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.

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 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 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 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 The Great Smog Month and Spatial and Monthly Variation in Air Quality in Ambient Air in Delhi, India

2020 ◽  
Vol 10 (27) ◽  
Author(s):  
Anchal Garg ◽  
N.C. Gupta
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Quality Index ◽  
Ambient Air ◽  
Air Quality Index ◽  
Monthly Variation ◽  
Ambient Air Quality ◽  
Episodic Events ◽  
Traffic Intersection ◽  
Stubble Burning

Background. In recent years, poor urban air quality in Delhi, India has gained significant attention. Episodic events including crop stubble burning and Diwali celebrations are considered major factors in the worsening quality of ambient air. Objective. This study aimed to investigate spatial and monthly variation as well as the role of episodic events in ambient air quality in Delhi, including the ‘Great Smog' month of November 2017. Methods. Monitoring of air pollutants (particulate matter (PM10, PM2.5, PM1) and nitrogen dioxide (NO2)) was carried out at three distinct locations of Delhi from April 2017–February 2018. The concentration of NO2 was measured using a modified Jacob and Hochheiser method and PM was measured using a GRIMM aerosol spectrometer. Air quality index was also determined to identify the effects of air pollution on human health. Results. Overall, the levels of air pollution were found to be approximately 2.1–3.2 times higher along a traffic intersection and about 1.4–2.0 times higher in a commercial area compared with an institutional area. The highest average monthly concentrations of PM10, PM2.5, PM1 and NO2 were 768, 374, 298 and 149 μg/m3, respectively, during the Great Smog month of November 2017. November and August were recorded as the most polluted and cleanest months, respectively, in the city. Generally, poor to severe categories of the air quality index (AQI) were obtained from October to February. Higher concentrations during November were attributed to stubble burning in the nearby states of Delhi with the additive effect of fireworks during Diwali celebrations. Conclusions. Severe ambient air quality as observed in the present study is a serious matter of concern for the health of Delhi's population. To control spikes in poor air quality during episodic events, it is imperative to raise awareness among farmers regarding the severe health hazards of stubble burning. Competing Interests. The authors declare no competing financial interests.

scholarly journals Analysis of Air Quality Index Distribution of PM10 and PM2.5 Concentrations in Ambient Air of Al-Hillah City, Iraq

2021 ◽  
Vol 1058 (1) ◽  
pp. 012014
Author(s):  
Ruqayah Ali Grmasha ◽  
Shahla N. A. Al-Azzawi ◽  
Osamah J. Al-sareji ◽  
Talal Alardhi ◽  
Mawada Abdellatif ◽  
...  
Keyword(s):  
Air Quality ◽  
Quality Index ◽  
Ambient Air ◽  
Air Quality Index ◽  
Pm10 And Pm2.5 ◽  
Index Distribution

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.

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