Aerosol-PM2.5 Dynamics: In-situ and satellite observations under the influence of regional crop residue burning in post-monsoon over Delhi-NCR, India.

The increasing air pollution in the urban atmosphere is adversely impacts the environment, climate and human health. The alarming degradation of air quality, atmospheric conditions, economy and human life due to air pollution needs significant in-depth studies to ascertain causes, contributions and impacts for developing and implementing an effective policy to combat these issues. This work lies in its multifaceted approach towards comprehensive understanding and mitigating severe pollution episodes in Delhi and its surrounding areas. We investigated the aerosol dynamics in the post-monsoon season (PMS) from 2019 to 2022 under the influence of both crop residue burning and meteorological conditions. The study involves a broad spectrum of factors, including PM2.5 concentrations, active fire events, and meteorological parameters, shedding light on previously unexplored studies. The average AOD550 (0.79) and PM2.5 concentration (140.12 µg/m³) were the highest in 2019. PM2.5 was higher from mid-October to mid-November each year, exceeding the WHO guideline of 15 µg/m³ (24 h) by 27-34 times, signifying a public health emergency. A moderate to strong correlation between PM2.5 and AOD was found (r = 0.65) in 2021. The hotspot region accounts for almost 50% (2019), 47.51% (2020), 57.91% (2021) and 36.61% (2022) of the total fire events. A statistically significant negative non-linear correlation (r) was observed between wind speed (WS) and both AOD and PM2.5 concentration, influencing air quality over the region. HYSPLIT model and Windrose result show the movement of air masses predominated from the North and North-West direction during PMS. This study suggest to promotes strategies such as alternative waste management, encouraging modern agricultural practices in hot-spot regions, and enforcing strict emission norms for industries and vehicles to reducing air pollution and its detrimental effects on public health in the region and also highlights the need for future possibilities of research to attract the global attention.

Integration of hydrogeological data, GIS and AHP techniques applied to delineate groundwater potential zones in sandstone, limestone and shales rocks of the Damoh district, (MP) central India.

The Damoh district, which is located in the central India and characterized by limestone, shales, and sandstone compact rock. The district has been facing groundwater development challenges and problems for several decades. To facilitate groundwater management, it is crucial to monitoring and planning based on geology, slope, relief, land use, geomorphology, and the types of the basaltic aquifer in the drought-groundwater deficit area. Moreover, the majority of farmers in the area are heavily dependent on groundwater for their crops. Therefore, delineation of groundwater potential zones (GPZ) is essential, which is defined based on various thematic layers, including geology, geomorphology, slope, aspect, drainage density, lineament density, topographic wetness index (TWI), topographic ruggedness index (TRI), and land use/land cover (LULC). The processing and analysis of this information were carried out using Geographic Information System (GIS) and Analytic Hierarchy Process (AHP) methods. The validity of the results was trained and tested using Receiver Operating Characteristic (ROC) curves, which showed training and testing accuracies of 0.713 and 0.701, respectively. The GPZ map was classified into five classes such as very high, high, moderate, low, and very low. The study revealed that approximately 45% of the area falls under the moderate GPZ, while only 30% of the region is classified as having a high GPZ. The area receives high rainfall but has very high surface runoff due to no proper developed soil and lack of water conservation structures. Every summer season show a declined groundwater level. In this context, results of study area are useful to maintain the groundwater under climate change and summer season. The GPZ map plays an important role in implementing artificial recharge structures (ARS), such as percolation ponds, tube wells, bore wells, cement nala bunds (CNBs), continuous contour trenching (CCTs), and others for development of ground level. This study is significant for developing sustainable groundwater management policies in semi-arid regions, that are experiencing climate change. Proper groundwater potential mapping and watershed development policies can help mitigate the effects of drought, climate change, and water scarcity, while preserving the ecosystem in the Limestone, Shales, and Sandstone compact rock region. The results of this study are essential for farmers, regional planners, policy-makers, climate change experts, and local governments, enabling them to understand the groundwater development possibilities in the study area.

Land cover and crop types mapping using different spatial resolution imagery in a Mediterranean irrigated area.

