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Atmosphere
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Measurement, Evaluation and Modeling of Particulate Matter and Air Quality...
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Measurement, Evaluation and Modeling of Particulate Matter and Air Quality Index

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Air Quality".

Deadline for manuscript submissions: closed (19 July 2024) | Viewed by 6352

Special Issue Editors

Dr. Milan Guzan

E-Mail Website
Guest Editor
Department of Theoretical and Industrial Electrical Engineering, Faculty of Electrical Engineering and Informatics, Technical University of Košice, 04200 Kosice, Slovakia
Interests: measurement; sensors; particulate matter; air quality index; correlation; chaos; autonomous circuit; boundary surface
Dr. Tibor Vince

E-Mail Website
Guest Editor
Department of Theoretical and Industrial Electrical Engineering, Faculty of Electrical Engineering and Informatics, Technical University of Košice, 042 00 Košice, Slovakia
Interests: industrial electronic engineering; industrial application; IoT; simulation and modeling applications; automated measurement systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Although the area of measuring particular matter (PM), modeling the development of PM, or the Air Quality Index (AQI), and measuring the individual components affecting AQI (PM2.5, PM10, CO, NO2, O3, SO2) is well established, it continues to lack a wider global range of measurements and modeling that expands knowledge in this field.

Therefore, we would like to invite you to contribute research articles reflecting your new measurements, proposed measurement chains, and novel findings related to PM/AQI developments, including new locations around the world, in the journal Atmosphere, and a Special Issue entitled “Measurement, Evaluation and Modeling of Particulate Matter and Air Quality Index”.

Dr. Milan Guzan
Dr. Tibor Vince
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Atmosphere is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • sensors
  • particulate matter
  • ultrafine particles
  • air quality index
  • correlation
  • typical particle size
  • number concentration
  • mass concentration

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Published Papers (6 papers)

