Correlation of oxygenation indices in invasive mechanical ventilated adult patients.

BACKGROUND: 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. OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSION: 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.

Source apportionment, health risk assessment, and trajectory analysis of black carbon and light absorption properties of black and brown carbon in Delhi, India.

Black Carbon (BC) is an important atmospheric pollutant, well recognized for adverse health and climatic effects. The present work discusses the monthly and seasonal variations of BC sources, health risks, and light absorption properties. The measurement was done from January to December 2021 using a seven wavelength aethalometer. Annual average BC concentration during the study period was 12.2 ± 8.8 µg/m3 (ranged from 1.9 - 52.2 µg/m3). Results represent highest BC concentration during winter (W), followed by post-monsoon (P-M), summer (S), and monsoon (M) seasons where the fossil fuel (FF) combustion is the major source during W, S, and M seasons and biomass burning (BB) during the P-M season. The health risk assessment revealed that individuals in Delhi are exposed to BC levels equivalent to inhaling the smoke from 36 passively smoked cigarettes (PSC) everyday. The risk is highest during W reaching upto 71 PSC and minimum during M i.e., 9 PSC. The light absorption properties were calculated for BC (AbsBC) and Brown carbon (AbsBrC). AbsBC and varied from 229-89 Mm-1 between 370-950 nm and AbsBrC varied from 87-12 Mm-1 between 370-660 nm. AbsBC contributed substantially to total absorption at all wavelengths, while AbsBrC contribution is quite significant in the UV region only. Trajectory analysis confirmed significant influence of regional sources (e.g., biomass-burning aerosols from northwest and east direction) on air quality, health risks, and light absorption properties of BC over Delhi especially during the P-M season. The BB events of Punjab, Haryana, Uttar Pradesh, and eastern Pakistan seems to have significant influence on Delhi's air quality predominantly during P-M season.

Spatiotemporal variability and health risk assessment of PM2.5 and NO2 over the Indo-Gangetic Plain: A three years long study (2019-21).

Studying the spatiotemporal variability of pollutants is necessary to identify the pollution hotspots with high health risk and enable the agencies to implement pollution abatement strategies in a targeted manner. Present study reports the spatio-temporal variability and health risk assessment (HRA) of PM2.5 (Particulate matter with aerodynamic diameter <2.5µm) and NO2 over IGP from 2019-2021. The HRA is expressed as passively smoked cigarettes (PSC) for four different health outcomes i.e., low birth weight (LBW), percentage decreased lung function (DLF) in school aged children, lung cancer (LC), and cardiovascular mortality (CM). The findings confirm very high PM2.5 and NO2 mass concentrations and high health risk over middle IGP and Delhi as compared to upper and lower IGP. Within Delhi, north Delhi region is the most polluted and at highest risk as compared to central and south Delhi. The health risk associated with PM2.5 over IGP is highest for DLF, equivalent to 21.63 PSCs daily, followed by CM (11.69), LBW (8.27) and LC (6.94). For NO2, the health risk is highest for DLF (3.09 PSCs) and CM (2.95), followed by LC (1.47) and LBW (1.04). PM2.5 and NO2 concentrations, along with the associated health risks, are highest during the post-monsoon and winter seasons and lowest during the monsoon season.

Effect of Urinary Trypsin Inhibitor (Ulinastatin) Therapy in COVID-19.

Purpose: End-organ damage in coronavirus disease-2019 (COVID-19) is linked to "cytokine storm" and excessive release of inflammatory mediators. Various novel therapies have been used in COVID-19 including urinary trypsin inhibitor therapy. This study explores the efficacy of ulinastatin in COVID-19. Materials and methods: We retrieved the medical records of patients admitted during one month and did a propensity score analysis to create matched treatment and control groups. We analyzed these groups and the outcomes were presented with appropriate statistics. Survival curve was prepared to compare the survival effect of ulinastatin therapy at the end of hospitalization, among both the groups. Results: A total of 736 patients were admitted, and after adjusting the data with propensity score matching, 55 cases were selected by the system. On the final outcome analysis, we found that intensive care unit (ICU) length of stay [median (interquartile range) days 3 (3.5-7.8) vs 2 (0-4); p-value 0.28] in control vs intervention groups, and in hospital mortality (odds ratio: 0.491, CI 95%: 0.099-2.44, p-value 0.435) were not statistically different among the groups. In survival plot analysis also, there was no statistical difference (p-value 0.414) among both the groups.Conclusion: In this retrospective study, we conclude that the final outcome of the ICU length of stay, and overall, in hospital mortality were not different among both the groups. Hence, adequately powered randomized control trials are urgently required to confirm any benefit of ulinastatin therapy in COVID-19 treatment. How to cite this article: Jain A, Kasliwal R, Jain SS, Jain R, Gupta D, Gupta P, et al. Effect of Urinary Trypsin Inhibitor (Ulinastatin) Therapy in COVID-19. Indian J Crit Care Med 2022;26(6):696-703.

