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A significant rural-urban disparity in unsafe child stool disposal practices exists in India, yet existing research falls short in identifying the contributing factors to this gap. This study addresses the research gap by contextualizing the rural-urban divide in unsafe child stool disposal using data from the fifth round of the National Family Health Survey (NFHS-5, 2019-21). In particular, the study examines the prevalence and predictors of unsafe disposal practices, exploring associated contributing factors to this gap. The study involves a sample of 78,074 women aged 15-49 with a living child under 2 years, without any missing data related to the study interest. Employing descriptive statistics, the Pearson chi-square test, multilevel logistic regression, and the Fairlie decomposition model, the research aims to fulfill its objectives. The rural-urban gap in unsafe child stool disposal practices among the study participants was 22.3 percentage points (pp), with a more pronounced gap among the Scheduled Tribes (ST). Notably, the gap was particularly wide in Madhya Pradesh (33.9 pp), Telangana (27.5 pp), Gujarat (26.1 pp), and Rajasthan (25.8 pp). Predictors such as mother's education, mass media exposure, household wealth quintile, and sanitation facilities proved significant irrespective of residence. However, religion, social group, and water facility on household premises emerged as significant factors in rural areas only. The study identified that 67% of the explained gap in unsafe child stool disposal practices was attributed to the rural-urban difference in household wealth. Other noteworthy contributors were 'household sanitation facility' (21.3%), 'mother's education level' (3.9%), and 'water facility on household premises' (3.9%). These findings underscore the need for population and area-specific policy interventions, especially for individuals from socio-economically disadvantaged backgrounds, those with lower education levels, and limited exposure to mass media, particularly in states with a high prevalence of unsafe disposal practices. Such interventions are crucial to mitigating the existing rural-urban gap in unsafe child stool disposal practices., (© 2024. The Author(s).)

Sugarcane crop is irrigated using surface, overhead, and drip irrigation methods. Increased water use in sugarcane is a major concern around the world, implying the need for water accounting, developing water-efficient hybrids and water-saving agro-techniques for long-term conservation and use of water. "Water Footprint (WF)" is a measure of both direct and indirect water usage accountable for any product and/or process. In praxis, 'Green Water Footprint' (GWF) and 'Blue Water Footprint' (BWF) are extremely crucial for the restoration of essential ecosystem services (ES), such as sugarcane production. The WF metric was used as a priority tool in our study to evaluate water-efficient sugarcane hybrids, germplasm clones, deficit irrigation scheduling, crop geometry, and water conservation measures. Precise and accurate WF quantification would supplement the decision-making processes for managing available water resources in sugarcane agriculture. In split plot experimental design two research investigations on water management in sugarcane were undertaken at the ICAR-Sugarcane Breeding Institute, Coimbatore, Tamil Nadu, India. The major objective of the research trails was to find out suitable sugarcane hybrids and agronomic management practices to minimise water usage in sugarcane cultivation in water stressed and drought prone areas of tropical India. Our investigation comprised two phases; the first one being assessment of the impact of deficit irrigation scheduling, planting techniques and water conservation measures in sugarcane production, while the second phase dealt with genotypic evaluation under variable irrigation scheduling. Results showed that BWF reduced significantly in the first ratoon crop due to deficit irrigation scheduling coupled with planting of two budded setts and application of sugarcane trash at the rate of 5 t ha -1 . Sugarcane hybrids viz., Co 85019, Co 10026, Co 12009, Co 13014, Co 14002, Co 14025, Co 15015, and Co 15018 were more water efficient, with a lower total WF. Among the germplasm clones, Fiji 55, ISH 111, ISH 107, Pathri, and Gungera exhibited lower GWF, BWF and total WF., (© 2023. Springer Nature Limited.)

The Early Jurassic and Cretaceous deposits of India are known for their diverse sauropod fauna, while little is known from the Middle and Late Jurassic. Here we report the first ever remains of a dicraeosaurid sauropod from India, Tharosaurus indicus gen. et sp. nov., from the Middle Jurassic (early-middle Bathonian) strata of Jaisalmer Basin, western India. Known from elements of the axial skeleton, the new taxon is phylogenetically among the earlier-diverging dicraeosaurids, and its stratigraphic age makes it the earliest known diplodocoid globally. Palaeobiogeographic considerations of Tharosaurus, seen in conjunction with the other Indian Jurassic sauropods, suggest that the new Indian taxon is a relic of a lineage that originated in India and underwent rapid dispersal across the rest of Pangaea. Here we emphasize the importance of Gondwanan India in tracing the origin and early evolutionary history of neosauropod dinosaurs., (© 2023. Springer Nature Limited.)