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Research

22 pages, 3270 KiB  
Article
The Effects of Air Quality and the Impact of Climate Conditions on the First COVID-19 Wave in Wuhan and Four European Metropolitan Regions
by Marina Tautan, Maria Zoran, Roxana Radvan, Dan Savastru, Daniel Tenciu and Alexandru Stanciu
Atmosphere 2024, 15(10), 1230; https://doi.org/10.3390/atmos15101230 - 15 Oct 2024
Viewed by 625
Abstract
This paper investigates the impact of air quality and climate variability during the first wave of COVID-19 associated with accelerated transmission and lethality in Wuhan in China and four European metropolises (Milan, Madrid, London, and Bucharest). For the period 1 January–15 June 2020, [...] Read more.
This paper investigates the impact of air quality and climate variability during the first wave of COVID-19 associated with accelerated transmission and lethality in Wuhan in China and four European metropolises (Milan, Madrid, London, and Bucharest). For the period 1 January–15 June 2020, including the COVID-19 pre-lockdown, lockdown, and beyond periods, this study used a synergy of in situ and derived satellite time-series data analyses, investigating the daily average inhalable gaseous pollutants ozone (O3), nitrogen dioxide (NO2), and particulate matter in two size fractions (PM2.5 and PM10) together with the Air Quality Index (AQI), total Aerosol Optical Depth (AOD) at 550 nm, and climate variables (air temperature at 2 m height, relative humidity, wind speed, and Planetary Boundary Layer height). Applied statistical methods and cross-correlation tests involving multiple datasets of the main air pollutants (inhalable PM2.5 and PM10 and NO2), AQI, and aerosol loading AOD revealed a direct positive correlation with the spread and severity of COVID-19. Like in other cities worldwide, during the first-wave COVID-19 lockdown, due to the implemented restrictions on human-related emissions, there was a significant decrease in most air pollutant concentrations (PM2.5, PM10, and NO2), AQI, and AOD but a high increase in ground-level O3 in all selected metropolises. Also, this study found negative correlations of daily new COVID-19 cases (DNCs) with surface ozone level, air temperature at 2 m height, Planetary Boundary PBL heights, and wind speed intensity and positive correlations with relative humidity. The findings highlight the differential impacts of pandemic lockdowns on air quality in the investigated metropolises. Full article
(This article belongs to the Special Issue Measurement, Evaluation and Modeling of Particulate Matter and Air Quality Index)
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15 pages, 3254 KiB  
Article
A Comprehensive Study on Winter PM2.5 Variation in the Yangtze River Delta: Unveiling Causes and Pollution Transport Pathways
by Yong Pan, Jie Zheng, Fangxin Fang, Fanghui Liang, Lei Tong and Hang Xiao
Atmosphere 2024, 15(9), 1037; https://doi.org/10.3390/atmos15091037 - 28 Aug 2024
Viewed by 531
Abstract
To thoroughly investigate the impact of meteorological conditions and emission changes on winter PM2.5 variation in the Yangtze River Delta (YRD) from 2015 to 2019, we leveraged advanced modeling techniques, namely, the Weather Research and Forecasting (WRF) model and the Nested Air [...] Read more.
To thoroughly investigate the impact of meteorological conditions and emission changes on winter PM2.5 variation in the Yangtze River Delta (YRD) from 2015 to 2019, we leveraged advanced modeling techniques, namely, the Weather Research and Forecasting (WRF) model and the Nested Air Quality Prediction Model System (NAQPMS). The results revealed that a notable trend of high-PM2.5-concentration regions shifted from coastal areas towards to the inland regions. While emission reduction can effectively reduce the concentration of PM2.5, meteorological changes exert a significant impact on PM2.5 concentration. Unfavorable meteorological changes in 2018 and 2019 emerged as crucial factors driving PM2.5 pollution in the region (up 0~50 µg·m−3). Our findings also shed light on the potential sources and transport pathways of PM2.5 pollution in key cities within the YRD, indicating that the coastal channel of Hebei–Shandong–Jiangsu and the inland channel bordering Hebei, Henan, Shandong, and Anhui serve as major contributors. Light and moderate pollution was predominantly influenced by the medium-distance coastal channel (48~70%). Remarkably, short-distance inland (19~54%) and coastal transportation (33~53%) channels emerged as the primary causes of severe PM2.5 pollution in the YRD. To effectively combat this issue, it is imperative to bolster key control and prevention measures in these regions. Full article
(This article belongs to the Special Issue Measurement, Evaluation and Modeling of Particulate Matter and Air Quality Index)
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15 pages, 1786 KiB  
Article
Elucidating Decade-Long Trends and Diurnal Patterns in Aerosol Acidity in Shanghai
by Zhixiao Lv, Xingnan Ye, Weijie Huang, Yinghui Yao and Yusen Duan
Atmosphere 2024, 15(8), 1004; https://doi.org/10.3390/atmos15081004 - 20 Aug 2024
Viewed by 648
Abstract
Aerosol acidity is a critical factor affecting atmospheric chemistry. Here, we present a study on annual, monthly, and daily variations in PM2.5 pH in Shanghai during 2010–2020. With the effective control of SO2 emissions, the NO2/SO2 ratio increased [...] Read more.
Aerosol acidity is a critical factor affecting atmospheric chemistry. Here, we present a study on annual, monthly, and daily variations in PM2.5 pH in Shanghai during 2010–2020. With the effective control of SO2 emissions, the NO2/SO2 ratio increased from 1.26 in 2010 to 5.07 in 2020 and the NO3−/SO42− ratio increased from 0.68 to 1.49. Aerosol pH decreased from 3.27 in 2010 to 2.93 in 2020, regardless of great achievement in reducing industrial SO2 and NOx emissions. These findings suggest that aerosol acidity might not be significantly reduced in response to the control of SO2 and NOx emissions. The monthly variation in pH values exhibited a V-shape trend, mainly attributable to aerosol compositions and temperature. Atmospheric NH3 plays the decisive role in buffering particle acidity, whereas Ca2+ and K+ are important acidity buffers, and the distinct pH decline during 2010–2016 was associated with the reduction of Ca2+ and K+ while both temperature and SO42− were important drivers in winter. Sensitivity tests show that pH increases with the increasing relative humidity in summer while it is not sensitive to relative humidity in winter due to proportional increases in Hair+ and aerosol liquid water content (ALWC). Our results suggest that reducing NOx emissions in Shanghai will not significantly affect PM2.5 acidity in winter. Full article
(This article belongs to the Special Issue Measurement, Evaluation and Modeling of Particulate Matter and Air Quality Index)
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21 pages, 6563 KiB  
Article
