# Runoff microbiome quality assessment of a city center rainwater harvesting zone shows a differentiation of pathogen loads according to human mobility patterns

Rayan Bouchali, Claire Mandon, Emmanuelle Danty - Berger, Alain Géloën, Laurence Marjolet, Benjamin Youenou, Adrien C.M. Pozzi, Sophie Vareilles, Wessam Galia, Gislain Lipeme Kouyi, et al.

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## ► To cite this version:

Rayan Bouchali, Claire Mandon, Emmanuelle Danty - Berger, Alain Géloën, Laurence Marjolet, et al. Runoff microbiome quality assessment of a city center rainwater harvesting zone shows a differentiation of pathogen loads according to human mobility patterns. International Journal of Hygiene and Environmental Health, 2024, 260, pp.114391. 10.1016/j.ijheh.2024.114391. hal-04649723

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# Runoff microbiome quality assessment of a city center rainwater harvesting zone shows a differentiation of pathogen loads according to human mobility patterns

Rayan Bouchali<sup>a</sup>, Claire Mandon<sup>b</sup>, Emmanuelle Danty - Berger<sup>a</sup>, Alain Géloën<sup>a</sup>, Laurence Marjolet<sup>a</sup>, Benjamin Youenou<sup>a</sup>, Adrien C.M. Pozzi<sup>a</sup>, Sophie Vareilles<sup>b</sup>, Wessam Galia<sup>a</sup>, Gislain Lipeme Kouyi<sup>c</sup>, Jean-Yves Toussaint<sup>b</sup>, Benoit Cournoyer<sup>a,*</sup>

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## ARTICLE INFO

**Keywords:**

Urban microbiology

Rainwater harvesting

Pathogens

DNA metabarcoding

Hydrocarbon

Pseudomonas paraeruginosa

## ABSTRACT

The hygienic quality of urban surfaces can be impaired by multiple sources of microbiological contaminants. These surfaces can trigger the development of multiple bacterial taxa and favor their spread during rain events through the circulation of runoff waters. These runoff waters are commonly directed toward sewer networks, stormwater infiltration systems or detention tanks prior a release into natural water ways. With water scarcity becoming a major worldwide issue, these runoffs are representing an alternative supply for some usage like street cleaning and plant watering. Microbiological hazards associated with these urban runoffs, and surveillance guidelines must be defined to favor these uses. Runoff microbiological quality from a recently implemented city center rainwater harvesting zone was evaluated through classical fecal indicator bacteria (FIB) assays, quantitative PCR and DNA meta-barcoding analyses. The incidence of socio-urbanistic patterns on the organization of these urban microbiomes were investigated. FIB and DNA from Human-specific *Bacteroidales* and pathogens such as *Staphylococcus aureus* were detected from most runoffs and showed broad distribution patterns. 16S rRNA DNA meta-barcoding profilings further identified core recurrent taxa of health concerns like *Acinetobacter*, *Mycobacterium*, *Aeromonas* and *Pseudomonas*, and divided these communities according to two main groups of socio-urbanistic patterns. One of these was highly impacted by heavy traffic, and showed recurrent correlation networks involving bacterial hydrocarbon degraders harboring significant virulence properties. The *tpm*-based meta-barcoding approach identified some of these taxa at the species level for more than 30 genera. Among these, recurrent pathogens were recorded such as *P. aeruginosa*, *P. paraeruginosa*, and *Aeromonas caviae*. *P. aeruginosa* and *A. caviae* *tpm* reads were found evenly distributed over the study site but those of *P. paraeruginosa* were higher among sub-catchments impacted by heavy traffic. Health risks associated with these runoff *P. paraeruginosa* emerging pathogens were high and associated with strong cytotoxicity on A549 lung cells. Recurrent detections of pathogens in runoff waters highlight the need of a microbiological surveillance prior allowing their use. Good microbiological quality can be obtained for certain typologies of sub-catchments with good hygienic practices but not all. A reorganization of Human mobility and behaviors would likely trigger changes in these bacterial diversity patterns and reduce the occurrences of the most hazardous groups.

## 1. Introduction

Water supply is becoming a worldwide critical issue. Multiple

solutions and technologies to face the deficit and increasing demand in water are getting implemented (Askarizadeh et al., 2015; Lapointe et al., 2022). Sustainable Urban Drainage Systems (SUDS) aiming at restoring

<footnote><sup>a</sup> Université de Lyon, Université Claude Bernard Lyon 1, VetAgro Sup, UMR Ecologie Microbienne / Microbial Ecology (LEM), CNRS 5557, INRAE 1418, 69280, Marcy L'Etoile, France</footnote>

<footnote><sup>b</sup> Université de Lyon, INSA Lyon, UMR Environnement, Ville, Société, CNRS 5600, 18 rue Chevreul, 69362, Lyon, France</footnote>

<footnote><sup>c</sup> INSA Lyon, UR7429 DEEP, 11 rue de la physique, 69621, Villeurbanne, France</footnote>

<footnote>* Corresponding author. UMR Microbial Ecology / Ecologie Microbienne, CNRS 5557, INRAE 1418, VetAgro Sup, Main building, aisle 3, 1st floor, 69280, Marcy-L'Etoile, France.</footnote>

<footnote>E-mail address: benoit.cournoyer@vetagro-sup.fr (B. Cournoyer).</footnote>
the quality and natural water balance over cities and peri-urban watersheds through the transfer of runoffs into rain gardens and storm-water infiltration systems are being built. The filtered waters getting through these SUDS can recharge groundwater supplies and provide a base flow for local streams (Askarizadeh et al., 2015). Furthermore, when the water balance has been re-equilibrated, the exceeding runoffs can be harvested for other purposes. In fact, with the increasing water shortages observed in several countries, direct uses of these exceeding runoffs for street cleaning, plant watering, car washings, toilet flushing, etc are being considered. However, the safety of these latter uses remain to be validated by national health agencies. Quality guidelines must be defined. Several studies have, in fact, reported high concentrations of chemical pollutants and infectious agents in urban runoffs that could represent Human health threats (Lapointe et al., 2022). For example, petrol motor engines have been shown to heavily contaminate street runoffs (Bernardin-Souibgui et al., 2018; Revitt et al., 2014) through the emissions of metallic trace elements (MTE) (Cu, Pb, Zn, Cr, Ni and Cd), and polycyclic aromatic hydrocarbons (PAH) (Gasperi et al., 2014; Lapointe et al., 2022; Leite et al., 2011). Furthermore, microbiological contaminants representing health hazards could also be found (Colin et al., 2020; Graham et al., 2021; Lapointe et al., 2022). Infectious viruses have been detected among runoff e. g. adenoviruses, enteroviruses, and noroviruses but at low concentrations (Graham et al., 2021). Key taxa to be used for assessing runoff microbiological safety remain to be defined (O'Shea and Field, 1992).

A sustainable use of urban runoffs for local purposes also requires a better understanding of the circulation and mixing of contaminants over the city surfaces. Urban runoffs are the products of rainwaters flowing over various surfaces which are leading to a re-suspension of multiple micro-organisms but also of pollutants, particles, dusts, soils, plant debris, and wastes. They are thus a mirror of the hygienic state (sanitation quality defined according to the recorded quantities of pollutants and pathogens representing human health hazards) of these surfaces and of their microbiomes. These surfaces are colonized by multiple micro-organisms which are likely structured according to the microbial community coalescence and r/K ecological theories (Andrews and Harris, 1986a; Mansour et al., 2018; Song et al., 2017). The observed core and sub-core taxa would thus have gene pools better fit for the colonized urban settings. These communities would have emerged through community assemblage processes, and re-assortments (changes in the relative numbers of particular taxa) leading to the emergence of the best fit functional organization. Opportunistic taxa could be transiently detected among these microbial assemblages, and outgrow the core and sub-core taxa when short term opportunities would occur such as accidental oil spills. These disturbances would modify community assemblages through increased mortality of certain core and sub-core taxa, and rapid growth of the opportunistic colonizers. So far, few investigations described these street surface microbiomes and their ecological trends. The microbiomes of urban sediments and runoffs (soil, dusts, wastes, plant debris) found over urban surfaces and accumulating in stormwater infiltration systems were documented (e. g. Aigle et al., 2021; Colin et al., 2020; Joyner et al., 2019). These microbiomes were found to be structured by the occurrences of pollutants (Marti et al., 2017a). The accumulation of poorly degradable pollutants in street sediments was associated with an increase in extremophiles such as *Acidibacter* and *Haliangium*, and a global drop in *Aeromonas* cells (Aigle et al., 2021).