Providencia Causing Urinary Tract Infections: Are We Reaching a Dead End?

Background: The genus Providencia, earlier considered a rare pathogen, is now increasingly recognized as a notorious opportunistic pathogen capable of causing serious nosocomial infections, mainly urinary tract infections (UTIs). Treating these infections is an onerous task given the resistance seen in clinical strains to many currently available antimicrobials. The objective of the present study is to provide an overall view into the prevalence of Providenciaspp. causing UTIs, their antibiotic susceptibility pattern, and respective clinical outcomes. Materials and methods: This is a retrospective observational study carried out in a tertiary care teaching referral hospital located in Jaipur, India from March 2021 to May 2021. All Providenciaspp. strains isolated from urine samples were included in the study. Data were entered in Microsoft Office Excel worksheet. Results are presented in numbers and percentages. Results: Out of 1,261 urine samples processed in the laboratory during the study period, 426 were culture positive and the majority were gram-negative isolates and included Escherichia coli (46.0%) and Klebsiellaspp. (28.0%). Providenciaspp. was the fourth most common gram-negative pathogen (6.0%). The median age of patients was 65 years. The male:female ratio was 3:2 and maximum patients belonged to the 30-60-year age-group. Diabetes was the commonest associated comorbidity. All patients had an indwelling urinary catheter. Three (20.0%) patients succumbed to infections. Conclusion: Providencia is an opportunistic pathogen that cannot be neglected due to escalating antibiotic resistance. Effective infection control and antibiotic stewardship policies are required to prevent the development of further antibiotic resistance. How to cite this article: Rajni E, Jain A, Garg VK, Sharma R, Vohra R, Jain SS. Providencia Causing Urinary Tract Infections: Are We Reaching a Dead End? Indian J Crit Care Med 2022;26(4):446-451.

Association between Acute Exposure to PM2.5 Chemical Species and Mortality in Megacity Delhi, India.

The association between daily all-cause mortality and short-term fine particulate matter (PM2.5) exposure is well established in the literature. However, association between acute exposure to PM2.5 chemical species and mortality is not well known, especially in developing countries like India. Here we examined associations between mortality and acute exposure to PM2.5 mass concentration and their 15 chemical components using data from 2013 to 2016 in megacity Delhi using a semiparametric quasi-Poisson regression model, adjusting for mean temperature, relative humidity, and long-term time trend as the major potential confounders. Mortality estimates were further checked for effect modification by sex, age group, and season. The subspecies of NO3-, NH4NO3, Cr, NH4+, EC, and OC showed a higher mortality impact than the total PM2.5 mass. Males were at higher risk from NO3-, SO42-, and their NH4+ compounds along with carcinogen Cr, whereas female group was at higher risk from EC and OC. Among all age groups, the elderly above 65 years were the most vulnerable group prone to mortality effects from maximum species. The major mortality risk from all hazardous species arose from their winter exposures. Our study provides the first evidence of association between acute exposure to PM2.5 chemical species and mortality anywhere in India and recommends similar studies in other regions so that sectoral mitigation emitting the most toxic species can be prioritized to maximize the health benefits.

Comparison of Predictive Ability of Epidemiological Factors, Inflammatory Biomarkers, and CT Severity Score for Mortality in COVID-19.

INTRODUCTION: COVID-19 patients are categorized as per their clinical severity and their level of care is decided based on the clinical severity. Apart from clinical severity of patients, a need for robust predictors was also felt for early categorization and accurate prediction of final fatal outcome in hospitalized patients. MATERIAL AND METHOD: In this retrospective observational cohort study all the adult patients admitted during November month were included. Available data for epidemiological factors, inflammatory biomarkers and CT severity score were collected and analyzed by univariate and multivariate logistic regression analysis to know predictive ability of each variable. A Receiver operating characteristic analysis was done to compare the predictive ability of each factor for final outcome of death. RESULTS: We analyzed records of 735 total patients. Most of them were male (72.38%), have a median (IQR) age of 60 years (50-69). Diabetes (42.85%), and hypertension (39.86%) were the most common co-morbidities. After univariate and multivariate regression analysis we could find that CRP, D-Dimer and CT severity score levels only can predict final outcome of death. During multivariate regression and receiver operative characteristic (ROC) analysis also, age and Charlson's co-morbidity index failed to predict in hospital mortality. CRP and D-Dimer on admission positively predicts final outcome of in hospital mortality with AUROC of 0.749(p=0.007, CI 0.61-0.88), and 0.864(p= 0.000, CI 0.74-0.99) respectively. Whereas, CT severity score had AUROC 0.73 (p= 0.014, CI 0.575-0.83). Cut off for CRP was 45 mg/L (Sn 0.8, Sp 0.56), D-dimer was 1000 µg/L (Sn:0.8, Sp: 0.9), and CT severity score was 15 (Sn 0.8, Sp 0.58). CONCLUSION: CRP level of 45 mg/l, D-dimer level of 1000 µg/L and CT severity level of >15 at the time of admission can be added to conventional clinical severity algorithm to more accurately predicting final outcome and stratifying the level of care offered at the time of admission, and hence may improve odds off survival.

