Season and city shape urban bioaerosol composition beyond vegetation and socioeconomic gradients

Season and city shape urban bioaerosol composition beyond vegetation and socioeconomic gradients | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Season and city shape urban bioaerosol composition beyond vegetation and socioeconomic gradients Sarah Poirier , View ORCID Profile Jonathan Rondeau-Leclaire , View ORCID Profile Maria Faticov , Alexis Roy , View ORCID Profile Gaële Lajeunesse , View ORCID Profile Jean-François Lucier , Sarah Tardif , View ORCID Profile Steven W. Kembel , View ORCID Profile Carly Ziter , View ORCID Profile Catherine Laprise , View ORCID Profile Alain Paquette , View ORCID Profile Catherine Girard , View ORCID Profile Isabelle Laforest-Lapointe doi: https://doi.org/10.1101/2025.09.25.678581 Sarah Poirier 1 Département de biologie, Université de Sherbrooke , Sherbrooke, Québec, Canada 2 Réseau de recherche en santé durable lié à la qualité de l’air et de l’environnement sonore (AIRS), CHU Sainte-Justine , Montréal, Québec, Canada 3 Centre d’Étude de la Forêt, Département des sciences biologiques, Université du Québec à Montréal , Montréal, Québec, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: poiriersarah{at}hotmail.com Jonathan.Rondeau.Leclaire{at}gmail.com isabelle.laforest.lapointe{at}gmail.com Jonathan Rondeau-Leclaire 1 Département de biologie, Université de Sherbrooke , Sherbrooke, Québec, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jonathan Rondeau-Leclaire For correspondence: poiriersarah{at}hotmail.com Jonathan.Rondeau.Leclaire{at}gmail.com isabelle.laforest.lapointe{at}gmail.com Maria Faticov 1 Département de biologie, Université de Sherbrooke , Sherbrooke, Québec, Canada 3 Centre d’Étude de la Forêt, Département des sciences biologiques, Université du Québec à Montréal , Montréal, Québec, Canada 5 Department of Wildlife, Fish, and Environmental Studies, Swedish University of Agricultural Sciences , Umeå, Sweden Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Maria Faticov Alexis Roy 1 Département de biologie, Université de Sherbrooke , Sherbrooke, Québec, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gaële Lajeunesse 1 Département de biologie, Université de Sherbrooke , Sherbrooke, Québec, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Gaële Lajeunesse Jean-François Lucier 1 Département de biologie, Université de Sherbrooke , Sherbrooke, Québec, Canada 6 Centre de calcul scientifique, Université de Sherbrooke , Sherbrooke, Québec, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jean-François Lucier Sarah Tardif 3 Centre d’Étude de la Forêt, Département des sciences biologiques, Université du Québec à Montréal , Montréal, Québec, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Steven W. Kembel 4 Département des sciences biologiques, Université du Québec à Montréal , Montréal, Québec, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Steven W. Kembel Carly Ziter 7 Department of Biology, Concordia University , Montréal, Québec, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Carly Ziter Catherine Laprise 8 Département des Sciences Fondamentales, Université du Québec à Chicoutimi , Chicoutimi, Québec, Canada 9 Centre intersectoriel en santé durable, Université du Québec à Chicoutimi , Saguenay, QC, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Catherine Laprise Alain Paquette 3 Centre d’Étude de la Forêt, Département des sciences biologiques, Université du Québec à Montréal , Montréal, Québec, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Alain Paquette Catherine Girard 2 Réseau de recherche en santé durable lié à la qualité de l’air et de l’environnement sonore (AIRS), CHU Sainte-Justine , Montréal, Québec, Canada 8 Département des Sciences Fondamentales, Université du Québec à Chicoutimi , Chicoutimi, Québec, Canada 9 Centre intersectoriel en santé durable, Université du Québec à Chicoutimi , Saguenay, QC, Canada 10 Département de biochimie, de microbiologie et de bio-informatique, Université Laval , Québec, Canada 11 Centre d’études nordiques (CEN), Université Laval , Québec, Québec, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Catherine Girard Isabelle Laforest-Lapointe 1 Département de biologie, Université de Sherbrooke , Sherbrooke, Québec, Canada 2 Réseau de recherche en santé durable lié à la qualité de l’air et de l’environnement sonore (AIRS), CHU Sainte-Justine , Montréal, Québec, Canada 3 Centre d’Étude de la Forêt, Département des sciences biologiques, Université du Québec à Montréal , Montréal, Québec, Canada Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Isabelle Laforest-Lapointe For correspondence: poiriersarah{at}hotmail.com Jonathan.Rondeau.Leclaire{at}gmail.com isabelle.laforest.lapointe{at}gmail.com Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF ABSTRACT Urban vegetation varies with socio-economic gradients, as lower-income neighborhoods often host sparser and less diverse green spaces. This disparity may affect respiratory health by influencing exposure to bioaerosols. Understanding the characteristics of this aerobiome could help anticipate risks related to allergies and other respiratory conditions. Here, we hypothesized that urban vegetation cover and socio-economic status shape urban bioaerosols. We sampled bioaerosols at 65 sites across three Canadian cities of varying population size and density using an active air sampler over four months, and characterized their bacterial, fungal, and pollen composition using amplicon sequencing. Seasonal alpha diversity varied significantly for fungi and pollen. Based on beta diversity, sampling period alone explained up to 40% of pollen, 29% of fungal, and 11% of bacterial community composition variation. In contrast, vegetation cover explained only a minor portion of the variance in bioaerosol composition, and median household income, almost none. These findings provide a critical baseline for understanding the urban aerobiome and highlight the need to study how vegetation identity and diversity, rather than cover alone, may shape bioaerosol dynamics in cities. As cities grow and urban greening initiatives expand, demystifying the aerobiome dynamics becomes an urgent public health priority. INTRODUCTION Biodiversity, from microscopic bacteria to majestic trees, provides countless ecosystem services that are essential for human population health worldwide 1 . However, the benefits of biodiversity are increasingly threatened as global change drives a rise in disturbances across biomes, including higher temperatures, more frequent and intense natural disasters, and land-use change 2 . Such pressures are leading to losses of biodiversity 3 and ecosystem functions 4 , 5 , threatening human health and well-being 6 . In urban areas, where 77% of Canadians 7 and 57% of the global population 8 currently reside, these impacts are particularly acute. Cities across the world are expanding rapidly and are projected to host over 70% of the global human population in the next 30 years 9 . Urban centers are major sources of airborne particles and gases due to dense human activity, infrastructure, and land-use change 10 . While there is significant attention on urban vegetation as a way to mitigate pollutant exposure in cities – alongside other co-benefits 1 , 11 – vegetation also influences the quantity and diversity of bioaerosols 12 , 13 (also known as the “aerobiome”), which in turn can influence human health 14 , 15 . Yet, the determinants of urban bioaerosol composition and dynamics remain poorly understood. In this work, we focus on two key components of urban bioaerosols: plant tissues and microbes (bacteria and fungi), aiming to uncover how urban environmental characteristics influence their presence and dynamics. Bioaerosols are airborne biological particles originating from diverse sources, including soil, water bodies, plant surfaces, animal waste, and human activities such as agriculture and industry 16 . In urban areas, they commonly