Modeling and optimization of transesterification of Jatropha oil to fatty acid methyl ester: application of response surface methodology (CCD) and Taguchi orthogonal method.

The study effectively optimized the transesterification process of Jatropha oil to fatty acid methyl ester using response surface methodology (CCD) and Taguchi orthogonal method, leading to enhanced efficiency and product quality. The optimization of five process parameters was conducted to predict the biodiesel yield (%) from the transesterification of Jatropha oil. The procedure was made easier with the use of a nanocatalyst made from calcium oxide obtained from snail shells using the hydrothermal technique. The RSM approach yielded an optimal FAME of 94.10% under specific conditions: a methanol to oil ratio of 5 : 1, catalyst weight of 1.5 w/w, reaction temperature of 50 °C, reaction duration of 45 minutes, and an agitation speed of 250 rpm across 32 experimental trials. On the other hand, the Taguchi method predicted a higher FAME yield of 86.36% using specific operational parameters. These parameters included a methanol to oil ratio of 4 : 1, catalyst weight of 0.5 w/w, reaction temperature of 60 °C, reaction duration of 25 minutes, and an agitation speed of 200 rpm. These parameters were determined through 16 experimental trials. The RSM technique yielded impressive results with a determined coefficient of determination (R2) of 0.9943, adjusted R2 of 0.9838, predicted R2 of 0.8470, and a coefficient of variance (CV) of 0.65. On the other hand, the Taguchi method had coefficients of 0.8994, 0.7483, and 1.95. The FAME yield of RSM was slightly higher, but the Taguchi method was much more cost-efficient. The analysis of variance (ANOVA) results showed that the methanol to oil ratio had the highest influence on the yield, accounting for 49.61% of the variation. This highlights the significant impact of this factor on the overall process. The study highlights the significance of utilizing advanced techniques such as TOA and RSM, which are known for their effectiveness. The study aims to enhance the yield and efficiency of the transesterification process, thereby increasing the overall production of fatty acid methyl ester from Jatropha oil. This innovative approach efficiently generates biodiesel from renewable resources, like Jatropha oil, in a manner that is both environmentally friendly and maximizes the effectiveness of the process parameters.

Dataset of geophysical electrical resistivity and subsurface profiling for natural resources exploration in a hard rock terrain of Tamil Nadu, India.

Geophysical resistivity technique; vertical electrical sounding (VES)/earth resistivity test (ERT) was carefully done at 35 locations in a hard rock terrain of Tamil Nadu, India to evaluate natural resources such as groundwater, economic mineral deposits, etc., Data acquisition was done by CRM-500 Aquameter along with GPS, topographic map, Brunton compass, measuring tape, field notebook, hammer, iron rods (electrodes), and batteries. Furthermore, the major four subsurface layers' thickness, resistivity, and pseudo-section profiles were identified from the resistivity dataset using IPI2WIN. The resistivity curve type is also evaluated from the consecutive subsurface layers' resistivities. These can be helpful in groundwater potential zone identification studies. The entire dataset from this research can be useful in groundwater exploration, management, economic mineral exploration, waste disposal sites, reservoir, and dam site selections, and identifying the structural controls such as fractures, joints, buried anticlines, etc., The data also can be coupled with other regional geological and geophysical datasets for many natural resource exploration and exploitation studies.

Delineation of groundwater potential zonation using geoinformatics and AHP techniques with remote sensing data.

Among all other valuable natural resources, groundwater is crucial for global economic growth and food security. This study aimed to delineate groundwater potential zones (GWPZ) in the Gidabo watershed of the Main Ethiopian Rift. The demand for groundwater supplies for various applications has risen recently in the watershed due to rapid population upsurge. An integrated Geographical Information System, Remote Sensing, and Analytical Hierarchy Process (AHP) has been utilized. Eight groundwater regulating factors, including rainfall, elevation, drainage density, soil types, lineament density, slope, lithology, and land use/land cover, have been taken in the analysis. To assign suitable weights to each factor, AHP was employed, as each element contributes differently to groundwater occurrence. The weighted overlay analysis (WOA) technique was then used in the ArcGIS environment to integrate all thematic layers and generate a GWPZ map. The delineated GWPZ in the watershed was classified into five categories. The poor GWPZ covered 18.7 %, the low GWPZ covered 33.8 %, the moderate GWPZ covered 23.4 %, the high GWPZ covered 18.1 %, and the very high GWPZ covered 5.8 % of the area. Well and spring data were used to validate the model, and the ROC (Receiver Operating Characteristic) curve method was applied. The results showed good accuracy of 76.8 %. The result of this research can be valuable for planning and managing groundwater resources in the Gidabo watershed.