Whilst there is growing evidence on the increased vulnerability of older adults to depression, there is limited research on potentially mitigative factors against symptoms of depression at a population level. This research examined associations of possible protective factors (personal efforts and beliefs) and depressive symptoms among older adults in India. This cross-sectional study used data from the Longitudinal Aging Study in India with 31,464 respondents aged 60 years and above. Depressive symptoms were assessed using the 10-item Centre for Epidemiologic Studies Depression Scale. Multivariable linear regression was used while exploring the associated factors of depressive symptoms. The mean score of depressive symptoms was 2.94 (CI 2.92, 2.96). Older adults who engaged in moderate [aCoef: -0.11, CI -0.18, -0.05], vigorous [aCoef: -0.09, CI -0.16, -0.03], or both types of physical activity [aCoef: -0.10, CI -0.19, -0.02] had lower likelihood of depressive symptoms in comparison to those who were physically inactive. Older adults who participated in social activities were less likely to have depressive symptoms [aCoef: -0.44, CI -0.50, -0.39] compared to their socially inactive counterparts. Further, older adults who perceived religion as very important [aCoef: -0.29, CI -0.41, -0.17], who had high life satisfaction [aCoef: -0.78, CI -0.82, -0.73], who had good self-perceived health [aCoef: -0.29, CI -0.33, -0.25] and those who had high self-perceived social standing [aCoef: -0.39, CI -0.47, -0.31] had lower likelihood of depressive symptoms in comparison to their respective counterparts. Physical activity, social participation, voluntary work and financial contribution to family, religiosity, life satisfaction, self-perceived health and self-perceived social standing are associated with lower likelihood of depressive symptoms among community-dwelling older adults in this study. Future longitudinal studies should explore these factors that can guide interventions against depression in old age., (© 2022. The Author(s).)

An increased nitrate (NO 3 - ) concentration in groundwater has been a rising issue on a global scale in recent years. Different consumption mechanisms clearly illustrate the adverse effects on human health. The goal of this present study is to assess the natural and anthropogenic NO 3 - concentrations in groundwater in a semi arid area of Rajasthan and its related risks to human health in the different groups of ages such as children, males, and females. We have found that most of the samples (n = 90) were influenced by anthropogenic activities. The background level of NO 3 - had been estimated as 7.2 mg/L using a probabilistic approach. About 93% of nitrate samples exceeded the background limit, while 28% of the samples were beyond the permissible limit of 45 mg/L as per the BIS limits. The results show that the oral exposure of nitrate was very high as compare to dermal contact. With regard to the non-carcinogenic health risk, the total Hazard Index (HI Total ) values of groundwater nitrate were an average of 0.895 for males, 1.058 for females, and 1.214 for children. The nitrate health risk assessment shows that about 38%, 46%, and 49% of the samples constitute the non-carcinogenic health risk to males, females, and children, respectively. Children were found to be more prone to health risks due to the potential exposure to groundwater nitrate.

We studied neurodevelopmental outcomes and behaviours in healthy 2-year old children (N = 1306) from Brazil, India, Italy, Kenya and the UK participating in the INTERGROWTH-21 st Project. There was a positive independent relationship of duration of exclusive breastfeeding (EBF) and age at weaning with gross motor development, vision and autonomic physical activities, most evident if children were exclusively breastfed for ≥7 months or weaned at ≥7 months. There was no association with cognition, language or behaviour. Children exclusively breastfed from birth to <5 months or weaned at >6 months had, in a dose-effect pattern, adjusting for confounding factors, higher scores for "emotional reactivity". The positive effect of EBF and age at weaning on gross motor, running and climbing scores was strongest among children with the highest scores in maternal closeness proxy indicators. EBF, late weaning and maternal closeness, associated with advanced motor and vision maturation, independently influence autonomous behaviours in healthy children.

