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).
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•Simultaneous sampling of PM2.5 and PM10 was carried out for 4 years (2013–2016).•Seasonal variations in composition and sources of PM2.5 and PM10 are studied in Delhi.•Secondary inorganic aerosol accounts for 21% of PM10 and 27% of PM2.5 mass with contrasting seasonal variations.•Traffic emission contributes greatly to PM10 while biomass burning to PM2.5, both being maximum in winters.•Maximum concentrations of PM2.5 and PM10 were coming from North West direction of Delhi (CBPF plots).
The present work explores the temporal and seasonal variabilities in composition and contributions of different sources to fine and coarse fractions of particulate matter over Delhi.
The removal of Cr(VI) from aqueous solution by batch adsorption technique using different low-cost adsorbents was investigated. Adsorbents such as clarified sludge—a steel industry waste material, ...rice husk ash, activated alumina, fuller's earth, fly ash, saw dust and neem bark were used to determine the adsorption efficiency. The influence of pH, adsorbent type and concentration, initial Cr(VI) concentration and contact time on the selectivity and sensitivity of the removal process were investigated. Adsorption process was found to be highly pH dependent. The optimum pH range for adsorption of Cr(VI) was found to be between 2 and 3. Kinetics studies were performed to understand the mechanistic steps of the adsorption process and the rate kinetics for the adsorption of Cr(VI) was best fitted with the pseudo-2nd-order kinetic model. Langmuir and Freundlich adsorption isotherms were applicable to the adsorption process and their constants were evaluated. The thermodynamic equilibrium constant and the Gibbs free energy were determined for each system. The adsorption capacity (
q
max) calculated from Langmuir isotherm and the Gibbs free energy (Δ
G
o) value obtained for the different adsorbents showed that clarified sludge was the most effective among the selected adsorbents for the removal of Cr(VI) from aqueous solution. The adsorption efficiencies of rice husk ash and activated alumina were also equally comparable with that of clarified sludge.
Here we show carbon stock is lower in the tropical mangrove forest than in the terrestrial tropical forest and their annual increase exhibits faster turn over than the tropical forest. Variable for ...above ground biomass are in decreasing order of importance, breast height diameter (
d), height (
H) and wood density (
ρ). The above ground biomass (AGB) and live below ground biomass (LBGB) held different biomass (39.93 ± 14.05 t C ha
−1 versus 9.61 ± 3.37 t C ha
−1). Carbon accrual to live biomass (4.71–6.54 Mg C ha
−1 a
−1) is more than offset by losses from litter fall (4.85 Mg C ha
−1 a
−1), and carbon sequestration differs significantly between live biomass (1.69 Mg C ha
−1 a
−1) and sediment (0.012 Mg C ha
−1 a
−1). Growth specific analyses of taxon density suggest that changes in resource availability and environmental constrains could be the cause of the annual increase in carbon stocks in the Sundarbans mangrove forest in contrast to the disturbance – recovery hypotheses.
► Mixed species mangrove biomass regression models have been developed. ► This model can be applied to estimate spatial variation of carbon sequestration. ► Annual increase of carbon stock exhibits faster turn over than the tropical forest. ► Carbon sink in terms of live biomass is several fold greater than that of sediment. ► Resource availability is more important over recovery from a significant disturbance.
In this study we have analysed the elemental composition of fine particulate matter (PM
2.5
) to examine the seasonal changes and sources of the elements in Delhi, India from January, 2017 to ...December, 2021. During the entire sampling period, 19 elements (Al, Fe, Ti, Cu, Zn, Cr, Ni, As, Mo, Cl, P, S, K, Pb, Na, Mg, Ca, Mn, and Br) of PM
2.5
were identified by Wavelength Dispersive X-ray Fluorescence Spectrometer. The higher annual mean concentrations of S (2.29 µg m
-3
), Cl (2.26 µg m
-3
), K (2.05 µg m
-3
), Ca (0.96 µg m
-3
) and Fe (0.93 µg m
-3
) were recorded during post-monsoon season followed by Zn > Pb > Al > Na > Cu > Ti > As > Cr > Mo > Br > Mg > Ni > Mn > and P. The annual mean concentrations of elemental composition of PM
2.5
accounted for 10% of PM
2.5
(pooled estimate of 5 year). Principal Component Analysis (PCA) identified the five main sources crustal/soil/road dust, combustion (BB + FFC), vehicular emissions (VE), industrial emissions (IE) and mixed source (Ti, Cr and Mo rich-source) of PM
2.5
in Delhi, India.
