The present work deals with the seasonal variations in the contribution of sources to PM
2.5
and PM
10
in Delhi, India. Samples of PM
2.5
and PM
10
were collected from January 2013 to December 2016 ...at an urban site of Delhi, India, and analyzed to evaluate their chemical components organic carbon (OC), elemental carbon (EC), water-soluble inorganic components (WSICs), and major and trace elements. The average concentrations of PM
2.5
and PM
10
were 131 ± 79 μg m
−3
and 238 ± 106 μg m
−3
, respectively during the entire sampling period. The analyzed and seasonally segregated data sets of both PM
2.5
and PM
10
were used as input in the three different receptor models, i.e., principal component analysis-absolute principal component score (PCA-APCS), UNMIX, and positive matrix factorization (PMF), to achieve conjointly corroborated results. The present study deals with the implementation and comparison of results of three different multivariate receptor models (PCA-APCS, UNMIX, and PMF) on the same data sets that allowed a better understanding of the probable sources of PM
2.5
and PM
10
as well as the comportment of these sources with respect to different seasons. PCA-APCS, UNMIX, and PMF extracted similar sources but in different contributions to PM
2.5
and PM
10
. All the three models extracted 7 similar sources while mutually confirmed the 4 major sources over Delhi, i.e., secondary aerosols, vehicular emissions, biomass burning, and soil dust, although the contribution of these sources varies seasonally. PCA-APCS and UNMIX analysis identified a less number of sources (besides mixed type) as compared to the PMF, which may cause erroneous interpretation of seasonal implications on source contribution to the PM mass concentration.
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•Comparisons of receptor models, i.e. PCA/APCS, UNMIX and PMF, were conducted.•Source profiles and contributions of PM10 were achieved by receptor models.•VE, SA and SD were dominant ...contributors to PM10 in Delhi.•PMF allowed to obtain better results than PCA/APCS and UNMIX.
Source apportionment of particulate matter (PM10) measurements taken in Delhi, India between January 2013 and June 2014 was carried out using two receptor models, principal component analysis with absolute principal component scores (PCA/APCS) and UNMIX. The results were compared with previous estimates generated using the positive matrix factorization (PMF) receptor model to investigate each model’s source-apportioning capability. All models used the PM10 chemical composition (organic carbon (OC), elemental carbon (EC), water soluble inorganic ions (WSIC), and trace elements) for source apportionment. The average PM10 concentration during the study period was 249.7±103.9μg/m3 (range: 61.4–584.8μg/m3). The UNMIX model resolved five sources (soil dust (SD), vehicular emissions (VE), secondary aerosols (SA), a mixed source of biomass burning (BB) and sea salt (SS), and industrial emissions (IE)). The PCA/APCS model also resolved five sources, two of which also included mixed sources (SD, VE, SD+SS, (SA+BB+SS) and IE). The PMF analysis differentiated seven individual sources (SD, VE, SA, BB, SS, IE, and fossil fuel combustion (FFC)). All models identified the main sources contributing to PM10 emissions and reconfirmed that VE, SA, BB, and SD were the dominant contributors in Delhi.
Chemical characterization of PM
2.5
organic carbon, elemental carbon, water soluble inorganic ionic components, and major and trace elements was carried out for a source apportionment study of PM
2.5
...at an urban site of Delhi, India from January, 2013, to December, 2014. The annual average mass concentration of PM
2.5
was 122 ± 94.1 µg m
−3
. Strong seasonal variation was observed in PM
2.5
mass concentration and its chemical composition with maxima during winter and minima during monsoon. A receptor model, positive matrix factorization (PMF) was applied for source apportionment of PM
2.5
mass concentration. The PMF model resolved the major sources of PM
2.5
as secondary aerosols (21.3 %), followed by soil dust (20.5 %), vehicle emissions (19.7 %), biomass burning (14.3 %), fossil fuel combustion (13.7 %), industrial emissions (6.2 %) and sea salt (4.3 %).
