scholarly journals Does Air Quality Influence the Spread of the SARS - COV2 in Metropolitan Cities? - A Case Study from Urban India

2021 ◽  
Vol 16 (2) ◽  
pp. 628-648
Author(s):  
Souradip Basu ◽  
Rajdeep Das ◽  
Sohini Gupta ◽  
Sayak Ganguli
Keyword(s):  
Air Quality ◽  
Quality Index ◽  
Quality Parameters ◽  
Air Pollutant ◽  
Air Quality Index ◽  
Pollution Level ◽  
Metropolitan Cities ◽  
The Government

COVID 19 pandemic has gradually established itself as the worst pandemic in the last hundred years around the world after initial outbreak in China, including India. To prevent the spread of the infection the Government implemented lockdown measure initially from 24th March to 14th April, 2020 which was later extended to 3rd May, 2020. This lockdown imposed restrictions in human activities, vehicular movements and industrial functioning; resulting in reduced pollution level in the cities. This study was initiated with the objective to identify the change in the air quality of seven megacities in India and to determine any correlation between the active COVID cases with the air quality parameters. Air quality dataset of the most common parameters (PM2.5, PM10, SO2, NO2, NH3, CO and Ozone) along with air quality index for 70 stations of seven megacities (Delhi, Mumbai, Kolkata, Bengaluru, Hyderabad, Chennai and Chandigarh) were analysed. Comparison was made between AQI of pre lockdown and during lockdown periods. The results obtained indicate sufficient improvement in air quality during the period of the lockdown. For the next part of the study active COVID cases during the lockdown were compared to the air quality change of that period. A significant correlation between active COVID case and change in the air quality was observed for Delhi and Kolkata with 0.51 and 0.64 R2 values respectively. A positive correlation was also observed between air pollutant parameters and incidents of COVID cases in this study. Thus from the analysis it was identified that air quality index improved considerably as a result of the nationwide lockdown however, there was no significant impact of this improvement on the infection rate of the prevailing pandemic.

Ambient Air Quality Monitoring Studies in Four Specific Location of Tamilnadu, India

2019 ◽  
pp. 677-683
Author(s):  
Mageshkumar P ◽  
Ramesh S ◽  
Angu Senthil K
Keyword(s):  
Air Quality ◽  
Quality Index ◽  
High Volume ◽  
Ambient Air ◽  
Quality Parameters ◽  
Air Quality Index ◽  
Gaseous Pollutants ◽  
Ambient Air Quality ◽  
Air Sampler

A comprehensive study on the air quality was carried out in four locations namely, Tiruchengode Bus Stand, K.S.R College Campus, Pallipalayam Bus Stop and Erode Government Hospital to assess the prevailing quality of air. Ambient air sampling was carried out in four locations using a high volume air sampler and the mass concentrations of PM10, PM2.5, SO2, NOX and CO were measured. The analyzed quality parameters were compared with the values suggested by National Ambient Air Quality Standards (NAAQS). Air quality index was also calculated for the gaseous pollutants and for Particulate Matters. It was found that PM10 concentration exceeds the threshold limits in all the measured locations. The higher vehicular density is one of the main reasons for the higher concentrations of these gaseous pollutants. The air quality index results show that the selected locations come under moderate air pollution.

scholarly journals Analisis Framing Detik.com dan Kompas.com Terhadap Pemberitaan Kualitas Udara Jakarta Terburuk di Dunia

Koneksi ◽  
2020 ◽  
Vol 3 (2) ◽  
pp. 366
Author(s):  
Nishya Gavrila ◽  
Farid Rusdi
Keyword(s):  
Air Quality ◽  
Moral Judgment ◽  
Quality Index ◽  
Poor Quality ◽  
Air Quality Index ◽  
Treatment Recommendation ◽  
The Poor ◽  
The World ◽  
The Government

On July 29, 2019, Jakarta's air quality was ranked first on the AirVisual.com website with the worst air quality statement in the world. According to AirVisual.com, Jakarta's Air Quality Index (AQI) stands at 188, which means the air quality in Jakarta is not healthy. On the same date, Detik.com and Kompas.com reported on the poor quality of Jakarta's air. The reason the author chose the latter is because based on the Alexa.com site, both news portals have the highest number of visitor readers in Indonesia. This study aims to determine and analyze Detik.com and Kompas.com in framing unhealthy air quality in Jakarta. The approach in this study uses a constructivist paradigm. by using Robert N. Entman's framing model that defines problems, diagnoses causes, makes moral judgment and recommendation treatment. From the results of this study, Detik.com further explained the response of Anies Baswedan regarding poor air quality in Jakarta, while on Kompas.com that poor air quality in Jakarta was a challenge for the government and the government could be convicted if it continued. Pada tanggal 29 Juli 2019, kualitas udara Jakarta menempati peringkat pertama di situs AirVisual.com dengan pernyataan kualitas udara terburuk di dunia. Menurut AirVisual.com, Air Quality Index (AQI) Jakarta berada pada angka angka 188, yang artinya kualitas udara di Jakarta tidak sehat. Pada tanggal yang sama Detik.com dan Kompas.com memberitakan tentang buruknya kualitas udara Jakarta. Alasan penulis memilih kedua tersebut karena berdasarkan situs Alexa.com, kedua portal berita tersebut memiliki jumlah pengunjung pembaca terbanyak di Indonesia. Penelitian ini bertujuan untuk mengetahui dan menganalisis Detik.com dan Kompas.com dalam membingkai kualitas udara di Jakarta yang tidak sehat.Pendekatan dalam penelitian ini memakai paradigma konstruktivis, dengan menggunakan framing model Robert N. Entman yakni define problems, diagnose cause, make moral judgement dan treatment recommendation. Dari hasil penelitian ini, Detik.com lebih menjelaskan tanggapan dari Anies Baswedan terkait buruknya kualitas udara Jakarta, sementara pada Kompas.com bahwa buruknya kualitas udara di Jakarta merupakan tantangan pemerintah dan pemerintah bisa dipidana jika terus dibiarkan.

