A study to evaluate efficacy of Tinospora cordifolia (Guduchi) as analgesic agent using albino wistar rats as an experimental animal model

Background: Pain is a very well-known symptom of many diseases and analgesics are used to relieve pain. The main problem with these drugs remains that of side effects. Herbal medicines are better in view of their cultural acceptability, better compatibility with human body systems and lesser incidence of side effects. Extract of Tinospora cordifolia (Guduchi) plant have been traditionally used to treat pain in traditional medicine.Methods: Commercially available preparation of T. cordifolia plant has been used as test drug (aqueous extract). Healthy albino rats of either sex, weighing between 140-200 g were selected for the study, divided into 4 groups of 6 each (control, standard, 100 mg/kg, 300 mg/kg). Central analgesic activity was assessed by tail flick model (morphine as standard drug I.P). Acetic acid 1% 10 ml/kg aqueous solution I.P. was used for abdominal writhing model. Diclofenac 150 mg/kg oral as standard drug for assessment of peripheral analgesic activity. Results were analysed using SPSS version 16 and Microsoft office excel 2007.Results: T. cordifolia extract significantly increased the tail flick latency time (sec) (mean tail flick latency control, T100, T300 6.833±0.25 sec, 8.65±017 sec, 10.01±0.14 respectively) (p value control vs T100, T300 at 90 min, 120 min, 0.0573, 0.0198, 0.0198 in between group) and decreased number of abdominal writhing in comparison with the control group (p value <0.0001).Conclusions: Extract of T. cordifolia was found to possess analgesic activity and also exhibited dose and time dependant increase involving central and peripheral mechanisms. The analgesic activity of T. cordifolia found to be comparable to standard drug used

Investigating the seasonal variability in source contribution to PM(2.5)and PM(10)using different receptor models during 2013-2016 in Delhi, India

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 mu g m(-3)and 238 +/- 106 mu g 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 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

Particle size distribution from municipal solid waste burning over National Capital Territory, India

Proceeding paper, presented at the 5th International Electronic Conference on Atmospheric Sciences, 16–31 July 2022. Emission of particulate matter (PM) of different sizes from Municipal Solid Waste (MSW) burning may have an impact on air quality and human health of the National Capital Territory (NCT) of India, particularly during winter months. MSW samples were collected from three sanitary landfill sites in the NCT Delhi. Experiments were performed to mimic real world burning during different stages of sample combustion (ignition, flaming smoldering, smoldering and pyrolysis). We determined the emission factor for the number and mass concentration of particles of different sizes, ranging from 0.34 to 9.05 µm, for MSW burning. Present results confirm the assumption that MSW burning emits the maximum number concentration (No/cm3) of particles (90%) in the range < 1.0 µm, or fine-mode aerosol

Chemical Characterization of Fine Atmospheric Particles of Water-Soluble Ions and Carbonaceous Species in a Tropical Urban Atmosphere over the Eastern Indo-Gangetic Plain

Ambient fine aerosols and their sources were evaluated in an eastern Indian megacity, Kolkata (KOL), from September 2010 to August 2011. A submicron aerosol sampler (SAS) with two stage stacked filter units (SFU) was devised for simultaneous but discrete collection of water-soluble inorganic ions (WSII) and carbonaceous aerosols (CA; elemental carbon (EC) and organic carbon (OC)). Characteristics of the WSII and CA were identified using ion chromatography and an OC-EC analyzer, respectively, adopting the Interagency Monitoring of PROtected Visual Environments (IMPROVE-A) protocol. The mean annual concentrations of the WSII showed a predominance of cations (anions), consisting of Ca2+, Mg2+, and Na+ (Cl-, NO3-, and SO42-), with secondary aerosols (NH4+, NO3-, and SO42-) and Ca2+ each constituting 25% and 30%, respectively, of the total WSII (T-WSII). The highest mean monthly concentration of SO42- and NO3- was observed during the winter month of February and the summer months of March and May, respectively. A pronounced peak in the monthly mean for the non-sea salt-K+ (nss-K+) concentration was noticed during October and April, implying the strong influence of biomass burning emissions during these months. Among the sea salt (SS), anthropogenic (AN), and dust (DT) sources of the T-WSII, a predominant contribution from DT in August and from AN in November, April, and May was inferred. The mean annual concentration of OC was three times higher than that of EC, with 43% of it being secondary OC. Whereas the major sources of OC were inferred to be paved dust, coal combustion, and biomass burning, those of EC were industrial and motor-vehicle non-exhaust emissions, coal combustion, and motor vehicle exhaust