Crop type identification is critical for agricultural sustainability policy development and environmental assessments. Therefore, it is important to obtain their spatial distribution via different approaches. Medium-, high- and very high-resolution optical satellite sensors are efficient tools for acquiring this information, particularly for challenging studies such as those conducted in heterogeneous agricultural fields. This research examined the ability of four multitemporal datasets (Sentinel-1-SAR (S1), Sentinel-2-MSI (S2), RapidEye (RE), and PlanetScope (PS)) to identify land cover and crop types (LCCT) in a Mediterranean irrigated area. To map LCCT distribution, a supervised pixel-based classification is adopted using Support Vector Machine with a radial basis function kernel (SVMRB) and Random Forest (RF). Thus, LCCT maps were generated into three levels, including six (Level I), ten (Level II), and fourteen (Level III) classes. Overall, the findings revealed high overall accuracies of >92%, >83%, and > 81% for Level I, Level II, and Level III, respectively, except for Sentinel-1. It was found that accuracy improves considerably when the number of classes decreases, especially when cropland or non-cropland classes are grouped into one. Furthermore, there was a similarity in performance between S2 alone and S1S2. PlanetScope LCCT classifications outperform other sensors. In addition, the present study demonstrated that SVM achieved better performances against RF and can thereby effectively extract LCCT information from high-resolution imagery as PlanetScope.

Flexible microtubule anchoring modulates the bi-directional motility of the kinesin-5 Cin8.

Two modes of motility have been reported for bi-directional kinesin-5 motors: (a) context-dependent directionality reversal, a mode in which motors undergo persistent minus-end directed motility at the single-molecule level and switch to plus-end directed motility in different assays or under different conditions, such as during MT gliding or antiparallel sliding or as a function of motor clustering; and (b) bi-directional motility, defined as movement in two directions in the same assay, without persistent unidirectional motility. Here, we examine how modulation of motor-microtubule (MT) interactions affects these two modes of motility for the bi-directional kinesin-5, Cin8. We report that the large insert in loop 8 (L8) within the motor domain of Cin8 increases the MT affinity of Cin8 in vivo and in vitro and is required for Cin8 intracellular functions. We consistently found that recombinant purified L8 directly binds MTs and L8 induces single Cin8 motors to behave according to context-dependent directionality reversal and bi-directional motility modes at intermediate ionic strength and according to a bi-directional motility mode in an MT surface-gliding assay under low motor density conditions. We propose that the largely unstructured L8 facilitates flexible anchoring of Cin8 to the MTs. This flexible anchoring enables the direct observation of bi-directional motility in motility assays. Remarkably, although L8-deleted Cin8 variants exhibit a strong minus-end directed bias at the single-molecule level, they also exhibit plus-end directed motility in an MT-gliding assay. Thus, L8-induced flexible MT anchoring is required for bi-directional motility of single Cin8 molecules but is not necessary for context-dependent directionality reversal of Cin8 in an MT-gliding assay.

Correction: Development of ultrasensitive and As(III)-selective upconverting (NaYF4:Yb3+,Er3+) platform.

Correction for 'Development of ultrasensitive and As(III)-selective upconverting (NaYF4:Yb3+,Er3+) platform' by Suman Duhan et al., Analyst, 2020, 145, 6378-6387, DOI: 10.1039/D0AN00717J.

Development of fuzzy analytic hierarchy process based water quality model of Upper Ganga river basin, India.

The ecological sustainability of rivers is in question due to severe pollution and lack of stringent regulations. Long term (1990-2016) water quality data of five stations namely Haridwar, Bareilly, Kanpur, Prayagraj and Varanasi of Upper Ganga river, India was considered for analysis using fuzzy analytical process (FAHP) based water quality index (WQI) to assess surface water quality. The value of water physical, biological and chemical parameters of temporal resolution (monthly, seasonal and yearly) indicate that value of electrical conductivity (EC), total dissolved solids (TDS), biological oxygen demand (BOD), chemical oxygen demand (COD), total alkalinity (Mg CaCO3), total hardness (Mg CaCO3), calcium (Ca), magnesium (Mg), sodium (Na), chlorine (Cl) and bicarbonate (HCO3) were observed very high compared to recommended value of Bureau of Indian Standards (BIS) and World Health Organization (WHO) at Kanpur, Prayagraj and Varanasi stations. However, low value of parameters is observed at Haridwar and Bareilly stations. Also, the high deviation was observed in water quality parameters during 1990-2010 whereas the deviation of parameters is decreased in 2011-2016. It is observed from the piper diagram that magnesium and bicarbonate at Haridwar, sodium, potassium and bicarbonate in Bareilly, Kanpur, Prayagraj and Varanasi stations are dominant during monthly and seasonal periods. The fuzzy based WQI value indicate that water quality is excellent to poor at Haridwar, while poor to unsuitable in Bareilly, Kanpur, Prayagraj and Varanasi during monthly and seasonal periods. The water quality ranges from poor to unsuitable during the 1990-2010 period and good to very poor during the 2011-2016 period at Bareilly, Kanpur, Prayagraj and Varanasi stations. Whereas very good to good during 1990-2010 and excellent to good during 2011-2016 at Haridwar. It was also determined that water quality parameters (Ca, Na+K, SO4, Hardness, Cl and Mg) and WQI values were increased with length of the stream. It indicates that drain discharge, urban growth, urban functions, ecological footprints and crop area increment were key sources of pollution.