The microbiology of several urban compartments likely acting as sources of micro-organisms that can seed these urban sediments and runoffs was also investigated. Recurrent bacterial taxa in these urban settings were recorded such as *Acinetobacter*, *Pseudomonas*, *Flavobacterium*, *Paludibacter*, *Peredibacter*, *Mycobacterium*, *Arthrobacter*, and *Psychrobacter* (Bouchali et al., 2022; Danko et al., 2021; Leung et al., 2014; Marti et al., 2017a; McLellan et al., 2015; Robertson et al., 2013). Several bacterial taxa emitted by man were recorded such as skin *Micrococcus*, *Propionibacterium*, and *Staphylococcus* (Leung et al., 2014;

Robertson et al., 2013) and species thriving in the intestinal tract (Afshinnekoo et al., 2015; Cui et al., 2017; Hsu et al., 2016; Leung et al., 2014; Marti et al., 2017a; Chong et al., 2013; Bernardin-Souibgui et al., 2018; Dueker et al., 2018; Arnone and Walling, 2007). Bacteria of health concerns were detected including *Legionella pneumophila*, *Pseudomonas aeruginosa*, *Aeromonas hydrophila*, *Nocardia cyriacigeorgica*, *Salmonella enterica* and non-tuberculous *mycobacteria* (Aigle et al., 2021; Bernardin-Souibgui et al., 2018; Bouchali et al., 2022; Dong et al., 2019; Falkinham, 2015; Lee et al., 2020; Vautrin et al., 2021).

The relationships between the microbiological diversity observed among the above sources and socio-urbanistic patterns including particular human behaviors and modes of mobility were rarely addressed. These patterns could likely lead to great variations in the concentrations of microbiological contaminants among runoffs. They could be highly explanatory of microbial distribution patterns and become an important key in the assessment of behaviors that can induce exposures to undesirable microbiological contaminants. In this work, we thus tested the hypothesis that common and specific features observed among urban catchments could be identified and used to differentiate urban settings associated with particular runoff microbiological water qualities. These investigations were performed on a recently implemented rainwater harvesting area named "Garibaldi" and located in Lyon (France). This area allows a storage of runoffs for local cleaning and watering uses. Several sub-catchments were analyzed over this rainwater harvesting area, and could be differentiated according to their socio-urbanistic patterns. Microbiological quality assessment of their runoffs were performed through the 16S rRNA gene and *tpm* metabarcoding approaches but also classical quantitative (q) PCR approaches and fecal indicator bacteria (FIB) monitorings. Recurrent and opportunistic bacterial taxa including pathogenic species could be tracked, and led to the observation of significant distribution patterns matching a differentiation of sub-catchments according to Human mobility patterns. High occurrences of FIB and bacterial pathogens were recorded including a hyper-virulent species named *Pseudomonas paraeruginosa*.

## 2. Materials and methods

### 2.1. Experimental site and socio-urbanistic surveys

The Lyon-Garibaldi rainwater harvesting area (France) was selected for this study (see https://www.graie.org/graie/BaseDonneesTA/07_69_Lyon6_Garibaldi.pdf). This area is part of the city center of Lyon and has undergone a rehabilitation from 2010 to reduce local heat islands and favor a use of the stored runoffs from sidewalks, pedestrian promenade areas, and bicycle paths for cleaning and plant watering. Runoffs coming from the main roads are transferred to the sewer system. Socio-urbanistic patterns were analyzed, in spring, summer and fall, by recording technical objects and human activities, and their traces. These surveys allowed defining runoff water sampling points (n = 12; Fig. 1) according to the urbanistic parameters shown in Table S1, S2 and S3a. Sixteen surveys were performed at day (n = 13) and night (n = 3) times during 30-min-long sessions per point and over the perimeter leading to the observed runoffs (Table S1). This led to the identification of 21 socio-urbanistic variables illustrative of the main anthropogenic constraints impacting the investigated city center rainwater harvesting zone (Table S2).

### 2.2. Runoff water samplings and their main physico-chemical and microbiological parameters

Three rain events were considered in this investigation (Table S3a) and their meteorological features are shown in Fig. S1. These events occurred in autumn, winter and summer. The total rainfall depth for each event was of less than 15 mm. Maximal flow and volume were computed and are given in Table S3a. These events represented about 80
<figure><img src="image_5.png" /><figcaption>**Fig. 1.** Aerial view of the rainwater harvesting area of the Garibaldi sector of the Lyon city center (France). The Garibaldi street and the Part-Dieu shopping mall are indicated as well as latitudinal (x axis) and longitudinal (y axis) co-ordinates. The runoff sampling points (P codes) are shown, and a color code was used to differentiate these points according to their associations with the monitored socio-urbanistic patterns (Table S1; Fig. 2): grey = cluster 1 (high traffic zone); orange = group II (low traffic, high numbers of pedestrians). RW DU: meteoric rainwater sampling zone. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

% of those observed in Lyon based on a thorough analysis carried out by the Lyon Metropolis from 1980 (https://data.grandlyon.com/portail/f/r/accueil), and were similar to those reported by Zhu and Lipeme Kouyi (2019). Runoffs were collected from the sites in less than 1h using a manual pump. Waters were pumped from the runoff without touching the solid surfaces, and just before reaching the drainage grid found at each sampling point. Rain (meteoric waters) control samples (RW DU) were recovered from sterile buckets. Runoff samples were stored at 4 °C, and then used within 24h in the following manipulations. It is to be noted that samplings over three independent rain events and seasons were performed to obtain a significant and representative variance in the microbiological datasets, and bring strength to the statistical inferences performed in the next sections (as performed by Bouchali et al., 2022). A multi-parametric sensor probe (Horiba, Piscataway, USA) was used to measure electrical conductivity, turbidity, pH, oxygen, and water temperature of the runoffs. Suspended matters (SM; NF EN 872), total organic carbon (TOC; NF EN 1484), dissolved organic carbon (DOC; NF EN 1484), Kjeldahl-nitrogen (N-tot; NF EN 25663) and total-phosphorus (P-tot; NF EN ISO 6878) were quantified according to Namour et al. (2015) and Voisin et al. (2018) by the Carso analytical lab (Lyon, France). Runoff intensity and flow were estimated using the Canoe modeling software (Chocat, 2013). Total heterotrophic bacterial counts were estimated from the collected runoffs according to Bernardin-Souibgui et al. (2018) by plating serial dilutions of runoff on 1/10 TSA (tryptic soy agar) medium amended with 1.5 % agar. Plates were incubated at 30 °C for 72h. Number of intestinal enterococci and Escherichia coli were estimated using the IDEXX Colilert and Enterolert type E kits (IDEXX, Westbrook, USA).

DNA extractions were performed as described in Colin et al. (2020). Briefly, approximatively 1 L of runoff water were filtered on a 0.22 µm polycarbonate filter which was then used for the DNA extraction. DNA extractions were performed using the FastDNA SPIN® Kit for Soil (MP

Biomedicals, Illkirch, France). Quality and concentrations of DNA ex-
tracts were checked using a NanoDrop UV-Vis spectrophotometer. The
MIQE-guidelines for performing qPCR assays were then applied in the
following manipulations (Bustin et al., 2009; Courts et al., 2019). PCR
primers, normalization strategy and instrumentation used to estimate
gene copy numbers were described in Marti et al. (2017a, 2017b).
Table S3b indicates the gene targets and qPCR conditions. Microbial
source tracking PCR assays of fecal contaminations were performed as
previously reported (Marti et al., 2017b; Shibata et al., 2010). The
Brilliant II qRT-PCR SYBR Green Low ROX Master Mix (Agilent,
Vénissieux, France) was used. Integrons were quantified using PCR
primers targeting integrase genes of class 1, 2 and 3 according to Marti
et al. (2017a). *L. monocytogenes* and *S. aureus* were quantified according
respectively to Nogva et al. (2000) and Martinon and Wilkinson (2011).
Human and dog fecal contaminations were tracked using respectively
the HF183 (Seurinck et al., 2005) and BacCan assays (Kildare et al.,
2007). For each bacterial marker detected by qPCR, ratios over 16S
rRNA gene copies were computed to normalize the datasets.