Source apportionment of particulates by receptor models over Bay of Bengal during ICARB campaign

Aerosol Radiation Budget (ICARB) in the pre-monsoon (March-April 2006) and winter (December-January 2008-09) seasons. Positive matrix factorization (PMF) was applied to identify sources of ambient particulate matter using daily chemical composition data collected in the pre-monsoon (total suspended particles, TSP) and winter season (particles with a diameter < 10 mu m, PM10). Sea salt (SS), secondary aerosol (SA), Si-dust, fossil fuel combustion (FFC), biomass burning (BB) sources have been identified in both seasons, however their relative contributions were different. The combined contribution of Si-dust, secondary aerosol and fossil fuel combustion, constitute. similar to 67% of particulate matter in pre-monsoon, whereas, secondary aerosols and biomass burning were the major contributors (63.2%) to particulate matter in winter. The identified sources effectively predict the measured particulate concentration in the pre-monsoon (r(2) = 0.74) and winter season (r(2) = 0.82). Another receptor model, principal component analysis (PCA) was done to increase the plausibility of the results obtained by PMF. PCA resulted in the identification of the sources that were comparable to the PMF outputs. PCA of TSP in the pre-monsoon season resulted in the extraction of three components (crustal dust + secondary aerosol, biomass burning, fossil fuel combustion + industrial emissions) that explained the 83% of the variance in the data. Similarly, in winter season, PCA resulted in the extraction of four components (biomass burning + secondary aerosol, industrial emission, crustal dust, sea salt) that explained the 86% of the variance of the data

Influence of ozone precursors and particulate matter on the variation of surface ozone at an urban site of Delhi, India

Continuous measurements of surface O3 and its precursors (NO, NO2, CO, CH4 and NMHCs) at an urban site of Delhi, India during January 2012 to December 2013 are presented. In the present study, the annual average mixing ratios of surface O3, NO, NO2, CO, CH4 and NMHC were 30 ± 6 ppb, 24 ± 6 ppb, 15 ± 4 ppb, 1.5 ± 0.4 ppm, 2.4 ± 0.4 ppm and 0.4 ± 0.1 ppm, respectively. The maximum average mixing ratios of surface O3, NO and NO2 were observed during the summer, whereas, the minimum average mixing ratios of ambient NO and NO2 were during monsoon seasons. The surface O3, NO and NO2 have shown the prominent diurnal variations during all the seasons at the observational site of Delhi. The result reveals that the surface O3 was negatively correlated with NOx and CO during the study. The linear scatter plot analysis shows that the PM2.5 and PM10 present in the ambient air of Delhi influence the production of surface O3 at observational site

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 mu m) 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 mu g 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

Long-Term (2012&ndash;2021) Variation in Carbonaceous Aerosols of PM2.5 at an Urban Site of Megacity Delhi Situated over Indo-Gangetic Plain of India

A long-term (January 2012 to December 2021) study on carbonaceous aerosols of fine particulates (PM2.5) was conducted over the megacity of Delhi, India, to evaluate their seasonal and yearly variations. During the entire study period, the observed annual mean levels (&micro;g m&minus;3) of PM2.5 and its carbonaceous components (OC, POC, SOC, EM, EC, TCM, and TC) were recorded as 126 &plusmn; 72, 15.6 &plusmn; 11.6, 9.3 &plusmn; 6.3, 6.4 &plusmn; 5.1, 8.2 &plusmn; 5.6, 7.3 &plusmn; 5.1, 33.2 &plusmn; 21.9, and 23.1 &plusmn; 16.5, respectively. On average, the CAs/TCM ratio accounts for 26% of PM2.5 concentrations. During the monsoon (minimum) and post-monsoon (maximum) season, significant seasonal variability in PM2.5 and its carbonaceous species (OC, EC, POC, SOC, and TCM) was observed. Based on the linear association (OC vs. EC) and ratios (OC/EC as well as EC/TC) of species, three significant sources of CAs (vehicular emissions (VE), fossil fuel combustion (FFC), and biomass burning (BB)) were identified