Societal knowledge, attitude, and practices towards dengue and associated factors in epidemic-hit areas: Geoinformation assisted empirical evidence.

Dengue is one of Pakistan's major health concerns. In this study, we aimed to advance our understanding of the levels of knowledge, attitudes, and practices (KAPs) in Pakistan's Dengue Fever (DF) hotspots. Initially, at-risk communities were systematically identified via a well-known spatial modeling technique, named, Kernel Density Estimation, which was later targeted for a household-based cross-sectional survey of KAPs. To collect data on sociodemographic and KAPs, random sampling was utilized (n = 385, 5 % margin of error). Later, the association of different demographics (characteristics), knowledge, and attitude factors-potentially related to poor preventive practices was assessed using bivariate (individual) and multivariable (model) logistic regression analyses. Most respondents (>90 %) identified fever as a sign of DF; headache (73.8 %), joint pain (64.4 %), muscular pain (50.9 %), pain behind the eyes (41.8 %), bleeding (34.3 %), and skin rash (36.1 %) were identified relatively less. Regression results showed significant associations of poor knowledge/attitude with poor preventive practices; dengue vector (odds ratio [OR] = 3.733, 95 % confidence interval [CI ] = 2.377-5.861; P < 0.001), DF symptoms (OR = 3.088, 95 % CI = 1.949-4.894; P < 0.001), dengue transmission (OR = 1.933, 95 % CI = 1.265-2.956; P = 0.002), and attitude (OR = 3.813, 95 % CI = 1.548-9.395; P = 0.004). Moreover, education level was stronger in bivariate analysis and the strongest independent factor of poor preventive practices in multivariable analysis (illiterate: adjusted OR = 6.833, 95 % CI = 2.979-15.672; P < 0.001) and primary education (adjusted OR = 4.046, 95 % CI = 1.997-8.199; P < 0.001). This situation highlights knowledge gaps within urban communities, particularly in understanding dengue transmission and signs/symptoms. The level of education in urban communities also plays a substantial role in dengue control, as observed in this study, where poor preventive practices were more prevalent among illiterate and less educated respondents.

Enhancing groundwater vulnerability assessment: Comparative study of three machine learning models and five classification schemes for Cuddalore district.

Most of the groundwater vulnerability assessment methods using machine learning are binary classification. This study attempts multi-class classification models to map the groundwater vulnerability against Nitrate contamination. Further, the significance of the number of classes used in the multi-class classification is studied by considering three and five classes. Three machine learning models, namely Random Forest, Extreme Gradient Boosting and CART, with two classification schemes, were developed for the present study. The parameters used in the conventional DRASTIC method and with an additional parameter, Landuse, have been employed for the study. Evaluation metrics such as Accuracy, Kappa, Positive Predictive Value, Negative Predictive Value, and Area Under the Curve of the Receiver Operating Characteristic (AUC-ROC) were compared among all six models to select the optimal one. Based on the model evaluation metrics and consistent distribution of area among the classes Random Forest model with a three-class classification with an AUC of 0.95 is considered optimum for the selected objective. This study highlights the importance of the data classification process and the selection of the number of classes for ML model prediction in assessing groundwater vulnerability. Leveraging the effectiveness of the Geographic Information system and advanced machine learning techniques, the proposed approach offers valuable insights for enhanced groundwater management and contamination mitigation strategies.

Using Sentinel-2 data to estimate the concentration of heavy metals caused by industrial activities in Ust-Kamenogorsk, Northeastern Kazakhstan.