include bacteria, fungi, and plant tissues such as pollen 17 , 18 , a well-known allergen. While typically harmless, pollen can trigger allergic reactions in sensitized individuals, with symptoms varying seasonally and geographically 19 . Urban vegetation can exacerbate airborne pollen exposure, particularly if the urban canopy is dominated by dioecious male trees and anemophilous tree species that produce more pollen 18 , 20 , 21 . This suggests that the health impacts of urban vegetation extend beyond its benefits 22 , warranting a broader focus on bioaerosols in public health and urban biodiversity research. However, the current methods for predicting airborne pollen concentration in urban environments are limited by the sparse distribution of monitoring stations and the labor-intensive nature of microscopic identification, which restricts both spatial resolution and real-time responsiveness 23 . Microorganisms represent the most abundant and diverse component of urban biodiversity, colonizing soils, plants, animals, insects, the built environment, with many taxa dispersing through the air 24 . Land-use patterns have been shown to shape airborne microbial communities or “microbiomes” 25 , 26 , and recent studies highlight the role of local vegetation in influencing airborne bacterial composition in both urban 27 and natural settings 28 . Advances in high-throughput sequencing are expanding our understanding of the urban microbiome 29 . Nevertheless, most studies to date have focused on indoor environments, leaving the outdoor “aerobiome” comparatively understudied 27 , 30 , 31 . This is in part due to the difficulty of studying highly dynamic microbial communities, which are characterized by rapid evolution and high dispersal potential 32 – 34 . Understanding these dynamics is especially important in cities, where biodiversity, including microbial exposure, is unevenly distributed and closely tied to social inequalities 35 . Despite its recognized benefits for health and well-being, access to urban vegetation remains inequitably distributed in many cities. Indeed, studies have shown that high-income neighborhoods tend to have more abundant and diverse tree communities than low-income ones 36 – 39 . This may result in large disparities in bioaerosol exposure and respiratory health outcomes. The urban aerobiome and its variation across socioeconomically diverse districts are critical, yet often-overlooked, determinants of population health. Exposure to diverse microbial and plant-derived bioaerosols, particularly during early life, has been linked to a decrease in the risk of developing asthma and allergies 40 through immune system development 41 . Autoimmune and allergic diseases, such as asthma and atopic dermatitis, affect between 10% and 30% of the population globally, with higher prevalence observed in high-income countries and among children 42 – 44 . Moreover, children in urban centers face a 70% higher risk of asthma, independent of ethnicity and income 45 . To mitigate these trends, urban planning initiatives must integrate a mechanistic understanding of how vegetation influence human health through bioaerosols. In cities, local vegetation diversity has been linked to microbial communities in key buildings (e.g., schools, hospitals, and homes) 46 – 48 . A recent study in Finland demonstrated that a 28-day biodiversity intervention in urban children increased both skin and gut diversity of Gammaproteobacteria , with positive effects on immunoregulatory pathways 49 . These findings support the idea that enhancing urban biodiversity could reduce the risk of immune-mediated diseases 49 . However, historical legacies have led to higher vegetation cover and diversity in low-risk (high income) neighborhoods, thus potentially reducing their public health potential 50 . Maintaining or enhancing vegetation cover and diversity in high-risk (low-income) neighborhoods, where respiratory illnesses are more prevalent 51 , could yield greater health gains and help reduce environmental health disparities. These socioeconomic disparities in access to urban vegetation highlight the need to consider social equity in urban greening strategies 50 , 52 , 53 . Ignoring the disparate spatial stratification of vegetation from low-income to high-income districts could limit the equity and effectiveness of urban biodiversity interventions. Therefore, understanding how vegetation cover and median income interact to shape bioaerosol exposure is essential as a first step towards designing equitable and health-promoting cities. In this study, we assessed the composition and diversity of airborne microorganisms (bacterial and fungal) and pollen (but also additional plant debris) across three Canadian cities (Montréal, Québec City, and Sherbrooke) differing in population size and density ( Fig. 1 ; Supplementary Fig. S1, Table S1;). Samples were collected across the summer by active air sampling at 65 sites ( Fig. 1A ) in Montréal (25 sites; Fig. 1B ), in Québec City (25 sites; Fig. 1C ), and in Sherbrooke (15 sites; Fig. 1D ). For each city, sampling site coordinates were determined according to two gradients: (1) median household income data by postal code 54 and (2) vegetation cover using the normalized difference vegetation index (NDVI) 55 . This design allowed us to identify sites along contrasting levels of both income and vegetation gradients (e.g., low vegetation cover and low socioeconomic status vs. high vegetation cover and low socioeconomic status). Download figure Open in new tab Figure 1. Sampling locations and their characteristics. (A) Geographic location of the three study cities in Québec, Canada. (B–D) Sampling distribution of the 25 sites within Montréal (B) , 25 sites within Québec City (C) , and 15 sites within Sherbrooke (D) . Circle size indicates median household income in Canadian dollars (thousands of CA$), with smaller circles representing lower-income areas. Circle color corresponds to the Normalized Difference Vegetation Index (NDVI), reflecting vegetation density. The vegetation density gradient (NDVI) is categorized in three bins (1: 0–0.3, 2: 0.3–0.5, 3: 0.5–1) and the median household income gradient is categorized in four bins ( 100k CA$). The Montréal study design is part of the Montreal Urban Observatory . Our main objective was to characterize the composition and diversity of urban bioaerosols and to estimate the relative influence of urban vegetation and socio-economic gradient on bioaerosol dynamics. Specifically, we evaluated how bacterial, fungal, and pollen composition, diversity and abundance vary in relation to (i) city identity, (ii) sampling period, (iii) vegetation cover (NDVI), and (iv) median household income. To achieve this, we employed high-throughput sequencing targeting the 16S rRNA (bacteria), ITS (fungi), and trnL (pollen) genes, complemented by quantitative PCR (qPCR) to estimate total bacterial load in bioaerosols. RESULTS Urban bioaerosol composition varies across season and cities We assessed the influence of city identity, sampling period, vegetation cover, and median household income on the composition of bacterial, fungal, and pollen bioaerosols ( Table 1 ; Fig. 2 ). Of note, Québec City could only be sampled in spring and fall because of limited access to the active air sampler. Based on Bray-Curtis dissimilarities, sampling period was the strongest determinant of variation in community composition of bacteria, fungi, and pollen (R² = 10.7%, 29.2%, and 39.7%, respectively), followed by city identity (R² = 7.9%, 8.6%, 7.6%,). Vegetation cover, measured via normalized difference vegetation index (NDVI) 55 , was a statistically significant but weak predictor of bioaerosol composition (R² = 1.2%, 0.5%, 0.5%,). Median household income showed a minor effect, limited to bacterial community composition (R² = 0.8%). We also found a significant interaction between city and sampling period (R² = 3.0%, 7.7%, 9.3%). Turnover-nestedness analyses indicated that compositional changes