff under current and future climate scenarios in the study area. The results indicate that (a) SRM has efficiently simulated the flow in Shyok River with average Nash–Sutcliff coefficient value (R2) of 0.8 (0.63–0.93) for all six years (2000–2006) of basin-wide and zone-wise simulations, (b) an increase of 10% (by 2050) and 20% (by 2075) in SCA will result in a flow rise of ∼11% and ∼20%, respectively, and (c) an increase of 1°C (by 2025), 2°C (by 2050), 3°C (by 2075) and 4°C (by 2100) in mean temperature will result in a flow rise of ∼26%, ∼54%, ∼81% and ∼118%, respectively. This study suggests that SRM equipped with remotely sensed snow cover data is an effective tool to estimate snowmelt runoff in high mountain data-scarce environments.

A major proportion of flow in the Indus River is contributed by its snow and glacier-fed river catchments situated in the Karakoram Range. It is therefore essential to estimate the snowmelt runoff from these catchments (with no or scarce precipitation records) for water resources management. The snowmelt runoff model (SRM) integrated with MODIS remote-sensing snow cover products was selected to simulate the daily discharges and to study the climate change impact on these discharges in the previous termHunzanext term River basin (the snow- and glacier-fed sub-catchment of the Indus River). The results obtained suggest that the SRM can be used efficiently in the snow- and glacier-fed sub-catchments of the Upper Indus River Basin (UIB). The application of the SRM under future climate (mean temperature, precipitation and snow cover) change scenarios indicates a doubling of summer runoff until the middle of this century. This analysis suggests that new reservoirs will be necessary for summer flow storage to meet with the needs of irrigation supply, increasing power generation demand, flood control and water supply.