This study aims to investigate the change in heavy metal concentration and evaluate pollution intensity using Sentinel-2 data. Sixty samples collected from the surface soil in the area were used to determine the concentration of lead, copper, and zinc using atomic absorption spectroscopy. Then, the step-by-step regression method was used in ArcGIS software to determine the relationship between the concentration of heavy metals and the ranking of the influential spectral bands of Sentinel-2 to monitor heavy metals in the relevant sampling points. According to the results, lead monitoring was effective through the blue channel, the ratio of green to near infrared-IV channels, and the ratio of short-wave infrared-III to near infrared-II channels. At the same time, copper was monitored through reflectance values in the red channel, the ratios of green to near infrared-IV channels, and the ratio of short-wave infrared-III to near infrared-II channels. The blue channel and the ratio of green to near infrared-IV channels the ratio of near infrared-II to near infrared-IV channels were efficient for zinc monitoring. Pollution Load Indices (PLI) and Geographical Accumulation Index (Igeo) were calculated to classify the contaminated soils of the region. The efficiency of each relationship obtained was evaluated using the root mean square error (RMSE) and Pearson's correlation coefficient (R). In summary, the copper, lead, and zinc equations had RMSE values of 1.8, 2.5, and 1.60 mg/kg, respectively. The Pearson correlation coefficients (R) for copper, lead, and zinc were 0.80, 0.76, and 0.72, respectively, which indicated good agreement between measured and estimated values.

Hydrochemistry and quality appraisal of groundwater in Birr River Catchment, Central Blue Nile River Basin, using multivariate techniques and water quality indices.

Due to the continuous population growth and the expansion of industry and agriculture in Ethiopia, groundwater demand has been increasing to supplement the erratic surface water. Therefore, the availability of sufficient and clean groundwater should be appraised and tracked regularly to secure its multi-purposes. This work aims to assess the appropriateness of groundwater for drinking, and irrigation uses and identifies the key factors controlling groundwater quality in the Birr River Catchment (BRC), Blue Nile River Basin, Ethiopia. For this purpose, a total of 79 groundwater samples were assessed for physicochemical parameters. Major ion analysis, multivariate techniques (MCA, HCA, and PCA), and multi-hydrochemical indices were applied in the analysis of groundwater quality data. Hydrochemical analysis indicated that the principal cation and anion were Ca2+ and HCO3-, respectively. The spatial analysis of the major ions revealed a positive trend for Mg2+, Na+, K+, HCO3-, Cl-, and SO42- along the groundwater flow path from the upland to the Birr river valley. Conversely, Ca2+ shows a deleterious tendency along the groundwater flow direction. The aquifer has three principal hydrochemical facies: Ca-HCO3, Ca/Mg-HCO3, and Na-HCO3. The water quality analysis indicates that with the exception of TDS, Ca2+, Mg2+, and HCO3- in a few locations, most of the parameters analyzed are within the WHO allowable limits and are thus considered suitable for drinking water. The combined use of Gibbs and ionic ratio plots confirmed that silicate weathering was invariably prevailing in the region. The Chloro-Alkali Indices (CAIs) have indicated that cation exchange occurs in more than 85% of groundwater samples. However, there were indications of the influence of reverse ion exchange in the rest of the data. The MCA, PCA, and HCA disclosed that geo-genic sources accompanied by human activities mainly control the groundwater quality of the catchment. However, water quality assessment indices show that groundwater in the highest proportion of the catchment is suitable for human consumption and agricultural use.

An integrated approach to explore the suitability of nitrate-contaminated groundwater for drinking purposes in a semiarid region of India.

