Nicolas Boria
email: boria@ensicaen.fr

Sébastien Bougleux
email: bougleux@unicaen.fr

Luc Brun
email: luc.brun@ensicaen.fr

Approximating GED using a Stochastic Generator and Multistart IPFP

Keywords: Graph Edit Distance, Parallel Gradient Descents, Multistart, Stochastic Warm Start

The Graph Edit Distance defines the minimal cost of a sequence of elementary operations transforming a graph into another graph. This versatile concept with an intuitive interpretation is a fundamental tool in structural pattern recognition. However, the exact computation of the Graph Edit Distance is N P-complete. Iterative algorithms such as the ones based on Franck-Wolfe method provide a good approximation of true edit distance with low execution times. However, underlying cost function to optimize being neither concave nor convex, the accuracy of such algorithms highly depends on the initialization. In this paper, we propose a smart random initializer using promising parts of previously computed solutions.

Introduction

Computing a similarity or a dissimilarity measure between graphs is a major challenge in pattern recognition. One of the most well-known and used approach to compute a distance between two graphs is the Graph Edit Distance (GED) [START_REF] Sanfeliu | A distance measure between attributed relational graphs for pattern recognition[END_REF]. Computing the GED consists in finding a sequence of graph edit operations (insertions, deletions and substitutions of vertices and edges) that transforms a graph into another with a minimal cost. Such a sequence of edit operations is called an edit path, and the edit distance between two graphs G and H is defined by GED(G, H) = min γ∈Γ (G,H) e∈γ c(e), where Γ (G, H) denotes the set of edit paths between G and H and c(e) denotes the cost of an elementary operation e belonging to the edit path γ.

If both graphs are simple and if the cost between vertices and edges remains fixed, one can show [START_REF] Bougleux | Graph edit as a quadratic assignment problem[END_REF] that the edit distance between two graphs G and H of respective orders n and m may be formulated as the following quadratic problem: GED(G, H) = min x∈Πn,m 1 2 x t ∆x + c t x, where Π n,m denotes the set of Work supported by Region Normandie under project RIN AGAC vectorized assignment matrices between V G and V H . Such a matrix x encodes for each element of V G one and only one operation (either substitution or deletion). In the same way, x encodes for each element of V H either a substitution or an insertion. The matrix ∆ encodes the cost of edge operations while c encodes the cost of operations on vertices. Computing the GED is NP-hard, so several heuristics were proposed to compute approximate solutions in polynomial time.

The design of approximate solutions to the GED problem has been strongly stimulated by the introduction of an approximation of the GED problem into a Linear Sum Assignment Problem with Edition (LSAPE) [9]. This approximation consists in associating to each node of two graphs G and H a substructure and to populate a cost matrix encoding: the costs of matching two substructures, the cost of inserting one substructure in H and the cost of removing one substructure from G. Given such a cost matrix c, the assignment matrix x minimizing ct x provides a set of elementary operations on vertices from which an edit path may be deduced. The cost of this edit path provides an upper bound for the graph edit distance. This minimization step may be solved in polynomial time. This transformation of an N P-complete problem into a minimization problem with a polynomial complexity is the major advantage of the LSAPE approximation. However the computation of the cost matrix c may require non polynomial execution times. Different types of substructures have been defined in [9, [START_REF] Zeng | Comparing stars: On approximating graph edit distance[END_REF][START_REF] Gaüzère | Approximating graph edit distance using GNCCP[END_REF][START_REF] Carletti | Approximate graph edit distance computation combining bipartite matching and exact neighborhood substructure distance[END_REF].

However, LSAPE is based on a linear approximation of a quadratic problem. In order to get a finer approximation of the graph edit distance, several methods use variants of local search such as simulated annealing in order to improve an initial estimation of the edit distance [START_REF] Ferrer | A first step towards exact graph edit distance using bipartite graph matching[END_REF][START_REF] Riesen | Improving bipartite graph edit distance approximation using various search strategies[END_REF][START_REF] Riesen | Improved graph edit distance approximation with simulated annealing[END_REF]. A slightly different approach [START_REF] Bougleux | Graph edit as a quadratic assignment problem[END_REF], consists in using the Franck Wolfe minimization scheme [7] from an initial guess. This algorithm converges by iterations towards a local minimum of the quadratic function usually close from the initial guess. This heuristic provides close approximations of the graph edit distance on small graphs but is sensitive to the solution used to initialize the method. An heuristic to reduce the influence of the initial guess has been proposed in [5]. This heuristic is based on the use of multiple initial guesses either deduced from a set of solutions of the LSAPE problem or based on the generation of random assignment matrices. The common drawback of these two heuristics is that the generation of initial solutions does not take into account information provided by runs of Franck-Wolfe method which may have converged.

In this paper we propose a new heuristic based on alternative runs of the generation of initial solutions and the determination of their associated local minima using Franck-Wolfe method. This method is described in Section 3. The proposed method is evaluated in Section 4 through several experiments.

Preliminaries

Throughout the paper, we will use the same concepts and notations as those introduced in [START_REF] Bougleux | Graph edit as a quadratic assignment problem[END_REF]. Vertices of graphs G and H are numbered respectively from 1 to n and from 1 to m, and two virtual vertices indexed as n + 1 in G and as In this context, a solution for GED will be described as an error-correcting assignment matrix x, where all vertices of G are assigned to a single element of H ∪ { H }, and all vertices of H are assigned to a single element of G ∪ { G }. The polytope Π n,m of error-correcting assignment matrices thus contains all matrices of dimensions (n + 1) × (m + 1), with binary values, and with a single 1 in each line and in each column, except for the last line and the last column. We naturally extend the concept to matrices with fractional values: we call errorcorrecting bistochastic matrix any matrix where the sum of all cells for each line except the last one and each column except the last one amounts exactly to 1.

Q(x + α(b -x)) 4 Update x : x ← x + α (b -x) 5 Repeat steps 2 to 4 until x T ∆(x -b ) < β Q(x) + x T ∆(b -x)
Given a cost matrix ∆ and an initial continuous or discrete candidate solution x, Algorithm 1 describes Frank-Wolfe algorithm FW(∆, x). In the following, we denote by IPFP the method that consists in running FW(∆, x) and projecting the returned solution in the discrete space. See [START_REF] Bougleux | Graph edit as a quadratic assignment problem[END_REF] for more details.

RANDPOST Algorithm

The conception of algorithm RANDPOST finds its origin in the following intuition regarding the -often conflicting -criteria that a smart initial solution generator should fulfill. On the one hand, a smart generator should propose solutions that are well distributed inside the Π n,m polytope, in order to increase the diversity among local minima that are ultimately returned. We call this criterium exploration criterium. On the other hand, we obviously wish that one of the initial solutions ultimately led to a global minimum, so that a good generator should somehow already generate solutions that includes "smart" assignments. We call this criterium quality criterium.

Building up on the progresses of the multistart IPFP (mIPFP) algorithm [5], we propose a new algorithm that explores the polytope and generates new solutions by taking advantage of the whole statistical information contained in the set of solutions returned by mIPFP. This algorithm is presented in Sec. 3.2. We also devise a new parameterized random generator that is described in Sec. 3.1.

Generating initial solutions with parameterized number of insertions and deletions

With respect to the initial random generator used in [5], where vertices were randomly assigned by the use of the std::random shuffle procedure of the C++ standard library, resulting in a random proportion of insertions and deletions, we decided to use a random generator with a parameterized proportion of insertions and deletions. We focus our analysis on the number of deletions, as -for given n = |G| and m = |H| -the number of deletions is completely determined by it, namely, #insertions ≡ #deletions -n + m.

Namely, given a parameter α ∈ (0, 1), we used a new initial random generator RANDGEN(α) which generates solutions with an expected number of deletions equal to αn + (1 -α) max{(n -m), 0}. In other words, α denotes the expected proportion of "unnecessary" deletions of vertices in the set of initial solutions, reminding that if n > m any feasible assignment will have to assign at least n -m vertices to the virtual node ε H which thus correspond "necessary" to deletions.

Stochastic generation of new initial solutions based on several refined solutions

We describe here the functioning of RANDPOST (Algorithm 2). Given a pair of graphs G and H, the algorithm starts by running mIPFP, which outputs a set S * of r solutions for the problem. Each of these r solutions is represented as an errorcorrecting assignment matrix x. A matrix Ψ is then created by simply summing all of these r matrices and dividing all values by r. Hence, ∀i ∈ [1, n + 1], j ∈ [1, m + 1], Ψ i,j represents the proportion of solutions where i is assigned to j among the r solutions of S * . Matrix Ψ (which is an error-correcting bistochastic matrix) is then used as a probability distribution to compute a new set of k solutions which are subsequently refined using IPFP, and the first r that have converged among these k parallel processes are added to the set S * . The whole Generate a new set S of k solutions using a random generator that uses the probability distribution described by Eq. (1).

