In Europe, particulate matter sanitary alerts are based on PM10 measurements. PM10 was collected in the downtown area of Chamonix (45∘55′21.53′′ N, 6∘52′11.68′′ E; Auvergne-Rhône Alpes, France; 1035 m.a.s.l.) in the Alpine Arve valley. This urban location is heavily impacted in winter by biomass burning (wood combustion used for domestic heating) and traffic emissions. Further, because of their topography, specific weather conditions and anthropogenic activities, the European daily limit value for PM10 is often exceeded in many sites in the Alpine valleys during the winter period (Chevrier, 2016), including this site in Chamonix.

In the framework of the DECOMBIO project (biomass burning contribution to PM10 in the Arve valley) PM10 sampling and detailed chemical characterizations have been achieved and are described elsewhere (Chevrier, 2016). Briefly, ambient particles were collected by filtration during 24 h (24 × 30 m3 h-1) with a DIGITEL DA-80 on 150 mm quartz filters (Tissuquartz Pallflex) using the European standard protocol NF EN 16450. DIGITEL DA-80 was automatically programmed to stock before and after sampled filters, and the samples were then collected every week. The filters were calcined at 500 ∘C for 8 h before use. After sampling, the filters were folded, wrapped in aluminum foils, sealed in polyethylene bags, and stored at -25 ∘C until chemical analyses and at 4 ∘C until OP analysis. PM10 mass measurements were achieved at the sampling site with TEOM-FDMS (tapered element oscillating microbalance–filter dynamics measurement system) as part of the regular Atmo-AURA network of air quality observation (https://www.atmo-auvergnerhonealpes.fr/donnees/acces-par-station).

Figure 1a shows the mass concentration of PM10 over the sampling period in Chamonix, taking into account the days with filters analyzed for OP (Table S1). Corresponding temperatures are illustrated in Fig. 1b. The data are clearly characterized by two periods – a cold period (late November to late February, n=30) and the warm period (late May to late September, n=29), with average temperatures of 1.4 and 14.9 ∘C, respectively. PM10 concentrations were 2.6 times higher during the cold period (Table 1) (29 ± 14 µg m3 and 10 ± 2 µg m3 for the cold and warm periods, respectively). The significant differences (Fig. 1c) in PM10 concentration between these two periods can be explained by different PM10 sources and frequent temperature inversions in this narrow valley in winter. Investigation of the former using a positive matrix factorization (PMF) approach (Chevrier, 2016) indicates that, during winter, the dominant emission source is biomass burning (60 % of PM mass on average), with 10 % due to traffic and about 18 % related to secondary inorganic aerosols (SIA). In summer, the main sources are biogenic activity (40 %), SIA (35 %) and traffic (10 %).

Methodological results of the nonlinear DTT response to PM concentrations are in agreement with Charrier et al. (2016) and were avoided thanks to isomass concentration extraction. For the AA, ASC and GSH assays, nonlinear issues were not encountered. Finally, the nonlinear ESR response to PM concentrations was solved with back correction of the ESR signal using a linear curve. Altogether these methodological points allowed avoiding the non-additive effects in OP assays and allowed PM samples inter-comparison.

The seasonal variation in OPv (OP measurements expressed per m3) is reported in Fig. 2a, c, e, g, i. Overall, higher and more variable OPv values were observed in the cold period than during the warm period where overall values remain low and close to each other. However, patterns did differ depending on the OP measurement method used. Significant differences between cold and warm periods were observed for the DTT, AA, GSH and ASC assays (Fig. 2b, d, f, h, j), but not for OP ESR (Fig. 2f). For the ESR assay, the yearly pattern could be attributed to a possible scavenging effect of HO⚫ by carbonaceous materials (Hellack et al., 2015) leading to the observed non-seasonal variation. Table 1 presents the median ratios calculated between the two periods. OP ESRv values were equivalent in the two periods with a ratio of the medians of 0.8 (close to 1 and the interquartile range includes 1) while for the other assays, the ratio was always higher than 2. The OPv DTT was only 2 times higher during the cold period compared with the warm period, whilst higher contrasts of 5, 7.1 and 8.3 were found using the GSH, AA and ASC assays, respectively.