The main objective of the present study is to perform risk assessment of groundwater contaminated by nitrate (NO3-) and evaluate the suitability of groundwater for domestic purposes in the Palani region of South India. Thirty groundwater samples were collected in the study area. Various groundwater quality analysis parameters such as the pH, electrical conductivity, total dissolved solids, total hardness, major cations (Ca2+, Mg2+, Na+, and K+), and major anions (Cl-, SO42-, F-, CO32-, and HCO3-) were adopted in this study to evaluate the drinking water suitability according to 2011 World Health Organization (WHO) standards. Piper and Gibbs's diagrams for the tested groundwater indicated that, due to the influence of rock-water interactions, evaporation, and reverse ion exchange, the chemical composition of groundwater varied. According to water quality index (WQI) mapping results, 46.67% of the sample locations was identified as contaminated zones via GIS spatial analysis. Multivariate statistical analysis methods, such as principal component analysis, cluster analysis, and the Pearson correlation matrix, were applied to better understand the relationship between water quality parameters. The results demonstrated that 40% of the samples could be identified as highly affected zones in the study region due to a high nitrate concentration. The noncarcinogenic health risks among men, women, and children reached 40, 50, and 53%, respectively. The results illustrated that children and women occurred at a higher risk than did men in the study region. The major sources of contamination included discharge from households, uncovered septic tanks, leachate from waste dump sites, and excess utilization of fertilizers in the agricultural sector. Furthermore, using the nitrate health hazard integrated method with the conventional indexing approach ensures that groundwater reliability can be guaranteed, contamination can be explored, and appropriate remedial measures can be implemented.

Evaluation of Vulnerability Status of the Infection Risk to COVID-19 Using Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA): A Case Study of Addis Ababa City, Ethiopia.

COVID-19 is a disease caused by a new coronavirus called SARS-CoV-2 and is an accidental global public health threat. Because of this, WHO declared the COVID-19 outbreak a pandemic. The pandemic is spreading unprecedently in Addis Ababa, which results in extraordinary logistical and management challenges in response to the novel coronavirus in the city. Thus, management strategies and resource allocation need to be vulnerability-oriented. Though various studies have been carried out on COVID-19, only a few studies have been conducted on vulnerability from a geospatial/location-based perspective but at a wider spatial resolution. This puts the results of those studies under question while their findings are projected to the finer spatial resolution. To overcome such problems, the integration of Geographic Information Systems (GIS) and Multi-Criteria Decision Analysis (MCDA) has been developed as a framework to evaluate and map the susceptibility status of the infection risk to COVID-19. To achieve the objective of the study, data like land use, population density, and distance from roads, hospitals, bus stations, the bank, markets, COVID-19 cases, health care units, and government offices are used. The weighted overlay method was used; to evaluate and map the susceptibility status of the infection risk to COVID-19. The result revealed that out of the total study area, 32.62% (169.91 km2) falls under the low vulnerable category (1), and the area covering 40.9% (213.04 km2) under the moderate vulnerable class (2) for infection risk of COVID-19. The highly vulnerable category (3) covers an area of 25.31% (132.85 km2), and the remaining 1.17% (6.12 km2) is under an extremely high vulnerable class (4). Thus, these priority areas could address pandemic control mechanisms like disinfection regularly. Health sector professionals, local authorities, the scientific community, and the general public will benefit from the study as a tool to better understand pandemic transmission centers and identify areas where more protective measures and response actions are needed at a finer spatial resolution.

SARS-CoV-2 phase I transmission and mutability linked to the interplay of climatic variables: a global observation on the pandemic spread.

The study aims to determine the impact of global meteorological parameters on SARS-COV-2, including population density and initiation of lockdown in twelve different countries. The daily trend of these parameters and COVID-19 variables from February 15th to April 25th, 2020, were considered. Asian countries show an increasing trend between infection rate and population density. A direct relationship between the time-lapse of the first infected case and the period of suspension of movement controls the transmissivity of COVID-19 in Asian countries. The increase in temperature has led to an increase in COVID-19 spread, while the decrease in humidity is consistent with the trend in daily deaths during the peak of the pandemic in European countries. Countries with 65°F temperature and 5 mm rainfall have a negative impact on COVID-19 spread. Lower oxygen availability in the atmosphere, fine droplets of submicron size together with infectious aerosols, and low wind speed have contributed to the increase in total cases and mortality in Germany and France. The onset of the D614G mutation and subsequent changes to D614 before March, later G614 in mid-March, and S943P, A831V, D839/Y/N/E in April were observed in Asian and European countries. The results of the correlation and factor analysis show that the COVID-19 cases and the climatic factors are significantly correlated with each other. The optimum meteorological conditions for the prevalence of G614 were identified. It was observed that the complex interaction of global meteorological factors and changes in the mutational form of CoV-2 phase I influenced the daily mortality rate along with other comorbid factors. The results of this study could help the public and policymakers to create awareness of the COVID-19 pandemic.

