Comunità Batterica e Composizione Chimica in Campioni di PM10: Nuovi Approcci di Analisi Integrando Differenti Tecniche
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Comunità Batterica e Composizione
Chimica in Campioni di PM10:
Nuovi Approcci di Analisi Integrando
Differenti Tecniche
Salvatore Romano1, Silvia Becagli2, Franco Lucarelli3,
Gennaro Rispoli1, Maria Rita Perrone1
1 Dipartimento di Matematica e Fisica, Università del Salento, Lecce, 73100
2 Dipartimento di Chimica, Università di Firenze, Sesto Fiorentino (FI), 50019
3 Dipartimento di Fisica, Università di Firenze, Sesto Fiorentino (FI), 50019
Romano S. et al. “Comunità Batterica e Composizione Chimica in Campioni di PM10: Nuovi Approcci di Analisi Integrando Differenti Tecniche”
Introduction BIOAEROSOL
BIOAEROSOL
number concentration ∼ 104 m−3
mass concentration ∼ 1 μg m−3
Fröhlich-Nowoisky et al. (2016) Atmos. Res.
Bioaerosol represents about
10% of the total aerosol mass
Wei et al. (2015) Sci. Bull.
AEROSOL
Cao et al. (2014)
Environ. Sci. Technol.
Size Distribution (µm)
IX Convegno Nazionale sul Particolato Atmosferico (Lecce, 14-16 Ottobre 2020) (2/12)
Romano S. et al. “Comunità Batterica e Composizione Chimica in Campioni di PM10: Nuovi Approcci di Analisi Integrando Differenti Tecniche”
Outline and Main Goals
• The structure of the airborne bacterial community was characterized in PM10 samples
to investigate its relationships with the chemical composition in a Central Mediterranean
site affected by aerosol particles of different type and origin.
• We analyzed both winter (January-March 2018) and spring (May-June 2018) PM10
samples to study the seasonal variations of the relationships between the bacterial
community structure and the chemical elements.
• The 16S rRNA gene metabarcoding approach was used for the DNA extraction and the
PM10 sample bacterial structure characterization (both at the phylum and at the genus
taxonomic level). PM10 samples were chemically characterized for more than 30 species,
including OC, EC, ions, and metals.
• The PCA (Principal Component Analysis) and the RDA (Redundancy Discriminant
Analysis) were applied to the bacterial phylum/genus relative abundances and the chemical
species mass concentrations, allowing identifying the main relations among the
investigated parameters.
IX Convegno Nazionale sul Particolato Atmosferico (Lecce, 14-16 Ottobre 2020) (3/12)
Romano S. et al. “Comunità Batterica e Composizione Chimica in Campioni di PM10: Nuovi Approcci di Analisi Integrando Differenti Tecniche”
Monitoring Site MARINE
AEROSOL ANTHROPOGENIC
AEROSOL
MARINE DESERT
AEROSOL DUST
DESERT
DUST
Aerosol & Climate Laboratory
Mathematics and Physics Department
University of Salento (Lecce, Italy)
from Perrone et al. (2015)
Environ. Sci. Pollut. Res.
