Yanis Boussad

Leslie ) Chen

Arnaud Legout
email: arnaud.legout@inria.fr

Augustin Chaintreau

Walid Dabbous

Longitudinal Study of Exposure to Radio Frequencies at Population Scale

Keywords: radiofrequency, population exposure, crowdsource, personal measurements, large-scale

. Here, to the best of our knowledge, we report the largest crowd-based measurement of population exposure to RF produced by cellular antennas, Wi-Fi access points, and Bluetooth devices for 254,410 unique users in 13 countries from January 2017 to December 2020. All measurements were obtained from the ElectroSmart Android app [7], and we applied a thorough methodology to clean and consolidate the measurements. We show that total exposure has been multiplied by 2.3 in the four-year period considered, with Wi-Fi as the largest contributor. The cellular exposure levels are orders of magnitude lower than the regulation limits and not significantly impacted by national regulation policies. Therefore, the mere comparison of exposure levels to regulation limits is a poor way to describe the real evolution of exposure. The population tends to be more exposed at home; for half of the study subjects, personal Wi-Fi routers and Bluetooth devices contributed to more than 50% of their total exposure. We make our dataset publicly available to provide a starting point for sound epidemiological studies on the health impacts of RF, and for other types of studies interested in population exposure to RF or the usage of wireless communication technologies.

Introduction

The long-term impact of radio frequencies on 2 health is a long-standing scientific question that is 3 well illustrated by the classification of radio frequen-4 cies as a Group 2B carcinogen by the WHO [START_REF] Research On Cancer | Iarc classifies 896 radiofrequency electromagnetic fields as possibly 897 carcinogenic to humans[END_REF]. This 5 classification means that there is some evidence that 6 it can cause cancer in humans but at present it is far 7 from conclusive [START_REF]Standard IARC classification Glossary[END_REF]. Total exposure to various sources 8 of radio frequencies is considered a critical factor for • It creates a timestamp with the local time in UTC. This is a slight approximation as signals might not be measured at exactly the same time in a given measurement scan. However, by considering a window of a few seconds, it is easy to attribute all measured signals to a given measurement scan and timestamp (we specifically discuss the case of Bluetooth in the section Bluetooth scan synchronization).

• It collects characteristics of the smartphone (brand and model) and its Android version.

• It measures the smartphone location in terms of latitude and longitude. Android provides this information by combining GPS, Wi-Fi access points, and cell tower information using a proprietary algorithm.

• It measures the downlink Received Signal Strength Indicator (RSSI) of all measurable Wi-Fi access points, Bluetooth devices, and cell towers (we discuss limitations below), along with several source-specific data.

-For Wi-Fi access points, we collect the SSID, the BSSID, the frequency, and whether the user is connected to this access point.

-For Bluetooth devices, we collect the device name, the device MAC address, and whether the user is bonded to this device.

-For cell towers, we identify whether the cell is using a 2G, 3G, 4G, or CDMA/EVDO technology. We determine whether the cell is serving (that is, the user is currently connected to this cell), and we collect cell identification information, such as the Mobile Network Code (MNC), Mobile Country Code (MCC), or Cell ID (CID), to generate a unique identity for each cell tower.

Ethical and legal considerations

We submitted the study protocol to our institutional ethical committee (Inria COERLE [START_REF]Operational Committee for the assesment of Legal and Ethical risks[END_REF]). They provided guidelines for respecting user privacy, consent, and data protection.

