scholarly journals Impact of COVID-19 on Air Quality in Central and Eastern China

2021 ◽  
Author(s):  
Haixia Feng ◽  
Erwei Ning ◽  
Haiying Feng ◽  
Jian Li ◽  
Qi Wang
Keyword(s):  
Air Quality ◽  
Traffic Congestion ◽  
Quality Index ◽  
Southern China ◽  
Eastern China ◽  
Product Data ◽  
Epidemic Period ◽  
Abnormal Increase ◽  
The Impact ◽  
Traffic Behavior

Abstract The focus of this paper is mainly on COVID-19’s impact on the air quality in central and eastern China using MCD19A2 aerosol optical depth (AOD) product data as well as the impact of human activities (mainly traffic behavior) on air quality. The main conclusions are the following: Significant data are still missing in MCD19A2 AOD product data, which led to the abnormal increase of AOD in southern China in February and the decline of analysis accuracy in AOD and air quality; COVID-19 had the important impact on air quality index (AQI) and peak congestion delay index (PCDI), resulting in the precipitous decrease of AQI and PCDI in Q1 2020, and the peaks of the AQI during the epidemic period were almost closely related to people's activities. AQI, PM2.5, and NO2 was significantly positively correlated with PCDI. Therefore, the alleviation of traffic congestion plays an important role in improving the air quality.

Evaluating the impact of partial driving restrictions on local air quality in Chongqing using regression discontinuity design

2021 ◽  
pp. 239980832110108
Author(s):  
Xin Li ◽  
Shuhan Jiang ◽  
Tianqi Wang ◽  
Jia Hu ◽  
Yun Yuan
Keyword(s):  
Air Quality ◽  
Traffic Congestion ◽  
Quality Index ◽  
Regression Discontinuity ◽  
Principal Component ◽  
River System ◽  
Air Quality Index ◽  
Regression Discontinuity Design ◽  
Driving Restrictions ◽  
The Impact

Driving restriction is used to mitigate traffic congestion and improve air quality. A partial bridge restriction policy is created in Chongqing, China since the bridges are natural network bottlenecks due to the local river system. Is such a strategy really capable of reducing air pollution and further improving local air quality? Employing an integration of principal component analysis and a regression-discontinuity design approach, this study examines the short-run effect of the partial driving restrictions on the local air quality index in Chongqing, China. The examination is first conducted to the city level, and then its eight administrative districts are tested separately. The findings reveal that the air quality index of the whole city area has experienced deterioration after the introduction of restrictions in Chongqing. Among eight districts, Yuzhong is the only one experiencing an improvement of air quality index.

scholarly journals THE EFFECTS OF ENVIRONMENTAL ISSUES ON RESIDENTIAL HOUSE PRICE (IN THE CASE OF ULAANBAATAR CITY)

2018 ◽  
pp. 117-130
Author(s):  
Oyunjargal D ◽  
Byambatseren Ch
Keyword(s):  
Air Quality ◽  
Quality Index ◽  
House Price ◽  
Measurement Data ◽  
Quality Measurement ◽  
Air Quality Index ◽  
Quality Level ◽  
Average Price ◽  
Residential House ◽  
The Impact

The purpose of this research is to determine the impact of the environment, especially the quality of air on house price. In addition, it also includes the research of the linkage between the index of air quality and average price of residential house which located in the most crowded districts of Ulaanbaatar such as Bayangol, Bayanzurkh, Chingeltei, Sukhbaatar, Songinokhairkhan and Khan-Uul. The statistical analysis and statistics determination methods were applied to identify the relationship utilizing the air quality index, determined from the air quality measurement data recorded in 2015-2017, and the average price per square meter of newly built apartment houses in the selected districts. The research findings suggest that there is little direct link between the house prices and air quality level, and the air quality levels of Ulaanbaatar districts do not have a significant impact on the price per square meter. Therefore, the air quality index should not considered as a house price determinant.

scholarly journals The impact of air quality on its Baidu index: grey model analysis

2021 ◽  
Author(s):  
Leping Tu ◽  
Yan Chen
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Quality Index ◽  
Air Quality Index ◽  
Grey Correlation ◽  
Convolution Model ◽  
Grey Correlation Analysis ◽  
And Control ◽  
The Impact ◽  
The Relationship