Determination of vulnerable regions of SARS-CoV-2 in Malaysia using meteorology and air quality data.

This study aims to explore the state-wise assessment of SARS-CoV-2 (COVID-19) pandemic spread in Malaysia with focus on influence of meteorological parameters and air quality. In this study, state-wise COVID-19 data, meteorological parameters and air quality index (AQI) were collected from March 13 to April 30, 2020, which encompass three movement control order (MCO) periods in the country. Overall, total infected cases were observed to be higher in MCO phase 1 and 2 and significantly reduced in MCO phase 3. Due to the variation in the spatial interval of population density and individual immunity, the relationship of these parameters to pandemic spread could not be achieved. The study infers that temperature (T) between 23 and 25 °C and relative humidity (RH) (70-80%) triggered the pandemic spread by increase in the infected cases in northern and central Peninsular Malaysia. Selangor, WP Kuala Lumpur and WP Putrajaya show significantly high infected cases and a definite trend was not observed with respect to a particular meteorological factor. It is identified that high precipitation (PPT), RH and good air quality have reduced the spread in East Malaysia. A negative correlation of T and AQI and positive correlation of RH with total infected cases were found during MCO phase 3. Principal component analysis (PCA) indicated that T, RH, PPT, dew point (DP) and AQI are the main controlling factors for the spread across the country apart from social distancing. Vulnerability zones were identified based on the spatial analysis of T, RH, PPT and AQI with reference to total infected cases. Based on time series analysis, it was determined that higher RH and T in Peninsular Malaysia and high amount of PPT, RH and good air quality in East Malaysia have controlled the spreading during MCO phase 3. The predominance of D614 mutant was observed prior to March and decreases at the end of March, coinciding with the fluctuation of meteorological factors and air quality. The outcome of this study gives a general awareness to the public on COVID-19 and the influence of meteorological factors. It will also help the policymakers to enhance the management plans against the pandemic spreading apart from social distancing in the next wave of COVID-19. Supplementary Information: The online version contains supplementary material available at 10.1007/s10668-021-01719-z.

Geochemical evaluation and human health risk assessment of nitrate-contaminated groundwater in an industrial area of South India.

The primary goal of this study is to evaluate the groundwater quality and conduct a non-carcinogenic risk assessment of nitrate contamination in an industrialized and high-density region of South India. A total of 40 sampling sites were identified in and around the industrial area, and samples were collected during the pre-monsoon and post-monsoon seasons. Piper and Gibbs' diagram shows that rock-water interaction, lithological characteristics and ion-exchange processes are the primary factors determining groundwater quality. The novel entropy water quality index (EWQI) indicated that 32 and 37.5% of the water in the study area were unsuitable for drinking purposes during both the pre-monsoon and post-monsoon seasons, respectively. Due to landfill leachate and modern agricultural activity, the nitrate concentration in groundwater post-monsoon had increased by 17.11%. The nitrate pollution index (NPI) value of groundwater exceeded the contaminated level by 22.77%. The non-carcinogenic human health risk assessment revealed that 35 and 40% of adult males, 37.5 and 52.5% of adult females and 42.5 and 55% of children during the pre-monsoon and post-monsoon periods were exposed to an increased concentration of nitrate in groundwater. The non-carcinogenic risk level to the exposed population in the study region descends in the following order: children > > females > males. The study suggests that low body weight in children is a direct result of consumption of low-quality water and that adult men and women suffer less severe consequences.

Isotopic signatures, hydrochemical and multivariate statistical analysis of seawater intrusion in the coastal aquifers of Chennai and Tiruvallur District, Tamil Nadu, India.

