Interpretation of air quality data using an air quality index for the city of Kanpur, India

2003 ◽  
Vol 2 (6) ◽  
pp. 453-462 ◽  
Author(s):  
Mukesh Sharma ◽  
Reenu Pandey ◽  
Mayank Maheshwari ◽  
B Sengupta ◽  
B P Shukla ◽  
...  
Keyword(s):  
Air Quality ◽  
Quality Index ◽  
Air Quality Index ◽  
Quality Data ◽  
The City ◽  
Air Quality Data

scholarly journals Analysis of air quality data in the city of Bogotá through clustering techniques

2020 ◽  
Vol 872 ◽  
pp. 012027
Author(s):  
Jesús Silva ◽  
Luz Adriana Londoño ◽  
Noel Varela ◽  
Omar Bonerge Pineda Lezama ◽  
Liliana Patricia Lozano Ayarza ◽  
...  
Keyword(s):  
Air Quality ◽  
Quality Data ◽  
Clustering Techniques ◽  
The City ◽  
Air Quality Data

scholarly journals Computer Simulations of Air Quality and Bio-Climatic Indices for the City of Sofia

Atmosphere ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1078
Author(s):  
Georgi Gadzhev ◽  
Kostadin Ganev
Keyword(s):  
Air Quality ◽  
High Risk ◽  
Quality Index ◽  
Air Quality Index ◽  
Space Distribution ◽  
Heat Index ◽  
City Center ◽  
Climatic Indices ◽  
The City ◽  
Very High

Air pollution is responsible for many adverse effects on human beings. Thermal discomfort, on the other hand, is able to overload the human body and eventually provoke health implications due to the heat imbalance. Methods: The aim of the presented work is to study the behavior of two bio-climatic indices and statistical characteristics of the air quality index for Sofia city—the capital of Bulgaria for the period 2008–2014. The study is based on the WRF-CMAQ model system simulations with a spatial resolution of 1 km. The air quality is estimated by the air quality index, taking into account the influence of different pollutants and the thermal conditions by two indices, respectively, for hot and cold weather. It was found that the recurrence of both the heat and cold index categories and of the air quality categories have heterogeneous space distribution and well manifested diurnal and seasonal variability. For all of the situations, only O3 and PM10 are the dominant pollutants—these which determine the AQI category. It was found that AQI1, AQI2, and AQI3, which fall in the “Low” band, have the highest recurrence during the different seasons, up to more than 70% in some places and situations. The recurrence of AQI10 (very high) is rather small—no more than 5% and concentrated in small areas, mostly in the city center. The Heat index of category “Danger” never appears, and the Heat index of category “Extreme caution” appears only in the spring and summer with the highest recurrence of less than 5% in the city center. For the Wind-chill index category, “Very High Risk” never appears, and the category “High Risk” appears with a frequency of about 1–2%. The above leads to the conclusion that both from a point of view of bioclimatic and air quality indices, the human health risks in the city of Sofia are not as high.

scholarly journals The trend of air quality index (AQI) in Tehran during (2011-2016)

2019 ◽  
Author(s):  
Farhad Taghizadeh ◽  
Ahmad Jonidi Jafari ◽  
Majid Kermani
Keyword(s):  
Air Quality ◽  
Quality Index ◽  
Power Plants ◽  
Thermal Power ◽  
Temperature Inversion ◽  
Air Quality Index ◽  
Quality Data ◽  
Evaluative Study ◽  
Moderate Condition ◽  
Inversion Phenomenon

Introduction: Tehran city with the most population, about 4 million cars, million liters of fuels consumption, the presence of polluting industries such as petrochemicals and refineries, thermal power plants, and surrounding industrial towns is considered as one of the most populous and most polluted cities in the world . This study aims to investigate the trend of variation in air quality index in Tehran. Materials and methods: In this descriptive and evaluative study, the air quality data of 7 monitoring stations in 2012 were taken from the Tehran Department of Environment and Tehran Air Quality Control Company(AQCC). The calculation of AQI was done according to the EPA guidelines. Results: According to the results of this study, highest AQI averaging for 2016 (208.49±42.13) and the lowest for 2011 (134.13±46.80). Also observed that during the study period PM2.5 particles with an average of 71.59% is the most important factor in increasing the air quality index. Conclusion: It was observed that in the cold seasons of the year, due to the temperature inversion phenomenon in Tehran and the increase in the concentration of pollutants, air quality in most regions of Tehran is in unhealthy conditions, but in other season of the year the air quality is in moderate condition. Among the index pollutants, particulates are the major cause of Tehran’s air quality decline.