7

Start refining all solutions in S using IPFP, and add the r first to have converged to S * Algorithm 2: RANDPOST(k, r, l)

sequence that updates Ψ , generates new solutions and finally refines them is repeated l times.

Parameters k and r enables to speed up the algorithm when k r: some of the k initial solutions might require many iterations of IPFP in order to converge to a local minimum, so that by launching k parallel IPFPs and stopping all them when r of them have converged, the whole process is likely to be faster than launching r parallel IPFPs and waiting for all of them to converge.

The intuitive idea behind algorithm RANDPOST is the following: if an assignment i → j appears with a high frequency within solutions of the set S * , then this assignment is likely to be part of many good solutions for the problem at hand. Hence, the algorithm generates k new solutions in a stochastic way, where the probability for a given assignment to be part of a solution is higher for assignments with high Ψ i,j value, and thus high frequency.

To be even more precise, the random generator assigns each vertex of G to a vertex of H ∪ { H } following a greedy procedure. The matrix x of dimensions (n + 1) × (m + 1) (which will eventually contain the solution returned by the algorithm) is initialized with zeros, and whenever an assignment i → j is made by the algorithm, this translates to x i,j ← 1. At the end of the algorithm x should be an error-correcting permutation. The random generator assigns iteratively vertices from 1 to n based on the following probabilistic distribution P i , where P i (j) denotes the probability for vertex i of G to be assigned to vertex j of H by the generator, given a partial assignment of vertices from 1 to i -1: ∀j = 1, ..., m + 1, P 1 (j) = Ψ (j) ∀i = 2, . . . , n, ∀j = 1, ..., m + 1

P i (j) =      0 if j = m + 1 and ∃h < i, s.t x h,j = 1 Ψ i,j 1 - m h=1 i-1 l=1 x h,l Ψ i,l otherwise (1)
Finally, all vertices of H that have been left unassigned at the end of the procedure are all assigned to G . First let us prove that matrix x produced by the proposed random generator is always an error-correcting permutation matrix. This is done by putting together the two following facts: 1) by construction, each line of x but the last one has exactly one value set to 1 and all the others set to 0 (a single assignment is made for a single line at each step of the generator), 2) the probability distribution described by [START_REF] Abu-Aisheh | A graph database repository and performance evaluation metrics for graph edit distance[END_REF] ensures that no vertex j of H is assigned twice (once assigned, its probability of being assigned again is zero), and the very last step ensures that each one is assigned at least once.

Let us briefly prove that (1) defines a proper probability distribution. It is easy to verify that m+1 j=1 P 1 (j) = 1 by simply reminding that Ψ is an errorcorrecting bistochastic matrix. For i = 2, . . . , n, consider a matrix Ψ which values are as follows:

Ψi,j = 0 if j = m + 1 and ∃h < i, s.t x h,j = 1 Ψ i,j otherwise It is easy to verify that:

∀i = 2, . . . , n m+1 j=1 Ψi,j = 1 - m h=1 i-1 l=1 x h,l Ψ i,l (2)
We finally prove that (1) defines a proper probability distribution: ∀i = 2, . . . , n

m+1 j=1 P (i → j) = m+1 j=1 Ψi,j 1 - m h=1 i-1 l=1 x h,l Ψ i,l = (2)
1 Finally, whenever the stochastic generator produces a candidate solution that has already been generated earlier, the solution is discarded and a new solution is produced using a slightly flatter (and thus more explorative) distribution.

Experimental results

In this section, we evaluate the proposed method through several experiments, in order to determine as clearly as possible the relevance and importance of the exploration and quality criteria described in Sec. 3.

Datasets and protocol

Table 1 presents the chemical datasets that were used in our experiments. MAO, PAH, MUTA10-70 and MUTAmix were considered in ICPR 2016 -Graph Distance Contest [START_REF] Abu-Aisheh | Graph edit distance contest: Results and future challenges[END_REF]. We also extracted 25 graphs from ClinTox [START_REF] Wu | Moleculenet: a benchmark for molecular machine learning[END_REF], and 10 graphs with more than 100 vertices from MUTA.

We evaluated the four following versions of RANDPOST(k, r, l): RANDPOST(40, 40, 0), RANDPOST(40, 20, 1), RANDPOST(40, 10, 3) and RANDPOST(40, 5, 7). This choice of parameters is determined by the following idea: considering two algorithms RANDPOST(k, r 1 , l 1 ) and RANDPOST(k, r 2 , l 2 ) such that r 1 (l 1 + 1) = r 2 (l 2 + 1) and r 1 > r 2 , their relative performances can be compared without bias as the overall number of candidate solutions is the same (in our case, all four algorithms generate exactly 40 candidates), while the latter algorithm performs better on the

(b) MAO, anti-metric costs c3

Fig. 1: Behavior of RANDPOST w.r.t. parameter α, metric vs. anti-metric costs quality criteria, and the former on the exploration one. We thus consider that r represents the exploration parameter, while l represents the quality one.

Regarding cost functions, we tested all algorithms with four different sets of costs: c 1 , c 2 and c 3 correspond to the costs used in [START_REF] Abu-Aisheh | Graph edit distance contest: Results and future challenges[END_REF], while c 4 is the cost function used in [5] and references therein. Note that c 1 , c 2 and c 4 correspond to metric costs where a substitution cost of two elements is lower or equal to the cost of the removal of the first element together with the insertion of the second one. Conversely, c 3 is an anti-metric cost violating this last inequality. The main idea between these two classes of cost functions is that metric cost functions favor substitutions while the anti-metric ones favor deletions and insertions.

All tests were performed using 4 AMD Opteron processors at 2.6Ghz with 512G of RAM. The number of parallel threads was limited to 40 (which corresponds to parameter k). The code for the algorithm is written in C++.

Behavior of the algorithm w.r.t. parameter α

We tested three versions of RANDPOST with several values for parameter α of RANDGEN, and several cost functions (see the previous section). The most significant results are presented in Figure 1 for MAO, a dataset with enough and relatively simple instances so that interesting statistical tendencies can emerge. Contrasting tendencies can be observed with the metric cost function c 4 and the anti-metric one c 3 . Interestingly, the algorithm performs better as the initial proportion of "unfavored" choice rises. We believe that this is due to the design of the IPFP gradient descent, which is likely to find a better local minimum when starting from a solution including a greater number of "neutral" (in the sense of easily improvable) assignments.

Unfortunately, the behavior that we observe on MAO does not emerge with the same clarity on more complex datasets containing bigger or unlabeled graphs. However, it seems that IPFP requires a medium value for α (around 0.4) to perform best when dealing with unlabeled graphs. For bigger graphs (more than 40 vertices) high values of α seem to produce better starting points for IPFP, independently of cost functions. 2 presents the performance of the four versions of RANDPOST(k, l) that we mentioned earlier, plus RANDPOST(1, 0) which corresponds to a single run of IPFP starting from a random candidate solution. For each pair of graphs in each dataset, we extracted the best known GED among those returned by a set of 14 algorithms (9 algorithms of [START_REF] Abu-Aisheh | Graph edit distance contest: Results and future challenges[END_REF] + 5 versions of RANDPOST), except for ClinTox and MUTA100+ that weren't part of the benchmark in [START_REF] Abu-Aisheh | A graph database repository and performance evaluation metrics for graph edit distance[END_REF]. For these two datasets, the best GED was extracted from our 5 algorithms alone. The "err." column presents the mean error w.r.t. best known solutions, while the "%best" column presents the proportion of pairs of graphs for which the best known GED was found. For each dataset, we selected the value of α leading to a minimal mean GED over all 5 tested algorithms. The selected value is indicated in the table. Due to space restrictions, we present results regarding the metric cost c1 only. The same tendencies can be observed with all the other cost functions. The tendencies that emerge from Table 2 are quite clear: the more qualitative versions of RANDPOST(k, r, l) perform better than all algorithms presented in [START_REF] Abu-Aisheh | Graph edit distance contest: Results and future challenges[END_REF] on datasets with labeled graphs containing at least 60 vertices. Under this threshold, the balance between exploration and quality criteria that performs better GED favors more exploratory methods as the size of the graphs decreases. Further analysis shows that the phenomenon is deeply linked to the speed and quality of convergence of the algorithms: a more exploratory version of RANDPOST will ultimately converge to better GED estimations, but it will also converge at a slower rate. On the other hand, bigger graphs lead to slower overall convergence rates. These two phenomenons are visible in Figure 2. Both plots represent the improvement in GED estimations over the successive loops of RANDPOST. Each stairstep measures the best GED computed in a loop, and as the x-axis represents the number of computed solution, the length of the steps equals r for each algorithm RANDPOST(k, r, l).