Development of ultrasensitive and As(III)-selective upconverting (NaYF4:Yb3+,Er3+) platform.

The development of a sensitive α-NaYF4:Yb3+,Er3+ solid-phase upconverting platform (UCP) was realized using Moringa oleifera leaf extract for selective detection of the trivalent arsenic ion [As(iii)] contamination in drinking water. The presence of polyphenols in the leaf extract were shown to induce luminescence resonance transfer (LRET), thereby diminishing the Er3+ emission (red and green band) when activated by 980 nm excitation. However, the addition of As(iii) species interrupted the LRET process and restored the emissions proportionately. This feature allowed the platform to selectively detect arsenic pollution in water below the safe limit of 10 ppt. The uniqueness of UCP lies in monitoring the As(iii) contamination in samples containing heavy ions (Cd2+ and Hg2+) also, without an apparent effect on the signal reproducibility. The UCP was also found to be insensitive to other interfering ions including Pb2+, H2PO4-, F-, Cl-, Ca2+, Mg2+, Sn2+, Cr6+, Fe2+ and Co2+, if present.

Early warning systems development for agricultural drought assessment in Nigeria.

The existing drought monitoring mechanisms in the sub-Saharan Africa region mostly depend on the conventional methods of drought monitoring. These methods have limitations based on timeliness, objectivity, reliability, and adequacy. This study aims to identify the spread and frequency of drought in Nigeria using Remote Sensing/Geographic Information Systems techniques to determine the areas that are at risk of drought events within the country. The study further develops a web-GIS application platform that provides drought early warning signals. Monthly NOAA-AVHRR Pathfinder NDVI images of 1 km by 1 km spatial resolution and MODIS with a spatial resolution of 500 m by 500 m were used in this study together with rainfall data from 25 synoptic stations covering 32 years. The spatio-temporal variation of drought showed that drought occurred at different times of the year in all parts of the country with the highest drought risk in the north-eastern parts. The map view showed that the high drought risk covered 5.98% (55,312 km2) of the country's landmass, while low drought risk covered 42.4% (391,881 km2) and very low drought risk areas 51.5% (476,578 km2). Results revealed that a strong relationship exists between annual rainfall and season-integrated NDVI (r2 = 0.6). Based on the spatio-temporal distribution and frequency of droughts in Nigeria, drought monitoring using remote sensing techniques of VCI and NDVI could play an invaluable role in food security and drought preparedness. The map view from the web-based drought monitoring system, developed in this study, is accessible through localhost.

Bidirectional motility of kinesin-5 motor proteins: structural determinants, cumulative functions and physiological roles.

Mitotic kinesin-5 bipolar motor proteins perform essential functions in mitotic spindle dynamics by crosslinking and sliding antiparallel microtubules (MTs) apart within the mitotic spindle. Two recent studies have indicated that single molecules of Cin8, the Saccharomyces cerevisiae kinesin-5 homolog, are minus end-directed when moving on single MTs, yet switch directionality under certain experimental conditions (Gerson-Gurwitz et al., EMBO J 30:4942-4954, 2011; Roostalu et al., Science 332:94-99, 2011). This finding was unexpected since the Cin8 catalytic motor domain is located at the N-terminus of the protein, and such kinesins have been previously thought to be exclusively plus end-directed. In addition, the essential intracellular functions of kinesin-5 motors in separating spindle poles during mitosis can only be accomplished by plus end-directed motility during antiparallel sliding of the spindle MTs. Thus, the mechanism and possible physiological role of the minus end-directed motility of kinesin-5 motors remain unclear. Experimental and theoretical studies from several laboratories in recent years have identified additional kinesin-5 motors that are bidirectional, revealed structural determinants that regulate directionality, examined the possible mechanisms involved and have proposed physiological roles for the minus end-directed motility of kinesin-5 motors. Here, we summarize our current understanding of the remarkable ability of certain kinesin-5 motors to switch directionality when moving along MTs.

