Air Quality Index-A Comparative Study for Assessing the Status of Air Quality

Nigam, Shivangi; Rao, B. Padma S.; Kumar, Navneet; Mhaisalkar, V. A.

doi:10.5958/2321-581x.2015.00041.0

Cited by 46 publications

(16 citation statements)

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“…To see the impact of bursting of fireworks during Diwali festival on air quality the real time air quality monitoring was carried out at residential site NEERI, Nagpur (Figure 1) [3]. In the present study ambient air quality was…”

Section: Methodsmentioning

confidence: 98%

Real Time Ambient Air Quality Status during Diwali Festival in Central, India

Nigam¹,

Kumar²,

Mandal³

et al. 2016

GEP

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In India, festivals are celebrated with lot of enthusiasm and Diwali is the major festival of light. In this festival, houses are illuminated by lights and sky is illuminated by fireworks. These fireworks though create lot of amusement but also pollute the atmosphere in terms of air pollution. The continuous air pollution monitoring was undertaken during Diwali festival (2014) at residential site NEERI, Nagpur. Air quality parameters were compared with CPCB standard. On Diwali day, PM 10 and PM 2.5 concentration achieve its highest value of 900 µg/m 3 and 950 respectively µg/m 3. This high concentration is maintained in atmosphere for two days of this festival in atmosphere which is approximately 8-9 times more than that regulatory standard. These particles carry all the components of the cracker including heavy metals, alkali metals, alkaline earth and change the atmosphere with positive and negative ions apart from impaction of sulfur and other acid gases to the atmosphere.

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Section: Methodsmentioning

confidence: 98%

Real Time Ambient Air Quality Status during Diwali Festival in Central, India

Nigam¹,

Kumar²,

Mandal³

et al. 2016

GEP

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“…The AQI of an individual air pollutant is used to determine air quality. The mathematical equation for calculating sub‐indices of the AQI is as follows:

I_{normalp} = true(\frac{I_{normalH normalI} - I_{normalL normalO}}{B_{normalP normalH normalI} - B_{normalP normalL normalO}} \times (C_{normalp} - B_{L P O}) true) + I_{normalL normalO}

where I P is the AQI for a pollutant P , C P is the actual ambient concentration of the pollutant P , B PHI is the upper end breakpoint concentration ≥ C P , B PLO is the lower end breakpoint concentration that is ≤ C P , I LO is the sub‐index or the AQI value corresponding to B PLO , and I HI is the sub‐index or the AQI value corresponding to B PHI .…”

Section: Methodsmentioning

confidence: 99%

Assessment of Fractionated Aerosols at a Semiarid Region over the Indo‐Gangetic Basin

Singh¹,

Gupta²,

Jangid³

et al. 2019

CLEAN Soil Air Water

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The present study examines the mass concentration of particulate matter (PM) of different sizes between January and December 2015 at Agra, a semiarid region in the Indo‐Gangetic basin. The mean mass concentrations of PM10 and PM2.5 are 146.9 and 54.8 μg m−3, respectively. The observed PM2.5 and PM10 concentrations are considerably higher than standard values set by the World Health Organization, United States Environmental Protection Agency, and National Ambient Air Quality Standard (India). Higher concentrations of PM10 and PM2.5 are observed in November and January, respectively. The highest and lowest concentrations of PM10 and PM2.5 are measured in winter and monsoon seasons, respectively. The PM2.5/PM10 mean mass concentration ratio is 0.44, indicating the dominance of coarse particles. The daily average air quality index is 107.7 for PM2.5 and 132.8 for PM10. The exceedance factor (EF) for PM10 is >1.5, indicating a critical level of PM10, whereas the EF for PM2.5 is <1.5 indicating a high level of pollution load. The trajectory analysis showed the effect of long‐range‐transported particles on this region.

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“…The work [ 20 ] considers five methods of AQI calculation: as an arithmetic mean of the ratio of pollutant measured in the air to its standard value (with and without weights for pollutants), as a geometric mean of the same ratio, according to the Oak Ridge National Laboratory formula, and according to the algorithm proposed by EPA. All methods were tested in India.…”

Section: Related Workmentioning

confidence: 99%

“…Some examples of Smart Health platforms are: Kaa IoT is a versatile, multifunctional, open-source platform for the implementation of complete IoT solutions, connected applications, and intelligent products. Kaa has professional-grade IoT functions that can be connected and used to implement a vast majority of IoT use cases [ 20 ]. Vista data vision is a comprehensive software solution perfect for data management and visualization environmental monitoring projects.…”

Section: Underlaying Conceptsmentioning

confidence: 99%

See 1 more Smart Citation

Decision Support Algorithm Based on the Concentrations of Air Pollutants Visualization

Svertoka

Bălănescu²,

Suciu³

et al. 2020

Sensors

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As medical technologies are continuously evolving, consumer involvement in health is also increasing significantly. The integration of the Internet of Things (IoT) concept in the health domain may improve the quality of healthcare through the use of wearable sensors and the acquisition of vital and environmental parameters. Currently, there is significant progress in developing new approaches to provide medical care and maintain the safety of the life of the population remotely and around the clock. Despite the standards for emissions of harmful substances into the atmosphere established by the legislation of different countries, the level of pollutants in the air often exceeds the permissible limits, which is a danger not only for the population but also for the environment as a whole. To control the situation an Air Quality Index (AQI) was introduced. For today, many works discuss AQI, however, most of them are aimed rather at studying the methodologies for calculating the index and comparing air quality in certain regions of different countries, rather than creating a system that will not only calculate the index in real-time but also make it publicly available and understandable to the population. Therefore we would like to present a decision support algorithm for a solution called “Environmental Sensing to Act for a Better Quality of Life: Smart Health” with the primary goal of ensuring the transformation of raw environmental data collected by special sensors (data which typically require scientific interpretation) into a form that can be easily understood by the average user; this is achieved through the proposed algorithm. The obtained result is a system that increases the self-awareness and self-adaptability of people in environmental monitoring by offering easy to read and understand suggestions. The algorithm considers three types of parameters (concentration of PM10 (particulate matter), PM2.5, and NO2) and four risk levels for each of them. The technical implementation is presented in a step-like procedure and includes all the details (such as calculating the Air Quality Index—AQI, for each parameter). The results are presented in a front-end where the average user can observe the results of the measurements and the suggestions for decision support. This paper presents a supporting decision algorithm, highlights the basic concept that was used in the development process, and discusses the result of the implementation of the proposed solution.

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Air Quality Index-A Comparative Study for Assessing the Status of Air Quality

Cited by 46 publications

References 1 publication

Real Time Ambient Air Quality Status during Diwali Festival in Central, India

Real Time Ambient Air Quality Status during Diwali Festival in Central, India

Assessment of Fractionated Aerosols at a Semiarid Region over the Indo‐Gangetic Basin

Decision Support Algorithm Based on the Concentrations of Air Pollutants Visualization

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