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 Spatial and temporal variability of air quality in Shanghai in 2015

2020 ◽  
Vol 194 ◽  
pp. 05063
Author(s):  
Li Liu ◽  
Lihuan He ◽  
Yu Bai ◽  
Mi Zhou ◽  
Fengying Zhang ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Air Quality ◽  
Urban Area ◽  
Spatial Patterns ◽  
Temporal Variability ◽  
Air Pollutants ◽  
Quality Index ◽  
Air Quality Index ◽  
Spatial And Temporal Variability ◽  
The City

In order to understand the temporal-spatial patterns of air pollutants in Shanghai, the concentrations of six air pollutants (SO2, NO2, PM10, PM2.5, CO, O3) and air quality index (AQI) were analyzed. The results showed that, the percentage of excellent/good-AQI days was 70.7% during the whole year of 2015, and PM2.5, O3 and NO2 were the main air pollutants in Shanghai. Besides, the air quality in winter was most serious, followed by that in spring and autumn, and the air quality in summer was the best. From the perspective of spatial distribution, monitoring stations in YangpuSipiao and Putuo which were located in the urban area were affected by traditional industries and the air quality were poor, while those in Chuansha and Zhangjiang of Pudong which were located on the edge of the city showed relatively good air quality.

scholarly journals Computer Simulations of Air Quality and Bio-climatic Indices for the City of Sofia

2021 ◽  
Author(s):  
Georgi Gadzhev ◽  
Kostadin Ganev
Keyword(s):  
Air Quality ◽  
Quality Index ◽  
Thermal Conditions ◽  
Air Quality Index ◽  
Statistical Characteristics ◽  
Cold Weather ◽  
Human Beings ◽  
Spatial Features ◽  
The City ◽  
Quality Categories

Air pollution is responsible for any adverse effects on human beings. Thermal discomfort, on the other hand, is able to overload the human body and eventually provoke health implications due to the heat imbalance. Methods: The aim of the present work is to study the behavior of two bio-climatic indexes and statistical characteristics of the air quality index for Sofia city - the capital of Bulgaria for the period 2008 - 2014. The study is based on WRF-CMAQ model system simula-tions with a spatial resolution of 1 kilometer. The air quality is estimated by the air quality index, taking into account the influence of different pollutants and the thermal conditions by two indi-ces, respectively, for hot and cold weather. Results: It was found that half of the heat and cold index categories are present in the simulations. Their distribution has some spatial features. All air quality categories are present in the domain, with dominance only of the O3 and PM10. Conclu-sions: It was found that Sofia is not so hot and air polluted place, but in some situations, people have to have some concerns when intend to be outdoors for a prolonged time.

scholarly journals Study of the Properties of Aerosols and the Air Quality Index Using a Backscatter Lidar System and Aeronet Sunphotometer in the City of São Paulo, Brazil

2010 ◽  
Vol 60 (4) ◽  
pp. 386-392 ◽  
Author(s):  
Eduardo Landulfo ◽  
Fábio J.S. Lopes ◽  
Glauber L. Mariano ◽  
Ani S. Torres ◽  
Wellington C. de Jesus ◽  
...  
Keyword(s):  
Air Quality ◽  
Quality Index ◽  
Sao Paulo ◽  
Air Quality Index ◽  
São Paulo ◽  
Lidar System ◽  
The City

scholarly journals High Density Real-Time Air Quality Derived Services from IoT Networks

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5435
Author(s):  
Claudio Badii ◽  
Stefano Bilotta ◽  
Daniele Cenni ◽  
Angelo Difino ◽  
Paolo Nesi ◽  
...  
Keyword(s):  
Air Quality ◽  
Real Time ◽  
Mobile Applications ◽  
Data Analytics ◽  
High Density ◽  
Quality Data ◽  
Control Room ◽  
Dense Grid ◽  
The City ◽  
Air Quality Data

In recent years, there is an increasing attention on air quality derived services for the final users. A dense grid of measures is needed to implement services such as conditional routing, alerting on data values for personal usage, data heatmaps for Dashboards in control room for the operators, and for web and mobile applications for the city users. Therefore, the challenge consists of providing high density data and services starting from scattered data and regardless of the number of sensors and their position to a large number of users. To this aim, this paper is focused on providing an integrated solution addressing at the same time multiple aspects: To create and optimize algorithms for data interpolation (creating regular data from scattered), making it possible to cope with the scalability and providing support for on demand services to provide air quality data in any point of the city with dense data. To this end, the accuracy of different interpolation algorithms has been evaluated comparing the results with respect to real values. In addition, the trends of heatmaps interpolation errors have been exploited to detected devices’ dysfunctions. Such anomalies may often be useful to request a maintenance action. The solution proposed has been integrated as a Micro Services providing data analytics in a data flow real time process based on Node.JS Node-RED, called in the paper IoT Applications. The specific case presented in this paper refers to the data and the solution of Snap4City for Helsinki. Snap4City, which has been developed as a part of Select4Cities PCP of the European Commission, and it is presently used in a number of cities and areas in Europe.

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