(ASU), an integer value between 0 and 31. It is converted to dBm according to the formula: dBm = ASU * 2 -113. For this reason, the granularity of the cellular RSSI is 2 dB. Fourth, each wireless technology comes with some additional limitations. Bluetooth sources can only be measured when they are discoverable. Wi-Fi sources can only be measured when they are configured as access points, that is, the emitting power of the connected devices is not measured. Measurements of cellular sources suffer from several limitations. i) A smartphone with an active SIM card can only measure the RSSI from the operators declared in the SIM card. In practice, it is either the cellular operator that owns the SIM card (MNO), the cellular operator that is operating the cellular infrastructure for the virtual operator (MVNO), or the operators that partner with the MNO of the SIM card in foreign countries (Roaming). We explain in the Dataset Processing section how we mitigate this issue. ii) The measurement coverage is largely dependent on the version of Android and the cell phone maker. Indeed, the Android API can return the RSSI of the serving cell for all smartphones, but only the most recent versions of Android can also return the neighboring cells' RSSI. In addition, this API tends to be quite buggy due to the Android RIL (Radio Interface Layer, which is closed-source and vendor-specific. In particular, some smartphones return invalid RSSI measurements (outside of the range given in Table 1). We discuss in Dataset Cleaning how we identify and remove invalid measurements. iii) Smartphones periodically scan for cellular networks to ensure continuity of service. To speed up network scanning, smartphones follow priority rules that are defined by the network and stored in the SIM card. This means that a given smartphone may not scan for all the cellular Radio Access Technology (RAT), but instead, scan only high priority RATs. For example it may scan only 4G and 3G networks, excluding 2G. As a result, we expect the cellular scans not to include all the cellular generations in a single scan. Last, the received power is measured using the Received Signal Strength Indicator (RSSI). Therefore, our measurements do not take into account the effective load of the wireless channel.

Dataset characteristics

In this study, we use all the exposure scans collected from January 2017 to December 2020 (4 years) representing 506,100 user profiles and 6,438 million measured RSSI.

We first clean this raw dataset as follows: i) we remove all measurements with invalid GPS coordinates, ii) we remove all measurements with invalid RSSI values, iii) we keep only measurements from the 13 countries with the largest number of measurements, iv) we remove all CDMA/EVDO measurements.

Then, we process the remaining measurements: v) we convert all timestamps to the local time of the country of origin, vi) we identify the Wi-Fi physical sources, vii) we attribute each Bluetooth measurement to an atomic scan. The following sections detail each of these seven steps.

Dataset cleaning

Invalid GPS coordinates removal. Background measurements are quite fast (typically a few seconds). There is usually not enough time to get a valid GPS coordinate from scratch, that is, when the GPS was not activated before the scan or when no prior information is cached to help the GPS converge faster to a location. However, location is a system-wide property, so if another app or the system has recently accessed the device location, we will benefit from this when we make the scan. Also, when the device is not power-constrained, we can allow more time to get a valid GPS location.

When a GPS coordinate cannot be retrieved in the ElectroSmart app, we set both the latitude and the longitude associated with a scan to either 0 or -1 depending on the root cause (in this paper, we do not exploit this root cause). As one of our goals is to explore the evolution of the exposure per country, we removed all scans with a GPS coordinate set to either 0 or -1. We removed 7.9% of the Wi-Fi measurements, 9% of the Bluetooth measurements, 18.2% of the 2G measurements, 19.8% of the 3G measurements, and 12.8% of the 4G measurements. Overall, we removed 11.2% of all the raw measurements by filtering out invalid GPS coordinates.

Invalid RSSI removal. The Android OS is an open-234 source software program that is common to all An-235 droid devices, but each smartphone manufacturer 236 adapts it to their hardware by performing customiza-237 tion and developing drivers, all of which are propri-238 etary. Therefore, each smartphone model can come 239 with specific bugs [START_REF] Mazloum | Xmobisenseplus: An updated application for the assessment of human exposure to rf-emfs[END_REF]. This step focuses on the RSSI, 240 which is produced by the proprietary Radio Interface 241 Layer (RIL).

242

Fortunately, each wireless standard comes with a 243 valid range for the RSSI value, as shown in Table 1. 244 We can therefore easily filter out each measurement 245 with an out-of-range RSSI value. We removed 0.07% 246 of the Wi-Fi measurements, 0.04% of the Bluetooth 247 measurements, 0.8% of the 2G measurements, 2.4% 248 of the 3G measurements, and 14.1% of the 4G mea-249 surements. After this removal step, 85.9% of all the 250 raw measurements remained.

