Positive effects of COVID-19 lockdown on air quality of industrial cities (Ankleshwar and Vapi) of Western India

AbstractOn January 30, 2020, India recorded its first COVID-19 positive case in Kerala, which was followed by a nationwide lockdown extended in four different phases from 25th March to 31st May, 2020, and an unlock period thereafter. The lockdown has led to colossal economic loss to India; however, it has come as a respite to the environment. Utilizing the air quality index (AQI) data recorded during this adverse time, the present study is undertaken to assess the impact of lockdown on the air quality of Ankleshwar and Vapi, Gujarat, India. The AQI data obtained from the Central Pollution Control Board was assessed for four lockdown phases. We compared air quality data for the unlock phase with a coinciding period in 2019 to determine the changes in pollutant concentrations during the lockdown, analyzing daily AQI data for six pollutants (PM10, PM2.5, CO, NO2, O3, and SO2). A meta-analysis of continuous data was performed to determine the mean and standard deviation of each lockdown phase, and their differences were computed in percentage in comparison to 2019; along with the linear correlation analysis and linear regression analysis to determine the relationship among the air pollutants and their trend for the lockdown days. The results revealed different patterns of gradual to a rapid reduction in most of the pollutant concentrations (PM10, PM2.5, CO, SO2), and an increment in ozone concentration was observed due to a drastic reduction in NO2 by 80.18%. Later, increases in other pollutants were also observed as the restrictions were eased during phase-4 and unlock 1. The comparison between the two cities found that factors like distance from the Arabian coast and different industrial setups played a vital role in different emission trends.

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Oil and gas impacts on air quality in federal lands in the Bakken region: an overview of the Bakken Air Quality Study and first results

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-15-28749-2015 ◽

2015 ◽

Vol 15 (20) ◽

pp. 28749-28792 ◽

Cited By ~ 1

Author(s):

A. J. Prenni ◽

D. E. Day ◽

A. R. Evanoski-Cole ◽

B. C. Sive ◽

A. Hecobian ◽

...

Keyword(s):

Air Quality ◽

Power Plants ◽

Oil And Gas ◽

Quality Data ◽

Gas Emissions ◽

Horizontal Drilling ◽

Federal Lands ◽

Pollutant Concentrations ◽

Quality Study ◽

The Impact

Abstract. The Bakken formation contains billions of barrels of oil and gas trapped in rock and shale. Horizontal drilling and hydraulic fracturing methods have allowed for extraction of these resources, leading to exponential growth of oil production in the region over the past decade. Along with this development has come an increase in associated emissions to the atmosphere. Concern about potential impacts of these emissions on federal lands in the region prompted the National Park Service to sponsor the Bakken Air Quality Study over two winters in 2013–2014. Here we provide an overview of the study and present some initial results aimed at better understanding the impact of local oil and gas emissions on regional air quality. Data from the study, along with long term monitoring data, suggest that while power plants are still an important emissions source in the region, emissions from oil and gas activities are impacting ambient concentrations of nitrogen oxides and black carbon and may dominate recent observed trends in pollutant concentrations at some of the study sites. Measurements of volatile organic compounds also definitively show that oil and gas emissions were present in almost every air mass sampled over a period of more than four months.

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Prediction of Environmental Pollution Using Hybrid PSO-K-Means Approach

International Journal of E-Health and Medical Communications ◽

10.4018/ijehmc.2021030104 ◽

2021 ◽

Vol 12 (2) ◽

pp. 65-76

Author(s):

Manish Mahajan ◽

Santosh Kumar ◽

Bhasker Pant

Keyword(s):

Air Quality ◽

Environmental Pollution ◽

Productivity Loss ◽

Hybrid Approach ◽

Quality Data ◽

Quality Prediction ◽

Clustering Methods ◽

Air Quality Prediction ◽

The Impact

Air pollution is increasing day by day, decreasing the world economy, degrading the quality of life, and resulting in a major productivity loss. At present, this is one of the most critical problems. It has a significant impact on human health and ecosystem. Reliable air quality prediction can reduce the impact it has on the nearby population and ecosystem; hence, improving air quality prediction is the prime objective for the society. The air quality data collected from sensors usually contains deviant values called outliers which have a significant detrimental effect on the quality of prediction and need to be detected and eliminated prior to decision making. The effectiveness of the outlier detection method and the clustering methods in turn depends on the effective and efficient choice of parameters like initial centroids and number of clusters, etc. The authors have explored the hybrid approach combining k-means clustering optimized with particle swarm optimization (PSO) to optimize the cluster formation, thereby improving the efficiency of the prediction of the environmental pollution.