Abstract To investigate the relationship between air quality and its Baidu index, we collect the annual Baidu index of air pollution hazards, causes and responses. Grey correlation analysis, particle swarm optimization and grey multivariate convolution model are used to simulate and forecast the comprehensive air quality index. The result shows that the excessive growth of the comprehensive air quality index will lead to an increase in the corresponding Baidu index. The number of search for the causes of air quality has the closest link with the comprehensive air quality index. Strengthening the awareness of public about air pollution is conducive to the improvement of air quality. The result provides a reference for relevant departments to prevent and control air pollution.

scholarly journals A psychophysical measurement on subjective well-being and air pollution

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Yuan Li ◽  
Dabo Guan ◽  
Yanni Yu ◽  
Stephen Westland ◽  
Daoping Wang ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Quality Index ◽  
Well Being ◽  
Air Quality Index ◽  
Subjective Well Being ◽  
Atmospheric Conditions ◽  
Survey Techniques ◽  
Physical Effects ◽  
The Impact

AbstractAlthough the physical effects of air pollution on humans are well documented, there may be even greater impacts on the emotional state and health. Surveys have traditionally been used to explore the impact of air pollution on people’s subjective well-being (SWB). However, the survey techniques usually take long periods to properly match the air pollution characteristics from monitoring stations to each respondent’s SWB at both disaggregated spatial and temporal levels. Here, we used air pollution data to simulate fixed-scene images and psychophysical process to examine the impact from only air pollution on SWB. Findings suggest that under the atmospheric conditions in Beijing, negative emotions occur when PM2.5 (particulate matter with a diameter less than 2.5 µm) increases to approximately 150 AQI (air quality index). The British observers have a stronger negative response under severe air pollution compared with Chinese observers. People from different social groups appear to have different sensitivities to SWB when air quality index exceeds approximately 200 AQI.

Analyzing the impact of heating emissions on air quality index based on principal component regression

2018 ◽  
Vol 171 ◽  
pp. 1577-1592 ◽  
Author(s):  
Han Li ◽  
Shijun You ◽  
Huan Zhang ◽  
Wandong Zheng ◽  
Wai-ling Lee ◽  
...  
Keyword(s):  
Air Quality ◽  
Quality Index ◽  
Principal Component Regression ◽  
Principal Component ◽  
Air Quality Index ◽  
The Impact

scholarly journals Assessment of AIR Quality Index for Delhi region: A comparison between odd-even policy 2019 and Lock Down Period.

2021 ◽  
Author(s):  
Chesta Dhingra
Keyword(s):  
Air Quality ◽  
Wind Speed ◽  
Ambient Temperature ◽  
Quality Index ◽  
Winter Season ◽  
Air Quality Index ◽  
Machine Learning Algorithms ◽  
Accurate Estimation ◽  
Pm10 And Pm2.5 ◽  
The Impact

The aim behind doing this research is to analyse the impact of odd-even policy andlockdown implementation on the air quality index of Delhi by doing the case study on the fourregions Ashok Vihar, Anand Vihar, Dwarka and R.K. Puram. The data is been collected fromDPCC and the main parameters we looked for are PM10 and PM2.5. In which we find out that.highest levels of the pollutants PM10 and PM2.5 been observed during the time of odd-evenpolicy implementation for the year 2019 (04 November 2019- 15 November 2019) whereasduring the lockdown period (23 March 2020-31st August 2020) a great decline in pollutantlevels is been detected. This we further try to correlate with the spatial variations of Delhiregion and able to discern that meteorological parameters (Ambient Temperature, RelativeHumidity, Wind Speed and Solar Radiations) in respect with seasonal variations have a majorinfluence on PM 10 and PM 2.5 levels. During the winter season both the parameters PM10& PM2.5 are touching the peak because of the impact of three major meteorological parametersAmbient Temperature, Wind Speed and Solar Radiation and during the monsoon season airquality conditions are quite favourable because of Ambient Temperature and Wind Speedparameters. In the end we use the ensembled machine learning algorithms like Random Forestand Extra trees regressor to have an accurate estimation of PM2.5 levels for all the four regionsof Delhi and perceived that these ensembled learning techniques are better than other machinelearning algorithms like Neural Networks, Linear regression and SVMs. The Random Forestand Extra trees regressor models give the R2value 0.75 and 0.78 respectively for estimation ofPM2.5; R2 value is a statistical measurement which explains the variance of dependent variablebased on the independent variables of a regression model.

