remote sensing Article Variability and Geographical Origin of Five Years Airborne Fungal Spore Concentrations Measured at Saclay, France from 2014 to 2018 Roland Sarda-Estève 1, *, Dominique Baisnée 1 , Benjamin Guinot 2 , John Sodeau 3 , David O’Connor 4 , Jordina Belmonte 5,6 , Jean-Pierre Besancenot 7 , Jean-Eudes Petit 1 , Michel Thibaudon 7 , Gilles Oliver 7 , Charlotte Sindt 7 and Valérie Gros 1 1 Laboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, Unité mixte de recherche CEA-CNRS-UVSQ, 91191 Gif sur Yvette, France 2 Laboratoire d’Aérologie, Université Toulouse III, CNRS, UPS, 31400 Toulouse, France 3 School of Chemistry and Environmental Research Institute, University College Cork, T12 YN60 Cork, Ireland 4 Technological University of Dublin, D08NF82 Dublin, Ireland 5 Dept. Biologia Animal, Biologia Vegetal i Ecologia, Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès, Spain 6 Institut de Ciència i Tecnologia Ambientals (ICTA-UAB), Universitat Autònoma de Barcelona, 08193 Cerdanyola del Vallès, Spain 7 Réseau National de Surveillance Aérobiologique, 69690 Brussieu, France * Correspondence: sarda@lsce.ipsl.fr; Tel.: +33-1-69-08-97-47  Received: 30 May 2019; Accepted: 9 July 2019; Published: 13 July 2019  Abstract: Airborne fungal spores (AFS) represent the major fraction of primary biological aerosol particles (PBAPs), and they are studied worldwide largely due to their important role within the Earth system. They have an impact on climate and human health, and they contribute to the propagation of diseases. As their presence in the air depends largely on studied ecosystems, a spore trap was used to monitor their atmospheric concentrations from 2014 to December 2018 in Saclay, a suburban area in the megacity of Paris. The main objective of this work was: (1) to understand the atmospheric variability of AFS in relation to different variables such as meteorological factors, agricultural practice, and (2) to identify their geographical origin by using a source receptor model. During our period of observation, 30 taxa have been identified under a light microscope. In order of importance, Ascospores, Cladosporium, Basidiospores, Tilletiopsis, Alternaria were found to be the most abundant types respectively (50.8%, 33.6%, 7.6%, 1.8%, and 1.3%) accounting for 95% of the atmospheric concentrations. We observed a general decrease associated with a strong interannual variability. A bimodal seasonal cycle was identified with a first maximum in July and a second in October. The main parameters driving the atmospheric concentration are temperature and precipitation. The daily variability is strongly activated by successive periods of hot weather and rainfall, multiplying the concentration by a factor of 1000 in less than 12 hours. Results from the source receptor model ZeFir point out unambiguous different origins of AFS due to specific sources impacting the observation site. Our study also indicated that a hydrological stress has a direct effect on the daily concentrations. This last point should be taken into account for every stressed ecosystem studied in a global warming context. This is particularly important for Mediterranean areas where water is a key control of the growth and dispersion of fungal spores. Keywords: Airborne fungal spores; meteorological factors; source receptor model; pathogen transport; polluted environments; hydrological stress Remote Sens. 2019, 11, 1671; doi:10.3390/rs11141671 www.mdpi.com/journal/remotesensing Remote Sens. 2019, 11, 1671 2 of 29 1. Introduction Biogenic aerosols and more specifically fungal spores are present in all ecosystems on Earth, and even in space habitats such as the international space station [1,2]. They are studied worldwide due to their impacts on climate, outdoor/indoor air quality, human health, plant illnesses, and decay [3–7]. It is known that these types of biogenic particles are emitted directly from the biosphere to the atmosphere [8] and can indirectly influence the global Earth energy budget due to their capacity to form Giga Cloud Condensation Nuclei (GCCN) or Ice Nuclei (IN) [9]. They can and do actively participate in regional and global hydrological cycles in line with their degree of hydrophobicity and the condensation surfaces they provide to initiate droplet formation [10]. As fungal spores are present in the coarse mode, they have an impact on the concentration of particulate matter below 10 µm (PM10) [11]. Concerning the health effect on humans and animals, [12–14] reported that they have been identified to both cause and exacerbate respiratory diseases. The global atmospheric distribution of such particles depends on the biotope; the most abundant divisions being Ascomycota (AMC) and Basidiomycota (BMC). Over continental areas, the ratio of BMC/AMC is markedly higher compared to marine counterparts figuring out the relatively strong emissions of AMC from the vegetation cover [15]. Monitoring the different classes of fungal spores in the air and subsequently establishing global emission inventories are of key importance for the characterization of their possible Cloud Condensation Nuclei (CCN) properties and noxiousness for ecosystems and humans [16]. As fungal spore propagules are for the most part present in the coarse mode (defined as particulate matter (PM) with an aerodynamic diameter (Da ) greater than 2.5 µm but below 10µm), it is possible to estimate their total concentrations by measuring specific chemical tracers present in all airborne fungal spores; namely, mannitol and arabitol [17]. On a global scale, based on measurements made in Amazonia, it has been estimated that concentrations of fungal spores could be as high as 50 Tg/y for wet and dry discharge in the size range of 1 µm to 10 µm [18]. Results from optimized modeling studies reported 28 Tg/y over vegetated regions with 25% in the PM fine mode, which particle sizes are typically below a Da of 2.5 µm [19]. Such a two-fold difference can be explained by consideration of the cut-off chosen for the measurement of mannitol and the conversion factor used: ng.m−3 of mannitol as a linear function of Nb#.m−3 of total fungal spores. As several others sources have been identified [20], the calculation of the conversion factor between real atmospheric concentrations of total fungal spores concentrations and molecular tracers must be determined carefully and validated by on-site collocated measurements. This result points out the importance to measure with precision the on-site fungal spore concentrations with a spore trap [21] to quantify properly the conversion factor between the chemical tracer and the atmospheric concentration of airborne fungal spores. Airborne fungal spores vary in size from 2 to 50 µm (Da ) with the most allergenic ones being found between 2 and 10 µm. The atmospheric transport of fungal spores is an important variable for evaluating the potential of disease propagation on any terrestrial species (plants, animals, and human). To date it is known that certain fungal spores can be transported over long distances due to their relatively small diameter. They can survive periods of prolonged environmental stress and are able to cross the oceans [22]. As an example, fourteen new genera in the atmosphere of Havana have been found including the Mauritania fungi [23], probably transported together with desert dust from the African Sahel region. In this way, they can be transported in regions where climatic conditions are more favorable for their development than in their environment of origin [24–27]. This observation means that studying transport processes, meteorological parameters, specific sources, and atmospheric processes relevant to spores and their measured concentrations are important to assess contamination sources on plants [28], animals and allergenic effects on human health [29,30]. It was shown that at Saclay, France the concentrations of fungal spores were mostly found in the coarse mode rather than the fine mode and therefore can be assimilated into the dust fraction for transport model purposes [31]. Regarding spore transport mechanisms, atmospheric dispersion models have been used to describe the spatio–temporal dispersal of fungal pathogens [32,33]. Additionally, descriptive, predictive or conceptual modeling of particulate matter have been proven to be interesting investigative tools. In 2018, this approach was used to explain and predict the variability of birch pollen [34]. In essence, Remote Sens. 2019, 11, 1671 3 of 29 this means that both meteorological parameters and topography should be included in models to describe the complex spatial and temporal distribution and variability of PBAPs in the context of global warming (including wet/dry deposition/discharge). For example, in Europe a comparison between predictive models has been made to forecast levels of Alternaria and Cladosporium [35]. These species are important due to their pathogenic effects on plants and their impact on mammalian cells through the presence of mycotoxins. On-site meteorological data associated with multiple observation sites has been used by [36] to identify the origin and potential sources of airborne fungal propagules. More recently, the importance of specific agricultural practices in the release of many types of fungal spores, especially harmful to crops, has been studied using a real-time method [37]. In our study, the objectives were first to understand the variability of total atmospheric fungal spores and second to identify their origin using the ZeFir tool kit [38]. This user-friendly software has been recently developed and successfully applied to identify pollen origins and potential sources that affect the Saclay observatory [39]. Here, 234 weeks of daily monitoring of airborne fungal propagules have been analyzed to attempt a localization of the five main fungal spores present in the Saclay ecosystem, which are Ascospores, Basidiospores, Cladosporium, Alternaria, and Tilletiopsis. The model has also been used to investigate the geographical origins of some specific species known to be allergenic AFS implicated in human health diseases, plant contamination or lethal parasites such as Ustilago, Ganoderma, Aspergillaceae, Myxomycetes, Dydimella, Helicomyces, Botrytis, and Entomophthora [40,41]. We also attempt to understand the link between the geographical origin of AFS and the agricultural practices at a regional scale. 