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Animal-Assisted and Pet-Robot Interventions for Ameliorating Behavioral and Psychological Symptoms of Dementia: A Systematic Review and Meta-Analysis

Biomedicines ◽

10.3390/biomedicines8060150 ◽

2020 ◽

Vol 8 (6) ◽

pp. 150 ◽

Cited By ~ 1

Author(s):

Sangki Park ◽

Ahream Bak ◽

Sujin Kim ◽

Yunkwon Nam ◽

Hyeon soo Kim ◽

...

Keyword(s):

Psychological Symptoms ◽

Meta Analysis ◽

Inappropriate Behaviors ◽

Positive Effects ◽

Dementia Patients ◽

The Impact ◽

Animal Assisted Intervention ◽

Behavioral And Psychological Symptoms

Patients with dementia suffer from psychological symptoms such as depression, agitation, and aggression. One purpose of dementia intervention is to manage patients’ inappropriate behaviors and psychological symptoms while taking into consideration their quality of life (QOL). Animal-assisted intervention (AAI) and pet-robot intervention (PRI) are effective intervention strategies for older people with cognitive impairment and dementia. In addition, AAI and PRI have been shown to have positive effects on behavioral and psychological symptoms of dementia (BPSD). However, studies into the association between AAI/PRI and BPSD have elicited inconsistent results. Thus, we performed a meta-analysis to investigate this association. We analyzed nine randomized controlled trials on AAI and PRI for dementia patients published between January 2000 and August 2019 and evaluated the impact of AAI/PRI on agitation, depression, and QOL. We found that AAI and PRI significantly reduce depression in patients with dementia. Subsequent studies should investigate the impact of AAI and PRI on the physical ability and cognitive function of dementia patients and conduct a follow-up to investigate their effects on the rate of progression and reduction of symptoms of dementia. Our research will help with neuropsychological and environmental intervention to delay or improve the development and progression of BPSD.

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Multiple‐Factor Influence on Air Quality of Road Motor Vehicles Tail Number Limit in Administrative Area of Beijing, China

Journal of Advanced Transportation ◽

10.1155/2020/8853180 ◽

2020 ◽

Vol 2020 ◽

pp. 1-20

Author(s):

Bakhrom Rajabov ◽

Lan Liu ◽

Jamshid Rajabov

Keyword(s):

Air Quality ◽

Motor Vehicles ◽

Quality Data ◽

Peak Time ◽

Spring Festival ◽

Administrative Area ◽

The Impact ◽

Beijing China ◽

Air Quality Data

From December 2, 2013, to October 31, 2019 (total 2160 days), Beijing official air quality data was used as the research object. The article analyzes the end of days 4 and 9 and the end of the nonrestricted 4 and 9 days, working and nonworking days, restricted and nonrestricted working days, long holidays (Spring Festival and National Day), and nonlong holidays (short holidays other than the Spring Festival and National Day and working days) of AQI, PM2.5, PM10, SO2, CO, NO2, and O3. According to the statistical analysis of the data, the air quality of the 4 and 9 limit is worse than that of the non-4 and 9 limit. Motor vehicles restricted in traffic had an objective effect on air AQI, PM2.5, PM10, CO, and NO2, whereas there was almost no difference in O3. Some peak values of AQI, PM2.5, PM10, SO2, CO, and NO2 on nonrestricted working days were significantly higher than those on restricted working days. At the same time, there was a peak time of the impact of motor vehicles on AQI, PM2.5, PM10, SO2, CO, and NO2 in Beijing. This time should be between 3 and 5 days, or 72 and 120 hours.

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Oil and gas impacts on air quality in federal lands in the Bakken region: an overview of the Bakken Air Quality Study and first results

Atmospheric Chemistry and Physics ◽

10.5194/acp-16-1401-2016 ◽

2016 ◽

Vol 16 (3) ◽

pp. 1401-1416 ◽

Cited By ~ 37

Author(s):

A. J. Prenni ◽

D. E. Day ◽

A. R. Evanoski-Cole ◽

B. C. Sive ◽

A. Hecobian ◽

...

