Anskje Van Mensel
email: anskje.vanmensel@uantwerpen.be

Karen Wuyts

Pedro Pinho

Babette Muyshondt

Cristiana Aleixo

Marta Alos Orti

Joan Casanelles-Abella

François Chiron

Tiit Hallikma

Lauri Laanisto

Marco Moretti

Ülo Niinemets

Piotr Tryjanowski

Roeland Samson

The magnetic signal from trunk bark of urban trees catches the variation in particulate matter exposure within and across six European cities

Keywords:

niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

Air pollution is responsible for millions of premature human deaths worldwide every year (WHO 2018).

Particulate matter (PM) -a complex and varying mixture of particles -is one of Europe's most harmful pollutants for human health (EEA 2020). Suspended in the air, PM can be inhaled and cause various adverse health effects, including aggravation of respiratory and cardiovascular diseases (Pöschl 2005). This is of particular concern in cities since urban air quality is worse than in the rural environment (EEA 2020). Air pollution has a direct economic impact, with rising cost for health insurances, reduced labour productivity and higher absenteeism at work (European Environment Agency (EEA) 2020).

To estimate exposure to air pollution and potential health risks, it is important to monitor local air quality. Especially valuable are easy to measure proxies that integrate the pollution signal over time and provide a reliable estimate of air pollution level in any chosen locality. Biomagnetic monitoring, i.e. the measurement of magnetic variables such as the Saturation Isothermal Remanent Magnetisation (SIRM) of biological matrices exposed to the atmosphere, has been used increasingly over the years to provide proxies of atmospheric PM concentration and of the amount and composition of surface-accumulated PM and metals [START_REF] Hofman | Biomagnetic Monitoring of Atmospheric Pollution: A Review of Magnetic Signatures from Biological Sensors[END_REF]. It is a straightforward, quick method that allows the analysis of a lot of samples to create time-integrated air pollution maps at high spatial resolution while taking into account specific local conditions (as opposed to air quality sampling with sensors or air pollution modelling). Via deposition of PM on exposed surfaces, SIRM is related to atmospheric mass concentrations of coarse and fine PM fractions (PM10 and PM2.5, i.e. PM with an aerodynamic diameter smaller than 10 and 2.5 µm, respectively ; [START_REF] Hofman | Impact of urban street canyon architecture on local atmospheric pollutant levels and magneto-chemical PM10 composition: an experimental study in Antwerp, Belgium[END_REF][START_REF] Revuelta | Partitioning of magnetic particles in PM10, PM2.5 and PM1 aerosols in the urban atmosphere of Barcelona (Spain)[END_REF]) and increases with exposure to motorised road and railroad traffic [START_REF] Kardel | Intra-urban spatial variation of magnetic particles: Monitoring via leaf saturation isothermal remanent magnetisation (SIRM)[END_REF][START_REF] Wuyts | A new opportunity for biomagnetic monitoring of particulate pollution in an urban environment using tree branches[END_REF]) and industrial emissions [START_REF] Matzka | Magnetic biomonitoring of roadside tree leaves: Identification of spatial and temporal variations in vehicle-derived particulates[END_REF][START_REF] Declercq | A multi-proxy magnetic approach for monitoring large-scale airborne pollution impact[END_REF]. For more background information on SIRM we refer to the review paper by [START_REF] Hofman | Biomagnetic Monitoring of Atmospheric Pollution: A Review of Magnetic Signatures from Biological Sensors[END_REF].

Mosses, lichens, plant leaves and branches and even animals and human tissues have been used for biomagnetic monitoring [START_REF] Hofman | Biomagnetic Monitoring of Atmospheric Pollution: A Review of Magnetic Signatures from Biological Sensors[END_REF]. So far, the magnetic signal of tree bark from trunk and branches has been only sparsely studied before, although some benefits can be deduced from its use.

Trunk bark from Acer rubrum trees near a highway was studied by [START_REF] Kletetschka | Magnetic anomalies on the tree trunks[END_REF] and showed higher SIRM values at the forest-facing side as opposed to the road-facing side of trees. This difference was presumed to be due to differences in surface wetness. Also [START_REF] Zhang | Biomonitoring of atmospheric particulate matter using magnetic properties of Salix matsudana tree ring cores[END_REF] found that the acquisition of magnetic particles by Salix matsudana trunk bark depends on orientation and height. However, they found higher SIRM values at the pollution-source facing sides of trees. [START_REF] Brignole | Chemical and magnetic analyses on tree bark as an effective tool for biomonitoring: A case study in Lisbon (Portugal)[END_REF] and [START_REF] Chaparro | Fine air particles trapped by street tree barks: in situ magnetic biomonitoring[END_REF] showed that magnetic analysis of trunk bark is a good proxy for ironrich fine particles trapped in the bark and overall load of trace elements in the bark and that it could be used to make a distinction between locations with differences in air pollution. A study on branch bark of Platanus x acerifolia [START_REF] Wuyts | A new opportunity for biomagnetic monitoring of particulate pollution in an urban environment using tree branches[END_REF]) confirmed the presence of metal-containing particles on and in bark and the potential of bark SIRM to distinguish between sites with different traffic intensity, but also pointed to the accumulation of particles and SIRM with exposure time. To develop a reliable methodology for PM exposure assessment based on biomagnetic monitoring with tree bark, potentially confounding factors need to be identified and knowledge on the influence of tree characteristics and sampling methods on the magnetic signal of tree bark is necessary. The available evidence is, however, limited and ambiguous. Moreover, previous studies are predominantly singlecity studies which limits the number of species, climate types, PM exposure levels and PM source types, and hence questions the generalizability of the biomagnetic approach.

In this study, we aimed to evaluate the variation in trunk bark magnetism of urban trees at large spatial scale (i.e. in cities across the European continent), at local scale (i.e. within cities) and microscale (i.e. within trees), in relation with levels of exposure to PM and genera. Therefore, the tree trunk bark from 39 genera was sampled in over 170 urban green areas (UGAs) in six European cities and analysed magnetically for the SIRM. A multiple-city approach has the advantage of including multiple different environmental and human-induced conditions. Particulate matter source abundance (e.g. surface area of roads), measured and modelled ambient PM concentration, time of exposure, and orientation of the sampled trunk were considered as indicators of PM exposure. The following research questions were addressed: (1) Does trunk bark SIRM differ between cities in accordance with their mean atmospheric PM concentrations? (2) Is the variation in trunk bark SIRM related to differences in tree genus and does it influence the capacity of trunk bark SIRM to differentiate between cities regarding PM? (3) Is variation in SIRM related to difference in levels of PM exposure, as indicated by modelled atmospheric PM concentration, land use (to quantify PM source abundance), tree diameter (as an indicator of time of exposure) and trunk side (orientation)? (4) What are the potential, drawbacks and advantages of magnetic analysis of tree trunk bark to discriminate between sites with different PM concentrations? We expect to see higher SIRM values on tree trunk bark in cities with worse air quality (i.e. higher PM concentrations), in areas with more PM sources, in trees with a greater trunk circumference (as indicator of tree age and as such exposure time) and at trunk sides oriented towards the prevailing wind direction (more PM deposition due to greater chance of wet bark from rain and increased impaction of particles from the air flow approaching the tree trunk).

