Bioaerosols in the atmosphere: A comprehensive review on detection methods, concentration and influencing factors

In the past few decades, especially since the outbreak of the coronavirus disease (COVID-19), the effects of atmospheric bioaerosols on human health, the environment, and climate have received great attention. To evaluate the impacts of bioaerosols quantitatively, it is crucial to determine the types of bioaerosols in the atmosphere and their spatial-temporal distribution. We provide a concise summary of the online and offline observation strategies employed by the global research community to sample and analyze atmospheric bioaerosols. In addition, the quantitative distribution of bioaerosols is described by considering the atmospheric bioaerosols concentrations at various time scales (daily and seasonal changes, for example), under various weather, and different underlying surfaces. Finally, a comprehensive summary of the reasons for the spatiotemporal distribution of bioaerosols is discussed, including differences in emission sources, the impact process of meteorological factors and environmental factors. This review of information on the latest research progress contributes to the emergence of further observation strategies that determine the quantitative dynamics of public health and ecological effects of bioaerosols.

Uncertainties in source allocation of carbonaceous aerosols in a Mediterranean region

Understanding the atmospheric processes involving carbonaceous aerosols (CAs) is crucial for assessing air pollution impacts on human health and climate. The sources and formation mechanisms of CAs are not well understood, making it challenging to quantify impacts in models. Studies suggest residential wood combustion (RWC) and traffic significantly contribute to CAs in Europe's urban and rural areas. Here, we used an atmospheric chemistry model (MONARCH) and three different emission inventories (two versions of the European-scale emission inventory CAMS-REG_v4 and the HERMESv3 detailed national inventory for Spain) to assess the uncertainties in CAs simulation and source allocation (from traffic, RWC, shipping, fires and others) in Northeast Spain. For this, black carbon (BC) and organic aerosol (OA) measurements performed at three supersites representing different environments (urban, regional and remote) were used. Our findings show the importance of model resolution and detailed emission input data in accurately reproducing BC/OA observations. Even though emissions of total particulate matter are rather consistent between inventories in Spain, we found discrepancies between them mainly related to the spatiotemporal disaggregation (particularly relevant for traffic and RWC) and the treatment of the condensable fraction of CAs in RWC (changes in the speciation of elemental/organic carbon). The main source contribution to BC concentrations in the urban site is traffic, accounting for 71.1%/65.2% (January/July) in close agreement with the fossil contribution derived from observations (78.8%/84.2%), followed by RWC (12.8%/3%) and shipping emissions (5.4%/13.8%). An over-representation of RWC (winter) and shipping (summer) is obtained with CAMS-REG_v4. Noteworthy uncertainties arise in OA results due to condensables in emissions and a limited secondary aerosol production in the model. These findings offer insights into MONARCH's effectiveness in simulating CAs concentrations and source contribution in Northeast Spain. The study highlights the benefits of combining new datasets and modeling techniques to refine emission inventories and better understand and mitigate air pollution impacts.

The new WHO air quality guidelines for PM2.5: predicament for small/medium cities

The global burden of disease estimated that approximately 7.1 million deaths worldwide were related to air pollution in 2016. However, only a limited number of small- and middle-sized cities have air quality monitoring networks. To date, air quality in terms of particulate matter is still mainly focused on mass concentration, with limited compositional monitoring even in mega cities, despite evidence indicating differential toxicity of particulate matter. As this evidence is far from conclusive, we conducted PM_2.5 bioaccessibility studies of potentially harmful elements in a medium-sized city, Londrina, Brazil. The data was interpreted in terms of source apportionment, the health risk evaluation and the bioaccessibility of inorganic contents in an artificial lysosomal fluid. The daily average concentration of PM_2.5 was below the WHO guideline, however, the chemical health assessment indicated a considerable health risk. The in vitro evaluation showed different potential mobility when compared to previous studies in large-sized cities, those with 1 million inhabitants or more (Curitiba and Manaus). The new WHO guideline for PM_2.5 mass concentration puts additional pressure on cities where air pollution monitoring is limited and/or neglected, because decision making is mainly revenue-driven and not socioeconomic-driven. Given the further emerging evidence that PM chemical composition is as, or even more, important than mass concentration levels, the research reported in the paper could pave the way for the necessary inter- and intra-city collaborations that are needed to address this global health challenge.

