Atmospheric Particulate Matter Hazard Mapping

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

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 11193

Special Issue Editor


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Guest Editor
Faculty of Geosciences and Civil Engineering, Institute of Science and Engineering, Kanazawa University, Kanazawa 920-1192, Japan
Interests: aerosol; air pollution; biomass burning; carbon; emission inventory; forest fires; health risks; particulate matter; remote sensing; wildfire hazard
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Special Issue Information

Dear Colleagues,

Recently, airborne particulate matter (PM), which is supposed to be strongly associated with adverse human health consequences and the global climate system, is considered a significant contributor to environmental pollution in many countries. The PMs studied are currently based on PM10 and PM2.5 measurements and attempt to extend to PM1.0 and nanoparticle observation via ground monitoring, atmospheric modeling, and satellite remote sensing. Consequently, there is not enough information on the status and characteristics of atmospheric PM and emission sources yet, especially in urban areas with various PM emission sources.

In the past decade, it has been found that the smaller, respirable particles (PM2.5 or, particularly, PM0.1) pose a higher risk of human health problems. The atmospheric PM is associated with increased morbidity and mortality in humans. There is significant evidence that PM harms the respiratory, nervous, and cardiovascular systems. Additionally, the effects of PM related to chemical compositions, e.g., black carbon, affect the atmosphere by having both a direct and an indirect impact on the extent of atmospheric radiation. Therefore, this Special Issue aims to provide a recent study of ambient particulate matter with special-temporal variation in advance. Contributions from observations, field experiments, chemical transport modeling, and data science investigations are welcome.

This Special Issue invites original research studies, reviews, and perspective articles that aim to investigate Particulate Matter in the atmosphere. Subject areas may include, but are not limited to, the following:

  • Observations and modeling of ambient PMs
  • Physical, optical, and chemical characterization of PMs
  • Size-fractionated PM down to the nano-size range
  • Source apportionment of ambient PMs
  • Impacts of meteorology and emission reduction of PMs
  • Impacts on human health, ecosystems, and economic burden.

Dr. Worradorn Phairuang
Guest Editor

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Keywords

  • particulate matter
  • biomass burning
  • PM2.5
  • local sources
  • long-range transport
  • atmospheric modeling
  • air monitoring
  • health risks
  • emission inventory
  • nanoparticles

Published Papers (4 papers)

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Research

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13 pages, 2527 KiB  
Article
Investigation of Secondary Organic Aerosol Formation during O3 and PM2.5 Episodes in Bangkok, Thailand
by Pornpan Uttamang, Parkpoom Choomanee, Jitlada Phupijit, Surat Bualert and Thunyapat Thongyen
Atmosphere 2023, 14(6), 994; https://doi.org/10.3390/atmos14060994 - 7 Jun 2023
Cited by 1 | Viewed by 1701
Abstract
In Bangkok, the megacity of Thailand, concentrations of fine particulate matter (PM2.5) have often exceeded the National Ambient Air Quality standards. During severe smog events over Bangkok, the air quality has exhibited moderate to unhealthy atmospheric conditions, according to the air [...] Read more.
In Bangkok, the megacity of Thailand, concentrations of fine particulate matter (PM2.5) have often exceeded the National Ambient Air Quality standards. During severe smog events over Bangkok, the air quality has exhibited moderate to unhealthy atmospheric conditions, according to the air quality index of the United States. To investigate the formation of secondary organic aerosols (SOA), a field campaign to estimate secondary organic carbon (SOC) in Bangkok using the EC tracer method was conducted in January 2021, when the concentrations of PM2.5 were high. The monthly period was classified into three pollution groups, including high pollution, high PM, and low pollution events. The study showed that the correlations between PM2.5 and O3 were negative during both the daytime and night-time. The OC/EC ratios varied from 4.32 to 5.43, while the moderate OC/EC values implied that fossil fuel combustion was the major carbonaceous aerosol in Bangkok. The EC tracer-estimated SOC and POC showed that SOC contributed between 32.5 and 46.4% to OC, while the highest SOC contribution occurred during the low pollution event. The heightened formation of SOA during the low pollution event was perhaps owing to the levels of oxides of nitrogen (NOx). Since Bangkok is more likely to have a NOx-rich photochemical reaction regime, an increase in the NOx level tended to decrease the SOA yield ([NOx] was 21.6 ppb, 20.8 ppb, and 17.1 ppb during the high pollution, high PM, and low pollution events, respectively). Together with the high humidity and high light intensity during the low pollution event, the SOA formation was enhanced. Even though the driving factors of SOA formation over Bangkok remain unclear, the results of this study reveal the significance and urgency of local actions to reduce NOx and O3 towards more habitable and sustainable urban environments. Full article
(This article belongs to the Special Issue Atmospheric Particulate Matter Hazard Mapping)
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15 pages, 3771 KiB  
Article
Statistical PM2.5 Prediction in an Urban Area Using Vertical Meteorological Factors
by Jutapas Saiohai, Surat Bualert, Thunyapat Thongyen, Kittichai Duangmal, Parkpoom Choomanee and Wladyslaw W. Szymanski
Atmosphere 2023, 14(3), 589; https://doi.org/10.3390/atmos14030589 - 19 Mar 2023
Cited by 8 | Viewed by 2967
Abstract
A key concern related to particulate air pollution is the development of an early warning system that can predict local PM2.5 levels and excessive PM2.5 concentration episodes using vertical meteorological factors. Machine learning (ML) algorithms, particularly those with recognition tasks, show [...] Read more.
A key concern related to particulate air pollution is the development of an early warning system that can predict local PM2.5 levels and excessive PM2.5 concentration episodes using vertical meteorological factors. Machine learning (ML) algorithms, particularly those with recognition tasks, show great potential for this purpose. The objective of this study was to compare the performance of multiple linear regression (MLR) and multilayer perceptron (MLP) in predicting PM2.5 levels. The software was trained to predict PM2.5 levels up to 7 days in advance using data from long-term measurements of vertical meteorological factors taken at five heights above ground level (AGL)—10, 30, 50, 75, and 110 m—and PM2.5 concentrations measured 30 m AGL. The data used were collected between 2015 and 2020 at the Microclimate and Air Pollutants Monitoring Tower station at Kasetsart University, Bangkok, Thailand. The results showed that the correlation coefficients of PM2.5 predicted and observed using MLR and MLP were in the range of 0.69–0.86 and 0.64–0.82, respectively, for 1–3 days ahead. Both models showed satisfactory agreement with the measured data, and MLR performed better than MLP at PM2.5 prediction. In conclusion, this study demonstrates that the proposed approach can be used as a component of an early warning system in cities, contributing to sustainable air quality management in urban areas. Full article
(This article belongs to the Special Issue Atmospheric Particulate Matter Hazard Mapping)
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Review