2. Materials and Methods 2.1. Experimental Site Description The observation site and the monitoring procedure have been previously described by [39], as well as the occurrence of pollution events impacting the Saclay site [38]. Briefly, the observatory is located at Saclay, France (48.7247◦ N, 2.1488◦ E), 30 kilometers in the southwest of Paris. The observatory is known as the SIRTA station from the EU-ACTRIS network. The site is not under the direct influence of any chemical source, thus ideal for atmospheric gas, aerosol and bioaerosol observation. It is now referenced as an international intercomparison site for aerosol and bioaerosol instrumentation [42,43]. The sampling site is surrounded by crops, forest, and small residential villages, and can be impacted by strong winter and summer pollution events. Recently, this region has become more urbanized due the Paris-Saclay University building program, which is a part of the “Grand Paris” project; the subsequent drastic change in land use is expected to be modifying biogenic and anthropogenic emissions in the area. 2.2. Airborne Fungal Spores Monitoring Procedure Airborne fungal spores (AFS) have been continuously monitored since July 2014 at Saclay, France. The observation period presented in this study runs from July 2014 to December, 2018 (234 weeks). Daily AFS concentrations were obtained with a volumetric impaction sampler (VIS), via a Hirst-type spore trap (VPPS 2000, Lanzoni, Bologna, Italy). The VPPS 2000 spore trap was located on the roof of the observatory at 15 m above the ground (152 m above sea level) without any neighboring vegetation. Under these conditions, the measurements obtained were expected to be representative of the regional sources. The air was continuously pumped through an orifice of 14 × 2 mm (28 mm2 ) always facing the prevailing winds at a flow rate of 10 L per minute. This instrument collected the impacted particles efficiently with an aerodynamic diameter between 2 µm and 200 µm on a rotating drum mounted with a 19 mm sticky tape (cellophane tape coated with silicone) [44]. The drum was changed weekly on the same day at the same time to minimize the sampling procedure errors. The counting and identification were performed by the French Monitoring Network of Aerobiology (Réseau National de Surveillance Aérobiologique, RNSA, Brussieu, France). Since RNSA belongs to the European Aeroallergen Network Remote Sens. 2019, 11, 1671 4 of 29 (EAN), it strictly follows the recommendations regarding the minimum requirements for the counting procedure. The standard analytical method used accounted for 10% of the surface area. Standard sampling, processing and analysis techniques have been well documented [45]. The quality assurance and the quality control of theses method were regularly made during the international intercomparison campaigns [46]. The daily concentrations obtained are expressed as concentrations of fungal spores per cubic meter of air. Analogous to pollen studies, the Main Spores Season (MSS) is defined as the duration time when fungal spores are present in the atmosphere in significant concentrations in a specific ecosystem. Therefore, it defines the main season start and end points. The MSS used in this study is the period during which the sum of daily mean fungal spore concentrations stands between 5% and 95% of the total sum. The terminology and the selection criteria used in this study follow the recommendation of [47]. 2.3. Investigation of the Geographical Origins of Airborne Fungal Spores The investigation of the geographical origins of AFS was performed by coupling ambient concentrations with on-site measured wind data. At the Saclay observatory, meteorological parameters are provided by a weather station WXT520 (Vaisala, France). The measurements of wind speed (WS, m/s), wind direction (WD, Degrees), temperature (T, ◦ C), relative humidity (RH, %), and cumulative rain (R, mm) are acquired every minute. A variant of two-dimension non-parametric wind regression (NWR) originally developed by [48], called the sustained wind incidence method (SWIM) developed by [49] has been used to identify the geographical origin of AFS. This variant takes into account the standard deviation of the wind speed and the wind direction on a daily basis. Equation (1) below describes the calculation of SWIM, Ci ·Υi δ Si = · (1) max(Ci ·Υi ) δi where Ci , Υi , and δ represents respectively wind speed, wind direction, and wind direction standard deviation. This actually allows downwind daily concentration values associated with high atmospheric variability to be obtained during that day. Wind direction standard deviation was estimated by the 1-pass Yamartino equations [50]. This entire study was performed with ZeFir, a user-friendly tool for wind analysis [38]. More information can be found here: https://sites.google.com/site/ZeFirproject. This method was successfully applied for the first time to determine the geographical origin of pollen by [39]. 2.4. Meteorological Aspects of the Saclay Ecosystem France belongs to the temperate climate zone and four major climate types influence the territory regarding the climate over more than 30 years period [51]. The main one is in the west, mainly influenced by the ocean (Figure 1a, Oceanic, and Oceanic degraded). This climate is characterized by significant rainfall during autumn, winter and spring periods; less precipitation is observed during the summer period. Mean temperatures vary from 5 ◦ C in winter to 21 ◦ C in summer, as illustrated in Figure 1b. The Saclay observation site is dominated by an oceanic degraded climate (heavy rainfall during the dormancy period), relatively mild winter conditions with less than 47 days below 0 ◦ C per year occurring between November to March and a relatively hot summer. Remote Sens. Remote 2019, Sens. 11, 1671 2017, 9, x FOR PEER REVIEW 5 of5 of 32 29 Remote Sens. 2017, 9, x FOR PEER REVIEW 5 of 32 Figure 1. (a) Climate of France adapted from [49] and (b) monthly mean precipitations and Figure Figure (a)1.Climate (a) Climate 1. temperature of 2014 offrom France France to adapted adapted from 2018 fromand [49] at Saclay,[49] and (b) France (b) monthly monthly typical mean mean precipitations of an precipitations Oceanic degraded and and temperature climate. from 2014 to 2018 atfrom temperature Saclay, 2014France typical to 2018 of anFrance at Saclay, Oceanic degraded typical of anclimate. Oceanic degraded climate. The Eastern region is characterized by a continental climate and the south of France exhibits TheEastern The Easternregion regionisischaracterized characterizedbybya continental a continental climate climate and the south of France exhibits a a typical Mediterranean climate. The French mountains haveand theirtheown south of France climate exhibits characteristics, typical a typical Mediterranean climate. The French mountains have their own climate characteristics, mainly mainlyMediterranean driven by theclimate. altitudeThe whereFrench mountainsare temperatures have theirand lower ownprecipitation climate characteristics, higher on the driven by mainly the altitude driven by the where altitude temperatures are lower and where temperatures areAprecipitation lower and higher on the precipitation slopeson higher exposed the to slopes exposed to winds charged with humidity. correct understanding of the ecosystem winds charged slopes exposed with to humidity. winds chargedA correct with understanding humidity. A of the ecosystem correct understanding studiedof isthe necessary ecosystem to grasp studied is necessary to grasp fungal spores purpose as their atmospheric concentration can be fungal studied spores purpose is necessary as their atmospheric concentration can be extremely variable [7]. extremely variableto[7]. grasp fungal spores purpose as their atmospheric concentration can be extremely variable [7]. 2.5. Wind Prevalence at Saclay 2.5.Wind Prevalence at Saclay 2.5.Wind Wind Prevalence at Saclay factor for the dispersal and transport of (bio)aerosols far from their is an important sources Wind [35]. isorder In an important to better factor for thethe characterize dispersal Saclay and transport prevailing winds ofpattern (bio)aerosols between farJuly from2014their and Wind sources is an [35]. important In order tofactor better for the dispersal characterize theand transport Saclay prevailingof (bio)aerosols winds patternfar from betweentheir July December sources 2018 [35]. In the ZeFir order source-receptor to2018 better toolthe wasSaclay used. prevailing The wind rose presented in Figure 2Julyindicate that2014 air and December masses generally thecharacterize originate ZeFir from source‐receptor west to tool (oceanic), south-west was used. windsThepattern with wind rose speeds between presented ranging between in 5 2014 and December Figure 2 indicate 2018 that the air ZeFir source‐receptor masses generally tool was originate from used. west The to wind rose presented south‐west (oceanic), in with to 12 km/h. Figure A second 2 indicate that wind regimegenerally air masses is characterized by north originate (5◦ ) totosouth-east fromregime west south‐west (125 ◦ ) winds, at speeds (oceanic), with speeds ranging rangingranging from 2 to between 5 to 7 km/h,5 bringing 12 km/h. rather A second sunny wind wind skies, regime dry air isand is characterized higher temperatures by north as (5°) to reported speeds between to 12 km/h. A second characterized by north (5°) to by [39]. (125°) winds, at speeds ranging from 2 to 7 km/h, bringing rather sunny skies, dry air air south‐east south‐east (125°) winds, at speeds ranging from 2 to 7 km/h, bringing rather sunny skies, dry and higher temperatures as reported by [39]. and higher temperatures as reported by [39]. N0 JOINT PROBABILITY N0 20 JOINT PROBABILITY NW 315 20 NE 45 50 NW 315 15 NE 45 50 40 15 10 40 x10 10 30 5 x10 -3 5 30 W E -3 270 0 5 10 15 90 20 W 270 0 5 510 10 E 15 15 90 20 20 5 10 15 20 10 10 0 0 SW 225 SE 135 225 SW 180 SE 135 180 S S Figure 2. Joint probability polar plot, equivalent to a wind rose from July 2014 to December 2018. Figure 2. Joint probability polar plot, equivalent to a wind rose from July 2014 to December 2018. Figure 2. Joint probability polar plot, equivalent to a wind rose from July 2014 to December 2018. 3. Results 3. Results In our study the complete identification of the fungal propagules at a genus level was not possible 3. Results In our due to the usedstudy the complete identification identification method (e.g 2.2). Atof the thisfungal stage of propagules our study at noaDNA genusorlevel RNAwas not analysis In our possible study due the complete to the used identification identificationand of method the fungal propagules (e.g 2.2).forAtAscospores, at a genus this stage ofBasidiosporeslevel was our study no DNA not or were done to complete the observations particularly and some possible RNA due to thewere analysis used done identification to method complete the(e.g 2.2). At thisand observations stageparticularly of our studyfor noAscospores, DNA or optically unidentified spores. As it is the first time that these results are presented, they need to be RNA analysis were Basidiospores and done optically some to complete the observations unidentified and particularly for these Ascospores, compared with the literature to interpret the mainspores. As it parameters isdriving the firstthe time that AFS resultsin concentration are the Basidiospores and some optically unidentified spores. As it is the first time that these presented, they need to be compared with the literature to interpret the main parameters driving results are presented, the AFSthey need to be compared concentration with in the Saclay the literature ecosystem. Thistostep interpret is as the main parameters a necessary driving prerequisite to use the AFS concentration in the Saclay ecosystem. This step is as a necessary prerequisite to use Remote Sens. 2017, 9, x FOR PEER REVIEW 6 of 32 Remote Sens. 2019, 11, 1671 6 of 29 Zefir source receptor modeling tool to identify the geographical origin of airborne fungal spores impacting Saclay the observation ecosystem. This step site. is as a necessary prerequisite to use Zefir source receptor modeling tool to identify the geographical origin of airborne fungal spores impacting the observation site. 3.1. Interannuality of the Airborne Annual Fungal Spore Integral at Saclay 3.1. Interannuality of the Airborne The interannuality Annual (AFSIn) of AFS Integral Fungal Spore Integral in was studied at depth. Saclay The annual sum of fungal spores Theranges from 2.5 of interannuality 10AFS 6 Nb#/m3 to 5.2 106 Nb#/m3. We observed a strong interannuality of the Integral (AFSIn) was studied in depth. The annual sum of fungal spores AFSIn and no biannual cycle ranges from 2.5 10 Nb#/m3 to 5.2 6 has10been found. 