Analyses statistiques

Des analyses de variance (ANOVA) à mesure répétées ont été réalisées afin de répondre au premier et deuxième objectif qui était d’étudier la variabilité des concentrations de métaux et d’hydrocarbures aromatiques polycycliques dans les tissus des abeilles, entre les ruches localisées dans différents environnements de la région de Québec (rural vs urbain et intra-urbain : milieu favorisé et défavorisé). Ces tests permettent de vérifier si les

elles. Dans le cas présent, si les concentrations de métaux et d’HAP dans les abeilles sont différentes selon l’environnement.

Afin de répondre au troisième objectif, qui consistait à comparer la variabilité des données de BE de l’abeille aux données des stations d’échantillonnage du RSQAQ, des courbes de lissage ont été générées à partir de modèles additifs généralisés (GAM). Ce type de modèle permet de prédire le comportement d’une variable au fil du temps, dans le cas présent la concentration de contaminants dans les abeilles et dans les PST au fil du temps (Hastie & Tibshirani, 1990). Les courbes générées à partir du modèle pour ces deux variables ont par la suite été comparées en fonction du chevauchement des courbes, mais également de la tendance générale de la courbe (parallèle ou non). Avant de procéder à cette comparaison, les données ont été ramenées sur une même unité (µg/g).

3.7 Considérations éthiques

Une approbation du comité d’éthique de la recherche de l’Université Laval (CERUL) n’a pas été nécessaire dans le cadre de cette recherche puisqu’elle n’implique pas de sujets humains. De plus, les abeilles ne sont pas soumises au comité d’éthique de protection des animaux de l’université Laval (CPAUL). Dans le cadre de cette recherche, les analyses de contaminants ont été effectuées sur des abeilles et non sur le miel afin d’éviter tous conflits potentiels avec l’industrie apicole et notre partenaire principale, l’entreprise Alvéole. De plus, tel que mentionné dans le chapitre 1 de ce mémoire, le miel ne serait pas la matrice la plus adéquate pour assurer le suivi des polluants.

Chapitre 4 – Résultats

4.1 Article scientifique

Honeybees as a Biomonitoring Tool to Document Exposure Gradient

Émilie Grenier1_{, Pierre Giovenazzo}2_{, Carl Julien}3_{, Isabelle Goupil-Sormany}1

1_{Laval University, (social and preventive medicine), Québec, (Québec), Canada} 2_{Laval University, (biology), Québec, (Québec), Canada}

3_{Deschambault Animal Sciences Research Center (CRSAD), Deschambault, (Québec),}

Canada

Corresponding author e-mail address: emilie.grenier.4@ulaval.ca

Keywords

Biomonitoring, honeybee, heavy metals exposition, polycyclic aromatic hydrocarbon, PAH, social inequalities

Declarations

Not applicable.

Funding

This research is part of a larger project focusing on air quality in Québec City and has been funded by the city of Québec.

Conflicts of interest

Nothing to declare.

Author Contributions

EG, PG and IGS, contributed to the design of the experiment and the interpretation of the results. EG collected the data and performed statistical analysis. EG and CJ developed and performed lab analysis. EG drafted the manuscript. PG and IGS proofed the manuscript.

Acknowledgments

We thank Dominic Larivière team for the heavy metals analysis, Jacinthe Julien for her laboratory help, Gaétan Daigle for his statistical support and beekeepers Émile Houle, Jérémie Doyon, Alexia D. Drouin and Gabriel Gagnon-Anctil.

Journal

Environmental Monitoring and Assessment

Résumé

Cette étude visait à explorer le potentiel de la biosurveillance par l’abeille pour assurer le suivi de quatre métaux et six hydrocarbures aromatiques polycycliques (HAP) dans de la Ville de Québec. La complémentarité de cette méthode, aux stations d’échantillonnage de l’air conventionnel (SEC), a également été examinée. Huit ruches ont été installées en milieu rural et urbain, ce dernier étant divisé en zone favorisée et défavorisée. L’échantillonnage d’abeilles s’est déroulé de mai à septembre. Des différences significatives ont été détectées pour les contaminants entre le milieu rural et urbain et entre la zone favorisée et défavorisée. Seul l’arsenic a montré un gradient d’exposition clair. Bien qu’il ne fût pas possible de conclure sur la complémentarité des méthodes, puisque seul le plomb était corrélé entre les SEC et la biosurveillance, l’abeille représente néanmoins un bio-indicateur suffisamment sensible pour détecter des différences entre des milieux où les niveaux de contaminants sont similaires.