2.3. Meta-barcoding analyses of V5–V7 16S rRNA and tpm genes

2.3.1. Illumina DNA sequencings of the PCR products

Amplification and sequencing of the V5–V7 16S rRNA regions were
performed as described by Colin et al. (2020). V5–V7 16S rRNA PCR
amplifications were carried out using DNA primers 799F and 1193R
(Beckers et al., 2016) and the following temperature cycles: 94 °C for 3
min, followed by 28 cycles of 94 °C for 30 s, 53 °C for 40 s and 72 °C for
1 min, with a final elongation step at 72 °C for 5 min. *tpm* PCR products
were generated as described by Aigle et al. (2021). Briefly, the PCR
products were obtained using an equimolar mix of the PTCF2, PTCF2m,
PTCR2 and PTCTR2m primers, and the following PCR cycles: 94 °C for 5
min, 35 cycles consisting of 94 °C for 30 s, 58 °C for 30 s and 72 °C for 30
s, and a final extension of 5 min at 72 °C. Illumina V3 MiSeq DNA
sequencing of V5–V7 16S rRNA and *tpm* amplicons were performed
respectively by MrDNA services (Shallowater, Texas, USA) and Biofidal
(Vaulx-en-Velin, France). The 16S rRNA and *tpm* gene sequences re-
ported in this work are available at the European Nucleotide Archive
(https://www.ebi.ac.uk/ena) respectively under the accessions
#PRJEB33507 and #PRJEB36973.

2.3.2. Processing of meta-barcoding V5–V7 16S rRNA and tpm gene DNA sequences, computation of diversity indices, and taxonomic allocations

DNA sequences were processed using the DADA2 package for R
(Callahan et al., 2016). Blinker, barcode and primers of *tpm* raw reads
were discarded according to Aigle et al. (2021). Regarding DADA2, the
Standard Operating Procedure (SOP) for merged reads was used for the
processing of the 16S rRNA gene (*rrs*) raw reads (https://benjjneb.github.io/dada2/bigdata_1_2.html) while the SOP for paired-end reads
was used for the *tpm* ones (https://benjjneb.github.io/dada2/bigdatampaired.html). The Decontam package for R (Davis et al., 2018) was used to
remove contaminating sequences from (i) the extraction kit, the 0.2 µm
pore size polycarbonate filters and sequencing process, and (ii) the rain
meteoric water bacterial communities recovered prior reaching urban
surfaces. This led to a detection of 536 16S rRNA-based potential
contaminant ASVs (Amplified Sequence Variants) (3.20 % of the total
reads) (Table S4), and of 34 contaminant ASVs for the *tpm* dataset (0.09
% of the total reads) (Table S5). The contaminant ASVs were removed
from the two datasets and the final ASV contingency tables are given in
Tables S6 and S7. ASVs taxonomic allocations were made at > 80 %
bootstrap cutoff values using the “Wang” text-based Bayesian classifier
(Wang et al., 2007) and comparisons with the Silva_nr_v123 (16S rRNA
gene) or the “BD_TPM_Mar18_v1.unique_770seq” database (Pozzi et al.,
2021). The Faprotax software (Louca et al., 2016) was used to perform
functional inferences from the ASV taxonomic allocations.
### 2.3.3. Statistical analyses

Socio-urbanistic variables were compared and clustered using the ClustOfVar package for R (Chavent et al., 2011). Alpha diversity indices (Shannon, Simpson and Evenness) and Hellinger transformations were computed using the Vegan package for R (Dixon, 2003). PCA were computed using the prcomp function of the R software. Spearman correlations were computed using the R package Hmisc (https://cran.r-project.org/web/packages/Hmisc/index.html). Co-occurrence networks between bacterial taxa among the runoff samples were investigated using the MENA Pipeline (Deng et al., 2012). Relations between these networks and the socio-urbanistic variables were computed using the fast greedy modularity optimization method of the MENA pipeline and the Cytoscape software (Kohl et al., 2011).

## 2.4. Isolation of Pseudomonas paraeruginosa strains from runoffs, and cytotoxicity assays

P. paraeruginosa strains were isolated using the procedure described by Lavenir et al. (2007, 2014) for the isolation of P. aeruginosa cells. Briefly, urban runoff waters were plated on a Pseudomonas agar base medium (Oxoid, Basingstoke Hampshire, UK) supplemented with 5% glycerol, 200 mg/L cetrimide and 15 mg/L nalidixic acid, and incubated at 37 °C. Strains were affiliated to the *P. paraeruginosa* species according to the phylogenetic analysis of the *tpm* gene as described in Aigle et al. (2021). Cytotoxicity of these urban isolates were investigated on Human carcinoma pulmonary A549 cells (ATCC, Massanas, VA, USA). For these assays, *P. paraeruginosa* cells were grown in LB medium until reaching an absorbance (at 560 nm) of 0.6 (≈10⁸ CFU/mL). A549 cells were cultivated as described by Lima de Albuquerque et al. (2021) in high glucose conditions (4.5 g/L). A549 cells were trypsinized using 5 % trypsin (Sigma-Aldrich, St. Quentin Fallavier, France) and seeded in 96 wells xCELLigence E-plates (ACEA, Agilent, Santa Clara, United States) without antibiotics over a three-day incubation period. A549 cells at 80 % confluence (≈12 500 cells/well) were exposed to the urban *P. paraeruginosa* isolates at MOI (multiplicity of infection) 100. Cytotoxicity assays were run using the xCELLigence RealTime Cell Analyses (RCTA) Single Plate (SP) system according to Junka et al. (2012). A549 cells were then rinsed, trypsinized (5 % trypsin) and fixed (5 % of formalin solution; Sigma Aldrich). Trypsinized A549 cells were enumerated by flow cytometry using a Novocyte flow cytometer (ACEA, Agilent). Microscopic phase contrast observations of the A549 cells were performed using a Cytation 3 realtime imaging plateform (Biotek Instruments, Agilent).

# 3. Results and discussion

## 3.1. Socio-urbanistic patterns

Socio-urbanistic patterns including mobility patterns were used to define categories representing the main constraints impacting the urban microbiome of the investigated city center rainwater harvesting zone. The surveyed areas showed low industrial activities but high numbers of offices involved in banking and consulting services, and of residential buildings (4–5 floors high) including small stores, shops and restaurants at ground level (Table S1). Highest numbers of counts for a variable were related to mobility (Table S2). This included active travel modes such as walking (n = 7517 in total), cycling (n = 1630) or implying the use of manual (electric) scooters (n = 200) or gasoline engines (cars, n = 4669; two wheels, n = 251; trucks, n = 198; bus, n = 28) (Table S2). Technical objects such as park benches (n = 388), tables (n = 158), chairs (n = 58), cigarettes (n = 103) and food products, and wastes such as cigarette butts (n = 70), trash cans (n = 49), plastic particles (n = 16), urine traces (n = 19) and sanitary wastes (n = 7), were also observed (Table S2). A Principal Component Analysis (PCA) was computed (Fig. 2A) to illustrate trends in this dataset and find convergent variables. This allowed a differentiation of the city sampling points into four

<figure><img src="image_6.png" /><figcaption>**Fig. 2.** Socio-urbanistic patterns observed over the investigated city center rainwater harvesting area. (A) Principal Component Analysis (PCA) computed on the 19 socio-urbanistic variables. Runoff sampling points (P codes) are indicated. The PCA was computed using the FactoExtra package for R. Ovals indicate strong relationships between socio-urbanistic patterns and the sampling points. Group II sampling points are colored in red. (B) ClustOfVar dendrogram showing the relations between the socio-urbanistic variables monitored over the Garibaldi sector of the Lyon city center (France) computed from the dataset shown in Table S2. The ClustOfVar best fit clustering cutoff value is indicated. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

clusters. High correlations were observed between numbers of “trucks”, “cars”, “buses” and “motorized two wheels”, and led to a grouping of sampling sites P03.1, P05.1 and P06.1 into cluster 1 (Fig. 1). These sites are located on the main road (Garibaldi street) of the investigated rainwater harvesting zone, and their runoffs have been directed towards a combined sewer network for safety reasons. High correlations were also observed between numbers of “food products”, “park benches”, “tables”, “people eating” and “trash cans”. Highest values for these variables were recorded at points P3 and P5 which were then grouped into cluster 2 (Fig. 2A). Runoffs of these sites are transferred into rain gardens and an underground container for future uses like street cleaning. These cluster 2 sites were clearly associated to socialization (restaurants, pubs) and rain gardens. Counts of non-motorized mobilities (“manual scooters”, “pedestrians” and “bikes”) were high at P01, P02, P04 and P04.1 and reflected the attraction of an important commercial mall and its closely connected subway and train stations (Table S1). Runoffs of site P01 were transferred to an infiltration garden. Runoffs from P02 and P04 were transferred to the sewer network. These points were grouped into cluster 3. Finally, cluster 4 sampling points (P04.2 and P06) were characterized by high records of “joggers”, “dogs” and “urine” (Fig. 2A). Runoffs from P06 were transferred to a rain garden. The ClustOfVar package was used to confirm the significance of the PCA clusters. This approach led to the identification of five groups of variables (Fig. 2B): (i) traffic - gasoline engines, (ii) active non-motorized mobility, (iii) food catering, (iv) domestic wastes, and (v)
leisure. ClustOfVar was used to build synthetic variables representing groups (i) to (v), and to estimate their relative contributions in the emergence of functional bacterial networks and occurrences of bacterial pathogens.