Methods

Sampling locations

For this research, samples were collected in six European cities in the framework of the BiodivERsA project 'BIOVEINS -Connectivity of green and blue infrastructures: living veins for biodiverse and healthy cities'. These cities included Antwerp, Belgium; Lisbon, Portugal; Paris, France; Poznan, Poland; Tartu, Estonia and Zurich, Switzerland (Table 1, (WD, Meteoblue 2021), mean annual precipitation (in mm;reference period An: 1991-2020[START_REF] Kmi | Climate statistics of the Belgian municipalities[END_REF]), Lx: 1991-2018(IPMA 2021), Pa: 1981-2010(Météo-France 2021), Po: 1991-2020(Climate Change Knowledge Portal 2021), Ta: 2016-2020(Estonian Weather Service 2021), Zu: 1991-2020(MeteoSwiss 2021), climatic zone according to the Köppen -Geiger climate classification [START_REF] Kottek | World map of the Köppen-Geiger climate classification updated[END_REF]Cfb: Inside each selected UGA, four trees, preferably of the same species, were selected in the four main wind directions around the centroid of the UGA. The centroid was predetermined as the centre of gravity of the UGA. When there were no four suitable trees available inside the UGA, trees were selected outside the UGA but as close to the UGA as possible. The genus of each sampled tree was identified in the field. Trees preferably had a diameter between 20 cm and 80 cm to avoid young and very old trees and as such to ensure, as much as possible, uniformity in trunk bark structure within genera. We expected variation in PM accumulation on the tree trunks due to influence of wind and rain according to their prevailing direction and the position of PM sources relative to the trees. Since these are expected to be tree-specific and difficult to determine accurately for each tree, particularly for trees inside UGAs, we opted for uniformity and chose to collect bark at two fixed, opposite sides, i.e. the north-and south-facing sides of the trunk, from here on mentioned as N-and S-facing sides.

Samples were taken at a height of 1.5 m above ground level using a stainless steel knife, by scraping off the surface of the trunk. The bark scrape was intercepted with a plastic kidney dish, stored in a labelled paper bag and transported to the lab. Tree stem circumference was measured using a measuring tape at a height of 1.5 m above the ground. The knife and kidney dish were cleaned between the collection of different samples. throughout this manuscript, data of a genus do not represent all species within the genus but yet reflect the data of several but not all unidentified species in the genus.

Sample preparation and magnetic analysis

Before storage, samples were dried at room temperature for at least a week in the lab. From each bark sample, a subsample (with median mass of 617 mg) was taken and weighed using an electronic balance with an accuracy of 0.0001 g. This subsample was thoroughly packed in cling film, to avoid movement of the bark, and pressed into a plastic sample pot with a volume of 6.7 10 -6 m 3 . For magnetising the subsample, we followed the protocol described by [START_REF] Kardel | Leaf saturation isothermal remanent magnetization (SIRM) as a proxy for particulate matter monitoring: Inter-species differences and in-season variation[END_REF]. We used a pulsed DC magnetic field of 0.8 T to magnetise the subsample with the Molspin pulse magnetiser (Molspin Ltd, UK). The volume magnetisation of the subsample was measured immediately after magnetisation using a JR-6 magnetometer with a sensitivity of 10 -8 A m -1 (AGICO, Czech Republic), as the average of two measurements. Method blanks were included as sample pots with the same amount of cling film as was used to pack the subsamples. The obtained volume magnetisation values (in A m -1 ) were multiplied by the volume of the sample pot to obtain the magnetic moment (A m 2 ). The magnetic moment was normalised by dividing by the mass of the subsample, which resulted in the massnormalised saturation isothermal remanent magnetisation (SIRM) expressed as A m 2 kg -1 . Samples were weighted and magnetically analysed between August 2018 and December 2019.

Land-use classification

Land In the next step, to gain better insight on the possible interactions of the explanatory variables with the genus effect, we focused on the four genera that occurred in all of the six cities, namely Acer, Tilia, Quercus and Fraxinus. These four genera represent 72% of the full 12-genera dataset (Table 1) and is further on called the 4-genera dataset (Ntotal = 852: NAntwerp = 185; NLisbon = 30; NParis = 122; NPoznan = 207; NTartu = 138; NZurich = 170). Hereon, we applied and optimized a similar mixed model as above with only four levels for the variable genus.

Next, we investigated the relationship of SIRM values with PM exposure at city level, taking the cityspecific mean annual measured PM2.5 concentrations of 2019-2020 (Table 1) as indicator. A mixed model was built to evaluate influence of the mean annual PM2.5 concentration at city level on the SIRM values and taking into account genus, circumference, tree trunk side and cover of industry and roads.

This was done for both the 12-genera and the 4-genera dataset. Acer was the only genus that was represented in each city with at least five trees. Therefore, at city level, the correlation between median bark SIRM values of Acer with 2-year-mean annual PM concentrations was tested with a Pearson correlation test (N = 6).

Correlation of modelled PM data with bark SIRM values for Antwerp

For the locations of all trees sampled in Antwerp (N = 129), modelled PM10 and PM2.5 concentrations for the years 2018 and 2019 were extracted from concentration maps derived from the ATMO-Street model chain [START_REF] Lefebvre | Presentation and evaluation of an integrated model chain to respond to traffic-and health-related policy questions[END_REF][START_REF]List of surface weather stations -long series[END_REF][START_REF]List of surface weather stations -long series[END_REF] using QGIS software (QGIS Development Team 2020). The SIRM and the modelled PM10 and PM2.5 data were right-skewed and were lntransformed prior to analysis. A mixed model was built to investigate the effect of modelled PM concentrations on the measured SIRM values. Orientation (factor, 2 levels), genus (factor, 12 levels), tree trunk circumference (continuous) and GIS-extracted modelled PM concentrations (continuous)

were used as fixed factors. UGA-ID and tree-ID were used as random factors, with the latter nested in the first. The same model (but without the explanatory variable "genus") was run for subsets of the three most common genera in Antwerp: Quercus (N = 76), Acer (N= 47) and Tilia (N = 44). N-and S-sides of the tree). All statistical analyses were performed in R 4.1.2 (R Core Team 2021). The linear mixed models were built on ln-transformed SIRM data using the package nlme [START_REF] Pinheiro | nlme: Linear and Nonlinear Mixed Effects Models Pöschl U (2005) Atmospheric aerosols: Composition, transformation, climate and health effects[END_REF]. Images were made using the package ggplot2 [START_REF] Wickham | ggplot2: Elegant Graphics for Data Analysis[END_REF]. In all statistical tests, p-values ≤ 0.05 were considered as significant.

Results

City, genus and PM exposure: the 12-genera dataset

The tree trunk bark SIRM value ranged between 3.0 and 10346.6 µA m 2 kg -1 over the six cities and 12 genera (i.e. those with at least 20 samples), with a median value of 406.2 µA m 2 kg -1 . Genera to climate relationships were observed e.g., Catalpa and Juglans were only represented in the southern cities.

According to the mixed model, with UGA-ID and tree-ID as nested random factors, city (p < 0.0001), genus (p < 0.0001), circumference (p < 0.0001), orientation (p = 0.0009), cover of industrial area (p = 0.0028) and cover of roads (p = 0.0011) contributed significantly to the variation in bark SIRM of the 12 genera. There was a significant interaction between city and circumference (p = 0.0063), city and orientation (p < 0.0001), city and cover of industrial area (p = 0.0038, Fig. S3 in SI) and between city and cover of roads (p < 0.0001, Fig. S3 in SI). The lowest SIRM values occurred in Zurich, while the highest values were observed in Poznan (Fig. 1). The median SIRM values of the bark including the 12 genera increased in the following order: Zurich, Tartu, Paris, Antwerp, Lisbon, Poznan. The SIRM values were significantly higher in Antwerp, Lisbon and Poznan than in Paris, Tartu and Zurich. as shown in Table 1 and values can be read off from the right y-axis. Mind the logarithmic scale of the y-axes.

