Integrated water resources management framework and other frameworks that approach sustainable resources management from a catchment perspective can benefit from the availability of a characterized and harmonized dataset of hydrological units. Harmonization is particularly relevant for a highly linguistically diverse continent such as Africa where high-resolution topographic relief datasets are not readily available, and most of the continent's waters fall within transboundary drainage units. This article describes and implements a hierarchical coding framework that harmonizes the description, classification, and coding of drainage units on the African continent. The continent is divided and subdivided into successively smaller drainage units, classified into six levels: systems, regions, subregions, basins, subbasins, and catchments, primarily determined by topology, location, type, and size of drainage units. Drainage units are consistently assigned identification codes that show their location and classification level. The classification groups the 1007 catchments (with areas ranging from 10,000 to 50,000 km2) on the mainland continent into 274 subbasins (50,000–250,000 km2), 119 basins (100,000–500,000 km2), and thirty-two subregions, twelve regions and five systems including islands. The characterization provides a high-resolution basin boundary dataset that improves data compatibility and accuracy and is expected to support catchment-based research and application on the continent.

The rapid urbanization in Delhi-NCR has been inducing enormous anthropogenic pressure, result in degraded ecological infrastructure. Therefore, in the present study, the changing landscape pattern in Delhi's urban and peri-urban regions was investigated over the last five decades (1973–2020) using satellite remote sensing and landscape metrics to understand its trends and impacts on ecological infrastructure, especially on green cover space (UGS) that provide an opportunity to evaluate cities' sustainability. The study exhibits significant built-up growth in the peri-urban (516.9% growth; net growth 2407 km2) compared to Delhi urban (236.9%; 431.7 km2), with an overall decline in UGS (−14.3%; −168.60 km2). A large segment of UGS deterioration observed in the peri-urban (31.36%; −327.41 km2 loss) compared to Delhi urban, showed significant UGS recovery (120.25%; 158.81 km2 growth) during the later period. The landscape modeling and Shannon entropy-based study exhibited coalescence in Delhi urban (Hn ≤ 1.5) and dispersion in peri-urban (Hn > 2). The zonal analysis showed a significant dispersion in Panipat, Gurgaon, and Faridabad (Delhi NCR) with the establishment of major socio-economic development and population aggregation. The UGS availability and land consumption analysis highlighted the major hotspots of UGS decline in Gurgaon, Ghaziabad, and Shahdara, within Delhi urban, while in Jhajjar, Gurgaon, and Panipat in the peri-urban regions, having insufficient per capita green spaces (9 m2/person) and high land consumption (>85 m2/person). Moreover, recovery in UGSs (256% during 2014–2020) was observed in the Delhi urban. The study necessitates efficient functioning of the urban ecosystem for making the cities safe, resilient, and inclusive.

Precipitation is highly sensitive to climate change, and monitoring its trends over time is essential for understanding the effects of global warming on the Earth's water cycle. This study investigates the effects of climate change on monthly precipitation in the Susurluk Basin located in northwestern Turkey, using innovative trend analysis methods. Innovative Polygon Trend Analysis (IPTA) using star graphics and Improved Visualization for Innovative Trend Analysis (IV-ITA) techniques were used to analyze precipitation data from nine stations in the basin over 50 years (1970–2019), and the findings were compared to classical Mann-Kendall (MK) results. According to the trend analysis results, the MK method detected significant trends in only two months in the decreasing direction. In these decreasing trends observed at Simav and Keles stations in November, z values were calculated as −2.38 and −2.05, respectively. IPTA method detected increasing trends in 54 months and decreasing trends in 40 months; this means there is a change of more than 5% between two half series. The IV-ITA method indicated an increase of more than 40% in the low values for January, March, August, and September. In addition, there was an increasing trend of more than 40% in the high values observed in September at all stations. It was observed that mean precipitation heights increased from September to December and then decreased throughout the basin. It is predicted that decreases in precipitation during dry seasons may put intense pressure on agricultural water use and affect water quality. In addition, the increase in precipitation in spring months may increase flood events. It was determined that graphical innovative trend analysis methods are more effective in trend identification than MK, and these methods' results have much higher visual inspection and linguistic interpretation possibilities. Innovative methods allow for more flexible and in-depth analysis using different statistical parameters.