Algorithms MAO α = 0 PAH α = 0.3 ClinTox α = 0.
When dealing with smaller graphs, qualitative methods converge very rapidly to suboptimal solutions, while the exploratory ones converge more slowly to better GED estimations. On the other hand, when dealing with bigger graphs, the fast rate of convergence of qualitative methods becomes a strength rather than a flaw, Fig. 2b shows that when the number of computed solutions is limited to 40 (which corresponds to the results in Table 2), none of the algorithms has yet converged, so that the faster converging algorithm yields better results. This phenomenon eventually reverses on the long run: as an example, Fig. 2b suggests that the limit on the number of computed solutions must be brought as high as 90 for RANDPOST(40, 10, 20) to outperform RANDPOST(40, 5, 40) on MUTA70.

Conclusion

Using a new iterative IPFP-based algorithm relying on stochastically generated solutions, we investigated the relative importance of exploration and quality criteria when generating candidate solutions for a multistart version of IPFP. Our results suggest that the balance leading to better GED estimations depends mostly on some ratio between the dimension of the problem at hand and the overall number of generated solutions.

1 begin 2 (x ∆)b 3

 123 Minimize a linear approximation of Q around the current solution x in the discrete domain by solving an LSAP:b ← argmin b∈πn,m, Perform the descent by minimizing Q along the segment [x, b] in the continuous domain: α ← argmin α∈[0,1]

1 begin 2 * 4 for i=1 to l do 5

 1245 Generate an initial set S ⊂ Dn,m,ε of k solutions using RANDGEN(α)3Start refining all solutions in S using IPFP, and stop the refinement process when r of them have converged, which results in a set of r improved solutions S Update matrix Ψ in the following way: Ψ ← x∈S * x/|S * |.

6

 6

Fig. 2 :

 2 Fig. 2: Convergence of RANDPOST on datasets MUTA-20 and MUTA-70

  are added. These virtual vertices, denoted by G and H , correspond respectively to insertions and deletions. An assignment i → H (resp. G → j) corresponds to the deletion of vertex i of G (resp. insertion of vertex j of H).

	holds for
	a given scalar β ∈ (0, 1), or if a given number of iterations is reached
	Algorithm 1: FW(∆, x)
	m + 1 in H

Table 1 :

 1 Characteristics of datasets

	Dataset	#graphs Avg order Labels on nodes/edges
	MAO	68	18.4	labeled
	PAH	94	20.7	unlabeled
	MUTA10-70	10	10-70	labeled
	MUTAmix	10	45	labeled
	MUTA100+	10	131.6	labeled
	ClinTox	25	115.7	labeled

Table 2 :

 2

	9

Experimental results of RANDPOST(k, r, l), cost c1 4.3 Performance of RANDPOST Table


Marine Désert

Sylvain Ravier

Gregory Gille

Angélina Quinapallo

Alexandre Armengaud

Gabrielle Pochet

Jean-Luc Savelli

Henri Wortham

Etienne Quivet
email: etiennequivet@univ-amu.fr

Grégory Gille

Grégory Gille

Spatial and temporal distribution of current-use pesticides in ambient

Keywords: Pesticides, Atmosphere, Monitoring, Transport Highlights

teaching and research institutions in France or abroad, or from public or private research centers.

Introduction

Ubiquitous in today's farming culture, pesticides are used to protect the crops against weeds (herbicides), fungi (fungicides), or insects (insecticides), and to ensure a regularity in agricultural production necessary for people's diet. Not surprisingly, the current agricultural practice is considered as the main source of atmospheric pesticide pollution [START_REF] Samsonov | Physicochemical model and kinetics of pesticide constituent evaporation out of multi-ingredient polydisperse aerosols[END_REF][START_REF] Lichiheb | Measuring Leaf Penetration and Volatilization of Chlorothalonil and Epoxiconazole Applied on Wheat Leaves in a Laboratory-Scale Experiment[END_REF], even if other important sources of pesticide pollution include production, industrial, and urban applications.

In 2016, according to the most recent statistics on agriculture, forestry, and fisheries for European Union (Eurostat, 2018), the total quantity of pesticide sales in Europe amounted to close to 360,000 tons with a major use of fungicides and bactericides (44%) and herbicides (32%). Looking at the individual EU Member States, France ranks at the second pesticide consumer, with 20.2% of the total agricultural yield (i.e., around 72,000 tons of active substances). France has a wide variety of agricultural crops (cereals, sugar beets, oleaginous, potatoes, and perennial crops such as vineyards and orchards). With about 27 million hectares of Utilized Agricultural Land (30% of the total surface area), i.e., around 2.7 kilograms of active substances sold per hectare, France is one of the first countries to export foodstuffs (Agreste, 2010). Despite a protective role (mosquito control, allergenic plants, mycotoxins for example), the use of pesticides is currently a real health issue. Several reports highlighted a worrying situation for pesticide exposure in terms of public health (Inserm, 2013) and environmental hazards (EFSA, 2013). Toxicity of pesticides and their health hazards have been the subject of many studies. All these studies demonstrated that pesticides pose adverse health effects, from skin and eye irritation to asthma and bronchial diseases [START_REF] Canal-Raffin | Physicochemical characteristics and bronchial epithelial cell cytotoxicity of Folpan 80 WG® and Myco 500®, two commercial forms of folpet[END_REF], 2008), decrease of fertility [START_REF] Al-Thani | Assessment of reproductive and fertility effects of amitraz pesticide in male mice[END_REF][START_REF] Clementi | Pesticides and fertility: an epidemiological study in Northeast Italy and review of the literature[END_REF][START_REF] Petrelli | Environmental risk factors and male fertility and reproduction[END_REF], birth defects and fetal death [START_REF] Clementi | A study of the impact of agricultural pesticide use on the prevalence of birth defects in northeast Italy[END_REF]Redigor et al., 2004), Parkinson disease [START_REF] Hatcher | Parkinson's disease and pesticides: a toxicological perspective[END_REF][START_REF] Le Couteur | Pesticides and Parkinson's disease[END_REF], neurotoxicity [START_REF] Axelrad | Interactions between pesticides and components of pesticide formulations in an in vitro neurotoxicity test[END_REF][START_REF] Raffaele | The use of developmental neurotoxicity data in pesticide risk assessments[END_REF], and finally to very severe illnesses such as lung, prostate and breast cancer (de Brito Sa Stoppelli and Crestana, 2005;[START_REF] A N U S C R I P T A C C E P T E D Accepted Manuscript Lee | Pesticide exposure and lung cancer mortality in Leningrad province in Russia[END_REF][START_REF] Van Maele-Fabry | Occupation related pesticide exposure and cancer of the prostate: a meta-analysis[END_REF].

On the other hand, the main environmental concern lies in the fact that most pesticides are persistent particularly in the atmosphere [START_REF] Socorro | Heterogeneous Reactions of Ozone with Commonly Used Pesticides Adsorbed on Silica Particles[END_REF][START_REF] Socorro | The persistence of pesticides in atmospheric particulate phase: An emerging air quality issue[END_REF]Mattei et al., 2018 and reference therein). Hence, the atmosphere is an important spread vector at local, regional, and global scales. As proof, a wide variety of pesticides was found in the atmosphere, including

M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT

remote areas where they are not spread (Arctic, Antarctic, mountain peaks, etc.) [START_REF] Ruggirello | Current use and legacy pesticide deposition to ice caps on Svalbard, Norway[END_REF].

Nevertheless, there is currently no regulatory threshold for the content of pesticides in the atmosphere and no obligation to monitor them. However, atmospheric pesticide contamination was observed in urban and rural areas with concentration levels from some picograms per cubic meter (pg m -3 ) to several nanograms per cubic meter (ng m -3 ) [START_REF] Coscollà | Particle size distributions of currently used pesticides in a rural atmosphere of France[END_REF][START_REF] Coscollà | Particle size distributions of currently used pesticides in ambient air of an agricultural Mediterranean area[END_REF][START_REF] Estellano | Assessing Levels and Seasonal Variations of Current-Use Pesticides (CUPs) in the Tuscan Atmosphere, Italy, Using Polyurethane Foam Disks (PUF) Passive Air Samplers[END_REF]Zivan et al., 2016Zivan et al., , 2017)). This contamination can be due to spray drift during pesticide applications (about 15 to 40%; [START_REF] Sinfort | Influence des conditions et matériels de pulvérisation sur les pertes de pesticides au sol et dans l'air en viticulture Languedocienne[END_REF][START_REF] Yates | Emissions of 1,3-Dichloropropene and Chloropicrin after Soil Fumigation under Field Conditions[END_REF]Zivan et al., 2016Zivan et al., , 2017)), post-application volatilization from treated plants (Zivan et al., 2016(Zivan et al., , 2017)), soil [START_REF] White | Ambient Air Concentrations of Pesticides Used in Potato Cultivation in Prince Edward Island, Canada[END_REF], or aquatic surfaces [START_REF] Luo | Modeling complexity in simulating pesticide fate in a rice paddy[END_REF]) (about 0.1 to several dozen %; [START_REF] Lichiheb | Measuring Leaf Penetration and Volatilization of Chlorothalonil and Epoxiconazole Applied on Wheat Leaves in a Laboratory-Scale Experiment[END_REF], and wind erosion [START_REF] Glotfelty | Volatilization and wind erosion of soil surface applied atrazine, simazine, alachlor and toxaphene[END_REF].