The Indian cheetah was hunted to extinction by the mid-20th century. While analysis of 139 bp of mitochondrial DNA (mtDNA) has confirmed that the Indian cheetah was part of the Asiatic subspecies (Acinonyx jubatus venaticus), the detailed relationships between cheetah populations remains unclear due to limited genetic data. We clarify these relationships by studying larger fragments of cheetah mtDNA, both from an Indian cheetah museum specimen and two African cheetah, one modern and one historic, imported into India at different times. Our results suggest that the most recent common ancestor of cheetah mtDNA is approximately twice as ancient as currently recognised. The Indian and Southeast African (Acinonyx jubatus jubatus) cheetah mtDNA diverged approximately 72 kya, while the Southeast and Northeast African (Acinonyx jubatus soemmeringii) cheetah mtDNA diverged around 139 kya. Additionally, the historic African cheetah sampled from India proved to have an A. j. jubatus haplotype, suggesting a hitherto unrecognised South African route of cheetah importation into India in the 19 th century. Together, our results provide a deeper understanding of the relationships between cheetah subspecies, and have important implications for the conservation of A. j. venaticus and potential reintroduction of cheetahs into India.

In this paper, NeuralProphet (NP), an explainable hybrid modular framework, enhances the forecasting performance of pandemics by adding two neural network modules; auto-regressor (AR) and lagged-regressor (LR). An advanced deep auto-regressor neural network (Deep-AR-Net) model is employed to implement these two modules. The enhanced NP is optimized via AdamW and Huber loss function to perform multivariate multi-step forecasting contrast to Prophet. The models are validated with COVID-19 time-series datasets. The NP's efficiency is studied component-wise for a long-term forecast for India and an overall reduction of 60.36% and individually 34.7% by AR-module, 53.4% by LR-module in MASE compared to Prophet. The Deep-AR-Net model reduces the forecasting error of NP for all five countries, on average, by 49.21% and 46.07% for short-and-long-term, respectively. The visualizations confirm that forecasting curves are closer to the actual cases but significantly different from Prophet. Hence, it can develop a real-time decision-making system for highly infectious diseases., (© 2024. The Author(s).)

Healthcare systems worldwide are grappling with the challenge of providing high-quality healthcare in the face of evolving disease patterns. India, like many other countries, faces a significant treatment gap for various curable impairments, non-communicable diseases (NCDs), and cardiovascular diseases (CVDs). To address their healthcare needs, individuals often relocate in search of better treatment options. However, no studies were conducted to understand the spatial mobility. This paper explores the determinants of spatial mobility for treatment in India using data from NSS 75th round (2017-2018). A total of 64,779 individual medical cases of different diseases were taken into consideration for our analysis. Fixed effect and multinomial regression models were used to understand diseases specific mobility for treatment. It was found that those with CVDs, NCDs, and disabilities are more prone to travel outside their district for medical care. Rural and economically disadvantaged individuals also tend to travel further for treatment. The key factors impacting treatment-seeking mobility include insurance coverage, hospital quality, cost of medicine, and cost of X-rays/surgeries. The study highlights the need for improved policies to address the gap between healthcare needs and infrastructure in India, with a focus on prioritizing the development of local healthcare facilities for disabilities, NCDs, and CVDs., (© 2024. The Author(s).)

Pregnancy-associated anemia is a significant health issue that poses negative consequences for both the mother and the developing fetus. This study explores the triggering factors of anemia among pregnant females in India, utilizing data from the Demographic and Health Survey 2019-21. Chi-squared and gamma tests were conducted to find out the relationship between anemia and various socioeconomic and sociodemographic elements. Furthermore, ordinal logistic regression and multinomial logistic regression were used to gain deeper insight into the factors that affect anemia among pregnant women in India. According to these findings, anemia affects about 50% of pregnant women in India. Anemia is significantly associated with various factors such as geographical location, level of education, and wealth index. The results of our study indicate that enhancing education and socioeconomic status may serve as viable approaches for mitigating the prevalence of anemia disease developed in pregnant females in India. Employing both Ordinal and Multinominal logistic regression provides a more comprehensive understanding of the risk factors associated with anemia, enabling the development of targeted interventions to prevent and manage this health condition. This paper aims to enhance the efficacy of anemia prevention and management strategies for pregnant women in India by offering an in-depth understanding of the causative factors of anemia., (© 2023. The Author(s).)