## 3.2. Relations between socio-urbanistic patterns, runoff physico-chemical properties and their contents in recurrent bacterial fecal indicators

Physico-chemical variables of the runoff waters were monitored, and a PCA of this dataset was performed (Table S3a; Fig. S2A). This analysis did not differentiate runoff samples according to the sampling campaigns. This indicates that the events generated similar remobilization of sediments, wastes, and dusts. However, runoff turbidity was positively correlated with the synthetic variable “traffic intensity” and negatively with the waste occurrences (Fig. S3). This suggests that traffic induces turbulences during rain events that favored a re-mobilization of dusts and sediments. Coarse wastes appeared to reduce this re-mobilization process of small surface matters. Furthermore, suspended matters (MES), total-P and to a lesser extent total-N were negatively correlated with active mobility, catering and leisure synthetic variables (Fig. S3). These activities thus also tended to prevent conditions favorable for a re-mobilization of surface matters. There was no significant correlation between the physico-chemical parameters and any of the PCA clusters sampling points defined in Fig. 2 (Kruskal-Wallis test; $p > 0.05$).

Classical bacterial indicators and total heterotrophic bacteria were monitored among the sampled runoff waters (Table S3a), and trends were also illustrated by a PCA and correlation tests (Fig. S2, S4, S5). Heterotrophic bacterial plate counts were negatively correlated to the synthetic variable “active mobility” (Fig. S5). All runoff samples showed contamination by intestinal *enterococci* and *Escherichia coli* (Table S3a, Fig. 3), making them recurrent colonizers likely because of concomitant fecal organic contaminations supporting their growth or circulation. Strong correlations between these fecal indicator bacteria (FIB) and the socio-urbanistic parameters were not detected (Table S8) meaning that all sites were impacted by these fecal contaminations. The observed broad distribution of FIB was in line with previous records which showed them to be recurrent in urban settings (Bouchali et al., 2022;

Ellis, 2004) and that urban impervious surfaces can increase their numbers (Paule-Mercado et al., 2016). Sources of these FIB were investigated by qPCR assays targeting *Bacteroidales* species specific to Human (HF183 marker) and dog gastro-intestinal tracts as done by Monteiro et al. (2021) and Sidhu et al. (2013). Here, 17 out of 31 samples harbored the HF183 DNA marker while the dog-specific *Bacteroidales* were detected in 26 of these samples (Table S3a). Total, Human and dog *Bacteroidales* counts were higher among samples of socio-urbanistic cluster 1 related to high traffic intensity (Kruskal-Wallis test; $p < 0.05$) (Table S8). These bacterial indicators were positively correlated to the synthetic variables “traffic”, “catering” and “leisure” (Fig. S5) but not with the waste loads. These latter bacteria thus appeared to have broad distribution patterns over urban surfaces but high numbers mainly among runoffs from heavy traffic zones (Fig. 3). The HF183 marker was previously observed among urban sediments (dusts, soil, plant debris) remobilized by street runoff, and found to accumulate in a detention basin (Marti et al., 2017a). The Human practices associated with these FIB loads remain to be elucidated but could be due to emissions from the sewer networks (Sidhu et al., 2013), dissemination through vehicle tires or boots (Chambers et al., 2009), or deviant individual behaviors. Lyon mainly has combined sewers (fed by runoff and wastewaters) connected to wastewater treatment plants. During heavy rainfall, these networks can become saturated, leading to overflowing roadways.

Human bacterial pathogens were also detected among the sampled runoffs by qPCR assays (Tables S3a and b). *Listeria monocytogenes* and *Staphylococcus aureus* DNA targets were recurrent; being detected, respectively, in 24 and 18 samples over 31 (Table S3a). *L. monocytogenes* DNA targets were higher in group II sampling points (which was built by grouping datasets of clusters 2, 3 and 4 of Fig. 2A) than among samples of cluster 1 (high traffic zone) (Kruskal-Wallis test; $p < 0.05$; Table S8, Fig. 3). No significant differences were observed between the PCA clusters 2 to 4. *S. aureus* DNA targets were more abundant among samples of cluster 1 than group II (Kruskal-Wallis test; $p < 0.05$; Fig. 2A; with no difference between PCA clusters 2 to 4) indicative of a relation with petrol-derived contaminants (Fig. 3). *S. aureus* cells were likely to come from human spillovers but appeared to have colonized urban areas

<figure><img src="image_7.png" /><figcaption>**Fig. 3.** Summary of the main bacterial distribution patterns observed over the investigated rainwater harvesting zone. See text for a complete description of the classical fecal indicator bacteria (FIB) analyses, qPCR assays, and 16S rRNA and *tpm* meta-barcoding datasets used to build this figure. The upper panel indicates the intensity of the socio-urbanistic variables related with significant changes in the microbiological dataset, and matching divergences observed in Fig. 2. The bottom panel indicates the main bacterial taxa observed over the study that showed significant distribution biases in favor of cluster 1 or group II socio-urbanistic patterns. Group II associated all sites with low traffic and high socialization/pedestrian levels. Taxa representing human health hazards have been indicated in red, phytopathogens in green, and those involved in alkane degradation were surrounded by dotted lines. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>
impacted by high gasoline engine traffic. In fact, the use of hydrocarbons by *S. aureus* was previously observed (Shekhar et al., 2015; Shukor et al., 2009), and would explain this distribution bias. Interestingly, increases in antibiotic resistances in *S. aureus* strains exposed to soot were observed (Amala et al., 2021), and questions the incidence of urban petrol-derivatives on the evolution of this species within urban biomes. These *S. aureus* strains may therefore have diverged genetically from those colonizing the human skin. *L. monocytogenes* DNA counts were positively correlated with the “catering” synthetic variable (Fig. S5). While this species was previously reported over urban settings (Fagerlund et al., 2022; Sauders et al., 2006), this strong relation is in agreement with its ecology since *L. monocytogenes* is known to be associated with spoiled food products (Jordan and McAuliffe, 2018). Integrons of class 1, 2 and 3 which can take part in the spread of antibiotic resistances (Deng et al., 2015) were also tracked by qPCR assays (Table S3a). Integron 1 was detected in 26 out of 31 samples. Counts of integron 1 DNA targets were negatively correlated to the synthetic variable “traffic intensity” but were positively correlated to the synthetic variable “wastes” (Fig. S5, Fig. 3). Integron 1 is indicative of the likely occurrences of antibiotic resistance genes over city surfaces. Integrons 2 and 3 were detected among respectively 26 and 30 out of 31 runoff samples, confirming their high prevalence in urban settings (Marti et al., 2017a). Integron 3 was previously observed in *E. coli*, *P. aeruginosa*, *P. putida*, and *Klebsiella pneumoniae* (Deng et al., 2015). Three of these species were observed during this investigation, and were found to be recurrent urban surface colonizers (see section 3.4).

### 3.3. Recurrent and opportunistic bacterial taxa inferred from the V5–V7 16S rRNA DNA meta-barcoding datasets and relations with socio-urbanistic patterns

#### - Recurrent taxa

Processing of the 16S rRNA gene meta-barcoding sequences by the DADA2 and decontam computer packages generated a dataset of 3 377 965 reads distributed into 5819 ASVs (Table S9). Rarefaction curves are presented in Fig. S6A. Diversity indices were computed from this dataset. To illustrate, Shannon indices ranged from 3.59 to 6.00. These values were similar to those reported for urban deposits (Aigle et al., 2021). Sampling campaigns did not show significant differences (Kruskal Wallis test: p-value >0.05) in terms of Shannon, Simpson or Evenness diversity indices (Table S9). Anosim analyses were performed on the Bray-Curtis dissimilarities (Table S10) computed on (i) the normalized contingency table of these ASVs expressed in percentage of reads per ASV per sample, and on (ii) a normalized table grouping replicates under an averaged single value per ASV and sampling point. Anosim with the full dataset did not reveal significant trends while the latter approach showed a significant differentiation of the meta-barcoding profiles between samples from cluster 1 (impacted by a heavy traffic of gasoline engines) and those of group II (grouping sampling points of clusters 2, 3, and 4 shown in Fig. 2). This trend suggested that constraints associated with a growth over city surfaces can likely trigger a significant differentiation of bacterial assemblages, and the emergence of functional networks involving multiple taxa. In order to confirm this trend, the dataset was further explored with the aim of detecting the key bacterial taxa involved in these differentiations and leading to specialized bacterial networks. The 16S rRNA-based ASVs were thus allocated to bacterial taxa using the Silva nr database (Table S6). Contingency tables were then built per phyla and genera, and used to further infer distribution biases in line with the investigated socio-urbanistic patterns.