The aim of this work is to establish a diagnostic of pesticides concentrations in the air of two French regions, Provence-Alpes-Côte d'Azur (PACA) and Corsica, in different contexts of sources (non-agricultural and various agricultural sectors: field crops, orchards, vegetable crops, vineyards, etc.). For six years (2012)(2013)(2014)(2015)(2016)(2017), 59 active substances were monitored in 6 urban sites (Arles, Avignon, Cannes, Port-de-Bouc, Nice, and Toulon) and 2 rural sites (Aléria and Cavaillon). The compounds under study were selected based on their regional sales quantity, their toxicity, and their atmospheric lifetime. They included authorized, banned or relatively recent banned classes of herbicides, fungicides, and insecticides (Table 1).

Spatial and temporal distributions of detection frequencies and atmospheric concentrations were analyzed according to sampling sites.

Material and methods

Chemicals

Pesticide standards were purchased from Sigma-Aldrich (PESTANAL, analytical standard) and their purity was guaranteed at least 95%. The main physicochemical properties, the agricultural uses and the legal situation of pesticides studies are summarized in Table 1. Acetonitrile (ACN) was purchased from Fisher Scientific (Optima LC/MS Grade, 99.99%). Dichloromethane (DCM) and acetone were purchased from Sigma-Aldrich (Chromasolv for HPLC, ≥ 99.8%). The Ultra-High Quality water (UHQ water, 18.2 MΩ cm -1 at 25°C) was obtained from a MilliQ water purification system (Direct 8 MilliQ, Merck Millipore).

M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT

Triphenyl phosphate (TPP; ≥ 99%) and anthracene-d10 (≥ 98%) were used as internal standards and were purchased from Sigma-Aldrich.

Sampling and site characterization

A total of 59 active substances were monitored during sampling campaigns (2012-2017), including 25 herbicides (H), 19 fungicides (F), 14 insecticides (I), and 1 synergist (i.e., Piperonyl butoxide (PBO) which will be considered as insecticide thereafter). According to the octanol-air partition coefficient model [START_REF] Bidleman | Sorption to aerosols. Chapter 10[END_REF], the estimated distribution of the active substances between gas and particulate phases (Table S1) showed a large distribution among the compounds under study ranging from less than 1% sorbed to atmospheric particulates (Dimethenamid-P, H) to almost 100% (Fenhexamid, F).

Sampling was undertaken at eight sites located throughout the Provence-Alpes-Côte-d'Azur (PACA) region and Corsica, France (Figure S1). The description of sampling sites and the sampling periods are summarized in Table 2. All the samplings of the urban sites were implemented in their city center. As regards the two rural sites, Cavaillon (hamlet of Les Vignères) is located in an intensive arboriculture area, whereas Aléria (hamlet of Teppe Rosse) is located near fields of cereals (corn, barley), vineyards and orchards (clementine). The sampling was carried out according to the AFNOR standard XP X43-058 (AFNOR, 2007a) using a high-volume sampler (Digitel Aerosol Sampler DHA-80) equipped with a Total Suspended Particulates (TSP) inlet. Samplings were done two meters above groundlevel. Particulate and gaseous samples were collected simultaneously on 150 mm diameter ashless quartz microfibre filter (ALBET LabScience) for particulate pesticides followed by a combination of two polyurethane foams (PUF; Tisch Environmental, Inc.) and 20 g of Amberlite XAD-2 resin (Supelco) for gaseous pesticides. Prior to use, filters, PUF, and resin were subject to clean-up by warming at 900°C and with DCM and acetone, respectively. The sampling flow was 10 m 3 h -1 for 48 h, giving a total volume of filtered air around 480 m 3 .

A total of approximately 24 samples per site and per year were collected, for a total of 726 samples. The sampling frequency was higher during spring and summer (April to September) corresponding to application periods. Once collected, samples were stored and protected from light at -18°C until their analysis.

Moreover, in order to estimate the sampling matrixes contamination induced by sample handling and storage, field air blanks were regularly carried out at each site. They consist of filter, PUFs, and resin that were briefly placed in the high-volume sampler then stored and analyzed according to the same protocol than the other samples. No contamination was detected.

Sample extraction and analysis

According to the AFNOR standard XP X43-058 (AFNOR, 2007b), both particulate (filter)

and gaseous (PUF and resin) phases were extracted simultaneously using an accelerated solvent extractor (ASE 350,Dionex). Each sample was introduced in a 99 mL stainless-steel cell with two internal standard solutions of TPP (20 µL; 50 mg L -1 ) and anthracene-d10 (20 Afterward, the extracts were concentrated under a nitrogen flow using a concentration workstation (TurboVap II, Biotage) with pressure 1.1 bar and a water bath at 40°C, until a 500 µL extract was obtained.

A first aliquot portion was directly analyzed by gas chromatography coupled to tandem mass spectrometry (GC-MS/MS), with a Trace GC Ultra (Thermo Scientific) coupled to a TSQ QuantumTM Triple Quadrupole (Thermo Scientific) using electron impact ionization (70 eV) according to the following parameters: column THERMO TG-5MS (internal diameter 0.25 mm, length 30 m, film thickness 0.25 µm), carrier gas: helium with 1 mL min -1 flow rate, split/splitless injector: splitless time of 2 min with surge pressure of 300 kPa during 2 min, injection volume: 1 µL, inlet temperature: 250°C, interface temperature: 330°C, with the following temperature program: hold 3 min at 75°C; increase temperature to 180°C at a rate of 25°C min -1 ; increase temperature to 300°C at 5°C min -1 ; hold 3 min at 300°C. The characteristic selected ions for pesticides quantification and limits of detection (LOD) are presented in Table S2. Data acquisition and treatments of selected-reaction mass chromatograms were provided by the Xcalibur software (v.2.2, Thermo Scientific).

DCM was removed from the second aliquot portion and was replaced by acetonitrile (TurboVap II) prior to an analysis by ultra-performance liquid chromatography (Acquity, Waters) interfaced with a Quadrupole-Time-of-Flight Mass Spectrometer (Synapt G2 HDMS, Waters) (UPLC-MS/MS) equipped with an electrospray ion source (ESI). The mass spectrometer was used in its resolution mode, up to 18 000 FWHM (Full width at half maximum) at 400 Th. The chromatographic separation was carried out on an Acquity UPLC column BEH C18, 1.7 µm particle size, 100 mm × 2.1 mm i.d. (Waters), at 40°C. The mobile phase consisted in (A) UHQ water + 0.1% formic acid and (B) ACN + 0.1% formic acid. The gradient elution was performed at a flow rate of 0.6 mL min -1 using 5% to 80% of B within 2.5 min and held at 80% of B for 0.5 min. The injection volume was 7.5 µL. Optimum ESI conditions were found using a -1 kV capillary voltage in negative mode and 0.5 kV in positive mode, 450°C desolvation temperature, 150°C source temperature, 20 L h -1 and 1000 L h -1 cone gas and desolvation gas flow rate, respectively. Dwell times of 0.20 s scan -1 were

M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT chosen.
For MS/MS experiment, collision gas was Argon 99.995% (Linde) with a pressure of approximately 1.4.10 -4 mbar in the collision cell. The characteristic selected ions for pesticides quantification, optimum cone voltage, collision energy, and limits of detection (LOD) are presented in Table S3. Data acquisition and treatments of selected-reaction mass chromatograms were provided by the MassLynx software (v.4.1, Waters).

The extraction and analysis methods have been validated by a national intercomparison exercise [START_REF] Marliere | Pesticides dans l'air ambiant : Intercomparaison analytique[END_REF].

Results and discussion

Detection frequencies and atmospheric concentrations

The maximum and median concentrations and the frequency of detection of measured pesticides in the eight sampling sites are summarized in Table 3. The minimum concentration is below the LOD for each compound, except for Lindane (min. 0.007 ng m -3 ) and Chlorpyrifos-methyl (min. 0.180 ng m -3 ) at Aléria where these pesticides were quantified in all samples.