Drought is a complex and devastating natural disaster that needs to be constantly investigated. In this study, standardized precipitation indexes (SPI-3 and SPI-6) were computed using daily precipitation data collected from six meteorological stations in a Mediterranean coastal basin (Northwestern Morocco) during the period from 1984 to 2021. Subsequently, we examined the spatio-temporal distribution of three agricultural indices namely Vegetation Condition Index (VCI), Temperature Condition Index (TCI), and Vegetation Health Index (VHI). Additionally, to predict SPI at two scales (SPI-3 and SPI-6), we investigated the ability of four machine learning (ML) models such as random subspace, random forest, M5P, and REPTree. ML model's performance was evaluated using statistical metrics such as R2, MAE, RMSE, RAE, and RRSE. As per SPI results, 1995, 1999, 2005, 2015, and 2017 were observed as severe driest years. Agricultural drought’ magnitude differs over time and space from 1984 to 2021. Besides, findings showed that the REPTree model achieved the best performance during the testing and validation phases, with R2 (0.64–0.85), MAE (0.37–0.58), RMSE (0.51–0.74), RAE (48.10–75.34%) and RRSE (53.24–76.68%). In contrast, RF was found to offer the lowest performance accuracy, despite outperforming during the training phase. Moreover, SPI-6 has higher prediction accuracy than SPI-3. Furthermore, our findings offer a reliable model for drought prediction, which may further assist policymakers and authorities in developing better adaptation and mitigation strategies to reduce drought-related losses. In future research, we suggest exploring alternative ensemble or hybrid ML algorithms to further improve prediction accuracy and capability.

Groundwater contamination by arsenic (As) is a major health/environmental concern due to its high toxicity to humans. Despite substantial research, there is scarce data in many countries regarding spatial distribution and As levels in groundwater, as well as its possible accumulation in humans/animals and associated health hazards. In this study, a total of 124 drinking water, 20 animal milk, 21 human hair, and 14 human nail samples were collected and analyzed for As contents from four tehsils (Bahawalnagar, Chishtian, Haroonabad and Minchinabad) of District Bahawalnagar. Geostatistical analysis and risk assessment models were used to predict the spatial distributions and health hazards of As in drinking water. Arsenic groundwater contents ranged between below detection limit (BDL) to 31.5 μg.L−1 with a mean of 7.6 μg.L−1. About 6 water samples (5%) contained As level > World Health Organization (WHO) limit. All these samples containing As contents > WHO limit were collected from Chishtian tehsil. In the case of milk samples, mean As contents were 75 μg.L−1 with a range of 42–111 μg.L−1. Arsenic was also detected in human hair (mean value of 640 μg kg−1 and range 71–2139 μg kg−1). However, As contents were BDL for all the nail samples. Risk assessment indices (cancer risk and hazard quotient) revealed possible health hazards due to their high values (3.0 and 0.0013, respectively). The principal component analysis (PCA) revealed that the potential sources of As release in the groundwater of study area do not match with those of other water variables. Hence, there is a need to monitor the groundwater quality of the Chishtian tehsil for any potential negative health impacts, as well as the possible sources of As in the groundwater of the study area.

Temperature is the main element in producing asphalt mixture for asphalt pavement. The presence of Evotherm 3G in the asphalt can help to reduce the temperature of asphalt production and hence reduce energy usage. This study presents the bitumen properties with Evotherm 3G and the effect of this additive on asphalt mixture performance in reducing production temperature compared to hot mix asphalt. The bitumen tests were performed on the bitumen penetration grade of 60/70 using the penetration, softening point, and viscosity tests, while the Marshall samples were produced for the asphalt properties evaluation. The results showed that asphalt containing Evotherm 3G produced at a lower compaction temperature has comparable stability and flow to the conventional hot mix asphalt. The viscosity data of the bitumen added with Evotherm 3G does not give a significant difference compared to the penetration value. This justifies the applicability of Evotherm 3G as a surfactant in warm mix asphalt production.