were predominantly driven by amplicon sequencing variants (ASVs, see Methods) turnover rather than shifts in the relative abundance of persistent bioaerosol taxa, a pattern consistent across bacterial, fungal, and pollen composition (Supplementary Fig. S4). The magnitude of compositional differences between cities and sampling periods was so pronounced that it produced a horseshoe effect in the PCoA ordinations ( Fig. 2 ), a well-documented artifact in unconstrained ordination methods that typically arises when strong gradients (e.g., in Fig. 2BC ) dominate the dataset 56 . This pattern motivated us to analyze bioaerosol datasets separately for each city, Montréal ( Fig. 3 ), Québec City ( Fig. 4 ), and Sherbrooke ( Fig. 5 ), rather than aggregating them, allowing for clearer interpretation of local dynamics. Download figure Open in new tab Figure 2. Principal Coordinates Analysis (PCoA) ordinations on Bray-Curtis dissimilarities of bioaerosol communities across three Canadian cities. Ordinations are shown for (A) bacteria, (B) fungi, and (C) pollen sampled in Montréal (red), Québec City (blue), and Sherbrooke (green). Sampling periods are represented by distinct shapes: spring (squares), summer (diamonds), and fall (circles). Download figure Open in new tab Figure 3. Composition and diversity of aerosols in Montréal. (A-C) Relative abundance of dominant families for bacteria (A) , fungi (B) , and pollen (C) in urban air. The barcharts are structured in three sampling periods and show the mean relative abundance by date of sampling. The n shows the number of samples aggregated per day but sequencing was performed on each sample separately. (D) Alpha diversity (Shannon index) across sampling periods for each biological group. (E) Principal coordinates analysis (PCoA) of Bray-Curtis dissimilarities showing seasonal shifts in community structure. Statistical significance was assessed using the Wilcoxon signed-rank test with p-value correction using the Holm procedure; asterisks indicate significant differences (*p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001). Download figure Open in new tab Figure 4. Composition and diversity of aerosols in Québec. (A-C) Relative abundance of dominant families for bacteria, fungi, and pollen in urban air. The barcharts are structured in three sampling periods and samples are aggregated by date of sampling. The n shows the number of samples aggregated per day. (D) Alpha-diversity (Shannon index) across sampling periods for each biological group. (E) Principal coordinates analysis (PCoA) of Bray-Curtis dissimilarities showing seasonal shifts in community structure. Statistical significance was assessed using the Wilcoxon signed-rank test with p-value correction using the Holm procedure; asterisks indicate significant differences (*p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001). Download figure Open in new tab Figure 5. Composition and diversity of aerosols in Sherbrooke. (A-C) Relative abundance of dominant families for bacteria, fungi, and pollen in urban air. The barcharts are structured in three sampling periods and samples are aggregated by date of sampling. The n shows the number of samples aggregated per day. (D) Alpha-diversity (Shannon index) across sampling periods for each biological group. (E) Principal coordinates analysis (PCoA) of Bray-Curtis dissimilarities showing seasonal shifts in community structure. Statistical significance was assessed using the Wilcoxon signed-rank test with p-value correction using the Holm procedure; asterisks indicate significant differences (*p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001). View this table: View inline View popup Download powerpoint Table 1. Variation in bioaerosol community composition across cities and seasons assessed by PERMANOVA. Statistical analysis of bacterial, fungal, and pollen communities reveals significant differences in composition across urban environments (Montréal, Québec City, Sherbrooke) and sampling periods (SP; spring, summer, fall), as determined by permutational multivariate analysis of variance (PERMANOVA) on Bray-Curtis dissimilarities. F: pseudo-F value, DF: degrees of freedoms, R 2 : % r-squared, p : observed p -value. Distinct taxonomic and relative abundance patterns in urban bioaerosols Urban aerosols composition was generally more stable in bacterial communities compared to fungi and pollen ( Figs. 2 –4ABC; Supplementary Figs. S5–S7). Specifically, within any given sampling time and city combination, Bray-Curtis dissimilarities between bacterial ASV compositions were much lower and less variable than between fungal or plant compositions (Supplementary Fig. S8). Most bacterial sequences were assigned to two phyla: Actinomycetota (65.7%) and Pseudomonadata (15.0%) ( Figs. 3A , 4A , 5A ; Supplementary Fig. S5, Table S2A). To explore the concept of a core microbiome, we identified ASVs present in at least 95% of samples and with an overall mean relative abundance of at least 0.1% 57 . Using this criterion, we identified eight bacterial ASVs as core members, all of them from the Actinobacteria (Supplementary Table S3). These taxa included two variants from the genus Nocardioides (family Nocardioidaceae ), three from the genus Blastococcus (family Geodermatophilaceae ), and one unclassified member of the order Frankiales . Additionally, two variants from the family Micrococcaceae , belonging to the genera Arthrobacter and Kocuria , were also identified as core taxa. Fungal sequences were assigned to the phyla Basidiomycota (63.0%) and Ascomycota (37.0%) ( Figs. 3B , 4B , 5B ; Supplementary Fig. S6, Table S2B). We identified eight fungal ASVs as core members (Supplementary Table S3). These taxa included three ASVs from the Ascomycota (class Dothideomycetes ) and five from the Basidiomycota (class Agaricomycetes ). Within the Ascomycota , two ASVs belonged to the genus Cladosporium (family Cladosporiaceae ), and one to Alternaria (family Pleosporaceae ), both genera are well-known contributors to airborne fungal spores 58 , 59 . Among the Basidiomycota , two ASVs were assigned to Bjerkandera (family Phanerochaetaceae), and one each to Sistotremastrum ( Hydnodontaceae ), Trametes ( Polyporaceae ), and Resinicium ( Rickenellaceae ). These five ASVs are wood-decaying fungi commonly found in forested and urban green spaces 60 , 61 . The plant (pollen, but also including other aerial plant debris) component of bioaerosols ( Figs. 3C , 4C , 5C ; Supplementary Fig. S7, Table S2C) was dominated by the Pinaceae family across all cities (35.0% overall; 33.3% in Montréal, 24.8% in Québec, 47.0% in Sherbrooke), comprising conifers such as Abies balsamea (balsam fir), Pinus spp. (pines), Picea spp. (spruces), and Larix laricina (tamarack). These species typically release pollen around June, aligning with the peak observed during the second sampling time (93.3% of sequences). Notably, Pinaceae ASVs remained prevalent into the fall sampling period (September-October), accounting for 32.8% of sequences. The Asteraceae family were also strongly detected in our sequences (13.9%; 28.0% in Montréal, 3.1% in Québec, 2.9% in Sherbrooke), with a strong presence in fall in Montréal (59.6%). Oleaceae , which includes Fraxinus spp. (ashes) and Syringa spp. (lilacs), followed (9.8% overall; 24.0%, 34.7%, and 10.1%, respectively in spring), with peak abundance in early May. Only one ASV was identified as a core member of the pollen dataset, and it was assigned to the genus Syringa (family Oleaceae ; Supplementary Table S3). The Sapindaceae (e.g., Acer saccharum , Acer platanoides ) showed early-season peaks (9.8% overall; 52.8%, 5.2%, 21.0%, respectively in spring). The Betulaceae , including Corylus spp. (hazels), Betula spp. (birches), Alnus (alders), and Ostrya virginiana (hop-hornbeams), contributed significantly to spring bioaerosols (8.7% overall; 7.5%, 21.1%, and 50.4%, respectively. Finally, the Poaceae , a family of grasses and weedy species linked to hay fever (e.g., Poa pratensis , Phleum pratense , Bromus inermis , Digitaria sanguinalis , Elytrigia repens ), were primarily detected in fall, and especially outside Montréal (9.5% and 9.3% in the fall for Québec and Sherbrooke). Contrasting patterns in alpha and beta diversity across urban bioaerosol types Across all cities, for bacteria, diversity measured by the Shannon index remained stable across sampling periods (Supplementary Fig. S9). In contrast, fungal alpha diversity was lower during spring compared to summer (p < 0.001) and fall (p < 0.001). Pollen alpha diversity, on the other hand, showed a distinct seasonal trend, with higher diversity in the fall and lower in the summer (all pairwise comparisons: p < 0.001). City-level comparisons (Supplementary Fig. S10) revealed occasional season-specific trends in alpha diversity. In spring, only fungal alpha diversity was found to be significantly lower in Sherbrooke compared to Montreal (p < 0.05). In summer, pollen alpha diversity was higher in Sherbrooke than in Montréal (p < 0.0001). In the fall, bacterial alpha diversity was highest in Montréal, compared to Québec (p < 0.01) and Sherbrooke (p < 0.01). Montréal exhibited lower fungal alpha diversity than Québec (p < 0.01), and lower pollen alpha diversity than both Québec City (p < 0.01) and Sherbrooke (p < 0.001). Temporal trends within each city further highlighted these dynamics. In Montréal ( Fig. 3D ), pollen alpha diversity was drastically lower in summer compared to spring and fall (both p < 0.0001). In Québec ( Fig. 4D ), bacterial alpha diversity was higher in spring (p < 0.01), while both fungal and pollen diversity were higher in fall (p < 0.001 and p < 0.01, respectively). In Sherbrooke ( Fig. 5D ), fungal alpha diversity was higher in summer (p < 0.01) and fall (p < 0.0001) compared to spring, while pollen diversity peaked in fall compared to spring (p < 0.001) and summer (p < 0.0001). Finally, no significant differences were found in alpha diversity across vegetation index category (NDVI) or median household income (Supplementary Figs. S9–S13). For beta diversity, principal coordinates analysis (PCoA) revealed a distinct clustering of samples by sampling period for all three amplicons studied ( Fig. 3E , 4E , 5E ). In Montreal, the first two axes captured 21%, 38.2% and 61.6% of inertia in Bray-Curtis dissimilarities for bacteria, fungi and pollen, respectively. In Québec, they captured 22.8%, 51.8% and 55.1%, respectively, while in Sherbrooke, 17.9%, 53.3% and 58.8%, respectively. Differential abundance of bioaerosol taxa We performed differential abundance analyses using ANCOM-BC2 to identify temporal shifts in absolute abundances of bioaerosol genera, setting summer as the reference season and controlling for city identity ( Fig. 6 ; effect sizes and p-values listed in Supplementary Table S7). Genera were identified as differentially abundant if they met 3 conditions: enriched or depleted in at least one season compared to summer; p adj. < 0.01; and passed ANCOM-BC2’s sensitivity test (see Methods). Download figure Open in new tab Figure 6. Differentially abundant genera across seasons identified by ANCOM-BC. Differential abundance analysis of bioaerosols using ANCOM-BC2 Dunnett tests, with summer as the reference season and city identity included as a covariate. Positive values indicate enrichment in spring (blue) or fall (green) relative to summer, while negative values indicate depletion in spring (yellow) or fall (orange). Error bars represent standard errors of the estimated log-fold (LF) changes in absolute abundance. (A) Bacterial genera; (B) plant (pollen) genera; (C) fungal genera. Only genera with at least 20% prevalence across samples were tested, and only those with at least one significant comparison having passed the pseudocount sensitivity test, and with an absolute LF-change >1.5 for fungi are shown. Significance threshold is q < 0.01, where q is the p-value adjusted for multiple testing using the Holm procedure. *genus incertae sedis . Among bacterial genera ( Fig. 6A ), Exiguobacterium showed significantly lower abundance in spring, while Cryobacterium and Paenarthrobacter showed lower abundance in fall. Fall samples were characterized by increased abundance of Frigobacterium , Pseudokineococcus , Aureimonas , and Methylobacterium sequences. Interestingly, all Dothiodeomycetes and Leotiomycetes identified as differentially abundant were enriched in summer compared to fall and often spring too. Similarly, all Tremelloycetes identified by this analysis were depleted in the summer relative to either spring or fall. In contrast, no consistent patterns of enrichment were observed for the Agaricomycetes ( Fig. 6A ). Multiple fungal genera were identified as differentially abundant, revealing distinct temporal patterns in urban air ( Fig. 6B ). Several genera, including Irpex , Exidia , and Ramularia , were significantly enriched in summer samples, suggesting a peak in fungal sporulation or dispersal during warmer months. In contrast, spring samples were enriched in genera such as Daedaleopsis and Aspergillus , many of which include cold-tolerant or early-season taxa. Multiple genera were enriched in fall, including Amphinema , Perenniporia, Postia , Trametes , and Ceriporia , suggesting a shift in urban fungal community composition as temperatures decline. Some genera showed reduced abundance in specific seasons. For example, Gloeophyllum , Fraxinicola , and Venturia were less abundant in fall compared to summer, while Steccherinum , Coprinopsis , and Hyphodermella , were less abundant in spring. Interestingly, the genus Formitopsis was more abundant in both spring and fall compared to summer, suggesting a bimodal seasonal pattern. In addition, the genera Ganoderma and Gloeoporus showed significant depletion in spring compared to summer, followed by an enrichment in fall, suggesting a gradual seasonal increase ( Fig. 6B ). Finally, differential abundance analysis of pollen composition also demonstrated strong temporal variation ( Fig. 6C ). Pinus was significantly more abundant in summer compared to both spring and fall, consistent with its expected mid-season pollination peak. In contrast, several taxa, including Fraxinus , Juniperus , Dipteronia , and Equisetum , were enriched in spring, suggesting early-season pollen release. Additional spring-associated genera included Grimmia , Syringa , Betula , and Acer , many of which are common in urban landscapes and contribute substantially to early-season airborne pollen loads ( Fig. 6C ). Bacterial bioaerosol quantity varies between cities and to lesser extent across seasons In terms of quantity estimates, the qPCR analysis showed that bacteria were more abundant in summer than fall in Montréal ( p adj. = 0.028) Fig. 7A ). There were no significant differences in Québec or Sherbrooke across sampling periods. When comparing cities, our results show that there were less bacterial bioaerosols in Sherbrooke than in Montréal or Québec City at each sampling periods ( Fig. 7B ). None of the other gradients (median income and vegetation) could be associated with airborne bacteria quantity (Fig. S14). Download figure Open in new tab Figure 7. Abundance of bacterial urban bioaerosols across cities and sampling periods. Extreme values with copy number > 5e+06 were excluded from the statistical test and plots. Values shown are log10(mean copy number). Mean copy number is the mean of three qPCR replicates per sample. Statistical significance established with pairwise Wilcoxon test with p-value correction using the Holm procedure. Statistical significance was assessed using the Wilcoxon signed-rank test with p-value correction using the Holm procedure; asterisks indicate significant differences (*p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001). DISCUSSION In this study, we investigated the composition, diversity, and abundance of urban bioaerosols, focussing on bacteria, fungi, and pollen (including plant debris), across three Canadian cities varying in size