The purpose of this research work is to investigate the biomechanics of pelvis, hip, knee, and ankle joint motion using a Kinect sensor & inertial measurement unit (IMU) sensor during the normal ...walk. In this paper, a very cost-effective gait analysis system based on Microsoft Kinect v2 and Inertial Measurement Unit (IMU) device is presented. Kinect sensor is used for acquiring 3D skeleton data (camera (x, y, z), depth (x, y) orientation (x, y, z, w), color (x, y)) with 25 human body joints. For this analysis, the lower extremities joints i.e. spinal cord joint, hip, knee, and ankle joints of both left and right legs are being considered. The main contribution of this research work is the joint angle calculation of lower extremities of human gait based on Microsoft Kinect sensor V2 and IMU sensor. From the law of cosine, the joint angle is calculated between the two joints and plotted for a single subject. We came with the observation that the characteristics of the human knee joint and ankle joint are inversely related to each other. There are two sharp humps for knee and ankle joints during the normal walk. During the swing phase, the knee joint is highly activated while during toe-off and heel strike it is least activated. This analysis of clinical data is very useful for prosthesis limb and exoskeleton design. The stability of calculated joints trajectories is validated using the limit cycle curve. A system is designed for real-time analysis of biomechanics of different lower limbs joints using gait.
•A mathematical model has been proposed to analyse the pandemic COVID-19.•The model has been analysed both theoretically and numerically.•The procedure to control the basic reproduction number R0 has ...been provided.•We have formed an optimal control problem where governmental policy is the control.•The model is used for short term prediction of COVID-19 in three states of India.
As there is no vaccination and proper medicine for treatment, the recent pandemic caused by COVID-19 has drawn attention to the strategies of quarantine and other governmental measures, like lockdown, media coverage on social isolation, and improvement of public hygiene, etc to control the disease. The mathematical model can help when these intervention measures are the best strategies for disease control as well as how they might affect the disease dynamics. Motivated by this, in this article, we have formulated a mathematical model introducing a quarantine class and governmental intervention measures to mitigate disease transmission. We study a thorough dynamical behavior of the model in terms of the basic reproduction number. Further, we perform the sensitivity analysis of the essential reproduction number and found that reducing the contact of exposed and susceptible humans is the most critical factor in achieving disease control. To lessen the infected individuals as well as to minimize the cost of implementing government control measures, we formulate an optimal control problem, and optimal control is determined. Finally, we forecast a short-term trend of COVID-19 for the three highly affected states, Maharashtra, Delhi, and Tamil Nadu, in India, and it suggests that the first two states need further monitoring of control measures to reduce the contact of exposed and susceptible humans.
The highly populated Indian regions are currently in a phase of rapid economic growth resulting in high emissions of carbonaceous aerosols. This leads to poor air quality and impact on climate. The ...chemical composition of carbonaceous aerosols has rarely been studied in industrial areas of India. Here, we investigated carbonaceous aerosols in particulate matter (PM) monthly in the industrial area of Delhi in 2011. The concentrations of organic C and elemental C in PM₁₀ fraction were analyzed. Results show a clear seasonal variability of organic and elemental C. PM₁₀ ranged 95.9–453.5 μg m⁻³, organic C ranged 28.8–159.4 μg m⁻³, and elemental C ranged 7.5–44.0 μg m⁻³; those values were higher than reported values. Organic and elemental C were correlated with each other in pre-monsoon and winter seasons, implying the existence of similar emission sources such as coal combustion, biomass burning and vehicular exhaust. The annual average contribution of total carbonaceous aerosols in PM₁₀ was estimated as 62 %.
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 %).
Biomass burning emits large amount of aerosols and trace gases into the atmosphere, which have significant impact on atmospheric chemistry and climate. In the present study, we have selected seven ...Indian states (Delhi, Punjab, Haryana, Uttar Pradesh, Uttarakhand, Bihar and West Bengal) over the IGP, India. Samples of biomass fuel (Fuel Wood, Crop Residue and Dung Cake) from rural household have been collected (Saud et al., 2011a). The burning process has been simulated using a dilution sampler following the methodology developed by Venkatraman et al. (2005). In the present study, emission factor represents the total period of burning including pyrolysis, flaming and smoldering. We have determined the emission factors of organic carbon (OC) and elemental carbon (EC) from different types of biomass fuels collected over the study area. Average emission factors of OC from dung cake, fuel wood and crop residue over IGP, India are estimated as 3.87 ± 1.09 g kg−1, 0.95 ± 0.27 g kg−1, 1.46 ± 0.73 g kg−1, respectively. Similarly, average emission factors of EC from dung cake, fuel wood and crop residue over IGP, India are found to be 0.49 ± 0.25 g kg−1, 0.35 ± 0.07 g kg−1 and 0.37 ± 0.14 g kg−1, respectively. Dung cake and crop residue are normally not used in Uttarakhand. Annual budget of OC and EC from biomass fuels used as energy in rural households of IGP, India is estimated as 361.96 ± 170.18 Gg and 56.44 ± 29.06 Gg respectively. This study shows the regional emission inventory from Indian scenario with spatial variability.
► Determination of EF of OC and EC of residential fuels in the Laboratory. ► State wise EF of carbonaceous aerosol from residential fuels over IGP, India. ► Estimation of emission budget of OC and EC over seven states in IGP, India. ► Refinement of estimation of emission budget of OC and EC over India.
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