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In addition to the out-of-range values, we also ob-252 served in-range abnormal values for cellular measure-253 ments (2G, 3G, 4G). Abnormal values are in the valid 254 range but tend to appear with higher frequency in 255 the same exposure scan. The root cause of these ab-256 normal values is hard to pinpoint as it most likely 257 comes from bugs in the proprietary RIL. In par-258 ticular, we observed that all smartphones with an 259 Exynos [START_REF] Exynos | [END_REF] System on Chip (SoC) 1 have an abnor-260 mally high number of -51 dBm measurements: for all 261 cellular measurements performed from smartphones 262 with an Exynos SoC, the -51 dBm values represent 263 71% of all cellular measurements, whereas, they rep-264 resent 1.91% for all smartphones running any SoC 265 other than Exynos.

266

We found that the cells reporting abnormal values 267 correspond to fake cells, that is, when the RIL reports 268 a cell, but it does not correspond to a real measured 269 cell. Indeed, when a smartphone connects to a cel-270 lular operator, it measures various performance indi-271 cators (including the RSSI), and connects to the cell 272 with the best performance indicator; we call this cell 273 the serving cell. All the other cells are called neigh-274 boring cells. We found that for 3G, the percentage of neighboring cells measured by smartphones with an Exynos SoC is 21.8% of all measured cells, whereas it is 2.7% for smartphones running any SoC other than Exynos. This is a clear indication that smartphones with an Exynos SoC report fake neighboring cells, at least for 3G.

Due to the bogus behavior of smartphones running an Exynos SoC, we decided to adopt a conservative strategy by removing all measurements (Wi-Fi, Bluetooth, 2G, 3G, 4G) performed by a smartphone with an Exynos SoC. Even if the issue does not concern Wi-Fi and Bluetooth, removing only cellular measurements (while keeping Wi-Fi and Bluetooth measurements) would have affected our discussion of personal exposure by changing the proportion of the sources of exposure. We removed 24.4% of the Wi-Fi measurements, 33.5% of the Bluetooth measurements, 7.9% of the 2G measurements, 40.6% of the 3G measurements, and 10.8% of the 4G measurements. After this removal step, 62.6% of all raw measurements remained.

For the sake of completeness, we note that we also observed an abnormally large number of measurements with a -113 dBm RSSI for 2G and, to a lesser extent, for 3G. We did not, however, find any correlation between these -113 dBm measurements and a specific SoC, device brand, or Android version. As dBm are in a logarithmic scale, and since we perform all our computations in Watt, which is in a linear scale, the impact of these measurements on the rest of this paper is negligible.

Included countries. ElectroSmart was released in August 2016 in two languages, English and French.

We added Italian and German in March 2019, and Spanish and Portuguese in January 2020. France is the country with the largest number of measurements (36% of all measurements after removing invalid GPS and RSSI), followed by the USA (27.5%), Italy (7.9%), and Germany (4.6%).

We restricted this study to the 13 countries with the largest number of measurements. In addition to France, the USA, Italy, and Germany, we included (in order from the highest to the lowest number of measurements) Canada, the United Kingdom, Switzer-land, Belgium, Spain, the Netherlands, India, Aus-320 tralia, and Brazil. Although Brazil accounts for only 321 0.5% of all measurements, this still represents 21.6 322 million measurements and 2668 unique users.

323

Altogether, the excluded countries represent 9.3% 324 of all measurements. So, after this step, 56.8% of 325 all raw measurements and 50.3% of all user profiles 326 remained.