and population density ( Fig. 1 ). Our central objective was to disentangle the relative influence of environmental and socio-economic drivers, including vegetation cover, neighborhood socio-economic status, and sampling period, on bioaerosol dynamics. While vegetation is known to shape bioaerosol abundance and composition 12 , 13 and often correlates with socio-economic gradients 36 – 39 , the interplay between these factors and the urban aerobiome remains poorly understood. Our findings revealed that sampling period was the most influential driver of community composition across all bioaerosol types, explaining 10.7–39.7% of the observed variation ( Table 1 ). City identity and its interaction with sampling period also showed significant effects, accounting for 7.6–8.6% and 3.0–9.3% of the variation, respectively ( Table 1 ). In contrast, vegetation cover and socio-economic status appeared to have limited influence on bioaerosol community structure at this spatial resolution. These results underscore the importance of temporal dynamics in shaping urban bioaerosols, revealing that temporal variation far outweighs local vegetation or socio-economic gradients in driving microbial and pollen composition. This insight challenges prevailing assumptions that urban vegetation and neighborhood characteristics are dominant determinants of airborne biodiversity and instead points to the need for time-sensitive monitoring and policy interventions. Understanding when bioaerosols shift most dramatically can inform public health strategies, urban planning, and climate resilience efforts, especially as cities face increasing pressures from environmental change and population growth. In line with previous studies, our results confirm that sampling period is the dominant driver of variation in bioaerosols, including bacteria, fungi, and pollen, across urban environments 62 – 65 ( Table 1 , Fig. 2 – 5 ). This strong temporal signal likely reflects seasonal shifts in abiotic conditions such as temperature, humidity, rainfall, and plant phenology that influence the growth, release, and aerosolization of biological particles 66 . Additionally, microbial succession on plant surfaces (the phyllosphere) varies seasonally, contributing to fluctuations in airborne microbial communities 13 , 67 . The second most influential factor was city identity, which may reflect differences in vegetation identity 68 , topography 69 , land use 26 , road traffic 70 , pollution 71 , and climate 72 . For instance, higher traffic volumes and distinct climatic conditions in Montréal compared to Québec City and Sherbrooke (Table S4) may contribute to divergent bioaerosol dynamics. While previous studies have reported that urban vegetation influences airborne microbial communities 12 , 13 , our results suggest only a minor effect of vegetation cover on bioaerosol composition. This discrepancy may stem from differences in spatial resolution or from the fact that earlier studies often inferred vegetation influence based on the presence of plant-associated taxa in the air, rather than direct measurements of surrounding vegetation 73 . These findings highlight the primacy of time and place in shaping the urban aerobiome, suggesting that when and where we sample matters more than how green or affluent a neighborhood is. This has important implications for urban health monitoring, allergen forecasting, and environmental justice, as it calls for dynamic, city-specific strategies rather than static, one-size-fits-all approaches to managing bioaerosol exposure in cities. The most relatively abundant bacterial families detected across all seasons and cities included the Nocardioidaceae , Geodermatophilaceae , Micrococcaceae , and Microbacteriaceae ( Figs. 3A , 4A , 5A ). All belong to the phylum Actinobacteriota , class Actinobacteria , which includes many taxa commonly found in soil and aquatic environments 74 , both recognized as major sources of airborne microorganisms 75 . Notably, Nocardioidaceae has been previously identified in bioaerosols 75 , and its members are known for their resilience in harsh conditions. These Gram-positive bacteria can survive long-range atmospheric transport, including via sandstorms originating from desert topsoils 76 , and have also been detected in urban air samples 77 . Our results also showed limited variation in alpha diversity across seasons and cities, with only a slight decrease from spring to fall in Québec ( Fig. 4 ), and a modest increase in Montréal compared to Québec and Sherbrooke during the fall (Supplementary Fig. S10). The consistent presence of Actinobacteria across diverse urban environments and seasons suggests a stable core of airborne bacterial taxa that may be shaped more by regional environmental reservoirs rather than by local urban features. This stability at the ASV level, coupled with the resilience of taxa such as Nocardioidaceae , highlights the potential for long-distance microbial dispersal and raises important questions about the ecological roles and health implications of persistent airborne bacteria in cities. Interestingly, our results show that Cryobacterium , a genus of obligately psychrophilic bacteria that has been sampled from glaciers in Antarctica 78 , 79 , is more abundant in summer compared to fall ( Fig. 6 ). Additionally, Methylobacterium ( Fig. 6 ), known to be present in air, soil, and on plant leaves, where some of them can produce plant growth promoting substances 80 , was most abundant in the fall. This could be caused by falling and decaying of leaves, during which leaf bacteria could get aerosolized in the air. This genus is also part of the Beijerinckiaceae family, which contains numerous plant-colonizing taxa. These findings reveal how temporal ecological processes, such as plant growth and decay, shape the composition of airborne microbial communities in cities. The differential depletion of Cryobacterium in fall and enrichment of Methylobacterium in fall highlights the dynamic link between terrestrial ecosystems and urban air, suggesting that bioaerosol composition reflects not only environmental conditions but also biological activity on the ground. This has implications for understanding seasonal exposure risks, urban ecosystem connectivity, and the potential for airborne microbes to influence plant and human health, particularly as climate change alters phenological patterns, microbial dispersal dynamics, and the timing of allergen peaks in urban environments. The identification of a small, consistent set of core ASVs across diverse urban environments suggests the presence of a resilient airborne bacterial community that persists regardless of local conditions (Table S3). The dominance of Actinobacteria , many of which are known for their environmental robustness, points to a shared microbial signature in urban air, potentially shaped by common sources such as soil, dust, and plant surfaces. Recognizing these core taxa is essential for understanding baseline urban bioaerosol composition, which can inform future studies on air quality and microbial exposure. Although genomic sequencing offers a powerful means of characterizing the composition and diversity of bioaerosols, it remains limited in its ability to quantify absolute abundances. To address this limitation, we employed quantitative PCR (qPCR) to estimate bacterial bioaerosol concentrations. Our results indicate a temporal trend, with bacterial abundance peaking in summer and declining in fall ( Fig. 7A ). This pattern contrasts with previous studies reporting lower airborne bacterial loads during summer months 81 . Among the three cities studied, Sherbrooke consistently exhibited the lowest concentrations of airborne bacteria ( Fig. 7B ), a finding that diverges from observations by Xie et al., who reported comparable bacterial bioaerosol levels across large urban centers 82 . One possible explanation for Sherbrooke’s lower levels is its urban morphology: the relative absence of tall buildings may reduce the