Dual targeting of MDM2 with a novel small-molecule inhibitor overcomes TRAIL resistance in cancer.

Mouse double minute 2 (MDM2) protein functionally inactivates the tumor suppressor p53 in human cancer. Conventional MDM2 inhibitors provide limited clinical application as they interfere only with the MDM2-p53 interaction to release p53 from MDM2 sequestration but do not prevent activated p53 from transcriptionally inducing MDM2 expression. Here, we report a rationally synthesized chalcone-based pyrido[ b ]indole, CPI-7c, as a unique small-molecule inhibitor of MDM2, which not only inhibited MDM2-p53 interaction but also promoted MDM2 degradation. CPI-7c bound to both RING and N-terminal domains of MDM2 to promote its ubiquitin-mediated degradation and p53 stabilization. CPI-7c-induced p53 directly recruited to the promoters of DR4 and DR5 genes and enhanced their expression, resulting in sensitization of TNF-related apoptosis-inducing ligand (TRAIL)-resistant cancer cells toward TRAIL-induced apoptosis. Collectively, we identified CPI-7c as a novel small-molecule inhibitor of MDM2 with a unique two-prong mechanism of action that sensitized TRAIL-resistant cancer cells to apoptosis by modulating the MDM2-p53-DR4/DR5 pathway.

The prophage-encoded hyaluronate lyase has broad substrate specificity and is regulated by the N-terminal domain.

Streptococcus equi is the causative agent of the highly contagious disease "strangles" in equines and zoonotic meningitis in human. Spreading of infection in host tissues is thought to be facilitated by the bacterial gene encoded extracellular hyaluronate lyase (HL), which degrades hyaluronan (HA), chondroitin 6-sulfate, and dermatan sulfate of the extracellular matrix). The clinical strain S. equi 4047 however, lacks a functional extracellular HL. The prophages of S. equi and other streptococci encode intracellular HLs which are reported to partially degrade HA and do not cleave any other glycosaminoglycans. The phage HLs are thus thought to play a role limited to the penetration of streptococcal HA capsules, facilitating bacterial lysogenization and not in the bacterial pathogenesis. Here we systematically looked into the structure-function relationship of S. equi 4047 phage HL. Although HA is the preferred substrate, this HL has weak activity toward chondroitin 6-sulfate and dermatan sulfate and can completely degrade all of them. Even though the catalytic triple-stranded ß-helix domain of phage HL is functionally independent, its catalytic efficiency and specificity is influenced by the N-terminal domain. The phage HL also interacts with human transmembrane glycoprotein CD44. The above results suggest that the streptococci can use phage HLs to degrade glycosaminoglycans of the extracellular matrix for spreading virulence factors and toxins while utilizing the disaccharides as a nutrient source for proliferation at the site of infection.

Modeling groundwater quality over a humid subtropical region using numerical indices, earth observation datasets, and X-ray diffraction technique: a case study of Allahabad district, India.

