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Article: The influence of meteorological variables and lockdowns on COVID-19 cases in urban agglomerations of Indian cities.

Chelani, Asha B / Gautam, Sneha

Stochastic environmental research and risk assessment : research journal

2022 Volume 36, Issue 9, Page(s) 2949–2960

Abstract: Coronavirus has been identified as one of the deadliest diseases and the WHO has declared it a pandemic and a global health crisis. It has become a massive challenge for humanity. India is also facing its fierceness as it is highly infectious and ... ...

Abstract	Coronavirus has been identified as one of the deadliest diseases and the WHO has declared it a pandemic and a global health crisis. It has become a massive challenge for humanity. India is also facing its fierceness as it is highly infectious and mutating at a rapid rate. To control its spread, many interventions have been applied in India since the first reported case on January 30, 2020. Several studies have been conducted to assess the impact of climatic and weather conditions on its spread in the last one and half years span. As it is a well-established fact that temperature and humidity could trigger the onset of diseases such as influenza and respiratory disorders, the relationship of meteorological variables with the number of COVID-19 confirmed cases has been anticipated. The association of several meteorological variables has therefore been studied in the past with the number of COVID-19 confirmed cases. The conclusions in those studies are based on the data obtained at an early stage, and the inferences drawn based on those short time series studies may not be valid over a longer period. This study attempted to assess the influence of temperature, humidity, wind speed, dew point, previous day's number of deaths, and government interventions on the number of COVID-19 confirmed cases in 18 districts of India. It is also attempted to identify the important predictors of the number of confirmed COVID-19 cases in those districts. The random forest model and the hybrid model obtained by modelling the random forest model's residuals are used to predict the response variable. It is observed that meteorological variables are useful only to some extent when used with the data on the number of the previous day's deaths and lockdown information in predicting the number of COVID-19 cases. Partial lockdown is more important than complete or no lockdown in predicting the number of confirmed COVID-19 cases. Since the time span of the data in the study is reasonably large, the information is useful to policymakers in balancing the restriction activities and economic losses to individuals and the government.
Language	English
Publishing date	2022-01-25
Publishing country	Germany
Document type	Journal Article
ZDB-ID	1481263-0
ISSN	1436-3259 ; 1435-151X ; 1436-3240 ; 0931-1955
ISSN (online)	1436-3259 ; 1435-151X
ISSN	1436-3240 ; 0931-1955
DOI	10.1007/s00477-021-02160-4
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: The influence of meteorological variables and lockdowns on COVID-19 cases in urban agglomerations of Indian cities

Chelani, Asha B. / Gautam, Sneha

Stochastic environmental research and risk assessment. 2022 Sept., v. 36, no. 9

2022

Abstract	Coronavirus has been identified as one of the deadliest diseases and the WHO has declared it a pandemic and a global health crisis. It has become a massive challenge for humanity. India is also facing its fierceness as it is highly infectious and mutating at a rapid rate. To control its spread, many interventions have been applied in India since the first reported case on January 30, 2020. Several studies have been conducted to assess the impact of climatic and weather conditions on its spread in the last one and half years span. As it is a well-established fact that temperature and humidity could trigger the onset of diseases such as influenza and respiratory disorders, the relationship of meteorological variables with the number of COVID-19 confirmed cases has been anticipated. The association of several meteorological variables has therefore been studied in the past with the number of COVID-19 confirmed cases. The conclusions in those studies are based on the data obtained at an early stage, and the inferences drawn based on those short time series studies may not be valid over a longer period. This study attempted to assess the influence of temperature, humidity, wind speed, dew point, previous day’s number of deaths, and government interventions on the number of COVID-19 confirmed cases in 18 districts of India. It is also attempted to identify the important predictors of the number of confirmed COVID-19 cases in those districts. The random forest model and the hybrid model obtained by modelling the random forest model's residuals are used to predict the response variable. It is observed that meteorological variables are useful only to some extent when used with the data on the number of the previous day’s deaths and lockdown information in predicting the number of COVID-19 cases. Partial lockdown is more important than complete or no lockdown in predicting the number of confirmed COVID-19 cases. Since the time span of the data in the study is reasonably large, the information is useful to policymakers in balancing the restriction activities and economic losses to individuals and the government.
Keywords	COVID-19 infection ; Orthocoronavirinae ; algorithms ; dewpoint ; humidity ; influenza ; models ; pandemic ; research ; risk assessment ; temperature ; time series analysis ; wind speed ; India
Language	English
Dates of publication	2022-09
Size	p. 2949-2960.
Publishing place	Springer Berlin Heidelberg
Document type	Article
ZDB-ID	1481263-0
ISSN	1436-3259 ; 1435-151X ; 1436-3240 ; 0931-1955
ISSN (online)	1436-3259 ; 1435-151X
ISSN	1436-3240 ; 0931-1955
DOI	10.1007/s00477-021-02160-4
Database	NAL-Catalogue (AGRICOLA)