This article examines the performance of initial public offerings (IPOs) issued by the economic sectors in India. It analyses the level of underpricing measured by market adjusted initial return ...(MAIR), short-run performance measured by market-adjusted abnormal return (MAAR) and long-run performance measured by 3-year buy and hold abnormal return (BHAR) methodology relative to Sensex and Nifty for 40 IPOs approaching the capital market during the period 2006–2016. The selection of IPOs is based on the foreign direct investment (FDI) limit of USD 3,000 million in each economic sector, that is, primary sector, secondary sector and tertiary sector. The long-run analysis is done at the end of the first year, the second year and the third year. It is found using the ordinary least square (OLS) regression that variables like age of the issuing firm and volume traded on the first day of listing have a positive relation with initial returns, while offer size of the IPO has a negative relation with the initial return. Further, this study also finds out that the secondary sector performs poorly in the long run relative to the primary and tertiary sectors. This study can be of importance to investors for assessing different sectors before investing.
Ovarian cancer is an aggressive gynaecological cancer with extremely poor prognosis, due to late diagnosis as well as the development of chemoresistance after first-line therapy. Research advances ...have found stem-like cells present in ovarian tumours, which exist in a dynamic niche and persist through therapy. The stem cell niche interacts extensively with the immune and non-immune components of the tumour microenvironment. Significant pathways associated with the cancer stem cell niche have been identified which interfere with the immune component of the tumour microenvironment, leading to immune surveillance evasion, dysfunction and suppression. This review aims to summarise current evidence-based knowledge on the cancer stem cell niche within the ovarian cancer tumour microenvironment and its effect on immune surveillance. Furthermore, the review seeks to understand the clinical consequences of this dynamic interaction by highlighting current therapies which target these processes.
The COVID-19 pandemic has forced upon sudden lifestyle changes because of nationwide lockdowns mandating isolation at home, affecting daily habits and lifestyle changes. The present study was ...conducted with an aim to assess these changes brought about because of COVID-19 lockdown restrictions.
The web-survey aimed to understand the immediate impact of the COVID-19 lockdown on people by using a structured questionnaire collecting demographic, lifestyle, and dietary information. The survey was disseminated online among the literate, urban, adult population with internet access.
Of the 1,200 people who received the survey, a total of 1,008 respondents participated in the study, aged between 18 and 81 years (Median- 24). An increase in daily screen time has been observed in 56.7% of the population. A decrease in work-related stress was observed in 43% of the population, sleep pattern improved in 36.7% people, and 27.1% of the inactive population showed increased physical activity. A significant decrease in the proportion of people consuming junk food (73.8%), alcohol (27.6%), and smoking (8.1%) was observed.
The present web-based survey study suggests a significant change in the lifestyle and dietary patterns of people brought about because of the COVID-19 lockdown most highly seen as a major increase in screen usage and a decrease in junk food consumption.
The present work depicts the spatial and temporal variations in chemical characteristics and sources of PM2.5 and PM10 over Indo Gangetic Plain (IGP) of India from January 2015 to December 2016. ...PM2.5 and PM10 samples were collected at three typical urban sites of Delhi, Varanasi, and Kolkata of IGP, India and characterized to evaluate their chemical components. The average concentrations of PM2.5 at Delhi, Varanasi, and Kolkata were 135 ± 64, 99 ± 33, and 116 ± 38 μg m−3, respectively. Whereas the average concentrations of PM10 over Delhi, Varanasi, and Kolkata were 242 ± 95, 257 ± 90, and 179 ± 77 μg m−3, respectively. Source apportionment was carried out using the three receptor models i.e. Principal Component Analysis-Absolute Principal Component Score (PCA-APCS), UNMIX, and Positive Matrix Factorization (PMF), implemented on the same data sets to obtain the conjointly validated results. All the models identified that vehicular emissions, secondary aerosols, biomass burning, and soil dust were the dominant sources of PM2.5 and PM10 over IGP, India. Hybrid receptor models revealed the presence of strong local emission sources as well as traversing of pollutants from the parts of Pakistan, Punjab, Haryana, Rajasthan, Uttar Pradesh, Bihar, and Bangladesh.