Water is undoubtedly the vital commodity for all living creatures and required for well-being of the human society. The present work is based on the surveys and chemical analyses performed on the collected groundwater samples in a part of the Ganga basin in order to understand the sources and evolution of the water quality in the region. The two standard indices such as water quality index and synthetic pollution index for the classification of water in the region are computed. The soil and sediment analysis are carried out with the help of X-ray diffractometer (XRD) for the identification of possible source of ions in water from rock and soil weathering. The dominant minerals which include quartz, muscovite, plagioclase, and orthoclase are reported in the area. The study further utilizes the multivariate statistical techniques for handling large and complex datasets in order to get better information about the groundwater quality. The following statistical methods such as cluster analysis (CA), factor analysis (FA), and principal component analysis (PCA) are applied to handle the large datasets and to understand the latent structure of the data. Through FA/PCAs, we have identified a total of 3 factors in pre-monsoon and 4 factors in post-monsoon season, which are responsible for the whole data structure. These factors explain 77.62 and 82.39% of the total variance of the pre- and post-monsoon datasets. On the other hand, CA depicted the regions that have similar pollutants origin. The average value of synthetic pollution index of groundwater during pre-monsoon is 9.27, while during post-monsoon, it has been recorded as 8.74. On the other hand, the average values of water quality index of groundwater during pre-monsoon and post-monsoon seasons are found as 217.59 and 233.02, respectively. The study indicates that there occurs an extensive urbanization with gradual vast development of various small- and large-scale industries, which is responsible for degradation in water quality. The overall analysis reveals that the agricultural runoff, waste disposal, leaching, and irrigation with wastewater are the main causes of groundwater pollution followed by some degree of pollution from geogenic sources such as rock and soil weathering, confirmed through XRD analysis.

Evaluation of Surface Water Quality in the Betwa River Basin through the Water Quality Index Model and Multivariate Statistical Techniques.

The Betwa River Basin (BRB), a sub-basin of the River Yamuna, is the oldest flowing water system in Central India. The water quality of the rivers are under stress, hence regular monitoring and appraisal is required to know the health of the rivers. Factor analysis and principal component analysis (FA/PCA) multivariate statistical techniques were used to extract three and four varimax factors that explained 96.408 and 100.00 percent of the total variance in water quality, respectively. Cluster analysis (CA) categorizes observed items into distinct quality categories based on correlations between stations and years. Point industrial/sewage effluents, diffuse pollution as runoff from arable land, erosion, and natural source pollution contribute to the pollution of the BRB. As a result, water quality is threatened or impaired, and conditions often departed from natural or desirable levels at Rajghat, Garrauli, Mohana, and Shahijina stations. According to the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI), the surface water quality at the Rajghat and Mohana stations corresponds to fair ecological status. However, the surface water quality of the Garrauli and Shahijina stations has a marginal water quality as per CCME-WQI. From 1985 to 2018, the Shahijina had the most considerable load of nutrients and organic matter, as determined by the CCME-WQI and by comparing the water quality data. A thorough examination had revealed a fluctuating trend in the BRB pollution, particularly at all stations. Results indicate that between 1985 and 2018, the only defense mechanism of the river was the auto purification mechanism, which is strongly influenced by the drought, point pollution source, and extreme meteorological events that probably cause these fluctuations.

Mycobacterial biofilms: Understanding the genetic factors playing significant role in pathogenesis, resistance and diagnosis.

Even though the genus Mycobacterium is a diverse group consisting of a majority of environmental bacteria known as non-tuberculous mycobacteria (NTM), it also contains some of the deadliest pathogens (Mycobacterium tuberculosis) in history associated with chronic disease called tuberculosis (TB). Formation of biofilm is one of the unique strategies employed by mycobacteria to enhance their ability to survive in hostile conditions. Biofilm formation by Mycobacterium species is an emerging area of research with significant implications for understanding its pathogenesis and treatment of related infections, specifically TB. This review provides an overview of the biofilm-forming abilities of different species of Mycobacterium and the genetic factors influencing biofilm formation with a detailed focus on M. tuberculosis. Biofilm-mediated resistance is a significant challenge as it can limit antibiotic penetration and promote the survival of dormant mycobacterial cells. Key genetic factors promoting biofilm formation have been explored such as the mmpL genes involved in lipid transport and cell wall integrity as well as the groEL gene essential for mature biofilm formation. Additionally, biofilm-mediated antibiotic resistance and pathogenesis highlighting the specific niches, sites of infection along with the possible mechanisms of biofilm dissemination have been discussed. Furthermore, drug targets within mycobacterial biofilm and their role as potential biomarkers in the development of rapid diagnostic tools have been highlighted. The review summarises the current understanding of the complex nature of Mycobacterium biofilm and its clinical implications, paving the way for advancements in the field of disease diagnosis, management and treatment against its multi-drug resistant species.

Facile green synthesis of silver nanoparticles derived from the medicinal plant Clerodendrum serratum and its biological activity against Mycobacterium species.

