Isolation, Characterization, and Antimicrobial Activity of Bacterial and Fungal Representatives Associated With Particulate Matter During Haze and Non-haze Days

Particulate matter (PM) has been a threat to the environment and public health in the metropolises of developing industrial countries such as Beijing. The microorganisms associated with PM have an impact on human health if they are exposed to the respiratory tract persistently. There are few reports on the microbial resources collected from PM and their antimicrobial activities. In this study, we greatly expanded the diversity of available commensal organisms by collecting 1,258 bacterial and 456 fungal isolates from 63 PM samples. A total of 77 bacterial genera and 35 fungal genera were included in our pure cultures, with Bacillus as the most prevalent cultured bacterial genus, Aspergillus, and Penicillium as the most prevalent fungal ones. During heavy-haze days, the numbers of colony-forming units (CFUs) and isolates of bacteria and fungi were decreased. Bacillus, Paenibacillus, and Chaetomium were found to be enriched during haze days, while Kocuria, Microbacterium, and Penicillium were found to be enriched during non-haze days. Antimicrobial activity against common pathogens have been found in 40 bacterial representatives and 1 fungal representative. The collection of airborne strains will provide a basis to greatly increase our understanding of the relationship between bacteria and fungi associated with PM and human health.

Linkages between the atmospheric transmission originating from the North Atlantic Oscillation and persistent winter haze over Beijing

In this study, the persistent winter haze that occurred over Beijing during 1980 to 2016 is examined using reanalysis and station data. On both interannual and daily-to-weekly timescales, the winter haze weather in Beijing is found to be associated with a pronounced atmospheric teleconnection pattern from the North Atlantic to Eurasia (Beijing). A positive western-type North Atlantic Oscillation (WNAO+) phase and a positive East Atlantic/West Russia (EA/WR+) phase are observed as part of this teleconnection pattern (an arched wave train). This study focuses on the role of the WNAO pattern, because the WNAO+ pattern acts as the origin of the atmospheric transmission, 8–10 d before the persistent haze events. Further analyses reveal that the WNAO+ pattern can increase the number of haze days and persistent haze events on interannual and daily-to-weekly timescales. Specifically, strong WNAO+ winters (above the 95th percentile) can increase the number of haze days and persistent haze events by 26.0 % and 42.3 %, respectively. In addition, a high WNAO index for the 5 d average (above the 95th percentile) predicts a 16.9 % increase in the probability of haze days on Day 8 and a higher proportion of persistent haze days compared with an unknown WNAO state. Thus, the WNAO+ pattern is as a necessary prior background condition for the formation of the wave train and is a skillful predictor for persistent hazy weather. Corresponding to the WNAO+ pattern, intensified zonal wind and a north–south sea surface temperature tripolar mode over the North Atlantic also appear before persistent haze events on the daily-to-weekly timescale. On the interannual timescale, winters with a greater number of persistent haze days are also associated with a tripolar sea surface temperature (SST) mode over the North Atlantic that is situated farther northward.

Elemental Carbon Observed at a Peri-Urban Forest Site near the Seoul Metropolitan Area as a Tracer of Seasonal Haze Occurrence

In order to identify the seasonal variability and source of carbonaceous aerosols in relation to haze occurrence, organic carbon (OC) and elemental carbon (EC) were continuously measured at the Taehwa Research Forest (TRF) near the Seoul metropolitan area from May 2013 to April 2014. For the entire experiment, the mean OC (5.1 µgC/m3) and EC (1.7 µgC/m3) concentrations of TRF were comparable to those of Seoul, with noticeably higher concentrations in winter and spring than in other seasons, and during haze days (6.6 ± 3.2 and 2.1 ± 1.0 μgC/m3) than during non-haze days (3.5 ± 2.2 and 1.3 ± 0.8 μgC/m3). The seasonal characteristics of OC and EC reveal the various sources of haze, including biomass combustion haze either transported for long distances or, in spring, from domestic regions, the greatest contribution of secondary organic carbon (SOC) in summer, and fossil fuel combustion in winter and fall. In addition, the seasonal OC/EC ratios between haze and non-haze days highlights that the increase in EC was more distinct than that of OC during haze episodes, thus suggesting that EC observed at a peri-urban forest site serves as a useful indicator for seasonally varying source types of haze aerosols in the study region.