4 fungicides (Dimethomorph, Fenhexamid, Folpet, and Tebuconazole), 5 herbicides (Chlorpropham, Diflufenican, Oxadiazon, Pendimethalin, and Propyzamide), and 6 insecticides (Chlorpyrifos, Cypermethrin, Lambda-cyhalothrin, Lindane, Permethrin, and PBO) were quantified in all sampling sites.

Max Median % ng m -3 ng m -3 % ng m -3 ng m -3 % ng m -3 ng m -3 % ng m -3 ng m -3

Fungicides (19)

Herbicides (25) 2,4D 0 - - 4 0.320 - 5 2.690 - 0 - - 2,4MCPA 0 - - 2 0.160 - 1 0.270 - 6 0.080 - Aclonifen 0 - - 0 - - 0 - - 0 - - Amitrole 0 - - 0 - - 0 - - 0 - -
Dimethenamid-P 0 - - non-targeted 0 - - non-targeted Flazasulfuron 0 - - 0 - - 0 - - 0 - - Flumioxazin 0 - - non-targeted 0 - - non-targeted Flurochloridone 0 - - 15 0.016 - 11 0.013 - 0 - - Fluroxypyr 0 - - 0 - - 0 - - 0 - - Isoproturon 0 - - non-targeted 0 - - non-targeted Lenacil 0 - - non-targeted 0 - - non-targeted Linuron 0 - - 0 - - 0 - - 0 - - Metazachlor 0 - - 15 0.039 - 15 0.113 - 0 - - S-
- 0 - - Sulcotrione 0 - - 0 - - 0 - - 0 - - Terbuthylazine 0 - - 0 - - 0 - - 0 - -
- - 0 - - 3 0.407 - 0 - - Diflubenzuron 0 - - 0 - - 1 0.440 - 0 - - Esbiothrin 0 - - 0 - - 0 - - 0 - - Fenoxycarb 0 - - 28
Herbicides (25) 2,4D 4 0.470 - 0 - - 0 - - 3 0.180 - 2,4MCPA 1 0.490 - 0 - - 0 - - 3 0.540 - Aclonifen 0 - - 0 - - 0 - - 0 - - Amitrole 0 - - 0 - - 0 - - 0 - -
Dimethenamid-P 0 - - 0 - - 0 - - non-targeted Flazasulfuron 0 - - 0 - - 0 - - 0 - - Flumioxazin 0 - - 0 - - 0 - - 0 - - Flurochloridone 13 0.006 - 1 0.010 - 6 0.050 - 2 0.002 - Fluroxypyr 1 0.570 - 0 - - 0 - - 0 - - Isoproturon 0 - - 0 - - 0 - - 0 - - Lenacil 0 - - 0 - - 0 - - non-targeted Linuron 0 - - 0 - - 0 - - 0 - -
0 - - 3 0.031 - 0 - - Thiamethoxam 0 - - 0 - - 0 - - 0 - - M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT (-) means < Limit of Detection

Detection frequencies

Figure 1 presents the detection frequency of the 59 active substances. The number in brackets refers to the total samples where the active substance was searched. This number of samples differ from a compound to another either because of sampling or analytical problems or because of the inclusion of the active substance during the period under study (e.g.,

Thiamethoxam was sampled for the first time in 2015).

Figure 1. Detection frequency of the 59 active substances. 'Found' is based on samples with concentrations above the limit of detection, and the number in brackets refers to total samples where the active substance was searched. Iprodione ( 506) Cyprodinil ( 707) Flurochloridone ( 726) Fenpropimorph ( 726) Epoxiconazole ( 64) Spiroxamine ( 506) Fenhexamid ( 726) Difenoconazole ( 701) Prosulfocarb ( 726) Fipronil ( 689) Metazachlor ( 726) Tolylfluanid ( 64) Fenoxycarb ( 726) Oxadiazon ( 711) Permethrin ( 693) Cypermethrin ( 726) Pyrimethanil ( 726) Folpet ( 726

M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT
Among the 14 active substances (i.e., 23.7% of searched compounds) which have never been detected, there are 1 fungicide (Fenpropidin), 2 insecticides (Esbiothrin and Thiamethoxam), and 11 herbicides (Aclonifen, Amitrole, Clomazone, Dimethenamid-P, Flazasulfuron, Flumioxazin, Isoproturon, Lenacil, Linuron, Sulcotrione, and Terbuthylazine). In France, even though they were authorized during the sampling period, some of them were subject to restrictive derogation. Thiamethoxam was only used for ornamental crops because of their potential responsibility in the mortality of pollinating insects [START_REF] Tsvetkov | Chronic exposure to neonicotinoids reduces honey bee health near corn crops[END_REF].

Terbuthylazine was only used for corn because of their genotoxic effects such as DNA damage [START_REF] Lovakovic | Effects of sub-chronic exposure to terbuthylazine on DNA damage, oxidative stress and parent compound/metabolite levels in adult male rats[END_REF]. On the other hand, Esbiothrin was only used as a biocide against household pest insects, making its ambient air detection more unlikely.

Fungicides: The most frequently detected fungicide was Tebuconazole (64.6% It should be noted that Flusilazole (2.1%) was banned since 2013 and that its detections were all made during 2012. Tolylfluanid (17.2%) was banned for agricultural uses but was authorized as antifouling agent biocide. Therefore, it is not unusual to find it from sampling sites close to ports (mainly Nice).

Herbicides: Of the 14 herbicides detected, four have a detection frequency higher than 20%

namely Pendimethalin (65.7%), Diflufenican (27.7%), Chlorpropham (27.0%), and Propyzamide (25.4%). Pendimethalin is a selective dinitroaniline herbicide used in pre-and post-emergence applications to control certain broadleaf weeds and most annual grasses [START_REF] Koblizkova | Sorbent impregnated polyurethane foam disk passive air samplers for investigating current-use pesticides at the global scale[END_REF]. [START_REF] Schummer | Temporal variations of concentrations of currently used pesticides in the atmosphere of Strasbourg, France[END_REF]2012-2015;[START_REF] Villiot | Temporal and seasonal variation of atmospheric concentrations of currently used pesticides in Champagne in the centre of Reims from 2012 to 2015[END_REF]. In Tuscany region (Italy), Pendimethalin was less frequently detected (25%) but sampling was made using polyurethane foam disks as passive air samplers which do not take into account the particulate phase (2008[START_REF] Marliere | Observation des niveaux de concentration en pesticides dans l'air ambiant (2/2)[END_REF][START_REF] Estellano | Assessing Levels and Seasonal Variations of Current-Use Pesticides (CUPs) in the Tuscan Atmosphere, Italy, Using Polyurethane Foam Disks (PUF) Passive Air Samplers[END_REF].

The detection frequencies of Diflufenican (non-detected) and Propyzamide (4%) in Ile-de-France region [START_REF] A N U S C R I P T A C C E P T E D Accepted | The 2010 European Union Report on Pesticide Residues in Food[END_REF]-2014;[START_REF] Airparif | Les pesticides dans l'air francilien -Partie II[END_REF] were much lower than those found in PACA and Corsica, except for Nice. According to the sales data for areas under study (BNVD, 2017), Diflufenican was sold for rapeseed crop areas but also non-cropped areas while Propyzamide was sold for a wide range of uses, such as seed crops, vineyards, orchards, rapeseed, and ornamental crops. The difference could be due to the LOD, lower in this study. In the same way, Chlorpropham, used for potatoes, was detected in a lower frequency (5%) in the Valencia region (2008-2014;[START_REF] López | Risk assessment of airborne pesticides in a Mediterranean region of Spain[END_REF].

Insecticides: 13 insecticides were detected including four above 50% of detection:

Chlorpyrifos-methyl (50.0%), PBO (52.6%), Chlorpyrifos (71.5%), and Lindane (98.6%).

PBO is a synergist widely used with insecticides as pyrethroids and carbamates. It was rarely monitored in the ambient atmosphere but its wide presence in samples implies the presence of other active substances. Field of uses of Chlorpyrifos and Chlorpyrifos-methyl currently concerns vineyards and orchards (Index Acta Phytosanitaire, 2018), both agricultural areas broadly represented around most of the sampling sites.

Despite its ban since 1998 for agricultural uses and 2007 for biocidal treatments, Lindane is the most frequently detected active substance. Classified as a persistent organic pollutant (UNEP, 2001), its persistence in the environment could explain its highest detection frequency.