The emergence of multidrug-resistant mycobacterial strains is a significant crisis that has led to higher treatment failure rates and more toxic and expensive medications for tuberculosis (TB). The urgent need to develop novel therapeutics has galvanized research interest towards developing alternative antimicrobials such as silver nanoparticles (AgNPs). The current study focused on the anti-mycobacterial activity of green-synthesized AgNPs and its polyethylene glycol encapsulated derivative (PEG-AgNPs) with improved stability using the leaves extract of Clerodendrum serratum. Different characterization methods were used to analyze them. DLS analysis revealed a lower polydispersity index of PEG-AgNPs, suggesting a more uniform size distribution than that of AgNPs. The HR-TEM results revealed that the AgNPs and PEG-AgNPs have predominantly spherical shapes in the size range of 9-35 nm and 15-60 nm, respectively, while positive values of Zeta potential indicate their stability. FTIR-ATR analysis confirmed the presence of functional groups responsible for reducing and capping the bio-reduced AgNPs, whereas the XRD data established its crystalline nature. Impressively, the PEG-AgNPs exhibited maximum inhibitory activity against different Tubercular and Non-Tuberculous Mycobacterium species i.e., Mycobacterium smegmatis, Mycobacterium fortuitum and Mycobacterium marinum, relative to those of AgNPs and Linezolid. The flow cytometry assay showed that the anti-mycobacterial action was mediated by an increase in cell wall permeability. Notably, the results of AFM confirm their ability to inhibit mycobacterial biofilm significantly. We demonstrated the nontoxic nature of these AgNPs, explicated by the absence of hemolytic activity against human RBCs. Overall, the results suggest that PEG-AgNPs could offer a novel therapeutic approach with potential anti-mycobacterial activity and can overcome the limitations of existing TB therapies.

Biophysical and in-silico studies on the structure-function relationship of Brugia malayi protein disulfide isomerase.

Human Lymphatic filariasis is caused by parasitic nematodes Wuchereria bancrofti, Brugia malayi, and Brugia timori. Protein disulfide isomerase (PDI), a redox-active enzyme, helps to form and isomerize the disulfide bonds, thereby acting as a chaperone. Such activity is essential for activating many essential enzymes and functional proteins. Brugia malayi protein disulfide isomerase (BmPDI) is crucial for parasite survival and an important drug target. Here, we used a combination of spectroscopic and computational analysis to study the structural and functional changes in the BmPDI during unfolding. Tryptophan fluorescence data revealed two well-separated transitions during the unfolding process, suggesting that the unfolding of the BmPDI is non-cooperative. The binding of the fluorescence probe 8-anilino-1-naphthalene sulfonic acid dye (ANS) validated the results obtained by the pH unfolding. The dynamics of molecular simulation performed at different pH conditions revealed the structural basis of BmPDI unfolding. Detailed analysis suggested that under different pH, both the global structure and the conformational dynamics of the active site residues were differentially altered. Our multiparametric study reveals the differential dynamics and collective motions of BmPDI unfolding, providing insights into its structure-function relationship.Communicated by Ramaswamy H. Sarma.

Role of site-site interaction on the phase equilibria of multiple-site associating fluids in a functionalized slit pore.

Vapor-liquid phase equilibria for multiple sites associating fluids with different associating strengths are investigated in a slit pore using grand-canonical transition matrix Monte Carlo method. The increase of critical temperature from two-site to four-site associating fluids at constant site strength is quite significant as compared to that of the one-site to two-site associating fluids, which is more pronounced at higher associating strength (ϵ* = 6). Monomer fraction and cluster size distribution are used to investigate the association of fluid particles in coexistence phases. The monomer fraction for both phases decreases with increased associating sites on the fluid particles due to more site-site interaction with neighboring fluid particles and forming a larger cluster. Therefore, the number of associating sites and their distribution play a vital role in the association of fluid particles. Moreover, the saturation chemical potential changes with the arrangement of the sites. For two-site associating fluids, we observe early vapor-liquid transition when the sites are oppositely placed, and when the sites are placed at 90°, the vapor-liquid transition is observed at the higher chemical potential. Moreover, four-site associating fluids with a square arrangement show early vapor-liquid phase transition, mainly because these arrangements of sites effectively interact with surface sites and the molecules in the next layer.

