Assessment of Air Quality in Commercial Places of Chennai through Air Quality Index

2014 ◽  
Vol 984-985 ◽  
pp. 1190-1194 ◽  
Author(s):  
R. Ravinder ◽  
R. Kesavan ◽  
P. Thilagaraj
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Suspended Particulate Matter ◽  
Sulphur Dioxide ◽  
Quality Index ◽  
Air Pollutant ◽  
Air Quality Index ◽  
Oxides Of Nitrogen ◽  
Suspended Particulate ◽  
Respirable Suspended Particulate Matter

Air quality indices are used for local and regional air quality management in many metro cities of the world. The present investigation was carried out to find the significance of air pollutant concentrations at commercial areas of Chennai. Suspended Particulate Matter (SPM), Respirable Suspended Particulate Matter (RSPM), Sulphur dioxide (SO2) and Oxides of nitrogen (NOX) were analyzed over two sites namely T.Nagar and Kilpauk in Chennai. Both the sampling stations selected are located in commercial areas. Several concepts and indicators exist to measure and rank the urban areas in terms of their socio-economic infrastructure and environment related parameters. In this paper an air quality index (AQI) considering the combined level of three criteria pollutants (oxides of nitrogen, sulphur dioxide, and Respirable suspended particulate matter ) is proposed.

scholarly journals Regional Energy, CO2, and Economic and Air Quality Index Performances in China: A Meta-Frontier Approach

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 2119 ◽  
Author(s):  
Ying Li ◽  
Yung-Ho Chiu ◽  
Liang Lu
Keyword(s):  
Particulate Matter ◽  
Energy Consumption ◽  
Air Quality ◽  
Quality Index ◽  
Air Pollutant ◽  
Air Quality Index ◽  
Significant Difference ◽  
Desirable Output ◽  
Reduce Emissions ◽  
Carbon Dioxide Co2

Rapid economic development has resulted in a significant increase in energy consumption and pollution such as carbon dioxide (CO2), particulate matter (PM2.5), particulate matter 10 (PM10), SO2, and NO2 emissions, which can cause cardiovascular and respiratory diseases. Therefore, to ensure a sustainable future, it is essential to improve economic efficiency and reduce emissions. Using a Meta-frontier Non-radial Directional Distance Function model, this study took energy consumption, the labor force, and fixed asset investments as the inputs, Gross domestic product (GDP) as the desirable output, and CO2 and the Air Quality Index (AQI) scores as the undesirable outputs to assess energy efficiency and air pollutant index efficiency scores in China from 2013–2016 and to identify the areas in which improvements was necessary. It was found that there was a large gap between the western and eastern cities in China. A comparison of the CO2 and AQI in 31 Chinese cities showed a significant difference in the CO2 emissions and AQI efficiency scores, with the lower scoring cities being mainly concentrated in China’s western region. It was therefore concluded that China needs to pay greater attention to the differences in the economic levels, stages of social development, and energy structures in the western cities when developing appropriately focused improvement plans.

scholarly journals Spatio-Temporal Variation of Particulate Matter(PM2.5) Concentrations and Its Health Impacts in a Mega City, Delhi in India

2018 ◽  
Vol 12 ◽  
pp. 117863021879286 ◽  
Author(s):  
Amit Kumar Gorai ◽  
Paul B Tchounwou ◽  
SS Biswal ◽  
Francis Tuluri
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Environmental Protection Agency ◽  
Quality Index ◽  
Temporal Variations ◽  
Air Quality Index ◽  
Capital City ◽  
Health Impacts ◽  
Quality Level ◽  
Spatio Temporal

Rising concentration of air pollution and its associated health effects is rapidly increasing in India, and Delhi, being the capital city, has drawn our attention in recent years. This study was designed to analyze the spatial and temporal variations of particulate matter (PM2.5) concentrations in a mega city, Delhi. The daily PM2.5 concentrations monitored by the Central Pollution Control Board (CPCB), New Delhi during November 2016 to October 2017 in different locations distributed in the region of the study were used for the analysis. The descriptive statistics indicate that the spatial mean of monthly average PM2.5 concentrations ranged from 45.92 μg m−3 to 278.77 μg m−3. The maximum and minimum spatial variance observed in the months of March and September, respectively. The study also analyzed the PM2.5 air quality index (PM2.5—Air Quality Index (AQI)) for assessing the health impacts in the study area. The AQI value was determined according to the U.S. Environmental Protection Agency (EPA) system. The result suggests that most of the area had the moderate to very unhealthy category of PM2.5-AQI and that leads to severe breathing discomfort for people residing in the area. It was observed that the air quality level was worst during winter months (October to January).

scholarly journals Air Quality Index and Air Pollutant Concentration Prediction Based on Machine Learning Algorithms

2019 ◽  
Vol 9 (19) ◽  
pp. 4069 ◽  
Author(s):  
Huixiang Liu ◽  
Qing Li ◽  
Dongbing Yu ◽  
Yu Gu
Keyword(s):  
Machine Learning ◽  
Air Pollution ◽  
Air Quality ◽  
Regression Models ◽  
Quality Index ◽  
Air Pollutant ◽  
Air Quality Index ◽  
Machine Learning Algorithms ◽  
Coefficient Of Determination ◽  
Support Vector

Air pollution has become an important environmental issue in recent decades. Forecasts of air quality play an important role in warning people about and controlling air pollution. We used support vector regression (SVR) and random forest regression (RFR) to build regression models for predicting the Air Quality Index (AQI) in Beijing and the nitrogen oxides (NOX) concentration in an Italian city, based on two publicly available datasets. The root-mean-square error (RMSE), correlation coefficient (r), and coefficient of determination (R2) were used to evaluate the performance of the regression models. Experimental results showed that the SVR-based model performed better in the prediction of the AQI (RMSE = 7.666, R2 = 0.9776, and r = 0.9887), and the RFR-based model performed better in the prediction of the NOX concentration (RMSE = 83.6716, R2 = 0.8401, and r = 0.9180). This work also illustrates that combining machine learning with air quality prediction is an efficient and convenient way to solve some related environment problems.

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

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