scholarly journals Short-term environmental impact of ambient air quality trends in during the COVID-19 pandemic in India

2021 ◽  
Vol 5 (1) ◽  
pp. 017-025
Author(s):  
Karuppasamy Manikanda Bharath ◽  
Natesan Usha ◽  
Periyasamy Balamadeswaran ◽  
S Srinivasalu
Keyword(s):  
Air Quality ◽  
Ambient Air ◽  
Air Pollutant ◽  
Quality Monitoring ◽  
Quality Data ◽  
Air Quality Monitoring ◽  
The North ◽  
Air Quality Monitoring Stations ◽  
Air Quality Data ◽  
Monitoring Stations

The lockdown, implemented in response to the COVID-19 epidemic, restricted the operation of various sectors in the country and its highlights a good environmental outcome. Thus, a comparison of air pollutants in India before and after the imposed lockdown indicated an overall improvement air quality across major Indian cities. This was established by utilizing the Central Pollution Control Board’s database of air quality monitoring station statistics, such as air quality patterns. During the COVID-19 epidemic, India’s pre-to-post nationwide lockdown was examined. The air quality data was collected from 30-12-2019 to 28-04-2020 and synthesized using 231 Automatic air quality monitoring stations in a major Indian metropolis. Specifically, air pollutant concentrations, temperature, and relative humidity variation during COVID-19 pandemic pre-to-post lockdown variation in India were monitored. As an outcome, several cities around the country have reported improved air quality. Generally, the air quality, on a categorical scale was found to be ‘Good’. However, a few cities from the North-eastern part of India were categorized as ‘Moderate/Satisfactory’. Overall, the particulate matters reduction was in around 60% and other gaseous pollutants was in 40% reduction was observed during the lockdown period. The results of this study include an analysis of air quality data derived from continuous air quality monitoring stations from the pre-lockdown to post-lockdown period. Air quality in India improved following the national lockdown, the interpretation of trends for PM 2.5, PM 10, SO2, NO2, and the Air Quality Index has been provided in studies for major cities across India, including Delhi, Gurugram, Noida, Mumbai, Kolkata, Bengaluru, Patna, and others.

scholarly journals VARMA-EGARCH Model for Air-Quality Analyses and Application in Southern Taiwan

Atmosphere ◽  
2020 ◽  
Vol 11 (10) ◽  
pp. 1096
Author(s):  
Edward Ming-Yang Wu ◽  
Shu-Lung Kuo
Keyword(s):  
Time Series ◽  
Air Quality ◽  
Air Pollutants ◽  
Quality Monitoring ◽  
Quality Data ◽  
Air Quality Monitoring ◽  
Egarch Model ◽  
Southern Taiwan ◽  
Air Quality Monitoring Stations ◽  
Monitoring Stations

This study adopted the Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) model to analyze seven air pollutants (or the seven variables in this study) from ten air quality monitoring stations in the Kaohsiung–Pingtung Air Pollutant Control Area located in southern Taiwan. Before the verification analysis of the EGARCH model is conducted, the air quality data collected at the ten air quality monitoring stations in the Kaohsiung–Pingtung area are classified into three major factors using the factor analyses in multiple statistical analyses. The factors with the most significance are then selected as the targets for conducting investigations; they are termed “photochemical pollution factors”, or factors related to pollution caused by air pollutants, including particulate matter with particles below 10 microns (PM10), ozone (O3) and nitrogen dioxide (NO2). Then, we applied the Vector Autoregressive Moving Average-EGARCH (VARMA-EGARCH) model under the condition where the standardized residual existed in order to study the relationships among three air pollutants and how their concentration changed in the time series. By simulating the optimal model, namely VARMA (1,1)-EGARCH (1,1), we found that when O3 was the dependent variable, the concentration of O3 was not affected by the concentration of PM10 and NO2 in the same term. In terms of the impact response analysis on the predictive power of the three air pollutants in the time series, we found that the asymmetry effect of NO2 was the most significant, meaning that NO2 influenced the GARCH effect the least when the change of seasons caused the NO2 concentration to fluctuate; it also suggested that the concentration of NO2 produced in this area and the degree of change are lower than those of the other two air pollutants. This research is the first of its kind in the world to adopt a VARMA-EGARCH model to explore the interplay among various air pollutants and reactions triggered by it over time. The results of this study can be referenced by authorities for planning air quality total quantity control, applying and examining various air quality models, simulating the allowable increase in air quality limits, and evaluating the benefit of air quality improvement.

scholarly journals Evaluation of Ambient Air Quality Monitoring System in Jakarta: A Literatur Review

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Michelle Gunawan ◽  
Riri Asyahira ◽  
Filson M Sidjabat
Keyword(s):  
Air Quality ◽  
Monitoring System ◽  
Ambient Air ◽  
Air Pollutant ◽  
Data Availability ◽  
Quality Monitoring ◽  
Quality Data ◽  
Air Pollution Control ◽  
Air Quality Monitoring ◽  
Ambient Air Quality

<p>As the first step to air pollution control and public health protection, Air quality monitoring systems provides information that indicate the extend of pollution in an area, the source of pollution and the types of pollutants. Therefore, the aim of this study is to evaluate Jakarta’s air quality monitoring system by comparing it to the US, which participates in Indonesia’s air quality monitoring system by using their own system.  In specific, parameters such as air quality index, monitoring stations, regulation and data availability are to be compared through reviewing various literatures in detail.  The result obtained shows that the monitoring station amount is already ideal and complies to the U.S regulation. Indonesia’s ambient air quality standard need to be stricter and Air Pollutant Standard Index should include PM<sub>2.5</sub> as a parameter obtains significantly better results. Air quality data is available and accessible, although it needs to be integrated and provide real time information in a simple and effective way.</p>

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