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

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.

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

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.984-985.1190 ◽

2014 ◽

Vol 984-985 ◽

pp. 1190-1194 ◽

Cited By ~ 1

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.

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

Environmental Health Insights ◽

10.1177/1178630218792861 ◽

2018 ◽

Vol 12 ◽

pp. 117863021879286 ◽

Cited By ~ 16

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).

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

Applied Sciences ◽

10.3390/app9194069 ◽

2019 ◽

Vol 9 (19) ◽

pp. 4069 ◽

Cited By ~ 2

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.

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Assessment of spatial variations of particulate matter (PM10 and PM2.5) in Bahrain identified by air quality index (AQI)

Arabian Journal of Geosciences ◽

10.1007/s12517-016-2808-9 ◽

2017 ◽

Vol 10 (1) ◽

Cited By ~ 12

Author(s):

Majeed S. Jassim ◽

Gulnur Coskuner

Keyword(s):

Particulate Matter ◽

Air Quality ◽

Quality Index ◽

Spatial Variations ◽

Air Quality Index ◽

Pm10 And Pm2.5

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AMBIENT AIR QUALITY STATUS OF DEWAS INDUSTRIAL AREA OF MADHYA PRADESH, INDIA

EPRA International Journal of Economic Growth and Environmental Issues ◽

10.36713/epra5304 ◽

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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SMOG IN BIALYSTOK IN POLAND. DATA OF PM 2.5 AND PM 10 PARTICULATE MATTER IN OUTDOOR AIR MEASURED IN 2017-2018 BY "THE LABORATORY OF ENERGY-EFFICIENT ARCHITECTURE AND RENEWABLE ENERGIES" AT FACULTY OF ARCHITECTURE OF BIALYSTOK UNIVERSITY OF TECHNOLOGY

GEOLINKS Conference proceedings, Book 1 Volume 2 ◽

10.32008/geolinks2020/b1/v2/05 ◽

2020 ◽

Author(s):

Adam Turecki

Keyword(s):

Particulate Matter ◽

Air Quality ◽

Energy Efficient ◽

Quality Index ◽

Renewable Energies ◽

Air Quality Index ◽

University Of Technology ◽

Energy Efficient Architecture ◽

The Government ◽

Pm2.5 And Pm10

The differences between what in the winter 2017 was presented by the government measurement station of air quality, belonging to the Chief Inspectorate of Environmental Protection (CIEP) in Bialystok in Poland, and what the citizens could see and smell, were the reason for installing the monitoring system of PM10 and PM2.5 particulate matter, in the "Laboratory of Energy-efficient Architecture and Renewable Energies" (LEARE) at the Faculty of Architecture of Bialystok University of Technology. The measurements were compared with done by CIEP and the information of “The World Air Quality Index” (WAQI). This project started in 2007. It is proving a transparent Air Quality information for more than 70 countries, covering more than 9000 stations in 600 major cities. Since 16 Nov 2017, data was also downloaded from the new European Air Quality Index (EAQI) website, created by the European Environment Agency (EEA). From the beginning of 2018, data from the public-private service AIRLY was added to the study. They installed four online dust meters in Bialystok. The density of the dust measurement network was still insufficient, so the mobile measurements were started. Recently, the use of a drone equipped with a dust meter for tests at various heights has begun. Measurements denies EAQI presentation of so good air quality in Bialystok. The levels of PM2.5 and PM10 are often much higher than those presented by EAQI and CIEP. Government measuring station, located in the center of Bialystok, poorly reflect air pollution in peripheral districts.

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Evaluation of daily pollutant standard index and air quality index in a university campus in Nigeria using PM10 and PM2.5 particulate matter

Journal of Science Technology and Environment Informatics ◽

10.18801/jstei.070219.54 ◽

2019 ◽

Vol 7 (2) ◽

pp. 517-532

Author(s):

O. J. Osimobi ◽

B. Yorkor ◽

C. A. Nwankwo

Keyword(s):

Particulate Matter ◽

Air Quality ◽

Quality Index ◽

Air Quality Index ◽

University Campus ◽

Standard Index ◽

Pm10 And Pm2.5

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Particulate matter (PM10) concentration, mineralogical characteristics and traffic-related element (TRE) composition of urban traffic particles in Ibadan, Nigeria

10.5194/egusphere-egu2020-15274 ◽

2020 ◽

Author(s):

Olalekan Tesleem Kolawole ◽

Akinade Shadrach Olatunji ◽

Khanneh Wadinga Fomba

Keyword(s):