The Air Quality Index (AQI) in India is steadily deteriorating, leading to a rise in the mortality rate due to Lung Cancer. This decline in air quality can be attributed to various factors such as PM 2.5, PM 10, and Ozone (O3). To establish a relationship between AQI and Lung Cancer, several predictive models including Linear Regression, KNN, Decision Tree, ANN, Random Forest Regression, and XGBoost Regression were employed to estimate pollutant levels and Air Quality Index in India. The models relied on publicly available state-wise Air Pollution Dataset. Among all the models, the XGBoost Regression displayed the highest accuracy, with pollutant level estimations reaching an accuracy range of 81% to 98% during training and testing. The second-highest accuracy range was achieved by Random Forest. The paper also explores the impact of increasing pollution levels on the rising mortality rate among lung cancer patients in India., (© 2023. The Author(s).)

A diet high in sodium contributes to a significant proportion of Disability Adjusted Life Years (DALYs) due to cardiovascular diseases. This paper describes the awareness, behaviour and determinants related to dietary salt intake in an adult population of 18-69 years that were assessed as part of the National NCD Monitoring Survey (NNMS) in India. A sub-sample of 3000 adults selected through simple random sampling from 150 nationally representative Primary Sampling Units (PSUs) was included. Data regarding awareness and behaviour related to dietary salt intake were collected. Urinary sodium excretion in spot urine samples was estimated and used to calculate dietary salt intake. The dietary salt intake's sociodemographic, behavioural and metabolic determinants were also analysed. Less than one-third of the adults of both genders in all age groups in rural and urban areas were aware that daily high salt intake could affect health. The estimated mean daily salt intake was 8.0 g (8.9 g/day for men and 7.1 g/day for women). The salt intake was significantly higher in men [Adjusted OR = 17.66 (5.24-59.46)], rural areas [Adjusted OR = 6.14 (1.83-20.60)], overweight and obese respondents [Adjusted OR = 17.62 (3.17-98.07)]. The perception of the harmful effects of high salt intake and practices to limit salt intake was low in the study population. The mean daily salt intake was higher than the WHO recommendation of up to 5 g daily. The mean dietary salt intake is high in the Indian population, which calls for planning and implementing control of dietary salt consumption measures., (© 2023. Springer Nature Limited.)

This paper investigates the role of nuclear energy in promoting ecological sustainability in India, focusing on three ecological indicators: ecological footprint (EF), CO2 emissions (CO 2 ), and load capacity factor (LF). In addition to nuclear energy, the study considers the influence of gas consumption and other drivers of ecological sustainability using data spanning from 1970 to 2018. The analysis also takes into account the impact of the 2008 global financial crisis on the model, employing the autoregressive distributed lag (ARDL) and frequency domain causality approaches to assess the relationships. Unlike previous studies, this research evaluates both the Environmental Kuznets Curve (EKC) and load capacity curve (LCC) hypotheses. The ARDL results support the validity of both the EKC and LCC hypotheses in the Indian context. Furthermore, the findings reveal that nuclear energy and human capital contribute positively to ecological quality, while gas consumption and economic growth have a negative impact on ecological sustainability. The study also highlights the increasing effect of the 2008 global financial crisis on ecological sustainability. Additionally, the causality analysis demonstrates that nuclear energy, human capital, gas consumption, and economic growth can serve as predictors of long-term ecological sustainability in India. Based on these findings, the research presents policy recommendations that can guide efforts towards achieving SDGs 7 and 13., (© 2023. The Author(s).)

For countries with high maternal mortality and morbidity, on-time initiation of antenatal care (ANC) is indispensable. Therefore this paper aims for studying the median survival time (MST) of first ANC among pregnant women as well as understanding the contextual factors that influence a mother's decision to access ANC services in India. The study used cross-sectional survey data obtained from the NFHS-4 conducted in 2015-2016. The MST of the timing of the first ANC visit was estimated using the Kaplan-Meir estimate. A multivariate Cox-proportional hazard regression model was used to identify the factors related to the timing of the first ANC visit with a 95% confidence interval (CI). Overall at least one ANC checkup was assessed by 60.15% of women and the median survival time for the first ANC checkup was found to be 4 months. Early initiation of ANC in pregnant women increased by 37% (AHR: 1.37, CI:1.34-1.39) for primary education, and 88% (AHR:1.88, CI:1.86-1.90) for secondary education compared to women having no formal education. Results of the current study revealed that the median survival time of the first ANC visit was 4 months in India which is delayed compared to recommendations of WHO. Therefore boosting the access and utilization of antenatal care coverage among pregnant women can ensure the best health outcomes for their pregnancy., (© 2023. The Author(s).)

