Vertical Profiling of Fresh Biomass Burning Aerosol Optical Properties over the Greek Urban City of Ioannina, during the PANACEA Winter Campaign

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.

Formation and Evolution of Secondary Particulate Matter During Heavy Haze Pollution Episodes in Northeast China in Winter

Based on the simultaneous observation of fine particulate matter (PM2.5) and its chemical components in four heavy haze pollution episodes at 14 sampling sites in northeast China from 2017 to 2019, the formation and existence of sulfate (SO42-) and nitrate (NO3-) secondary contaminants under different stages of the pollution episodes, and different meteorological and emission conditions were compared. The results yielded three main findings. (1) Organic carbon (OC) was the most important component of PM2.5, followed by NO3-,SO42-,and ammonium (NH4+). Nitrate surpassed sulfate as the most important secondary inorganic component over the study period. (2) The significant increase in atmospheric OC, SO42-, and NO3-concentrations was an important reason for haze formation. Meteorological factors such as wind direction, wind speed, temperature (T), relative humidity (RH), and atmospheric oxidability played an important role in secondary pollutant formation. (3) There were two potential SO42- formation mechanisms. The first was the gas-phase reaction of the hydroxyl radical(OH·) leading to the oxidation of nitrogen dioxide (NO2) and sulfur dioxide (SO2),and high ozone (O3) concentrations. A high atmospheric oxidability and high winter Ts were very important for SO42- formation. The second mechanism occurred under neutral or weakly alkaline conditions when large amounts of SO2 could enter aerosol droplets, and NO2 was more likely to react in the aqueous phase with SO2 to increase the output of SO42-. Nitrate formation was may be mainly due to the homogeneous gas-phase reaction of OH· with NO2, SO2, and ammonia(NH3). The highest NO3 concentration was observed under mild winter Ts, high RH, high atmospheric oxidability (O3 and nitrous acid (HONO)), high NH3 concentrations, and suitable light conditions. The differences in SO42- formation between northeast China and other regions were mainly a result of the suppression of the aqueous reaction of SO42- due to the low T in winter and low-sulfur coal emissions, which resulted in the gas-phase oxidation process with the highest SO42- production capacity becoming an important process. However, the aqueous reaction process was the most common mechanism of SO42- production in northeast China.

The Occurrence of Heavy Air Pollution during the COVID-19 Outbreak in Beijing, China: Roles of Emission Reduction, Meteorological Conditions, and Regional Transport

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.

Exposure to Non-exhaust Emission in Central Seoul using an Agent-based Framework

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.

Seasonal variations in the synoptic climatology of air pollution in Birmingham, UK

A synoptic typing approach was undertaken to examine the seasonal relationship (winter versus summer) between air mass types and pollutant concentrations of O3, PM10, NOx, NO2 and CO in Birmingham, UK, from 2000 to 2015. Daily means of seven surface meteorological variables were entered into a P-mode principal component analysis. Three principal components explained 72.2% (72.9%) of the variance in winter (summer). Cluster analysis was used to group together days with similar PC scores and thus similar meteorological conditions. Six clusters provided the best air mass classification in both seasons. High pollutant concentrations were associated with anticyclonic types. In particular, tropical (polar) continental air mass type was most likely to produce extremely high concentrations in summer (winter). In winter, a sequence of Polar Continental (cool and humid) and Binary Mid-latitude Anticyclonic Maritime—Sub-Polar Cyclonic Maritime (cold and dry) induced severe pollution episodes in all pollutants. Whilst the mean duration of severe pollution episodes varied little between winter and summer (O3 was an exception, with severe episodes lasting 20% longer in summer), high pollutant extremes were more common in winter. This was due to more favourable meteorological conditions (e.g. temperature inversions) and increased anthropogenic emissions during the cold season.