Abstract

Honeybees have been widely used in Europe as environmental bioindicators for heavy metals and polycyclic aromatic hydrocarbons (PAHs). However, their potential in North America has been poorly explored, especially in cities with low levels of pollution. Many Québec city citizens are preoccupied with air quality, mainly when it comes to gradients of exposure to industrial pollution. Thus, the aim of this study was to evaluate the use of honeybees as bioindicators to assess exposure to heavy metals and PAHs in Québec city and how this method could be complementary to atmospheric physicochemical sampling stations. We sampled honeybees over a 5-month period (May to September) at six locations distributed in two urban areas distinguished by their location and social level (lower town socially deprived and upper town socially favorized) and two control rural locations. Six

PAHs were analyzed by ultra-performance liquid chromatography (UPLC), while 4 heavy metals were analyzed by inductively coupled plasma mass spectrometry. Significant differences were detected at certain sampling times for heavy metals and PAHs between rural and urban environments, but also between the two urban areas. Nevertheless, except for arsenic, we did not detect a gradient of exposures for measured pollutants. Despite the fact that we were unable to find a significant link between atmospheric sampling and honeybees as bioindicators, our results show that honeybees are good biological indicators and are sensitive enough to detect significative differences in contaminant levels between areas much alike.

Introduction

Environmental pollutants such as polycyclic aromatic hydrocarbons (PAHs) and heavy metals are of a great concern in public health because of their toxic properties and their environmental persistence (Jaishankar et al., 2014; Kampa & Castanas, 2008; Kim et al., 2013; Porrini et al., 2003; Rengarajan et al., 2015). PAHs are generated during the incomplete combustion of organic matter (Kim et al., 2013). In cities they are mainly emitted by anthropogenic sources such as motor vehicle exhaust, waste incineration and domestic heating (Rengarajan et al., 2015; Slezakova et al., 2013). Likewise, industrial activities, forest fires and other combustion processes also contribute to release PAHs in the environment (Kim et al., 2013; Rengarajan et al., 2015). These contaminants are carcinogenic, teratogenic and/or mutagenic (Abdel-Shafy & Mansour, 2016; Deng et al., 2006; Kim et al., 2013). Heavy metals are emitted through human activities like the use of fossil fuel, motor vehicle exhaust, atmospheric emissions from industries and maritime traffic (Jaishankar et al., 2014; DSP, 2018; Kampa & Castanas, 2008; Porrini et al., 2003). Several heavy metals and their compounds are classified by the IARC as carcinogenic to humans and cause many adverse effects on various systems (nervous, digestive, reproductive, respiratory) (Cui et al., 2019; Giglio et al., 2017; IARC, 2020; Jaishankar et al., 2014; Kampa & Castanas, 2008). Populations are mainly exposed to these heavy metals through the digestive pathway but also, although to a lesser extent, through atmospheric pollution, by breathing fine particles (PM2.5) containing heavy metals

(Tchounwou et al., 2012). However, heavy metals may also be detected in total suspended particles (TSP) which have a diameter of 100 microns or less (Canadian Government [GC], 2013). These particles usually reflect a more local contamination than PM2.5. Moreover,

ground and surfaces (Fang et coll., 2003; Walsh & Brière, 2018) which may especially affect children (Calabrese et al., 1997).

In 2012, a major industrial incident resulted in the deposition of visible red dust (iron oxide) in a socially deprived area of Québec city (lower town). This raised many concerns among citizens in this area regarding their exposure to environmental contaminants coming from nearby industrial sources. They strongly believed to be more exposed to heavy metals through TSP but also to several other environmental pollutants (Bencze & Pouliot, 2017).