Natural disasters always have a damaging effect on our way of life. Landslides  cause serious damage to both human and natural resources around the world. In this paper, the prediction accuracy of five hybrid models for landslide occurrence in the Uttarkashi, Uttarakhand (India) was evaluated and compared. In this approach, the Rough Set theory coupled with five different models namely Bayesian Network (HBNRS), Backpropagation Neural Network (HBPNNRS), Bagging (HBRS), XGBoost (HXGBRS), and Random Forest (HRFRS) were taken into account. The database for the models development was prepared using fifteen conditioning factors that had 373 landslide and 181 non-landslide locations that were then randomly divided into training and testing locations with a ratio of 75%:25%. The appropriateness and predictability of these conditioning factors were assessed using the multi-collinearity test and the least absolute shrinkage and selection operator approach. The accuracy, sensitivity, specificity, precision, and F-Measures, and the area under the curve (AUC)-receiver operating characteristics curve, were used to evaluate and compare the performance of the individual and hybrid created models. The findings indicate that the constructed hybrid model HXGBRS (AUC = 0.937, Precision = 0.946, F1-score = 0.926 and Accuracy = 89.92%) is the most accurate model for predicting landslides when compared to other models (HBPNNRS, HBNRS, HBRS, and HRFRS). Importantly, when the fusion is performed with the rough set method, the prediction capability of each model is improved. Simultaneously, the HXGBRS model proposed shows superior stability and can effectively avoid overfitting. After the core modules were developed, the user-friendly platform was designed as an integrated GIS environment using dynamic maps for effective landslide prediction in large prone areas. Users can predict the probability of landslide occurrence for selected region by changing the values of a conditioning factors. The created approach could be beneficial for predicting the impact of landslides on slopes and tracking landslides along national routes., (© 2022. The Author(s).)

The present paper utilizes entropy theory and Google earth engine cloud computing technique to investigate system state and river recovery potential in two large sub-basins of the Mahanadi River, India. The cross-sectional intensity entropy (CIE) is computed for the post-monsoon season (October-March) along the selected reaches. Further, a normalized river recovery indicator (NRRI) is formulated to assess the temporal changes in river health. Finally, NRRI is related to a process-based variable-LFE (low flow exceedance) to comprehend the dominating system dynamics and evolutionary adjustments. The results highlight the existence of both threshold-modulated and filter-dominated systems based on CIE and NRRI variabilities. In addition, the gradual decline in CIE and subsequent stabilization of vegetated landforms can develop an 'event-driven' state, where floods exceeding the low-flow channel possess a direct impact on the river recovery trajectory. Finally, this study emphasizes the presence of instream vegetation as an additional degree of freedom, which further controls the hierarchy of energy dissipation and morphological continuum in the macrochannel settings., (© 2022. The Author(s).)

The overarching goal of this paper is to shed light on the human influence on the changing patterns of heat waves in India using the Heat Wave Magnitude Index daily (HWMId). The HWMId obtained from the observational data sets shows a large increase in the heat waves during the past decades. Investigating the effects of natural (e.g., solar variations and volcanic forcings) and anthropogenic (e.g., greenhouse gas emissions, anthropogenic, land use, and land cover) forcings revealed that the anthropogenic factors have cause a two-fold increase in the occurrence probability of severe heat waves in central and mid-southern India during twentieth century. The spatial distribution of maximum HWMId values under natural and all forcings (including anthropogenic) indicates that in most places human activities have increases the frequency, duration and intensity of extreme heat waves. Under the Representative Concentration Pathway (RCP) 4.5, the risk of heat waves is projected to increase tenfold during the twenty-first century. More than ~ 70% of the land areas in India is projected to be influenced by heat waves with magnitudes greater than 9. Furthermore, we find a significant relationship between heat waves and deficits in precipitation. Results show that concurrent heat waves and droughts are projected to increase in most places in India during the twenty-first century., (© 2022. The Author(s).)