In coastal aquifers, seawater intrusion is a significant groundwater issue. The research paper contributes to the understanding of the consequences of seawater intrusion in the Chennai coastal aquifer from Foreshore Estate to Thirunilai along the coastline. 110 groundwater samples were collected and analyzed for physicochemical parameters such as pH, (EC), (TDS), (TH), major anions (Cl-, NO3-, HCO32-, and SO42-), and cations (Ca2+, Mg2+, Na+, and K+) during the pre-monsoon (June 2014) and post-monsoon (January 2015) seasons. Stable isotopic analyses of 18O were performed on 24 groundwater samples collected from various locations throughout the research region based on EC, TDS, Na, and Cl- concentrations for both seasons. The stable isotopic composition of 18O and Deuterium in groundwater samples was determined for the study region. According to the Correlation matrix and Factor analysis, the main contributors to groundwater salinity as a result of seawater intrusion into the coastal aquifer are EC, TDS, Na+, and Cl-. GMWL exhibits a similar pattern, and the samples have been classified into various molar ratio diagrams to identify seawater intrusions for better evaluation. The result revealed that seasonal, geogenic, and anthropogenic factors always make a significant contribution to the heterogeneous chemistry of groundwater.

Assessment of seasonal variation in distribution and abundance of plankton and ichthyofaunal diversity in relation to environmental indices of Karankadu Mangrove, South East Coast of India.

Karankadu mangrove situated along the Southeast coast of India is known for its unique and extreme species diversity and richness. Ecological functions of this mangrove comprise of nutrient cycling, coastal protection, fish fauna production and carbon sequestration besides providing livelihood to nearby coastal communities. The current study having assessed seasonal fluctuations of various Physico-chemical factors viz., rainfall, pH, surface water temperature, salinity, dissolved nutrients in the water and sediments, primary productivity and plant pigments (chlorophylls a,b,c) during the study period from July 2018 to June 2019, recorded a total of 29 species of zooplankton, 26 species of phytoplankton, and 19 species of fish with observed maximum density at summer and pre-monsoon period of the year. Relationship between the biotic and abiotic components of the ecosystem from the baseline data collected, was established through the statistical analysis performed.

Potential Human Health Risks Due to Groundwater Fluoride Contamination: A Case Study Using Multi-techniques Approaches (GWQI, FPI, GIS, HHRA) in Bilate River Basin of Southern Main Ethiopian Rift, Ethiopia.

The main focus of the present research was to examine the appropriateness of groundwater resources for drinking purposes in the Bilate River Basin of Southern Main Ethiopian Rift, Ethiopia. The groundwater quality index (GWQI), fluoride pollution index (FPI), and human health risk were used to examine the human health risk factors associated with the intake of high fluoride groundwater. For this purpose, 29 groundwater samples were collected from the existing wells and were analyzed for various physicochemical parameters. The dominant cation was Na+, followed by Ca2+, Mg2+, and K+. The dominant anion was HCO3-, followed by Cl-, SO42-, and F-. The Gibbs plot shows that rock-water interactions are the dominant factor controlling the groundwater chemistry. By using the GWQI, the quality of groundwater samples was 31% excellent, 21% good, 31% poor, and 17% very poor. The fluoride concentration in groundwater ranges from 0.2 to 5.60 mg/L (mean, 2.10 mg/L). 59% (i.e., 17 wells) of the groundwater samples were not suitable for drinking, because they surpassed the drinking water quality limit of 1.5 mg/L. The remaining 41% (i.e., 12 wells) of the samples were suitable for drinking. The FPI indicates that 51.72% of the wells were highly polluted by fluoride. The noncarcinogenic health risk varies from 0.75 to 8.44 for children (83%), 0.34-3.84 for women (62%), and 0.27-3.01 for men (52%), which indicates that children are at higher health risk than women and men due to the physiological condition and the rates of ingestion.

Occurrence of Heavy Metals in Groundwater Along the Lithological Interface of K/T Boundary, Peninsular India: A Special Focus on Source, Geochemical Mobility and Health Risk.

