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

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

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 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 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 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

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.

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

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.

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

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

<p>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 (PM<sub>10</sub>) concentrations and mineralogical composition of the PM<sub>10</sub> particles.</p><p>Sixty particulate matter (PM<sub>10</sub>) 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 PM<sub>10</sub> cut-off on cellulose filter. PM<sub>10</sub> concentration was calculated from the difference in weight of the filter and flow rate of the sampler while the mineralogical composition of the PM<sub>10</sub> 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).</p><p>The results of the PM<sub>10</sub> concentration showed that NHR had the highest concentration of 1194.30 µg/m<sup>3</sup>, while the lowest concentration was observed in RR (36.33 µg/m<sup>3</sup>), 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 PM<sub>10</sub> 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 µg/m<sup>3</sup>) was observed in NHR, while those of Cu, Mo and Mn (5.45 µg/m<sup>3</sup>, 6.67 µg/m<sup>3</sup> and 11.78 µg/m<sup>3</sup> 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.</p><p> </p><p>Keywords: Traffic-related elements; Air quality index; National highway roads; Major roads; Rural roads</p>

scholarly journals COVID19: Forecasting Air Quality Index and Particulate Matter (PM2.5)

2021 ◽  
Vol 67 (3) ◽  
pp. 3363-3380
Author(s):  
R. Mangayarkarasi ◽  
C. Vanmathi ◽  
Mohammad Zubair Khan ◽  
Abdulfattah Noorwali ◽  
Rachit Jain ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Quality Index ◽  
Air Quality Index
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