In a culturally different and low-resource setting, where lifestyle habits, including dietary pattern and physical activities differ from those in high-income countries, the association between physical activity and cognition is expected to differ. We aimed to investigate the association between physical activity and cognitive functioning after controlling for potential confounders among older adults in India. Furthermore, gender differences in this relationship were analyzed. Using a national-level data from the Longitudinal Ageing Study in India (2017-2018), this paper employed propensity score matching (PSM) approach to examine the association between physical activities and cognitive functioning among Indian older adults. Cognitive impairment was measured through five broad domains (memory, orientation, arithmetic function, executive function, and object naming). We limit our sample to older adults aged 60 + years, and our final dataset contains 31,464 participants (men = 16,366, and women = 15,098). The results indicated that older adults who engaged in frequent physical activity have greater cognitive functioning than older adults without physical activity after adjusting for various individual, health, lifestyle, and household factors. This association holds true for both older men and older women. The results from the PSM revealed that the cognitive function score was increased by 0.98 and 1.32 points for the frequently physically active older men and women population, respectively. The results demonstrate the possible beneficial effects of frequent physical activity on cognitive functioning among older adults. Thus, regular physical activity can be considered as an effective lifestyle factor to promote healthy cognitive aging., (© 2022. The Author(s).)

India is a hotspot of the COVID-19 crisis. During the first wave, several lockdowns (L) and gradual unlock (UL) phases were implemented by the government of India (GOI) to curb the virus spread. These phases witnessed many challenges and various day-to-day developments such as virus spread and resource management. Twitter, a social media platform, was extensively used by citizens to react to these events and related topics that varied temporally and geographically. Analyzing these variations can be a potent tool for informed decision-making. This paper attempts to capture these spatiotemporal variations of citizen reactions by predicting and analyzing the sentiments of geotagged tweets during L and UL phases. Various sentiment analysis based studies on the related subject have been done; however, its integration with location intelligence for decision making remains a research gap. The sentiments were predicted through a proposed hybrid Deep Learning (DL) model which leverages the strengths of BiLSTM and CNN model classes. The model was trained on a freely available Sentiment140 dataset and was tested over manually annotated COVID-19 related tweets from India. The model classified the tweets with high accuracy of around 90%, and analysis of geotagged tweets during L and UL phases reveal significant geographical variations. The findings as a decision support system can aid in analyzing citizen reactions toward the resources and events during an ongoing pandemic. The system can have various applications such as resource planning, crowd management, policy formulation, vaccination, prompt response, etc., (© 2022. The Author(s).)

In the absence of an effective vaccine or drug therapy, non-pharmaceutical interventions are the only option for control of the outbreak of the coronavirus disease 2019, a pandemic with global implications. Each of the over 200 countries affected has followed its own path in dealing with the crisis, making it difficult to evaluate the effectiveness of measures implemented, either individually, or collectively. In this paper we analyse the case of the south Indian state of Kerala, which received much attention in the international media for its actions in containing the spread of the disease in the early months of the pandemic, but later succumbed to a second wave. We use a model to study the trajectory of the disease in the state during the first four months of the outbreak. We then use the model for a retrospective analysis of measures taken to combat the spread of the disease, to evaluate their impact. Because of the differences in the trajectory of the outbreak in Kerala, we argue that it is a model worthy of a place in the discussion on how the world might best handle this and other, future, pandemics., (© 2022. The Author(s).)

The Novel Coronavirus which emerged in India on January/30/2020 has become a catastrophe to the country on the basis of health and economy. Due to rapid variations in the transmission of COVID-19, an accurate prediction to determine the long term effects is infeasible. This paper has introduced a nonlinear mathematical model to interpret the transmission dynamics of COVID-19 infection along with providing vaccination in the precedence. To minimize the level of infection and treatment burden, the optimal control strategies are carried out by using the Pontryagin's Maximum Principle. The data validation has been done by correlating the estimated number of infectives with the real data of India for the month of March/2021. Corresponding to the model, the basic reproduction number [Formula: see text] is introduced to understand the transmission dynamics of COVID-19. To justify the significance of parameters we determined the sensitivity analysis of [Formula: see text] using the parameters value. In the numerical simulations, we concluded that reducing [Formula: see text] below unity is not sufficient enough to eradicate the COVID-19 disease and thus, it is required to increase the vaccination rate and its efficacy by motivating individuals to take precautionary measures., (© 2021. The Author(s).)

