A comparison between national air quality index, india and composite air quality index for Ahmedabad, India

Shah, Dipsha; Patel, Piyushkumar

doi:10.1016/j.envc.2021.100356

Cited by 11 publications

(5 citation statements)

References 14 publications

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“…AAQI characterizes the exposure to all monitored pollutants, which may be more valuable to describe air quality and more feasible for environmental management (Zhang and Lin, 2020). Some researchers consider AAQI more efficient than USEPA-AQI in urban air quality evaluation, as its values are more than USEPA-AQI and they represent the exposure to all the monitored pollutants (Shah and Patel, 2021). In case of more than one of the monitored pollutants exceeded the limits, it may be logic to use AAQI, while if some of the pollutants levels is good, it may be not acceptable for these pollutants to contribute in increasing air quality index.…”

Section: Resultsmentioning

confidence: 99%

“…Therefore, a method is needed to determine the effect of multiple pollutants and its health risk ranking as a single number [Oakes et al, 2014]. In addition, policy-makers have recommended displaying air quality in indices to notify the public and alert them about the risk of pollution [Shah and Patel, 2021]. Air quality index (AQI) is a model used to convert the concentrations of air pollutants into a single number representing the level of air quality condition.…”

Section: Introductionmentioning

confidence: 99%

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Assessment of Air Quality through Multiple Air Quality Index Models – A Comparative Study

Shihab¹

2023

J. Ecol. Eng.

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It is important to indicate air quality level in metropolitan areas as it is harmful to public health. Air quality index (AQI) is a useful tool to convert air quality pollutants concentrations into a single number representing air quality level. There are many air quality index models in the literature to represent air quality level. Two models were selected to assess the air quality in Mosul city. The first model utilizes the highest sub-index depending on USEPA pollutants standards. The second model includes the weights of all pollutants in the model as an aggregated air quality index (AAQI) model. Air quality concentrations were collected using a fixed monitoring station located in the courtyard of the public library for a year. The results illustrate that the values of aggregated Air quality index model were higher than those of the USEPA model. Air quality category "Moderate" was dominant in winter and spring for the two models. In contrast, the category "Unhealthy for sensitive group" was dominant in summer and autumn. Furthermore, the category "Unhealthy" appeared only with aggregated model in autumn. The contribution of air pollutants in AQI can be ranked as PM 10 , SO 2 , NO 2 , CO and O 3 from higher to lower. The study concluded that the AAQI is comprehensive and more workable in environmental management.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Assessment of Air Quality through Multiple Air Quality Index Models – A Comparative Study

Shihab¹

2023

J. Ecol. Eng.

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“…Several methods for determining AQI or API have been developed in the past by various researchers / environmental agencies (Bishoi et al, 2009 ; Chen & Promotion, 2013 ; World Health, 2021 ), but there is no universally accepted method that is suitable for all situations (Kanchan et al, 2015 ). In this study, AQI has been estimated following National Air Quality Index (NAQI), India methodology which is based on maximum operating function (Shah & Patel, 2021 ). It involves sub-index formulation by linear segmented principle or linear interpolation between breakpoint concentration values for selected pollutants (at least 3), one of which has to be PM10 or PM 2.5.…”

Section: Methodsmentioning

confidence: 99%

Understanding Responses of Atmospheric Pollution and its Variability to Contradicting Nexus of Urbanization–Industrial Emission Control in Haldia, an Industrial City of West Bengal

Yadav

Mitra

Santra

et al. 2023

J Indian Soc Remote Sens

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Poor atmospheric health has been causing millions of deaths globally by attacking the respiratory system. According to the World Health Organization, India has the most heavily polluted cities overall and contributes significantly to the annual particle concentration at a global level. This situation demands intensive and extensive monitoring systems for strategy optimization at various levels of governance to combat health and environmental. These strategies include an efficient air quality control strategy, the management of pollution sources while taking into account both local and national controls, etc. However, inadequacy in monitoring stations, which are primarily targeted at cities, has kept most of the small towns and villages out of the radar. This poses difficulty in assessing pollution levels at the local and regional scales, thereby causing a poor understanding of the intensity and spatio-temporal variability of pollutant concentration. Addressing this issue, an attempt has been made in this study to understand the atmospheric pollution of an industry-based city, Haldia in West Bengal, where all the monitoring stations are situated in industrial centers and function nearby making a larger part unmonitored. Handheld instruments were used to collect field data for five other stations selected mainly from non-industrial areas experiencing rapid detrimental land use conversion. Despite being spatially consistent in higher (2015–2019 mean of 95) and lower Air Quality Index (2015–2019 mean of 65), respectively, in industrial and non-industrial areas, contradicting temporal trend is quite distinct in them. Pollution has increased in areas experiencing rapid LULC change, especially in rapidly urbanized areas, whereas industrial emission controls and implementation of various pollution control strategies, including implementation of pollution base profiling and baseline policy action, graded response action plan, etc., have been successful in abating pollution in areas near industries. The study pinpoints the requirement of spatially consistent awareness in rapidly urbanized sections of Haldia to avoid possible environmental hazards caused by urbanization and the necessity of having a dense network of monitoring stations to capture local variations in pollutant concentrations for a better understanding of atmospheric health.

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“…The air quality index (AQI) functions as a measure used to assess the degree of air pollution or the air quality in a specific location [1]- [3]. Typically, this measure is employed by governmental bodies to depict the condition of air quality within a particular area [4], [5]. Various countries around the world adopt different air quality indices, depending on the specified air quality standards inherent to each nation [6], [7].…”

Section: Introductionmentioning

confidence: 99%

The development of color histogram method to identify air quality index based on sky images

Enggari,

Sumijan,

Tajuddin

2024

IJEECS

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Air quality index measurements in Indonesia are carried out by ministry of environment and forestry (KLHK). The Ministry divides air quality levels into 5 categories, namely good, moderate, unhealthy, very unhealthy and dangerous. In this study, 3 air quality categories were used as primary research data, namely good, moderate and unhealthy because the others, never occurred in Indonesia from the time this research was conducted until its completion. This research develops the color histogram method in order to recognize the shape of an object in an image. First stage in this research is inputting the sky image into the system. Then carry out pre-processing in the form of cropping the image to obtained is only an image of sky. Next, convert the red, green and blue (RGB) colored sky image to Grayscale, then image enhancement, then noise reduction. After that is processed using development of the color histogram method. Refinement of color histogram method has yielded an impressive accuracy level of 90%, validated through the analysis of 30 sky images. The method successfully detected 27 images accurately, while three images posed detection challenges. The findings of this research is color histogram method can be used to identify objects especially air pollution from sky images.

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A comparison between national air quality index, india and composite air quality index for Ahmedabad, India

Cited by 11 publications

References 14 publications

Assessment of Air Quality through Multiple Air Quality Index Models – A Comparative Study

Assessment of Air Quality through Multiple Air Quality Index Models – A Comparative Study

Understanding Responses of Atmospheric Pollution and its Variability to Contradicting Nexus of Urbanization–Industrial Emission Control in Haldia, an Industrial City of West Bengal

The development of color histogram method to identify air quality index based on sky images

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