Seven core phyla (Table S11) were detected among the runoff samples, and were allocated to 523 genera (Table S12) of which 32 were conserved core taxa (detected among all samples) with *Flavobacterium* being the most abundant, followed by *Massilia*, *Chryseobacterium*, *Acinetobacter*, *Alkanindiges Mycobacterium*, and *Pseudomonas*. These core

genera represented more than 90 % of the total reads. Most of these had already been recorded among other urban settings (Danko et al., 2021; Leung et al., 2014; Marti et al., 2017a; McLellan et al., 2015; Robertson et al., 2013) except *Alkanindiges* and *Novosphingobium*. In addition to these highly conserved core taxa, 78 recurrent core taxa recovered in at least 50 % of the samples were observed such as the *Aeromonas* (30/31 samples), *Blastococcus* (30/31 samples), *Bacteroides* (24/31 samples) and *Prevotella* (23/31 samples) (Table S12). Sixty sub-core taxa (found in 30–50 % of the samples) were also identified (Table S12).

#### - incidence of socio-urbanistic patterns on conserved core taxa

Genus distribution biases related to the socio-urbanistic PCA clusters, and groups of sampling areas (cluster 1 against group II sites) were explored using DeSeq2 and Kruskal-Wallis rank tests (Table S13, Fig. 4). Read numbers affiliated to *Methylobacterium-Methylorubrum* (core), *Blastococcus* (recurrent core), *Alkanindiges* (core), *Bacteroides* (recurrent core), and *Prevotella* (recurrent core) were significantly higher in cluster 1 sampling points (heavy traffic) than those of group II (DeSeq2: p < 0.05) (Figs. 3 and 4; Table S13a). These distribution patterns were in line with the observation of higher numbers of taxa affiliated to hydrocarbon degraders among the cluster 1 samples (p < 0.05) than group II. It is to be noted that *Prevotella* and *Bacteroides* are indicative of fecal contaminations as indicated by McLellan and Roguet (2019), and that their 16S rRNA gene reads distribution patterns (Fig. 4) were in agreement with the observed higher occurrences of Human- and dog-fecal contaminations in PCA cluster 1 sampling points. *Cloacibacterium* (core) and *Macellibacteroides* (sub-core) which are mainly related to sewer and fecal contaminations according to McLellan and Roguet (2019) were also detected in the dataset, but did not show significant distribution biases between the PCA clusters defining socio-urbanistic groups. Interestingly, reads from five genera showed a tropism according to DeSeq2 analyses for group II sampling points such as those allocated to the highly abundant *Chryseobacterium* (core), the *Paracoccus* (core) and *Nubsella* (sub-core) (Table S13a, Fig. 4). The genus *Paracoccus* (*alpha-proteobacteria*) contains over 50 species colonizing pristine and polluted environments (Lasek et al., 2018). Many *Paracoccus* have been isolated from soil, sewage, and biofilters. *Chryseobacterium* spp. have been isolated from polluted soils, decaying plant materials, and freshwater sediments (Loch and Faisal, 2015). Regarding the *Nubsella*, a single species was described so far from a water sample of unknown origin (Asker et al., 2008). Our report thus presents an ecological perspective explaining their observation which appears not limited to Japan, the country of their actual description. Looking at the 16S rRNA gene read contingency table reported by Colin et al. (2020), *Nubsella* were also observed among industrial runoffs and sediments, and underground waters. DeSeq2 inferences were confirmed by Kruskal-Wallis rank tests (p < 0.05) (Table S13b). However, these Kruskal-Wallis tests identified additional groups with peculiar distribution patterns matching with the investigated socio-urbanistic variables. 49 genera were found to have significantly higher counts (p < 0.05) among cluster 1 (high traffic zone) sampling sites than group II ones. Reads allocated to *Agrococcus* (sub-core), *Paracoccus* (core), *Aureimonas* (recurrent core), *Spirosoma* (core), *Sphingorhabdus* (sub-core), *Haliangium* (sub-core) and *Paenibacillus* (recurrent core) were found to be higher in group II (clusters 2, 3 and 4) than cluster 1 samples (p < 0.05) (Figs. 3 and 4; Table S13b). The harsh conditions building up among old heavily contaminated urban deposits likely explained the occurrences of *Haliangium* in these samples as suggested for sediments accumulating in an urban detention basin (Marti et al., 2017a). However, *Acidibacter* spp. also reported to survive under such extreme conditions (Marti et al., 2017a) were not found to be recurrent or to be sub-core taxa for this urban setting. *Acidibacter* thus appears to be favored by growth conditions prevailing in runoff detention basins. All these trends are in line with an occurrence of conserved bacterial taxa over city surfaces being able to thrive over urban C-sources (fecal matters, pollutants, food wastes) and survive under
<figure><img src="image_8.png" /><figcaption>**Fig. 4.** Pairwise distribution patterns of bacterial taxa inferred from the 16S rRNA gene meta-barcoding dataset according to cluster 1 (high traffic) and group II (low or no traffic but high numbers of wastes and recreational activities) socio-urbanistic patterns. See Fig. 2 for the socio-urbanistic variables considered to generate these categories. From left to right, views (dashed boxes) were enlarged to allow a more accurate positioning of the taxa according to their relative abundances. For each view, the 1:1 identity line (solid black line) indicates a bias towards high (above line) or low (below line) traffic. This figure was drawn from the relative analyses (%) of the fifty genera showing the most significant distribution biases over the dataset (DeSeq2 and Kruskal-Wallis test; Table S13).</figcaption></figure>

extreme growth conditions like desiccation and exposure to toxic pollutants.

- opportunistic taxa

The 16S rRNA genes ASV contingency table was further analyzed to infer unusual distribution patterns indicative of environmental bursts at particular sampling points. Such increases (representing >1 % of the observed taxa) were detected among the following sub-core taxa: *Dysgonomomas* (at P05_S1; catering, food wastes), *Morganella* (at P04.2_S1; close to shrubs at the proximity of restaurants, and a pedestrian zone), *Malikia* (at P05.1_S2; large avenue impacted by heavy traffic), *Burkholderia-Caballeronia-Paraburkholderia* (at P04.2_S2), *Myroides* (at P06_S3; pedestrian zone close to a rain garden) (Table S12). These bursts occurred at a single site and sampling period but relationships with certain socio-urbanistic variables could be detected. *Malikia* 16S rRNA gene reads were found significantly higher in group II samples than those of cluster 1 (Kruskal-Wallis tests, p < 0.05) (Figs. 3 and 4; Table S13b). This genus was previously associated to sewer systems (Spring et al., 2005). Additional bursts were recorded among the genera found in less than 30 % of the samples and considered to strictly harbor taxa awaiting growth opportunities. Among these, two genera showed a burst in 16S rRNA gene reads >1 % at a sampling site: *Chania* (at P01_S1; pedestrian zone close to a commercial mall), and *Sporosarcina* (at P06_S3; pedestrian zone close to a rain garden). *Chania* was previously reported in soils (Ee et al., 2016), and *Sporosarcina* in association with plants (Hashmi et al., 2020). Their observed bursts are thus likely related to their physical proximity with vegetated zones. Other opportunistic taxa showing signs of a burst were *Azospira*, *Qipengyuania*, *Giesbergeria*, *Zoogloea*, *Dechloromonas*, *Klebsiella*, and *Corallococcus*. The *Azospira* 16S rRNA gene reads were found significantly higher in group II than cluster 1 (heavy traffic zone) samples (p < 0.05) (Figs. 3 and 4; Table S13b). *Azospira* reads were higher at the proximity of rain gardens, in line with the previously reported occurrences of *Azospira* in association with plants (Reinhold-Hurek and Hurek, 2000).

