scholarly journals The Quality of Urban Air in Barcelona: A New Approach Applying Compositional Data Analysis Methods

2020 ◽  
Vol 4 (2) ◽  
pp. 113-121 ◽  
Author(s):  
Jose Gibergans-Baguena ◽  
Carme Hervada-Sala ◽  
Eusebi Jarauta-Bragulat
Keyword(s):  
Air Pollution ◽  
Data Analysis ◽  
Air Quality ◽  
Quality Index ◽  
Compositional Data ◽  
Air Quality Index ◽  
Compositional Data Analysis ◽  
Atmospheric Air ◽  
Large Cities

The main goal of this paper is to go some steps further to improve the understanding and manageability of air quality. Quality of atmospheric air in large cities is a matter of great importance because of its impact on the environment and on the health of the population. Recently, measures restricting access of private vehicles to the centre of large cities and other measures to prevent atmospheric air pollution are currently topical. The knowledge of air quality acquires special relevance to be able to evaluate the impact of those great social and economic measures. There are many indices to express air quality. In fact, quite every country has its own, depending on the main pollutants. In general, all indices ignore the compositional nature of the concentrations of air pollutants and do not apply methods of Compositional Data Analysis and have some other weak points such as leak of standardized scale. Therefore, the methodology used is founded on Compositional Data Analysis. The air quality index has an adequate correlation between input (concentrations) and output (air quality index), it distinguishes between air pollution and air quality and it has a 0-100 reference scale which makes easier interpretation and management of air quality expression. To illustrate the proposed method, an application is made to a series of air pollution data (Barcelona, 2001-2015). The results show the effectiveness of the 2008 European directive on ambient air quality.

scholarly journals Analysis of existing air monitoring technologies

2021 ◽  
Vol 3 (134) ◽  
pp. 67-78
Author(s):  
Volodymyr Tarasov ◽  
Bohdan Molodets ◽  
Тatyana Bulanaya ◽  
Oleg Baybuz
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Quality Index ◽  
Ambient Air ◽  
Human Condition ◽  
Air Quality Index ◽  
Air Monitoring ◽  
Atmospheric Air ◽  
Monitoring Technologies ◽  
The Impact

Atmospheric air monitoring is a systematic, long-term assessment of the level of certain types of pollutants by measuring their amount in the open air. Atmospheric air monitoring is an integral part of an effective air quality management system and is carried out through environmental monitoring networks, which should support timely provision of public information about air pollution, support compliance with ambient air quality standards and development of emission strategies, support for air pollution research.The work is devoted to existing air monitoring technologies: ground (sensors, diffusion tubes, etc.) and remote resources (satellites, aircraft, etc.). In addition, standards of air quality assessment (European and American) are described. As an example, we consider the European Air Quality Index (EAQI) and the Air Quality Index according to EPF standards: indicators by which these indices are calculated, the ranking of air status depending on the value of the index are described.AQI (Air Quality Index) is used as an indicator of the impact of air on the human condition. The European Air Quality Index allows users to better understand air quality where they live, work or travel. By displaying information for Europe, users can gain an understanding of air quality in individual countries, regions and cities. The index is based on the values of the concentration of the five main pollutants, including particles less than 10μm (PM10), particles less than 2.5μm (PM2.5), ozone (O3); nitrogen dioxide (NO2); sulfur dioxide (SO2). To conclude, ground stations give a more accurate picture of the state of the air at a point, while satellite image data with a certain error (due to cloud cover, etc.) can cover a larger area and solve the problem of coverage of stations in the area. There is no single standard for calculation. Today, the European Air Quality Index (EAQI) is used in Ukraine and Europe.

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 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 The impact of air quality on its Baidu index: grey model analysis

2021 ◽  
Author(s):  
Leping Tu ◽  
Yan Chen
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Quality Index ◽  
Air Quality Index ◽  
Grey Correlation ◽  
Convolution Model ◽  
Grey Correlation Analysis ◽  
And Control ◽  
The Impact ◽  
The Relationship

Abstract To investigate the relationship between air quality and its Baidu index, we collect the annual Baidu index of air pollution hazards, causes and responses. Grey correlation analysis, particle swarm optimization and grey multivariate convolution model are used to simulate and forecast the comprehensive air quality index. The result shows that the excessive growth of the comprehensive air quality index will lead to an increase in the corresponding Baidu index. The number of search for the causes of air quality has the closest link with the comprehensive air quality index. Strengthening the awareness of public about air pollution is conducive to the improvement of air quality. The result provides a reference for relevant departments to prevent and control air pollution.

