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 Analysis of Air Quality Index Distribution of PM10 and PM2.5 Concentrations in Ambient Air of Al-Hillah City, Iraq

2021 ◽  
Vol 1058 (1) ◽  
pp. 012014
Author(s):  
Ruqayah Ali Grmasha ◽  
Shahla N. A. Al-Azzawi ◽  
Osamah J. Al-sareji ◽  
Talal Alardhi ◽  
Mawada Abdellatif ◽  
...  
Keyword(s):  
Air Quality ◽  
Quality Index ◽  
Ambient Air ◽  
Air Quality Index ◽  
Pm10 And Pm2.5 ◽  
Index Distribution

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.

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.

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 Investigation of Air Quality Index and PM10 and PM2.5 in Arak

2013 ◽  
Vol 1 (3) ◽  
pp. 12-17 ◽  
Author(s):  
Fatemeh Fazelinia ◽  
Ali Akbar Khodabandehlou ◽  
Lida Rafati ◽  
Amir Hossein Mahvi ◽  
◽  
...  
Keyword(s):  
Air Quality ◽  
Quality Index ◽  
Air Quality Index ◽  
Pm10 And Pm2.5

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 Understanding the Spatial-Temporal Patterns and Influential Factors on Air Quality Index: The Case of North China

2019 ◽  
Vol 16 (16) ◽  
pp. 2820 ◽  
Author(s):  
Wenxuan Xu ◽  
Yongzhong Tian ◽  
Yongxue Liu ◽  
Bingxue Zhao ◽  
Yongchao Liu ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Wind Speed ◽  
Quality Index ◽  
North China ◽  
Ambient Air ◽  
Air Quality Index ◽  
Rapid Expansion ◽  
Spatial Data Analysis ◽  
Prevailing Wind Direction

North China has become one of the worst air quality regions in China and the world. Based on the daily air quality index (AQI) monitoring data in 96 cities from 2014–2016, the spatiotemporal patterns of AQI in North China were investigated, then the influence of meteorological and socio-economic factors on AQI was discussed by statistical analysis and ESDA-GWR (exploratory spatial data analysis-geographically weighted regression) model. The principal results are as follows: (1) The average annual AQI from 2014–2016 exceeded or were close to the Grade II standard of Chinese Ambient Air Quality (CAAQ), although the area experiencing heavy pollution decreased. Meanwhile, the positive spatial autocorrelation of AQI was enhanced in the sample period. (2) The occurrence of a distinct seasonal cycle in air pollution which exhibit a sinusoidal pattern of fluctuations and can be described as “heavy winter and light summer.” Although the AQI generally decreased in other seasons, the air pollution intensity increased in winter with the rapid expansion of higher AQI value in the southern of Hebei and Shanxi. (3) The correlation analysis of daily meteorological factors and AQI shows that air quality can be significantly improved when daily precipitation exceeds 10 mm. In addition, except for O3, wind speed has a negative correlation with AQI and major pollutants, which was most significant in winter. Meanwhile, pollutants are transmitted dynamically under the influence of the prevailing wind direction, which can result in the relocation of AQI. (4) According to ESDA-GWR analysis, on an annual scale, car ownership and industrial production are positively correlated with air pollution; whereas increase of wind speed, per capita gross domestic product (GDP), and forest coverage are conducive to reducing pollution. Local coefficients show spatial differences in the effects of different factors on the AQI. Empirical results of this study are helpful for the government departments to formulate regionally differentiated governance policies regarding air pollution.

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