APPLICATION OF AIR QUALITY INDEX AND INVERSE DISTANCE WEIGHTING FOR MAPPING THE DISTRIBUTION OF AIR POLLUTION AT SEVERAL URBAN DISTRICTS OF HANOI CITY

2020 ◽  
Vol 65 (10) ◽  
pp. 189-200
Author(s):  
Khac Dang Vu ◽  
Anh Nguyen Thi Van
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Quality Index ◽  
Ambient Air ◽  
Inverse Distance Weighting ◽  
Air Quality Index ◽  
Pollution Level ◽  
Urban Districts ◽  
Distance Weighting ◽  
Inverse Distance

The air pollution level can be assessed using air quality index - AQI calculated from the concentration of some gases and particle matters which are measured at ambient air quality monitoring stations. The calculated AQI values are characterized by temporal continuity but spatial discontinuity. However, AQI values of each monitoring station is interpolated by the IDW (Inverse Distance Weighting) method in GIS which helps us to assess the air quality at a detailed and specific level for every location in the study area by establishing distribution maps of air pollution. The interpolation of AQI values for zoning air quality in several urban districts of Hanoi during the Winter (October, November, December 2019) shows that in general, the areas with a very bad level of air quality occupied an important surface in the Northwest of urban districts (on the territory of Bac Tu Liem, Ba Dinh, Tay Ho, Cau Giay) for last 3 months of the year. The areas with a bad level of air quality occupied a large surface in the Southeast in October and December, but its surface became narrow in November. But in November, areas having a bad level of air quality were expanded to the Southeast while they occupied only a small surface at the center of the study area in October and December. Although the distribution of each level vary in terms of coverage, their common pattern has been conserved during three months of Winter. The distribution map of air quality provides the complete picture of the air pollution situation and it helps to adequately evaluate this issue in the urban districts of Hanoi city.

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 Estimating Short- and Long-Term Associations Between Air Quality Index and COVID-19 Transmission: Evidence From 257 Chinese Cities

2021 ◽  
Vol 66 ◽  
Author(s):  
Ru Cao ◽  
Yuxin Wang ◽  
Xiaochuan Pan ◽  
Xiaobin Jin ◽  
Jing Huang ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Quality Index ◽  
Air Quality Index ◽  
Pollution Level ◽  
Short Term ◽  
Migration Index ◽  
Long Run ◽  
Long Term Impact

Objectives: To evaluate the long- and short-term effects of air pollution on COVID-19 transmission simultaneously, especially in high air pollution level countries.Methods: Quasi-Poisson regression was applied to estimate the association between exposure to air pollution and daily new confirmed cases of COVID-19, with mutual adjustment for long- and short-term air quality index (AQI). The independent effects were also estimated and compared. We further assessed the modification effect of within-city migration (WM) index to the associations.Results: We found a significant 1.61% (95%CI: 0.51%, 2.72%) and 0.35% (95%CI: 0.24%, 0.46%) increase in daily confirmed cases per 1 unit increase in long- and short-term AQI. Higher estimates were observed for long-term impact. The stratifying result showed that the association was significant when the within-city migration index was low. A 1.25% (95%CI: 0.0.04%, 2.47%) and 0.41% (95%CI: 0.30%, 0.52%) increase for long- and short-term effect respectively in low within-city migration index was observed.Conclusions: There existed positive associations between long- and short-term AQI and COVID-19 transmission, and within-city migration index modified the association. Our findings will be of strategic significance for long-run COVID-19 control.

scholarly journals Understanding the Spatial-Temporal Patterns and Influential Factors on Air Quality Index: The Case of North China

2019 ◽  
Vol 16 (16) ◽  
pp. 2820 ◽  
Author(s):  
Wenxuan Xu ◽  
Yongzhong Tian ◽  
Yongxue Liu ◽  
Bingxue Zhao ◽  
Yongchao Liu ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Wind Speed ◽  
Quality Index ◽  
North China ◽  
Ambient Air ◽  
Air Quality Index ◽  
Rapid Expansion ◽  
Spatial Data Analysis ◽  
Prevailing Wind Direction