- correlation networks

Co-occurrence networks indicative of functional complementarities between bacterial genera inferred by the 16S rRNA gene meta-barcoding approach were analyzed using the MENA pipeline (Fig. 5A). Statistics of the observed modules and networks are presented in Table S14. Fifteen genera (from *Proteobacteria* and *Actinobacteria*) formed a large module (*Mod1*) and were significantly correlated to the synthetic variables “traffic” and “leisure”. All of these *Mod1* genera were core taxa (*Roseomonas*, *Methylobacterium*, *Noviherbaspirillum*, *Alkanindiges*, and *Friedmanniella*), and were indicative of the occurrences of recurrent colonizers in these groups. Faprotax analysis indicated these bacterial genera to be involved in hydrocarbon degradation. Module 2 (*mod2*) was also made of core genera (Fig. 5A) but did not show significant correlations with the investigated socio-urbanistic variables. *mod1* and *mod2* were connected because of positive correlations between the number of reads allocated to the *Roseomonas* and *Rubritepida*. *Roseomonas* spp. have been isolated from soil, freshwater sediment, plants, and opportunistic Human infections (De et al., 2004). *Rubritepida* ecology remains to be documented. A single species isolated from a hot spring in Hungary was described so far (Alarico et al., 2002). 16S rRNA gene reads of this genus were recently reported among industrial run-offs, urban sediments and aquifer waters (Bouchali et al., 2022; Colin et al., 2020).

### 3.4. Recurrent and opportunistic bacterial taxa inferred from the TPM DNA meta-barcoding datasets and relations with socio-urbanistic patterns

- Recurrent taxa

The *tpm* meta-barcoding approach allowed to track species from about twenty genera of *Proteobacteria* over the investigated urban surfaces (Aigle et al., 2021). A *tpm* dataset of 2 454 357 reads was generated for this investigation, and these reads were found to be distributed into 2891 ASVs (Table S7). Rarefaction curves are presented in Fig. S6B. The
<figure><img src="image_9.png" /><figcaption>**Fig. 5.** Correlation networks computed from (A) genera inferred from the 16S rRNA gene meta-barcoding dataset, and (B) species inferred from the *tpm* meta-barcoding one. Correlation networks were computed using the fast greedy modularity optimization method of MENA. Spearman correlations above 0.70 (A) and 0.62 (B) are presented. Properties of these modules are presented in Tables S14 and S20. Keystone taxa i.e. showing the maximum node degree, node betweenness values and stress values are highlighted in red. Hydrocarbon-degraders are indicated in black bold font. Relationships between networks and socio-urbanistic variables were explored using the Module-EigenGene analyses and most significant Spearman correlations are indicated in grey boxes. Asterisks indicate Spearman correlations at p-values <0.001. Inferred core and opportunistic taxa are represented by, respectively, a grey and red dotted circle. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)</figcaption></figure>

third sampling campaign showed significantly higher ASV numbers than campaigns 1 and 2 (Kruskal-Wallis test, p < 0.001) but this did not yield significant differences between Shannon, Simpson and Evenness diversity indices (Kruskal-Wallis test, p > 0.05) (Table S9). A total of 76 % of these *tpm* sequences could be allocated to a bacterial genus (Table S7; Table S15). Reads affiliated to the *Pseudomonas* and *Aeromonas* were detected among all the runoff samples, indicating occurrences of highly recurrent species in these groups. The 16S rRNA gene dataset had previously indicated the *Pseudomonas* to be a core group (found in all samples), and the *Aeromonas* to be part of the recurrent core groups (found in 96 % of the samples; Table S12, Fig. 3). 16 genera tracked by the *tpm* approach showed heterogeneous distribution patterns in the runoffs. Bursts of *Stenotrophomonas* were observed at P05_S1 (28 % of the reads; cluster 1 – high traffic), and P02_S2 (4 % of the reads; cluster 2 associated with high active mobility), of *Aeromonas* at P05.1_S2 (26 %; cluster 3 – high catering activities), P06_S2 (24 %; cluster 4 – high wastes records), P04.1_S2 (19 %; cluster 2 – active mobility) and P04_S2 (18 %; cluster 4 – leisure activities), of *Xanthomonas* at P06.1_S2 (13 %; cluster 1 – traffic) and P06_S2 (7 %; cluster 4 – leisure/wastes), and of *Aqua-spirillum* at P04.1_S2 (11 %; cluster 2 – active mobility) (Table S15).

A total of 25 % of the *tpm* reads were allocated to 76 well-defined species (Table S16). The other reads were allocated to group of sequences from undefined species (among the above cited genera).

*P. anguilliseptica*-like *tpm* reads showed the highest relative abundances per sample (6.84 %), then those allocated to *P. koreensis* (6 %) and the complex *P. aeruginosa/P. paraeruginosa* (1.53 %). *P. paraeruginosa* corresponds to the PA7 sub-clade which was previously considered to be part of *P. aeruginosa* (Rudra et al., 2022). It represented about 0.07% of the total *tpm* relative read numbers, and showed highest numbers at sites P01 and P02 (see ASV_136 in Table S8) which are close to the “Part-Dieu” commercial mall and high speed train (TGV) railway station. Nine core species were detected in more than 80 % of the runoff samples (Table S16): *Pseudomonas koreensis* (31/31 samples), *Pseudomonas syringae* (30/31), *Pseudomonas simiae* (29/31), *Pseudomonas viridiflava*, *Pseudomonas lurida* (27/31), *Pseudomonas azotoformans* (27/31), *Pseudomonas anguilliseptica*-like (26/31) and *Pseudomonas rhodesiae* (26/31) (Table S16). All of these species except *P. simiae* were previously reported among urban settings. *P. simiae* was first defined as a pathogen of monkey (Vela et al., 2006) but is now considered a plant-associated species with significant plant growth promoting properties (Pieterse et al., 2021). *P. koreensis* and the *P. anguilliseptica*-like species were found to increase in numbers in heavily contaminated anoxic bottom sediments from urban settling tanks of runoff detention basins (Aigle et al., 2021). Similarly, *P. syringae* phytopathogens were reported to increase in numbers among urban sediments showing increasing concentrations of toxic metallic elements like Cd and Cr, and of naphthalene
(Aigle et al., 2021). *P. koreensis* was previously shown to grow with hydrocarbons as C-sources such as hexadecane, engine oil, pyrene and phenanthrene (Bučková et al., 2013). *P. viridiflava* *tpm* reads were previously reported among industrial runoffs, soil of an infiltration basin, and among an urban aquifer (Colin et al., 2020). In addition to these conserved core species, 9 species were recurrent and found in more than 48 % of the runoff samples: *Stenotrophomonas terrae* (23/31), *Pseudomonas lactis* (23/31), *P. aeruginosa* (22/31), *Aeromonas GpCl1* (22/31), *Pseudomonas putida* (22/31), *Pseudomonas fragi* (21/31), *Aeromonas hydrophila* (20/31), *Pseudomonas fluorescens* (19/31), *Aeromonas caviae* (17/31), *Pseudomonas grimontii* (17/31), and *P. paraeruginosa* (15/31). Most of these species were previously recorded among industrial runoffs and urban sediments (Aigle et al., 2021; Colin et al., 2020) except *Pseudomonas lactis* and *P. paraeruginosa*. *P. lactis* was originally described as a colonizer of bovine raw milk (von Neubeck et al., 2017) but was recently found to be an endophyte of boxwood (*Buxus*) in public gardens (Kong, 2019). Furthermore, *P. lactis* strains resistant to metallic trace elements have been reported (Havryliuk et al., 2020). *P. aeruginosa* had previously been detected over London city surfaces (Ellis, 2004), in urban sediments (Aigle et al., 2021; Bernardin-Souibgui et al., 2018), among industrial runoffs and urban aquifers (Bouchali et al., 2022; Colin et al., 2020), transit systems (Danko et al., 2021) and urban recreational waters (Cui et al., 2017) but not its outlier sub-clade which was recently renamed *P. paraeruginosa*. This is the first well-documented record (detection in 15/31 samples) of a significant environmental spread of *P. paraeruginosa* cells. An environmental burst of *P. paraeruginosa* was recorded at site P02, and a burst of *P. aeruginosa* at site P04. An additional group of twelve sub-core species (found in >30% of the samples) could also be defined, and was found mainly made of recurrent colonizers showing no sign of high opportunistic bursts at a particular sampling point (Table S16).