327

CDMA removal. The term CDMA refers to a large 328 family of cellular protocols (cdmaOne, CDMA2000, 329 EVDO) deployed mainly in North America. Elec-330 troSmart can measure CDMA cells, but, apart from 331 in the USA, we did not find CDMA measurements 332 in any of the selected countries. In the USA, all 333 CDMA measurements represent 0.95% of all cellu-334 lar measurements (4G measurements represent 64% 335 of all cellular measurements). As CDMA measure-336 ments are only used in the USA in our filtered dataset 337 and represent a negligible fraction of all cellular mea-338 surements, we decided to remove all CDMA measure-339 ments from our dataset. 340 Cleaned dataset characteristics. In the rest of 341 this paper, we will only refer to the cleaned dataset 342 that resulted from the previous removal steps. This 343 dataset contains 254,410 user profiles and 3,656 mil-344 lion measured RSSI. This represents 56.8% of all the 345 measurements and 50.3% of all the profiles available 346 in the raw dataset.

347

In this cleaned dataset, Wi-Fi represents 58.3% of 348 all measured RSSI, Bluetooth 6.6%, 2G 10.5%, 3G 349 7.6%, and 4G 17%.

Dataset processing 351

Adapting to local time. All the raw measurements in 352 the dataset are associated with a timestamp in UTC 353 that corresponds to the instant the corresponding sig-354 nal was detected. In order to identify day and night 355 periods, we need to convert all timestamps into local 356 time. To do so, we reverse-geocode the GPS coor-357 dinate of each measurement using OpenStreetMap's 358 Nominatim [START_REF]OpenStreetMap Nominatim[END_REF] to determine the corresponding coun-359 try. Then we convert the timestamp in UTC to a 360 timestamp in the local timezone of the GPS coordi-361 nate using timezonefinder python library [START_REF] Timezonefinder | [END_REF].

Identifying physical and logical WiFi sources. Identifying the physical sources of radio frequencies is particularly important for assessing exposure. This notion of physical source can be tricky. In this paper, a physical source is the source of a carrier signal, that is, the source of a signal at a specific frequency. For Bluetooth, 2G, 3G, and 4G, one detected signal corresponds to one physical source, but this is not the case for Wi-Fi.

A Wi-Fi access point usually has one or two physical sources of emission, but the signals we measure correspond to logical sources, and it is common to have multiple logical sources for one physical source.

We can obtain the carrier signal frequency for each measured source, and one might argue that this information is enough to identify the physical sources. However, it is not the case, as different physical sources can use the same frequency. This is a common issue in Wi-Fi as the number of available frequencies (called channels) is limited, and the density of sources is high.

Wi-Fi networks are based on the notion of a service set, that is, the idea that logical networks can be layered on top of a physical network. Such logical networks are identified by a Service Set ID (SSID) (usually a human-readable string) associated with a Basic Service Set ID (BSSID), which is a 6-byte, internationally unique identifier usually derived from the MAC address of the access point. The strategy used to derive a BSSID from a MAC address depends on the equipment and administrator. We observed three strategies: the BSSID differs from the MAC address by the first byte, the last byte, or both the first and last bytes.

Therefore, the rule we apply to identify a physical source in a user scan is the following: if several Wi-Fi measurements report the same frequency and have the same BSSID (excluding the first and last bytes in the comparison), we associate them to the same physical source. In addition, as logical sources for the same physical source might report different RSSI (because the measurements might not be performed at the exact same time), we consider that the RSSI of the physical source is the maximum RSSI of all the associated logical sources for a given scan.

In the rest of this paper, all results we report for Wi-Fi are for physical sources.

409

Bluetooth scan synchronization. When counting the 410 number of sources, it is important to use the concept 411 of an atomic scan, that is, a scan that reflects the 412 instantaneous exposure as measured by the smart-413 phone. Cellular and Wi-Fi scans are atomic because 414 the Android API returns all current sources in a sin-415 gle call or callback. However, this is not the case 416 for Bluetooth. When we start a Bluetooth scan, the 417 smartphone will perform a Bluetooth inquiry request 418 and wait for an answer from devices in the vicin-419 ity [START_REF] Chakraborty | Analysis of the bluetooth device discovery protocol[END_REF]. Therefore, devices will reply one by one, 420 usually within 15 seconds of the start of the scan.