scholarly journals Analysis of Ambient Air Quality Based On Exceedance Factor and Air Quality Index for Siliguri City, West Bengal

2020 ◽  
pp. 236-246 ◽  
Author(s):  
Subham Roy ◽  
Nimai Singha
Keyword(s):  
Air Quality ◽  
Quality Index ◽  
Ambient Air ◽  
Air Quality Index ◽  
National Standard ◽  
Pollution Level ◽  
Pm2.5 And Pm10 ◽  
The City ◽  
Exceedance Factor

Bad air is one of the key concerns for most of the urban centres today, and Siliguri is no exceptions to this. In order to assess the air quality of Siliguri, Exceedance factor (EF) method was applied based on the average annual concentration of the pollutants named as; NO2, SO2, PM2.5 and PM10 and it is found that PM2.5 and PM10 are the major pollutants that pose a severe threat for the city. After applying the EF method, it is found that the values of PM2.5 was between moderate to high pollution level and for PM10 it falls under high to critical pollution level. On the other hand, the concentration of NO2 and SO2 falls under moderate to low pollution level. Through trend analysis of the various pollutants, it is found that their concentration was varying in nature. In case of PM10, the trend shows high concentration which exceeds national standard; whereas PM2.5 shows its concentration near towards violating the national standard soon if not checked. In contrast, trends of NO2 and SO2 were recorded lower than the national standard. The present situation of ambient air of Siliguri was analyzed based on Air Quality Index which reveals that air quality of the city can be classified into two seasons, i.e. clean air period (from April to October) and polluted period (from November to March). Lastly, the annual trends of PM2.5 and PM10 were constructed as they are the major pollutants, and it shows their skewed nature during winter months which results in smog episodes. It unveils how critical the situation of air quality of Siliguri became especially during winter months which seek immediate attention. Thus the study tries to present a vivid scenario about the present air quality of Siliguri, which concludes with some of the suggestions to restrain the air quality.

Changes in Air Quality of the Yuxi City Urban Area

2014 ◽  
Vol 1021 ◽  
pp. 225-228
Author(s):  
Cheng Qiu ◽  
Hong Chen ◽  
Chun Li Ye ◽  
Yan Jun Yang ◽  
Chang Bing Ye
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Urban Area ◽  
Health Problem ◽  
Quality Index ◽  
Quality Standards ◽  
Air Pollutant ◽  
Air Quality Index ◽  
Air Quality Standards

Air pollution causes health problem. The paper simply analyzed the changes of air quality in the Yuxi city urban area from 2006 to 2012. In the Yuxi city urban area between 2006 and 2012, SO2 levels increased about 43.9 percent; NO2 levels increased about 13.3 percent; PM10 levels in 2012 decreased about 1.5 percent. By evaluating the air quality in the Yuxi city urban area, the results showed that air quality index was the maximum in 2009, and the quality of the air in Yuxi became worse from 2006 to 2012, air pollution in 2009 was the heaviest between 2006 to 2012. After adopting P.R.C EPA air quality standards (GB3095-2012) in 2013, the first air pollutant in Yuxi is PM10, and then it is SO2 among SO2, NO2 and PM10.Much should beend done to reduce the amount of PM10 and SO2 released.

scholarly journals Air Quality Index Estimation Based on Image Analysis

2021 ◽  
Vol 7 (05) ◽  
pp. 172-177
Author(s):  
Mohd Ashraf., Niket., Devender & Dr. Vinod Kumar
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Quality Index ◽  
Large Scale ◽  
Air Quality Index ◽  
Control Measures ◽  
Pollution Level ◽  
The Government ◽  
Index Estimation ◽  
Harmful Effects

Air pollution is an issue that is out of the control of an average citizen. Controlling air pollution requires preventive and control measures on a large scale implemented by the government. However, what an individual can dois protect him/her from the harmful effects of pollution by taking precautions such as not going out in times of severe pollution or wearing an air mask when travelling out. It will be very helpful if a person is able to find out the pollution level around him. Government provides measures of pollution in terms of AIR QUALITY INDEX (AQI). However this is provided only at certain centre places. AQI may change drastically between these centres. In this report, an effort was made to solve this problem by enabling an individual to find an estimate of the Air Quality Index near them with their smartphone, even without an Internet connection, by simply clicking an image of their surroundings. Using this information a person can take preventive measures to take care of his health. This will not only spread awareness about air pollution but also protect people from the harmful effects of air pollution. We have used Machine Learning to achieve this goal. We prepared a dataset of images of sky and trained a model using several algorithms and compared them. We then used this model to recognise almost accurate AQI of the surroundings.

