Influence of seasonality, air mass origin and particulate matter chemical composition on airborne bacterial community structure in the Po Valley, Italy

Innocente, Elena; Squizzato, Stefania; Visin, Flavia; Facca, Chiara; Rampazzo, Giancarlo; Bertolini, Valentina; Gandolfi, Isabella; Franzetti, Andrea; Ambrosini, Roberto; Bestetti, Giuseppina

doi:10.1016/j.scitotenv.2017.03.199

Cited by 84 publications

(47 citation statements)

References 94 publications

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“…Supporting the former, it has been suggested that there is a well-mixed collection of "background" microbiota originating from largearea sources, like extensive forest, grass fields, or marine environments (Gandolfi et al, 2013;Seifried et al, 2015), and that they tend to be highly resilient against the hostile conditions of long-range atmospheric transport (Fierer et al, 2008;Wéry et al, 2018). Despite high-level similarity of our results to those of other bacterial surveys, at finer taxonomic resolutions we uncovered subtle compositional patterns related to sitescale features, such as land cover and management, as well as temporal shifts possibly associated with regional agricultural activities, prevailing wind direction, and vegetation phenology, suggesting a constant interplay between microbes transmitted by regional and continental air masses and those emitted from local sources (Seifried et al, 2015;Innocente et al, 2017). To begin understanding this spatiotemporal interplay, we developed a conceptual model of potential influences on aerobiome assembly across different scales (Figure 8) to guide our discussion.…”

Section: Discussionmentioning

confidence: 41%

Spatiotemporal Controls on the Urban Aerobiome

et al. 2019

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Greater exposure to environmental microorganisms has been hypothesized to reduce the likelihood of developing autoimmune disorders, and vegetation is known to be a source of diverse microbiota to the air. However, the spatiotemporal dynamics of airborne microbial communities in urban environments with varying amounts and types of vegetation are poorly understood. In this study we used high-throughput sequencing of the bacterial 16S rRNA gene to assess whether fine-scale variation in urban vegetation influences the diversity, composition, or structure of airborne bacterial communities over time. We used passive settling dishes to collect airborne bacteria from 36 sites representing three urban land cover types (forest, grassland, paved) over a 3-month period in Eugene-Springfield, Oregon, USA. We used remote sensing data (aerial 4-band orthoimagery and LiDAR) and geographic information systems (GIS) to assess detailed site characteristics (e.g., total vegetation cover and structural diversity) for each site. Our initial analysis indicated that site was the most important factor explaining variation in bacterial community structure (R 2 = 0.32, p < 0.001), followed by sampling date (R 2 = 0.24, p < 0.001), while land cover type was a significant but weak predictor (R 2 = 0.06, p < 0.001) and other vegetation metrics were even less predictive. However, when samples were analyzed separately by date, the explanatory power of land cover type increased substantially; six of nine dates showed significant effects (p < 0.05) with R 2 ranging from 0.16-0.31, indicating that land cover type had a marked influence on bacterial community structure that was obscured by the effects of site and sampling date. Despite the importance of site as a predictor of bacterial community structure, Mantel tests for spatial correlation were insignificant for most sampling dates, suggesting that localized site characteristics were driving this relationship. We use our results to propose a space-time conceptual model of the interactions between site-scale environmental features (e.g., vegetation characteristics) and regional-scale temporal processes and events (e.g., agricultural harvesting) to understand and perhaps manage intraurban airborne bacterial communities.

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Section: Discussionmentioning

confidence: 41%

Spatiotemporal Controls on the Urban Aerobiome

et al. 2019

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“…The Redundancy Discriminant Analysis represents a widespread chemometric procedure to compare different types of data as chemical species or meteorological parameters [37,38]. The RDA could be considered as the multivariate extension of a simple linear regression applied to some sets of variables [39].…”

Section: Discussionmentioning

confidence: 99%

“…In particular, the selected datasets were first standardized and then used as input (samples as rows and variables as columns) of the function f_rda included in the Fathom Toolbox estimating all the statistical parameters needed for the RDA triplot. As described by [38], Monte Carlo tests were also used by setting 499 unrestricted permutations as input for the f_rda function. Then, the structure of outputs from f_rda function, the datasets of the transformed response variables, the weighted average scores, and a selected scaling factor (optimized to better visualize data) represented the inputs of the f_rdaPlot function.…”

Section: Discussionmentioning

confidence: 99%

Oxidative Potential Sensitivity to Metals, Br, P, S, and Se in PM10 Samples: New Insights from a Monitoring Campaign in Southeastern Italy

et al. 2020

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Different analytical techniques were used in this work to investigate the relationships between oxidative potential (OP) and metal, Br, P, S, and Se concentration in PM10 samples. Dithiothreitol and ascorbic acid acellular assays were used to determine the oxidative potential (OP) in PM10 samples. The particle-induced X-ray emission technique was used to estimate the mass concentration of specific chemical elements. PM10 samples were collected in Lecce, a coastal site of the Central Mediterranean away from large sources of local pollution. Both winter and spring samples were analyzed to study the seasonal dependence of the relationships between OP values and chemical element concentrations. The Redundancy Discriminant Analysis (RDA) was applied to (volume- and mass-normalized) OP values as response variables and metal, Br, P, S, and Se concentrations as explanatory variables. RDA triplots allowed to visualize the main relationships between PM10 OP values and corresponding chemical element concentrations. Spearman correlation coefficients were also used to investigate the relationships between OP values and metal, Br, P, S, and Se concentrations, besides comparing RDA outcomes. The integrated approach based on two different techniques allowed to better highlight the potentially harmful effects associated with specific metals and other chemical elements in PM10 samples.

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“…Air mass origin was different for the four sampling periods. Differential source regions and transport have been shown to influence microbial composition of the atmosphere (DeLeon-Rodriguez et al 2013;Innocente et al 2017). Zweifel et al (2012) also suggested annual variation, succession of species in airborne communities or random variation as explanatory mechanisms.…”

Section: Seasonal Differences In Air Community Compositionmentioning

confidence: 99%

Microbial composition in seasonal time series of free tropospheric air and precipitation reveals community separation

et al. 2019

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Primary biological aerosols are transported over large distances, are traveling in various media such as dry air masses, clouds or fog, and eventually deposited with dry deposition, especially for larger particles, or precipitation like rain, hail or snow. To investigate relative abundance and diversity of airborne bacterial and fungal communities, samples have been collected with a liquid impinger (Coriolis l) from the top of Mount Sonnblick (3106 m asl, Austrian Alps) from the respective sources under a temporal aspect over four seasons over the year to include all climatic conditions. Bacterial and fungal

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Influence of seasonality, air mass origin and particulate matter chemical composition on airborne bacterial community structure in the Po Valley, Italy

Cited by 84 publications

References 94 publications

Spatiotemporal Controls on the Urban Aerobiome

Spatiotemporal Controls on the Urban Aerobiome

Oxidative Potential Sensitivity to Metals, Br, P, S, and Se in PM10 Samples: New Insights from a Monitoring Campaign in Southeastern Italy

Microbial composition in seasonal time series of free tropospheric air and precipitation reveals community separation

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