There is a strong coordinated effort by vaccination groups all over the world to put an end to the current crisis of COVID-19. Now sufficient data are available to analyse and compare some results to explore the aftereffects of vaccination. Some influence variables on transmissions of the disease were discussed e.g., mass vaccination, lockdown and seasonality. Most studies covered here are up to the beginning of July 2022, while some analyses focused on the earlier period of mass vaccination. Well established, simple statistical techniques to evaluate results were presented those used open data sources of authoritative bodies. Some comparisons between vaccinated vs. unvaccinated were also discussed based on data from UK Government Health Security Agency (UHSA). In terms of mass vaccination, adverse reactions after vaccination received attention, as health and safety issues of the general public are of prime importance. Apart from direct side effects, the secondary effect of mass vaccination needs attention too. After the initiation of the vaccination programme, almost all countries experienced a sudden surge in transmission and most countries had to impose strict lockdown measures. Many countries, with a low prevalence of disease, suddenly showed a steep jump and some countries even followed a synchronized pattern between the rate of transmissions and the variation of vaccine doses. Time series analyses and bar diagram presentations were able to capture those features. In that context, fast mutation of the virus and new variants after mass vaccination and possible mechanisms/consequences were also attended. To understand the effect of seasonality, similarities between COVID-19 and the seasonal Flu are discussed for Europe and US to gain useful insight. Using time series analyses and spatial plots of regional temperature composites we showed, like Flu, seasonality played a dominant role in transmissions of COVID-19 in the Europe. Regulations of vaccine dose and policy implication were explored too. From 22nd December 2021, global vaccine doses were reduced substantially, which followed a dramatic reduction in cases and thereafter deaths with around one month's lag between each. As strong dependency on seasonality is noticed in certain countries and observing that regulation of vaccine doses has roles in modulating the transmission with certain lags, globally as well as regionally, our results have policy implications for the management of COVID. Debating, questioning and criticism are always the foundation of great science and the major pillars of its progress. Following that objective, it is an effort to explore pragmatically, supported by scientific analyses, areas relating to the effectiveness of the COVID-19 vaccine and the exit strategy via the pathway of vaccination.

Microplastics, as growing contaminants in current scenario, are an ecological threat to the surface water ecosystem. Our primary goal is to investigate the identification, characterization, distribution and abundance of microplastics throughout India's east coast, from Ennore to Kovalam. In accordance with the NOAA protocol, microplastics were examined in surface water samples. At all sampling sites, polymers like polyethylene (43%), polypropylene (42%) and polystyrene (15%) were often found. Surface water (total sample location = 34) were found to be contaminated with various types of microplastics ranging from 6 to 30 items/Location with a mean value of 12 items/Location. Microplastics examined from surface water in a variety of colours, including white (26%), black (16%), grey (12%), red (14%), blue (12%), yellow (10%) and green (10%). Microplastics identified by stereo microscope as well as analytical methods such as SEM and FTIR were utilized to explore the characterization of microplastics. The following are the most typical types of microplastic found in surface water: Fibres (59%), films (24%), fragments (10%) and pellets (7%). This research work is a baseline for study about microplastics contamination site from Ennore to Kovalam on East Coast of India.

Adequate information about the variations and trends of climatic variables is indispensable for the better management of the water resources and agricultural sectors. In this study, the variability and trends of the monthly, seasonal and annual temperatures ((TMIN, TMAX and TMEAN) time series were studied over Niger Central Hydrological Area, Nigeria (NCHA) during 1911–2015 using the Climate Research Unit (CRU) data. Different statistical operations analysis comprising descriptive statistics, linear regression model, Mann–Kendall test, Pettitt's test and standard normal homogeneity test (SNHT) were employed for the analysis. The tests were used to examine the temperature variability, presence of monotonic trends and change points of the time series. The monthly TMEAN variability over the entire area was between 2.10% and 3.61%. Periods 1914–1925 and 1974–1980 showed cooling trends, while 1930–1935, 1981–1988 and 2001–2015 exhibited warming trends. The annual TMIN and TMEAN showed positive trends across the 33 sub-basins of NCHA, while the TMAX showed mixed trends. The increasing trend was most significant on the monthly, seasonal and annual timescales of the TMIN, followed by TMEAN. In contrast, the mixed trends in TMAX were not significant. The global rate of change trends of TMIN, TMAX and TMEAN are 0.091, −0.007 and 0.043 °C/decade respectively. The overall warming of TMEAN also had a significant upward shift change of 0.43 °C since 1980 and the change spatially is characterized by a SW-NE orientation. There is a need to embrace green settlement and a green economy to reduce the impact of the ever-increasing warming trend over the region.