North China has become one of the worst air quality regions in China and the world. Based on the daily air quality index (AQI) monitoring data in 96 cities from 2014–2016, the spatiotemporal patterns of AQI in North China were investigated, then the influence of meteorological and socio-economic factors on AQI was discussed by statistical analysis and ESDA-GWR (exploratory spatial data analysis-geographically weighted regression) model. The principal results are as follows: (1) The average annual AQI from 2014–2016 exceeded or were close to the Grade II standard of Chinese Ambient Air Quality (CAAQ), although the area experiencing heavy pollution decreased. Meanwhile, the positive spatial autocorrelation of AQI was enhanced in the sample period. (2) The occurrence of a distinct seasonal cycle in air pollution which exhibit a sinusoidal pattern of fluctuations and can be described as “heavy winter and light summer.” Although the AQI generally decreased in other seasons, the air pollution intensity increased in winter with the rapid expansion of higher AQI value in the southern of Hebei and Shanxi. (3) The correlation analysis of daily meteorological factors and AQI shows that air quality can be significantly improved when daily precipitation exceeds 10 mm. In addition, except for O3, wind speed has a negative correlation with AQI and major pollutants, which was most significant in winter. Meanwhile, pollutants are transmitted dynamically under the influence of the prevailing wind direction, which can result in the relocation of AQI. (4) According to ESDA-GWR analysis, on an annual scale, car ownership and industrial production are positively correlated with air pollution; whereas increase of wind speed, per capita gross domestic product (GDP), and forest coverage are conducive to reducing pollution. Local coefficients show spatial differences in the effects of different factors on the AQI. Empirical results of this study are helpful for the government departments to formulate regionally differentiated governance policies regarding air pollution.

scholarly journals The Relationship Between the Actual Level of Air Pollution and Residents’ Concern about Air Pollution: Evidence from Shanghai, China

2019 ◽  
Vol 16 (23) ◽  
pp. 4784 ◽  
Author(s):  
Daxin Dong ◽  
Xiaowei Xu ◽  
Wen Xu ◽  
Junye Xie
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Quality Index ◽  
Air Quality Index ◽  
Pollution Level ◽  
Daily Data ◽  
Internet Search Engine ◽  
Beneficial Impact ◽  
Local Residents ◽  
The Relationship

This study explored the relationship between the actual level of air pollution and residents’ concern about air pollution. The actual air pollution level was measured by the air quality index (AQI) reported by environmental monitoring stations, while residents’ concern about air pollution was reflected by the Baidu index using the Internet search engine keywords “Shanghai air quality”. On the basis of the daily data of 2068 days for the city of Shanghai in China over the period between 2 December 2013 and 31 July 2019, a vector autoregression (VAR) model was built for empirical analysis. Estimation results provided three interesting findings. (1) Local residents perceived the deprivation of air quality and expressed their concern on air pollution quickly, within the day on which the air quality index rose. (2) A decline in air quality in another major city, such as Beijing, also raised the concern of Shanghai residents about local air quality. (3) A rise in Shanghai residents’ concern had a beneficial impact on air quality improvement. This study implied that people really cared much about local air quality, and it was beneficial to inform more residents about the situation of local air quality and the risks associated with air pollution.

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.

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.

scholarly journals A Literature Review on Prediction of Air Quality Index and Forecasting Ambient Air Pollutants using Machine Learning Algorithms

2020 ◽  
Vol 5 (8) ◽  
pp. 1148-1152
Author(s):  
Radhika M. Patil ◽  
Dr. H. T. Dinde ◽  
Sonali. K. Powar
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Air Quality ◽  
Literature Review ◽  
Air Pollutants ◽  
Quality Index ◽  
Ambient Air ◽  
Air Quality Index ◽  
Machine Learning Algorithms ◽  
Ambient Air Pollutants

Day by day the air pollution becomes serious concern in India as well as in overall world. Proper or accurate prediction or forecast of Air Quality or the concentration level of other Ambient air pollutants such as Sulfur Dioxide, Nitrogen Dioxide, Carbon Monoxide, Particulate Matter having diameter less than 10µ, Particulate Matter having diameter less than 2.5µ, Ozone, etc. is very important because impact of these factors on human health becomes severe. This literature review focuses on the various techniques used for prediction or modelling of Air Quality Index (AQI) and forecasting of future concentration levels of pollutants that may cause the air pollution so that governing bodies can take the actions to reduce the pollution.