6 Nb#/m Instead, 3 . We observeda general a strong decrease of the atmospheric interannuality of the AFSIn and concentration was found, as illustrated in Figure 3. The AFSIn data from January no biannual cycle has been found. Instead, a general decrease of the atmospheric concentration to June 2014 was have been estimated by a linear modeling with the most basic model which is found, as illustrated in Figure 3. The AFSIn data from January to June 2014 have been estimated the simple linear by a regression where a variable X is explained and modeled by an affine function of linear modeling with the most basic model which is the simple linear regression where a variable X a comparable isvariable Y. The explained andinterpolation modeled by an of the Saclay affine data set function of afrom January to comparable June 2014 variable hasinterpolation Y. The been done byof the comparing the daily concentrations of Paris and Saclay data set for year 2014 provided by the Saclay data set from January to June 2014 has been done by comparing the daily concentrations of Paris RNSA observations in Paris. The results obtained without the data set of the January to June and Saclay data set for year 2014 provided by the RNSA observations in Paris. The results obtained 2014 were also showing a decrease of the atmospheric concentration (Figure A1, Appendix B) without the data set of the January to June 2014 were also showing a decrease of the atmospheric and pointed out the importance to follow the AFS all over the year as it is done at the Saclay concentration (Figure A1, Appendix B) and pointed out the importance to follow the AFS all over the observatory. year as it is done at the Saclay observatory. Figure 3. Annual Fungal Spores Integral (AFSIn) from January 2014 to December 2018. Figure 3. Annual Fungal Spores Integral (AFSIn) from January 2014 to December 2018. Such results are in accordance with those observed for total species records in Europe like reported by [52]Such withresults are in an AFSIn in 1989 accordance of 2.106with those Nb#/m observed in 3 . Similarly, forGreece, total species records a general in Europe decrease like spore in fungal reported levels by [52] with was in Thessaloniki an AFSIn in 1989 observed of 2.10 from 1987 Nb#/m 6 to 2005.with 3 Similarly, a mean in Greece, decreasea of general −52%decrease compared to in fungal spore levels in Thessaloniki was observed from 1987 to 2005 with Saclay (−52%) [53]. The annual sum of fungal spores observed at Saclay is in the range a mean decrease of has of what –52% compared to Saclay (–52%) [53]. The annual sum of fungal spores observed at Saclay is in been observed in other oceanic environments in Europe, and is generally 3 to 4 times higher than the range of what has been observed in other oceanic environments in Europe, and is generally in Mediterranean areas [54]. All the results obtained in these different ecosystems show a general 3 to 4 times higher than in Mediterranean areas [54]. All the results obtained in these different decrease indicating that our observations are not linked to fluctuating changes. ecosystems show a general decrease indicating that our observations are not linked to fluctuating 3.2. changes. Seasonality of All Taxa Combined Concentrations at Saclay 3.2. The monthly Seasonality mean of All Taxa distribution of all AFS at Combined Concentrations concentrations Saclay averaged over the five years of measurements displays a clear bimodal seasonal cycle as illustrated in Figure 4. P90, P75, P25, and P10 The the represent monthly mean25th, 90th, 75th, distribution and 10thof all AFS concentrations percentiles averaged respectively. The over monthly the found mean five years of MSS for the measurements displays a clear bimodal seasonal cycle as illustrated in Figure 4. P90, P75, P25, in the current study is found in May (11571 Nb#/m3 ) and ends in November (8472 Nb#/m3 ). The first and P10 represent the 90th, 75th, 25th, and 10th percentiles respectively. The monthly mean found maximum was observed in July (28,564 Nb#/m3 ) followed by a rapid decrease in September (12,456 Remote Sens. 2017, 9, x FOR PEER REVIEW 7 of 32 for 2019, Remote Sens. the MSS in the current study is found in May (11571 Nb#/m3) and ends in November7 of 11, 1671 (8472 29 Nb#/m ). The first maximum was observed in July (28,564 Nb#/m ) followed by a rapid decrease 3 3 in September (12,456 Nb#/m3). A secondary maximum was noted in October (14,538.103 Nb#/m3), Nb#/m3 ). A secondary maximum was noted in October (14,538.10 3 Nb#/m 3 ), followed by a sharp followed by a sharp decrease in December (3439 Nb#/m3). Data are available in Table A1, decrease in December (3439 Nb#/m3 ). Data are available in Table A1, Appendix A. In total, the fungal Appendix A. In total, the fungal spore season is significant over 7 months in a year (from May spore season is significant over 7 months in a year (from May to November), representing 58% of the to November), representing 58% of the year and 91% of the total AFS concentrations. year and 91% of the total AFS concentrations. Figure 4. Monthly means of Airborne fungal spores (AFS) concentrations from January 2014 to Figure 4. Monthly means of Airborne fungal spores (AFS) concentrations from January 2014 to December 2018. December 2018. During the period of observation, no significantly high concentrations appear to drive up the During the period of observation, no significantly high concentrations appear to drive up statistical values that determine the two AFS seasonal peaks. 2014 and 2017 exhibit a clear seasonal the statistical values that determine the two AFS seasonal peaks. 2014 and 2017 exhibit a clear cycle and the other years are more in a continuum from July to December as illustrated by Figure A2. seasonal cycle and the other years are more in a continuum from July to December as illustrated by Figure 3.3. Major A2. Driving the Seasonality of AFS Concentrations at Saclay Parameters The ◦ C), the monthly means of 3.3.observations made Major Parameters on thethemonthly Driving means Seasonality of Concentrations of AFS temperature (at Saclay precipitation (mm) and the monthly mean concentrations of fungal spores, as illustrated in Figure 5, showed thatThe theobservations MSS starts whenmadethe on mean the monthly meansreaches temperature of temperature an average (°C), the above value monthly11 means ◦ C andof not below (values are reported in Tables A1 and A2). This result is important to understand howin precipitation (mm) and the monthly mean concentrations of fungal spores, as illustrated Figure 5,of the production showed fungal that theoccurs: spores MSS starts when (1) the needthe formean temperature a certain amount of reaches water; an andaverage value (2) a mean aboveabove temperature 11 °C 11 and ◦ Cnot below to start the(values growingareprocess reported andinthe Table A1 and dispersal. subsequent A2). This result Thoseis important to observations understand how the production are in full accordance with literature [6]. of fungal spores occurs: (1) the need for a certain amount of water; and (2) a mean temperature above 11 °C to start the growing process and the subsequent dispersal. Those observations are in full accordance with literature [6]. Remote Sens. 2019, 11, 1671 8 of 29 Remote Sens. 2017, 9, x FOR PEER REVIEW 8 of 32 Figure Figure 5. Mean 5. Mean temperature, temperature, precipitation precipitation and seasonality and seasonality of fungal of fungal spores spores measured measured between between 20142014 and December 2018. and December 2018. 3.4. Main SporeSpore 3.4. Main Season and Daily Season Variability and Daily of Airborne Variability Fungal of Airborne SporeSpore Fungal Concentrations at Saclay Concentrations at Saclay The fungal propagule The fungal propagulepattern at Saclay pattern was found at Saclay was foundto be todifferent from one be different fromyearoneto theto year next thelikely next due likely to the meteorological factors of variability (temperature, insolation, precipitation, due to the meteorological factors of variability (temperature, insolation, precipitation, atmospheric pollutants) as previously atmospheric pollutants)reported [55,56]. For as previously each of reported the five [55,56]. Foryears eachreported of the fivein years the current reportedstudy, the in the startscurrent and the ends the study, of the MSS starts andarethe shown ends inof Table the MSS A3.are The calculation shown in Table applied A3. The was the same applied calculation as what has been was thepreviously same asemployed what has been [57]. previously employed [57]. As shown As shownin Figure 6, the6,fungal in Figure sporespore the fungal concentrations concentrations can becanextremely be extremelyvariable both both variable in timein and time intensity. As an example, a high concentration’s episode occurred in and intensity. As an example, a high concentration’s episode occurred in June 2015 as the June 2015 as the weather conditions weather were remarkably conditions werehot and dry for remarkably the hot season. and dry for Hence an increase the season. Hence in the AFS concentrations an increase in the AFS 3 10 × 10 Nb#/m fromconcentrations 3 on10 22 xJune × 10 3 3 from 103 rising Nb#/mto3 on25622 JuneNb#/mrising to on256 23 June x 103andNb#/mdecreasing 3 on 23 back June to 10 and × 103decreasing Nb#/m3 onback 24 June was noted. This exceptional episode has been characterized to 10 x 10 Nb#/m on 24 June was noted. This exceptional episode has been 3 3 using several on-line techniques using characterized to understand the processes several on‐line techniquesinvolved in creatingthe to understand theprocesses bloom ofinvolved bioaerosols that can in creating occurthein bloom water-stressed environments of bioaerosols that can[58]. It turned occur out that during in water‐stressed 2015 the temperature environments in the [58]. It turned outregion that of Saclay was greater by 1.5% from annual seasonal normal temperatures during 2015 the temperature in the region of Saclay was greater by 1.5% from annual seasonal with the month of June being particularly normal hotter and dryer temperatures (+5%) with the than month “normal” of Junerecords. On 23 June ahotter being particularly precipitation and dryer event occurred, (+5%) than and,“normal” at the endrecords. of the rain episode, the concentration of total fungal spores got multiplied On 23 June a precipitation event occurred, and, at the end of the rain episode, by a factor 25. This the observation concentration is consistent with other of total fungal studies spores got showing multiplied thatbyairatemperature factor 25. This is theobservation main physical is parameter driving consistent withAFS otherconcentrations studies showing but isthat not air effective withoutisthe temperature thepresence of waterparameter main physical and most typically, drivinga rain AFSshower which increase concentrations theeffective but is not bioaerosol concentrations without the presence afterofseveral water and hours [59,60]. most typically, a rain shower which increase the bioaerosol concentrations after several hours [59,60]. Remote Sens. 2019, 11, 1671 9 of 29 Remote Remote Sens. Sens. 2017, 2017, 9, 9, x FOR x FOR PEER PEER REVIEW REVIEW 9 9of of3232 Figure 6. Daily Figure Figure 6. 6. variability Daily Daily of of variability fungal variability of spores fungal fungal concentrations spores spores year concentrations concentrations by by year year year by from year year 2014 from from to 2018. 2014 2014 toto 2018. 2018. 