Future prediction of water balance using the SWAT and CA-Markov model using INMCM5 climate projections: a case study of the Silwani watershed (Jharkhand), India.

The aim of this research was to simulate the future water balance of the Silwani watershed, Jharkhand, India, under the combined effect of land use and climate change based on the Soil and Water Assessment Tool (SWAT) and Cellular Automata (CA)-Markov Chain model. The future climate prediction was done based on daily bias-corrected datasets of the INMCM5 climate model with Shared Socioeconomic Pathway 585 (SSP585), which represent the fossil fuel development of the world. After a successful model run, water balance components like surface runoff, groundwater contribution to stream flow, and ET were simulated. The anticipated change in land use/land cover (LULC) between 2020 and 2030 reflects a slight increase (3.9 mm) in groundwater contribution to stream flow while slight decrease in surface runoff (4.8 mm). The result of this research work helps the planners to plan any similar watershed for future conservation.

Modeling of land use change under the recent climate projections of CMIP6: a case study of Indian river basin.

The aim of the study was to investigate the land use change dynamics under CMIP6 projections using Land Change Modeler (LCM). The Global Sensitivity Analysis (GSA) techniques was applied to quantify the sensitivity of single parameter and combination of parameters. Land use and land cover (LULC) transitions of the baseline period (2006-2016) was assessed with a model performance accuracy of 80%. Receiver operating characteristic (ROC) analysis shows that the model has performed well for all the LULC classes except builtup land. Prediction under the SSP245 scenario depicts that areal extent of agricultural, forest, and snow, and glacier will decrease by the mid-century (2045). However, the grassland and barren land area will increase from the baseline period. A similar change pattern with a higher magnitude has also been predicted under SSP585 scenario. The CMIP6 forcing index considers socio-economic effects and LCM predicted an expansion in barren land which may be attributed to changes in cryosphere in the studied area.

Prediction of meteorological drought and standardized precipitation index based on the random forest (RF), random tree (RT), and Gaussian process regression (GPR) models.

Agriculture, meteorological, and hydrological drought is a natural hazard which affects ecosystems in the central India of Maharashtra state. Due to limited historical data for drought monitoring and forecasting available in the central India of Maharashtra state, implementing machine learning (ML) algorithms could allow for the prediction of future drought events. In this paper, we have focused on the prediction accuracy of meteorological drought in the semi-arid region based on the standardized precipitation index (SPI) using the random forest (RF), random tree (RT), and Gaussian process regression (GPR-PUK kernel) models. A different combination of machine learning models and variables has been performed for the forecasting of metrological drought based on the SPI-6 and 12 months. Models were developed using monthly rainfall data for the period of 2000-2019 at two meteorological stations, namely, Karanjali and Gangawdi, each representing a geographical region of Upper Godavari river basin area in the central India of Maharashtra state which frequently experiences droughts. Historical data from the SPI from 2000 to 2013 was processed to train the model into machine learning model, and the rest of the 2014 to 2019-year data were used for testing to forecast the SPI and metrological drought. The mean square error (MSE), root mean square error (RMSE), adjusted R2, Mallows' (Cp), Akaike's (AIC), Schwarz's (SBC), and Amemiya's PC were used to identify the best combination input model and best subregression analysis for both stations of SPI-6 and 12. The correlation coefficient ([Formula: see text]), mean absolute error (MAE), root mean square error (RMSE), relative absolute error (RAE), and root relative squared error (RRSE) were used to perform evaluation for SPI-6 and 12 months of both stations with RF, RT, and GPR-PUK kernel models during the training and testing scenarios. The results during testing phase revealed that the RF was found as the best model in forecasting droughts with values of [Formula: see text], MAE, RMSE, RAE (%), and RRSE (%) being 0.856, 0.551, 0.718, 74.778, and 54.019, respectively, for SPI-6 while 0.961, 0.361, 0.538, 34.926, and 28.262, respectively, for SPI-12 scales at Gangawdi station. Further, the respective values of evaluators at Karanjali station were 0.913 and 0.966, 0.541 and 0.386, 0.604 and 0.589, 52.592 and 36.959, and 42.315 and 31.394 for PUK kernel and RT models, respectively, during SPI-6 and SPI-12. Machine learning models are potential drought warning techniques because they take less time, have fewer inputs, and are less sophisticated than dynamic or scientific models.