This study evaluates performance of a series of freely available gridded precipitation datasets that have been developed from the satellite (CHIRPS and TRMM TMPA), rain-gauged interpolation (APHRODITE and IMD-gridded), and Coupled Model Intercomparison Project Phase 6 (CMIP6)-General Circulation Models (GCMs) with the gauged observations (n = 49) for assessing their ability in capturing annual and seasonal rainfall characteristics over Central Godavari River basin in India. A rank matrix was developed for the purpose of comparing the performance of gridded datasets with that of gauged observations. This matrix was generated on the basis of the goodness-of-fit (GOF) statistics. The results reveal that APHRODITE dataset (r = 0.91–0.94; NSE = 0.68 to 0.87) has performed the best across all three zones, followed by the IMD-gridded (r = 0.93–0.95; NSE = 0.57 to 0.77) and CHIRPS datasets (r = 0.89–0.92; NSE = 0.38 to 0.60). However, TRMM TMPA dataset show the poorest accuracy with the gauged observation for all three zones. Additionally, projections from the CMIP6-GCMs reveal that the western part of the basin will receive more rainfall as compared to the eastern and central parts of the basin in future (2050s). This is opposite from prevailing rainfall variability pattern in the basin. The Zone 1 and Zone 2 of the basin are found to be comparatively more susceptible to the climate change under Shared Socio-Economic Pathway (SSP)245 and SSP585 scenarios than Zone 3. These results will aid in the future development of actionable agricultural water management plans for the basin, especially for rainfed agriculture, by assisting in the selection of the most effective gridded precipitation and CMIP6-GCM datasets.

In the present study, direct runoff and Total runoff were estimated at temporal scales (2000–2018) focusing on flood potential in the catchments of Kashmir valley. The valley is recurrently prone to catastrophic floods with increased Total runoff frequency in recent decades (post-2010 periods). LANDSAT ETM+ satellite image based land use/land cover mapping exhibited the dominance of vegetation cover (46%) primarily in the peripheral hilly regions, in contrast to agricultural land (29%) and built-up (1.6%) in the elongated valley region. The high anomalous rainfall (>500 mm) in most of the catchments during 2014–2018 caused an increased runoff apparently leading to catastrophic floods inundation (as observed in 2014 with accumulated rainfall of 713 mm and cumulative Total Runoff of 55 LCM). A higher contribution towards Total Runoff in northern catchments (>56%) exhibited higher susceptibility to flood inundation as compared to the southern catchments (<42%). The study highlighted a high vulnerability in the large proportion of built-up land (148 km2) within close proximity to Jhelum river and below 1560m elevation range due to the erratic rainfall induced runoff floods. The insignificant proportion of built-up led to less contribution towards the high runoff yet increases the severity of runoff and flood inundation due to high imperviousness. The rapid built-up growth coupled with deterioration of natural wetlands in the urban-rural fringe increased the impact of catastrophic flood manifolds. The study provided a regional and comprehensive runoff assessment that can be utilized to develop adequate flood control measures at the catchment level in the valley.

Revealing the ionic sources of NO 3− pollution in groundwater and health risks to children and adults is essential to take necessary management measures before the development of urban activities, as nitrate (NO 3−) in drinking water poses serious health risks worldwide, including in India. Groundwater samples were collected from a semi-urban region of Titrol block, Jagatsinghpur district, Odisha, and analysed for chemical variables. The content of NO 3− ranged from 2 to 92 mg/L with an average of 34.26 mg/L, in which 83.33% of groundwater samples contained NO 3− more than 10 mg/L, which is the starting point of water pollution. Computed values of the Nitrate Pollution Index (NPI) varied from −0.40 to 8.20 with an average of 2.42. According to NPI, 16.67%, 16.67%, 10%, 13.33%, and 43.33% of groundwater samples were unpolluted, slightly polluted, moderately polluted, significantly polluted, and very significantly polluted, which cover 1.45%, 6.07%, 27.43%, 37.46%, and 27.59% of the study region. Correlation of NO 3− with well depth and other chemical variables indicated significant degradation of groundwater quality by agricultural activities, leading to vulnerability of health conditions. According to Chronic Health Risk (CHR), CHR varied from 0.063 to 3.067 (1.067) for children and 0.042 to 1.917 (0.712) for adults, with 56.67% and 23.33% of groundwater samples above the acceptable level of 1.0, respectively. Children are 1.50 times more susceptible to NO 3− poisoning than adults. Therefore, careful planning is needed to provide clean drinking water and reduce the health risk of NO 3−. This study contributes to UNDP's Sustainable Development goals 2030 for a healthy environment.