- incidence of socio-urbanistic patterns on recurrent *tpm*-harboring species

Distribution biases among the *tpm* dataset according to the socio-urbanistic patterns were further explored by DeSeq2 and Kruskal-Wallis rank analyses (Table S17). At the species level, reads affiliated to *Stenotrophomonas terrae* (core taxa) were more abundant in runoffs sampled from the socio-urbanistic group II (ACP clusters 2 to 4; soft mobilities) than those of cluster 1 (heavy traffic) (DeSeq2; *p* < 0.05) (Table S17a, Fig. 3). *S. terrae* is a nitrate-reducing soil bacterium (Heylen et al., 2007), and this classification is in line with the observation of higher numbers at the proximity of urban rain gardens. Reads affiliated to species involved in hydrocarbon degradation (according to Faprotax inferences) such as *Pseudomonas frederiksbergensis* (sub-core taxa) (Andersen et al., 2000), *Pseudomonas extremaustralis* (sub-core) (Tribelli et al., 2012), and *Pseudomonas anguilliseptica* (conserved core) (Ciric et al., 2010) were detected in higher numbers among runoffs of the cluster 1 points (heavy traffic) (Kruskal-Wallis test; *p* < 0.05) (Table S17b, Fig. 3). *Pseudomonas lactis* (conserved core) showed higher read numbers among cluster 1 samples (Kruskal-Wallis test; *p* < 0.05). This could be explained by the tolerance of this species to high concentrations of metallic trace elements (Havryliuk et al., 2020).

At the ASV level (Table S18), ASVs allocated to a *P. anguilliseptica*-like species (core taxa) (n = 11 ASVs), *P. koreensis* (core) (n = 15 ASVs) and *P. sp.* PGPP3 (n = 22 ASVs) (DeSeq2, *p* < 0.05) were found related to cluster 1 points (Table S18a; Fig. 3). These ASVs (their related strains) are thus likely to use hydrocarbons as C-sources or to be able to sustain exposure to high concentrations of urban pollutants. Group II sampling points showed higher read numbers affiliated to some *A. caviae* (recurrent core group) (n = 7 ASVs; Fig. 3) and *P. fragi* (recurrent core group) (ASV_348) ASVs (DeSeq2, *p* < 0.05) (Fig. 3). *A. caviae*, a Human opportunistic pathogen, and *P. fragi*, a psychrotrophic species responsible of dairy spoilage (Hebraud et al., 1994), were previously recorded among urban sediments (Aigle et al., 2021; Bernardin-Souibgui et al.,

2018) and urban aquifers (Colin et al., 2020). These distribution patterns are in line with the higher content of food wastes over surfaces of group II samples which are related to catering activities. Kruskal-Wallis rank tests allowed to resolve more bacterial distributions biases (Table S18b). They confirmed the occurrences of higher numbers of reads per ASVs affiliated to recurrent species of the sub-core taxa which are known hydrocarbon degraders such as *P. alcaligenes*, *P. stutzeri*, *P. frederiksbergensis* and *P. anguilliseptica* among cluster 1 samples (heavy traffic). Furthermore, pathogenic *P. paraeruginosa* ASV_136 showed a distribution bias indicative of a higher relative number of reads among cluster 1 sites than those of group II, even though an environmental burst could be observed at P02 during the sampling campaign 2 (Table S7). *P. paraeruginosa* strains were previously found to harbor resistances towards urban metallic trace elements (Deredjian et al., 2011). Interestingly, three ASVs from undefined bacterial groups (ASV_115, ASV_1407, ASV_1994) found in high numbers in cluster 1 samples had been previously recorded among urban road sediments heavily contaminated by chemical pollutants (Aigle et al., 2021) (Table S19). Distant urban sub-catchments with shared typologies thus appeared to disseminate similar taxa.

- opportunistic *tpm*-inferred species

Opportunistic bacterial taxa were identified among the *tpm*-inferred species (Table S16). Among the sub-core taxa (found in 30–50 % of the samples), only the distribution patterns of *Xanthomonas hortorum* *tpm* reads were indicative of an opportunistic behavior. A burst of *X. hortorum* *tpm* reads was observed at P06_S2 (5 % of the total reads) which is impacted by leisure activities and the presence of a rain garden. Other opportunistic taxa were rather inferred from the *tpm* dataset of species recorded in less than 30 % of the samples. *P. pseudoalcaligenes* *tpm* reads distribution patterns indicated a burst at P03.1_S1 (2 % of the total reads). This sampling point is impacted by a heavy traffic of gasoline engines. The most significant opportunistic bursts were observed for (i) *Aeromonas lusitana* (23 % of the reads at P06_S2) at the same sampling point and time than the one related to the *X. hortorum* burst, and (ii) for *Pseudomonas amygdali* (9 % at P05_S2), a *P. syringae*-related group causing disease on almond trees, observed at a sampling site with significant catering activities and closely located to a rain garden. *A. lusitana* and *X. hortorum* are plant-pathogens, and their higher occurrences at P06 are in line with plant growth changes occurring in fall among rain gardens. Bursts of another plant-pathogen, *Pseudomonas palleroniana*, were also observed in fall at P04.2_S2 (catering zone close to ornamental shrubs) and P03.1_S2 (close to a rain garden).

- correlation networks

Co-occurrence networks of *tpm* read numbers allocated at the species level were analyzed using the MENA pipeline including Spearman correlation tests (Fig. 5B). Statistics of modules and networks are presented in Table S20. This led to the characterization of two modules (*tpm-mod*) supported by Spearman correlation values < 0.620 and a p-value <0.05. *tpm-Mod1* was composed of 32 species including recurrent core hydrocarbon degraders such as *P. extremaustralis*, *P. alcaligenes*, *P. frederiksbergensis*, *P. putida*, *P. stutzeri* and *P. syringae*, and was positively correlated to the synthetic variable “traffic” (Spearman test: rho = 0.26, *p* < 0.01) (Figs. 2 and 5B) but also “leisure” (rho = 0.36, *p* < 0.01). *tpm-Mod1* was negatively correlated to the synthetic variable “catering activities” (rho = -0.30, *p* < 0.01). *tpm-Mod2* was made of 25 species and was positively correlated to the synthetic variables “catering activities” (Spearman test: rho = 0.51, *p* < 0.01) and “waste” accumulations (rho = 0.24, *p* < 0.01). *tpm-Mod2* was negatively correlated to synthetic variables “traffic” (Spearman test: rho = -0.33, *p* < 0.01) and “leisure activities” (Spearman test: rho = -0.75, *p* < 0.01).
### 3.5. Health hazards associated with recurrent *P. aeruginosa* and *P. paraeruginosa* urban lineages

The high occurrences of *tpm* reads allocated to *P. aeruginosa* and *P. paraeruginosa* pathogenic lineages over the rainwater harvesting zone led us to further analyze their population genetic diversity patterns (Table S21). The *P. aeruginosa* and *P. paraeruginosa* *tpm* reads were allocated to (1) sub-clades (PAO1, PA14 or PA7) and (2) Multi Locus Sequence Typing (https://pubmlst.org/) groups according to Colin et al. (2020) (Table S21). The *tpm* ASV_12 (n = 6087 reads) and ASV_472 (n = 655 reads) were affiliated to *tpmG1* of the PAO1 sub-clade. These ASVs matched with ST27 and ST234 isolates from non-cystic fibrosis infections (blood, eye or urinary infection), Cystic Fibrosis sputum or hospital settings. ASV_1655 was affiliated to the *tpmG08-24* type corresponding to the *P. aeruginosa* sub-clade PA14, and matched with ST253, ST1610 and ST1822 isolates. ST253 strains were previously isolated from soft tissues and urinary infections but also from soil and water samples. ASV_1178 (n = 293 reads) and ASV_1552 (n = 262) were respectively affiliated to *tpmG12_15_17_18_35* and *tpmG19_21-25-27-29* types but could not be allocated to a sub-clade (Table S21). ASV_136 (n = 5718 reads) was affiliated to the *tpmG3* type corresponding to *P. paraeruginosa*, and were found to match ST2228 and ST2230. Distribution patterns of these ASVs were investigated by Kruskal-Wallis rank tests (Table S18b). Samples from socio-urbanistic cluster 1 (high traffic) showed significantly higher reads of ASV_12 (PAO1-like) and ASV_136 (*P. paraeruginosa*) than among those of group II (p < 0.05).

