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.

Levofloxacin antibiotic is frequently being detected in the environment and regarded as an emerging contaminant. The present study was focused on the green synthesis of magnetite (Fe₃O₄ - gINPs) nanoparticles from Moringa olifera and its efficiency for removal of levofloxacin from aqueous solution. The adsorbent magnetite nanoparticles (Fe₃O₄) were prepared by green synthesis using Moringa olifera and coprecipitation method. Characterizations analyses of both chemically and green synthesized nanoparticles were performed by SEM, XRD, and FTIR. The average crystallite size of gINPs was 14.34 nm and chemically synthesized was 18.93 nm. The performance of the synthesized product was evaluated by adsorption capacity and removal efficiency. The parameters considered included adsorbent (gINPs) dosage, initial concentration of adsorbate, pH, contact time, and temperature. The obtained data were fitted to kinetic and isotherm models to determine the mechanism. Adsorption batch experiments were conducted to determine the reaction mechanism by studying kinetics while fitting isotherm models for samples analyzed using HPLC at 280 nm. Results showed that 86.15% removal efficiency of 4 mg L^-1 levofloxacin was achieved by 100 mg L^-1 gINPs in 24 h contact time when all other parameters (pH 7, temperature 25 °C) were kept constant. The maximum adsorption capacity achieved at equilibrium was 22.47 mg/g. Further, it was identified as a pseudo-second-order model with R² = 0.965 for adsorption kinetics while isotherm data better fitted to the Freundlich model compared to Langmuir isotherm with R² = 0.994. The potential pathway determined for levofloxacin removal was chemisorption with minor diffusion, multilayer, spontaneous and exothermic processes on the gINPs (Fe₃O₄). Reusability experiments were conducted in four cycles and removal efficiency varied from 85.35% to 80.47%, indicating very high potential of the adsorbent for re-use.

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.

The Upper Indus Basin (UIB) is a major source of supplying water to different areas because of snow and glaciers melt and is also enduring the regional impacts of global climate change. The expected changes in temperature, precipitation and snowmelt could be reasons for further escalation of the problem. Therefore, estimation of hydrological processes is critical for UIB. The objectives of this paper were to estimate the impacts of climate change on water resources and future projection for surface water under different climatic scenarios using soil and water assessment tool (SWAT). The methodology includes: (i) development of SWAT model using land cover, soil and meteorological data; (ii) calibration of the model using daily flow data from 1978 to 1993; (iii) model validation for the time 1994–2003; (iv) bias correction of regional climate model (RCM), and (v) utilization of bias-corrected RCM for future assessment under representative concentration pathways RCP4.5 and RCP8.5 for mid (2041–2070) and late century (2071–2100). The results of the study revealed a strong correlation between simulated and observed flow withR²and Nash–Sutcliff efficiency (NSE) equal to 0.85 each for daily flow. For validation,R²and NSE were found to be 0.84 and 0.80, respectively. Compared to baseline period (1976–2005), the result of RCM showed an increase in temperature ranging from 2.36°C to 3.50°C and 2.92°C to 5.23°C for RCP4.5 and RCP8.5 respectively, till the end of the twenty-first century. Likewise, the increase in annual average precipitation is 2.4% to 2.5% and 6.0% to 4.6% (mid to late century) under RCP4.5 and RCP8.5, respectively. The model simulation results for RCP4.5 showed increase in flow by 19.24% and 16.78% for mid and late century, respectively. For RCP8.5, the increase in flow is 20.13% and 15.86% during mid and late century, respectively. The model was more sensitive towards available moisture and snowmelt parameters. Thus, SWAT model could be used as effective tool for climate change valuation and for sustainable management of water resources in future.

This study focusses on identifying a set of representative climate model projections for the Upper Indus Basin (UIB). Although a large number of General Circulation Models (GCM) predictor sets are available nowadays in the CMIP5 archive, the issue of their reliability for specific regions must still be confronted. This situation makes it imperative to sort out the most appropriate single or small-ensemble set of GCMs for the assessment of climate change impacts in a region. Here a set of different approaches is adopted and applied for the step-wise shortlisting and selection of appropriate climate models for the UIB under two RCPs: RCP 4.5 and RCP 8.5, based on: (a) range of projected mean changes, (b) range of projected extreme changes, and (c) skill in reproducing the past climate. Furthermore, because of higher uncertainties in climate projection for high mountainous regions like the UIB, a wider range of future GCM climate projections is considered by using all possible extreme future scenarios (wet-warm, wet-cold, dry-warm, dry-cold). Based on this two-fold procedure, a limited number of climate models is pre-selected, from of which the final selection is done by assigning ranks to the weighted score for each of the mentioned selection criteria. The dynamically downscaled climate projections from the Coordinated Regional Downscaling Experiment (CORDEX) available for the top-ranked GCMs are further statistically downscaled (bias-corrected) over the UIB. The downscaled projections up to the year 2100 indicate temperature increases ranging between 2.3 °C and 9.0 °C and precipitation changes that range from a slight annual increase of 2.2% under the drier scenarios to as high as 15.9% in the wet scenarios. Moreover, for all scenarios, future precipitation will be more extreme, as the probability of wet days will decrease, while, at the same time, precipitation intensities will increase. The spatial distribution of the downscaled predictors across the UIB also shows similar patterns for all scenarios, with a distinct precipitation decrease over the south-eastern parts of the basin, but an increase in the northeastern parts. These two features are particularly intense for the “Dry-Warm” and the “Median” scenarios over the late 21st century.

A photocatalytic system for decolorization of double azo reactive black 5 (RB5) dye and water disinfection of E. coli was developed. Sol gel method was employed for the synthesis of Fe-TiO₂ photocatalysts and were characterized using thermogravimetric analysis (TGA), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscopy (SEM) coupled with energy dispersive X-ray analysis (EDX), transmission electron microscopy (TEM), diffuse reflectance spectroscopy (DRS) and Brunauer–Emmett–Teller (BET) analysis. Results showed that photocatalytic efficiency was greatly influenced by 0.1 weight percent iron loading and 300 °C calcination temperature. The optimized reaction parameters were found to be the ambient temperature, working solution pH 6.2 and 1 mg g⁻¹ dose to completely decolorize RB5. The isotherm studies showed that RB5 adsorption by Fe-TiO₂ followed the Langmuir isotherm with maximum adsorption capacity of 42.7 mg g⁻¹ and K_ads 0.0079 L mg⁻¹. Under illumination, the modified photocatalytic material had higher decolorization efficiency as compared to unmodified photocatalyst. Kinetic studies of the modified material under visible light irradiation indicated the reaction followed the pseudo-first-order kinetics. The illumination reaction followed the Langmuir-Hinshelwood (L-H) model as the rate of dye decolorization increased with an incremental increase in dye concentration. The L-H constant K_c was 1.5542 mg L^–1∙h^–1 while K_ads was found 0.1317 L mg^–1. The best photocatalyst showed prominent percent reduction of E. coli in 120 min. Finally, 0.1Fe-TiO₂-300 could be an efficient photocatalyst and can provide a composite solution for RB5 decolorization and bacterial strain inhibition.