3.5. Airborne Fungal Spore Characteristics at Saclay 3.5.Airborne 3.5. AirborneFungal FungalSporeSporeCharacteristics CharacteristicsatatSaclay Saclay To date, the classification regarding the phylum is still under progress due to novel technics ToTo date, date, the the classification classification regarding regarding the the phylum phylum isis still still under under progress progress due due toto novel novel technics technics of identifications [61]. In our study, 30 types of propagules were identified and classified under ofofidentifications identifications[61]. [61].InInour ourstudy, study,3030types typesofofpropagules propaguleswere wereidentified identifiedandandclassified classifiedunder under the Universal Biological Indexer and Organizer (uBio) [62]. The results are reported in Table A4. the the Universal Universal Biological Biological Indexer Indexer and and Organizer Organizer (uBio) (uBio) [62].The [62]. The results results are are reported reported inin Table Table A4. A4. During the period of observation, i.e., from July 2014 to December 2018, the relative proportion of the Duringthe During theperiod periodofofobservation, observation,i.e., i.e.,from fromJulyJuly2014 2014totoDecember December2018, 2018,the therelative relativeproportion proportion Ascomycota (AMC) and Basidiomycota (BMC) at a phylum level were calculated without DNA or ofofthethe Ascomycota Ascomycota (AMC)and (AMC) and Basidiomycota Basidiomycota (BMC) (BMC) atat a phylum a phylum levelwere level were calculated calculated without without RNA analysis in our atmospheric samples. The results were compared with the methodology proposed DNAororRNA DNA RNAanalysis analysisininour ouratmospheric atmosphericsamples. samples.The Theresults resultswerewerecompared comparedwith withthe the by [15] regarding the fungal diversity over land and oceans to determine a biogeography in the air. methodology methodology proposed proposed byby [15] [15] regarding regarding the the fungal fungal diversity diversity over over land land and and oceans oceans toto determine determine The ratio BMC/AMC in our ecosystem is dominated by AMC (Figure 7). This is consistent with the a abiogeography biogeographyininthe theair. air.The Theratio ratioBMC/AMC BMC/AMCininour ourecosystem ecosystemisisdominated dominatedbybyAMC AMC(Figure (Figure predominance of BMC and AMC in the biosphere, where they account for 98% of the known species 7).7).This Thisisisconsistent consistentwith withthethepredominance predominanceofofBMC BMCand andAMCAMCininthe thebiosphere, biosphere,wherewherethey they and also with the Oceanic Degraded climate characteristics of the Saclay site, in reference to marine account account for for 98% 98% ofofthethe known known species species and and also also with with the the Oceanic Oceanic Degraded Degraded climate climate characteristics characteristics sites as reported by [63]. ofofthetheSaclay Saclaysite, site,ininreference referencetotomarine marinesites sitesasasreported reportedbyby[63]. [63]. Figure 7. Relative abundance of principal fungal spores’ phyla from 01 July 2014 to 31 December 2018. Figure Figure 7. 7. Relative Relative abundance abundance ofof principal principal fungal fungal spores’ spores’ phyla phyla from from 0101 July July 2014 2014 toto 3131 December December 2018. 2018. 3.6. Airborne Fungal Spore Diversity at Saclay 3.6. 3.6. Airborne Airborne The FungalSpore mean Fungal SporeDiversity concentrations DiversityatatSaclay Saclay of the 30 atmospheric fungal propagules found at and percentages Saclay are Thereported Themean in Table A5. To meanconcentrations concentrations better and and understand percentages percentages the ofofthe variability the30 of AFS 30atmospheric atmospheric at Saclay fungal fungal and thefound propagules propagules relative found atatSaclay Saclayare arereported reportedininTable TableA5. A5.ToTobetter betterunderstand understandthe thevariability variabilityofofAFS AFSatatSaclay Saclayand andthe the relativeproportion relative proportionofofallergenic allergenicfungal fungalspores, spores,we weanalyzed analyzedthethemajor majorclasses classesofoffungal fungalspore spore Remote Sens. 2019, 11, 1671 10 of 29 Remote Sens. 2017, 9, x FOR PEER REVIEW 10 of 32 proportion of allergenic propagules present in fungal spores, the air. Figurewe analyzedthe 8 displays theobtained major classes patternof which fungaltakes sporeinto propagules account present in the air. Figure 8 displays the obtained pattern which takes into 95% of the total AFS. AMC contributes to 85% of the total AFS, driven by Ascopores account 95% of the total (51%), AFS.Cladosporium AMC contributes (33%),toand 85%Alternaria of the total AFS, (1%), driven while BMC byonly Ascopores (51%), represents 10%Cladosporium (33%), and of AFSIn through the Alternaria (1%), while BMC only represents 10% of AFSIn through the contribution contribution of the Basidiospores (8%) and Tilletiopsis (2%) taxa. Their seasonality was of the Basidiospores and Tilletiopsis (8%)investigated (2%) taxa. (Figure Their seasonality A3, Appendix B) but was the investigated time variable(Figure A3, appear does not Appendix B) but the time to determine the variable does not appear to determine the predominance of AMC in the Saclay environment, predominance of AMC in the Saclay environment, which therefore calls for screening the air which therefore mass calls for screening the air mass origins. origins. Figure 8. Relative abundance of principal fungal spores from 01 July 2014 to 31 December 2018. Figure 8. Relative abundance of principal fungal spores from 01 July 2014 to 31 December 2018. As illustrated in Figure 8, the known allergenic taxa monitored at Saclay, which are Cladosporium and Alternaria, represent in As illustrated 34% of the 8, Figure AFSIn the and in the known AMC phylum. allergenic Clearly, there taxa monitored is a major at Saclay, reservoir which are for allergenic species but, at this stage of development, only genotyping analysis could Cladosporium and Alternaria, represent 34% of the AFSIn and in the AMC phylum. Clearly, reveal what are there the species to be found in the Ascospores and Basidiospores reservoir. is a major reservoir for allergenic species but, at this stage of development, only genotyping analysis could reveal what are the species to be found in the Ascospores and Basidiospores 4. Discussion reservoir. The interannual, seasonal and daily variability of total and specific AFSIn concentrations at Saclay are discussed and compared to the literature. The geographical origins of the main AFS impacting 4. Discussion the observation site were thus assessed and discussed through the use of the Zefir source-receptor The interannual, seasonal and daily variability of total and specific AFSIn concentrations at model. For the purpose of investigating the origins and point sources of AFS impacting Saclay will be Saclay are discussed and compared to the literature. The geographical origins of the main AFS also discussed impacting the observation site were thus assessed and discussed through the use of the Zefir source‐receptor 4.1. Factors Controlingmodel. For the the Airborne purpose Fungal SporeofConcentrations investigating the origins and point sources of AFS impacting Saclay will be also discussed In the ecosystem of Saclay, water is not a limiting factor, which makes air temperature the only driver ofFactors 4.1. the AFSControling seasonality,theasAirborne illustrated by Figures Fungal A4 and A5. This result is in accordance with recent Spore Concentrations findings from [55]. In an attempt to model the seasonal cycle of AFS at Saclay, we explored different calculationIn procedures the ecosystem of Saclay, based on ourwater is not a limiting understanding factor, which of the impact makes of rainfall on air AFStemperature the concentrations. only driver We found that byofnormalizing the AFS seasonality, the monthly asmean illustrated by Figures temperature by theB4monthly and B5.mean Thisprecipitation, result is in accordance with recent findings from [55]. In an attempt to model the seasonal we could reasonably reproduce the seasonal cycle of AFS at Saclay (Figure 9) and values reported cycle of AFS atin Saclay, Table A2. we explored different calculation procedures based on our understanding of the impact of Inrainfall on AFS concentrations. the literature, rain has often beenWe found cited that by most as the normalizing efficientthe monthly agent mean for the wettemperature removal of bioaerosols [64]. However, precipitation alone would possibly be a poor drivingcycle by the monthly mean precipitation, we could reasonably reproduce the seasonal of AFS on parameter at Saclaymodels regression (Figure[35] 9) and for values two main reported in1) reasons Table A2. variation of daily precipitation values does the annual not closely correlate with the concentrations measured during dry or wet conditions, (2) precipitation on a daily basis presents high variability in time and space (rain patches). Therefore, instead of considering precipitation data, some works suggested to follow rain showers as a favorable trigger of PBAP generation [59]. The second BIOaerosol DETECTion campaign in 2015 (BIODETECT 2015) in Saclay provides an interesting illustration of the triggering property of rain showers on PBAPs. As described in Section 3.6, June in 2015 was particularly hot and dry. The AFS concentrations were Remote Sens. 2019, 11, 1671 11 of 29 low for the season compared to 2016 and 2017. A moderate rainfall event occurred on 23 June 2015 providing 4.8 mm of water in four hours (e.g., 1 mm measured is equivalent to 1 L/m2 ). Despite this relatively low amount of water, right after the shower, the concentrations in AFS have been multiplied by 25 from 12,000 Nb#/m3 to 226,056 Nb#/m3 , then went back to 14,000 Nb#/m3 the day after as reported in (Table A6) and illustrated by Figure 10. This event can be typically defined as a “splash dispersal” [65]. These spores are known to be hydrofugal and can be easily dispersed by the impact of raindrops [66]. But more interesting is the proportion of the different species. As expected, Ascospores were dominant; Cladosporium and Alternaria were not affected but Ustilago, Dydimella, and Helicomyces concentrations increased from 0 to 1131 Nb#/m3 , 103 to 232 Nb#/m3 , and 0 to 283 Nb#/m3 , respectively. Ustilago is known to be a cereal and herb pathogen (Table A2) and the strong increase observed coincided with the end of the Poaceae pollen season, as reported by [39]. In this specific period, Poaceae was in early decline due to the very hot climate conditions. The rain shower suddenly favored the release of these fungi which was living and growing in this type of habitat. We also observed that the concentrations of Cladosporium increased slowly to reach a maximum 2 days RemoteThis after. Sens. 2017, 9, x FOR PEER is consistent withREVIEW the study of [67] on the growing processes. 