entrapment of airborne particles, thereby enhancing air circulation and dispersal of bioaerosols. These findings highlight the importance of accounting for local urban characteristics in bioaerosol studies and open new avenues for research into how urban morphology and atmospheric dynamics shape the spatiotemporal distribution of airborne microbial communities. Three fungal families dominated the sample sequences across all cities: Cladosporiaceae (21.6%) Polyporaceae (13.2%), and Fomitopsidaceae (12.1%). In comparison with bacteria (R 2 = 10.7%), sampling period explained 29.2% of the variation in fungal community structure ( Table 1 ). In addition to this increase in compositional similarity within season, fungal alpha diversity was lower in spring compared to summer and fall in Québec and Sherbrooke, but not in Montréal ( Figs. 3 – 5 ). This increase in fungal alpha diversity in fall was also previously observed by Núñez et al. 165 . The identification of a fungal core microbiome composed of ubiquitous allergenic taxa (e.g., Cladosporium , Alternaria ) 58 , 83 and wood-decaying fungi highlights the diverse ecological origins of airborne fungi in cities (Table S3). These findings suggest that urban air consistently reflects inputs from vegetation, soil, and decaying organic matter. Understanding the composition and stability of fungal bioaerosols is crucial to assess seasonal allergen exposure, ecosystem connectivity, and the potential impacts of urban planning on airborne fungal diversity. For pollen, composition was primarily driven by sampling period ( Table 1 ; Figs. 2 – 5), consistent with previous studies 84 , 85 . These temporal and city-specific patterns likely reflect differences in local vegetation profiles, shaped by urban canopy composition and flowering phenology. Daily fluctuations in pollen composition ( Fig. 5 ) suggest that site-specific environmental factors, such as fine particles, temperature, and humidity, may further influence airborne pollen dynamics. These differences between cities could stem from variations in tree species composition, urban forestry practices, and microclimatic conditions, all of which contribute to the timing and intensity of pollen release. This work provides a critical baseline for understanding how urban vegetation and seasonal dynamics shape airborne pollen exposure. As cities continue to grow and climate conditions shift, such insights are essential to anticipate changes in allergen loads and informing urban planning and public health strategies aimed at mitigating respiratory health risks. For instance, city-specific pollen profiles and their seasonal dynamics could inform the development of targeted public health advisories or the strategic placement of air filtration systems in schools, hospitals, and elderly care facilities during peak allergen periods. While this study offers novel insights into the temporal dynamics of urban bioaerosols, several limitations should be considered when interpreting the results. Due to logistical constraints related to access to the active air sampler, we were unable to sample sites in Québec City during the summer, which may have limited our ability to fully capture seasonal variation. Additionally, although we initially planned to conduct shotgun metagenomic sequencing to quantify microbial communities, the quality and quantity of extracted DNA was insufficient. Future studies could explore alternative sampling materials or preservation methods to improve DNA integrity, as well as extend sampling durations to better capture temporal variability. It is also important to note that short-read amplicon sequencing detects DNA from both viable and non-viable organisms, meaning that the presence of a taxon does not necessarily indicate biological activity. Furthermore, the restricted combinations of socio-economic and vegetation gradients may have reduced our power to detect subtle effects. As such, the absence of statistically significant results should not be interpreted as evidence of no effect. Although we did not find strong evidence of differential exposure to bioaerosols across socio-economic gradients, this study provides a valuable foundation for understanding how urban infrastructure and environmental context shape airborne microbial communities in cities. Finally, we did not account for the potential influence of fine particulate matter (e.g., PM2.5), which may interact with bioaerosols and modulate their respiratory health impacts, including by enhancing the allergenicity of pollen. CONCLUSION In this study, we show that the composition of urban bioaerosols, including bacteria, fungi, and pollen, is strongly influenced by sampling period and city identity, while alpha diversity remained relatively stable across these factors. We did not detect strong effects of vegetation cover or median household income, suggesting that socio-economic status may not substantially alter exposure to diverse bioaerosols in the urban air, though further research is needed to reach consensus. As global change accelerates, shifts in temperature, humidity, and the frequency of extreme climatic events such as widespread fires are likely to influence bioaerosol dynamics. Future studies should explore the role of vegetation identity and diversity, which may better explain the presence of plant-associated microbial taxa in the air than vegetation cover (NDVI) alone. Given the rising prevalence of respiratory conditions such as allergies and asthma, it is crucial to understand the sources and ecological drivers of urban bioaerosols, including contributions from vegetation and human activity, to better anticipate and mitigate their impacts on public health. METHODS Study design The cities were chosen to represent a gradient of population size and density, and topography (Supplementary Table S1). We performed active air sampling to capture seasonal variation in urban bioaerosols (Supplementary Figs. S2–S3). Montréal is one of the most populated cities in Canada (population of ∼1,760,000; density of 4,500 inhabitants/km 2 ; 430 km 2 ), making it an important urban center to include in the study as it represents high-density urbanism and facilitates potential comparisons with cities of similar densities across the world. Montréal has the lowest cover of vegetation between the three cities with 69.1% (0.5827 normalized difference vegetation index [NDVI]) of vegetation 55 , and its topography is the one with the least variation and the lowest average elevation at 30 m 86 (Table S1). The Montréal study design is part of the “ Montreal Urban Observatory ”, a research platform which aims to monitor urban forest ecosystems for global change adaptation and health (Paquette et al. in review). In addition to considering vegetation cover and household income, the Montréal study design also accounted for population density. Québec City is a medium size city (population of ∼550,000; 1,230 inhabitants/km 2 ; 485 km 2 ), offers a contrasting vegetation cover with 85.9% (0.6518 NDVI) of vegetation 55 , and displays the highest variation in topography (Table S1) with an average altitude of 117 m 87 . Finally, Sherbrooke is a sparsely populated city (population of ∼170,000; 490 inhabitants/km 2 ; 370 km 2 ) with the highest vegetation cover of 90.6% (0.6837 NDVI) 55 and a varied topography (average altitude of 232 m 88 ; Table S1), thus representative of smaller urban areas that are surrounded by agricultural fields and forests. Within each city, to characterize environmental and socioeconomic gradients, we extracted normalized difference vegetation index (NDVI) values from Landsat-8 satellite imagery (USGS, 2021) at 2 m spatial resolution using Google Earth Engine. For each sampling site, we calculated the mean NDVI within a 200 m radius buffer to capture local vegetation cover. Household income data were obtained from the 2016 Canadian Census (Statistics Canada, 2017), which reports median total household income for 2015. Each sampling site was assigned the median