scholarly journals A Review of Chronological Evolution of Air Quality Indexing Systems (1966 To 2021)

2021 ◽  
Vol 16 (3) ◽  
pp. 704-725
Author(s):  
Dipsha Paresh Shah ◽  
Piyushkumar Patel
Keyword(s):  
Fuzzy Logic ◽  
Air Quality ◽  
Quality Index ◽  
Pollution Index ◽  
Ambient Air ◽  
Air Quality Index ◽  
Fuzzy Synthetic Evaluation ◽  
Quality Health ◽  
Unique Method ◽  
Past Experiences

Air quality index (AQI) also known as air pollution index (API) is the way of describing ambient air quality to assess the health risk associated with pollution. With the advent of time, there have been several air quality indexing systems starting from the first air Quality Index developed in 1966 by Marvin H. Green and various modifications have been made ever since to improve the accuracy of measurement. Such systems can assess the air quality by several factors like the concentration of different pollutants or by various empirically established formulas based on past experiences. In this review article, an effort has been made to chronologically evaluate the AQI system developed across the world from 1966 to 2021. Every indexing system has its own unique method for air quality determination and each method has its own merits and demerits. This pape rcovers various parameters, empirical relationships, standards, merits, and demerits, which in hind sight will help to develop an amalgamation of various indexing systems that can be used as a standard method for monitoring the quality of air. This paper also covers the AQI systems that prevail in India. A fuzzy logic system is very helpful in handling the uncertainty in air quality assessment. So, fuzzy-based air quality indexing systems developed from 2010 to 2017 have also been reviewed. The review of articles established that the results obtained through fuzzybased AQI aremore reliable than the other methods. Out of all the above describing methods, fuzzy synthetic evaluation-based AQI system and fuzzy air quality health index (FAQHI) are more powerful tools to describe the air quality. But till 2017, thereis no development of AQI systems based on fuzzy logic, considering PM2.5 as one of the pollutants. So, there is a need to develop the fuzzy-based AQI system considering PM2.5 as a pollutant with other air pollutants.

scholarly journals Geomatics Approach for Assessment of respiratory disease Mapping

2014 ◽  
Vol XL-8 ◽  
pp. 205-211 ◽  
Author(s):  
M. Pandey ◽  
V. Singh ◽  
R. C. Vaishya
Keyword(s):  
Fuzzy Logic ◽  
Air Quality ◽  
Human Health ◽  
Quality Index ◽  
Spatial Clustering ◽  
Statistical Significance ◽  
Ambient Air ◽  
Industrial Area ◽  
Air Quality Index ◽  
Statistical Techniques

Air quality is an important subject of relevance in the context of present times because air is the prime resource for sustenance of life especially human health position. Then with the aid of vast sums of data about ambient air quality is generated to know the character of air environment by utilizing technological advancements to know how well or bad the air is. This report supplies a reliable method in assessing the Air Quality Index (AQI) by using fuzzy logic. The fuzzy logic model is designed to predict Air Quality Index (AQI) that report monthly air qualities. With the aid of air quality index we can evaluate the condition of the environment of that area suitability regarding human health position. For appraisal of human health status in industrial area, utilizing information from health survey questionnaire for obtaining a respiratory risk map by applying IDW and Gettis Statistical Techniques. Gettis Statistical Techniques identifies different spatial clustering patterns like hot spots, high risk and cold spots over the entire work area with statistical significance.

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 Emission from Uyo Main Refuse Dumpsite and Potential Impact on Health

2020 ◽  
pp. 8-13
Author(s):  
S. A. Nta ◽  
M. J. Ayotamuno ◽  
A. H. Igoni ◽  
R. N. Okparanma
Keyword(s):  
Air Quality ◽  
Quality Index ◽  
Ambient Air ◽  
Air Quality Index ◽  
Volatile Organic Carbon ◽  
Highly Sensitive ◽  
Volatile Organic ◽  
Landfill Emissions ◽  
Potential Impact ◽  
Pm2.5 And Pm10

This paper presents potential impact on health of emission from landfill site on Uyo village road, Uyo local government area of Akwa Ibom State, Nigeria. Three sampling points were assessed for particulate matter (PM2.5 and PM10), nitrogen dioxide (NO2), sulphur dioxide (SO2), carbon monoxide (CO), hydrogen sulphide H2S, ammonia (NH3), total volatile organic carbon (TVOC) and hydrogen cyanide (HCN) using highly sensitive digital portable meters. The data obtained were expressed in terms of an air quality index. Air quality index indicates that the ambient air can be described as unhealthy for sensitive groups for NO2, unhealthy for SO2 and PM2.5 and moderate for CO, respectively. H2S, NH3, TVOC, HCN, PM10 were not indicated in USEPA air quality standards. It recommended that stringent and proper landfill emissions management together with appropriate burning of wastes should be considered in the study area to ease the risks associated with these pollutants on public health.

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 AMBIENT AIR QUALITY STATUS OF DEWAS INDUSTRIAL AREA OF MADHYA PRADESH, INDIA

2020 ◽  
pp. 4-12
Author(s):  
Reeta Kori ◽  
Alok Saxena ◽  
Harish Wankhade ◽  
Asad Baig ◽  
Ankita Kulshreshtha ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Quality Index ◽  
Ambient Air ◽  
Industrial Area ◽  
Madhya Pradesh ◽  
Air Quality Index ◽  
Particulate Phase ◽  
Ambient Air Quality ◽  
Quality Status

A study has been conducted to assess the ambient air quality status of Dewas industrial area of Madhya Pradesh, India. Total nine locations were selected in Dewas industrial area for ambient air quality monitoring. The eleven pollutants mainly particulate matter less than 10 µ size (PM10), particulate matter less than 2.5 µ size (PM2.5), nitrogen dioxide (NO2), sulphur dioxide (SO2), ozone (O3), ammonia (NH3), benzene (C6H6), benzo (a) Pyrene (BaP) – particulate phase, lead (Pb), Arsenic (As) and Nickel (Ni) were monitored during different four quarters from April 2019 to March 2020. The study revealed that average concentration of gaseous pollutants viz, NO2, SO2, O3, NH3, C6H6 in ambient air were well within standard limits at all selected locations, however concentration of particulate matter (PM10, PM2.5) and heavy metals (Pb & Ni) except As level were found exceeding the National Ambient Air Quality Standards (NAAQS) 2009, India at few monitoring locations. Benzo (a) Pyrene (BaP) –particulate phase in ambient air was not detected during this study. Ambient air Quality Index was found to be moderate (115.56-198.36) at six locations and satisfactory (17.60-94.94) at three locations in Dewas industrial area. Overall ambient Air Quality Index of Dewas industrial area was observed, satisfactory to moderate during this study w.r.t. Air Quality Index. KEY WORDS: Industrial Area, Ambient Air, Air Pollutants, Air Quality Index

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.

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