11 of 32 Figure 9. (a) Ratio of temperature/rain in ◦ C/mm (curve) and monthly mean concentrations of fungal Figure 9. (a) Ratio of temperature/rain in °C/mm (curve) and monthly mean concentrations of fungal spores from 1/1/2014 to 31/12/2018; (b) correlation between the temperature/rain and all fungal spores spores Remote from9,1/1/2014 Sens. 2017, x FOR to 31/12/2018; PEER REVIEW (b) correlation between the temperature/rain and all fungal12spores of 32 concentrations (monthly mean). concentrations (monthly mean). In the literature, rain has often been cited as the most efficient agent for the wet removal of bioaerosols [64]. However, precipitation alone would possibly be a poor driving parameter on regression models [35] for two main reasons 1) the annual variation of daily precipitation values does not closely correlate with the concentrations measured during dry or wet conditions, (2) precipitation on a daily basis presents high variability in time and space (rain patches). Therefore, instead of considering precipitation data, some works suggested to follow rain showers as a favorable trigger of PBAP generation [59]. The second BIOaerosol DETECTion campaign in 2015 (BIODETECT 2015) in Saclay provides an interesting illustration of the triggering property of rain showers on PBAPs. As described in Section 3.6, June in 2015 was particularly hot and dry. The AFS concentrations were low for the season compared to 2016 and 2017. A moderate rainfall event occurred on 23 June 2015 providing 4.8 mm of water in four hours (e.g., 1 mm measured is equivalent to 1 L/m2). Despite this relatively low amount of water, right after the shower, the concentrations in AFS have been multiplied by 25 from 12,000 Nb#/m3 to 226,056 Nb#/m3, then went back to 14,000 Nb#/m3 the day after as reported in (Table A6) and illustrated by Figure 10. This event can be typically defined as a “splash dispersal” [65]. These spores are known Figure 10. Effecttoofbe hydrofugal a moderate rainand canon shower bethe easily AFSdispersed by the concentrations on aimpact stressedofoceanic raindrops [66]. degraded Figureinteresting But more climate 10. JuneEffect 2015.of is a moderate rain shower the proportion on the of the AFS concentrations different species. As on a stressedAscospores expected, oceanic degraded were climateCladosporium dominant; June 2015. and Alternaria were not affected but Ustilago, Dydimella, and Helicomyces concentrations increased key Pathogenic AFS are a from concern which 0 to 1131 requires Nb#/m a better 3, 103 to 232understanding Nb#/m3, andof0their control to 283 factors Nb#/m 3, as they Pathogenic need a host respectively. AFS are a key (plantsisorknown Ustilago concern animals) to for which be their requires ownand a cereal a better development. understanding For lower herb pathogen (Table case, of their control A2)Entomophthora and the strong species, factors as they need a host (plants or animals) for their own development. increase observed coincided with the end of the Poaceae pollen season, as reported by [39]. In For lower case, Entomophthora species, this specific period, a lethal Poaceae wasfungal in earlyparasite declineaffecting due to thea type of fly, very hot the Musca climate domestica conditions. [41] The rain was observed for the first time in May 2016 at Saclay, in relatively high shower suddenly favored the release of these fungi which was living and growing in this type concentrations. The presence of habitat.ofWethis alsofungal observed parasite hasconcentrations that the been explained by the entomologist of Cladosporium increasedSandrine slowly toProvots reach a (personal communication) by the late produce egg hatching and larval maximum 2 days after. This is consistent with the study of [67] on the growing processes. formation. Indeed, the Remote Sens. 2019, 11, 1671 12 of 29 a lethal fungal parasite affecting a type of fly, the Musca domestica [41] was observed for the first time in May 2016 at Saclay, in relatively high concentrations. The presence of this fungal parasite has been explained by the entomologist Sandrine Provots (personal communication) by the late produce egg hatching and larval formation. Indeed, the late start of the spring due to cold weather [65] created a delay in the fly reproductions at a regional scale. Consequently, an explosion of flies occurs and, a remarkable proliferation of this pathogen in the early days of May as reported by the French National Survey of Crops contamination [66]. This episode has generated a drastic increase of the atmospheric concentrations of Entomophthora’s fungal spores and they were transported and detected at Saclay far from the point source. It pointed out one more time the indirect role of the local and regional meteorology in the exhalation or inhibition of AFS and particularly the dispersion of specific AFS pathogens with unknown pathogenicity consequences on human and animals. The variability of fungal spore concentrations is therefore strongly dependent on the related ecosystem and on its local and regional meteorological characteristics. In particular, some species undergo strong differences in growth following wet, dry, or “splash” discharges. These processes of discharge can subsequently affect the concentrations of the chemical tracers in the PM10 fraction. Moreover, to better understand the effects of rainfall events on fungal spore diversity, abundance and dispersal processes, models need to integrate rain and temperature as well as dew point data at the finest resolution possible. 4.2. Interannuality and Global Increase of Specific AFSIn Concentrations The total concentrations of AFS are in general analyzed in the literature in a context of global warming and for allergenic purposes [12,53,67]. It is admitted today that fungal spores are very sensitive to temperature and water (e.g., relative humidity and rain for their proper development [15]. It is the case on Earth but also in confined and much stressed environments like the International Space Station [68]. On a year-to-year basis, the results of our 5-year study display strong variations in the interannuality of the five main fungal taxa spores identified (Figure 11). We also analyze Ustilago as it is a strong plant pathogen on culture [36] and present on the strong allergenic Poaceae pollen Remote Sens. 2017, 9, x FOR PEER REVIEW 13 of 32 Figure11. Figure AFSIn: 11.AFSIn: Ascospores, Ascospores, Cladosporium, Cladosporium, Alternaria, Alternaria, Ustilago, Ustilago, Tilletiopsis, Tilletiopsis, andand Basidiospores Basidiospores from from 2014 2014 to 2018. to 2018. WeWe investigated investigated the theinfluence influenceofofthe the meteorological conditionsthat meteorological conditions thatthen thenoccurred occurredin in France. France. During Duringourour period of of period observation observation indeed, 2015 indeed, and 2015 2018 and where 2018 wherehothot and dry. and In In dry. more moredetails, 2015 details, was 2015 characterized by a deviation of +1.5 ◦ C compared to the seasonal normal and a loss close to 20% in was characterized by a deviation of +1.5 °C compared to the seasonal normal and a loss close to the20% totalinprecipitations all over theallcountry. the total precipitations The over the year 2018 country. Thewasyearthe hottest 2018 was since 1959 insince the hottest France 1959with in a measured anomaly France with of +1.4 anomaly a measured ◦ ◦ C (+2 C of in the +1.4continental °C (+2 °C part alone) in the and waspart continental relatively alone) dry andallwas over relatively dry all over France (global loss in precipitation of 10%). In the north half of the country, including Saclay, a loss of more than 20% in the total rain water was observed [69]. In contrast, 2014 was the second hottest year after 2018 but benefited in the northern half of France from an additional 20% of precipitations. The years 2016 and 2017 stood in the normal seasonal range (temperature and rain), but 2017 was hot and humid in the northern part of France and quite Remote Sens. 2019, 11, 1671 13 of 29 France (global loss in precipitation of 10%). In the north half of the country, including Saclay, a loss of more than 20% in the total rain water was observed [69]. In contrast, 2014 was the second hottest year after 2018 but benefited in the northern half of France from an additional 20% of precipitations. The years 2016 and 2017 stood in the normal seasonal range (temperature and rain), but 2017 was hot and humid in the northern part of France and quite close to what it has been observed for 2014. The general decrease of the concentration observed in the AFSIn was mainly due to the predominance of Ascospores in the Saclay ecosystem. Cladosporium shows moderate biannual cycle compared to the other taxa studied. Ustilago and Basidiospores presented similar patterns: (1) a strong biannual cycle and (2) strong sensitivity to hot temperature while Tilletiopsis concentrations are strongly affected by hot weather. Interestingly, given they are all BMC and that continental climate is expected to be hotter, our observations suggest one hypothesis of the enrichment in the ratio BMC/AMC in continental ecosystem. The effect of the Da of the fungal propagules as proposed by [15] is perhaps not the only factor controlling the ratio BMC/AMC and could change in the future. Moreover, the growing kinetics is strongly dependent on temperature as reported by [70]. To understand the differences between the annual variability of the different taxa, we investigated the impact in the cycle of the land use in “Big Cultures” which include wheat, maize, ragweed, onions, and tomatoes [71]. We did not find any correlation in the annual cycles, but evidenced a probable link with the general increasing trend in the total agricultural production for humans. This last point supports idea that the presence of agricultural fields is not a limiting factor for the emission of AFS in the air, unlike rain and temperature. These differences from-to-year can explain the differences observed in the individual ratio of BMC and AMC as illustrated by the Figure A3. Preliminary conclusions point out, as expected, that all taxa do not have the same response to an elevation of temperature and rainfalls—in particular, Ascospores and Tilletiopsis seem to be very sensitive to temperature and hydrological stress. It is noticeable that, since 1990, temperature in France has always stood between +0.5 and +1.4◦ C, up +2 ◦ C in some specific regions, over the normal annual mean computed from 1981 to 2010, as reported in [69]. Consequently, our 5-year study displays a strong interannuality of the monitored taxa. Cladosporium, Ustilago, and Basidiospores are characterized by a bi-annual cycle in accordance with that previously reported [53,67]. The general decrease observed in the total AFS is mainly due to the predominance of Ascospores followed by Basidiospores. 