The SIRM varied significantly between the genera (overview in Fig. S4 In all cities, the bark SIRM significantly increased with increasing circumference (Fig. 2a), following a linear relationship between the ln-transformed bark SIRM and the tree circumference and thus the tree trunk bark SIRM values increased exponentially with tree circumference. However, there was a significant interaction effect between circumference and city. The trend lines were significantly steeper for Paris and Poznan than for Antwerp, Tartu and Zurich (Fig. 2b). In general, the SIRM values at the N-facing side of trees were lower than at the S-facing side of trees (p < 0.0001) but there was a significant interaction between orientation and city (p < 0.0001). In Antwerp, the S-facing bark had significantly higher SIRM values (p < 0.0001), while in Lisbon, the SIRM of the N-facing bark was higher (p = 0.0002). In Antwerp, the median at the N-facing side was 35 % lower than at the S-facing side. In Lisbon, the SIRM median values at the N-facing side were double of those on the S-facing side. In Paris, Poznan, Tartu and Zurich, no significant differences occurred in bark SIRM between N and S orientation (Fig. 3). The cover of roads and cover of industry were not significantly correlated (r = 0.07, p = 0.3885) and contributed both to the variation in SIRM. The cover of industrial area in the buffer zones did significantly correlate with the cover of urban area (r = -0.34, p < 0.0001), agriculture (r = -0.22; p = 0.0038) and railways (r = 0.15, p = 0.0532). The cover of industrial area and roads around the UGA centroid had a positive significant effect on the SIRM values, but these effects were city-dependent.

The SIRM values increased significantly more per unit surface increase of industrial area in Zurich than in Antwerp, Lisbon and Paris. The least steep relationship between the SIRM value and cover of industrial area was found in Paris; only in Tartu and Zurich, this relationship was significantly steeper.

Also for the cover of roads, there was an interaction with the city effect: the SIRM values increased more with an increasing cover of roads in Poznan, Zurich and Paris than they did in Lisbon and Antwerp.

City, genus and PM exposure: the 4-genera dataset

When considering only the four genera that occurred in all of the six cities, namely Acer (N = 332), Tilia (N = 254), Quercus (N = 180) and Fraxinus (N = 86), the SIRM values ranged between 3.0 and 10346.6 µA m 2 kg -1 over the six cities and four genera (i.e. those that occur in all cities), with a median value of 412.1 µA m 2 kg -1 . The optimised mixed model, with UGA-ID and tree-ID as nested random factors, indicated a significant contribution of city (p < 0.0001), genus (p = 0.0004), circumference (p < 0.0001), orientation (p = 0.0019), cover of roads (p = 0.0036) and cover of industrial area (p = 0.0085) to the variation in bark SIRM, with a significant interaction between city and orientation (p = 0.0001), city and cover of roads (p = 0.0003) and city and cover of industrial area (p = 0.0117). The lowest SIRM values occurred in Zurich, while the highest values were observed in Poznan (Fig. 1). The median values of the bark SIRM including the four genera increased in the following order: Zurich, Paris, Tartu, Antwerp, Lisbon, Poznan.

The lowest SIRM values were for samples of the genera Fraxinus and Acer, while the genus Quercus had intermediate values and the values for Tilia samples were significantly higher than those of Acer (p = 0.0018) and Fraxinus (p = 0.0298) (overview in Fig. S4 in SI). Moreover, a significant increase in SIRM values was observed with tree circumference (p < 0.0001), irrespective of genus and city. In general, significantly higher SIRM values were observed at the S-facing side of the tree, as opposed to the N-facing side of the trees (p < 0.0001), but there was a significant interaction between city and orientation (p = 0.0001). There were significant differences between samples from N-and S-facing side of trees in Antwerp and Lisbon. In Antwerp, the median at the N-facing side was 34 % lower than at the S-facing side. In Lisbon, the opposite was true with median values at the N-facing side that were more than three times as high (314 %) as those on the S-facing side.

The covers of industrial area and roads in the vicinity of the UGAs had a positive significant effect on the SIRM values, but these effects were city-dependent. As with the 12-genera dataset, the SIRM values increased significantly more per surface of industrial area in Zurich then in Antwerp, Lisbon and Paris. The least steep relationship between SIRM value and cover of industrial area was found in Paris, only in Tartu and Zurich this relationship was significantly steeper. Also the cover of roads, interacted significantly with the city effect: SIRM values increased more with an increasing cover of roads in Poznan, Zurich and Paris than in Antwerp. In Tartu, the increase in SIRM values with cover of roads was significantly lower than in Paris, but did not significantly differ from the other cities.

Relationships of SIRM values with PM data

Following the mixed models outcome in which the circumference, genus, tree side and land-use classes were accounted for, the cities' mean annual measured PM2.5 concentration contributed significantly to the variation in SIRM, for both the 12-genera (p < 0.0001) and the 4-genera (p < 0.0001) dataset.

Positive relationships between the annual measured PM2.5 concentration and the SIRM were observed. For all of the three subsets of the most common genera in Antwerp, the same conclusions could be drawn.

Inter-genera correlation

There was a significant correlation between the SIRM values for Tilia trees (averaged at tree level) and the interpolated SIRM values for genus Acer extracted at the Tilia tree location (r = 0.74, p < 0.0001).

Similarly

Discussion

This study is the first to study tree bark SIRM on a continental scale, in different climate zones, in different urban settings and with a large variety of genera. Our results indicate the importance of surrounding land use, genus, tree circumference and orientation resulting in differences in bark SIRM related to PM exposure. The median tree trunk bark SIRM value of 406.2 µA m 2 kg -1 obtained in this study falls within the range of SIRM values for bark found by Hofman et al. 2017 (4.3 to 2980 µA m 2 kg - 1

) and [START_REF] Kletetschka | Magnetic anomalies on the tree trunks[END_REF]Kletetschka et al. (270 to 1530 µA m 2 kg -1 in Goddard, Maryland, US), but is lower than the range of 2500-19200 µA m² kg -1 observed by [START_REF] Chaparro | Fine air particles trapped by street tree barks: in situ magnetic biomonitoring[END_REF] in Mar del Plata in Argentina.

Moreover, a large range with SIRM values up to 10346.6 µA m 2 kg -1 was observed in this study. This large variation in SIRM value here is probably the result of the large extent of the study, including a large sample size in different urban settings and climate zones, and the large variation in factors that influence the bark SIRM value, i.e. tree genus, tree circumference and site-and city specific PM exposure.