Spearman correlations (rs) between the assays were calculated over the sampling year. Correlations were considered as strong for rs>0.70 and moderate for rs (> 0.45 and < 0.70), which are commonly criteria that can be found in the literature (Janssen et al., 2014; Yang et al., 2014). Table 2 shows that OPv values are all strongly correlated (rs>0.7) with the exception of OP ESRv values for which correlations ranged from 0.16 (OP ASCv) to 0.45 (OP DTTv). All assays are also strongly correlated with PM10 (rs>0.77), except for OP ESRv (rs=0.33). The strong correlations between OP DTTv and OP AAv are in agreement with another study on PM10 (Janssen et al., 2014), whereas they were not observed by Fang et al. (2016) on PM2.5. The weak correlations found between the OP ESR assay and both OP DTT and OP GSH assays are also in agreement with other studies on PM2.5 (Künzli et al., 2006; Yang et al., 2014). However, the OP ESR assay is usually highly correlated with OP AA and ASC assays in both PM10 and PM2.5 studies (Janssen et al., 2014; Künzli et al., 2006; Yang et al., 2014).

As shown in Table 3, these correlations also vary with the period, with higher rs values during the cold period (0.54<rs<0.92) than in the warm period for which the highest correlation (rs=0.65) was found between OP DTTv and OP AAv. Temporal variations of correlations were also observed by Fang et al. (2016) on PM2.5. During the cold period, rs between OP methods and PM10 ranged from moderate (0.59) for OP ESRv to strong (>0.7) for the other OP measurements. During the warm period, a strong correlation was observed between PM10 and OP DTTv.

To better understand the evolution of OP results, OPv were related to the chemical composition of PM10 by using both univariate and multiple regressions analyses.

Spearman correlations between OPv and the chemical composition of PM10 (expressed in ng m-3 or µg m-3) were calculated over the sampling year and are summarized in Table 4. The highest cumulative score of correlations above 0.45 is seen for OP DTTv (37/48; detailed information on cumulative scores is available in the Supplement, Sect. S4). Conversely, few correlations above 0.45 (5/48) are seen for OP ESRv. The other four assays (OP AAv, OP GSHv and OP ASCv) exhibit very similar correlations with the chemical species.

Whereas correlations are evident between OP DTTv, OP AAv, OP GSHv, OP ASCv, and some organic and inorganic species, in agreement with other studies regarding both PM2.5 and PM10 (Janssen et al., 2014; Saffari et al., 2014; Yang et al., 2014), for OP ESRv correlations are limited to transition metals (Cr, Cu, Mo, Zr) and Ba. All these metals may be associated with traffic emissions, including brake wear (Hulskotte et al., 2014; Sanders et al., 2003). These results can be explained by the specificity of the ESR assay towards hydroxyl radical generation and in the case of Ba (not redox-active) linked to its co-emission with the former redox-active compounds. These results agree with the study of Boogaard et al. (2012) where the OP ESRv of PM collected near major urban road was highly correlated with Cr, Cu and Ba. However, OP ESRv is also associated with organic compounds (OC, PAH) in other studies (Janssen et al., 2014; Yang et al., 2014).

While many chemical species have a cumulative score of 4/5 over the sampling year, Cu is the only species with moderate to strong correlations with all OP measurements (N=5/5). Several studies, including one in a subway system (Moreno et al., 2017) already pointed out the high impact of Cu concentrations on OP.

Multiple linear regressions were investigated in order to obtain further insight into the set of chemical species that can be among the dominant factors to the different OP measurements and for OP variations within time.

Such analysis requires the removal of extreme values and also requires transformations of the OP data in order to obtain distributions as close as possible to the normal one (detailed information about data set preparation can be found in the supporting information Sect. S5 and Table S2). The multiple linear regression models were obtained using the linear model (lm) function in R software. Forward variable selection and BIC number criteria were used to select the predictors that give the better explanation of the variance of the OPv (Sect. S5 model realizations and validations). The models obtained, for the set of samples excluding the extreme values, are presented in the following Eqs. (2, 3, 4, 5, and 6).

log⁡(OPDTTv)=-0.440+0.101×Cu+0.0259×∑PAH-0.0247×Mg2++0.273×As+0.318×Mo+0.00549×MSA+0.002×∑Polyols+0.00135×Na+R2=0.81

log⁡(OPAAv)=0.433+0.000387×∑Monosaccharides+0.756×Ni-0.372×Mo+0.000538×FeR2=0.93

log⁡(OPAAv)=-1.43+0.00163×∑Monosaccharides-0.00171×Ca2++1.69×Sb+0.117×Cu+0.00208×Na++0.00119×Fe-0.0435×ZnR2=0.87

OPESRv=339953+73037×Cu-6725×∑Alkanes-441351×Cd+20795×TiR2=0.62

OPGSHv=1.21+0.00161×∑Monosaccharides+0.210×Cu-0.0642×Mg2++0.0268×MSAR2=0.48

log⁡OPASCv=1.79+0.000659×NO3--0.00733×∑Polyols-0.00292×Na++0.000247×OC∗+0.103×CuR2=0.74

Two distinct models were associated for the AA single compound assay since the distribution is highly bimodal with two normal distributions: one in a cold period and one in a warmer period (Sect. S5 data set preparation). Strong coefficients of determination were found for the OP DTTv, AAv and ASCv (R2 adj > 0.70), indicating that the variance of these OPv was well explained. For the ESRv and GSHv, R2 values were lower: 0.62 and 0.48, respectively (Table S4).