Atmospheric concentrations

With respect to atmospheric concentrations, it is still difficult to classify them because there is no regulatory threshold. However, 3 categories of atmospheric concentrations such as, below 0.1 ng m -3 , between 0.1 and 1 ng m -3 , and above 1 ng m -3 , allow a distribution of detected active substances, with 74.4%, 20.5%, and 5.1% of samples, respectively (Figure 2). France (2007;[START_REF] Schummer | Temporal variations of concentrations of currently used pesticides in the atmosphere of Strasbourg, France[END_REF]Québec, Canada (2004;[START_REF] Aulagnier | Pesticides measured in air and precipitation in the Yamaska Basin (Québec): occurrence and concentrations in 2004[END_REF].

Nevertheless, in this literature, exceptions with concentrations higher than 1 ng.m -3 are observed on a few samples, i.e., Diflufenican until 2.25 ng m -3 (Ineris, 2008) and Epoxiconazole until 3.99 ng m -3 [START_REF] Coscollà | Human exposure and risk assessment to airborne pesticides in a rural French community[END_REF]. 17 active substances exceeded an atmospheric concentration of 1 ng m -3 for at least one sample. The most frequently detected at this concentration level were Imidacloprid (I, 4 times/4 detections), Folpet (F, 147/162), Chlorpyrifos (I, 56/520), and Pendimethalin (H, 29/464).

Imidacloprid was always detected at atmospheric concentrations higher than 1 ng m -3 . Only one pesticide was always detected at atmospheric concentrations above 1 ng m -3 . Indeed, on 2 July 2012, it was simultaneously detected at concentrations ranging from 3.3 to 7.3 ng m -3 in Arles, Avignon, Cavaillon, and Toulon. Imidacloprid is a neonicotinoid insecticide like

Imidacloprid (4) 2,4D (18)
Diflubenzuron ( 2)

Fluazinam ( 1)

Fluroxypyr ( 1) Folpet ( 162)

Cymoxanil ( 11)

Iprodione ( 25)

Cyprodinil (40) 2,4MCPA (9)
Permethrin ( 132)

Deltamethrin ( 12)

Spiroxamine ( 46)

Chlorpyrifos-methyl ( 32)

Fenoxycarb ( 125)

Pyrimethanil ( 162)

Difenoconazole ( 80)

Tolylfluanid ( 11) Lindane ( 701) Chlorpyrifos ( 519)

Pendimethalin ( 464)

Lambda-cyhalothrin (32) S-Metolachlor ( 122)

Prosulfocarb ( 87)

Triallate ( 19)

Oxadiazon ( 135)

Tebuconazole ( 469)

Flusilazole ( 15)

Dimethomorph ( 166)

Pirimicarb ( 16)

Tetraconazole ( 167)

Piperonyl Butoxide (PBO) ( 382)

Cypermethrin ( 154) Boscalid ( 215) Propyzamide ( 184)

Fipronil ( 84)

Metazachlor ( 99)

Fenpropimorph ( 53) Fenhexamid ( 66) Chlorpropham (196) Epoxiconazole ( 5 Cavaillon (Figure S2), which could explain the detection of Imidacloprid at several tens of kilometers away, suggesting a transport at a regional scale.

Folpet (max. 31.41 ng m -3 ) is a broad-spectrum fungicide generally used on vineyards (Index Acta Phytosanitaire, 2018). Its atmospheric monitoring has already regularly shown concentrations above 1 ng m -3 in British Columbia, Canada (2004Canada ( -2006;;[START_REF] Raina | Atmospheric Concentrations of Captan and Folpet in the Lower Fraser Valley Agricultural Region of Canada[END_REF] and in many French regions (2006[START_REF] A N U S C R I P T A C C E P T E D Accepted | The 2010 European Union Report on Pesticide Residues in Food[END_REF][START_REF] Schummer | Temporal variations of concentrations of currently used pesticides in the atmosphere of Strasbourg, France[END_REF][START_REF] Coscollà | Human exposure and risk assessment to airborne pesticides in a rural French community[END_REF] reaching the highest concentration at 3,950 ng m -3 in Grand Est region (2005;[START_REF] Marliere | Observation des niveaux de concentration en pesticides dans l'air ambiant (2/2)[END_REF].

French monitoring reported the presence of Pendimethalin (max. 13.35 ng m -3 ) at high concentrations ranged from 0.32 to 7.83 ng m -3 in Grand Est region [START_REF] Schummer | Temporal variations of concentrations of currently used pesticides in the atmosphere of Strasbourg, France[END_REF][START_REF] Villiot | Temporal and seasonal variation of atmospheric concentrations of currently used pesticides in Champagne in the centre of Reims from 2012 to 2015[END_REF] and from 0.13 to 117.32 ng m -3 in Centre region [START_REF] Coscollà | Human exposure and risk assessment to airborne pesticides in a rural French community[END_REF].

These maximum atmospheric concentrations were significantly higher than those measured in other countries, such as in Canada (max. 140 pg m -3 ; [START_REF] Gouin | Atmospheric concentrations of current-use pesticides across south-central Ontario using monthly-resolved passive air samplers[END_REF] or Australia (max.

200 pg m -3 ; [START_REF] Koblizkova | Sorbent impregnated polyurethane foam disk passive air samplers for investigating current-use pesticides at the global scale[END_REF].

Chlorpyrifos (max. 407.79 ng m -3 ) is one of the most searched pesticides in the atmosphere.

Surveys performed in France (range from 0.01 to 956.30 ng m -3 ; [START_REF] Marliere | Observation des niveaux de concentration en pesticides dans l'air ambiant (2/2)[END_REF][START_REF] Airparif | Les pesticides dans l'air francilien -Partie II[END_REF][START_REF] Coscollà | Human exposure and risk assessment to airborne pesticides in a rural French community[END_REF][START_REF] Villiot | Temporal and seasonal variation of atmospheric concentrations of currently used pesticides in Champagne in the centre of Reims from 2012 to 2015[END_REF], in Italy (range from 3 to 580 pg m -3 ; [START_REF] Estellano | Assessing Levels and Seasonal Variations of Current-Use Pesticides (CUPs) in the Tuscan Atmosphere, Italy, Using Polyurethane Foam Disks (PUF) Passive Air Samplers[END_REF], in Spain (range from 1 to 210 pg m -3 ; [START_REF] Yusà | New screening approach for risk assessment of pesticides in ambient air[END_REF], in Czech Republic (max. 360 pg m -3 ; [START_REF] Koblizkova | Sorbent impregnated polyurethane foam disk passive air samplers for investigating current-use pesticides at the global scale[END_REF], in Canada (range from 7 to 868 pg m -3 ; [START_REF] Yao | Spatial and temporal distribution of pesticide air concentrations in Canadian agricultural regions[END_REF][START_REF] Aulagnier | Pesticides measured in air and precipitation in the Yamaska Basin (Québec): occurrence and concentrations in 2004[END_REF][START_REF] Gouin | Atmospheric concentrations of current-use pesticides across south-central Ontario using monthly-resolved passive air samplers[END_REF][START_REF] Hayward | Levels and seasonal variability of pesticides in the rural atmosphere of Southern Ontario[END_REF], in USA (max. 2.9 ng m -3 ; [START_REF] Peck | Gas-phase concentrations of current-use pesticides in Iowa[END_REF][START_REF] A N U S C R I P T A C C E P T E D Accepted Manuscript | Semivolatile Endocrine-Disrupting Compounds in Paired Indoor and Outdoor Air in Two Northern California Communities[END_REF], in Japan (max. 51 ng m -3 ; [START_REF] Kawahara | Air pollution and young children's inhalation exposure to organophosphorus pesticide in an agricultural community in Japan[END_REF], and in China (range from 0.072 to 2.901 ng m -3 ; [START_REF] Li | Occurrence, seasonal variation and inhalation exposure of atmospheric organophosphate and pyrethroid pesticides in an urban community in South China[END_REF], have

shown variable concentrations but often associated with a high detection frequency (as in this study).

Spatial distribution of pesticides

Each sampling site has a specific typology (rural or urban) and is characterized by its environment, in particular by the surrounding crops (Table 2). However, none of the 8 sampling sites seem to be impacted by only one type of crop, since, in South of France,

M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT

agricultural practices associate generally several crops. The spatial distribution shows how important the use of some pesticide families (and sometimes some active substances) is at each sampling sites. The distribution between urban and rural typologies for individual active substance highlights the atmospheric transport phenomenon at local and regional scales. In view of atmospheric concentrations, two trends of pesticide distribution emerge 1/ a majority of fungicides, like Aléria, Arles, Avignon, Port-de-Bouc, and Toulon, and 2/ a majority of insecticides, like Cannes, Cavaillon, and Nice. On the eight sites, the herbicides contribution was negligible or at least minority (Cannes, 19%) although the herbicides family was the most represented in the searched pesticide list (25 out of 59 pesticides).