The United Nations' (UN) Sustainable Development Goals (SDGs) agenda has triggered numerous countries to harness solar energy from solar photovoltaic (PV) modules to increase the share of renewable energy in the global energy mix. However, geographical and climatic factors have a significant impact on the electrical performance of solar PV modules. In addition, since solar PV energy production models are the only physics-based approach to transferring ground-measured PV energy production to other locations, the authors developed 294 physical models from six different PV power technologies and validated them for the model's adaptability. To facilitate the possible determination of PV electric energy generation in the unique geographical and climatic environment of the experiment site, these models were built using machine learning, Gumbel's probabilistic approach (GPM), and hybridization of the two. The major challenges in this study are in developing the hybridized machine learning with the Gumbel probabilistic functional model, which resides in the mathematical transformation process, which required a great deal of repeated mathematical science knowledge to arrive at the final transformed and efficient model for predicting the potential of solar PV output. With a thorough coefficient of determination (R2) of 0.9998% and a root mean square error (RMSE) of 0.0063 kwh, the hybrid model with only the measurable solar radiation parameter is the closest to the measured PV energy production of all technologies. The best hybridized model was used to explore the potential impacts of climate change on the different solar PV technologies. This was achieved by using energy parameters from the Australian Community Climate and Global System Simulation (ACCESS-CM2) in Phase 6. On an annual basis, the effects of climate change on various PV technologies have had a small adverse impact (less than 1%) on these renewable energy technologies. It was also found that, compared to other technologies, CIGS thin film technology produced the least negative effects on climate change, with 10.94%–36.75% in the best-case, 35.71%–36.36% in the moderate-case, and 33.33–40.00% in the worst-case scenario for shared socioeconomic pathways (SSP126, SSP245, and SSP585) emissions. This suggests the intrinsic properties of Copper Indium Gallium Selenide (CIGS) thin film modules are more effective at withstanding high temperatures as they contribute 60.00–89.66% of their intrinsic module properties to PV energy production compared to other technologies. However, taking into account the time, resource availability, cost-effectiveness, commercialization, and consumption of various PV technologies studied in this era of global sustainability, poly-crystalline (p-Si) technology is highly recommended for harvesting solar PV energy products in Alice Springs, Australia.

This study aims to assess the risk of the wastewater plume from the oxidation ponds contaminating the underneath shallow groundwater aquifer. The study area is an industrial city comprised of three main zones: industrial, populated, and agricultural. The study area is bordered on the eastern side by a wastewater pond and the Rosseta Nile river branch. The geochemical investigation is based on the determination of groundwater salinity, major ions, nitrogen compounds (NH4−, NO2−, NO3−), phosphate (PO43−), heavy metals, and the isotopic content (δ18O and δ2H) as well as the geochemical groundwater modeling. The results show most groundwater samples (50%) in the study area are freshwater, 31% are marginal saline water, and 18% are brackish. Higher nitrogen and PO43− compound concentrations are found in the shallow groundwater close to the oxidation ponds and populated zone. The δ18O and δ2H in the Quaternary groundwater is relatively depleted; they range from −2.1 to 2.3‰ and −7 to 21‰, respectively. The integrations of the geochemistry and isotopes show that the Pleistocene aquifer receives considerable recharge percent (14–56%) from the Rosseta Nile river branch and has been polluted due to the seepage from the oxidation ponds (seepage percent range: 0.6–3.6%). The results suggest that excessive fertilizer usage in the agricultural zone, seepage from oxidation ponds into the subsurface aquifer, and the anthropological effects of population size and industrial zones all contribute to the decline in groundwater quality. The highly vulnerable groundwater sites extend from the northeastern areas close to the oxidation ponds and the southwestern areas close to the industrial and populated zones.