IX Convegno Nazionale sul Particolato Atmosferico (Lecce, 14-16 Ottobre 2020) (4/12)
Romano S. et al. “Comunità Batterica e Composizione Chimica in Campioni di PM10: Nuovi Approcci di Analisi Integrando Differenti Tecniche”
Instrumentation and Methodology
(Airborne Bacterial Community Characterization)
10 winter Hydra Dual Sampler
PM10 samples FAI Instruments
(January-March 2018)
10 spring PM Sampler
PM10 samples Gravimetric Method
(May-June 2018)
PM10 Sample 1
IX Convegno Nazionale sul Particolato Atmosferico (Lecce, 14-16 Ottobre 2020) (5/12)
Romano S. et al. “Comunità Batterica e Composizione Chimica in Campioni di PM10: Nuovi Approcci di Analisi Integrando Differenti Tecniche”
Instrumentation and Methodology
(Chemical Species and Oxidative Potential)
ION Mass Concentration Metal Mass Concentration
ION Chromatography Varian 720-ES Inductively
Gilson 222-XL auto sampler Coupled Plasma Atomic
(University of Florence) Emission Spectrometer
(University of Florence)
OC and EC Mass Concentration Oxidative Potential
Sunset Carbon DTT and AA
Analyzer Instrument depletion rate methods
(University of Salento) (University of Ferrara)
PM10 Sample 2
IX Convegno Nazionale sul Particolato Atmosferico (Lecce, 14-16 Ottobre 2020) (6/12)
Romano S. et al. “Comunità Batterica e Composizione Chimica in Campioni di PM10: Nuovi Approcci di Analisi Integrando Differenti Tecniche”
Characterization of Airborne Bacterial Phyla in PM10 Samples
geographical location and meteorological conditions
of the monitoring site
effects on the airborne bacteria dispersion
variability of the bacterial community composition
Cyanobacteria (18.80%)
Actinobacteria (11.43%)
Proteobacteria (45.56%)
winter spring
from Romano S. et al. (2019) «Airborne bacteria in the Central Mediterranean: Structure and role of meteorology and air mass transport» Sci. Total Environ. 697 (134020)
IX Convegno Nazionale sul Particolato Atmosferico (Lecce, 14-16 Ottobre 2020) (7/12)
Romano S. et al. “Comunità Batterica e Composizione Chimica in Campioni di PM10: Nuovi Approcci di Analisi Integrando Differenti Tecniche”
Characterization of Airborne Bacterial Genera in PM10 Samples
We found a different structure of the airborne
bacterial community at the phylum and at the
genus taxonomic level.
Pseudomonas (9.23%)
winter spring
Calothrix (16.84%)
Bacillus (4.27%)
from Romano S. et al. (2019) «Airborne bacteria in the Central Mediterranean: Structure and role of meteorology and air mass transport» Sci. Total Environ. 697 (134020)
IX Convegno Nazionale sul Particolato Atmosferico (Lecce, 14-16 Ottobre 2020) (8/12)
Romano S. et al. “Comunità Batterica e Composizione Chimica in Campioni di PM10: Nuovi Approcci di Analisi Integrando Differenti Tecniche”
Role of the Meteorology and the Long-Range Transport
Meteorological Parameters, Bacterial Phyla Relative Abundances PCA Input Parameters
Heavy Rain Samples
(S5, S9, S11, S13)
Continental Samples
(S1, S2, S10)
Desert Dust Samples
(S3, S6, S12)
Marine Samples
(S4, S7, S8)
Late Spring Samples
(S14, S15, S16, S17, S18, S19, S20)
from Romano S. et al. (2019) Sci. Total Environ. 697 (134020)
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Romano S. et al. “Comunità Batterica e Composizione Chimica in Campioni di PM10: Nuovi Approcci di Analisi Integrando Differenti Tecniche”
Redundancy Discriminant Analysis main pollution sources identified by Positive Matrix
Factorization (PMF) (black arrows): mixed anthropogenic MAN,
chemical species mass concentrations (red arrows) = predictive variables sulphate SUL, heavy oils – secondary marine OSM, soil dust
bacterial PHYLA relative abundances (green arrows) = response variables SDU, reacted dust RDU, and sea salt SES
0.5
SDU 0.25
Mn (a)
S3
0.4
Ca
Fe
Al (b) S13
Rb
Ba
Ti Si winter 0.2 spring SDU
SUL V RDU Sr
0.3 As Ti Si Al
OSM Verr - Gemm SES
PNi
Canonical Axis II (18.09 %)
SO2- NO 0.15 Fe RDU
4 3 2+ Mn
Canonical Axis II (21.79 %)
Cr Mg -
0.2 Pb Cl Zr
K Na+ K OSM Sr
S Br 0.1 SUL Pb Ni
Mo S6 Plan Zn Tene Mg2+
0.1 Zn Cu Zr Ther Tene Cu S19 OC V
Cd Firm
Acti
OC S2 S10 Chlo Br
S P
NH+4 Se Bact 0.05 + 2- RbS16
MAN NH4 SO4 Cyan Acti NO-3
EC Ba EC Arma Ca
0 Cyan Se Prot Nitr S18
Prot Cr
MAN MS- S4 0 Cd Acid
S1 S15 Firm Mo
-0.1 S11 Bact
S7 As Chla Ther MS-
S8 S14 Na+
S9 S20 S17
-0.05
-0.2 In spring, most of the bacterial Cl- SES
Bacterial phylum arrows were spread phyla were associated with
over a wider area of the canonical -0.1
-0.3 S5 chemical species characterizing the S12
axis plane in winter than in spring. OSM, RDU, and SES sources.