Next, these models have then been applied to the general data set (Fig. S6), but, when strongly overestimated, values were removed again (Sect. S5 application to the overall data set). The models tend to underestimate OP values (negative intercept and slope = 0.84) for the ASC assay. For the other assays, the models tend to overestimate high OP values and to underestimate low OP values (negative intercept and slope between 1.11 and 1.51). However, the coefficients of determination range from 0.73 (OP ASCv) to 0.89 (OP AAv). Altogether, these results indicate that, on average, the models correctly represent the OP of PM participating in the ROS exposure during the overall year.

Finally, the contribution of each predictor during cold and warm periods has been investigated (Fig. 3) (Sect. S5 contribution of each predictor). The intercepts, attributed to unknown species, were significantly > 0 in all models. Moreover, for some species, a negative contribution was found (Table S4) that can be explained by an antagonist effect of some atmospheric components on OP: soot particles for Hellack et al. (2015), Gram-positive bacteria for Samake et al. (2017) or metal–organic binding interactions (Verma et al., 2012; Tuet et al., 2016; Wang et al., 2017), or because of the weighting assignation of species in the models. During the cold period, traffic tracers or indicators (like Cu, Fe, Mo, Ti, or ∑ PAHs) are important positive factors to explain all OP measurements. Also, and with the exception of the ESR assay, biomass burning tracers ∑Monosaccharides, including levoglucosan or indicators such as ∑ PAHs or OC* (corresponding to total OC minus the molecular species measured), both strongly related to biomass burning emission (Bonvalot et al., 2016; Chevrier, 2016), are prominent positive factors for all assays for this cold period. These results agree with the observations on the seasonal evolution of OPv, with much larger values in the cold period when biomass burning emissions are dominant. Additionally, literature results on DTT reactivity towards organic compounds indicate higher impact of biomass burning species when compared to other organic compounds (Verma et al., 2015). During the warm period, traffic tracers and especially copper are important positive factors in OP measurements. In addition, road dust and industrial tracers (Zn, Ca2+) for the AA assay as well as primary biogenic indicators ∑polyols, MSA for the DTT and GSH assays seem to be prominent factors of these OP measurements.

Several limitations can be attributed to this study. Most important, all of these results have been obtained for a specific location and cannot be generalized as chemical composition of PM10 strongly differs from one location to another. PM10 chemistry is different from PM2.5 and the associations reported here are only valid for PM10. Some components that might mainly reside in the coarse mode are positive factors in the multiple linear regression models (e.g., Ti in OP ESRv). They can display a different final health impact, since a fraction of PM10 does not penetrate all the way to the lung. Also, the results of the ESR assay warrant caution due to our back correction of the ESR signal linked to the nonlinear response of the assay. Finally, the multiple model result for the GSH assay should be considered with caution since a normal distribution was not reached in the first step of the analysis. Moreover, multiple nonlinear regression models should also be investigated since several studies have shown that oxidative potentials from different PM components are not always additive (Tuet et al., 2016, 2018; Wang et al., 2017; Xiong et al., 2017; Yu et al., 2018). Finally, these analyses are only relevant for PM10 when some health studies are now taking PM2.5 into account. Additional studies addressing the comparison of OP results associated with PM10 and PM2.5 are needed (Gali et al., 2017; Styszko et al., 2017).

However, all results point out that biomass burning and vehicular emissions in winter are the main sources correlated with ROS generation. In summer, ROS burden is associated with vehicular emissions, biogenic emissions, road dust and industrial sources. All these results also suggest the association of assays in order to take into account this wide range of OP determinants. To achieve this, the DTT and the AA assays, or the DTT and the RTLF assays, can be associated to get the best information, which is in agreement with other studies on acellular OP measurements (Janssen et al., 2014; Yang et al., 2014). However, a definitive proposition for the best association of assays will most probably come from a final benchmark against epidemiological study outcomes (Fang et al., 2016; Strak et al., 2017; Weichenthal et al., 2016a).