421

The heuristic we use to attribute replying devices 422 to an atomic scan is to group together all Bluetooth 423 devices whose inter-arrival is less than 15 seconds.

424

In the rest of this paper, each time we count the 425 number of Bluetooth devices, we count the number of 426 devices in an atomic scan as defined in this section.

427

Mitigation of the cellular scans limited to the SIM op-428 erator. We have explained in the Limitations section 429 that the cellular measurements only take into account 430 the RSSI from the operator declared in the SIM card. 431 This limitation results in a significant underestima-432 tion of the cellular exposure. To mitigate this issue, 433 in each scan, we multiple the RSSI corresponding to 434 a cellular measurement with the number of operators 435 in the country in which the scan was performed. We define personal exposure as the received power 438 from all the electromagnetic field sources on the ra-439 dio frequency bands exposing humans. The received 440 power is a function of the emitting power that is 441 expressed in Equation ( 1) where P r is the received 442 power, P e is the emitting power, K is a constant 443 dependant on the emitting and receiving antennas' 444 characteristics, d is the distance to the source, and f 445 is the signal frequency [START_REF] Friis | A Note on a Simple Transmission Formula[END_REF]. We see in Equation [START_REF] Gupta | Non-ionizing 867 radiation as possible carcinogen[END_REF] 446 that distance plays an important role in personal ex-447 posure, as does signal frequency: higher frequency 448 signals fade faster than lower ones.

P r = K 1 4πdf 2 P e (1)
The analysis we perform in this paper is based on three main calculation steps that we describe and jus- avoids bias that could be introduced by months with a larger than average number of users.

Data availability

Upon publication, all data used in this paper will be available online for scientific exploitation. The data consists of timestamped measurements of RSSI for each of the five types of signals considered in this paper (Wi-Fi, Bluetooth, 2G, 3G, 4G). All user IDs have been anonymized (using a salted hash), and all GPS locations have been replaced by one of the 13 countries we consider. When required to preserve user anonymity, we provide aggregated data using pre-processing steps. For instance, we provide the identification of the unique physical sources using our own anonymous source counter. A detailed description of the format of the data will be available on the online publication site. Table 2 shows the evolution of the total personal 494 exposure in the 13 countries with the largest num-495 ber of measurements (as discussed in Materials and 496 Methods). We observe an overall trend of increased 497 exposure across all countries from 2017 to 2020. To 498 confirm this trend, we computed the Spearman cor-499 relation on the monthly average exposure to evalu-500 ate the relationship between time (months) and the 501 monthly average exposure for each country. Table 3 502 shows a significant positive correlation between time 503 and exposure for most countries. For each year, we take the yearly average exposure as given in Table 2, convert it to Watt, compute the mean for all 13 countries, and convert it back to dBm. The bars represent a 95% confidence interval for the mean using empirical bootstrap resampling with replacement (N=1,000) on the yearly average exposure per country. Plots are shifted horizontally to avoid confidence interval overlap. An increase of 3 dB results in the doubling of the exposure.

It is interesting to understand how each wireless 505 technology contributes to this exposure trend. Fig- 506 ure 1 shows that the total exposure (brown curve) has 507 been multiplied by 2. The yearly average exposure increased from 2017 to 2020 worldwide. This table represents the evolution of the yearly average exposure per country. We use an ISO 3166 [START_REF]Country Codes[END_REF] alpha-2 country code to represent each country using a two-letter code. We compute the mean and the 95% confidence interval for the mean using empirical bootstrap resampling with replacement (N=1,000) [START_REF] Efron | An introduction to the bootstrap[END_REF] on the monthly average exposure for each country. The change column shows the increased (in blue) or decreased (in red) exposure as a percentage compared to the previous year. This percentage change is computed in Watt instead of dBm to have a linear interpretation of the change in exposure.

we observe for each wireless technology corresponds to the adoption or decline of the corresponding technology. We observe a clear increase in the exposure due to Wi-Fi and Bluetooth technologies, but a decrease in the exposure due to 2G and 3G technologies.