Pesticide family distribution by sampling sites

Although it was not detected in 2016, Folpet was the most concentrated active substance in the five sampling sites where fungicides were predominant. In France, it is one of the most widely used fungicides in vineyards [START_REF] De Lozzo | Impact des pesticides sur la santé du vigneron[END_REF] with around 360 tons sold (2012)(2013)(2014)(2015)(2016) for the sampling areas (BNVD, 2017). Moreover, within a radius of 10 km around the sampling sites (Table 2), Avignon has the highest land-use rate by vineyards (30%). Folpet can also be used to cure the cancerous disease (Crumenulopsis mainly) of some Mediterranean forests [START_REF] Morelet | La maladie chancreuse du pin d'Alep -Recherche de moyens d'intervention[END_REF]. The development of this fungus favored by a higher rainfall was identified during the measurement campaign close to the three other sampling sites in PACA (DRAAF PACA, 2015).

Aléria Arles Avignon Cannes Cavaillon Nice Port-de-Bouc Toulon M A N U S C R I P T A C C E P T E D ACCEPTED MANUSCRIPT
With regard to insecticides, samples of Cannes and Nice were mainly composed by Lindane, Permethrin, and PBO (synergist widely used with pyrethroids and carbamates). Within a radius of 10 km, the urban fabric is the most important for Cannes (46%) and Nice (47%).

More, both cities have adopted a 'zero pesticide' policy for the maintenance of green spaces and roads. The ban on agricultural uses of Permethrin suggests therefore individual biocidal uses. From Cavaillon, Chlorpyrifos was the insecticide the most concentrated (Maximum:

407.79 ng m -3 ) which is consistent with the presence of apple, pear, and cherry orchards.

The largest contributions of herbicides were also at Cannes (Oxadiazon (mainly quantified in 2012), Pendimethalin, Chlorpropham, and Diflufenican). Except for Diflufenican which is only used for cereals, all other active substances can be used for ornamental and flower crops (Index Acta Phytosanitaire, 2018) as logical as Alpes-Maritimes (Cannes) is the most important area for horticulture and the production of cut flowers (France AgriMer, 2013).

Pesticide distribution by typology: rural vs. urban

Figure 4 shows the distribution of active substances between urban and rural sampling sites.

The number of detections is weighted by the number of samples collected on each site typology (urban and rural). sites. The number in brackets refers to total samples where the active substance was detected. Overall, active substances were generally more frequently found in rural sites (35 active substances over 45 detected). However, many compounds showed similar patterns and atmospheric concentrations on both site typologies (rural and urban) suggesting their important use even on urban sites.

Nevertheless, according to both the atmospheric persistence of the active substances and the small distances between agricultural areas and cities, pesticides can undergo atmospheric transport at the local and regional scale which contribute to explain their presence in urban sites. To test this hypothesis, wind speed and direction have to be considered. As an example, at the urban sampling site of Avignon, a very good fit is observed between the pollution roses model (v. 3.60, Petit et al., 2017).

Temporal distribution of pesticides

The atmospheric contamination level by pesticides can be described from a temporal viewpoint for each detected active substance. Therefore, based on a sampling dataset of 6years, a bunch of statistical distributions of the data can be calculated for monthly and yearly patterns. Ideally, the temporal distributions should be represented either by types of crops or sampling site typologies (urban or rural). However, it is difficult to consider a sampling area as wholly owned by one type of crop, since several crop types are generally associated (Table 2). Moreover, for pesticides having too low detection frequencies, it is impossible to realize monthly profiles.

Therefore, the monthly distributions were described considering all sampling sites (i.e., for all types of crops) while the yearly distributions depicted each sampling site. Both monthly (Figure 6) and yearly (Figure 7) distributions were only presented for the six active substances

Monthly distribution for all sampling sites

The monthly distribution provides information on seasonal applications of the active substances. Because of a lack of information about the farming practices (applied amounts and nature of commercial formulations, dates of treatment, application equipment) at each sampling area, these detection periods were not always easily correlated with their uses.

However, this detection timeline highlights the period(s) of population exposure and will be used to set up sampling strategies. (RH > 90%) and temperate (10-25°C) periods favor fungus growth. It is therefore logical that fungicides were mainly detected during the spring and early summer periods (April to July), and to a lesser extent, during the fall period (October to December) (Figure S3). The difference of action modes, preventive and curative actions for Tebuconazole and only preventive action for Folpet (Index Acta Phytosanitaire, 2018), could explain that Folpet was not detected during the fall period (Figure 6).

The weed control is ongoing throughout the year, due to the pre-emergence and postemergence treatments for a wide range of crops, such as winter and spring cereals, vegetable crops… A general trend is therefore hard to find. For example, due to their different fields of application, Propyzamide, Pendimethalin, and Prosulfocarb were more frequently detected during the winter, spring, and fall periods, respectively (Figure 6 and Figure S3). These observations are consistent with the conclusions of the French Agency for Food, Environmental and Occupational Health & Safety (ANSES, 2010).

The insecticide Chlorpyrifos was used extensively during the spring and summer periods especially in Cavaillon, which is characterized by intensive orchard crops. This timeline is consistent with atmospheric concentrations measured in Tuscany region (2008[START_REF] Marliere | Observation des niveaux de concentration en pesticides dans l'air ambiant (2/2)[END_REF][START_REF] Estellano | Assessing Levels and Seasonal Variations of Current-Use Pesticides (CUPs) in the Tuscan Atmosphere, Italy, Using Polyurethane Foam Disks (PUF) Passive Air Samplers[END_REF] and in Canada (2006Canada ( -2007;;[START_REF] Hayward | Levels and seasonal variability of pesticides in the rural atmosphere of Southern Ontario[END_REF]. Lindane is a particular case because it was banned since 1998 and 2007 for agricultural and biocidal uses, respectively. Despite these strong restrictions, moderate but regular concentrations were observed through the year in all sampling sites (Median: 0.065 ng m -3 ). The same pattern was previously observed in other French regions such as in the Centre region (2006-2008;Coscollà et al., 2010). Finally, according to French (2012-2015;[START_REF] Villiot | Temporal and seasonal variation of atmospheric concentrations of currently used pesticides in Champagne in the centre of Reims from 2012 to 2015[END_REF] and Chinese (2011Chinese ( -2012;;[START_REF] Li | Occurrence, seasonal variation and inhalation exposure of atmospheric organophosphate and pyrethroid pesticides in an urban community in South China[END_REF] studies, the other insecticides (including PBO) were detected throughout the year (Figure 5 and Figure S3). Figure 7. Yearly distribution -annual average concentrations for each sampling site.

Pesticide distribution for each sampling site

Therefore, a downward trend was observed for most of the atmospheric pesticide concentrations (Figure 7). This trend is particularly important for two insecticides, By contrast, for some pesticides such as Metazachlor (H) and Propyzamide (H), an increase of atmospheric concentrations is observed during the last sampling year (Figure S4).

Unfortunately, the step back is not enough to know if this increase is exceptional or if it is the start of a new upward trend in the concentrations of these active substances.

A sequential decrease (i.e., increase and decrease phases over the years) was observed for Pendimethalin (H), Folpet (F), and Tebuconazole (F) (Figure 7). Even if the sold quantities were smaller and smaller, these active substances remained the most used in the PACA and Corsica regions (BNVD, 2017). Some active substances like PBO (Figure 7), Boscalid (H), Prosulfocarb (F), and s-Metolachlor (F) (Figure S4) showed temporal profiles with similar concentrations throughout the six years.

Conclusion

The monitoring of atmosphere of six urban and two rural sites in the PACA and Corsica for 6years enabled the detection of 45 active substances, involving 18 fungicides, 14 herbicides, 12 insecticides, and 1 synergist. The most detected pesticides were Pendimethalin (H, 64.6%), Tebuconazole (F, 65.9%), Chlorpyrifos (I, 71.5%), and Lindane (I, 98.6%). Atmospheric concentrations can vary from few pg m -3 to several hundreds of ng m -3 , including pesticides regularly quantified above 1 ng m -3 such as Folpet, Chlorpyrifos, and Pendimethalin.

Although having mainly agricultural uses, the active substances were both detected in rural and urban sampling sites, suggesting a possible atmospheric transport from agricultural areas to city centers at local and regional scales. In an atmospheric concentration point of view, the spatial distribution also shows that fungicides were more quantified than insecticides and herbicides. Temporal monitoring shows a seasonally trend for each pesticide, depending on pest pressure. Fungicide concentrations were higher during the spring and early summer periods (April to July), whereas herbicide and insecticide concentrations were quite constant throughout the year. Inter-annual variation suggests a downward trend which is consistent with the available regional sales data.