scholarly journals Real Time Air Quality Index from Various Locations

2020 ◽  
Vol 9 (2) ◽  
pp. 368-372
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Real Time ◽  
Environmental Protection Agency ◽  
Quality Index ◽  
Ground Level ◽  
Air Quality Index ◽  
Time Data ◽  
Open Government Data ◽  
Government Data

The surveys regarding air pollution shows that there has been a hasty growth due to the emission of fuels and exhaust gases from factories. The Air Quality Index (AQI) has been launched to note the contemporary status of the air quality. The intent of AQI is to aid every individual know how the regional air quality will make an impact on them. The Environmental Protection Agency assess the AQI for five major air pollutants namely Nitrogen dioxide (NO2), ground-level ozone (O3), particle pollution (PM10, PM2.5), carbon monoxide (CO), and sulphur dioxide (SO2). The intent of the project is to congregate real-time Air Quality Index from distinct monitoring stations across India, analysing the data and reporting on it. Collect the real-time data using the API key provided by Open Government Data (OGD) platform India. This is done by making use of Microsoft Business Intelligence (MSBI) and Power BI Tools to transform, analyse and visualize the data. This project can be utilized to develop various programs like Ozone today in Europe and in mobile applications which acts as an alert system that can protect people from air pollution.

scholarly journals Air Pollution and Nasal Mucociliary Clearance Time among Urban and Rural Residents in Two Philippine Communities

2020 ◽  
Vol 35 (1) ◽  
pp. 33-35
Author(s):  
Soraya Joson ◽  
Joman Laxamana
Keyword(s):  
Air Pollution ◽  
Methylene Blue ◽  
Air Quality ◽  
Quality Index ◽  
Mucociliary Clearance ◽  
Air Quality Index ◽  
Clearance Time ◽  
Rural Residents ◽  
Air Index ◽  
Nasal Mucociliary Clearance

ABSTRACT Objective: To measure the nasal mucociliary clearance (NMC) time among adults residing in two Philippine communities with different air quality indices using the saccharin and methylene blue test. Methods: Design: Cross-Sectional Study Setting: Diliman, Quezon City and Puerto Princesa, Palawan Participantss: Fifty (50) participants, 25 residing in an urban city with fair air quality index and 25 residing in a rural province with good air quality index. Results: The mean NMC time of the urban group was 22.15±12.68 mins and was significantly longer than the NMC time of the rural group which was 5.29±2.87mins; t(48) = 6.643, p<0.0001). Conclusion: Increased air pollution may be associated with significant prolongation of nasal mucociliary clearance time among urban residents with fair quality air index compared to rural residents with good quality air index. Keywords: nasal mucociliary clearance, naso mucociliary clearance time, air pollution, air quality index, saccharin test, methylene blue

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 Endogenous or Exogenous? Examining Trans-Boundary Air Pollution by Using the Air Quality Index (AQI): A Case Study of 30 Provinces and Autonomous Regions in China

2018 ◽  
Vol 10 (11) ◽  
pp. 4220 ◽  
Author(s):  
Wenyang Huang ◽  
Huiwen Wang ◽  
Yigang Wei
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Linear Regression ◽  
Regression Model ◽  
Linear Regression Model ◽  
Quality Index ◽  
Air Quality Index ◽  
Oil Consumption ◽  
Sar Model ◽  
Autonomous Regions

China is experiencing severe environmental degradation, particularly air pollution. To explore whether air pollutants are spatially correlated (i.e., trans-boundary effects) and to analyse the main contributing factors, this research investigates the annual concentration of the Air Quality Index (AQI) and 13 polluting sectors in 30 provinces and autonomous regions across China. Factor analysis, the linear regression model and the spatial auto-regression (SAR) model are employed to analyse the latest data in 2014. Several important findings are derived. Firstly, the global Moran’s I test reveals that the AQI of China shows a distinct positive spatial correlation. The local Moran’s I test shows that significant high–high AQI agglomeration regions are found around the Beijing–Tianjin–Hebei area and the regions of low–low AQI agglomeration all locate in south China, including Yunnan, Guangxi and Fujian. Secondly, the effectiveness of the SAR model is much better than that of the linear regression model, with a significantly improved R-squared value from 0.287 to 0.705. A given region’s AQI will rise by 0.793% if the AQI of its ambient region increases by 1%. Thirdly, car ownership, steel output, coke output, coal consumption, built-up area, diesel consumption and electric power output contribute most to air pollution according to AQI, whereas fuel oil consumption, caustic soda output and crude oil consumption are inconsiderably accountable in raising AQI. Fourthly, the air quality in Beijing and Tianjin is under great exogenous influence from nearby regions, such as Hebei’s air pollution, and cross-boundary and joint efforts must be committed by the Beijing–Tianjin–Hebei region in order to control air pollution.

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