Effects of nationwide COVID-19 lockdown on lifestyle and diet: An Indian survey.

AIMS SETTINGS AND DESIGN: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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 Cancer Stem Cell Niche in Ovarian Cancer and Its Impact on Immune Surveillance.

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.

Investigating the seasonal variability in source contribution to PM2.5 and PM10 using different receptor models during 2013-2016 in Delhi, India.

The present work deals with the seasonal variations in the contribution of sources to PM2.5 and PM10 in Delhi, India. Samples of PM2.5 and PM10 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 PM2.5 and PM10 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 PM2.5 and PM10 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 PM2.5 and PM10 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 PM2.5 and PM10. 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.

A Comparative Study of Intralesional Vitamin D3, Measles Mumps Rubella Vaccine, and Tuberculin Purified Protein Derivative in the Treatment of Recalcitrant Warts: An Approach to Solve a Therapeutic Conundrum.

BACKGROUND: Human papilloma virus infects and proliferates in skin or mucosal cells to cause warts. Most of the current therapeutic modalities are ablative, act only on treated lesions, and lack a well-defined treatment endpoint. These being blind procedures, recurrence rates are high, owing to the remnant virus. Intralesional immunotherapy plays a significant role, as it potentially acts on treated and distant lesions. OBJECTIVES: We sought to study and compare the efficacy, safety profile, and recurrence rates of intralesional immunotherapy modalities (vitamin D3; measles, mumps, and rubella [MMR] vaccine; and tuberculin purified protein derivative [PPD]) in treating viral warts. METHODS: An open-label interventional study of 60 cases of cutaneous viral warts was performed in a tertiary care center attached to a medical college after obtaining approval from the institutional ethics committee. Each patient was consecutively assigned into Group 1 (vitamin D3: 0.2mL of 15mg/mL), Group 2 (MMR: 0.5mL), or Group 3 (tuberculin PPD: 0.1mL of 10TU). One or two warts were injected per session every two weeks. Response was assessed. Adverse effects were noted. Cases were followed up monthly for three months. RESULTS: The MMR group had the maximum patients with complete response (15 of 20, 75%) followed by tuberculin PPD group (13 of 20, 65%) and vitamin D3 group (12 of 20, 60%). No major adverse drug reactions were reported in any of the groups. CONCLUSION: Immunotherapy offers a safe and promising approach in patients with extensive cutaneous viral warts in difficult to treat sites.

Influence of chemical aging on physico-chemical properties of mineral dust particles: A case study of 2016 dust storms over Delhi.

The physico-chemical properties of dust particles collected During Dust Storm (DDS) and After Dust Storm (ADS) events were studied using Scanning Electron Microscope coupled with Energy Dispersive X-ray Spectroscopy (SEM-EDS), X-ray Fluorescence Spectroscopy (XRF) and X-ray Photoelectron Spectroscopy (XPS). Morphological and compositional change in dust particles were observed as they react with the anthropogenic pollutants present in the urban environment. The calcite rich particles were observed to transform into calcium chloride, calcium nitrate, and calcium sulfate on reacting with the chlorides, nitrates, and sulfates present in the urban atmosphere. The frequency distributions of Aspect Ratio (AR) for the DDS and ADS particles were observed to be bimodal (mode peaks at 1.2 and 1.5) and monomodal (mode peak at 1.1), respectively. The highly irregular shaped solid dust particles were observed to transform into nearly spherical semisolid particles in the urban environment. XPS analysis confirms the high concentration of oxides, nitrates, and chlorides at the surface of ADS samples which show the signatures of mineral dust particles aging. Species with a high value of imaginary part of refractive index (like Cr metal, Fe metal, Cr2O3, FeO, Fe2O3) were observed at the surface of dust particles. At 550 nm wavelength, the light-absorbing potential of the observed species along with black carbon (BC) was found to vary in the order; Cr metal > Fe metal > Cr2O3> FeO > BC > Fe2O3> FeOOH. The presence of the aforementioned species on the surface of ADS particles will tremendously affect the particle optical and radiative properties compared to that of DDS particles. The present work could reduce the uncertainty in the radiation budget estimations of mineral dust and assessment of their climatic impacts over Delhi.