Keyword(s):

Air Quality ◽

Power Plants ◽

Oil And Gas ◽

Quality Data ◽

Gas Emissions ◽

Horizontal Drilling ◽

Federal Lands ◽

Pollutant Concentrations ◽

Quality Study ◽

The Impact

Abstract. The Bakken formation contains billions of barrels of oil and gas trapped in rock and shale. Horizontal drilling and hydraulic fracturing methods have allowed for extraction of these resources, leading to exponential growth of oil production in the region over the past decade. Along with this development has come an increase in associated emissions to the atmosphere. Concern about potential impacts of these emissions on federal lands in the region prompted the National Park Service to sponsor the Bakken Air Quality Study over two winters in 2013–2014. Here we provide an overview of the study and present some initial results aimed at better understanding the impact of local oil and gas emissions on regional air quality. Data from the study, along with long-term monitoring data, suggest that while power plants are still an important emissions source in the region, emissions from oil and gas activities are impacting ambient concentrations of nitrogen oxides and black carbon and may dominate recent observed trends in pollutant concentrations at some of the study sites. Measurements of volatile organic compounds also definitively show that oil and gas emissions were present in almost every air mass sampled over a period of more than 4 months.

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Dampak pembatasan sosial berskala besar terhadap kualitas udara di Jakarta

Majalah Geografi Indonesia ◽

10.22146/mgi.64767 ◽

2021 ◽

Vol 35 (2) ◽

Author(s):

Rezky Yunita ◽

Mangapul Parlindungan Tambunan ◽

Rudy P. Tambunan

Keyword(s):

Spatial Distribution ◽

Air Quality ◽

Natural Environment ◽

Quality Parameters ◽

Outdoor Activity ◽

Horizontal Visibility ◽

Positive Effects ◽

Pollutant Concentrations ◽

The Impact ◽

Pm2.5 Concentration

Abstrak Beberapa negara di dunia memberlakukan pembatasan sosial dan karantina wilayah sebagai upaya untuk menekan laju penularan wabah virus COVID-19. Pembatasan sosial dan karantina wilayah memberikan dampak negatif bagi perekonomian, namun juga dapat berdampak positif bagi perbaikan kondisi lingkungan khususnya kualitas udara di suatu wilayah. Selama periode Pembatasan Sosial Berskala Besar (PSBB) di Jakarta tahun 2020, aktivitas penduduk di luar rumah menurun secara signifikan. Penelitian ini bertujuan untuk menganalisis secara kuantitatif perubahan parameter kualitas udara berupa PM2.5 dan visibility di Jakarta selama periode sebelum (2019) dan setelah pandemi (2020) menggunakan metode statistik. Pengaruh mobilitas penduduk dan distribusi spasial konsentrasi polutan juga dianalisis dalam penelitian ini. Hasil penelitian menunjukkan selama masa pandemi COVID-19, terdapat pengurangan konsentrasi polutan pada tahun 2020 hingga lebih dari 100 persen dibandingkan tahun 2019. Jarak pandang mendatar di Jakarta juga meningkat hingga 11 persen selama PSBB. Mobilitas penduduk mempengaruhi konsentrasi polutan di Jakarta sebesar 30 persen dan distribusi spasial menunjukkan adanya fluktuasi konsentrasi PM2.5 sebelum dan setelah diberlakukannya PSBB. Abstract Countries worldwide have implemented some sort of lockdowns to slow down COVID-19 infection and mitigate it. Lockdown due to COVID-19 has drastic effects on social and economic fronts. However, this lockdown also has some positive effects on the natural environment, especially on air quality. During the 2020 PSBB period in Jakarta, outdoor activity decreased significantly. This study quantitatively analyzes air quality parameters of PM2.5 and visibility changes in Jakarta during the period before (2019) and after the pandemic (2020) using statistical methods. The impact of mobility to polution also become a concern in this study. The results confirmed an improvement in air quality due to the implementation of social restrictions during the COVID-19 pandemic. PSBB has an impact on reducing pollutant concentrations by more than 100 percent during PSBB compared to 2019. The horizontal visibility in Jakarta also increased by 11 percent during the PSBB. Mobility has affected PM2.5 concentration by 30 percent in Jakarta, and spatial distribution of PM2.5 shows evidence of fluctuation during and before PSBB enacted.