Inter-city variations in SIRM

The trunk bark SIRM differed between cities, with higher values in Poznan, Lisbon and Antwerp and lower values in Paris, Tartu and Zurich (Fig. 1). This is in line with the higher recent mean annual concentrations of PM2.5 measured in Antwerp and Poznan and the lower PM2.5 concentrations for Tartu and Zurich (Table 1, Fig. 1), in spite of the difference in time span represented by bark SIRM (which increases with tree age (see §4.4.2) and thus can be considered a time-integrated PM proxy) and the recent PM2.5 concentrations. However, Paris and Lisbon seem to deviate slightly from this similarity:

trees in Paris had lower bark SIRM and those in Lisbon higher SIRM values than could be expected based on the mean annual PM2.5 concentrations. In Paris, site selection included parks outside the city center and sometimes in low urbanised landscape far away from the most urbanised sites; this can explain SIRM values being lower than expected from the city's mean annual PM2.5 concentration. In Lisbon, sampling was performed one year later than in the other cities, which may have contributed to higher SIRM values than expected, although this effect is expected to be marginal. Nonetheless, the measured PM2.5 concentrations can significantly explain variation in SIRM at city level when the influence of genus, circumference, tree trunk side and the cover of roads and industry are controlled for. The tree trunk SIRM increased with increasing levels of mean annual PM2.5 concentrations. More specifically, when only SIRM values of Acer were considered, i.e. the only genus with at least five trees sampled per city, also a significant and positive correlation was observed between the cities' mean annual measured PM2.5 concentrations and their median bark SIRM. Our results confirm that trunk bark SIRM differs between cities in accordance with their mean atmospheric PM concentrations (research question 1).

When considering the four genera that occurred in all cities, the interaction between city and genus was not significant, so the differentiating power of bark SIRM between cities with different PM2.5 levels was similar for each of the considered genera. We conclude that the power of trunk bark SIRM to differentiate between cities according to their mean annual PM concentration is irrespective of the four widespread genera considered in this study (research question 2).

Inter-tree variation in SIRM

4.2.1. PM sources in tree surroundings Across the six cities, the bark SIRM values were found to increase with increasing covers of industrial area and of roads in the surroundings of the UGAs. These results indicate the sensitivity of bark SIRM to magnetic PM emissions from industrial activities and traffic. Elevated SIRM signals in industrial areas and closer to roads with motorised traffic opposed to green areas have been observed previously for leaves [START_REF] Hansard | Biomagnetic monitoring of industry-derived particulate pollution[END_REF][START_REF] Castanheiro | Magnetic-and particle-based techniques to investigate metal deposition on urban green[END_REF][START_REF] Kardel | Intra-urban spatial variation of magnetic particles: Monitoring via leaf saturation isothermal remanent magnetisation (SIRM)[END_REF][START_REF] Moreno | Biomonitoring of traffic air pollution in Rome using magnetic properties of tree leaves[END_REF], the bark of tree branches [START_REF] Wuyts | A new opportunity for biomagnetic monitoring of particulate pollution in an urban environment using tree branches[END_REF]) and tree trunk bark [START_REF] Brignole | Chemical and magnetic analyses on tree bark as an effective tool for biomonitoring: A case study in Lisbon (Portugal)[END_REF]). This study, nonetheless, goes beyond the categorical comparison of land use classes and is, to our knowledge, the first to identify a relationship between tree trunk bark SIRM and the cover of industrial area and roads in the vicinity of the sampling location, and thus to quantitatively generalise the influence of land use on SIRM (research question 3).

Furthermore, the interactions of the city effect with the cover of industrial area and with the cover of roads suggest that the effect size of road and industry cover on bark SIRM was city-specific. As amount, and temporary conditions [START_REF] Lefebvre | Presentation and evaluation of an integrated model chain to respond to traffic-and health-related policy questions[END_REF]. Also, in a mixed-source environment, variation in SIRM reflects not only differences in atmospheric PM10 or PM2.5 concentrations but also in composition and magnetic grain size, since different PM sources may induce different leaf SIRM values at comparable Fe content or atmospheric PM mass concentration [START_REF] Baldacchini | How does the amount and composition of PM deposited on Platanus acerifolia leaves change across different cities in Europe?[END_REF][START_REF] Hansard | Biomagnetic monitoring of industry-derived particulate pollution[END_REF]. This was also pointed out by the different relationships between bark SIRM and the cover of roads and industrial land use ( §4.2.1). Our SIRM data were capable of capturing the high spatial variation in PM exposure typical of urban settings, both in amount and composition, and can take into account local conditions. Hence, we suggest measuring bark SIRM values to get a better idea of the spatial variation in anthropogenic coarse and fine PM concentrations at a higher spatial resolution in order to complement PM measurements and to be taken into account in PM concentration models, preferentially in areas dominated by the same anthropogenic PM source. and T. tomentosa) provide a rough surface as well, but yet with micro-roughness rather than Robinia-like macro-roughness. It should be noted also that a thick bark may dilute the magnetic signal per mass unit, as the accumulation is restricted mainly to the exposed surface [START_REF] Wuyts | A new opportunity for biomagnetic monitoring of particulate pollution in an urban environment using tree branches[END_REF]. This could cause underestimation of the PM accumulation on bark of species with thick bark, such as Quercus and

Populus. Nonetheless, rough bark surface seems to stimulate PM accumulation, but it is probable that other trunk bark traits also play a role in bark PM accumulation, such as its water holding capacity and wettability.

4.2.4. Tree circumference as a measure for exposure time Tree trunk bark SIRM increased with increasing tree trunk circumference, probably via its relationship with tree age. With increasing tree trunk circumference, the ln-transformed bark SIRM signal increased linearly (Fig. 2), and this for all genera studied and a broad range of circumferences, i.e. between 50 and 300 cm. For a given PM deposition velocity on bark, it can be expected that PM accumulates on bark with time, at least for trees with non-exfoliating bark, resulting in increasing PM per surface area and SIRM as trees age whilst their circumference increases. In conditions with low competition for light as can be expected in urban settings, tree circumference can be considered as a rough indicator of tree age [START_REF] Wegiel | Equations for predicting age of black locust (Robinia pseudoacacia L.) based on the tree circumference[END_REF]. According to [START_REF] Catinon | The anthropogenic atmospheric elements fraction: A new interpretation of elemental deposits on tree barks[END_REF], the accumulation of superficial deposits on young stems of F. excelsior trees reaches a maximum after 10-15 years, after which the accumulation per area unit of bark only slowly increases. Increasing SIRM values with tree age were also observed by [START_REF] Wuyts | A new opportunity for biomagnetic monitoring of particulate pollution in an urban environment using tree branches[END_REF] who investigated SIRM values of Platanus x acerifolia tree branches of different ages. The effect of surface roughening with aging, stimulating the deposition of PM on the surface, may also have played a role in the early years of tree development. It should be kept in mind that for trees in urban environments tree age and exposure time may not fully coincide: urban trees are brought in at variable ages and diameters from a tree nursery where they have experienced PM exposure different from that at their current urban location during a period that may also very from tree to tree. The time since tree planting is therefore probably more representative of the exposure time than tree diameter. The increase in SIRM values with tree trunk circumference was citydependent as not all slopes in the relationship were equally steep for each city (Fig. 2b) and a significant interaction occurred between circumference and city effects. Via the relation of circumference with tree age and thus exposure time, an explanation for this interaction can be found in differences in the growth rate of trees and accumulation rate in the different cities. The latter, in turn, could relate to differences in PM concentrations, PM size distribution and meteorology (e.g. wind speed and rain) between cities. Although the effect of exposure time on tree trunk circumference is city-dependent, we can conclude that tree trunk circumference will increase with tree age in all cities without reaching a maximum value and this irrespective of genus.