scholarly journals Regional Energy, CO2, and Economic and Air Quality Index Performances in China: A Meta-Frontier Approach

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2119 ◽  
Author(s):  
Ying Li ◽  
Yung-Ho Chiu ◽  
Liang Lu
Keyword(s):  
Particulate Matter ◽  
Energy Consumption ◽  
Air Quality ◽  
Quality Index ◽  
Air Pollutant ◽  
Air Quality Index ◽  
Significant Difference ◽  
Desirable Output ◽  
Reduce Emissions ◽  
Carbon Dioxide Co2

Rapid economic development has resulted in a significant increase in energy consumption and pollution such as carbon dioxide (CO2), particulate matter (PM2.5), particulate matter 10 (PM10), SO2, and NO2 emissions, which can cause cardiovascular and respiratory diseases. Therefore, to ensure a sustainable future, it is essential to improve economic efficiency and reduce emissions. Using a Meta-frontier Non-radial Directional Distance Function model, this study took energy consumption, the labor force, and fixed asset investments as the inputs, Gross domestic product (GDP) as the desirable output, and CO2 and the Air Quality Index (AQI) scores as the undesirable outputs to assess energy efficiency and air pollutant index efficiency scores in China from 2013–2016 and to identify the areas in which improvements was necessary. It was found that there was a large gap between the western and eastern cities in China. A comparison of the CO2 and AQI in 31 Chinese cities showed a significant difference in the CO2 emissions and AQI efficiency scores, with the lower scoring cities being mainly concentrated in China’s western region. It was therefore concluded that China needs to pay greater attention to the differences in the economic levels, stages of social development, and energy structures in the western cities when developing appropriately focused improvement plans.

scholarly journals Implementation of dataflow programming based Fuzzy Logic algorithm for gas concentration index in around of Sidoarjo mudflow, Indonesia

2018 ◽  
Vol 154 ◽  
pp. 03012
Author(s):  
Edita Rosana Widasari ◽  
Barlian Henryranu Prasetio ◽  
Hurriyatul Fitriyah ◽  
Reza Hastuti
Keyword(s):  
Air Pollution ◽  
Fuzzy Logic ◽  
Air Quality ◽  
Quality Index ◽  
Methane Concentration ◽  
Air Quality Index ◽  
Pollution Level ◽  
Affected Area ◽  
Fuzzy Logic Algorithm ◽  
Dataflow Programming

Sidoarjo mudflow or known as Lapindo mudflow erupted since 2006. The Sidoarjo mudflow is located in Sidoarjo City, East Java, Indonesia. The mudflow-affected area has high air pollution level and high health risk. Therefore, in this paper was implemented a system that can categorize the level of air pollution into several categories. The air quality index can be categorized using fuzzy logic algorithm based on the concentration of air pollutant parameters in the mudflow-affected area. Furthermore, Dataflow programming is used to process the fuzzy logic algorithm. Based on the result, the measurement accuracy of the air quality index in the mudflow-affected area has an accuracy rate of 93.92% in Siring Barat, 93.34% in Mindi, and 95.96% in Jatirejo. The methane concentration is passes the standard quality even though the air quality index is safe. Hence, the area is indicated into Hazardous level. In addition, Mindi has highest and stable methane concentration. It means that Mindi has high-risk air pollution.

scholarly journals Air Quality Index and Air Pollutant Concentration Prediction Based on Machine Learning Algorithms

2019 ◽  
Vol 9 (19) ◽  
pp. 4069 ◽  
Author(s):  
Huixiang Liu ◽  
Qing Li ◽  
Dongbing Yu ◽  
Yu Gu
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Air Quality ◽  
Regression Models ◽  
Quality Index ◽  
Air Pollutant ◽  
Air Quality Index ◽  
Machine Learning Algorithms ◽  
Coefficient Of Determination ◽  
Support Vector

Air pollution has become an important environmental issue in recent decades. Forecasts of air quality play an important role in warning people about and controlling air pollution. We used support vector regression (SVR) and random forest regression (RFR) to build regression models for predicting the Air Quality Index (AQI) in Beijing and the nitrogen oxides (NOX) concentration in an Italian city, based on two publicly available datasets. The root-mean-square error (RMSE), correlation coefficient (r), and coefficient of determination (R2) were used to evaluate the performance of the regression models. Experimental results showed that the SVR-based model performed better in the prediction of the AQI (RMSE = 7.666, R2 = 0.9776, and r = 0.9887), and the RFR-based model performed better in the prediction of the NOX concentration (RMSE = 83.6716, R2 = 0.8401, and r = 0.9180). This work also illustrates that combining machine learning with air quality prediction is an efficient and convenient way to solve some related environment problems.

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