scholarly journals Temporal Variations in the Air Quality Index and the Impact of the COVID-19 Event on Air Quality in Western China

2020 ◽  
Vol 20 (7) ◽  
pp. 1552-1568 ◽  
Author(s):  
Jiajia Zhang ◽  
Kangping Cui ◽  
Ya-Fen Wang ◽  
Jhong-Lin Wu ◽  
Wei-Syun Huang ◽  
...  
Keyword(s):  
Air Quality ◽  
Quality Index ◽  
Temporal Variations ◽  
Air Quality Index ◽  
Western China ◽  
The Impact

scholarly journals Mapping Comparison and Meteorological Correlation Analysis of the Air Quality Index in Mid-Eastern China

2017 ◽  
Vol 6 (2) ◽  
pp. 52 ◽  
Author(s):  
Zhichen Yu ◽  
Shaobo Zhong ◽  
Chaolin Wang ◽  
Yongsheng Yang ◽  
Guannan Yao ◽  
...  
Keyword(s):  
Air Quality ◽  
Correlation Analysis ◽  
Quality Index ◽  
Eastern China ◽  
Air Quality Index

scholarly journals Application of Fuzzy Inference System in the Prediction of Air Quality Index

2021 ◽  
Vol 6 (3) ◽  
pp. 75-85
Author(s):  
Nor Hayati Shafii ◽  
Nur Aini Mohd Ramle ◽  
Rohana Alias ◽  
Diana Sirmayunie Md Nasir ◽  
Nur Fatihah Fauzi
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Fuzzy Inference System ◽  
Quality Index ◽  
Fuzzy Inference ◽  
Average Concentration ◽  
Air Quality Index ◽  
Inference System ◽  
Proposed Model ◽  
The Impact

Air pollution is the presence of substances in the atmosphere that are harmful to the health of humans and other living beings. It is caused by solid and liquid particles and certain gases that are suspended in the air.  The air pollution index (API) or also known as air quality index (AQI) is an indicator for the air quality status at any area.  It is commonly used to report the level of severity of air pollution to public and to identify the poor air quality zone.  The AQI value is calculated based on average concentration of air pollutants such as Particulate Matter 10 (PM10), Ozone (O3), Carbon Dioxide (CO2), Sulfur Dioxide (SO2) and Nitrogen Dioxide (NO2).  Predicting the value of AQI accurately is crucial to minimize the impact of air pollution on environment and human health.  The work presented here proposes a model to predict the AQI value using fuzzy inference system (FIS). FIS is the most well-known application of fuzzy logic and has been successfully applied in many fields.  This method is proposed as the perfect technique for dealing with environmental well known and tackling the choice made below uncertainty.  There are five levels or indicators of AQI, namely good, moderate, unhealthy, very unhealthy, and hazardous. This measurement is based on classification made from the Department of Environment (DOE) under the Ministry of Science, Technology, and Innovation (MOSTI). The results obtained from the actual data are compared with the results from the proposed model.  With the accuracy rate of 93%, it shows that the proposed model is meeting the highest standard of accuracy in forecasting the AQI value.

scholarly journals Nottingham Express Transit

2018 ◽  
Vol 19 (1) ◽  
pp. 56-68 ◽  
Author(s):  
John Disney ◽  
Will Rossiter ◽  
David J Smith
Keyword(s):  
United Kingdom ◽  
Air Quality ◽  
Carbon Emissions ◽  
Traffic Congestion ◽  
New Technologies ◽  
Detailed Examination ◽  
Light Rail ◽  
The United Kingdom ◽  
The Impact

Traffic congestion at peak times has long been a problem facing cities in the United Kingdom.1 Latterly concern about combating congestion has been hightened by worries over carbon emissions and poor air quality. In tackling these problems, green innovations incorporating new technologies appear to have much to offer, although progress in implementing these sorts of innovation appears to have been slow. This case study analyses the efforts of one city to tackle these problems by pioneering a number of green innovations including the introduction of a light rail system employing trams known as Nottingham Express Transit as well as electric and gas-powered buses. The nature of these innovations is explored together with a detailed examination of how they came to be implemented and the impact they have had.

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