Seasonal characteristics of aerosols (PM2.5 and PM10) and their source apportionment using PMF: A four year study over Delhi, India.

The present study attempts to explore and compare the seasonal variability in chemical composition and contributions of different sources of fine and coarse fractions of aerosols (PM2.5 and PM10) in Delhi, India from January 2013 to December 2016. The annual average concentrations of PM2.5 and PM10 were 131 ± 79 µg m-3 (range: 17-417 µg m-3) and 238 ± 106 µg m-3 (range: 34-537 µg m-3), respectively. PM2.5 and PM10 samples were chemically characterized to assess their chemical components [i.e. organic carbon (OC), elemental carbon (EC), water soluble inorganic ionic components (WSICs) and heavy and trace elements] and then used for estimation of enrichment factors (EFs) and applied positive matrix factorization (PMF5) model to evaluate their prominent sources on seasonal basis in Delhi. PMF identified eight major sources i.e. Secondary nitrate (SN), secondary sulphate (SS), vehicular emissions (VE), biomass burning (BB), soil dust (SD), fossil fuel combustion (FFC), sodium and magnesium salts (SMS) and industrial emissions (IE). Total carbon contributes ∼28% to the total PM2.5 concentration and 24% to the total PM10 concentration and followed the similar seasonality pattern. SN and SS followed opposite seasonal pattern, where SN was higher during colder seasons while SS was greater during warm seasons. The seasonal differences in VE contributions were not very striking as it prevails evidently most of year. Emissions from BB is one of the major sources in Delhi with larger contribution during winter and post monsoon seasons due to stable meteorological conditions and aggrandized biomass burning (agriculture residue burning in and around the regions; mainly Punjab and Haryana) and domestic heating during the season. Conditional Bivariate Probability Function (CBPF) plots revealed that the maximum concentrations of PM2.5 and PM10 were carried by north westerly winds (north-western Indo Gangetic Plains of India).

Source Apportionment of PM10 Over Three Tropical Urban Atmospheres at Indo-Gangetic Plain of India: An Approach Using Different Receptor Models.

The present work is the ensuing part of the study on spatial and temporal variations in chemical characteristics of PM10 (particulate matter with aerodynamic diameter ≤ 10 µm) over Indo Gangetic Plain (IGP) of India. It focuses on the apportionment of PM10 sources with the application of different receptor models, i.e., principal component analysis with absolute principal component scores (PCA-APCS), UNMIX, and positive matrix factorization (PMF) on the same chemical species of PM10. The main objective of this study is to perform the comparative analysis of the models, obtained mutually validated outputs and more robust results. The average PM10 concentration during January 2011 to December 2011 at Delhi, Varanasi, and Kolkata were 202.3 ± 74.3, 206.2 ± 77.4, and 171.5 ± 38.5 µg m-3, respectively. The results provided by the three models revealed quite similar source profile for all the sampling regions, with some disaccords in number of sources as well as their percent contributions. The PMF analysis resolved seven individual sources in Delhi [soil dust (SD), vehicular emissions (VE), secondary aerosols (SA), biomass burning (BB), sodium and magnesium salt (SMS), fossil fuel combustion, and industrial emissions (IE)], Varanasi [SD, VE, SA, BB, SMS, coal combustion, and IE], and Kolkata [secondary sulfate (Ssulf), secondary nitrate, SD, VE, BB, SMS, IE]. However, PCA-APCS and UNMIX models identified less number of sources (besides mixed type sources) than PMF for all the sampling sites. All models identified that VE, SA, BB, and SD were the dominant contributors of PM10 mass concentration over the IGP region of India.

Carbonaceous Species of PM2.5 in Megacity Delhi, India During 2012-2016.