From 2018, France will strengthen its pesticide air monitoring to develop regulations regarding the pesticide toxicity. However, the implementation of an extensive air monitoring network in Europe for pesticide control is needed.

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Acknowledgment

The financial support for field sampling and laboratory analysis was provided by the PACA Region, the Regional Health Agencies of PACA and Corse, the Regional Directorate for Environment, Planning, and Housing of PACA, and the Regional Networks for Air Quality Monitoring AtmoSud and Qualitair Corse. The authors gratefully acknowledge the NOAA Air Resources Laboratory (ARL) for the provision of the HYSPLIT transport and dispersion model and READY website (http://www.ready.noaa.gov) used in this publication.

  mg L -1 ). The optimized extraction conditions were as follows: extraction solvent, dichloromethane; temperature, 100°C; pressure, 100 bars; heat up time, 5 min; static time, 6 min. The flush volume amounted to 70% of the extraction cell volume. The extracted analytes dissolved in dichloromethane were purged from the sample cell using pressurized nitrogen (100 bars) for 300 s. Four cycles per cell were done.

Figure 2 .

 2 Figure2. Distribution of the active substances between concentration below 0.1 ng m -3 , between 0.1 and 1 ng m -3 . The number in brackets refers to total samples where the active substance was quantified.

  induce substantial bee mortality(Zhu et al., 2017). Hence, there is a limitation of its use and a mandatory program to control the red palm weevil was put in place in summer 2012 in Toulon(JORF, 2012). The calculated trajectories (NOAA HYSPLYT model) show the surrounding air mass from Toulon to Arles, Avignon, and

Figure 3

 3 Figure 3 represents the distribution of pesticide family, i.e., herbicide, fungicide, and insecticide, for each sampling site. The percentage is determined from the sum of the atmospheric concentrations of each pesticide family during the 6-years sampling.

Figure 3 .

 3 Figure 3. Geographical distribution -sum of atmospheric concentrations by sampling sites. Fungicide = blue, Insecticide = orange, Herbicide = green.

Figure 4 .

 4 Figure 4. Typological distribution of active substances between urban (grey) and rural (green)

Fluroxypyr

  active substances detected, 41 compounds were detected both in urban and rural sites. Tolylfluanid (F) and Diflubenzuron (I) were detected only in urban sites, while Fluazinam (July 2017) and Fluroxypyr (May 2012) were found only once in the rural site of Cavaillon. Compounds the most frequently detected in urban sites, such as Tolylfluanid (Product-Type (PT) 7: Film preservatives, PT 8: Wood preservatives, PT 21: Antifouling products), Diflubenzuron (PT 18: Insecticides, acaricides and products to control other arthropods), Cypermethrin (PT 8, PT 18), PBO (PT 18), Permethrin (PT 8, PT 18), Imidacloprid (PT 18), Fenpropimorph (PT 8), and Tebuconazole (PT 7, PT 8, PT 10: Masonry preservatives), were authorized as biocide (Index Acta Phytosanitaire, 2018), suggesting wider urban uses than rural uses. Moreover, according to the sales data for studied areas (BNVD, 2017), Diflubenzuron (flowers, green plants), 2,4MCPA (grass weedkiller), and Diflufenican (urban weed control) are also used in the urban sites. In contrast, Epoxiconazole (cereals, beet) and Triallate (oleaginous, beet) have only agricultural uses, and their presence in urban samples suggests an atmospheric transport from rural to urban areas.

ofFigure 5 .

 5 Figure 5. Geographical origin analysis of Folpet (top) and Chlorpyrifos (down) in Avignon urban sampling site: (a) zone of 10 km radius around the urban sampling site of Avignon (Corine Land Cover nomenclature: 11X/12X-Urban fabric, 21X-Arable land, 221-Vineyards, 222-Fruit trees and berry plantations, 223-Olive groves, 24X-Heterogeneous agricultural areas, 31X-Forests, 32X-Scrub and/or herbaceous vegetation associations) and (b) ZeFir

  in terms of detection frequency and atmospheric concentration, i.e., Chlorpyrifos (I), Folpet (F), Lindane (I), Pendimethalin (H), PBO (I), and Tebuconazole (F).

Figure 6 .

 6 Figure 6. Monthly distribution -minimum, maximum, median, and average of atmospheric concentrations for all sampling sites.

Figure 7

 7 Figure7and FigureS4represent the annual average concentration of the detected active substances for each sampling site during the 6-years sampling. This inter-annual variation is related to the annual quantities sold that are often dependent on the pest pressure. In France, between 2012 and 2016, the sales for professional uses of fungicides (and bactericides), herbicides, and insecticides (and acaricides) increased by 16.7%, 8.4%, and 56.3%, respectively(Eurostat, 2018). For now, the sales data are available for individual compounds only at department-wide (i.e., Alpes-Maritimes for Cannes and Nice, Bouches-du-Rhône for Arles and Port-de-Bouc, Haute-Corse for Aléria, Var for Toulon, and Vaucluse for Avignon

  Chlorpyrifos and Lindane. Chlorpyrifos was detected at the highest concentrations in Cavaillon due to the large presence of orchards. In the Vaucluse department, since 2013, sales of Chlorpyrifos dropped by 97% (BNVD, 2017) and simultaneously the annual average concentration dropped by 98%. Moreover, at the national level, Chlorpyrifos was banned since the beginning of 2018 (Index Acta Phytosanitaire, 2018), suggesting a possible continuum in the decrease of their atmospheric concentrations. For Lindane, banned since 's no sales data available but the slow and continuous decrease of the atmospheric background concentration was explained by its slow environmental degradation.

Table 2 :

 2 Sampling sites description

	Sampling site (French department)	Latitude	Longitude Altitude	Typology	Description a	Sampling period	Total sample number
						Scrub and/or herbaceous
	Aléria (Haute-Corse)	42.10218 N	9.47368 E	29 m	Rural	vegetation associations (67%), Complex cultivation patterns (11%), Vineyards	11 Apr. 2016 04 Oct. 2017	36
						(8%)
						Rice fields (45%), Complex
						cultivation patterns (16%),
	Arles (Bouches-du-Rhône)	43.67514 N	4.62923 E	15 m	Urban	Non-irrigated arable land (14%), Pastures, meadows, and other permanent	13 Feb. 2012 12 Dec. 2013	46
						grasslands under agricultural
						use (10%)
						Complex cultivation patterns
	Avignon (Vaucluse)	43.93708 N	4.82496 E	21 m	Urban	(33%), Vineyards (30%), Fruit trees and berry plantations (14%) Urban	13 Feb. 2012 15 Dec. 2017	152
						fabric (10%)
	Cannes (Alpes-Maritimes)	43.56253 N	7.00672 E	79 m	Urban	Urban fabric (46%), Forests associations (10%) (34%), Scrub and/or herbaceous vegetation	18 Feb. 2012 12 Dec. 2013	35
						Complex cultivation patterns
	Cavaillon (Vaucluse)	43.88128 N	5.00611 E	60 m	Rural	(52%), Fruit trees and berry plantations (18%), Urban	13 Feb. 2012 15 Dec. 2017	142
						fabric (11%)
	Nice	43.70207	7.28539 E	0 m	Urban	Urban fabric (47%), Forests (24%), Scrub and/or	02 Apr. 2014

a Corine Land Cover nomenclature (zone of 10 km radius around the sampling site)

Table 3

 3

		Aléria			Arles			Avignon		Cannes
	Detection	Max	Median	Detection	Max	Median	Detection	Max	Median	Detection

. Frequency of detection, maximum, and median concentrations of pesticides in all sampling sites.

26.435 -48 16.705 -29 24.001 -9 2.481 -

Competing financial interests

The authors declare no competing financial interests.

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Zhu, Y.C., Yao, J., Adamczyk, J., Luttrell, R., 2017. Synergistic toxicity and physiological impact of imidacloprid alone and binary mixtures with seven representative pesticides on honey bee (Apis mellifera). PLoS One 12, e0176837. Zivan, O., Segal-Rosenheimer, M., Dubowski, Y., 2016. Airborne organophosphate pesticides drift in Mediterranean climate: The importance of secondary drift. Atmos. Environ., 127, 155-162. Zivan, O., Bohbot-Raviv, Y., Dubowski, Y., 2017. Primary and secondary pesticide drift profiles from a peach orchard. Chemosphere, 177, 303-310.

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Highlights

• 45 active substances were detected at frequencies ranging from 0.1 to 98.6%.

• Active substances were detected both in rural and urban sampling sites.

• The insecticide Chlorpyrifos has reached a concentration exceeding 400 ng m -3 .

• Pesticides can be transported from rural to urban areas at local scale.

• A downward trend was observed for most pesticides.