Experimental Characterization of Particulate and Gaseous Emissions from Biomass Burning of Six Mediterranean Species and Litter

Wildfires across the Mediterranean ecosystems are associated with safety concerns due to their emissions. The type of biomass determines the composition of particulate matter (PM) and gaseous compounds emitted during the fire event. This study investigated simulated fire events and analysed biomass samples of six Mediterranean species and litter in a combustion chamber. The main aims are the characterization of PM realized through scanning electron microscopy (SEM/EDX), the quantification of gaseous emissions through gas chromatography (GC-MS) and, consequently, identification of the species that are potentially more dangerous. For PM, three size fractions were considered (PM10, 2.5 and 1), and their chemical composition was used for particle source-apportionment. For gaseous components, the CO, CO2, benzene, toluene and xylene (BTXs) emitted were quantified. All samples were described and compared based on their peculiar particulate and gaseous emissions. The primary results show that (a) Acacia saligna was noticeable for the highest number of particles emitted and remarkable values of KCl; (b) tree species were related to the fine windblown particles as canopies intercept PM10 and reemit it during burning; (c) shrub species were related to the particles resuspended from soil; and (d) benzene and toluene were the dominant aromatic compounds emitted. Finally, the most dangerous species identified during burning were Acacia saligna, for the highest number of particles emitted, and Pistacia lentiscus for its high density of particles, the presence of anthropogenic markers, and the highest emissions of all gaseous compounds.

Classroom indoor PM2.5 sources and exposures in inner-city schools

Children spend over 6 h a day in schools and have higher asthma morbidity from school environmental exposures. The present study aims to determine indoor and outdoor possible sources affecting indoor PM_2.5 in classrooms. Weeklong indoor PM_2.5 samples were collected from 32 inner-city schools from a Northeastern U.S. community during three seasons (fall, winter and spring) during the years 2009 to 2013. Concurrently, daily outdoor PM_2.5 samples were taken at a central monitoring site located at a median distance of 4974 m (range 1065-11,592 m) from the schools. Classroom indoor concentrations of PM_2.5 (an average of 5.2 μg/m³) were lower than outdoors (an average of 6.5 μg/m³), and these averages were in the lower range compared to the findings in other schools' studies. The USEPA PMF model was applied to the PM_2.5 components measured simultaneously from classroom indoor and outdoor to estimate the source apportionment. The major sources (contributions) identified across all seasons of indoor PM_2.5 were secondary pollution (41%) and motor vehicles (17%), followed by Calcium (Ca)-rich particles (12%), biomass burning (15%), soil dust (6%), and marine aerosols (4%). Likewise, the major sources of outdoor PM_2.5 across all seasons were secondary pollution (41%) and motor vehicles (26%), followed by biomass burning (17%), soil dust (7%), road dust (3%), and marine aerosols (1%). Secondary pollution was the greatest contributor to indoor and outdoor PM_2.5 over all three seasons, with the highest contribution during spring with 53% to indoor PM_2.5 and 45% to outdoor PM_2.5. Lower contributions of this source during fall and winter are most likely attributed to less infiltration indoors. In contrast, the indoor contribution of motor vehicles source was highest in the fall (29%) and winter (25%), which was presumably categorized by a local source. From the relationship between indoor-to-outdoor sulfur ratios and each source contribution, we also estimated the local and regional influence on indoor PM_2.5 concentration. Overall, the observed differences to indoor PM_2.5 are related to seasonality, and the distinct characteristics and behavior of each classroom/school.

Assessing the impact of PM2.5 on respiratory disease using artificial neural networks