Evaluation of the hydrogeochemical processes governing the heavy metal distribution and the associated health risk is important in managing and protecting the health of freshwater resources. This study mainly focused on the health impacts due to the heavy metals pollution in a known Cretaceous-Tertiary (K/T) contact region (Tiruchinopoly, Tamilnadu) of peninsular India, using various pollution indices, statistical, and geochemical analyses. A total of 63 samples were collected from the hard rock aquifers and sedimentary formations during southwest monsoon and analysed for heavy metals, such as Li, Be, Al, Rb, Sr, Cs, Ba, pb, Mn, Fe, Cr, Zn, Ga, Cu, As, Ni, and Co. Ba was the dominant element that ranged from 441 to 42,638 µg/l in hard rock aquifers, whereas Zn was the major element in sedimentary formations, with concentrations that ranged from 44 to 118,281 µg/l. The concentrations of Fe, Ni, Cr, Al, Cr, and Ni fell above the permissible limit in both of the formations. However, the calculated heavy metal evaluation index (HEI), heavy metal pollution index (HPI), and the degree of contamination (Cd) parameters were higher in the sedimentary formation along the contact zone of the K/T boundary. Excessive health risks from consumption of contaminated groundwater were mostly confined to populations in the northern and southwestern regions of the study area. Carcinogenic risk assessment suggests that there are elevated risks of cancer due to prolonged consumption of untreated groundwater. Ba, Sr, and Zn were found to be geochemically highly mobile due to the partitioning between the rock matrix and groundwater, aided by the formation of soluble carbonato-complexes. Factor analysis indicates that the metals are mainly derived from the host rocks and anthropogenic inputs are relatively insignificant. Overall, this study indicated that groundwater in K/T contact zones is vulnerable to contamination because of the favorable geochemical factors. Long-term monitoring of such contact zones is required to avert the potential health hazards associated with consumption of the contaminated groundwater.

Air pollution improvement and mortality rate during COVID-19 pandemic in India: global intersectional study.

This research was carried out using the open-source database system along with the continuous air quality monitoring station results from global data sets during the COVID-19 pandemic lockdown in India and the global. Our purpose of this research is to study the improvement of air quality and human mortality rates in countries worldwide during the COVID-19 pandemic lockdown. Worldwide air quality data were collected from > 12,000 continuous air quality monitoring stations on six continents covering 1000 major cities from over 100 countries. Here, we discussed the implementation of the open-source data set of basic air pollutants such as PM 2.5, NO2, temperature, relative humidity, and Air Quality Index variation during the pre-lockdown and lockdown pandemic COVID-19 in India and described the global aspect. An average concentration of PM 2.5 (145.51 µg/m3), NO2 (21.64 µg/m3), and AQI index (55.58) continuously decreased. The variation of PM 2.5, NO2, normally shows more than 25 µg/m3 every year, but during the COVID-19 lockdown period (April 2020) continuously decreased below 20 µg/m3. Similarly, the AQI index and meteorological factors such as temperature, relative humidity, and wind speed variation decreased significantly in the many countries in the world. In Asian countries, air quality improved during the national lockdown especially in the most polluted cities globally such as Beijing, Delhi, and Nanjing and also in developed cities like Madrid, New York, Paris, Seoul, Sydney, Tokyo. Furthermore, the reduction of particulate matter was in about 46%, and other gaseous pollutants during the lockdown period were observed in a 54% reduction. We are witnessing pollution reductions which add significantly to improvements in air quality. This is due to the massive decrease in the use of fossil fuel, which in turn reduces production and traffic in general. People nowadays are now willing to see a comparatively healthier world with bleached skies and natural ecosystems. This research finding demonstrates potential safety benefits associated with improving air quality and mortality rates during the COVID-19 pandemic, resulting in decreases in mortality rates in India and around the world.

Application of Geospatial Technologies in the COVID-19 Fight of Ghana.

The SARS-CoV-2 infections continue unabated in Ghana and globally. The identification of country dynamics of the virus, its spread, and country-specific interventions in tackling the menace including the application of geospatial technologies. This research sought to highlight the use of geospatial technologies in the fight against COVID-19 in Ghana with best practices from China where the infections originated from; present the trends in Ghana and model near future trends of the virus. It was evident that just as other places, Ghana has employed geospatial technologies and continues to ply unchartered paths in solutions. The trend in Ghana is in line with a population concentration and tends to record higher figures in the southern parts. It is modeled that through incessant mobility patterns, infections will spread through to the middle parts and then the northern parts. The research, therefore, recommends the use of infrared scanners to augment testing practices and enhanced tracing of infected persons as well as the use of drones for the distribution of essential services.