-0.4 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3
-0.4 -0.2 0 0.2 0.4 0.6
Canonical Axis I (63.11 %)
Canonical Axis I (74.46 %)
The chemical species arrows in the RDA triplot were on average spread in the canonical axis plane over a wider angle in spring than in
winter, suggesting that the correlation coefficients among chemical species likely decreased in spring. The spring-summer air mass
aging, which favored the mixing of particles of different type/source, likely contributed to this result.
from Romano S. et al. (2020) Sci. Total Environ. 730, 138899
IX Convegno Nazionale sul Particolato Atmosferico (Lecce, 14-16 Ottobre 2020) (10/12)Romano S. et al. “Comunità Batterica e Composizione Chimica in Campioni di PM10: Nuovi Approcci di Analisi Integrando Differenti Tecniche”
DTT = dithiothreitol assay
Redundancy Discriminant Analysis AA = ascorbic acid assay
bacterial PHYLA relative abundances (red arrows) = predictive variables OPm = mass-normalized oxidative potential
oxidative potential (green arrows) = response variables OPV Analysis
= volume-normalized oxidative potential
Redundancy Analysis (Phyla and Oxidative Potential, WINTER) Redundancy (Phyla and Oxidative Potential, SPRING)
0.3 OP-AA and OP-DTT values
We found that in winter OPm was associated with OPvAA
OPvAA present similar associations with
0.3
0.25 Cyanobacteria (generally found in aquatic the analyzed bacterial phyla
habitat), the “marine samples” S4 and S8 and the
“heavy rain samples” S5 and S9. OPvDTT
0.2 0.2
Canonical Axis II (23.13 %)
Canonical Axis II (23.70 %)
S15
0.15
winter spring Tene Acti
S2
OPvDTT 0.1 S14 S13
Verr Ther
0.1 S3 ChloBact
Verr Plan
Acti
Gemm Firm Gemm Nitr Chla
0
0.05 S8 TherChlo Plan S20 S17 Cyan S16
OPmDTT Arma Prot S11
S6 Tene Firm S19
S7 S10 Bact
0 Cyan -0.1
S18
Acid
-0.05 OPmDTT S12
Prot S1
-0.2 In spring, OPm values were
S4 strictly related to Firmicutes
-0.1 OPmAA S5 S9
and the desert dust sample S12.
-0.3 OPmAA
-0.25 -0.2 -0.15 -0.1 -0.05 0 0.05 0.1 0.15 0.2 0.25 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3
Canonical Axis I (67.45 %) Canonical Axis I (70.91 %)
OPV (extensive parameter) and OPm (intensive parameter) arrows are located in a different region of the RDA triplot,
proving their different features in relation to the analyzed bacterial phyla
IX Convegno Nazionale sul Particolato Atmosferico (Lecce, 14-16 Ottobre 2020) (11/12)Romano S. et al. “Comunità Batterica e Composizione Chimica in Campioni di PM10: Nuovi Approcci di Analisi Integrando Differenti Tecniche”
Main Results
Proteobacteria (46%), Cyanobacteria (19%) and Actinobacteria (11%) were the most abundant
airborne bacterial phyla identified in winter and spring PM10 samples collected in Lecce, a
Central Mediterranean monitoring site.
“Continental”, “marine”, “heavy rain”, “desert dust”, and “late spring” clusters were
identified by PCA taking into account the long-range transport, the meteorological parameters,
and the bacterial phyla relative abundances.
The Redundancy Discriminant Analysis technique highlighted that most of the relationships
among bacterial phyla/genera and chemical species are highly season-dependent.
The oxidative potential values obtained by AA and DTT assays presented similar associations
with the analyzed bacterial phyla and chemical species, as proved by RDA triplots.
The analyses reported in this work have shown that the PM bacterial structure must be
accounted to properly estimate the toxicity of PM10 samples and to assess the chemical species
role.
IX Convegno Nazionale sul Particolato Atmosferico (Lecce, 14-16 Ottobre 2020) (12/12)Grazie per l’attenzione!!! salvatore.romano@unisalento.it
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