Understanding the impact on human health during peak episodes in air pollution is invaluable for policymakers. Particles less than PM_2.5 can penetrate the respiratory system, causing cardiopulmonary and other systemic diseases. Statistical regression models are usually used to assess air pollution impacts on human health. However, when there are databases missing, linear statistical regression may not process well and alternative data processing should be considered. Nonlinear Artificial Neural Networks (ANN) are not employed to research environmental health pollution even though another advantage in using ANN is that the output data can be expressed as the number of hospital admissions. This research applied ANN to assess the impact of air pollution on human health. Three well-known ANN were tested: Multilayer Perceptron (MLP), Extreme Learning Machines (ELM) and Echo State Networks (ESN), to assess the influence of PM_2.5, temperature, and relative humidity on hospital admissions due to respiratory diseases. Daily PM_2.5 levels were monitored, and hospital admissions for respiratory illness were obtained, from the Brazilian hospital information system for all ages during two sampling campaigns (2008-2011 and 2014-2015) in Curitiba, Brazil. During these periods, the daily number of hospital admissions ranged from 2 to 55, PM_2.5 concentrations varied from 0.98 to 54.2 μg m^-3, temperature ranged from 8 to 26 °C, and relative humidity ranged from 45 to 100%. Of the ANN used in this study, MLP gave the best results showing a significant influence of PM_2.5, temperature and humidity on hospital attendance after one day of exposure. The Anova Friedman's test showed statistical difference between the appliance of each ANN model (p < .001) for 1 lag day between PM_2.5 exposure and hospital admission. ANN could be a more sensitive method than statistical regression models for assessing the effects of air pollution on respiratory health, and especially useful when there is limited data available.

Characterization of fine particulate matter in ambient air by combining TEM and multiple spectroscopic techniques--NMR, FTIR and Raman spectroscopy

This paper reports a systematic study of the microstructures and spectroscopic characteristics of PM2.5 and its potential sources in Beijing by combining transmission electron microscopy and multiple spectroscopic techniques: nuclear magnetic resonance, Fourier transform infrared and Raman spectroscopy. TEM images showed that dominant components of PM2.5 are airborne organic substances with many trace metal elements which are associated with combustion sources. NMR spectra precisely determined the percentage of carbonaceous speciation in both PM2.5 (with spatial and temporal distribution) and its potential sources, and distinguished the similarities and differences among them. In FTIR spectra, a remarkable peak at 1390 cm(-1) that appeared only in PM2.5 samples was attributed to NH4NO3, representing the occurrence of secondary processes. Raman spectra revealed certain inorganic compounds including sulfate and nitrate ions. Based on the analysis of the decomposition of Raman spectra, spectral parameters provided structural information and helped to find potential sources of PM2.5. In the space of carbon aromaticity index and ID1/IG, PM2.5 points followed a linear distribution which may also be useful in source tracing. The result shows that the combined non-destructive methods are efficient to trace the sources of PM2.5.

PM₂.₅., EC and OC in atmospheric outflow from the Indo-Gangetic Plain: temporal variability and aerosol organic carbon-to-organic mass conversion factor

Temporal variability (November'09-March'10) in the mass concentrations of PM2.5, mineral dust, organic carbon and elemental carbon (OC and EC), water-soluble organic carbon (WSOC) and inorganic species (WSIS) has been studied in the atmospheric outflow to the Bay of Bengal from a sampling site [Kharagpur: 22.02°N, 87.11°E] in the Indo-Gangetic Plain (IGP). Based on diagnostic ratios of carbonaceous species [OC/EC ≈ 7.0 ± 2.2, WSOC/OC ≈ 0.52 ± 0.16, and K(+)/EC≈0.48±0.17], we document dominant impact from biomass burning emissions (wood-fuel and post-harvest agricultural-waste burning) in the IGP-outflow. Relatively high concentration of sulphate (SO4(2-) ≈ 6.9-25.3 μg m(-3); SO4(2-)/ΣWSIS=45-77%) and characteristic ratios of nss-SO4(2-)/EC (3.9 ± 2.1) and nss-SO4(2-)/OC (0.61 ± 0.46) provide information on absorption/scattering properties of aerosols. Based on quantitative assessment of individual components of PM2.5, we document aerosol organic carbon-to-organic mass (OC to OM) conversion factor centring at 1.5 ± 0.2 (range: 1.3-2.7) in the atmospheric outflow from IGP. The aerosol composition over the Bay of Bengal shows striking similarity with the diagnostic ratios documented for the IGP-outflow. Relatively high conversion factor for assessing the mass of organic aerosols over the Bay of Bengal (1.1-3.7) provides evidence for their oxidation during long-range atmospheric transport.

Biogenic potassium salt particles as seeds for secondary organic aerosol in the Amazon

The fine particles serving as cloud condensation nuclei in pristine Amazonian rainforest air consist mostly of secondary organic aerosol. Their origin is enigmatic, however, because new particle formation in the atmosphere is not observed. Here, we show that the growth of organic aerosol particles can be initiated by potassium-salt-rich particles emitted by biota in the rainforest. These particles act as seeds for the condensation of low- or semi-volatile organic compounds from the atmospheric gas phase or multiphase oxidation of isoprene and terpenes. Our findings suggest that the primary emission of biogenic salt particles directly influences the number concentration of cloud condensation nuclei and affects the microphysics of cloud formation and precipitation over the rainforest.