Intra-tree variation in SIRM: orientation

Although the bark SIRM values were in general lower at the N-facing than at the S-facing side of trees (Fig. 3), a significant interaction with city was observed. The SIRM values in Antwerp and Lisbon were 1.5-2 times higher at the side of the tree that corresponds to the prevailing wind direction (in Antwerp SW and in Lisbon NW). For the other cities, the sampling sides did not coincide with the prevailing wind direction, so no potential effect of prevailing wind direction could be detected. The prevailing windfacing side of trees is more likely to get wet from rain during a downpour than the other side of the tree. The presence of water facilitates the deposition of PM and stickiness for particles is higher on moist surfaces [START_REF] Kletetschka | Magnetic anomalies on the tree trunks[END_REF], but a possibly higher wash-off at this side may also occur.

Moreover, the wind-facing side of trees undergoes a larger impaction of upcoming PM by the direct air flow on the surface, a process which is most effective for coarse particles [START_REF] Fowler | Atmospheric composition change: ecosystemsatmosphere interactions[END_REF], i.e.

the particles with the highest magnetic concentration [START_REF] Revuelta | Partitioning of magnetic particles in PM10, PM2.5 and PM1 aerosols in the urban atmosphere of Barcelona (Spain)[END_REF].

Advantages and drawbacks of biomagnetic monitoring using tree trunk bark

In the light of research question 4, we here evaluate the potential, drawbacks and advantages of using magnetic analysis of tree trunk bark to discriminate between sites with different PM concentrations, based on our study's results and experience. As atmospheric PM will predominantly accumulate at the outer surface of the tree [START_REF] Wuyts | A new opportunity for biomagnetic monitoring of particulate pollution in an urban environment using tree branches[END_REF], it is better to collect bark with an object that takes a small and fixed surface (e.g. a corer or template). This enables the calculation of surface-normalised bark SIRM, making the samples independent of bark thickness while ensuring that the entire surface is sampled instead of only the protrusive bark parts. Alternatively, all samples need to be taken up to a fixed and not too large depth otherwise the signal might be diluted for each species differently.

Moreover, we suggest to use tree species that can be sampled rather easily, i.e. species with a smoother trunk (e.g. F. sylvatica), and avoid species with a very rough or exfoliating trunk (e.g. Platanus sp.). When there is limited time or resources, and only one sample per tree can be taken, it is advised to take samples according to the overall predominant wind direction as this will result in the highest SIRM values with the lowest relative measuring error in areas with low air pollution. Furthermore, the significant relationships between the SIRM values of Acer-Tilia and Acer-Quercus tree pairs (Fig. 4) endorse the possibility of increasing the spatial sampling resolution and cover of a biomagnetic monitoring study through the combination of different tree genera. SIRM values of different genera are then to be rescaled to the most abundant genus with inter-genera calibration formulae. A similar suggestion was made before for leaves by [START_REF] Kardel | Leaf saturation isothermal remanent magnetization (SIRM) as a proxy for particulate matter monitoring: Inter-species differences and in-season variation[END_REF] based on a linear relationship between the leaf SIRM of pairwise co-located Tilia sp. and Carpinus betulus trees.

Sampling trunk bark for biomagnetic monitoring has some advantages over the use of leaves and branches. It can be sampled during all seasons, as opposed to leaves of deciduous species. When working with leaves in temperate climates, research in winter periods is limited to evergreen species which will reduce the spatial resolution of the study. Moreover, trunk bark can be sampled at breathing height, which is more relevant for human PM exposure assessments. In many cases, this is a height where branches and leaves are not available. When sampling leaves or branches high up in the canopy, often a telemetric pruner or, even more complicated in urban settings, a crane needs to be used and the chance of contamination of the samples is high. However, sampling of the tree trunk also has disadvantages. In absence of a tree database with info on planting date, one cannot determine exactly in an non-destructive, easy and cheap way the age of the bark and thus its exposure time. Therefore, in order to use tree trunk magnetic sampling the tree trunk circumference must be controlled for either during sampling or during statistical analysis. For young branches and foliage, and particularly for leaves of deciduous species, surface exposure time generally is easier to assess. Moreover, preparing trunk bark for magnetic analysis takes more time and effort than using branches (which can be easily cut in pieces) or leaves (which can be rolled up) to fit in the plastic sample container for lab analysis.

Conclusion

The SIRM signal of trunk bark from urban trees varies greatly between and within cities, from continental to tree level. Through their effect on magnetic PM accumulation on the exposed bark, tree genus, cover of PM-emitting sources (i.e. roads and industry) near the tree, PM concentrations and tree circumference (as an indicator of tree age) explain the variation observed in tree trunk bark. Intratree variation occurs in relation with trunk side (orientation) relative to the prevailing wind direction.

We can conclude that the magnetic signal of tree trunk bark is a good proxy for ambient coarse to fine PM concentrations and enables to catch variation in anthropogenic PM exposure from traffic and industry at a local scale in areas dominated by the same anthropogenic PM source . However, our results point to the importance of monitoring bark SIRM with a standardised sampling protocol, in which genus, tree circumference (or age) and trunk side are thoughtfully selected. Nonetheless, the combination of different genera in one monitoring study with bark SIRM via inter-calibration seems valid and could enhance a biomonitoring study's spatial sample resolution and coverage. Therefore, we can recommend the use of bark from urban trees from different genera as an easy to measure, reliable and time-integrative proxy for source-specific, coarse to fine PM exposure at local scale.

Statements & Declarations

  Fig. S1 in supplementary information (SI)). The study encompassed small cities with moderate population density and relatively low PM2.5 concentrations, such as Tartu, as well as large cities with high population density (e.g. Paris) or high PM2.5 concentrations (e.g. Poznan). Three climate types are covered in this study, i.e. temperate (Antwerp, Paris, Poznan and Zurich), Mediterranean (Lisbon) and continental (Tartu). Long-term mean yearly precipitation amounts vary between 529 and 1200 mm. The prevailing wind directions are dominated by northwest to southwest winds except for the southeasterly winds prevailing in Zurich.

  -use classes in the neighbourhood of the UGAs were extracted from the Urban Atlas (UA) (Copernicus Land Monitoring Service 2012) within a buffer of 200 m radius around the centroid of the UGA. Land use categories were selected and combined into land-use classes to represent potential sources of PM: agricultural area (UA categories 21000, 22000, 23000), urban area (UA categories11100, 11210, 11220, 11230, 11240), roads (UA categories 12210, 12220), industrial area (UA categories 12300, 13100, 13300) and railways (UA category 12230). Land-use categories with a possible sink capacity for PM are combined in the land-use class green area (UA categories 14100, 14200, 31000, 32000). The area in square meters was calculated for each one of the six combined landuse classes in each buffer area of 200 m radius to represent the cover of this land-use class in the vicinity of the UGA.2.5. Statistical analysis2.5.1. City, genus and PM exposure To make the dataset more balanced, we only worked with data from genera for which we had at least 20 samples throughout all cities. This resulted in a dataset with 1178 observations from 593 trees of 12 genera in 164 UGAs, further called the full 12-genera dataset (number of samples N = 228 in Antwerp; 48 in Lisbon; 242 in Paris; 243 in Poznan; 175 in Tartu and 242 in Zurich). These 12 genera were: Acer (N = 332), Tilia (N = 254), Quercus (N = 180), Aesculus (N = 86), Fraxinus (N = 86), Populus (N = 72), Robinia (N = 46), Fagus (N = 32), Catalpa (N = 24), Juglans (N = 22), Schinus (N = 22) and Ulmus(N = 22). The median circumference of the sampled trees was 129 ± 51 cm. Striking was the significantly higher circumference for Tartu trees (170 ± 48 cm). Because the SIRM data were right-skewed, we lntransformed them before analysis. The covers of each of the six combined land-use classes were tested for correlation using the Spearman correlation test and only significantly uncorrelated PM sources were used to investigate whether variation in bark SIRM can be explained by the land-use class covers(PCA, Fig. S2 in SI). A mixed model was built with UGA-ID and tree-ID as random factors, with the latter nested in the first, and city (factor, 6 levels), orientation (factor, 2 levels), genus (factor, 12 levels), tree circumference (continuous), cover of industrial area (continuous), cover of roads (continuous) and their two-way interactions as fixed factors. This model was optimised stepwise for random and fixed factors based on the comparison of the Akaike Information Criterion (AIC). Model diagnostic plots were checked. P values of fixed effects were obtained with the function ANOVA.