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Exercise efficacy and prescription during treatment for pancreatic ductal adenocarcinoma: a systematic review

BMC Cancer ◽

10.1186/s12885-020-07733-0 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Dominic O’Connor ◽

Malcolm Brown ◽

Martin Eatock ◽

Richard C. Turkington ◽

Gillian Prue

Keyword(s):

Quality Of Life ◽

Systematic Review ◽

Pancreatic Cancer ◽

Adjuvant Therapy ◽

Physical Function ◽

Meta Analysis ◽

Ductal Adenocarcinoma ◽

Key Stakeholders ◽

The Impact

Abstract Background Surgical resection remains the only curative treatment for pancreatic cancer and is associated with significant post-operative morbidity and mortality. Patients eligible for surgery, increasingly receive neo-adjuvant therapy before surgery or adjuvant therapy afterward, inherently exposing them to toxicity. As such, optimizing physical function through exercise during treatment remains imperative to optimize quality of life either before surgery or during rehabilitation. However, current exercise efficacy and prescription in pancreatic cancer is unknown. Therefore, this study aims to summarise the published literature on exercise studies conducted in patients with pancreatic cancer undergoing treatment with a focus on determining the current prescription and progression patterns being used in this population. Methods A systematic review of four databases identified studies evaluating the effects of exercise on aerobic fitness, muscle strength, physical function, body composition, fatigue and quality of life in participants with pancreatic cancer undergoing treatment, published up to 24 July 2020. Two reviewers independently reviewed and appraised the methodological quality of each study. Results Twelve studies with a total of 300 participants were included. Heterogeneity of the literature prevented meta-analysis. Exercise was associated with improvements in outcomes; however, study quality was variable with the majority of studies receiving a weak rating. Conclusions High quality evidence regarding the efficacy and prescription of exercise in pancreatic cancer is lacking. Well-designed trials, which have received feedback and input from key stakeholders prior to implementation, are required to examine the impact of exercise in pancreatic cancer on key cancer related health outcomes.

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Feature and Language Selection in Temporal Symbolic Regression for Interpretable Air Quality Modelling

Algorithms ◽

10.3390/a14030076 ◽

2021 ◽

Vol 14 (3) ◽

pp. 76

Author(s):

Estrella Lucena-Sánchez ◽

Guido Sciavicco ◽

Ionel Eduard Stan

Keyword(s):

Air Quality ◽

Fundamental Problem ◽

Multivariate Time Series ◽

Quality Data ◽

Data Sets ◽

Air Quality Modelling ◽

Regression Problem ◽

Car Traffic ◽

Pollutant Concentrations ◽

Language Selection

Air quality modelling that relates meteorological, car traffic, and pollution data is a fundamental problem, approached in several different ways in the recent literature. In particular, a set of such data sampled at a specific location and during a specific period of time can be seen as a multivariate time series, and modelling the values of the pollutant concentrations can be seen as a multivariate temporal regression problem. In this paper, we propose a new method for symbolic multivariate temporal regression, and we apply it to several data sets that contain real air quality data from the city of Wrocław (Poland). Our experiments show that our approach is superior to classical, especially symbolic, ones, both in statistical performances and the interpretability of the results.

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Scalable kernel-based SVM classification algorithm on imbalance air quality data for proficient healthcare

Complex & Intelligent Systems ◽

10.1007/s40747-021-00435-5 ◽

2021 ◽

Author(s):

Shwet Ketu ◽

Pramod Kumar Mishra

Keyword(s):

Air Pollution ◽

Air Quality ◽

Class Imbalance ◽

Imbalanced Data ◽

Classification Algorithm ◽

Quality Data ◽

Pollution Level ◽

Classification Problems ◽

Chi Square ◽

The Impact

AbstractIn the last decade, we have seen drastic changes in the air pollution level, which has become a critical environmental issue. It should be handled carefully towards making the solutions for proficient healthcare. Reducing the impact of air pollution on human health is possible only if the data is correctly classified. In numerous classification problems, we are facing the class imbalance issue. Learning from imbalanced data is always a challenging task for researchers, and from time to time, possible solutions have been developed by researchers. In this paper, we are focused on dealing with the imbalanced class distribution in a way that the classification algorithm will not compromise its performance. The proposed algorithm is based on the concept of the adjusting kernel scaling (AKS) method to deal with the multi-class imbalanced dataset. The kernel function's selection has been evaluated with the help of weighting criteria and the chi-square test. All the experimental evaluation has been performed on sensor-based Indian Central Pollution Control Board (CPCB) dataset. The proposed algorithm with the highest accuracy of 99.66% wins the race among all the classification algorithms i.e. Adaboost (59.72%), Multi-Layer Perceptron (95.71%), GaussianNB (80.87%), and SVM (96.92). The results of the proposed algorithm are also better than the existing literature methods. It is also clear from these results that our proposed algorithm is efficient for dealing with class imbalance problems along with enhanced performance. Thus, accurate classification of air quality through our proposed algorithm will be useful for improving the existing preventive policies and will also help in enhancing the capabilities of effective emergency response in the worst pollution situation.

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