4.3. Seasonality of BMC and AMC Continuous measurements of AFS are important to increase the knowledge as the seasonality can be different in time and in variability as illustrated by Figure A6. In the study, as Saclay is not referenced as a marine or coastal site, it was expected a higher abundance for BMC. One explanation for this observation can be found in the size properties of AFS. BMC are expected to have shorter atmospheric residence times, therefore are unsuitable to undergo long-range transport. The BMC/AMC ratio is thus expected to decrease with increasing distance from the sea. As Saclay is 250 km from the ocean and impacted by marine air masses it would mean that the distance effect would need to be greater than 250 km in order to see a difference in the BMC/AMC ratio as reported by [15]. During our five-year observation period, we noticed that the ratio of BMC/AMC was changing during the season (Figure 12). This result does not affect the global ratio of BMC/AMC (e.g., Figure 7) but points out (1) the importance to consider BMC/AMC ratios by season for one given ecosystem and (2) to have a better understanding of the BMC/AMC ratio as a function of environmental considerations as proposed by [15]. In 2017, [72] showed similar and finer results regarding the seasonality of BMC/AMC using DNA analysis in several northern Europe locations and the observations suggested that the biodiversity of fungal spores can be observed after 900 km from an ecosystem to another one with consequences on the variation in the BMC/AMC ratios. BMC/AMC ratios by season for one given ecosystem and (2) to have a better understanding of the BMC/AMC ratio as a function of environmental considerations as proposed by [15]. In 2017, [72] showed similar and finer results regarding the seasonality of BMC/AMC using DNA analysis in several northern Europe locations and the observations suggested that the biodiversity of11, Remote Sens. 2019, fungal 1671 spores can be observed after 900 km from an ecosystem to another one 14 of 29 with consequences on the variation in the BMC/AMC ratios. Figure Figure 12.12. Seasonality Seasonality ofofthe AMC the (red(red AMC curve) and Basidiomycota curve) (BMC) (BMC) and Basidiomycota (blue curve) (blueincurve) mean in mean concentrations. concentrations. In In the the literature literature itit has has been been found found that that emissions emissionsof offungal fungalspores sporesfrom fromthe theoceans oceans(10 (10Mg Mg per per year)year) are several are several orders orders of magnitude of magnitude smaller smaller than than fromfrom inland inland surfaces surfaces (30–50 (30–50 TgTg perper year) year) [18,19]. [18,19]. Emissions Emissions are characterized are characterized by specificby specific BMC/AMC BMC/AMCratios: ratios: high ratios high ratios of BMC/AMC of BMC/AMC in or in marine marine or coastal coastal sites sites and low and of ratios low ratios of BMC/AMC BMC/AMC in continental in continental sites [15]. The sites [15].richness phyla The phyla richness of BMC has been of BMC hasregarding explained been explained the sizeregarding of the AFS. theBMCsize of arethe AFS. BMC considered toare be 5considered to 10 µm (D toabe ) in5size to 10and μmAMC (D ) inbetween to abe size and2 AMC and 5 µm to be(Dbetween a ). BMC 2isand 5 μm (D enhanced ). BMC inathe coarseis enhanced fraction (>2.5in the coarse µm), fraction whereas the taxa (>2.5 richnessμm),of whereas the taxa richness AMC is enhanced in the fineoffraction AMC is(<2.5 enhanced µm) ofin the fine fraction continental (<2.5 μm) air particulate of due matter continental air particulate matter due to their shorter atmospheric residence to their shorter atmospheric residence times. Our observation suggests that additional processes are times. Our observation involved in suggests that additional relation with temperature, processes rainfallareandinvolved in relation the ecosystem with temperature, characteristics, rainfall land in particular and use. the ecosystem characteristics, As continental areas present higher in particular temperatureland levels use. As continental during spring areas present than and summer higher marine temperature areas, Ascosporeslevelsor during spring and Basidiospores summer than proliferation can marine areas, In be inhibited. Ascospores or Basidiospores the same time, cereal and herbs proliferation can bein inhibited. are largely present continentalInregions the samewhich time, cerealincrease possibly and herbs are largely the presence present of BMC in like spores continental Ustilago andregions which possibly other Basidiospores. Asincrease describedtheby presence [54] in theof BMC regionspores like Ustilago of Madrid (hot andand dryother summer), Basidiospores. during the MSS, AsAMC described were bynot[54] in the regiondue predominating of Madrid to hot and(hot dry andweather. dry summer),Another during the preliminary MSS, AMCsuggests conclusion were not that predominating due is the size of the spores tonothottheand onlydry weather. factor that can Another explain the preliminary variation in the ratio of BMC/AMC. Other drivers could be (1) the climate of the ecosystem studied by determining the proportion of the main phyla and also the proportion in species in the same phyla and (2) the land use related to the culture of cereals or the presence of herbs—France among the European countries has the largest crops mainly located in the North and in its continental part (e.g., Figure 1) followed by Germany [73]. This aspect should be taken into account in the future in a context of global warming regarding the potential formation of GCCN, CCN, and IN [9,74]. 4.4. Geographical Origins of AFS Impacting the Observation Site The calculations using ZeFir from this 5-year dataset showed an interesting result regarding the general origin of AFS concentrations. The model designates a main origin from the northwest sector, that is, independent from the SW prevailing winds (Figure 2), however still in the general “wet” W sector, which mostly carries marine air masses. Two point-sources were identified, the major one is associated with wind speeds ranging from 10 km/h to 13 km/h and a minor one with wind speeds of 17 km/h (Figure 13). We also evaluate the geographical origin of total AFS by running the model year to year as illustrated by Figure A7 to investigate the effect of the meteorology on the Saclay ecosystem. sector, that is, independent from the SW prevailing winds (Figure 2), however still in the general “wet” W sector, which mostly carries marine air masses. Two point‐sources were identified, the major one is associated with wind speeds ranging from 10 km/h to 13 km/h and a minor one with wind speeds of 17 km/h (Figure 13). We also evaluate the geographical origin of total AFS by Sens. Remote running the1671 2019, 11, model year to year as illustrated by Figure A7 to investigate the effect of15the of 29 meteorology on the Saclay ecosystem. N0 ESTIMATED CONCENTRATION 600 20 NW 315 NE 45 Total_FS concentration 500 15 10 400 5 300 W 270 0 5 10 15 90E 200 5 10 15 20 100 0 SW 225 SE 135 180 S Figure Origin 13. 13. Figure of total Origin atmospheric of total atmosphericAFSAFS using sustained using wind sustained incidence wind method incidence (SWIM) method (SWIM)model model Origin. The dotted white circles represent the wind speed scale in kilometer per hour (km/h). Origin. The dotted white circles represent the wind speed scale in kilometer per hour (km/h). The colorThe gridcolor represents the estimated grid represents concentration the estimated (Nb#/m3 )(Nb#/m concentration for any3)wind speed for any and wind windand speed direction. wind direction. Figure 14 reveals that Saclay is impacted by both local and regional sources. Among the main Figure 14 reveals that Saclay is impacted by both local and regional sources. Among the fungal spores (95% of the species present in the air Section 2.2) Ascospores and Basiodospores which are main fungal spores (95% of the species present in the air Section 2.2) Ascospores and both wet spores are clearly originating from different areas: North West for Ascospores (marine/costal Basiodospores which are both wet spores are clearly originating from different areas: North area) and North East for Basiodospores (continental area). Cladosporium and Tilletiopsis were clearly West for Ascospores (marine/costal area) and North East for Basiodospores (continental area). originating from the NW sector. For Alternaria, the results pointed out a regional origin, possibly Cladosporium and Tilletiopsis were clearly originating from the NW sector. For Alternaria, the related to large agricultural parcels of rapeseed and sun flower (Figure A8) as Alternaria is known to be results pointed out a regional origin, possibly related to large agricultural parcels of rapeseed a plant pathogen ofx those crops [73,75]. and sun Remote Sens.flower 2017, 9,(Figure A8)REVIEW FOR PEER as Alternaria is known to be a plant pathogen of those crops [73,75]. 16 of 32 Figure Origin 14.14. Figure Originofoftotal totalatmospheric atmosphericfungal fungalspores spores concentration of Ascospores concentration of Ascospores(a), Cladosporium(b), (a),Cladosporium (b),Basidiospores Basidiospores(c), Tilletiopsis(d), (c),Tilletiopsis (d),and and Alternaria Alternaria (e)(e) using using SWIM SWIM model model forfor July July 2014 2014 to December to December 2018. 2018. TheThe white white circles circles representthe represent thewind windspeed speed scale scale in kilometer kilometer per perhour hour(km/h). (km/h).TheThecolor grid color grid represents thethe estimated concentration (Nb#/m 3 ) for anyany wind speed and wind direction. represents estimated concentration (Nb#/m 3) for wind speed and wind direction. 4.5. Case of Studies: The Geographical Origins of Plant or Human Pathogens 4.4. Case of Studies: the Geographical Origins of Plant or Human Pathogens To go further in our understanding on the geographical origin of specific AFS we ran the model To go further in our understanding on the geographical origin of specific AFS we ran the for eight species representing 65 % on the remaining 5% of the AFS (Figure A9), thus less abundant model for eight species representing 65 % on the remaining 5% of the AFS (Figure A9), thus less but determining as most of them being referenced as human or plant pathogens. The result obtained abundant but determining as most of them being referenced as human or plant pathogens. The forresult Aspergillaceae, obtained forGanoderma, Myxomycetes Aspergillaceae, Ganoderma, Ustilago depicts andMyxomycetes anda clear North Ustilago to North depicts East a clear origin North as illustrated by Figure 15. Aspergillaceae and Myxomycetes are two taxa known to grow in open to North East origin as illustrated by Figure 15. Aspergillaceae and Myxomycetes are two taxa forest on decaying known to grow material (Table on in open forest A4),decaying while thematerial habitat of Ustilago (Table A4),iswhile known tohabitat the be Poaceae and cereals, of Ustilago is known to be Poaceae and cereals, as already mentioned. This happens to be in close agreement with our results on the geographical origin of Betulaceae and Poaceae pollen grains impacting Saclay [39]. To go further in our understanding on the geographical origin of specific AFS we ran the model for eight species representing 65 % on the remaining 5% of the AFS (Figure A9), thus less abundant but determining as most of them being referenced as human or plant pathogens. The result obtained for Aspergillaceae, Ganoderma, Myxomycetes and Ustilago depicts a clear North to North East origin as illustrated by Figure 15. Aspergillaceae and Myxomycetes are two taxa Remote Sens. 2019, 11, 1671 16 of 29 known to grow in open forest on decaying material (Table A4), while the habitat of Ustilago is known to be Poaceae and cereals, as already mentioned. This happens to be in close agreement as with already ourmentioned. results on theThis happens toorigin geographical be in close agreement of Betulaceae with and our results Poaceae pollenon the geographical grains impacting origin of Betulaceae Saclay [39]. and Poaceae pollen grains impacting Saclay [39]. Figure Geographical 15.15. Figure Geographicalorigin of Aspergillaceae, origin of Aspergillaceae,Ganoderma, Ganoderma, Myxomycetes Myxomycetesand Ustilago and using Ustilago SWIM using SWIM model from model July from 2014 July to December 2014 to December 2018. The 2018. white The circles white represent circles thethe represent wind speed wind speed scale in in scale kilometer kilometer perper hour (km/h). The color grid represents thethe estimated concentration (Nb#/m 3 ) for any wind speed hour (km/h). The color grid represents estimated concentration (Nb#/m 3) for any wind speed and wind direction. and wind direction. We also run the model for Didymella, Helicomyces, Botryris, and Entomophthora, which ended to We also run the model for Didymella, Helicomyces, Botryris, and