COVID-19 related restrictions lowered particulate matter and trace gas concentrations across cities around the world, providing a natural opportunity to study effects of anthropogenic activities on emissions of air pollutants. In this paper, the impact of sudden suspension of human activities on air pollution was analyzed by studying the change in satellite retrieved NO 2 concentrations and top-down NOx emission over the urban and rural areas around Delhi. NO 2 was chosen for being the most indicative of emission intensity due to its short lifetime of the order of a few hours in the planetary boundary layer. We present a robust temporal comparison of Ozone Monitoring Instrument (OMI) retrieved NO 2 column density during the lockdown with the counterfactual baseline concentrations, extrapolated from the long-term trend and seasonal cycle components of NO 2 using observations during 2015 to 2019. NO 2 concentration in the urban area of Delhi experienced an anomalous relative change ranging from 60.0% decline during the Phase 1 of lockdown (March 25-April 13, 2020) to 3.4% during the post-lockdown Phase 5. In contrast, we find no substantial reduction in NO 2 concentrations over the rural areas. To segregate the impact of the lockdown from the meteorology, weekly top-down NOx emissions were estimated from high-resolution TROPOspheric Monitoring Instrument (TROPOMI) retrieved NO 2 by accounting for horizontal advection derived from the steady state continuity equation. NOx emissions from urban Delhi and power plants exhibited a mean decline of 72.2% and 53.4% respectively in Phase 1 compared to the pre-lockdown business-as-usual phase. Emission estimates over urban areas and power-plants showed a good correlation with activity reports, suggesting the applicability of this approach for studying emission changes. A higher anomaly in emission estimates suggests that comparison of only concentration change, without accounting for the dynamical and photochemical conditions, may mislead evaluation of lockdown impact. Our results shall also have a broader impact for optimizing bottom-up emission inventories.

COVID-19 is a global crisis where India is going to be one of the most heavily affected countries. The variability in the distribution of COVID-19-related health outcomes might be related to many underlying variables, including demographic, socioeconomic, or environmental pollution related factors. The global and local models can be utilized to explore such relations. In this study, ordinary least square (global) and geographically weighted regression (local) methods are employed to explore the geographical relationships between COVID-19 deaths and different driving factors. It is also investigated whether geographical heterogeneity exists in the relationships. More specifically, in this paper, the geographical pattern of COVID-19 deaths and its relationships with different potential driving factors in India are investigated and analysed. Here, better knowledge and insights into geographical targeting of intervention against the COVID-19 pandemic can be generated by investigating the heterogeneity of spatial relationships. The results show that the local method (geographically weighted regression) generates better performance ([Formula: see text]) with smaller Akaike Information Criterion (AICc [Formula: see text]) as compared to the global method (ordinary least square). The GWR method also comes up with lower spatial autocorrelation (Moran's [Formula: see text] and [Formula: see text]) in the residuals. It is found that more than 86% of local [Formula: see text] values are larger than 0.60 and almost 68% of [Formula: see text] values are within the range 0.80-0.97. Moreover, some interesting local variations in the relationships are also found.

In this paper, we describe the isolation of an unusual type of high molecular weight monorhamnolipid attached to esters of palmitic, stearic, hexa and octadecanoic acids as against the routinely reported di-rhamnolipids linked to hydroxydecanoic acids. The bioemulsifier was column-purified and the chemical nature of the compound was elucidated using FT-IR, GC-MS and 1D [ 1 H and 13 C] and 2D NMR. This monorhamnolipid is extracted from a bacterium, Pseudomonas guganensis and is not reported to have biological activities, let alone emulsification abilities. The bacterium continually produced rhamnolipids when nourished with n-hexadecane as its lone carbon source. The extracellularly secreted monorhamnolipids are capable of degrading hydrocarbons, with most preference to n-hexadecane [EI 24 of 56 ± 1.42% by 2 mL of the spent medium]. Whilst the crude ethyl acetate partitioned extract had an EI 24 of 65 ± 1.43%; the purified rhamnolipid product showed 78 ± 1.75% both at 12.5 mg/mL concentration. The used-up n-hexadecane is biotransformed to prepare its own rhamnolipids which in return is utilized to degrade n-alkanes thus creating a circular pathway which is proposed herein. This bacterium can be seen as a new source of bioemulsifier to reduce hydrocarbon in polluted waters.