Particulate Matter ◽

Air Quality ◽

Quality Index ◽

Mineralogical Composition ◽

Air Quality Index ◽

Engine Oil ◽

Emission Spectrometry ◽

Rural Roads ◽

Wide Range ◽

National Highway

Atmospheric traffic-related elements (TRE) generated from traffic-related emissions have been linked to a wide range of human diseases and also affect the ecosystem. This study focuses on data from the Nigerian air quality network along the segment of the National Highway Roads (NHR), inner-city Major Roads (MR) and Rural Roads (RR) in Ibadan. The aim of this near-road monitoring was to assess the levels of TRE, determine the particulate matter (PM10) concentrations and mineralogical composition of the PM10 particles.Sixty particulate matter (PM10) samples were collected from 5 traffic-related stations (2-NHR; 2-MR; 1-RR) (six samples from each station) in the study area using traffic-related high-volume air sampler with PM10 cut-off on cellulose filter. PM10 concentration was calculated from the difference in weight of the filter and flow rate of the sampler while the mineralogical composition of the PM10 was determined by single-particle analysis using scanning electron microscopy and energy-dispersive x-ray spectroscopy (SEM/EDXS) techniques, and the TRE were determined by inductively coupled plasma-optical emission spectrometry (ICP-OES).The results of the PM10 concentration showed that NHR had the highest concentration of 1194.30 &#181;g/m3, while the lowest concentration was observed in RR (36.33 &#181;g/m3), these correspond to the level of traffic density in both stations, the former having 60,000 vehicle/day while the later had <2000 vehicle/day. More than 80% of the PM10 concentrations in the NHR and the MR were classified as being unhealthy-hazardous to humans living very close to this environment on the basis of the air quality index (AQI). The most abundant mineral particles were clay (53%), quartz (9%) and rock-forming minerals (<3%) sourced from roadside soil and fly ash from construction rock dust. Other particles such as clay+sulphate (17%), sulphur-rich particle (8%), soot (7%) and tarballs (8%) were generated from anthropogenic input from traffic-related activities. The highest average concentration of TRE such as Ba, Cd, La, Pb, V and Zn (2.81, 1.61, 1.21, 6.92, 8.92 and 10.73 respectively all in &#181;g/m3) was observed in NHR, while those of Cu, Mo and Mn (5.45 &#181;g/m3, 6.67 &#181;g/m3 and 11.78 &#181;g/m3 respectively) was observed in MR. Principal component analysis (PCA) revealed four factors (PC1 to PC4). In PC1 26.57% of the variability was observed and loaded with Ba (0.76), Pb (0.82), V (0.85) while PC 2 could explain 17.94% variability and had La (0.67), Mn (0.83) and Mo (0.68), PC 3 explained 15.91% variance loaded with Cd (0.84) and Zn (0.77), and PC 4 gave account of 13.83% of the variance and was loaded with Cu (0.86). PC1 and PC2 were products of both gasoline and diesel engine while PC3 and PC4 were generated from engine oil, brake and tyre wares. The calculated enrichment factor classified the TRE as being moderate to highly contaminated in both NHR and MR while RR was considered relatively uncontaminated.&#160;Keywords: Traffic-related elements; Air quality index; National highway roads; Major roads; Rural roads

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Impact of the COVID-19 Outbreak on Air Quality in Korea

Atmosphere ◽

10.3390/atmos11101137 ◽

2020 ◽

Vol 11 (10) ◽

pp. 1137 ◽

Cited By ~ 3

Author(s):

Ji Hoon Seo ◽

Hyun Woo Jeon ◽

Ui Jae Sung ◽

Jong-Ryeul Sohn

Keyword(s):

Air Quality ◽

Human Activity ◽

Quality Index ◽

Air Pollutant ◽

Air Quality Index ◽

Control Action ◽

Quality Improvements ◽

Social Distancing ◽

Two Cities ◽

Activity Restrictions

The COVID-19 pandemic has led countries to take action, which has included practicing social distancing or lockdown. Many cities are experiencing air quality improvements due to human activity restrictions. The purpose of this study was to compare the air quality between 2020 and the previous three years, focusing on the two cities (Seoul and Daegu) where coronavirus is spreading the fastest in Korea. Significant decreases in PM2.5, PM10, CO, and NO2 were observed in both cities. In particular, compared to the same period of 2017-2019, in March 2020, PM2.5 showed remarkable reductions of 36% and 30% in Seoul and Daegu, respectively. The effects of social distancing have maximized improvements in air quality due to reduced transboundary pollutants. The PM2.5/PM10 ratio was significantly reduced after social distancing, indicating that the contribution of traffic-related PM2.5 declined. Air quality improved overall from January to July, and the most noticeable drop in the air quality index (AQI) was observed in April. These findings indicate that relatively weak social distancing measures compared to a COVID-19 lockdown can help reduce air pollutant levels. At the same time, however, changes in air quality in the neighboring countries caused by COVID-19 control action are affecting Korea.

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