scholarly journals The Butterfly Diversity in Bhilai Mahila Mahavidyalaya College Campus

2016 ◽  
Vol 1 (2) ◽  
Author(s):  
Shikha Shrivastava ◽  
Ruby Alleppa

In the midst of the industrial area of Bhilai steel plant, the greenery on the campus of this college provides a home for many butterflies. The diversity of butterflies was investigated within the college campus of Bhilai Mahila Mahavidyalaya, Bhilai, which lies in the Durg district of Chattisgarh State. The state enjoys a tropical climate. The survey was done from February 2015 to October 2015. This period includes the summer and monsoon months. During the survey, a total of 45 species, belonging to five families of the Order Lepidoptera, were recorded in the study area. The predominance of family Nymphalidae was noted, which comprised of 37.77% of the butterflies surveyed,  followed by Pieridae (22.22%), Papilionidae (20%), Lycaenidae (11.11%), and Hesperidae (8.88%). Among the forty-five species of butterflies investigated, 11 species come under the Indian Wild Life (Protection) Act 1972.

2009 ◽  
Vol 42 (4) ◽  
pp. 34
Author(s):  
Rajesh Sood ◽  
Ajay Bedi ◽  
Alok Jha

2021 ◽  
Author(s):  
Ahmed Homoudi ◽  
Klemens Barfus ◽  
Gesa Bedbur ◽  
Dánnell Quesada-Chacón ◽  
Christian Bernhofer

<p>The Intertropical Convergence Zone (ITCZ) is recognised as the most crucial feature of the tropical climate producing more than 30% of the global precipitation. Its variability dramatically affects the people living in tropical areas. In the eastern Pacific, a pair of ITCZ, one at each side of the equator, during the boreal spring is evident. It is known as the Double Intertropical Convergence Zone (DITCZ). Generally, the ITCZ in the Pacific is located in the Northern Hemisphere (NH); however, during extreme El Niño events, it can cross the equator, or a wide band of deep convection extending over both hemispheres is to be observed. The DITCZ exists more frequently and with much more strength in General Circulation Models (GCMs), resulting in a spurious bias. The DITCZ bias has been a long-standing tropical bias in climate model simulations since the early beginning. Despite the intense research on the tropical climate and its features, fewer studies investigated the state of the ITCZs through an objective and automated algorithm. Also, much fewer studies have applied such an algorithm to the GCMs output. Unfortunately, far too little attention has been paid to examining how DITCZ bias is transmitted to Regional Climate Models (RCMs). Furthermore, the input variables to the RCM from GCM are prognostic such as wind, temperature and humidity. Since precipitation is not an input, it would be interesting to examine how the representation of ITCZs in the GCMs is unfolded in the RCMs. The method adopted in this study depends on an objective and automated algorithm developed and modified by earlier studies. The algorithm uses layer- and time-averaged winds in the lower troposphere (seven layers between 1000 and 850 hPa), in addition to wet-blub potential temperature, to automatically detect the centre latitude of the ITCZs. Furthermore, it uses GPCP or CMIP5 model precipitation to obtain the extents (i.e. boundaries) of the ITCZs and the precipitation intensities. From reanalysis datasets, the NH ITCZs are found near 8°N, while the Southern Hemisphere (SH) ITCZs are near 5°S. In CMIP5 models, the DITCZ is much stronger and more frequent, and it occurs year-round. Generally, the NH ITCZs are located between 8°N and 10°N while the SH ITCZs are located between 5°S and 10°S. Moreover, models overestimate the tropical precipitation mainly, the centre and full ITCZ intensities. Furthermore, it indicates more vigorous convection in the NH ITCZs than in the SH ITCZs. The study also found that the state of ITCZ in GCMs directly influences the bias in RCM monthly precipitation. However, it depends mainly on the RCM employed. The most affected nations by DITCZ bias are Ecuador and Peru. Quantitative in-depth analysis of precipitation of GCMs and RCMs is still <span>on</span>going.</p>


1998 ◽  
Vol 31 (25) ◽  
pp. 79-82
Author(s):  
M.R. Khare ◽  
N. Neogi

Atmosphere ◽  
2020 ◽  
Vol 11 (5) ◽  
pp. 469 ◽  
Author(s):  
Alessia Di Gilio ◽  
Jolanda Palmisani ◽  
Livia Trizio ◽  
Gaetano Saracino ◽  
Roberto Giua ◽  
...  