Size distributions of fine and ultrafine particles in the city of Strasbourg: correlation between number of particles and concentrations of NO(x) and SO(2) gases and some soluble ions concentration determination

An Electrical Low Pressure Impactor (ELPI) was used during spring and autumn 2003 in the centre of Strasbourg for the measurement of atmospheric aerosols size distribution. The concentration of NO(x) and SO(2) in air was simultaneously measured with specific analysers. Samples were collected in the range 0.007-10 microm in equivalent aerodynamic diameter size. Number distributions are representative of a pollution originating from urban traffic with a particle size distribution exhibiting a nucleation mode below 29 nm and an accumulation mode around 80 nm in size. A mean particle density equal to 39000+/-35000 total particles per cm(3) with a size ranging from 7 to 10 microm was obtained after a sampling period of 2 weeks in spring. About 86.9% of the number of particles have an aerodynamic diameter below 0.1 microm and 13.1% between 0.1 and 1 microm. Correlation coefficients between the number of particles impacted on each ELPI plate and gas concentrations (SO(2) and NO(x)) showed that the numbers of particles with diameter between 0.10 and 0.62 microm are highly related to the NO(x) concentration. This result indicates that particles are traffic induced since NO(x) is mainly emitted by cars as shown by measurements on various sites. Particles are less clearly correlated to the SO(2) concentration. Particle analysis on different ELPI plates for a sampling period of 2 weeks in autumn showed high level of soluble NO(3)(-), SO(4)(2-) and NH(4)(+) ions. Indeed, up to 90% b.w. of these three species were found in the particle range 0.1-1 microm. The formation of particulate NH(4)NO(3) is favoured by high NO(x) concentration, which induces the formation of gaseous HNO(3).

Large seasonal swings in leaf area of Amazon rainforests

Despite early speculation to the contrary, all tropical forests studied to date display seasonal variations in the presence of new leaves, flowers, and fruits. Past studies were focused on the timing of phenological events and their cues but not on the accompanying changes in leaf area that regulate vegetation-atmosphere exchanges of energy, momentum, and mass. Here we report, from analysis of 5 years of recent satellite data, seasonal swings in green leaf area of approximately 25% in a majority of the Amazon rainforests. This seasonal cycle is timed to the seasonality of solar radiation in a manner that is suggestive of anticipatory and opportunistic patterns of net leaf flushing during the early to mid part of the light-rich dry season and net leaf abscission during the cloudy wet season. These seasonal swings in leaf area may be critical to initiation of the transition from dry to wet season, seasonal carbon balance between photosynthetic gains and respiratory losses, and litterfall nutrient cycling in moist tropical forests.

Receptor modeling application framework for particle source apportionment

Receptor models infer contributions from particulate matter (PM) source types using multivariate measurements of particle chemical and physical properties. Receptor models complement source models that estimate concentrations from emissions inventories and transport meteorology. Enrichment factor, chemical mass balance, multiple linear regression, eigenvector. edge detection, neural network, aerosol evolution, and aerosol equilibrium models have all been used to solve particulate air quality problems, and more than 500 citations of their theory and application document these uses. While elements, ions, and carbons were often used to apportion TSP, PM10, and PM2.5 among many source types, many of these components have been reduced in source emissions such that more complex measurements of carbon fractions, specific organic compounds, single particle characteristics, and isotopic abundances now need to be measured in source and receptor samples. Compliance monitoring networks are not usually designed to obtain data for the observables, locations, and time periods that allow receptor models to be applied. Measurements from existing networks can be used to form conceptual models that allow the needed monitoring network to be optimized. The framework for using receptor models to solve air quality problems consists of: (1) formulating a conceptual model; (2) identifying potential sources; (3) characterizing source emissions; (4) obtaining and analyzing ambient PM samples for major components and source markers; (5) confirming source types with multivariate receptor models; (6) quantifying source contributions with the chemical mass balance; (7) estimating profile changes and the limiting precursor gases for secondary aerosols; and (8) reconciling receptor modeling results with source models, emissions inventories, and receptor data analyses.