  2.5.3. Spatial interpolation and inter-genera correlation To investigate relations between the bark SIRM values of the different genera, spatial interpolation was performed at city level for Acer data (the most sampled genus) for the cities with sufficient coverage of Acer trees: Antwerp (N = 24), Poznan (N = 63) and Zurich (N = 37). Interpolation was done on the mean values per Acer tree (i.e. mean of N and S samples) using QGIS (QGIS Development Team 2020). After this, the SIRM values for Acer were extracted from the interpolated maps at the locations of the trees from the two other abundant genera Tilia and Quercus in Antwerp (NTilia = 15, NQuercus = 20), Poznan (NTilia = 17, NQuercus = 6) and Zurich (NTilia = 18, NQuercus = 24). The Pearson correlation test was used on the ln-transformed data to check for correlations between the interpolated SIRM values of genus Acer and the mean measured SIRM values of Tilia and Quercus (i.e. the mean of samples at

Fig. 1

 1 Fig. 1 Median and quantile normalised SIRM values of tree trunk bark for the 12-genera dataset (a) and the 4-genera dataset (b) for the six cities under investigation (An= Antwerp, Lx = Lisbon, Pa = Paris, Po = Poznan, Ta = Tartu, Zu = Zurich; 12-genera dataset: NAn = 228, NLx = 48, NPa = 242, NPo = 243, NTa = 175, NZu = 242; 4-genera dataset: NAn = 185, NLx = 30, NPa = 122, NPo = 207, NTa = 138, NZu =170). The horizontal lines represent Q1, median and Q3. SIRM values can be read off from the left y-axis. Different letters indicate significant differences in SIRM values between the cities according to the mixed model outcome. Red triangles represent the, mean annual concentration of fine particulate matter in µg/m 3 for 2019 and 2020 (PM2.5, EEA (2021))

Fig. 2

 2 Fig. 2 Normalised SIRM values of tree trunk bark in function of tree trunk circumference for all cities (a) and for the six studied cities individually (b) (An = Antwerp, Lx = Lisbon, Pa = Paris, Po = Poznan, Ta = Tartu and Zu = Zurich). Given the logarithmic scale of the y-axis, there is an exponential trend of increasing SIRM values with expanding tree trunk circumference at a height of 1.5 m above ground plotted with geom_smooth. Grey bands show the 95% confidence interval.

Fig. 3

 3 Fig. 3 Normalised SIRM values at the north (N)-and south (S)-facing sides of the sampled trees. Results are shown for the 12 genera for which at least 10 trees were sampled In the six studied cities (An = Antwerp, Lx = Lisbon, Pa = Paris, Po = Poznan, Ta = Tartu and Zu = Zurich). Asterisk shows cities with significant differences between both sides (* = p ≤ 0.05). Mind the logarithmic scale of the y-axis.

  Moreover, the six median bark SIRM values of Acer -the only genus with at least five trees in each city -correlated significantly and positively with the cities' mean annual measured PM2.5 concentrations (r = 0.91, p = 0.0124, Fig.S5in SI).For the detailed analyses in Antwerp, the modelled PM10 and PM2.5 concentrations were correlated (in each year 2018 and 2019: p < 0.0001) and so were the PM concentrations of the different years 2018 and 2019 (in each size fraction PM2.5 and PM10: p < 0.0001). For this reason, only one series of data for PM concentrations was kept in the model at the same time. For none of the years and fractions did the modelled PM concentration at the sampling locations contribute to the explanation of the variation in SIRM values (PM2.5, 2018: p = 0.9033; PM10, 2018: p = 0.9451; PM2.5, 2019: p = 0.9125; PM10, 2019: p = 0.7716).

  , the SIRM values for trees of the genus Quercus were significantly correlated with the interpolated SIRM values for Acer extracted at the Quercus tree locations (r = 0.75, p < 0.0001). The relationship between these values is described by the linear relationships SIRMAcer = 0.3 * SIRMTilia + 531.5 and SIRMAcer = 0.2 * SIRMQuercus + 394.5 (Fig. 4, Fig. S6 in SI).

Fig. 4

 4 Fig. 4 Relationship between the spatially interpolated SIRM values for Acer trees at the locations of Tilia (a; y = 0.3*x 531.5, R 2 adj = 0.54, p < 0.0001) and Quercus (b; y = 0.2 x + 394.5, R 2 adj = 0.56, p < 0.0001) trees. The SIRM-values of Acer were extracted from the interpolation maps in Fig. S6 in SI for cities Antwerp, Poznan and Zurich. Grey bands indicate the 95% confidence interval.

  chemical composition and size fraction distribution of deposited PM influence its SIRM signal(Hofman et al. 2014a;[START_REF] Castanheiro | Morphological and elemental characterization of leafdeposited particulate matter from different source types: a microscopic investigation[END_REF], these interactions imply that industrial areas and roads in different cities emit magnetic PM in different amounts per surface area and/or in other chemical composition or size fraction distribution. Indeed, studies by[START_REF] Sawidis | Trees as bioindicator of heavy metal pollution in three European cities[END_REF] on accumulated metals in tree bark and by[START_REF] Baldacchini | How does the amount and composition of PM deposited on Platanus acerifolia leaves change across different cities in Europe?[END_REF] on PM on leaves at street sites found different chemical compositions across European cities. These differences were attributed to differences in PM sources, i.e. local geochemistry, population density, the type and location of industrial activities and traffic, and the rate of technological innovations and emission control programs. We, therefore recommend further research to look into the potential of tree trunk bark SIRM to differentiate between different source types, whether or not in combination with chemical analyses(Lopes Moreira et al. 2016).4.2.2. Modelled PM concentrationsBark SIRM values did not significantly correlate with the modelled PM10 and PM2.5 concentrations in Antwerp city. This is in line with the findings ofHofman et al. (2014b) who saw a bad agreement between SIRM values of ivy (Hedera sp.) leaves and modelled atmospheric PM10 concentrations. Using measured PM concentrations, however,[START_REF] Mitchell | Evaluation and application of biomagnetic monitoring of traffic-derived particulate pollution[END_REF] did find a strong correlation between leaf SIRM values and pumped air filter particulate mass. The disagreement between the modelled PM10 and PM2.5 concentrations and the bark SIRM values in our study can be explained by the time range during which PM has been accumulating on the sampled bark. When samples are collected from 50-year-old trees, they have been subjected to PM concentrations in the air at that specific location for all those years. The modelled PM10 and PM2.5 concentrations from the previous years are likely not representative of the PM exposure in the lifespan of the trees. Moreover, inaccuracies in the PM model outcomes are plausible due to limited or inaccurate input data (e.g. limited number of measuring stations and uncertainty in emission data), model assumptions and local