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Article: Optimal selection of “zero liquid discharge” (ZLD) system using “analytical hierarchy process” (AHP) and “grey relational analysis” (GRA)

Shende, Akshay D / Chelani, Asha B / Rao, N. N / Pophali, Girish R

Environment, development and sustainability. 2021 June, v. 23, no. 6

2021

Abstract: This article specifically presents three different zero liquid discharge (ZLD) options as “Desalination of industrial wastewater and associated rejects management” and deals with optimal selection of ZLD system using “analytical hierarchy process” and “ ... ...

Abstract	This article specifically presents three different zero liquid discharge (ZLD) options as “Desalination of industrial wastewater and associated rejects management” and deals with optimal selection of ZLD system using “analytical hierarchy process” and “grey relation analysis” (GRA). Commercially available ZLD systems, which differ from each other in some way or the other, were assessed for various factors. Hierarchy among the three factors, viz. economic, technical, and administrative criteria, was considered to arrive at the best treatment option. Each criterion again involved hierarchy of indices. The most suitable ZLD option was arrived at in two ways: 1) by comparing the commercially available treatment options and 2) by deriving the most suitable option based on the maximum beneficial unit operation and treatment process among all the options. A ZLD treatment system based on the combination of unit operation and processes of different treatment options was recommended since it provided maximum benefits. An assessment of annual expenditure on operation and maintenance of existing ZLD system revealed that there is an emergent need to develop more cost-effective techniques to make them environmentally and economically sustainable.
Keywords	cost effectiveness ; desalination ; economic sustainability ; environment ; industrial wastewater ; liquids
Language	English
Dates of publication	2021-06
Size	p. 8506-8523.
Publishing place	Springer Netherlands
Document type	Article
Note	NAL-AP-2-clean
ZDB-ID	1438730-x
ISSN	1387-585X
ISSN	1387-585X
DOI	10.1007/s10668-020-00979-5
Database	NAL-Catalogue (AGRICOLA)

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Article: Irregularity analysis of CO, NO2 and O 3 concentrations at traffic, commercial and low activity sites in Delhi

Chelani, Asha B

Stochastic environmental research and risk assessment. 2014 May, v. 28, no. 4

2014

Abstract: The irregularity analysis of exceedance time series of gaseous pollutants CO, NO₂and O₃is carried out using Shannon entropy and Fisher information measure. The data observed during 2007–2010 at three sites with different land-use activities in Delhi are ... ...

Abstract	The irregularity analysis of exceedance time series of gaseous pollutants CO, NO₂and O₃is carried out using Shannon entropy and Fisher information measure. The data observed during 2007–2010 at three sites with different land-use activities in Delhi are analyzed. CO and NO₂showed irregular behavior at both, low anthropogenic activity and commercial activity sites, whereas at traffic site both the pollutant concentrations showed regular behavior. The irregularity is attributed to the multiplicity in emission sources at low activity and commercial site and regular behavior is observed due to the uniformity and well defined source characteristics at the traffic site. O₃at three sites showed irregular behavior owing to its secondary nature. Fisher–Shannon information plane showed the grouping of three pollutants except CO and NO₂at traffic and O₃at low activity site suggesting the similar temporal characteristics of the pollutants even at the sites with different land-use activities.
Keywords	anthropogenic activities ; entropy ; land use ; nitrogen dioxide ; pollutants ; time series analysis ; traffic ; India
Language	English
Dates of publication	2014-05
Size	p. 921-925.
Publishing place	Springer-Verlag
Document type	Article
ZDB-ID	1481263-0
ISSN	1436-3259 ; 1436-3240 ; 0931-1955
ISSN (online)	1436-3259
ISSN	1436-3240 ; 0931-1955
DOI	10.1007/s00477-013-0791-1
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Prediction of daily maximum ground ozone concentration using support vector machine.

Chelani, Asha B

Environmental monitoring and assessment

2009 Volume 162, Issue 1-4, Page(s) 169–176

Abstract: The accurate predictions of ground ozone concentrations are required for proper management, control, and making public warning strategies. Due to the difficulties in handling phenomenological models that are based on complex chemical reactions of ozone ... ...