Interestingly, Wi-Fi is by far the largest contributor to exposure.

In summary, we observe an overall increase in total personal exposure with time (a 2.3-fold increase from 2017 to 2020), with Wi-Fi being the largest contributor to personal exposure.

Exposure growth is not explained by the multiplication of sources

We focus now on how each source contributes to total exposure. This is a central question because an improved understanding of the most exposing sources could inform strategies for reducing personal exposure.

Since the measurement of the number of sources is not reliable for cellular technologies (see Materials and Methods), we focus on Wi-Fi and Bluetooth technologies. We consider this limitation reasonable because, as shown in Figure 1, these two are the most 532 significant contributors to total exposure.

533

Table 3: The Spearman correlation shows a significant positive correlation between time and exposure for most countries. The Spearman correlation is computed on the monthly averages for each country from 01/2017 to 12/2019. We exclude 2020 from this correlation as the COVID-19 period would have significantly impacted the interpretation of this correlation. In blue, we show the positive correlations, and in red, the negative ones. The grey two-sided p-values are above the threshold of 0.05. When including 2020, we observe an increase in the Spearman coefficients between 0. and Wi-Fi (bottom) when there is a given number of (Bluetooth or Wi-Fi) sources in the scan (the boxplot convention is the following: the middle orange line shows the median, the lower and higher hinges show the first and third quartiles, respectively, and the lower and higher whiskers show a limit of 1.5x the interquartile range from the lower and higher hinges, respectively). For instance, the last box in the top figure represents the sum of the received power in Bluetooth for exposure scans with exactly 20 detected Bluetooth sources. We observe that beyond 4 to 5 sources in the vicinity, any additional sources marginally change the individual exposure. (see Equation 1). In addition, we see in Figure 3 541 that in 50% of the exposure scans, the most exposing 542 Wi-Fi source (resp. Bluetooth) represents at least 543 83% (resp. 91%) of the total exposure due to Wi-Fi 544 (resp. Bluetooth). Thus, the number of sources in 545 the vicinity is not a good predictor of personal expo-546 sure; rather, the most exposing source is the primary 547 contributor to exposure.

548

The question now is how actionable this informa- the middle line shows the median, the lower and higher hinges show the first and third quartiles, respectively, and the lower and higher whiskers show a limit of 1.5x the interquartile range from the lower and higher hinges, respectively). For instance, the first green box shows the distribution of the contribution of the most exposing Bluetooth source to the sum of the exposure of all Bluetooth sources for each exposure scan. We observe that for 75% of the exposure scans (containing at least one Bluetooth measurement), the most exposing Bluetooth source represents at least 56% of the entire Bluetooth exposure.

Bluetooth-bounded devices to which a user has already connected. Connected sources or bounded devices are usually owned or controlled by the user and can therefore be switched off or moved to reduce exposure. Taking all scans into account, we computed that 41% of the time, the most exposing of all the Wi-Fi sources is a connected one. For Bluetooth, the most exposing source is a bounded device 10% of the time. Then, we computed what the individual personal exposure would have been if all connected sources and bounded devices had been switched off. While this is an overly optimistic situation, the goal is to assess the degree to which an individual could control exposure. Figure 4 shows that, by switching off the connected sources and bounded devices, half of the users could have reduced their total exposure by 50% (a reduction by 3.1 dB), and 25% could have reduced their total exposure by 90% (a reduction by 10 dB).