4. 2

 2 .3. Genera and bark structure The bark SIRM significantly depended upon the genus of the sampled tree. Notable are the low values for Fagus trees and high values for Robinia and Schinus. In the smaller 4-genera dataset (with genera sampled in all cities) the SIRM values of the genera Acer and Fraxinus were significantly lower than those for Tilia. Roughness of the exposed surface is a variable known to increase deposition of PM as proven by Muhammad et al. (2020) for leaves, and our results suggest that it seems to play a role in accumulation of PM on bark too. The sole representative of Fagus in Europe is F. sylvatica, which displays a very smooth and thin bark at all ages. The genus Acer in this study encompassed A. pseudoplatanus and A. platanoides, both showing a smooth bark surface at young and intermediate age and a rougher bark surface only at later age (with shallow cracks and furrows, respectively). In addition, since the magnetic signal of bark is confined to the outermost surface (Wuyts et al. 2018), exfoliation as observed for A. pseudoplatanus bark scales at later age can reduce the magnetic signal. The very rough, non-exfoliating bark of Robinia pseudoacacia (the sole representative of the Robinia genus in this study) and the genus Schinus can explain their very high values of bark SIRM. The dense longitudinal fissures and cracks in the bark of the Tilia species in this study (T. platyphyllos, T. cordata

Table 1 .

 1 Summary of cities with their abbreviation (abb.), coordinates, mean annual concentration of fine particulate matter in µg/m 3 for 2019 and 2020 (PM2.5, EEA 2021), population density (inhabitants/km 2 in 2018 (Eurostat 2021)), prevailing wind direction

  temperate maritime climate, Csa: warm Mediterranean climate, Dfb: moderate continental climate with precipitation throughout the year) and number of sampled trees of the four main genera (Acer, Fraxinus, Quercus, Tilia) and all other genera with at least 10 trees sampled (Aesculus, Catalpa, Fagus, Juglans, Populus, Robinia, Schinus, Ulmus). *Some UGAs were located outside the Paris city center where population density might be lower.

	(2021). The Urban Atlas 2012 was developed by the EU's Copernicus Land Monitoring Service based
	on satellite image interpretation at 2.5 m resolution for the reference year 2012 (freely accessible at
	https://land.copernicus.eu/). The Proximity Index (PI) was used to calculate the connectivity of UGAs.
	It considered the area of and distance to nearby patches (radius of 5 km) with favourable habitat
	(patches with a high probability of having trees). All possible UGAs were divided into six size and six PI
	classes, giving 36 possible combinations. Those combined classes were used to do a random stratified
	sampling. In the end we were able to perform sampling in 173 UGAs, of which 34 in Antwerp, 12 in
	Lisbon, 36 in Paris, 34 in Poznan, 22 in Tartu and 35 in Zurich.
	city	abb.	coordinates	PM2.5	pop.	WD	prec.	clima	Acer	Fraxinus	Quercus Tilia	other
					dens.			te
	Antwerp	An	51° 15' 36.7''N	12.52	1,094	SW	853	Cfb	24	9	38	22	22
			4° 24' 10.0''E
	Lisbon	Lx	38° 43' 20.1''N	9.47	1,011	NW	815	Csa	5	6	2	2	9
			9° 8' 21.6''W
	Paris	Pa	48° 51' 23.8''N	10.52	21,070*	SW	638	Cfb	21	12	11	17	60
			2° 21' 8.0''E
	Poznan	Po	52°24'23.0''N	17.17	2,089	WSW 529	Cfb	63	6	7	28	18
			16°55'30.6''E
	Tartu	Ta	58°22'40.9''N	5.18	14	SW	690	Dfb	17	2	10	40	19
			26°43'42.6''E
	Zurich	Zu	47°22'36.8''N	8.59	915	SSE	1200	Cfb	37	8	24	18	36
			8°32'30.1''E
	2.2. Experimental set-up and sampling
	In each city, the aim was to select 36 urban green areas (UGAs) based on their size and connectivity in
	the urban matrix according to the Urban Atlas 2012, as described in detail by Villarroya-Villalba et al.

was within the framework of the ERA-NET BiodivERsA project 'BIOVEINS -Connectivity of green and blue infrastructures: living veins for biodiverse and healthy cities' (H2020 BiodivERsA32015104). A.

Van

Mensel, R. Samson and K. Wuyts acknowledge funding from the University of Antwerp. P. Pinho acknowledges Fundação para a Ciência e a Tecnologia (FCT, 2020.03415.CEECIND). B. Muyshondt acknowledges funding from a PhD grant Strategic Basic Research of the Research Foundation -Flanders (FWO, 1S84819N). C. Aleixo gratefully acknowledges FCT for the financial support (PhD grant reference SFRH/BD/141822/2018). M. Alos Orti was supported by the European Social Fund's Dora Plus Programme. J. Casanelles-Abella was supported by the Swiss National Science Foundation (project 31BD30_172467). P. Tryjanowski acknowledges the National Science Center Poland (NCN) for funding through NCN/2016/22/Z/NZ8/00004.

This study was supported by the Belgian Science Policy (Belspo) within the Belgian research action through interdisciplinary networks (BRAIN-be) with project number BR/175/A1/BIOVEINS-BE. This was within the framework of the ERA-NET BiodivERsA project 'BIOVEINS -Connectivity of green and blue infrastructures: living veins for biodiverse and healthy cities' (H2020 BiodivERsA32015104). A. Van Mensel, R. Samson and K. Wuyts acknowledge funding from the University of Antwerp. P. Pinho acknowledges Fundação para a Ciência e a Tecnologia (FCT, 2020.03415.CEECIND). B. Muyshondt acknowledges funding from a PhD grant Strategic Basic Research of the Research Foundation -Flanders (FWO, 1S84819N). C. Aleixo gratefully acknowledges FCT for the financial support (PhD grant reference SFRH/BD/141822/2018). M. Alos Orti was supported by the European Social Fund's Dora Plus Programme. J. Casanelles-Abella was supported by the Swiss National Science Foundation (project 31BD30_172467). P. Tryjanowski acknowledges the National Science Center Poland (NCN) for funding through NCN/2016/22/Z/NZ8/00004. Moreover, we are grateful to Leen Van Ham and Hanne Hendrickx for laboratory assistance and to Joana Marques and Piret Lõhmus for help with sample collection.

Funding

This study was supported by the Belgian Science Policy (Belspo) within the Belgian research action through interdisciplinary networks (BRAIN-be) with project number BR/175/A1/BIOVEINS-BE. This

Availability of data and materials

Not applicable

Competing interests

The authors have no relevant financial or non-financial interests to disclose.

Ethical Approval Not applicable

Consent to participate Not applicable

Consent to publish Not applicable


François Osiurak
email: francois.osiurak@univ-lyon2.fr

Angela Bartolo
email: angela.bartolo@univ-lille.fr

Scalab

One century after Liepmann's work on apraxia: Where are we now?

One century ago, Hugo Karl Liepmann carried out a series of famous works on patients with deficits in the execution of manual gestures [START_REF] Liepmann | Die linke Hemisphere und das Handeln[END_REF][START_REF] Liepmann | Drei Aufsatze aus dem Apraxiegebiet[END_REF][START_REF] Liepmann | Apraktische Störungen[END_REF].