Entomophthora, which ended originate from the NW and the SSW sectors (Figure 16). As reported in Table A3, Didymella and to originate from the NW and the SSW sectors (Figure 16). As reported in Table A3, Didymella Helicomyces can grow on barley and corn leaves. By comparing the geographical origin computed by and Helicomyces can grow on barley and corn leaves. By comparing the geographical origin the model and the map of barley and corn fields in France (Figure A9) we observed that the model computed by the model and the map of barley and corn fields in France (Figure A9) we observed accounted for the sources of these two fungi. In particular, Helicomyces originated from SSW brought by strong winds ranging from 15 to 20 kmh. This result is of importance for the optimization of the Zefir model to localize more precisely specific point sources by comparing the distance of the source and the spots founded by the model as a function of wind speed in km/h. Botrytris is polyphagous and the main point source was identified on the NW sector. We attempted to identify the geographical origin of Entomophthora and we found a regional origin in the NW sector. We also observed the limits of the model when the data set used to find the origin and point sources is too limited (only two days in 2016). The use of pesticides on large agricultural parcels can inhibit the production of AFS by the crops but have no significant disturbance on production from forests or prairies, allowing the natural growth of specific pathogens. The hypothesis regarding the use of chemicals as a control factor of the proliferation of fungi on crops needs to be investigated in more details in future works, since our work suggests a same point source for Ustilago and Poaceae pollen. This last point must be taken in account for cross contamination in land in rotating land use [76]. the optimization of the Zefir model to localize more precisely specific point sources by comparing the distance of the source and the spots founded by the model as a function of wind speed in km/h. Botrytris is polyphagous and the main point source was identified on the NW sector. We attempted to identify the geographical origin of Entomophthora and we found a regional origin in the NW sector. We also observed the limits of the model when the data set Remote Sens. 2019, 11, 1671 17 of 29 used to find the origin and point sources is too limited (only two days in 2016). Figure 16. Geographical origin of Didymella, Helicomyces, Botrytis, and Entomophthora using SWIM Figure 16. Geographical origin of Didymella, Helicomyces, Botrytis, and Entomophthora using SWIM model for July 2014 to December 2018. The white circles represent the wind speed scale in kilometer model for July 2014 to December 2018. The white circles represent the wind speed scale in kilometer per hour (km/h). The color grid represents the estimated concentration (Nb#/m3 ) for any wind speed per hour (km/h). The color grid represents the estimated concentration (Nb#/m3) for any wind speed and wind direction. and wind direction. 5. Conclusions The use of pesticides on large agricultural parcels can inhibit the production of AFS by the The objectives of this work were, firstly, to understand the atmospheric variability of the main crops but have no significant disturbance on production from forests or prairies, allowing the airborne fungal spores (AFS) present at Saclay, a suburban area of Paris and, secondly, to apply a natural growth of specific pathogens. The hypothesis regarding the use of chemicals as a control source receptor model to attempt identifying their geographical origin. Several case studies served factor of the proliferation of fungi on crops needs to be investigated in more details in future to investigate the link between land uses and production of AFS. In the Saclay oceanic degraded works, since our work suggests a same point source for Ustilago and Poaceae pollen. This last ecosystem, 30 fungal airborne spore taxa were identified following palynological methods. In relation point must be taken in account for cross contamination in land in rotating land use [76]. with the classification used in this work, Ascomycota (AMC) were predominant (89%) followed by Basidiomycota (BMC, 10%). Ascospores were the main taxa (51%), then Cladosporium (33%), 5. Conclusions Basidiospores (8%), Tilletiopsis (2%), and Alternaria (1%). We observed a strong interannuality and The decrease a general objectivesinofthethistotal work were, AFSi firstly, to understand concentrations over our the atmospheric 5-year variability study, mainly driven of bythethe “wet spores” Ascospores and Basidiospores, while the “dry spores” Cladosporium and Alternariatohad main airborne fungal spores (AFS) present at Saclay, a suburban area of Paris and, secondly, apply a source increasing trends. receptor model to The year-to-year attempt resulted variability identifying fromtheir geographical hot/cold temperature origin. Several combined case to dry/wet studies served to investigate the link between land uses and production of conditions. The AFS concentrations showed a clear bimodal seasonal cycle and the main spores season AFS. In the Saclay oceanic (MSS) degraded is strongly ecosystem, affected 30 fungal by meteorological airborne factors spore taxaand like temperature were identified rainfall. A meanfollowing of 11 ◦ C has palynological been found to allowmethods. In relation the start with the of the season andclassification used in the first maximum this work, occurs in JulyAscomycota (32,000 Nb#/m (AMC) 3 ) while were the predominant second (89%) followed occurs in October by Basidiomycota (17,000 Nb#/m 3 ) as illustrated (BMC, 10%). in Figure 4. Ascospores The BMC/AMC werealso theshowed main a taxa (51%), then Cladosporium (33%), Basidiospores (8%), Tilletiopsis (2%), and Alternaria seasonality suggesting that the size of the spores is not the only factor that can explain the variation in (1%). We the observed aratio. BMC/AMC strongPreliminary interannuality andpointed results a generalout decrease in thepropagules that fungal total AFSi concentrations do not have the oversame our 5‐year study, mainly driven by the “wet spores” Ascospores and Basidiospores, response to an elevation of temperature or can be very sensitive to temperature and hydrological while the “dry spores” stress. We also Cladosporium pointed out that andthey Alternaria do nothadhaveincreasing the same trends. The year‐to‐year geographical origin and thisvariability last point resulted from hot/cold temperature combined to dry/wet conditions. The AFS could explain the biogeography of fungal spores in the air. The source receptor model Zefir, has been concentrations successfully applied to identify the geographical origin of the total AFS impacting the observation site. Most of the fungi originated from the NW sector, hence not transported by the prevailing SW winds. The model was also applied to detect the origin of the major classes and several species of AFS. The results obtained showed that this user-friendly tool kit is accurate enough to locate individual source points and relate them to land cover and land use. The agricultural practice should be taken into account as some species undergo strong differences in growth following wet, dry, or “splash” discharges. These processes of discharge can subsequently affect the concentrations of chemical tracers Remote Sens. 2019, 11, 1671 18 of 29 in the PM10 fraction. As a recommendation, our results hence suggest that all sites dedicated to air quality monitoring providing measurement of chemical tracers in the PM10 fraction should be calibrated by measurements of AFS with the spore trap technic. Consequently, to better understand the effects of rainfall events on fungal spore diversity, abundance and dispersal processes, models need to integrate rain and temperature as well as dew point data at the finest resolution possible. To-date, the consequences of global warming on fungal growth and spore production, like on allergenic pollen production, is not documented enough. Recent studies showed similar implications for respiratory tract diseases in humans. Our results pointed out that release of fungal spores so-called wet or dry showed strong and significant interannual differences. Our hypothesis is that in following years we will observe increased levels in allergenic fungal spore production as well as changes in species diversity. This study suggests that further research is needed to revise the grouping system of fungal spores as either “dry” or “wet” and simple BMC/AMC ratios and their response to climate change. Moreover, as the Paris region is impacted by severe chemical pollution events from different origins, it is of interest to understand, in future studies, (1) how atmospheric pollutants can exacerbate the human allergenic response when fungal spores are present in the air at high concentrations during air advection from “splash dispersal”, (2) the interaction between pollen and fungal spores should be studied together as we observed that Ustilago has the same source point of the Poaceae pollen and they are together showing general decreasing concentrations, (3) to identify at a genus level all the fungal spore diversity present at the Saclay observatory. Author Contributions: R.S.E. has pioneered bioaerosol research at CEA/LSCE, he has conceived and designed the experiments and has lead the writing of this paper. D.B. has participated to the conception and design of the experiments, she has analyzed the data and contributed to the writing of this article. B.G. contributed to the interpretation of the dataset in correlation with pollen, health impact and writing of the paper. J.S., D.O., J.B., and J.P.B. contribute to supervise the science on fungal spores research and writing of the paper. J.-E.P. has developed the ZeFir tool and contributed to the interpretation of the outputs of wind analysis. M.T., G.O., C.S., and V.G. have contributed to the interpretation of the data, complete the data set and writing of the paper. Funding: This research was funded by CEA-NRBC-E grant number D34 and the APC was funded by STAE Foundation IRT Saint-Exupéry grant name: FUNGONAUTE. Acknowledgments: The authors want to thank all the team from the Réseau National de Surveillance Aérobiologique (RNSA) and particularly Nadine Dupuy and Jennifer Charbonnier. We are grateful to Francois Truong and Virgile Berson from LSCE for their help during the experiments. Conflicts of Interest: The authors declare no conflict of interest. Appendix A Table A1. Values of monthly averages of fungal spores concentrations from January 2014 to December 2018. 2014–2018 (Nb#/m3 ) Month Mean Max Min January 63,355 99,765 30,315 February 71,159 174,322 18,289 March 82,535 222,756 27,654 April 116,672 297,432 46,570 May 380,782 469,114 235,434 June 655,789 1,170,577 446,747 July 834,069 1,838,943 247,726 August 470,549 711,466 326,907 September 373,688 472,874 258,106 October 440,451 678,167 251,567 November 243,986 437,597 157,354 December 103,865 154,363 66,760 Remote Sens. 2019, 11, 1671 19 of 29 Table A2. Values of monthly averages temperature (◦ C) and relative humidity (RH%) for each year from 2014 to 2018. 2014–2018 Month Mean AFS (Nb#/m3 ) Mean T(◦ C) Total Rain (mm) Ratio (T/R) January 63,355 4.4 276 0.0158 February 71,159 4.3 194 0.0220 March 82,535 7.7 270 0.0283 April 116,672 11.3 221 0.0511 May 380,782 14.6 430 0.0340 June 655,789 18.4 276 0.0668 July 834,069 20.7 189 0.1092 August 470,549 19.7 302 0.0655 September 373,688 15.3 233 0.0655 October 440,451 11.7 182 0.0643 November 243,986 7.8 289 0.0272 December 103,865 5.8 262 0.0220 Table A3. Values of concentration in fungal spores and date of start and end season for each year from July 2014 to December 2018. 