No abstract

The commercial use and unrestricted disposal of synthetic dyes in soil and water-bodies, following the industrial revolution, has led to a major threat towards environmental safety. The azo-dye, Remazol Black B (RBB) is one of the most commonly used synthetic reactive dyes in textile industries. In the present study, the decolourization and biodegradation of RBB were investigated using a bacterium isolated from the marine environment, which was later identified as Pseudomonas aeruginosa AR-7 by 16S rRNA analysis. P. aeruginosa AR-7 showed 99% decolourization at 100mg/L dye concentration when cultured at optimum conditions of incubation i.e., 96h at 37⁰C under static conditions using minimal salts medium (pH 7-9) supplemented with 0.1% glucose and yeast extracts. However, the dye degradation ability of the isolate was reduced to 29% on increasing the dye concentration to 500mg/L. In addition, P. aeruginosa AR-7 showed decolourization and degradation of RBB in wastewater obtained after dyeing a cotton fabric. In further experiments, the Fe3O4 nanoparticles were synthesized using co-precipitation method and were used to immobilize the cells of P. aeruginosa AR-7 by adsorption, in order to compare the RBB degrading abilities of the free and coated cells. The prepared nanoparticles (50-150nm) were characterized by FTIR and SEM analysis to study its structural properties. Also, upon magnetization studies using SQUID magnetometer, Fe3O4 nanoparticles were shown to have a magnetization of about 63emu/g. Interestingly, the coated cells not only showed better degradation ability of RBB but also produced simpler products such as alkane, carboxylic acids, ketone, etc. on complete degradation. On the other hand, the free cells mainly produced esters as indicated by the comparison of GC-MS results.

Abstract. The increasing rate of occurrence of extreme events (droughts and floods) and their rapid transition magnify the associated socio-economic impacts with respect to those caused by the individual event. Understanding of spatio-temporal evolution of wet–dry events collectively, their characteristics, and the transition (wet to dry and dry to wet) is therefore significant to identify and locate most vulnerable hotspots, providing the basis for the adaptation and mitigation measures. The Upper Jhelum Basin (UJB) in South Asia was selected as a case study, where the relevance of wet–dry events and their transition has not been assessed yet, despite clear evidence of climate change in the region. The standardized precipitation evapotranspiration index (SPEI) at the monthly timescale was applied to detect and characterize wet and dry events for the period 1981–2014. The results of temporal variations in SPEI showed a strong change in basin climatic features associated with El Niño–Southern Oscillation (ENSO) at the end of 1997, with the prevalence of wet and dry events before and after 1997 respectively. The results of spatial analysis show a higher susceptibility of the monsoon-dominated region towards wet events, with more intense events occurring in the eastern part, whereas a higher severity and duration are featured in the southwestern part of the basin. In contrast, the westerlies-dominated region was found to be the hotspot of dry events with higher duration, severity, and intensity. Moreover, the surrounding region of the Himalaya divide line and the monsoon-dominated part of the basin were found to be the hotspots of rapid wet–dry transition events.

Abstract. Bias correction (BC) is often a necessity to improve the applicability of global and regional climate model (GCM and RCM, respectively) outputs to impact assessment studies, which usually depend on multiple potentially dependent variables. To date, various BC methods have been developed which adjust climate variables separately (univariate BC) or jointly (multivariate BC) prior to their application in impact studies (i.e., the component-wise approach). Another possible approach is to first calculate the multivariate hazard index from the original, biased simulations, and bias-correct the impact model output or index itself using univariate methods (direct approach). This has the advantage of circumventing the difficulties associated with correcting the inter-variable dependence of climate variables which is not considered by univariate BC methods. Using a multivariate drought index (i.e., SPEI) as an example, the present study compares different state-ofthe- art BC methods (univariate and multivariate) and BC approaches (direct and component-wise) applied to climate model simulations stemming from different experiments at different spatial resolutions (namely CORDEX, CORDEX-CORE and CMIP6). The BC methods are calibrated and evaluated over the same historical period (1986–2005). The proposed framework is demonstrated as a case study over a transboundary watershed, i.e. the Upper Jhelum Basin (UJB) in the Western Himalaya. Results show that (1) there is some added value of multivariate BC methods over the univariate methods in adjusting the inter-variable relationship, however, comparable performance is found for SPEI indices. (2) The best performing BC methods exhibits a comparable performance under both approaches with a slightly better performance for the direct approach. (3) The added value of the high-resolution experiments (CORDEX-CORE) compared to their coarser resolution counterparts (CORDEX) are not apparent in this study.

<p>Global warming and anthropogenic activities have significantly altered the hydrological cycle and amplified the extreme events (floods and droughts) in many regions of the world, with associated environmental, economic, and social losses. For effective hydro extremes hazards management, it is significant to understand how climate change influences the occurrence, duration, and severity of the regional dryness/wetness conditions (droughts/floods). The present study was carried out over Upper Jhelum Basin (UJB) in Pakistan which lies in the western Himalaya, a most effected mountainous range by Climate Change. Firstly, a suitable gridded precipitation dataset was selected/chosen among various datasets (APHRODITE, CHIRPS, ERA5, PGMFD, MSWEP) through spatio-temporal comparison against in situ data at monthly, seasonal, and annual scale. Secondly, selected gridded data was adjusted for biases using linear (Linear scaling-LS, Local intensity scaling-LOCI) and nonlinear (Power transformation-PT and Distribution mapping-DM) statistical methods. Finally, standardized precipitation index (SPI) at multiple time scale was used to analyses dryness/wetness conditions in the Upper Jhelum Basin over a 35-year period (1981–2015). Results show the higher capability of ERA5 data to represent the UJB precipitation patterns with correlation coefficient (r=0.79) and normalized standard deviation (nSD=1.1), despite of overestimation especially during peak months. Regarding precipitation bias adjustment, all methods were able to correct the mean values while LOCI and DM take advantage over other two methods to correct wet-day probability and precipitation intensity. The SPI analysis at different time scales showed that wet periods occurred more in the first half of the study period, but at later years, drying periods ranging from moderate to severe continue to be seen with increasing frequency. A strong change in dry/wet conditions was observed around years 1997/1998. This change may be the result of the strongest El Nino event (1997-98) occurred in the history. However, further studies are still needed to check whether there is only a large multi-decadal variation or dry conditions will prevail in future. Overall, these findings would assist to better understand the changing pattern of extreme events with climate variability and help water resources managers to develop basin wide appropriate mitigation and adaptation measures to combat climate change and its consequences. </p>