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Atmosphere ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 94
Author(s):  
Christina-Anna Papanikolaou ◽  
Alexandros Papayannis ◽  
Maria Mylonaki ◽  
Romanos Foskinis ◽  
Panagiotis Kokkalis ◽  
...  

Vertical profiling of aerosol particles was performed during the PANhellenic infrastructure for Atmospheric Composition and climatE chAnge (PANACEA) winter campaign (10 January 2020–7 February 2020) over the city of Ioannina, Greece (39.65° N, 20.85° E, 500 m a.s.l.). The middle-sized city of Ioannina suffers from wintertime air pollution episodes due to biomass burning (BB) domestic heating activities. The lidar technique was applied during the PANACEA winter campaign on Ioannina city, to fill the gap of knowledge of the spatio-temporal evolution of the vertical mixing of the particles occurring during these winter-time air pollution episodes. During this campaign the mobile single-wavelength (532 nm) depolarization Aerosol lIdAr System (AIAS) was used to measure the spatio-temporal evolution of the aerosols’ vertical profiles within the Planetary Boundary Layer (PBL) and the lower free troposphere (LFT; up to 4 km height a.s.l.). AIAS performed almost continuous lidar measurements from morning to late evening hours (typically from 07:00 to 19:00 UTC), under cloud-free conditions, to provide the vertical profiles of the aerosol backscatter coefficient (baer) and the particle linear depolarization ratio (PLDR), both at 532 nm. In this study we emphasized on the vertical profiling of very fresh (~hours) biomass burning (BB) particles originating from local domestic heating activities in the area. In total, 33 out of 34 aerosol layers in the lower free troposphere were characterized as fresh biomass burning ones of local origin, showing a mean particle linear depolarization value of 0.04 ± 0.02 with a range of 0.01 to 0.09 (532 nm) in a height region 1.21–2.23 km a.s.l. To corroborate our findings, we used in situ data, particulate matter (PM) concentrations (PM2.5) from a particulate sensor located close to our station, and the total black carbon (BC) concentrations along with the respective contribution of the fossil fuel (BCff) and biomass/wood burning (BCwb) from the Aethalometer. The PM2.5 mass concentrations ranged from 5.6 to 175.7 μg/m3, while the wood burning emissions from residential heating were increasing during the evening hours, with decreasing temperatures. The BCwb concentrations ranged from 0.5 to 17.5 μg/m3, with an extremely high mean contribution of BCwb equal to 85.4%, which in some cases during night-time reached up to 100% during the studied period.


2021 ◽  
Vol 13 (21) ◽  
pp. 12312
Author(s):  
Xiao Yan ◽  
Aijun Shi ◽  
Jingyuan Cao ◽  
Tingting Li ◽  
Xuesong Sun ◽  
...  

To prevent the spread of coronavirus disease (COVID-19) and mitigate the epidemic risk, strict lockdown measures were implemented in Beijing during the quarantine period, significantly reducing human activities. However, severe air pollution episodes occurred frequently in Beijing. To explore the occurrence of severe air pollution during the quarantine period, the impacts of emission reductions, meteorological conditions, and regional transport on heavy air pollution were individually evaluated using the Community Multiscale Air Quality (CMAQ) model. Observations showed that the more unfavorable meteorological conditions which occurred during the pandemic as compared to the corresponding 2019 levels, including higher temperature, relative humidity, and frequency of strong southerly winds, and lower HPBL, led to an increase in PM2.5 concentrations. The model results also showed that the meteorological conditions in February 2020 favored PM2.5 formation. The PM2.5 concentrations were mainly dominated by regional transport, which became more significant in the quarantine period than in 2019, suggesting the importance of joint control on regional sources for reducing heavy air pollution. This study highlights that, although the emissions in Beijing and surrounding regions were largely reduced during the quarantine period, severe air pollution in Beijing did not reduce due to the unfavorable meteorological conditions.


2021 ◽  
Author(s):  
Hyesop Shin ◽  
Mike Bithell

Abstract Non-exhaust emission (NEE) from brake and tyre wear cause deleterious effects on human health, but the relationship with mobility has not been thoroughly examined. We construct an in silico agent-based traffic simulator for Central Seoul to illustrate the coupled problems of emissions, behaviour, and the estimated exposure to PM10 (particles less than 10 microns in size) for groups of drivers and subway commuters. The results show that significant extra particulates relative to the background exist along roadways where NEEs contributed some 40% of the roadside PM10. In terms of health risk, 88% of resident drivers had an acute health effect in late March but that kind of emergence rarely happened. By contrast, subway commuters’ health risk peaked at a maximum of 30% with frequent oscillations whenever the air pollution episodes occurred. A 90% vehicle restriction scenario reduced PM10 by 18-24%, and reduced the resident driver's risk by a factor of 2, but not effective for subway commuters as the group generally walked through background areas rather than along major roadways. Using an agent-based traffic simulator in a health context can give insights into how exposure and health outcomes can depend on the time of exposure and the mode of transport.


2021 ◽  
Vol 156 ◽  
pp. 106732
Author(s):  
Lidia Morawska ◽  
Tong Zhu ◽  
Nairui Liu ◽  
Mehdi Amouei Torkmahalleh ◽  
Maria de Fatima Andrade ◽  
...  

Author(s):  
Kerry E. Kelly ◽  
Wei W. Xing ◽  
Tofigh Sayahi ◽  
Logan Mitchell ◽  
Tom Becnel ◽  
...  

2021 ◽  
pp. 101099
Author(s):  
Costas A. Varotsos ◽  
Yuri Mazei ◽  
Damir Saldaev ◽  
Maria Efstathiou ◽  
Tanya Voronova ◽  
...  

2021 ◽  
pp. 101068
Author(s):  
Bao Anh Phung Ngoc ◽  
Hervé Delbarre ◽  
Karine Deboudt ◽  
Elsa Dieudonné ◽  
Dien Nguyen Tran ◽  
...  

2021 ◽  
Vol 249 ◽  
pp. 118249
Author(s):  
Mathilde Pascal ◽  
Vérène Wagner ◽  
Anna Alari ◽  
Magali Corso ◽  
Alain Le Tertre

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