Particulate Matter in the Eastern Slovakia Region: Measurement, Monitoring, and Evaluation
by Simona Kirešová, Milan Guzan, Branislav Sobota, Tibor Vince, Štefan Korečko, Jozef Dziak, Ján Molnár, Patrik Jacko and Matej Bereš
Atmosphere 2024, 15(7), 802; https://doi.org/10.3390/atmos15070802 - 4 Jul 2024
Viewed by 1061
Abstract
The paper focuses on the measurement of PM and other meteorological parameters in a small region of central Europe—eastern Slovakia and northeastern Hungary. Due to the increasing availability of sensors measuring not only PM, but also temperature, humidity, pressure, VOC, NOx, and CO [...] Read more.
The paper focuses on the measurement of PM and other meteorological parameters in a small region of central Europe—eastern Slovakia and northeastern Hungary. Due to the increasing availability of sensors measuring not only PM, but also temperature, humidity, pressure, VOC, NOx, and CO2, new possibilities arise in terms of comparing (mainly in terms of correlation) PM and the other measured parameters, thus generating a large amount of data for evaluation. The correlations found are typical for inland conditions, thus able to map other regions of the world. The presented measurements can also be used to predict the evolution of PM with alerts for people with respiratory diseases, or in virtual reality using a digital twin of a humanoid robot. Full article
(This article belongs to the Special Issue Measurement, Evaluation and Modeling of Particulate Matter and Air Quality Index)
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20 pages, 4104 KiB  
Article
Research on CC-SSBLS Model-Based Air Quality Index Prediction
by Lin Wang, Yibing Wang, Jian Chen, Shuangqing Zhang and Lanhong Zhang
Atmosphere 2024, 15(5), 613; https://doi.org/10.3390/atmos15050613 - 19 May 2024
Viewed by 1038
Abstract
Establishing reliable and effective prediction models is a major research priority for air quality parameter monitoring and prediction and is utilized extensively in numerous fields. The sample dataset of air quality metrics often established has missing data and outliers because of certain uncontrollable [...] Read more.
Establishing reliable and effective prediction models is a major research priority for air quality parameter monitoring and prediction and is utilized extensively in numerous fields. The sample dataset of air quality metrics often established has missing data and outliers because of certain uncontrollable causes. A broad learning system based on a semi-supervised mechanism is built to address some of the dataset’s data-missing issues, hence reducing the air quality model prediction error. Several air parameter sample datasets in the experiment were discovered to have outlier issues, and the anomalous data directly impact the prediction model’s stability and accuracy. Furthermore, the correlation entropy criteria perform better when handling the sample data’s outliers. Therefore, the prediction model in this paper consists of a semi-supervised broad learning system based on the correlation entropy criterion (CC-SSBLS). This technique effectively solves the issue of unstable and inaccurate prediction results due to anomalies in the data by substituting the correlation entropy criterion for the mean square error criterion in the BLS algorithm. Experiments on the CC-SSBLS algorithm and comparative studies with models like Random Forest (RF), Support Vector Regression (V-SVR), BLS, SSBLS, and Categorical and Regression Tree-based Broad Learning System (CART-BLS) were conducted using sample datasets of air parameters in various regions. In this paper, the root mean square error (RMSE) and mean absolute percentage error (MAPE) are used to judge the advantages and disadvantages of the proposed model. Through the experimental analysis, RMSE and MAPE reached 8.68 μg·m−3 and 0.24% in the Nanjing dataset. It is possible to conclude that the CC-SSBLS algorithm has superior stability and prediction accuracy based on the experimental results. Full article
(This article belongs to the Special Issue Measurement, Evaluation and Modeling of Particulate Matter and Air Quality Index)
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24 pages, 7124 KiB  
Article
Analysis of Experimental Measurements of Particulate Matter (PM) and Lung Deposition Surface Area (LDSA) in Operational Faces of an Oil Shale Underground Mine
by Sergei Sabanov, Abdullah Rasheed Qureshi, Ruslana Korshunova and Gulim Kurmangazy
Atmosphere 2024, 15(2), 200; https://doi.org/10.3390/atmos15020200 - 5 Feb 2024
Cited by 1 | Viewed by 1596
Abstract
Particulate matter (PM) in the context of underground mining results from various operations such as rock drilling and blasting, ore loading, hauling, crushing, dumping, and from diesel exhaust gases as well. These operations result in the formation of fine particles that can accumulate [...] Read more.
Particulate matter (PM) in the context of underground mining results from various operations such as rock drilling and blasting, ore loading, hauling, crushing, dumping, and from diesel exhaust gases as well. These operations result in the formation of fine particles that can accumulate in the lungs of mineworkers. The lung deposited surface area (LDSA) concentration is a variant solution to evaluate potential health impacts. The aim of this study is to analyse PM and LDSA concentrations in the operational workings of the oil shale underground mine. Experimental measurements were carried out by a direct-reading real-time PM monitor, Dusttrak DRX, and a multimetric fine particle detector, Naneous Partector 2, during the loading and dumping processes using the diesel engine loader. Consequently, the analysis was conducted on PM, LDSA, particle surface area concentration (SA), average particle diameter (d), particle number concentration (PNC), and particle mass (PM0.3), producing a few valuable correlation factors. Averaged LDSA was around 1433 μm2/cm3 and reached maximum peaks of 2140 μm2/cm3 during the loading, which was mostly related to diesel exhaust emissions, and within the dumping 730 μm2/cm3 and 1840 μm2/cm3, respectively. At the same time, average PM1 was about 300 μg/ m3 during the loading, but within the dumping peaks, it reached up to 10,900 μg/ m3. During the loading phase, particle diameter ranged from 30 to 90 nm, while during the dumping phase peaks, it varied from 90 to 160 nm. On this basis, a relationship between PNC and particle diameter has been produced to demonstrate an approximate split between diesel particulate matter (DPM) and oil shale dust diameters. This study offers important data on PM and LDSA concentration that can be used for estimating potential exposure to miners at various working operations in the oil shale underground mines, and will be used for air quality control in accordance with establishing toxic aerosol health effects. Full article
(This article belongs to the Special Issue Measurement, Evaluation and Modeling of Particulate Matter and Air Quality Index)
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