2014 2015 2016 2017 2018 Total AFS (Nb#/m3) 5,395,236 2,982,527 4,038,344 4,351,542 2,465,766 5% 160 149,126 201,917 217,577 123,288 95% 4,500,000 2,833,401 3,836,427 4,133,965 2,342,478 Start Season 5 Apr 30 Apr 05 Apr 23 Mar 01 May End Season 15 Nov 19 Nov 21 Nov 16 Nov 27 Dec Remote Sens. 2019, 11, 1671 20 of 29 Table A4. Physical characteristics of the 30 propagules types identified at Saclay (A = Ascospore, B = Basidiospore, C = Conidia, M = Myxomycetes, S = Sporangiospore, T = Teliospore, U = Urediniospore) adapted from the Universal Biological Indexer and Organizer, [61]. Name Phylum Spore Type Optical Spore Size (µm) Habitat Alternaria Ascomycota C 20–80 Soil, plants, and vegetables, mainly develop on decaying plants, especially cereals and hay. Ascospores Ascomycota A 15–40 All the sexually produced fungal spores formed within an ascus. Aspergillaceae Ascomycota A 2–10 Everywhere in nature (e.g., soil, decaying organic debris, and compost), in grains storage areas. Basidiospores Basidiomycota B 5–15 Mainly in forests and woodlands, especially in the autumn. Botrytis Ascomycota C 5–10 Ubiquitous, polyphagous, and necrotrophic, also abundant in soil. Cercospora Ascomycota C 2–5 × 50–325 Leaf parasites of chard, sugar beet, carrot, lettuce, and maize. Chaetomium Ascomycota A 10 On plant debris, soil, straw, and dung, also on wooden products. Cladosporium Ascomycota C 4–11 Saprophyte, on soil, plants, and cereals, colonizes very varied substrates (foodstuff, paper, textile, etc.). Didymella Ascomycota A 3–15 × 1–4 On the leaves of barley and wheat, berries and vegetable cultivation (e.g., peas, tomatoes, gherkins, and cucumbers). Entomophthora Zygomycota C 11–18 × 8–15 On flies, causes a fatal disease. Epicoccum Ascomycota C 20 On soil and senescent, dying or dead plants, especially cereals, beans, potatoes, peas, and peaches. Erysiphe Ascomycota C 30–60 On plants, pathogens which cause powdery mildew. Fusarium Ascomycota C 25–68 × 3–6 Mainly in cereal crops (grains, straw, and hay), also invade fresh fruits and vegetables. Fusicladium Ascomycota A On plants, and most are pathogens. Ganoderma Basidiomycota B 6–10 At the base and on stumps of deciduous trees, (oak, beech, and poplar), also on the roots of some fruit trees. Helicomyces Ascomycota A and C 70–140 × 2–3 and 14–21 Most are aquatic, on marsh plants, on dead leaves, on grass stems, or on shelled wood. Helminthosporium Ascomycota C 40–118 × 11–20 In humid areas, on grasses (above all barley and corn), also on dead branches and fallen branches of most trees and shrubs. Myxomycetes Mycetozoa M 5–24 In open forests, on deadwood, the bark of living trees, rotting plant material, soil and animal excrements. Nigrospora Ascomycota C 13–15 × 10–13, 18–21 × In air, soil, various decaying plants, and some cereal grains; it is rarely found growing indoors. 14–15, 18–24 Peronospora Chromista S 15–35 Plant pathogens of herbaceous dicotyledonous plants, (mildew). Pithomyces Ascomycota C 15–25 Ubiquitous on soil, also on dead leaves and fodder grasses, occasionally in indoor environment. Pleospora Ascomycota A 30–33 × 14–15 A plant pathogen with a cosmopolitan distribution, infecting all kinds of herbaceous debris and crops. Polythrincium Ascomycota C 5 × 1.5 On leaves, especially on leaves of red clover Sporobolomyces Basidiomycota C 5–25 Yeast in air, on humans, mammals, birds, the environment, and plants. Stemphylium Ascomycota C 22–35 In the agricultural environment, on dead tissues, animal or plant fibers (straw), also parasitic plants. Tilletiopsis Basidiomycota T 1–3 Parasites of flowering plants. Torula Ascomycota C 3–4 Yeast, on stems of dead herbaceous plants and on the leaves of barley and mature wheat. Trichothecium Ascomycota C 8–10 × 12–18 Cosmopolitan, in various habitats ranging from leaf litter to fruit crops. Uredospores Basidiomycota U 15–25 Parasite of many plant families, cereals are the most common hosts. Ustilago Basidiomycota T 5–10 Parasite of herbs (Poaceae), mainly cereals (maize and teosinte). Remote Sens. 2019, 11, 1671 21 of 29 Table A5. Mean concentrations and percentages of the 30 fungal propagules identified through a light microscope at Saclay from July 2014 to December 2018. 2014–2018 2014 2015 2016 2017 2018 Avg Avg Avg Avg Avg Avg (Nb#/m3) (%) (Nb#/m3) (%) (Nb#/m3) (%) (Nb#/m3) (%) (Nb#/m3) (%) (Nb#/m3) (%) Alternaria 257 2.377 277 1.386 63 0.746 106 0.958 202 1.696 115 1.604 Ascospores 5568 51.548 11,560 57.881 3418 40.519 6888 62.082 5784 48.474 2686 37.407 Aspergillaceae 181 1.679 124 0.620 87 1.031 104 0.942 145 1.217 88 1.222 Unidentified 118 1.097 51 0.254 36 0.422 21 0.190 12 0.098 3 0.046 Basidiospores 960 8.887 1284 6.427 1099 13.030 449 4.049 1243 10.413 213 2.969 Botrytis 109 1.011 56 0.282 17 0.200 98 0.885 27 0.227 20 0.284 Cercospora 52 0.480 6 0.030 6 0.069 11 0.099 11 0.090 6 0.081 Chaetomium 33 0.306 0 0.001 1 0.012 1 0.006 0 0.003 1 0.016 Cladosporium 3731 34.544 5795 29.015 3289 38.989 2784 25.089 3888 32.585 3644 50.744 Didymella 190 1.760 4 0.022 31 0.364 56 0.501 89 0.744 47 0.657 Entomophthora 52 0.481 0 0.000 0 0.000 0 0.003 0 0.000 0 0.000 Epicoccum 80 0.743 77 0.384 26 0.305 24 0.217 28 0.238 17 0.239 Erysiphe 65 0.602 19 0.096 26 0.302 22 0.199 10 0.085 9 0.130 Fusarium 36 0.337 0 0.001 3 0.034 2 0.019 1 0.012 0 0.002 Fusicladium 34 0.314 3 0.017 4 0.045 1 0.010 2 0.018 2 0.025 Ganoderma 131 1.212 141 0.704 65 0.772 64 0.575 74 0.619 68 0.948 Helicomyces 225 2.084 15 0.077 38 0.453 58 0.523 61 0.510 60 0.837 Helminthosporium 32 0.297 1 0.007 1 0.010 0 0.002 1 0.008 0 0.006 Myxomycetes 92 0.854 44 0.220 19 0.220 28 0.252 40 0.334 44 0.609 Nigrospora 29 0.266 1 0.007 1 0.016 0 0.001 1 0.006 0 0.006 Peronospora 45 0.420 16 0.078 2 0.021 2 0.017 5 0.040 2 0.022 Pithomyces 136 1.260 21 0.103 5 0.055 12 0.107 11 0.095 15 0.203 Pleospora 52 0.481 0 0.000 0 0.004 0 0.002 0 0.000 0 0.000 Polythrincium 40 0.373 22 0.109 3 0.030 3 0.029 6 0.048 2 0.026 Sporobolomyces 160 1.477 43 0.218 15 0.179 0 0.000 0 0.001 0 0.000 Stemphylium 29 0.272 2 0.010 1 0.008 1 0.007 1 0.010 0 0.006 Tilletiopsis 393 3.640 291 1.457 103 1.221 306 2.758 231 1.932 98 1.360 Torula 49 0.456 23 0.115 9 0.112 12 0.105 12 0.103 7 0.101 Trichothecium 49 0.453 1 0.004 0 0.000 1 0.007 0 0.001 0 0.000 Uredospores 64 0.595 44 0.221 8 0.093 15 0.133 8 0.069 8 0.109 Ustilago 120 1.109 51 0.256 62 0.737 26 0.234 39 0.324 25 0.343 RemoteTable Sens. 2019, A6. 11, 1671concentrations (Nb#/m3) of the five principal propagules during the episode of June Daily 22 of 29 2015. Table A6. Daily concentrations (Nb#/m3) of Basidiospores Alternaria Ascospores the five principalCladosporium propagules during the episode of Tilletiopsis June 2015. 17/06/2015 0 746 52 3907 26 18/06/2015 Alternaria 0 Ascospores 1311 Basidiospores 78 Cladosporium 2313 Tilletiopsis 257 17/06/2015 19/06/2015 0 78 746 6194 052 3907 5989 26 1106 18/06/2015 0 1311 78 2313 257 20/06/2015 19/06/2015 78 78 1157 6194 1030 5166 5989 155 1106 20/06/2015 21/06/2015 78 103 1157 6888 103 180 5166 7068 155 2699 21/06/2015 103 6888 180 7068 2699 22/06/2015 22/06/2015 26 26 5397 5397 257 257 5269 5269 206 206 23/06/2015 23/06/2015 00 205,755 205,755 7878 13,133 13,133 1799 1799 24/06/2015 0 1208 155 16,037 309 24/06/2015 25/06/2015 780 1208 1311 155 129 16,037 18,068 30952 26/06/2015 25/06/2015 257 78 1028 1311 52 129 16,962 18,068 520 27/06/2015 52 360 26 13,005 0 26/06/2015 257 1028 52 16,962 0 27/06/2015 52 360 26 13,005 0 Appendix B Appendix B Remote Sens. 2017, 9, x FOR PEER REVIEW 2 of 32 Figure A1. Annual fungal spores integral (AFSIn) from July 2014 to December 2018. Figure A1. Annual fungal spores integral (AFSIn) from July 2014 to December 2018. Remote Sens. 2019, 9, x; doi: FOR PEER REVIEW www.mdpi.com/journal/remotesensing Figure A2. Seasonality of AFS by year (a) 2014, (b) 2015, (c) 2016, (d) 2017, and (e) 2018. Figure A2. Seasonality of AFS by year (a) 2014, (b) 2015, (c) 2016, (d) 2017, and (e) 2018. Remote Sens. 2019, 11, 1671 23 of 29 Figure A2. Seasonality of AFS by year (a) 2014, (b) 2015, (c) 2016, (d) 2017, and (e) 2018. Figure Figure A3.A3. Monthlyabundance Monthly abundanceofofmajority majoritytypes types of of fungal fungal spores fromfrom 01/07/2014 01/07/2014 to to31/12/2018: 31/12/2018:(a) (a) January, January, (b) (b) February, February, (c) (c) March, March, (d) (d) April, April, (e) (e) May, May, (f) (f) June, June, (g) (g) July, July, (h) (h) August, August, (i) (i) September, September, (j) Remote Sens. 2017, 9, x FOR PEER REVIEWand (l) December. November, 3 of 32 (j) October, (k) November, and (l) December. A4. Temperature ◦ C (curve) and annual sum of fungal spores from 1 January 2014 to 31 Figure Figure A4. Temperature in °Cin(curve) and annual sum of fungal spores from 1 January 2014 to 31 December December 2018:2018: (a). Correlation (a). Correlation between between the temperature the temperature andannual and the the annual sum sum of fungal of fungal spores: spores: (b). (b). Figure A5. Total rain in mm (curve) and annual sum of fungal spores (bars) from 1 January 2014 to Figure A4. Temperature in °C (curve) and annual sum of fungal spores from 1 January 2014 to 31 December 2018: (a). Correlation between the temperature and the annual sum of fungal spores: (b). Remote Sens. 2019, 11, 1671 24 of 29 Figure A5. Total rain in mm (curve) and annual sum of fungal spores (bars) from 1 January 2014 to 31 December 2018: (a). Linear regression between the total rain and the annual sum of fungal spores: (b). Figure A5. Total rain in mm (curve) and annual sum of fungal spores (bars) from 1 January 2014 to 31 Figure A5. Total rain in mm (curve) and annual sum of fungal spores (bars) from 1 January 2014 to December 2018: (a). Linear regression between the total rain and the annual sum of fungal spores: (b). 31 December 2018: (a). Linear regression between the total rain and the annual sum of fungal spores: (b). Remote Sens. 2017, 9, x FOR PEERFigure REVIEW Monthly abundance of the AMC and BMC. 4 of 32 Figure A6. A6. Monthly abundance of the AMC and BMC. Figure A6. Monthly abundance of the AMC and BMC. Figure A7. Origin of total atmospheric pollen grains year by year using SWIM model Origin. The Figure A7. Origin white circles of total represent atmospheric the wind pollen speed scale grains year in kilometer by year per hour usingThe (km/h). SWIM colormodel Origin. The grid represents the white circles represent the wind 3speed scale in kilometer per hour (km/h). estimated concentration (Nb#/m ) for any wind speed and wind direction. The color grid represents the estimated concentration (Nb#/m3) for any wind speed and wind direction. Figure A7. Origin of total atmospheric pollen grains year by year using SWIM model Origin. The Figurecircles white A7. Origin of total represent atmospheric the wind pollen speed scale grains year in kilometer perbyhour year(km/h). using The SWIM model color gridOrigin. The represents white circles represent the wind speed scale in kilometer per hour (km/h). the estimated concentration (Nb#/m ) for any wind speed and wind direction. 3 The color grid represents Remote the Sens.estimated 2019, 11, 1671 concentration (Nb#/m3) for any wind speed and wind direction. 25 of 29 Figure A8. Figure A8. Surface Surface of of big big crops in France crops in France adapted adapted from from [71]. [71]. Figure A8. 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