In this study, data on the hourly concentrations of the total particle-bound Polycyclic Aromatic Hydrocarbons (p-PAHs) collected between 1 August 2013 and 31 August 2014 by the air quality fence monitoring network of the biggest European steel plant, were analyzed. In contrast with what was predicted, the total p-PAH concentration did not decrease with distance from the steel plant, and higher concentrations were registered at the Orsini site, in the urban settlement, relative to the Parchi site, which is nearest to the coke ovens. Therefore, in order to identify and explain the cause of these high concentrations, a tailored monitoring experiment was carried out on a specific monitoring pathway by using a total p-PAHs monitor placed onto a cart. The real-time monitoring of the total p-PAH concentration on the road revealed to be a useful tool, which identified vehicular traffic as an important source of p-PAHs and highlighted the possible high short-term effect that vehicular traffic sources could have on the health of the exposed human population. Moreover, the study focused attention on the importance of the spatial representativeness of fixed monitoring stations, especially in a highly complex industrial area such as Taranto (Southern Italy).


2010 ◽  
Vol 25 (1-3) ◽  
pp. 118-124 ◽  
Author(s):  
T. S. Prasanna Kumar ◽  
S. Rath ◽  
U. Bhaskar

2000 ◽  
Vol 27 (3) ◽  
pp. 189-193 ◽  
Author(s):  
A.K. Ray ◽  
P.K. Bhor ◽  
D.S. Basu ◽  
S.K. Sahay ◽  
A. Paul ◽  
...  

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Oscar Breugelmans ◽  
Caroline Ameling ◽  
Marten Marra ◽  
Paul Fischer ◽  
Jan van de Kassteele ◽  
...  

We studied the spatial distribution of cancer incidence rates around a large steel plant and its association with historical exposure. The study population was close to 600,000. The incidence data was collected for 1995–2006. From historical emission data the air pollution concentrations for polycyclic aromatic hydrocarbons (PAH) and metals were modelled. Data were analyzed using Bayesian hierarchical Poisson regression models. The standardized incidence ratio (SIR) for lung cancer was up to 40% higher than average in postcodes located in two municipalities adjacent to the industrial area. Increased incidence rates could partly be explained by differences in socioeconomic status (SES). In the highest exposure category (approximately 45,000 inhabitants) a statistically significant increased relative risk (RR) of 1.21 (1.01–1.43) was found after adjustment for SES. The elevated RRs were similar for men and women. Additional analyses in a subsample of the population with personal smoking data from a recent survey suggested that the observed association between lung cancer and plant emission, after adjustment for SES, could still be caused by residual confounding. Therefore, we cannot indisputably conclude that past emissions from the steel plant have contributed to the increased risk of lung cancer.


2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhanar Oralbekova ◽  
Tamara Zhukabayeva ◽  
Kazizat Iskakov ◽  
Makpal Zhartybayeva ◽  
Nargiz Yessimova ◽  
...  

In order to ensure optimal operation of the existing environmental monitoring information system, it has become essential to use mathematical modeling based on the data assimilation algorithm. In this paper, a data assimilation algorithm has been designed and implemented. An algorithmic approach was tested for the assimilation of city atmosphere monitoring data from an industrial area. An industrial district of Karaganda city was selected for the investigation of the algorithm. The industrial district of Karaganda was taken as a research object due to the high level of atmospheric air pollution in industrial cities in the Republic of Kazakhstan. The result of our research and testing of the algorithm showed the effectiveness of the data assimilation algorithm for monitoring the atmosphere of the selected city. The practical value of the work lies on the fact that the presented results can be used to assess the state of atmospheric air in real time, to model the state of atmospheric air at each point of the city, and to determine the zone of increased environmental risk in an industrial city.


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