A collection of more than 200 urban runoff *P. aeruginosa* and *P. paraeruginosa* isolates was built to further explore the above distribution patterns. More than 88 % of these strains had *tpm* sequences matching with ASV_12, ASV_136, ASV_1178, ASV_1552, or ASV_1655 of the *tpm* meta-barcoding dataset (Table S22). Only strains related to ASV472 could not be isolated. Three strains were found to match with *P. paraeruginosa* *tpm* sequences. These *P. paraeruginosa* urban strains were further analyzed to assess their virulence since this sub-clade has been described as hyper-virulent (Reboud et al., 2016; Roy et al., 2010). Their observation over city surfaces represents a significant health concern. *P. paraeruginosa* reads were detected in about 50% of the runoff samples leading to a classification of this sub-clade into the recurrent Lyon city surface colonizers. Cytotoxicity assays on A549 pulmonary cells were performed to verify the virulence of these urban *P. paraeruginosa* isolates (Fig. 6; Fig. S7). High decrease of DCI (delta cell indices) indicative of cell death, monitored by the xCELLigence system, were recorded when the A549 cells were exposed to these three urban

*P. paraeruginosa* isolates. DCI values were significantly lower for the A549 cells infected by these isolates than by the *E. coli* K-12 MG1655 (Wilcoxon-test: p-value <0.001) and other control conditions (area under curve, Wilcoxon-test: p-value <0.001). The A549 mortality induced by the urban bpoe6714 isolate was equivalent to the one triggered by the hyper-virulent IHMA879472 (hereafter named IHMA) clinical strain (Wilcoxon-test: p-value >0.05), confirming its hyper-virulence. The cytotoxic activities of bpoe6685 and bpoe6713 induced a slower cell death than the ones observed with IHMA (Wilcoxon tests; p-value <0.001). However, after a 4 h exposure time period, DCI were similar between bpoe6685, bpoe6713, bpoe6714 and IHMA (Fig. 6). Cytotoxicity of the urban isolates were compared to those of the first *P. paraeruginosa* isolate named the PA7 strain (Roy et al., 2010; Rudra et al., 2022). All the urban strains were found to be more virulent than the PA7 one which is harboring the highest number of antibiotic resistances described so far in this species (Wilcoxon-test: p-value <0.05). The control *E. coli* K-12 MG1655 cells had no significant incidence on the integrity of the A549 cells over the period of these experiments. All these observations were confirmed by a monitoring of A549 cell counts by flow cytometry at the end of the experiments. A549 living cells were in significantly lower numbers when exposed to the urban isolates than to the PA7 reference strains (Wilcoxon-test: p-value <0.05) (Fig. 6). Drops of more than 50 % in these A549 cell numbers were recorded. These drops were similar to those observed using the IHMA strain (Wilcoxon-test: p-value >0.05). Phase contrast microscopy observations showed high alterations of the A549 cell membranes when exposed to the urban isolates, and these morphological changes were similar to those observed with the IHMA strain (Fig. S7). These data clearly indicated that urban isolates from *P. paraeruginosa* were as virulent as the clinical IHMA isolate (Reboud et al., 2016). They thus represent a clear health hazard for Human populations.

### 4. Conclusion

This investigation was designed with the aim of assessing the microbiological quality of urban runoffs according to socio-urbanistic patterns observed over sub-catchments of a rainwater harvesting area. These sub-catchments were clearly found colonized by recurrent conserved core pathogenic bacterial species, with several of these likely contributing to functional networks involved in hydrocarbon degradation. All sampled sites were found significantly contaminated by fecal bacteria, and most by the *S. aureus* and *L. monocytogenes* bacterial pathogens (summarized in Fig. 3). The fecal bacteria were, in part, found

<figure><img src="image_10.png" /><figcaption>**Fig. 6.** Cytotoxicity of *P. paraeruginosa* urban isolates on A549 pulmonary cells. (A) Real-time monitoring of delta cell indices (DCI) as proxy for mortality of A549 cells over time when exposed to *P. paraeruginosa* strains. DCI was monitored every 5 min. A549 cells were exposed for 2 h to these strains then washed and further incubated for 2 h using a fresh growth medium (DMEM 10 % FCS). The bpoe6714, bpoe6713 and bpoe6685 strains have been isolated in this work, and are from urban runoffs. PA7 is a multi-drug resistant non-respiratory Human strain of *P. paraeruginosa* isolated from Argentina. PA7 strain was made available by Microcide Pharmaceuticals Inc (Mountain View, CA, USA). *P. paraeruginosa* IHMA is a urinary tract isolate considered to be one of the most virulent strains of this species. Control indicates assays performed without bacterial cells. xCELLigence control curves were similar to those obtained with *E. coli* K-12 MG1655 cells. (B) A549 cell counts after the 4 h assay described in (A). The asterisks indicate significant differences between the control, PA7, and the *P. paraeruginosa* urban isolates (Wilcoxon-test: p-value <0.05).</figcaption></figure>
to have been emitted by dogs and humans, and thus likely to have been co-released with viral pathogens such as human polyomaviruses and adenoviruses as reported by McQuaig et al. (2012). The investigated city center sub-catchments could be differentiated into groups of land-use typologies that were found related to variations in runoff bacterial meta-barcoding community patterns. The high traffic areas showed strong correlations with the occurrences of DNA meta-barcoding signatures from core actinobacterial taxa, *Alkanindiges* and *Pseudomonas* species such as *P. frederiksbergensis*, *P. extremaustralis*, and the *P. aeruginosa* and *P. paraeruginosa* Human pathogens. These traffic-related taxa were found to be organized into large functional modules with likely complementary biodegradation properties of pollutants found among runoffs. Such runoffs were previously shown to contain PAHs, short- and long-chain alkanes, multiple metallic trace elements and several emerging pollutants such as nonylphenols (e. g. Aigle et al., 2021; Gasperi et al., 2014). Runoffs from such heavy traffic zones are classically directed towards a sewer network to avoid further spread of their chemical pollutants in the city. Here, these heavy traffic zone runoffs were found to represent infectious hazards. Runoffs from the other investigated sub-catchments were stored in underground tanks for street cleaning and watering. However, these runoff were clearly contaminated by hazardous bacterial agents and FIB. They will thus require further microbiological quality assessment prior any utilization. These runoff waters were frequently contaminated by *P. aeruginosa*, *P. paraeruginosa*, *Aeromonas caviae*, *S. aureus* and *L. monocytogenes*. All these species represent health hazards for Human populations, and, here, the city strains of *P. paraeruginosa* were clearly demonstrated to be highly cytotoxic for human pulmonary epithelial cells. These taxa should thus be considered in the assessment of runoff microbiological quality. It is to be noted that the bacterial contaminants detected in this study could be used to identify the sources which led to their occurrences. To illustrate, the HF183 marker could be used to identify the zones which led to human fecal contaminations, and this could allow a definition of curative strategies.

Overall, this study highlighted the complexity of microbial life operating over city surfaces and runoffs. These systems were found to harbor complex community structures harboring species representing health concerns. More functional analyses of the observed urban bacterial recurrent and opportunistic city dwellers will now need to be performed to better define the metabolic and stress-resistance properties associated with their success. On the long run, the presented meta-barcoding datasets will be useful to evaluate the benefits of novel urban practices such as the increasing use of electric cars on the diversity and microbiological safety of urban runoff microbiomes.

### CRediT authorship contribution statement

**Rayan Bouchali:** Conceptualization, Data curation, Formal analysis, Writing – original draft. **Claire Mandon:** Conceptualization, Data curation, Formal analysis. **Emmanuelle Danty - Berger:** Conceptualization, Data curation, Formal analysis. **Alain Géloën:** Conceptualization, Formal analysis. **Laurence Marjolet:** Data curation, Formal analysis. **Benjamin Youenou:** Data curation, Formal analysis. **Adrien C.M. Pozzi:** Data curation, Formal analysis. **Sophie Vareilles:** Conceptualization, Data curation. **Wessam Galia:** Conceptualization, Data curation, Formal analysis. **Gislain Lipeme Kouyi:** Conceptualization, Data curation, Formal analysis. **Jean-Yves Toussaint:** Data curation, Formal analysis. **Benoit Cournoyer:** Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Project administration, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing.

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence

the work reported in this paper.

### Acknowledgements

We thank the OTHU, an urban drainage field observatory of ZABR (Zone atelier bassin du Rhône) which is funded, in part, by the French water agency Rhône-Méditerranée-Corse, and the Greater Lyon Urban Community, for its technical support. We also thank the School of Integrated Watershed Sciences H2O'LYON (ANR 17-EURE-0018), and the Lyon Urban School (LUS) (ANR-17-CONV-0004) for their supports in the elaboration of this multi-disciplinary research initiative. R. Bouchali received a PhD fellowship awarded by LUS. We thank Stéphanie Bouillot and Philippe Huber for their advices on the implementation of the cytotoxicity assays, and the gift of strain IHMA879472. We thank the PARMIC platform (L. Loiseau and E. Brothier) of UMR LEM for access to the EML-BRC bacterial collection, and the conservation and distribution of the strains reported in this article. This work was funded by the French national research agency (ANR) through the ANR-22-CE34-0016-01 project termed CityBioHazards, by the French national research program for environmental and occupational health of ANSES under the terms of project "Iouqmer" EST 2016/1/120, and the MITI CNRS project termed Urbamic.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.ijheh.2024.114391.

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