Development of fluorescent carbon nanoparticles from Madhuca longifolia flower for the sensitive and selective detection of Cr6+: a collective experimental–computational approach

Herein, blue-emitting carbon nanoparticles (CNPs) were synthesized using the Madhuca longifolia flower for the highly selective and sensitive detection of Cr6+ ions in aqueous media using a simple, green, and cost-effective approach, and computational experiments were also performed. The prepared CNPs were well-dispersed in water with an average diameter of 12 nm and functionalized with carbonyl, hydroxyl and carboxylic acid groups. The decrease in the fluorescence intensity of the CNPs with an increase in the content of Cr6+ provided an important signal for the sensitive and selective detection of Cr6+ in aqueous media. The limit of detection for Cr6+ in an aqueous medium was found to be 103 ppb, which is more sensitive in comparison with the previously reported study. Furthermore, the validation of the proposed higher sensing feature and more selective nature of the CNPs towards Cr6+ was also explained using an in silico approach. The results from the theoretical calculations based on the DFT approach demonstrated that the binding energy (BE) of the CNPs with three transition metal (TM) cations (Cr6+, Fe3+, and Hg2+) follows the order of Cr6+ > Fe3+ > Hg2+, where the Cr6+ TM cation associated with the CNPs possesses the highest valence state, showing the highest sensing feature and highest selectivity among the investigated ions, as expected. The metal ions associated with the CNPs having a higher charge and a smaller radius indicated a higher BE and larger degree of deformation of the CNPs. Moreover, to achieve new insights into the structural, stability/energetics, and electronic features, some useful tools, such as NCI-plot, HOMO–LUMO gap, MESP, and QTAIM analysis were employed, which facilitated noteworthy outcomes

Chemical characterization and source apportionment of aerosol at an urban area of Central Delhi, India

The concentrations of organic carbon (OC), elemental carbon (EC), water soluble inorganic ionic components (WSIC), and major & trace elements of PM10 were studied in Delhi, an urban site of the Indo Gangetic Plain (IGP), India during January 2013 to June 2014. The average mass concentration of PM10 recorded as 249.7 +/- 103.9 mu g m(-3) (average +/- standard deviation) with a range of 61.4e584.8 mu g m(-3). The strong seasonal variation was noticed in the mass concentration of PM10 and its chemical composition with maxima during winter (PM10: 293.9 +/- 95.6 mu g m(-3); OC: 30.5 +/- 13.7 mu g m(-3); EC: 15.2 +/- 7.4 mu g m(-3)) and minima during monsoon (PM10: 143.9 +/- 36.3 mu g m(-3); OC: 19.9 +/- 16.2 mu g m(-3); EC: 7.4 +/- 5.4 mu g m(-3)). The average concentration of major and trace elements (Na, Mg, Al, P, S, Cl, K, Ca, Si, Cr, Ti, As, Br, Pb, Fe, Zn and Mn) was accounted for similar to 18.5% of PM10 mass. Results of Positive Matrix Factorization (PMF) model, HYSPLIT4 trajectory model, PSCF analysis and cluster analysis provide region of sources and its strength and types of sources of PM10 over Delhi. Positive PMF provides that the major source of PM10 are soil dust (22.7%) followed by secondary aerosols (20.5%), vehicle emissions (17.0%), fossil fuel burning (15.5%), biomass burning (12.2%), industrial emissions (7.3%) and sea salts (4.8%) at the observational site of Delhi. The cluster analysis of air mass trajectories calculated by HYSPLIT model indicates that the air mass approaches to the observational site mainly from 4 sides (north-western IGP, Pakistan (10%); northwestern IGP, Northwest Asia (45%); eastern IGP (38%); Pakistan and Arabian Sea (6%)) during study. Potential Source Contribution Function (PSCF) analysis also supports the cluster analysis indicating that the concentration of PM10 mass contributed, is mainly from IGP region (Uttar Pradesh, Haryana and Punjab etc.), Afghanistan, Pakistan and surrounding areas