Haze-day Trends from 2013 to 2020 and Analysis of Spatiotemporal Characteristics of a Haze Process in Ningbo, China

Air quality in Ningbo continues to improve with a constant decrease in the PM2.5 concentration. However, pollution levels occasionally increase during autumn and winter. To understand the regional and seasonal distributions and the interannual variation during haze days, we analyzed the haze monitoring data from 2013 to 2020 and the haze weather process during January 18–19, 2016. Our results showed a downward trend in the major pollutant concentrations. High PM2.5 concentrations persist in certain areas of Ningbo during winter owing to seasonal haze conditions. However, the annual number of haze days has decreased between 2013 and 2020. Regional variations in pollutant concentrations appear mainly in winter, especially in December and January. The observed concentrations were higher in the north and west, and lower in the south and east. The haze process during January 18–19, 2016, occurred within a height of 0–1.5 km, with high PM2.5 concentrations mainly occurring as small and spherical particles. A higher relative humidity, temperature drop, and stable weather assist in accumulating and sinking pollutants, which cause long-term effects and render diffusion difficult. Although recent national initiatives have been effective, the air quality in northern Ningbo requires further improvement during autumn and winter.

Intensified modulation of winter aerosol pollution in China by El Niño with short duration

El Niño–Southern Oscillation (ENSO), a phenomenon of periodic changes in sea surface temperature in the equatorial central-eastern Pacific Ocean, is the strongest signal of interannual variability in the climate system with a quasi-period of 2–7 years. El Niño events have been shown to have important influences on meteorological conditions in China. In this study, the impacts of El Niño with different durations on aerosol concentrations and haze days during December–January–February (DJF) in China are quantitatively examined using the state-of-the-art Energy Exascale Earth System Model version 1 (E3SMv1). We find that PM2.5 concentrations are increased by 1–2 µg m−3 in northeastern and southern China and decreased by up to 2.4 µg m−3 in central-eastern China during El Niño events relative to the climatological means. Compared to long-duration (LD) El Niño events, El Niño with short duration (SD) but strong intensity causes northerly wind anomalies over central-eastern China, which is favorable for aerosol dispersion over this region. Moreover, the anomalous southeasterly winds weaken the wintertime prevailing northwesterly in northeastern China and facilitate aerosol transport from southern and southeast Asia, enhancing aerosol increase in northeastern China during SD El Niño events relative to LD El Niño events. In addition, the modulation effect on haze days by SD El Niño events is 2–3 times more than that by LD El Niño events in China. The aerosol variations during El Niño events are mainly controlled by anomalous aerosol accumulation/dispersion and transport due to changes in atmospheric circulation, while El Niño-induced precipitation change has little effect. The occurrence frequency of SD El Niño events has been increasing significantly in recent decades, especially after the 1940s, suggesting that El Niño with short duration has exerted an increasingly intense modulation on aerosol pollution in China over the past few decades.

Superior PM2.5 Estimation by Integrating Aerosol Fine Mode Data from the Himawari-8 Satellite in Deep and Classical Machine Learning Models

Artificial intelligence is widely applied to estimate ground-level fine particulate matter (PM2.5) from satellite data by constructing the relationship between the aerosol optical thickness (AOT) and the surface PM2.5 concentration. However, aerosol size properties, such as the fine mode fraction (FMF), are rarely considered in satellite-based PM2.5 modeling, especially in machine learning models. This study investigated the linear and non-linear relationships between fine mode AOT (fAOT) and PM2.5 over five AERONET stations in China (Beijing, Baotou, Taihu, Xianghe, and Xuzhou) using AERONET fAOT and 5-year (2015–2019) ground-level PM2.5 data. Results showed that the fAOT separated by the FMF (fAOT = AOT × FMF) had significant linear and non-linear relationships with surface PM2.5. Then, the Himawari-8 V3.0 and V2.1 FMF and AOT (FMF&AOT-PM2.5) data were tested as input to a deep learning model and four classical machine learning models. The results showed that FMF&AOT-PM2.5 performed better than AOT (AOT-PM2.5) in modelling PM2.5 estimations. The FMF was then applied in satellite-based PM2.5 retrieval over China during 2020, and FMF&AOT-PM2.5 was found to have a better agreement with ground-level PM2.5 than AOT-PM2.5 on dust and haze days. The better linear correlation between PM2.5 and fAOT on both haze and dust days (dust days: R = 0.82; haze days: R = 0.56) compared to AOT (dust days: R = 0.72; haze days: R = 0.52) partly contributed to the superior accuracy of FMF&AOT-PM2.5. This study demonstrates the importance of including the FMF to improve PM2.5 estimations and emphasizes the need for a more accurate FMF product that enables superior PM2.5 retrieval.