Abstract	The accurate predictions of ground ozone concentrations are required for proper management, control, and making public warning strategies. Due to the difficulties in handling phenomenological models that are based on complex chemical reactions of ozone production, neural network models gained popularity in the last decade. These models also have some limitations due to problems of overfitting, local minima, and tuning of network parameters. In this study, the predictions of daily maximum ozone concentrations are attempted using support vector machines (SVMs). The comparison between the accuracy of SVM and neural network predictions is performed to evaluate their performance. For this, the daily maximum ozone concentration data observed during 2002-2004 at a site in Delhi is utilized. The models are developed using the available meteorological parameters. The results indicated the promising performance of SVM over neural networks in predicting daily maximum ozone concentrations.
MeSH term(s)	Air Pollutants/analysis ; Models, Theoretical ; Ozone/analysis ; Regression Analysis
Chemical Substances	Air Pollutants ; Ozone (66H7ZZK23N)
Language	English
Publishing date	2009-02-25
Publishing country	Netherlands
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	782621-7
ISSN	1573-2959 ; 0167-6369
ISSN (online)	1573-2959
ISSN	0167-6369
DOI	10.1007/s10661-009-0785-0
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Persistence analysis of extreme CO, NO₂ and O₃ concentrations in ambient air of Delhi

Chelani, Asha B

Atmospheric research. 2012 May, v. 108

2012

Abstract: Persistence analysis of air pollutant concentration and corresponding exceedance time series is carried out to examine for temporal evolution. For this purpose, air pollutant concentrations, namely, CO, NO₂ and O₃ observed during 2000–2009 at a traffic ... ...

Abstract	Persistence analysis of air pollutant concentration and corresponding exceedance time series is carried out to examine for temporal evolution. For this purpose, air pollutant concentrations, namely, CO, NO₂ and O₃ observed during 2000–2009 at a traffic site in Delhi are analyzed using detrended fluctuation analysis. Two types of extreme values are analyzed; exceeded concentrations to a threshold provided by national pollution controlling agency and time interval between two exceedances. The time series of three pollutants is observed to possess persistence property whereas the extreme value time series of only primary pollutant concentrations is found to be persistent. Two time scaling regions are observed to be significant in extreme time series of CO and NO₂, mainly attributed to implementation of CNG in vehicles. The presence of persistence in three pollutant concentration time series is linked to the property of self-organized criticality. The observed persistence in the time interval between two exceeded levels is a matter of concern as persistent high concentrations can trigger health problems.
Keywords	air ; nitrogen dioxide ; ozone ; pollutants ; time series analysis ; traffic ; India
Language	English
Dates of publication	2012-05
Size	p. 128-134.
Publishing place	Elsevier B.V.
Document type	Article
ISSN	0169-8095
DOI	10.1016/j.atmosres.2012.02.001
Database	NAL-Catalogue (AGRICOLA)

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Article: Prediction of daily maximum ground ozone concentration using support vector machine

Chelani, Asha B

Environmental monitoring and assessment. 2010 Mar., v. 162, no. 1-4

2010

Abstract	The accurate predictions of ground ozone concentrations are required for proper management, control, and making public warning strategies. Due to the difficulties in handling phenomenological models that are based on complex chemical reactions of ozone production, neural network models gained popularity in the last decade. These models also have some limitations due to problems of overfitting, local minima, and tuning of network parameters. In this study, the predictions of daily maximum ozone concentrations are attempted using support vector machines (SVMs). The comparison between the accuracy of SVM and neural network predictions is performed to evaluate their performance. For this, the daily maximum ozone concentration data observed during 2002-2004 at a site in Delhi is utilized. The models are developed using the available meteorological parameters. The results indicated the promising performance of SVM over neural networks in predicting daily maximum ozone concentrations.
Keywords	chemical bases ; chemical reactions ; equipment ; meteorological parameters ; neural networks ; ozone ; prediction
Language	English
Dates of publication	2010-03
Size	p. 169-176.
Publisher	Springer Netherlands
Publishing place	Dordrecht
Document type	Article
ZDB-ID	782621-7
ISSN	1573-2959 ; 0167-6369
ISSN (online)	1573-2959
ISSN	0167-6369
DOI	10.1007/s10661-009-0785-0
Database	NAL-Catalogue (AGRICOLA)

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Article: Air quality assessment in Delhi: before and after CNG as fuel.

Chelani, Asha B / Devotta, Sukumar

Environmental monitoring and assessment

2007 Volume 125, Issue 1-3, Page(s) 257–263

Abstract: A number of policy measures have been activated in India in order to control the levels of air pollutants such as particulate matter, sulphur dioxide (SO(2)) and nitrogen dioxide (NO(2)). Delhi, which is one of the most polluted cities in the world, is ... ...

