Evaluation of water quality with application of Bayes' rule and entropy weight method

Sahoo, Mrunmayee Manjari; Patra, Kanhu Charan; Swain, Janaki Ballav; Khatua, Kishanjit Kumar

doi:10.1080/19648189.2016.1150895

Cited by 97 publications

(44 citation statements)

References 36 publications

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“…Similarly, several studies had been conducted to find the association of air pollutants such as NO 2 , SO 2 and other particulate matters with the increased risk of respiratory infections and mortality rates (Ã et al, 2005;Ashikin et al, 2014;Galy-lacaux et al, 2016;Lau et al, 2020;Mason et al, 2019;Pan et al, 2010;Sahoo et al, 2017;Wang and Su, 2020;Jiandong Yang et al, 2020). It had been concluded from several researches that, exposure to critical air pollutants such as PM 2.5 , NO 2 and SO 2 were harmful to human health and led to persistent and increased risk of respiratory illness (Chen et al, 29 2019(Chen et al, 29 , 2020Dastoorpoor et al, 2019;Gao et al, 2019;Hu et al, 2020;Naclerio et al, 2020;Oh et al, 2020;Sangkharat et al, 2019;Thi et al, 2018;Yao et al, 2020).…”

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confidence: 99%

Significance between Air pollutants, Meteorological Factors and COVID-19 Infections: Probable Evidences in India

Sahoo

2020

Preprint

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SARS-CoV-2 (Coronavirus) disease represents the causative agent with a potentially fatal risk which is having great global human health concern. Earlier studies suggested that air pollutants and meteorological factors were considered as the risk factors for acute respiratory infection, which carries harmful pathogens and affects the immunity. The study intended to explore the correlation between air pollutants, meteorological factors and the daily reported infection cases caused by novel coronavirus in India. The daily positive infected cases, air pollution and meteorological factors in 288 districts were collected from January 30, 2020 to April 23, 2020 in India. Speraman’s correlation and generalised additive model were applied to investigate the correlations of four air pollutants (PM2.5, PM10, NO2 and SO2) and eight meteorological factors (Temp, DTR, RH, AH, AP, RF, WS and WD) with COVID-19 infected cases. The study indicated that a 10 µg/m3 increase during (Lag0-14) in PM2.5, PM10 and NO2 was resulted in 2.21% (95%CI: 1.13 to 3.29), 2.67% (95% CI: 0.33 to 5.01) and 4.56 (95% CI: 2.22 to 6.90) increase in daily counts of COVID 19 infected cases respectively. However, only 1 unit increase in meteorological factor levels in case of daily mean temperature and DTR during (Lag0-14) associated with 3.78% (95%CI: 1.81 to 5.75) and 1.82% (95% CI: -1.74 to 5.38) rise of COVID-19 infected cases respectively. In addition, SO2 and relative humidity were negatively associated with COVID-19 infected cases at Lag0-14 with decrease of 7.23% (95% CI: -10.99 to -3.47) and 1.11% (95% CI: -3.45 to 1.23) for SO2 and for relative humidity respectively. The study recommended that there is significant relationship between air pollutants and meteorological factors with COVID-19 infected cases, which substantially explain the effect of national lockdown and suggested positive implications for control and prevention of the spread of SARS-CoV-2 disease.

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confidence: 99%

Significance between Air pollutants, Meteorological Factors and COVID-19 Infections: Probable Evidences in India

Sahoo

2020

Preprint

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“…Wilson introduced the principle of maximum entropy in urban and regional modeling [31]. The popular entropy weight method can also be taken as an application of the maximum entropy method [32][33][34].…”

Section: Natural City Aggregated By Maximum Entropy Methodsmentioning

confidence: 99%

Delimitating the Natural City with Points of Interests Based on Service Area and Maximum Entropy Method

Liu

Xia

et al. 2019

Entropy

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The natural city, which is essential to understand urban physical scale and identify urban sprawling in urban studies, represents the urban functional boundaries of the city defined by human activities rather than the administrative boundaries. Most studies tend to utilize physical environment data such as street networks and remote sensing data to delimitate the natural city, however, such data may not match the real distribution of human activity density in the new cities or even ghost cities in China. This paper suggests aggregating the natural city boundary from the service area polygons of points of interest based on Reilly’s Law of Retail Gravitation and the maximum entropy method, since most points of interests provide services for surrounding communities, reflecting the vitality in a bottom-up way. The results indicate that the natural city defined by points of interests shows a high resolution and accuracy, providing a method to define the natural city with POIs.

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“…On the contrary, if the index's entropy is bigger, it can provide less information, the less its contribution to the overall evaluation and the lower its weight process of weight determination. The entropy method has been applied in many fields such as water quality measurement, risk assessment, network security assessment and so on and it is mainly used for index screening, processing and weight determination [30][31][32][33][34][35]. Delgado used integrated gray clustering and the entropy weight method to analyze the environmental conflicts in the northern Peru mine project.…”

Section: Weight Determination By the Entropy Weightmentioning

confidence: 99%

Investigating Low-Carbon City: Empirical Study of Shanghai

Yang

2018

Sustainability

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A low-carbon economy is an inevitable choice for achieving economic and ecological sustainable development. It is of significant importance to analyze a city's low-carbon economy development level scientifically and reasonably. In order to achieve this goal, we propose an urban low-carbon economic development level evaluation model based on the matter-element extension method. First, we select some indicators from the existing indicator system based on past research and experience. Then, a matter-element model is established on the basis of weight to evaluate the level of a city's low-carbon, the critical value of each index is determined through the classical domain and the section domain, calculating the correlation degree of a single index and a comprehensive index. Finally, we analyze the low-carbon economy development status and future development trends according to the analysis results. In this study, we select Shanghai as an empirical study-the results show that Shanghai is a city with a low-carbon level and there is a trend of further improvement in Shanghai's low-carbon economy. But its low carbon construction and low carbon technology investment are relatively low. In summary, this method can provide another angle for evaluating a city's low-carbon economy.

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Evaluation of water quality with application of Bayes' rule and entropy weight method

Cited by 97 publications

References 36 publications

Significance between Air pollutants, Meteorological Factors and COVID-19 Infections: Probable Evidences in India

Significance between Air pollutants, Meteorological Factors and COVID-19 Infections: Probable Evidences in India

Delimitating the Natural City with Points of Interests Based on Service Area and Maximum Entropy Method

Investigating Low-Carbon City: Empirical Study of Shanghai

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