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 Development of a Health-Based Index to Identify the Association between Air Pollution and Health Effects in Mexico City

Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 372
Author(s):  
Kevin Cromar ◽  
Laura Gladson ◽  
Mónica Jaimes Palomera ◽  
Lars Perlmutt
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Mexico City ◽  
Health Risks ◽  
Quality Index ◽  
Ambient Air ◽  
Air Quality Index ◽  
Ambient Air Pollution ◽  
Increased Risk ◽  
Ed Visits

Health risks from air pollution continue to be a major concern for residents in Mexico City. These health burdens could be partially alleviated through individual avoidance behavior if accurate information regarding the daily health risks of multiple pollutants became available. A split sample approach was used in this study to create and validate a multi-pollutant, health-based air quality index. Poisson generalized linear models were used to assess the impacts of ambient air pollution (i.e., fine particulate matter (PM2.5), nitrogen dioxide (NO2), and ground-level ozone (O3)) on a total of 610,982 daily emergency department (ED) visits for respiratory disease obtained from 40 facilities in the metropolitan area of Mexico City from 2010 to 2015. Increased risk of respiratory ED visits was observed for interquartile increases in the 4-day average concentrations of PM2.5 (Risk Ratio (RR) 1.03, 95% CI 1.01–1.04), O3 (RR 1.03, 95% CI 1.01–1.05), and to a lesser extent NO2 (RR 1.01, 95% CI 0.99–1.02). An additive, multi-pollutant index was created using coefficients for these three pollutants. Positive associations of index values with daily respiratory ED visits was observed among children (ages 2–17) and adults (ages 18+). The use of previously unavailable daily health records enabled an assessment of short-term ambient air pollution concentrations on respiratory morbidity in Mexico City and the creation of a health-based air quality index, which is now currently in use in Mexico City.

scholarly journals Forecasting of outdoor air quality index using adaptive Neuro Fuzzy Inference System

2022 ◽  
Author(s):  
Haripriyan Uthayakumar ◽  
Perarasu Thangavelu ◽  
Saravanathamizhan Ramanujam
Keyword(s):  
Air Quality ◽  
Fuzzy Inference System ◽  
Quality Index ◽  
Fuzzy Inference ◽  
Ambient Air ◽  
Air Quality Index ◽  
Quality Data ◽  
Pollution Level ◽  
Inference System ◽  
Neuro Fuzzy

Introduction: The estimation of air pollution level is well indicated by Air Quality Index (AQI), which tells how unhealthy the ambient air is and how polluted it can become in near future. Hence, the predictions or modeling of AQI is always of greater concern among researchers and this present study aims to develop such a model for forecasting the AQI. Materials and methods: A combination of Artificial Neural Network (ANN) and Fuzzy logic (FL) system, called Adaptive Neuro-Fuzzy Inference System (ANFIS) have been considered for model development. Daily air quality data (PM2.5 and PM10) and meteorological data (temperature and humidity) over a period of March 2020 to March 2021 were used as the input data and AQI as the output variable for the ANFIS model. The performances of models were evaluated based on Root Mean Square Error (RMSE), Regression coefficient (R2) and Average Absolute Relative Deviation (AARD). Results: A total of 100 datasets is split into training (70), testing (15) and simulation (15). Gaussian and Constant membership functions were employed for classifications and the final index consisted of 81 inference (IF/THEN) rules. The ANFIS Simulation result shows an R2 and RMSE value of 0.9872 and 0.0287 respectively. Conclusion: According to the results from this study, ANFIS based AQI is a comprehensive tool for classification of air quality and it is inclined to produce accurate results. Therefore, local authorities in air quality assessment and management schemes can apply these reliable and suitable results.

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