The data source and resolution of DEM will affect the extraction of watershed features and flood simulation precision. In this paper, based on the 7 cm resolution DEM of the Liulin watershed obtained by tilt photography technology, the nearest neighbor interpolation, bilinear interpolation, and cubic convolution methods were used to resample DEMs with 1m and 30m resolution, and 6 resampled DEMs were obtained. The Aster 30m resolution DEM was downloaded from the geospatial data cloud. The watershed features were extracted from the 7 DEMs and compared. Based on these DEMs, the HEC-HMS model was established. 20 flood events were selected in the Liulin watershed for model calibration and verification. The results showed that the resampling method has little effect on the topographic features, but there were some differences between the resampling and downloaded DEM topographic features. The flood simulation results of the 1m resolution DEM-based HEC-HMS model are slightly better than that of the 30m resolution DEM-based model. The resampled 30m resolution DEM-based model performed identically with Aster 30m resolution DEM-based model. Therefore, an Aster 30m DEM can be used for flood simulation in a watershed that lacks measured high-precision topographic data.

The understanding of groundwater recharge occurrence in drylands is central to water resources management for various uses. This study uses Remote Sensing and GIS techniques to understand where groundwater recharge occurs, and its implications for water and food security in Notwane River Basin located in the Botswana drylands. WetSpass, a distributed hydrological model was applied to map the potential groundwater recharge zones. Crop yield was predicted using the Commonwealth Science and Industrial Research Organization (CSIRO) precision weighing system. Model inputs were land use, soil texture class, topology, slope, groundwater level and catchment hydro meteorological patterns from 1987 to 2017- all sourced from satellite images. Image based classification was done to map the land cover changes in the catchment using ILWIS 30 software. Model outputs were evapotranspiration, surface runoff and groundwater recharge zone maps. The results of image-based land cover classification showed an increase of Settlements/buildup area (22.35%), grassland (5.24%) and a decline in forest cover (3.64%), agricultural land (22.23%) and bareland (3.16%). The results indicate that high recharge zones are associated with low surface runoff in rural, forested areas with sandy soils and the opposite is true for urban, buildup with clay soils. CSIRO predicts yield estimation of up to 2.037 × 103 tonnes of drought resistant maize or sorghum annually using 1100 × 106 L of the available 517.32–434.32 mm/year and 532.64–426.50 mm/year potential surface runoff and groundwater recharge, respectively. Runoff and potential recharge in Notwane sub-catchment suggest an existence of water resources worthy to be explored for food security in water scarce drylands.

Climate change is one of the major global threats which affects the streamflow variability. In order to disentangle the relationships between climatic oscillations (COs) and streamflows (SF), this study presents a framework for the identification of main climatic factors integrating Bivariate wavelet coherence (BWC), Multiple wavelet coherence (MWC), and Partial wavelet coherence (PWC) approaches. The application of this method is demonstrated by analysing the monthly SF-CO teleconnections of four stations belonging to the rivers of Greater Pampa region of Kerala, which was severely affected by the 2018 Kerala floods. To investigate the impact of these large-scale climatic patterns on the SF of this region, COs, specifically El Niño Southern Oscillation, Pacific Decadal Oscillation (PDO), Indian Ocean Dipole (IOD), and North Atlantic Oscillation (NAO), are taken into consideration. The relationships between different SF-COs are statistically quantified with the help of Average Wavelet Coherence (AWC) and the Percentage of Significant Coherence (PoSC). The BWC analysis showed that the IOD is the dominant predictor for SF1 (AWC = 0.46, PoSC = 26.05%) and SF2 (AWC = 0.5, PoSC = 30.03%) stations while NAO is the dominant predictor for SF3 (AWC = 0.43, PoSC = 18.25%) and SF4 (AWC = 0.48, PoSC = 24.83%) stations. Addition of NAO to the most significant combination of SF-PDO-IOD from the two-factor analysis produced the highest coherence values for all the stations. The PWC analysis indicated a drastic reduction in coherence values with respect to the BWC analysis, indicating a strong interrelationship between different COs and SF.