Organic carbon (OC) and elemental carbon (EC) in PM2.5 were estimated to study the seasonal and inter-annual variability of atmospheric total carbonaceous aerosols (TCA) at an urban site of megacity Delhi, India for 5 years from January, 2012 to December, 2016. The annual average (± standard deviation) concentrations of PM2.5, OC, EC and TCA were 128 ± 81, 16.6 ± 12.2, 8.4 ± 5.8 and 34.5 ± 25.2 µg m-3, respectively. During the study, significant seasonal variations in mass concentrations of PM2.5, OC, EC and TCA were observed with maxima in winter and minima in monsoon seasons. Significant correlations between OC and EC, and OC/EC ratio suggested that vehicular emissions, fossil fuel combustion and biomass burning could be major sources of carbonaceous aerosols of PM2.5 at the sampling site of Delhi, India.

Chemical characteristics and source apportionment of PM2.5 using PCA/APCS, UNMIX, and PMF at an urban site of Delhi, India.

The present study investigated the comprehensive chemical composition [organic carbon (OC), elemental carbon (EC), water-soluble inorganic ionic components (WSICs), and major & trace elements] of particulate matter (PM2.5) and scrutinized their emission sources for urban region of Delhi. The 135 PM2.5 samples were collected from January 2013 to December 2014 and analyzed for chemical constituents for source apportionment study. The average concentration of PM2.5 was recorded as 121.9 ± 93.2 µg m-3 (range 25.1-429.8 µg m-3), whereas the total concentration of trace elements (Na, Ca, Mg, Al, S, Cl, K, Cr, Si, Ti, As, Br, Pb, Fe, Zn, and Mn) was accounted for ∼17% of PM2.5. Strong seasonal variation was observed in PM2.5 mass concentration and its chemical composition with maxima during winter and minima during monsoon seasons. The chemical composition of the PM2.5 was reconstructed using IMPROVE equation, which was observed to be in good agreement with the gravimetric mass. Source apportionment of PM2.5 was carried out using the following three different receptor models: principal component analysis with absolute principal component scores (PCA/APCS), which identified five major sources; UNMIX which identified four major sources; and positive matrix factorization (PMF), which explored seven major sources. The applied models were able to identify the major sources contributing to the PM2.5 and re-confirmed that secondary aerosols (SAs), soil/road dust (SD), vehicular emissions (VEs), biomass burning (BB), fossil fuel combustion (FFC), and industrial emission (IE) were dominant contributors to PM2.5 in Delhi. The influences of local and regional sources were also explored using 5-day backward air mass trajectory analysis, cluster analysis, and potential source contribution function (PSCF). Cluster and PSCF results indicated that local as well as long-transported PM2.5 from the north-west India and Pakistan were mostly pertinent.

Spatio-temporal variation in chemical characteristics of PM10 over Indo Gangetic Plain of India.

The paper presents the spatio-temporal variation of chemical compositions (organic carbon (OC), elemental carbon (EC), and water-soluble inorganic ionic components (WSIC)) of particulate matter (PM10) over three locations (Delhi, Varanasi, and Kolkata) of Indo Gangetic Plain (IGP) of India for the year 2011. The observational sites are chosen to represent the characteristics of upper (Delhi), middle (Varanasi), and lower (Kolkata) IGP regions as converse to earlier single-station observation. Average mass concentration of PM10 was observed higher in the middle IGP (Varanasi 206.2 ± 77.4 µg m(-3)) as compared to upper IGP (Delhi 202.3 ± 74.3 µg m(-3)) and lower IGP (Kolkata 171.5 ± 38.5 µg m(-3)). Large variation in OC values from 23.57 µg m(-3) (Delhi) to 12.74 µg m(-3) (Kolkata) indicating role of formation of secondary aerosols, whereas EC have not shown much variation with maximum concentration over Delhi (10.07 µg m(-3)) and minimum over Varanasi (7.72 µg m(-3)). As expected, a strong seasonal variation was observed in the mass concentration of PM10 as well as in its chemical composition over the three locations. Principal component analysis (PCA) identifies the contribution of secondary aerosol, biomass burning, fossil fuel combustion, vehicular emission, and sea salt to PM10 mass concentration at the observational sites of IGP, India. Backward trajectory analysis indicated the influence of continental type aerosols being transported from the Bay of Bengal, Pakistan, Afghanistan, Rajasthan, Gujarat, and surrounding areas to IGP region.

Source Apportionment of PM2.5 in Delhi, India Using PMF Model.

Chemical characterization of PM2.5 [organic carbon, elemental carbon, water soluble inorganic ionic components, and major and trace elements] was carried out for a source apportionment study of PM2.5 at an urban site of Delhi, India from January, 2013, to December, 2014. The annual average mass concentration of PM2.5 was 122 ± 94.1 µg m(-3). Strong seasonal variation was observed in PM2.5 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 PM2.5 mass concentration. The PMF model resolved the major sources of PM2.5 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 %).