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15 pages, 1329 KiB  
Review
Ambient Nanoparticles (PM0.1) Mapping in Thailand
by Worradorn Phairuang, Suthida Piriyakarnsakul, Muanfun Inerb, Surapa Hongtieab, Thunyapat Thongyen, Jiraporn Chomanee, Yaowatat Boongla, Phuchiwan Suriyawong, Hisam Samae, Phuvasa Chanonmuang, Panwadee Suwattiga, Thaneeya Chetiyanukornkul, Sirima Panyametheekul, Muhammad Amin, Mitsuhiko Hata and Masami Furuuchi
Atmosphere 2023, 14(1), 66; https://doi.org/10.3390/atmos14010066 - 29 Dec 2022
Cited by 8 | Viewed by 3955 | Correction
Abstract
Nanoparticles (NPs), nanoaerosols (NAs), ultrafine particles (UFPs), and PM0.1 (diameters ≤ 0.1 µm or 100 nm) are used interchangeably in the field of atmospheric studies. This review article summarizes recent research on PM0.1 in Thailand. The review involved peer-reviewed papers that [...] Read more.
Nanoparticles (NPs), nanoaerosols (NAs), ultrafine particles (UFPs), and PM0.1 (diameters ≤ 0.1 µm or 100 nm) are used interchangeably in the field of atmospheric studies. This review article summarizes recent research on PM0.1 in Thailand. The review involved peer-reviewed papers that appeared in the Scopus and the Web of Science databases and included the most recently published articles in the past 10 years (2013–2022). PM0.1 mainly originate from combustion processes such as in motor vehicles. The highest mass concentration of PM0.1 occurs during the dry season, in which open fires occur in some regions of Thailand. The northern area of the country has higher PM0.1 mass concentrations, followed by the central and southern areas. Carbonaceous nanoaerosols are produced during normal periods, and the proportions of organic to elemental carbon and char to soot suggest that these originate from motor vehicles. However, in haze periods, biomass fires can also produce carbon-containing particles. PM0.1 pollution from local and cross-border countries also needs to be considered. The overall conclusions reached will likely have a beneficial long-term impact on achieving a blue sky over Thailand through the development of coherent policies and managing new air pollution challenges and sharing knowledge with a broader audience. Full article
(This article belongs to the Special Issue Atmospheric Particulate Matter Hazard Mapping)
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Other

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2 pages, 368 KiB  
Correction
Correction: Phairuang et al. Ambient Nanoparticles (PM0.1) Mapping in Thailand. Atmosphere 2023, 14, 66
by Worradorn Phairuang, Suthida Piriyakarnsakul, Muanfun Inerb, Surapa Hongtieab, Thunyapat Thongyen, Jiraporn Chomanee, Yaowatat Boongla, Phuchiwan Suriyawong, Hisam Samae, Phuvasa Chanonmuang, Panwadee Suwattiga, Thaneeya Chetiyanukornkul, Sirima Panyametheekul, Muhammad Amin, Mitsuhiko Hata and Masami Furuuchi
Atmosphere 2023, 14(4), 745; https://doi.org/10.3390/atmos14040745 - 20 Apr 2023
Viewed by 898
Abstract
There were errors in the original article [...] Full article
(This article belongs to the Special Issue Atmospheric Particulate Matter Hazard Mapping)
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