Since his works, deficits in the gesture production started being considered as an autonomous neuropsychological disorder called apraxia. One of Liepmann's main contributions was to characterize apraxia as a skilled movement disorder, independent from other cognitive defects like aphasia and agnosia (see [START_REF] Goldenberg | Apraxia and beyond: Life and work of Hugo Liepmann[END_REF]. He further proposed a complex assessment of this deficit by means of different kinds of manual gestures (i.e., meaningless postures, real tool use, pantomime of tool use, symbolic/expressive gestures), each of them having a specific interest in the evaluation and understanding of the praxis disease. One century later, the question is: Where are we now?

Regardless of one century of history on the studies of apraxia, Liepmann's legacy is still present. Even the most recent definitions of apraxia define this disorder by default, maintaining that a diagnosis of apraxia has to be posited by ascertaining first that the deficit in the execution of gestures cannot be explained by other cognitive defects [START_REF] Cubelli | Definition: Apraxia[END_REF][START_REF] Osiurak | Definition: Limb apraxia[END_REF]. However, the emergence of complex cognitive models for the understanding of apraxia together with the use of a more complete assessments bring to light that this independency from other disorders is far from being taken from granted. Several points of concerns leave the question of "apraxia" as an autonomous disorder still under debate.

Language and praxis rely on neural substrates in the left brain. Therefore, the hard job of Liepmann was to understand whether apraxia was an exaggeration of aphasia (see [START_REF] Goldenberg | Apraxia and beyond: Life and work of Hugo Liepmann[END_REF] or if the two disorders were independent. Results of his works have led him to affirm that apraxia and aphasia are two independent disorders, giving limb apraxia the authority to be included in the list of the neuropsychological deficits.

However, the several recent patients' and neuroimagery works carried out on the link between language and gesture processing reveal that the question is still an open issue [START_REF] Bidula | Structural asymmetry of the insula is linked to the lateralization of gesture and language[END_REF][START_REF] Frey | Tool use, communicative gesture and cerebral asymmetries in the modern human brain[END_REF][START_REF] Goldenberg | Apraxia in left-handers[END_REF][START_REF] Goldenberg | Shared neural substrates of apraxia and aphasia[END_REF][START_REF] Króliczak | Atypical lateralization of language predicts cerebral asymmetries in parietal gesture representations[END_REF][START_REF] Vingerhoets | Praxis and language are linked: evidence from co-lateralization in individuals with atypical language dominance[END_REF] Apraxia is assessed by means of complex praxis assessments, basically including the evaluation of multiple gestures (i.e. pantomime, transitive, intransitive and meaningless) to be performed under different task conditions (i.e. verbal, visual, imitation etc.). Furthermore, since the hand with which the gesture is performed cannot be separated from the body toward which some gestures are executed (e.g. the distinction between hand and finger meaningless postures) and neither from the external environment where gestures are directed (i.e. to interact with other objectsobject related actions -or with other individuals -expressive/symbolic gestures), a growing interest in the relationship between gestures and other aspects of cognition emerged. We list here some points of concern:

(a) Imitation of meaningless postures. A series of works initiated in the 1990's have proposed an alternative to the direct-matching hypothesis, demonstrating that difficulties in imitating meaningless postures (particularly hand meaningless postures) could be revealed by body schema/image deficits [START_REF] Dafsari | Deficient body structural description contributes to apraxic end-position errors in imitation[END_REF][START_REF] Goldenberg | Imitating gestures and manipulating a mannikin: The representation of the human body in ideomotor apraxia[END_REF][START_REF] Goldenberg | The meaning of meaningless gestures: A study of visuo-imitative apraxia[END_REF].

(b) Real tool use. The classical approach to apraxia emphasizes the importance of motor memories (also called manipulation knowledge, action semantics, or visuokinesthetic engrams) for the real use of familiar tools [START_REF] Buxbaum | Ideomotor Apraxia: A call to action[END_REF][START_REF] Buxbaum | Action knowledge, visuomotor activation, and embodiment in the two action systems[END_REF][START_REF] Cubelli | Cognition in action: Testing a model of limb apraxia[END_REF][START_REF] Heilman | Two forms of ideomotor apraxia[END_REF][START_REF] Rothi | A cognitive neuropsychological model of limb praxis[END_REF][START_REF] Van Elk | Action semantics: A unifying conceptual framework for the selective use of multimodal and modality-specific object knowledge[END_REF]. However, evidence indicates that tool-use disorders might also be explained by impaired reasoning about physical object properties (also called mechanical-problem solving or technical-reasoning skills; [START_REF] Goldenberg | Tool use and mechanical problem solving in apraxia[END_REF][START_REF] Goldenberg | The neural basis of tool use[END_REF][START_REF] Jarry | Apraxia of tool use: More evidence for the technical reasoning hypothesis[END_REF][START_REF] Osiurak | Mechanical problem-solving strategies in left-brain damaged patients and apraxia of tool use[END_REF][START_REF] Osiurak | Unusual use of objects after unilateral brain damage: The technical reasoning model[END_REF] for review, see Osiurak, Lesourd, Navarro, & Reynaud, in press).

(c) Pantomime of use. The classical approach to apraxia stresses the importance of motor memories for pantomime of tool use. However, other hypotheses have been proposed such as the working-memory (Bartolo, Cubelli, Della Sala, & Drei, 2007) or the social-cognition hypotheses [START_REF] Bartolo | A cognitive overview of limb apraxia[END_REF][START_REF] Finkel | It takes two to pantomime: Communication meets motor cognition[END_REF]. The latter has notably been supported by some studies indicating that pantomime production deficits are present in individuals with autism [START_REF] Ham | Exploring the relationship between gesture and language in ASD[END_REF]Ham, Bartolo, Corely, Swanson, & Rajendran, 2010[START_REF] Ham | Exploring the relationship between gestural recognition and imitation: Evidence of dyspraxia in autism spectrum disorders[END_REF] or schizophrenia [START_REF] Stegmayer | Structural brain correlates of defective gesture performance in schizophrenia[END_REF][START_REF] Viher | The cortical signature of impaired gesturing: Findings from schizophrenia[END_REF][START_REF] Walther | Impaired gesture performance in schizophrenia: Particular vulnerability of meaningless pantomimes[END_REF], who are not initially considered as apraxic patients.

(d) Symbolic/expressive gestures. Deficits in producing symbolic and expressive gestures have been found in individuals with autism (e.g., [START_REF] Ham | Exploring the relationship between gestural recognition and imitation: Evidence of dyspraxia in autism spectrum disorders[END_REF] or in right brain-damaged patients (see [START_REF] Lesourd | Cerebral correlates of imitation of intransitive gestures: An integrative review of neuroimaging data and brain lesion studies[END_REF]. Therefore, to what extent social cognitive deficits contribute to difficulties in the production of symbolic/expressive gestures? Can the social cognitive deficits be at the basis of the difficulties in the production of these gestures or are praxis and social cognition still two independent disorders? Such studies question on the independence of apraxia from other cognitive disorders. Thus, is apraxia a pure "gestural disorder" or is it the consequence of other cognitive dysfunctions that are expressed through gestures. This last possibility would suggest that apraxia may not be a unitary disorder with a unique neuropsychological basis. In this special issue, most of the major authors on the topic address the above provoking questions with experimental data or review articles.

Acknowledgements

This work was performed within the framework of the LABEX CORTEX (ANR-11-LABX-0042) of Université de Lyon, within the program "Investissements d'Avenir" (ANR-11-IDEX-0007) operated by the French National Research Agency (ANR).