Surveillance on Emission of Herbal Woods and Cow Dung for Refinement of Atmosphere with Vedic Mantra

Earth's atmosphere is made of two gases Nitrogen and Oxygen. Five major air pollutants are Ground level Ozone, Airborne particles or aerosols, Carbon monoxide, Sulfur dioxide, Nitrogen dioxide. Air pollutants risky to human health are Ground level Ozone and Aerosols. They are the main ingredients of Smog . The ground level ozone is formed when sunlight reacts with certain chemical emissions like nitrogen dioxide, carbon monoxide or methane These chemicals are emitted from industrial waste, car exhaust, gasoline vapors etc. Air quality is measured with the Air Quality Index. An AQI under 50 is considered as good air quality however as the AQI number increases , it becomes a concern for human health . Researcher measured the PM level (PM 2.5 and PM 10), temperature, Humidity and other related parameters continuously on different woods in different times in a fixed size room and constrained environment to establish that Yagya is a reliable source to reduce environment pollution .

Overtone Frequencies of NO2 using Lie algebraic model

In this paper, we used the one dimensional unitatry Lie algebraic model to compute the vibrational frequencies of nitrogen dioxide (NO2) molecule in the gas phase up to the sixth overtone. In this model, the Hamiltonian operator describes the stretching and bending vibrations with algebraic parameters. The calculated fundamental vibrational frequencies are compared with experimental values and results consistent with the reference values.

Change In Nitrogen Dioxide (No2 ) Concentration Due To The Lockdown Amid The Covid-19 Pandemic In India

The study aimed to examine the change in the concentration of nitrogen dioxide due to the lockdown amid the COVID-19 pandemic in India at the district level using Sentinel-5P TROPOMI. The spatio-temporal characteristics of the tropospheric column NO2 concentration during 45 days of the lockdown were compared with the same days of 2019. Further, to model spatially varying relationships of NO2 during the lockdown period, it was given as a dependent variable whereas NO2 during the pre-lockdown period was considered as an independent variable. Results show that the mean NO2 concentration was reduced from 0.00406 mol/m2 before the lockdown (2019-03-25 to 2019-05-10) to 0.0036 mol/m2 during the lockdown period (2020-03-25 to 2020-05-10). The maximum decline of NO2 concentration was observed in Gautam Buddha Nagar and Delhi. This indicates the high level of atmospheric pollution due to the excess use of fuel in human activities. The results of the Ordinary Least Squares (OLS) method show a strong positive relationship between both variables. Positive standard residuals indicate that the concentration of NO2 has reduced more than expected as per the OLS model. The z-score (24.11) was obtained from spatial autocorrelation. It indicates that residuals are highly clustered and there is less than a 1% likelihood that this clustered pattern could be a result of a random chance. The highest decrease was observed in districts/urban agglomerations of Gautam Buddha Nagar (-40%), Delhi (-37%), Greater Bombay (-31%), Hyderabad (-29%), Faridabad (-29%), Bangalore Urban (-28%), Gandhinagar (-27%), Chennai (-27%) and Gurgaon (-26%) respectively.

Modeling Emissions Sources and Characterization in a Dense Low-Income Township Near Industrial Zones and Mine Dumps (Case of Tsakane, South Africa).

The sources of pollution in Tsakane township, which is situated within the City of Ekurhuleni in the province of Gauteng, South Africa, are investigated in this paper. The City of Ekurhuleni has the most industrial activities reported on South Africa's National Atmospheric Emission Inventory System (NAEIS), accounting for 40% of all listed activities in the country. The problem of suburban air pollution in South Africa is mainly associated with dense low-income areas like townships. The aim of this paper was to investigate atmospheric concentration correlation parameters, emissions roses, and probability modelling functions in order to analyse and classify significant emission sources affecting the township. Sulfur dioxide, nitrogen dioxide, ozone, and PM10 were the focus of the investigation. The probability functions for identifying and characterizing unknown or hidden sources of pollution were developed using hourly data. Furthermore, K-clustering algorithm analysis technique was used to provide graphical context for sources. PM10, ozone, sulfur dioxide, and nitrogen dioxide have all been identified as having directional pollution sources that are problematic and the results provide baseline data for a detailed understanding of current emission levels and possible sources.