Exposure to air contaminants has been shown to differ on a small scale basis in populations, and has been associated with different adverse health outcomes (Hajat et al., 2015; Landrigan et al., 2018; Morello-Frosch & Shenassa, 2006; Sexton & Linder, 2011). With the joint action of other socioeconomic and environmental factors, the potentially additive or synergistic effects of environmental contaminants exposure, together with other health risk factors may pose an excessive threat for often deprived subgroups (Sexton & Linder, 2011). Reducing population exposure to air pollutants in neighborhoods which are characterized by unfavorable environmental conditions with the most socioeconomically deprived population (Crouse et al., 2009; Hajat et al., 2015), is thus relevant, especially in order to prioritize preventive actions in the most at-risk sectors. However, the relation between these social inequalities and the atmospheric pollution are not well documented in Canada and consequently, environmental equity policies are mostly underdeveloped (Haluza-Delay, 2007; Miao et al., 2015). Since atmospheric physicochemical sampling stations, usually used to measure air pollution, are often in limited numbers and costly to operate, new environmental sampling methods must be explored.

The use of honeybees as environmental bioindicators is promising for many reasons: i) they are sensitive to several environmental pollutants, (ii) they are easy to manage, (iii) present in high densities, (iv) have a restricted (and known) foraging area, (v) they are cost-friendly and (vii) they are present worldwide. Honeybees offer the opportunity to establish standardized and reproducible environmental monitoring protocols (Giglio et al., 2017; Gutiérrez et al., 2015; Lambert et al., 2012; Perugini et al., 2009; Porrini et al., 2003; Ruiz et al., 2013). Unlike pesticides, who often cause mortality within honeybee colonies, other pollutants like metals are latent and are not immediately toxic (Conti & Botrè, 2001). Therefore, they can bioaccumulate in honeybees tissues and/or products (Conti & Botrè,

2001). The same thing can be expected for PAHs who have been measured in honeybee tissues in many studies and do not seem to cause acute mortality (Amorena et al., 2009; Lambert et al., 2012; Perugini et al., 2009). The usefulness of using honeybees to biomonitor environmental pollution has been widely studied in European context but less in North America for PAHs and heavy metals specifically (Gutiérrez et al., 2015; Herrero-Latorre et al., 2017; Perugini et al., 2009).

To our knowledge, this is the first North American study analyzing the potential use of the honeybees as an environmental bioindicator for PAHs and heavy metal with the intention of applying the method to assess socioenvironmental inequalities. The aims of this study are:

1- To measure and compare the concentrations of 4 heavy metals (Lead (Pb), Nickel (Ni), Arsenic (As) and Cadmium (Cd) and 6 PAHs (benzo(a)pyrene (BaP), dibenz(a,h)anthracene (DahA), benz(a)anthracene (BaA), benzo(b)fluoranthene (BbF), benzo(k)fluoranthene (BkF) and benzo(j)fluoranthene (BjF) in foraging honeybees sampled in urban and rural environments;

2- To evaluate associations between the concentrations of heavy metals and PAHs concentrations in honeybees with the deprivation index in 2 urban areas (lower town socially and economically more deprived and upper town socially and economically more favorized);

3- To compare heavy metals concentrations obtained from atmospheric physicochemical sampling stations in TSP to those obtained from biomonitoring with honeybees in regards of heavy metals concentration.

Materials and methods

The study was performed in the summer of 2019 (May-September) along within a wider research project documenting environmental inequalities and their impact on populations living in deprived neighborhoods (DSP, 2018). Six atmospheric physicochemical sampling stations belonging to the Québec ministry of environment were selected. Four were located in the lower town of Québec City, corresponding to the socially deprived area and two in the upper town, corresponding to the socially favorized area (Fig. 1). Samples were collected weekly (every 6 days) during a 24 hour period (0h00 to 0h00) with a high volume sampler.

Eight apiaries managed by an urban beekeeping company Alvéole

(https://www.alveole.buzz/) were selected as biomonitoring stations according to their proximity to the physicochemical sampling stations located in urban areas, and two additional ones from Deschambault Animal Sciences Research Center (http://crsad.qc.ca/) located in rural area (Fig.1). Approximately 200 foraging honeybees were sampled every 3 weeks at each site, within a week. Honeybees were caught at the entrance of the hive, on the hive’s flight board with an adapted hand vacuum, and immediately stored in dry ice. A total of 46 honeybee samples were collected and stored at -20°C prior to the analysis. Smoke was not used during the sampling but was used once the sample was collected. A preliminary essay showed no significant impact of the use of a beehive smoker on PAHs measures (material and results on demand).