household income of its corresponding Dissemination Area (DA), the smallest standardized geographic unit in Canada (population 400–700). Thus, each site was characterized by one NDVI value and one household income value, which were used as continuous predictors in subsequent models. Bioaerosol sampling At each site, samples were collected by concentrating urban air on electret filters (electrostatically charged) with an aerosol collector (SASS®4100, Research International) which collects particles with an aerodynamic diameter ranging from 0.5 to 10 µm 89 . Air was sampled for one hour at 4000 L/min, 1 m aboveground 90 . The samples were taken over three sampling rushes: from May 2 nd to May 19 th , 2022 (65 samples: 25 in Montréal, 25 in Québec, and 15 in Sherbrooke), from May 31 st to June 13 th , 2022 (30 samples: 15 in Montréal and 15 in Sherbrooke), and from August 31 st to October 5 th , 2022 (65 samples: 25 in Montréal, 25 in Québec, and 15 in Sherbrooke). Climate measurements for temperature, relative humidity, and wind speed were taken with an enviro-meter TM (Fisherbrand). Public daily recordings of temperature and precipitation 91 are showed in Supplementary Figs. S2–S3 with dates of sampling in each city overlayed. Sampling was performed as much as possible 24h after the last precipitation, a strategy that had to be adjusted sometimes due to timeline constraints (Fig. S3). Public recordings of monthly abiotic conditions 91 are also showed in Supplementary Table S4. DNA extraction Electret filters were submitted to a treatment prior to DNA extraction modified from Mbareche et al. 92 . Filters were cut to remove the plastic ring with a sterilised scalpel inside a sterile biological hood. Filter membranes were transferred to a 50 mL sterile plastic tube with 10 mL of extraction buffer (1.9 mM of Na 2 H 2 PO 4 *2H 2 O, 8.1 mM of Na 2 HPO 4 , 138 mM of sodium chloride, 2.7 mM of potassium chloride, 15.4 mM of sodium azide and 0,05% (w/v) of Triton X-100®) and vigorously agitated using a Vortex for 10 min. Filter membranes were then squeezed to remove the excess liquid with sterile pliers. The extracted buffer was transferred to a Amicon® Ultra-15 Centrifugal Filter Unit from Millipore Sigma (UFC910096) and then centrifuged at 4000g for 2 min. The resulting 100 µL was then transferred to the first tube of the QIAGEN DNeasy PowerSoil Pro Kit (47016) to extract DNA, following the manufacturer’s instructions with a mean yield of 1.75 ng/µl. Controls were added to confirm that samples were not contaminated during sampling, cutting away the filter membranes from the plastic ring and extracting DNA. Quantitative analysis Following DNA extraction, quantitative real-time PCR (qPCR) was performed to determine the absolute abundance of bacteria and fungi in all samples. All reactions were conducted using a Bio-Rad CFX96 system with CFX Maestro software (v2.3). Each 10 µL reaction contained 1× SYBR™ Green, 10 µM of each primer, and 2 µL of sample DNA. For bacterial quantification, primers 799F (forward) and 1193R (reverse) were used. The amplification protocol consisted of an initial denaturation at 95°C for 10 min, followed by 40 cycles of 10 s at 95°C, 10 s at 55°C, and 10 s at 95°C, with a final extension at 72°C for 10 s. Melting curve analysis was performed at the end of each run to confirm primer specificity. All reactions were conducted in triplicate. Absolute bacterial abundance was calculated by converting Cq values to 16S rRNA gene copy numbers using standard curves generated from serial dilutions of Erwinia amylovora ATCC15854 DNA. The known 16S gene copy number was derived from genome size and DNA concentration. Standard curves were included on each qPCR plate to minimize amplification bias. Sequencing For bacteria, the 16S rRNA gene V5-V6 region was amplified using the chloroplast-excluding 799F and 1115R primers 93 . For fungi, the primers ITS1F 94 and ITS2 95 were used to amplify the ITS1 region of the ITS gene. For pollen, the c-A49325 and d-B49863 primers were used to amplify the trnL (UAA) intron gene 96 . The resulting DNA was submitted to marker gene short read sequencing (Illumina Miseq PE300; see primers in Table S5). Raw sequences contained 35,895 ± 12,817 (16S), 33,424 ± 15,106 (ITS) and 24,900 ± 13,839 ( trnL ) reads, respectively. Sample composition estimation Amplicon sequencing variants (ASVs) were resolved with the DADA2 1.26.0 97 pipeline using R 4.4.0 98 on high performance computer clusters hosted by the Digital Research Alliance of Canada. Amplicon sequence set (16S, ITS and trnL samples) were processed separately to create three distinct datasets. Briefly, primers were removed using cutadapt 2.10 99 , thus ensuring read-through reverse complements were removed. Reads with a maximum expected error of 2 or at least one base with a quality of Q = 2 were removed. 16S sequences were truncated at lengths of 230 bases (forward) and 120 bases (reverse), dropping sequences shorter than these thresholds, while the variable-length ITS and trnL sequences were dropped if they were shorter than 100 bases. Of note, after discussion with the DADA2 authors, it was determined that the highly variable length of the trnL would better resolve ASVs by using only the forward reads. Extensive testing of various read merging strategies on this dataset confirmed that this was the best way to prevent artificial inflating of ASV diversity while optimizing their taxonomic assignment (see the following discussion with the authors: https://github.com/benjjneb/dada2/issues/2091 ). Therefore, for the trnL dataset only, forward sequences were truncated at 275 bp and any shorter reads were discarded. For all dataset, the dada2 error model was used to resolve ASVs and infer sample composition using the “pseudo-pooling” method, and PCR chimera were removed using the “consensus” method. ASV taxonomic annotations ASV were taxonomically labeled using the naïve bayesian classifier 100 implemented in DADA2. Assignment of 16S and ITS ASV taxonomic labels was done using SILVA v138.1 101 and UNITE for fungi v10.0 102 . For trnL ASV taxonomic assignment, a custom database was generated using an in-house pipeline. Briefly, the complete NCBI core_nt database 103 was downloaded on May 21, 2025, comprising 115,113,413 genomic sequences from all domains of life. These sequences were indexed by Dicey (0.3.3) 104 , a bio-informatic tool that finds sequences that can be amplified by a given pair of primers. This tool was applied to the trnL primer pair, and the resulting set of sequence was formatted as a database to be used as reference with DADA2’s taxonomic classifier. ASV post-processing ASV abundance matrix, taxonomic labels and sample metadata were consolidated and filtered independently for each amplicon dataset to create a phyloseq (1.48.0) 105 object that served as input for all statistical analyses, ensuring referential integrity between ASVs, taxonomy, and samples. First, ASVs without a Phylum annotation or at least a sum of 50 sequences across all samples were dropped. Then, samples with fewer than 9,000 (16S) and 2,500 (ITS and trnL ) sequences were discarded. These thresholds were established visually using sequence count distribution, aiming to increase the smallest sample count of each dataset while discarding the smallest number of samples. The resulting number of sequences, ASVs, samples, as well as the number of sequences and ASVs per sample and ASV prevalence are summarized in Table S6 and taxonomic assignation in Fig. S15 for each dataset. Given recent research, we manually changed all instances of the genus Methylorubrum for Methylobacterium 106 . Plots were generated using ggplot2 (3.5.1), patchwork (1.3.0), cowplot (1.1.3), gtable (0.3.6), gridExtra (2,3) and MetBrewer (0.2.0). Diversity analyses Diversity analyses were performed on rarefied data to account for uneven sequencing depth. Each amplicon dataset was rarefied to the number of sequences of its smallest sample, using the rarefy_even_depth function