The study is aimed at investigating the pollution status of Takum town, Nigeria using environmental magnetism, geochemistry and geostatistics. A total of 79 topsoil (0–10 cm) samples were collected and used for the measurement of various environmental magnetic parameters and some heavy metals (Ti, Cr, Mn, Fe, Ni, Cu, Zn, Hg and Pb). Magnetic susceptibility (χ), saturation and anhysteric remanent magnetizations were highly variable having values that ranged from 32.11 to 1118.57 × 10−8 m3kg−1, 95.03–1206.59 × 10−8 m3kg−1 and 199.87–16002 × 10−5 Am2kg−1 respectively. These values reflect top soil enhancement due to the dominance of secondary ferrimagnetic minerals which is revealed by thermomagnetic runs to be mainly magnetite. The frequency dependent susceptibility χfd% had an average value of 10.63% implying that the samples are dominated by pedogenic superparamagnetic grains. However, the presence of multi-domains grains from vehicular sources are also evident. The mean concentration of the measured heavy metals decreased in the order: Fe > Ti > Mn > Zn > Pb > Cu > Ni > Hg. Based on various indices, Hg and Pb have been identified as the elements constituting the greatest risk to the area. Significant positive correlations (r = 0.402, P < 0.01 – r = 0.751, P < 0.001) were obtained between magnetic concentration parameters and Cr, Fe, Zn, Pb and Pollution Load Index, supporting the use of environmental magnetic properties for quick assessment and monitoring of pollution levels in the Takum soils. The sources of heavy metal pollution have been identified to originate from vehicular, agricultural, pedogenic and weathering activities. Different degrees of significant correlation were obtained between magnetic parameters, heavy metals and particle size classes, indicating the possibility of using magnetic techniques as proxy for textural parameters.

Landslides are the most common types of geological and environmental conditions, as well as long-term and short-term geological processes. Various extreme events, such as earthquakes, heavy precipitation, flooding, and anthropogenic actions, among others, cause landslides. InSAR is (Interferometric Synthetic Aperture Radar) technique is widely used for detecting and monitoring land surface deformation, including landslides. This technique measures the changes in the surface elevation by analyzing the phase differences of radar signals between two or more satellite images acquired at different times. In this research study, the main aim is to show how effective InSAR is until it can detect landslides using SNAP software. The data used is sentinel-1(S-1), obtained from Earth Science Data Systems (ESDS) Program and the area selected is Hulu Langat district. It processed by SNAP software using S-1 data are free and openly accessible via various sources. Next, the final product from the SNAP software will be Georeferenced in ArcGIS.The InSAR method utilised in the field of study is due to a suitable study area for this technique which is hilly with a mix of urban and rural divided. Most of the landslides in Malaysia, specifically at Hulu Langat, are on a small scale, which is the InSAR technique that can detect it. The coherence image showed that black represents water and vegetation areas while white represents buildings, rocks, and roads. This research showed that InSAR can help detect natural events which are landslides significantly using the SNAP software. The result showed a phase and coherence map.

In complex tropical granitic terrains, foundation rocks’ bearing strength is important in infrastructure design, while bedrock fractures that adversely impact engineering structures can boost groundwater productivity. Consequently, rock quality designation (RQD) can characterize these features effectively. This technique is difficult to determine in boreholes and relatively costly for a large area. As a result, novel approaches were employed to effectively determine RQD over a large aerial extent using statistically optimized borehole and seismic P-wave velocity (Vp) data with compensated resistivity modeling. This study was carried out in the typical granitic terrain of Penang Island, Malaysia. The first stage involves evaluating and developing borehole-based RQD models, and the results were correlated with Vp data and were regressively analyzed to develop lithology-based empirical relations. These empirical relations were used for developing 2D/3D SRT (seismic refraction tomography) based RQD models and were treated as novel models. Integrating the borehole-based and SRT-based RQD models with compensated resistivity models effectively delineate the surficial-to-subsurface soil-rock profiles based on their rock mass quality (RMQ) and conditions, as residual soil, completely weathered rock, relatively weathered rock, and integral/fresh granitic bedrock, including different directional and multiple axial fractures. Based on these results, the highlighted suitable sections for founding the foundations of infrastructure are pinned on the fresh bedrock at the depths of 8–25 m, with >2400 m/s, >2000 Ω m, and RQD >90%. The deep-weathered/fractured zones, with depth >35 m at localized sections, are water-bearing proposed for groundwater development. Overall, the methods and lithology-based empirical relationships can be adopted in granitic terrains to rapidly estimate RQD for a vast area with few and no borehole data.