Fig. 1 - Geographical location of the biomonitoring and physicochemical stations in A) rural

Heavy metals analysis

Lead (Pb), Nickel (Ni), Arsenic (As) and Cadmium (Cd) were the four heavy metals chosen for this study. They are considered to be without safe threshold of exposure and therefore are a great concern in public health (DSP, 2018; Jaishankar et al., 2014; Kampa & Castanas, 2008). Heavy metal concentrations were determined by inductively coupled plasma mass spectrometry (ICP-MS) on whole honeybees digestates, according to Van der Steen et al. (2016). Briefly, approximately 20 honeybees were cleaned of pollen and dried in an oven for 72 hours at 80°C. Organic matter was digested in 100 ml of nitric acid for 7h at 90°C. Milli- Q water was added to obtain a final matrix of 10% HNO3. 10 ml of the solution was then

filtered and injected with a 0.2 µm syringe in ICP-MS/MS model 8800 (Agilent technologies, Missisauga) using a platine simmer cone. 1000 ppb rhodium (Rh) was used as internal standard.

Heavy metals concentration in air was analyzed by Quebec Center of Expertise in Environmental Analysis (QCEEA) using ICP-MS. Detailed procedure can be found at :

http://www.ceaeq.gouv.qc.ca/methodes/pdf/MA200Met12.pdf.

PAHs analysis

Some PAHs, classified either as carcinogenic or probably carcinogenic by the International Agency for Research on Cancer (IARC), are also taken into consideration in the human health risk assessment protocol used by Canadian governmental agencies (GC, 1994a). It is the case for the 6 PAHs chosen in this study: BaP, DahA, BaA, BbF, BkF and BjF. We followed the QuEChERS extraction method as suggested by Guy Boisvert from Waters Corporation. Chrysene-D12 and Naphtalene-D8 were used as internal standards at a 20 µg/ml concentration. Honeybees were lyophilized and 2 g of honeybees was homogenized in a glass mortar in 10 ml of water. 1 ml of formic acid, internal standards and 9 ml of acetonitrile were added into the sample and shaked for 30 minutes. QuEChERS salts were added and shook by hand for 1 min. Each sample was then centrifuged at 3,500 g for 5 min. Clean-up method was performed according to Tran-Lam et al. (2018) protocol. The cartridge was conditioned with 0.4 ml of the supernatant obtained from the centrifugation. 6 ml of supernatant was then transferred to a 15 ml clean-up tube (MgSO4 900 mg, PSA 300 mg,

C18 300 mg). Sample was vortexed for 3 minutes and centrifuged at 3,500 g for 5 min. 4.5 ml supernatant was withdrawn into another tube and evaporated to dry state in a heating sand bath (100°C) under nitrogen. Samples were then resolubilized with 1 ml of acetronitrile, filtered on a 0.2 µm membrane and injected in an ACQUITY UPLC H-Class system equipped

with a CSH C18 2.1 x 100 mm, 1.7 µm, column. Detection and quantification were performed by a photodiode array (PDA) detector following the experimental conditions mentioned in Provatas et al. (2013).

Deprivation index

A social deprivation index was developed to represent the socioeconomic level in the urban environment, taking account several variables (Pampalon et al., 2009). In order to calculate this index for use in our study, postal codes in a radius of 1.5 km around the hives localization (Badiou-Bénéteau et al., 2013), corresponding approximately to the foraging area for honeybees, were first extracted using QGIS software (v. 3.4.5). The deprivation index around each apiary was then computed according to the regional deprivation index scale. A pooled deprivation index was then calculated in order to make this variable dichotomous (favorized and deprived environments). These calculations were performed by the Québec National Public Health Institute (https://www.inspq.qc.ca/).

Data analysis and interpretation

Statistical analysis was performed using RStudio (RStudio, version 1.2.5033). Data below the limit of quantification (LOQ) and limit of detection (LOD) were replaced by arbitrary values between 0 and LOD, according to a uniform law using set.seed argument to generate random numbers. The amount of PAHs in honeybees were adjusted using the recuperation recovery percentages of Chrysene-D12. For the first and second objective, which were to compare the average contaminant concentrations in honeybees between the urban and rural environments, as well as within the urban environment between the socially favorized ad deprived areas, we performed repeated measures analysis of variance (ANOVA) to detect significant differences (p<0.05) between the environment.