from the phyloseq package. Alpha diversity was computed using the Shannon index. Differences in alpha diversity across sampling periods were tested using a pairwise Wilcoxon test and p -values were adjusted using the Holm procedure. PCoAs were computed on rarefied data using the variance-stabilized Bray-Curtis dissimilarities between samples, which were computed using the varianceStabilizingTransformation function from the DESeq package and the vegdist function from the vegan (2.6.8) 108 package, respectively. PERMANOVA were performed using the adonis2 function of the vegan package, with 9999 permutations. For the global test across all cities, the following model evaluated by terms: Because PERMANOVA does not separate effects of species turnover from changes in species richness, the beta.multi function from the betapart package 109 was used to disentangle the contributions of species turnover and nestedness to total community dissimilarity. Analyses were conducted at the ASV level for 16S (bacteria), ITS (fungi), and trnL (pollen). Bacterial, fungal, and pollen datasets were split by city and season pair (spring–summer, summer–fall, spring–fall), retaining only replicates present in both sampling periods. For each subset, sample counts were aggregated by period and converted the data to presence/absence format using the decostand function from the vegan package. Pairwise Sørensen dissimilarities between periods were computed using beta.multi(index.family = “sorensen”) . This approach partitions total beta diversity (β SOR ) into two additive components: the turnover component (β SIM, where existing taxa are replaced by others) representing species replacement between periods, and the nestedness component (β SNE ) representing changes in species richness (e.g., species losses or gains). Differential abundance analysis Changes in absolute abundances were estimated using the log-linear model implemented in ANCOM-BC2 (2.4.0) 110 on non-rarefied data and are expressed in (natural) log-fold changes. This approach was designed to account for both sample- and taxon-specific biases. It was chosen because amplicon-based sequencing is known to introduce sequencing bias, resulting in the overrepresentation of certain taxa relative to their true relative abundances. This bias was observed based on a control sample added to the trnL sequencing run, which was made of a known amount of DNA from a set of 17 pollen vouchers (Supplementary Figure S16), justifying the choice of a statistical method that explicitly accounts for taxon-specific biases. The ANCOM-BC2’s Dunnett test was used to test pairwise differences in genera found in at least 20 % of samples, using summer as a reference group. Fold-changes (FC) are expressed on the natural log scale, showing relative enrichment (or depletion) in absolute abundance relative to summer. P -values were corrected using the Holm procedure. As the ancombc2 function was executed once per amplicon dataset (Bacteria, Fungi, and Pollen), p -value correction was performed manually to account for multiple testing across these datasets, a total of 1172 tests. Only genera showing p < 0.01 were considered as differentially abundant. Additionally, ANCOM-BC2 uses pseudocounts for log ratios and performs tests to evaluate the sensitivity of inference to the pseudocount value; comparisons who did not pass this test were excluded. Considering the large number of differentially abundant taxa (especially fungi), only FCs with absolute values >1 (1.5 for fungi) are reported. qPCR analysis A copy number calculator 111 was used for each sample, with the length of the gene sequence for 16S, which was 1550 bp 112 . The average number of copies of the gene 16S (7 copies) was used, which ranged from 1 to 15 copies per organism 113 . Differences in bacterial load across sampling periods and across cities were tested using a pairwise Wilcoxon test and p -values were adjusted using the Holm procedure. AUTHOR CONTRIBUTION STATEMENT S.P. conceived the study with I.L.L., A.P., and C.G. S.P. led the writing of the first version of the manuscript and contributed to methodology, formal analysis, investigation, visualization, data curation, and both original draft and review/editing stages. J.R.-L. contributed to formal analysis, visualization, data curation, and writing – review and editing. M.F. contributed to methodology, formal analysis, visualization, and writing – review and editing. A.R. contributed to formal analysis and writing – review and editing. G.L. contributed to methodology, formal analysis, and writing – review and editing. J.-F.L. developed software. S.T. contributed to methodology and writing – review and editing. C.L. contributed to conceptualization, funding acquisition, resources, and writing – review and editing. S.W.K. contributed to conceptualization, funding acquisition, and writing – review and editing. C.G. contributed to conceptualization, methodology, funding acquisition, resources, supervision, and both original draft and review/editing stages. A.P. contributed to conceptualization, methodology, funding acquisition, supervision, resources, validation, and writing – review and editing. I.L.-L. led project administration and supervision, as well as contributed to conceptualization, methodology, funding acquisition, supervision, resources, investigation, validation, visualization, data curation, and both original draft and review/editing stages. DATA AVAILABILITY All the raw sequences have been deposited on ENA under accession number PRJEB96751. All scripts used to generate the results of this study from the raw sequences have been deposited on Github ( https://github.com/jorondo1/urban_bioaerosols ). These files will be rendered public upon publication. 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. FUNDING SOURCES This work was supported a New Frontiers: Exploration grant ( Social Sciences and Humanities Research Council [SSHRC]) held by Isabelle Laforest-Lapointe and Alain Paquette (NFRFE-2020-00597). This research was supported by two Canada Research Chairs (C.L holds the CRC1 in genomics of asthma and allergic diseases; I.L.L. holds the CRC2 in Applied Microbial Ecology) and two provincial networks (the Centre for Forest Research [CEF-CFR] and the Air, Intersectoriality, Respiratory, and Sound research network [AIRS]). ACKNOWLEDGEMENTS We are grateful to the members of the Paquette laboratory at UQÀM who worked on setting up the project, as well as to Ema Lussier, Jérémy Fraysse, Joey Chamard, Sarah Ishak, Karina Gisèle Mac Si Hone, Sophie Boutin, Ève Lebeau, Jennifer Fontaine and the Duchaine laboratory at the Institut universitaire en cardiologie et pneumologie de Québec (Université Laval) for their great help in field and laboratory work. We also acknowledge the support of Calcul Québec, Compute Canada (CCS), and the Digital Research Alliance of Canada for providing the high-performance computing resources essential to our data processing and analysis. Funder Information Declared New Frontiers: Exploration grant (Social Sciences and 761 Humanities Research Council [SSHRC] , NFRFE-2020-00597 awarded to ILL and AP CRC2 in Applied Microbial Ecology , Awarded to ILL CRC1 in genomics of asthma and allergic diseases , Awarded to CL Centre for Forest Research [CEF-CFR] Air, Intersectoriality, Respiratory, and Sound research 766 network [AIRS] Footnotes https://github.com/jorondo1/urban_bioaerosols References 1. ↵ Sandifer , P. A. , Sutton-Grier , A. E. & Ward , B. P . Exploring connections among nature, biodiversity, ecosystem services, and human health and well-being: Opportunities to enhance health and biodiversity conservation . Ecosyst. Serv . 12 , 1 – 15 ( 2015 ). OpenUrl 2. ↵ Potapov , P. , et al. Unprecedentedly high global forest disturbance due to fire in 2023 and 2024 . Proc. Natl. Acad. Sci. 122 , e2505418122 ( 2025 ). OpenUrl PubMed 3. ↵ Ripple , W. J. et al. World Scientists’ Warning to Humanity: A Second Notice . 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