Abstract	A number of policy measures have been activated in India in order to control the levels of air pollutants such as particulate matter, sulphur dioxide (SO(2)) and nitrogen dioxide (NO(2)). Delhi, which is one of the most polluted cities in the world, is also going through the implementation phase of the control policies. Ambient air quality data monitored during 2000 to 2003, at 10 sites in Delhi, were analyzed to assess the impact of implementation of these measures, specifically fuel change in vehicles. This paper presents the impact of policy measures on ambient air quality levels and also the source apportionment. CO and NO(2) concentration levels in ambient air are found to be associated with the mobile sources. The temporal variation of air quality data shows the significant effect of shift to CNG (Compressed Natural Gas) in vehicles.
MeSH term(s)	Air Pollutants/analysis ; Air Pollution ; Cities ; Environmental Monitoring/statistics & numerical data ; Fossil Fuels ; India ; Nitrogen Dioxide/analysis ; Particulate Matter ; Sulfur Dioxide/analysis ; Vehicle Emissions/analysis
Chemical Substances	Air Pollutants ; Fossil Fuels ; Particulate Matter ; Vehicle Emissions ; Sulfur Dioxide (0UZA3422Q4) ; Nitrogen Dioxide (S7G510RUBH)
Language	English
Publishing date	2007-02
Publishing country	Netherlands
Document type	Comparative Study ; Journal Article
ZDB-ID	782621-7
ISSN	1573-2959 ; 0167-6369
ISSN (online)	1573-2959
ISSN	0167-6369
DOI	10.1007/s10661-006-9517-x
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Nonlinear analysis and prediction of coarse particulate matter concentration in ambient air.

Chelani, Asha B / Devotta, Sukumar

Journal of the Air & Waste Management Association (1995)

2006 Volume 56, Issue 1, Page(s) 78–84

Abstract: This study attempts to characterize and predict coarse particulate matter (PM10) concentration in ambient air using the concepts of nonlinear dynamical theory. PM10 data observed daily from 1999 to 2002 at a site in Mumbai, India, was used to study the ... ...

Abstract	This study attempts to characterize and predict coarse particulate matter (PM10) concentration in ambient air using the concepts of nonlinear dynamical theory. PM10 data observed daily from 1999 to 2002 at a site in Mumbai, India, was used to study the applicability of the chaos theory. First, the autocorrelation function and Fourier power spectrum were used to analyze the behavior of the time-series. The dynamics of the time-series was additionally studied through correlation integral analysis and phase space reconstruction. The nonlinear predictions were then obtained using local polynomial approximation based on the reconstructed phase space. The results were then compared with the autoregressive model. The results of nonlinear analysis indicated the presence of chaotic character in the PM10 time-series. It was also observed that the nonlinear local approximation outperforms the autoregressive model, because the observed relative error of prediction for the autoregressive model was greater than the local approximation model. The invariant measures of nonlinear dynamics computed for the predicted time-series using the two models also supported the same findings.
MeSH term(s)	Air Pollutants/analysis ; Cities ; Dust ; Environmental Monitoring/statistics & numerical data ; India ; Nonlinear Dynamics ; Reproducibility of Results
Chemical Substances	Air Pollutants ; Dust
Language	English
Publishing date	2006-02-11
Publishing country	United States
Document type	Journal Article
ZDB-ID	1003064-5
ISSN	1096-2247 ; 0894-0630 ; 1047-3289
ISSN	1096-2247 ; 0894-0630 ; 1047-3289
DOI	10.1080/10473289.2006.10464432
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Article: Air Quality Assessment in Delhi: Before and After CNG as Fuel

Chelani, Asha B / Devotta, Sukumar

Environmental monitoring and assessment. 2007 Feb., v. 125, no. 1-3

2007

Abstract: A number of policy measures have been activated in India in order to control the levels of air pollutants such as particulate matter, sulphur dioxide (SO₂) and nitrogen dioxide (NO₂). Delhi, which is one of the most polluted cities in the world, is also ... ...

Abstract	A number of policy measures have been activated in India in order to control the levels of air pollutants such as particulate matter, sulphur dioxide (SO₂) and nitrogen dioxide (NO₂). Delhi, which is one of the most polluted cities in the world, is also going through the implementation phase of the control policies. Ambient air quality data monitored during 2000 to 2003, at 10 sites in Delhi, were analyzed to assess the impact of implementation of these measures, specifically fuel change in vehicles. This paper presents the impact of policy measures on ambient air quality levels and also the source apportionment. CO and NO₂ concentration levels in ambient air are found to be associated with the mobile sources. The temporal variation of air quality data shows the significant effect of shift to CNG (Compressed Natural Gas) in vehicles.
Keywords	air quality
Language	English
Dates of publication	2007-02
Size	p. 257-263.
Publisher	Kluwer Academic Publishers
Publishing place	Dordrecht
Document type	Article
ZDB-ID	782621-7
ISSN	1573-2959 ; 0167-6369
ISSN (online)	1573-2959
ISSN	0167-6369
DOI	10.1007/s10661-006-9517-x
Database	NAL-Catalogue (AGRICOLA)

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