For the last objective, data from the atmospheric physicochemical sampling stations were converted from µg/m3 _{to µg/g using the approximation 1,204 kg/m}3 _{in order to compare the}

data variability with the biomonitoring stations for heavy metals. Afterwards, we constructed a spline model based on a generalized additive model (GAM) to see how the concentration changed over time for both deprived and favorized environments. We then fitted a polynomial model, with a degree corresponding to the effective degrees of freedom of the GAM model, for each type of station (physicochemical and biomonitoring) (Hastie & Tibshirani, 1990). The interaction between time and the sampling stations allowed us to analyze heavy metal concentration changes over time.

Results

PAHs and heavy metals exposure gradients between rural and urban environment

All PAHs and heavy metals measured were detected in all honeybee samples. Some showed significantly different concentrations between rural and urban biomonitoring stations at various sampling times (Fig.2). Differences were significant for As in the beginning and the end of July (respectively p=0.0026, F=20.7845, and p=0.0280, F=7.6286), in August (p=0.0018, F= 23.9023) and in September (p=0.0003, F=45.5085). A gradient of exposure for As was detected with higher concentrations found in the urban environment compared to the rural one. This gradient is partial for other heavy metals. Pb may show a small gradient at the end of the summer in August (p=0.0438, F= 6.0270) and September (p=0.0193, F=9.1385) with significantly higher concentrations in the urban environment. A significant difference was also detected for Cd in September (p=0.0066, F=14.58476,) but no trend could be established between the rural and urban environment over time. Ni was similar from one environment to the other (p=0.3891).

Fig. 2 – Average heavy

metal concentration in honeybees (mean concentration in µg/g ± SE) for the rural (N=2) and urban environment (N=6) over a season (from May 2019 to September 2019). Significant differences (p<0.05 based on

Significant differences were detected for 4 PAHs (Fig.3): BaP (p= 0.0478, F= 5.7360), BkF (p= 0.0147, F= 10.3491), DahA (p=0.0458, F= 5.8806) and BjF (p= 0.0019, F= 23.27098) at the different sampling times (Fig. 3). Our results do not show a significant trend (p>0.05) between the rural and urban environment over time because significant differences were only detected once during the whole sampling period and were not always in the same direction as we can see for theBjF. In fact, this PAH was in higher concentration in the rural environment compared to the urban one (p=0.0453). Overall, higher concentrations of two PAHs, BjF and Bbf, were detected in honeybees regardless of the environment.

Fig. 3 - Average PAHs concentration in honeybees (mean concentration in µg/g ± SE) for

the rural (N=2) and urban environment (N=6) over a season (from May 2019 to September 2019). Significant differences (p<0.05 based on repeated ANOVA).

Associations between contaminants (PAHs and heavy metals) and the social deprivation index

On a finer scale, we were able to detect more differences within the urban environment for PAHs and heavy metal (deprived and favorized area) than when these two areas were combined together and compared to the rural environment (Fig.4-5). Results seem to be associated with the deprivation index of the area considered; in general, higher contaminant levels were found in the most deprived environments compared to the most favorized one and compared to the rural environments. Honeybees from the deprived environment showed significantly higher levels for As at the beginning of July (p=0.0052, F=18.34415,) and September (p=0.0115, F=12.8809). The other heavy metals, Ni and Cd, were significantly different in May (p=0.0225, F=9.3086) and early July (p=0.0473, F=6.1901), but not for other months. In both cases, their levels were higher in the deprived environments. Pb was not different from one environment to the other (p=0.8457). Even though more differences were detected in our dataset when stratified in smaller categories (deprived and favorized urban areas), there were no clear time trends as higher concentrations did not always point in the same direction towards higher concentrations in deprived areas as initially anticipated (Fig.5).

Fig. 4 - Average heavy metal concentration in honeybees (mean concentration in µg/g ±

SE) for the deprived (N=4), favorized (N=2) and rural environment (N=2) over a season (from May 2019 to September 2019). Significant differences (p<0.05 based on repeated ANOVA).

The correlation between the concentration gradient for various PAHs and the social inequalities are shown in Fig.5. Two PAHs (BkF and BbF) showed a significantly higher