•Spatial and temporal variations in chemical characteristics and sources of PM2.5 and PM10 over IGP, India.•Three receptor models (PCA-APCS, UNMIX, and PMF) were used for source apportionment.•Vehicular emissions, secondary aerosols, biomass burning, and soil dust were the dominant sources over the IGP.•PCA-APCS and UNMIX analysis estimated mixed type of sources as well.•Back trajectory, PSCF, and CWT analysis revealed the presence of strong local emission sources.
Mechanical ventilation is essential for managing acute respiratory failure, but traditional methods of assessing oxygenation, like the PaO2/FiO2 ratio, pose challenges due to invasiveness and cost.
...This single-centre prospective observational study aimed to assess the potential of the non-invasive Oxygen Saturation Index (OSI), utilising SpO2 measurements, to diagnose hypoxemia in mechanically ventilated adults. The study sought to establish correlations between OSI, oxygenation index (OI), PaO2/FiO2 ratio and SpO2/FiO2 ratio.
From August 2022 to July 2023, data was collected from 1055 mechanically ventilated intensive care unit patients. Statistical analysis included correlation tests, receiver operating curve (ROC) analysis and cut-off value determination for hypoxemia diagnosis.
We found that the P/F ratio had a statistically significant negative correlation with OI (correlation coefficient -0.832, P value: 0.000 in hypoxemic group and correlation coefficient -0.888, P value: 0.000 in the non-hypoxemic group), and OSI (correlation coefficient -0.746, P value: 0.000 in hypoxemic group and correlation coefficient -0.629, P value: 0.000 in non-hypoxemic group) and has a positive correlation with P/F ratio (correlation coefficient 0.92, P value: 0.000 in hypoxemic group and correlation coefficient -0.67, P value: 0.000 in non-hypoxemic group). OI and OSI had a statistically significant correlation (correlation coefficient 0.955, P value: 0.000 in hypoxemic group and correlation coefficient 0.815, P value: 0.000 in non-hypoxemic group). on ROC analysis P/F ratio was the most accurate in predicting hypoxia followed by OI and OSI. with a cut-off value, of OI being 7.07, and that for OSI being 3.90, at an 80% sensitivity level to diagnose hypoxemia.
OSI can serve as a dependable surrogate for OI, simplifying ARDS severity assessment. The P/F ratio is the most accurate predictor of hypoxia. Further research, especially in larger multicentre studies, is needed to validate these findings and explore the long-term clinical implications of using OSI for oxygenation monitoring in mechanically ventilated patients.
The key purpose of this study is to gauge the effect of yearly union budgets spanning from 2010 to 2021 through observing yields and volatility in the Indian bourse. Overall, 12 union budgets are ...considered in the Indian scenario. Further, the present work strives to relate the market returns with the investment activities adopted by foreign institutional investors (FII) and domestic institutional investors (DII). The event, i.e., the proclamation of the budget happens on February 1 every year and therefore 60 trading days which are sub-divided into 30 days before the budget and 30 days subsequent to the budget has been analyzed in the study. Moreover, the event window of 60 days has been bifurcated into a period of pre-and post-3 days, pre-and post- 15 days, and pre-and post- 30 days around the budget. Paired samples T-statistic and F-test have been used in the current study. The results suggest that the medium-term returns are significantly influenced by union budgets. The volatility is greatest in the short-term period subsequent to the budget proclamation and it reduces with increasing time. One startling thing to observe is that falling returns and rising volatility in the short-term period post-budget are due to the selling activities of foreign investors. It is recommended to hold the stocks in the post-budget short-term period irrespective of the falling returns till the long-term to book profits.
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