In summary, the growth of total exposure is not explained by a multiplication of sources. On the contrary, a handful of sources generate most of the personal exposure at any given time, and it is not uncom- In red, we show the median and in blue, the 75 th percentile. For each user and month, we first compute the per-user monthly average exposure. Then, for each user and month, we collect all connected Wi-Fi sources and bounded Bluetooth devices, and we re-compute the per-user monthly average exposure by removing all collected connected sources and bounded devices from the exposure scans. Finally, we compute the difference between the per-user monthly average exposure in each case. The result is the distribution shown in this figure for each user. Note that in some rare cases, the difference can be negative. This can occur when an exposure scan contains only one connected source. By removing connected sources, we change the number of samples on which we average. As a result, a user with only a few samples could end up with a higher average without connected sources. In this figure, we drop users with a negative gain; they represent 0.92% of all users.

mon that an individual's exposure is almost entirely 575 the result of sources they either own or associate with

576

(for a quarter of our subjects, such sources account 577 for 90% of exposure).

Cellular regulation

The maximum allowed exposure of the population is fixed by the ICNIRP international body [START_REF] Icnirp | ICNIRP Guidelines on Limiting Exposure to Electromagnetic Fields[END_REF]. However, each country is free to lower the maximum exposure depending on local policies. In addition, some countries have policies specific to some areas (e.g., Belgium has different limits for Flanders, Wallonia, and Brussels) or specific to some contexts (e.g., Italy enforces lower exposure near schools). Finally, the limits are specific to the frequencies used by cellular technologies. Here, we specifically focus on the frequencies 900 MHz, 1800 MHz, and 2100 MHz. For each country, we build a regulation limit triplet, one limit per frequency.

To the best of our knowledge, there is no central repository of exposure limits for all countries. To obtain a regulation limit triplet for each of the 13 countries we consider, we consolidated several sources [START_REF]WHO -Global Health Observatory data repository, Exposure limits for radio-frequency fields (public) data by country[END_REF][START_REF] Stam | National Institute for Public Health and the Environment, The Netherlands, Comparison of international policies on electromagnetic fields[END_REF][START_REF] Urbinello | Radio-frequency electromagnetic field (rf-emf) exposure levels in different european outdoor urban environments in comparison with regulatory limits[END_REF], and when multiple limits were provided (due to local policies or context), we keep the limit covering the largest population. Figure 5 does not show any clear correlation between regulation limits and exposure. We must be careful interpreting this result as there are several external factors that we do not control, such as the deployment strategy of the cellular operators. For example, operators might decide, in a densely populated area, to have a higher density of base stations (to increase the supported load) emitting at a lower power (to reduce interference). In such cases, base stations expose the population at a level that is significantly lower than what the regulation permits [START_REF] Urbinello | Radio-frequency electromagnetic field (rf-emf) exposure levels in different european outdoor urban environments in comparison with regulatory limits[END_REF][START_REF] Urbinello | Temporal trends of radio-frequency electromagnetic field (RF-EMF) exposure in everyday environments across European cities[END_REF]. Therefore, in practice, the regulation is an upper bound to the population exposure in some extreme cases, but in most cases, the population is exposed at levels much lower than the regulation limits.

To confirm this hypothesis, we computed the distribution of the cellular measurements in V/m. We obtain the electric field E in V/m from the measured received power in dBm with the formula:

E = 9.73f 50 × 10 P -30 10 c √ G ( 2
)
where G is the antenna gain, f is the frequency in Hz, We observe no correlation between regulation limits and exposure. This figure shows the correlation between the exposure and a regulation limit triplet for the three cellular technologies we measure, 2G, 3G, and 4G (the boxplot convention is the following: the middle orange line shows the median, the lower and higher hinges show the first and third quartiles, respectively, and the lower and higher whiskers show a limit of 1.5x the interquartile range from the lower and higher hinges, respectively). Here is the association between regulation limit triplets and countries: [START_REF] Sagar | Radiofrequency electromagnetic field exposure in everyday microenvironments in europe: A systematic literature review[END_REF][START_REF] Ramirez-Vazquez | Personal exposure assessment to wi-fi radiofrequency electromagnetic fields in mexican microenvironments[END_REF][START_REF] Gallastegi | Children's exposure assessment of ra-972 diofrequency fields: Comparison between spot 973 and personal measurements[END_REF] P is the power in dBm, and c is the speed of light [START_REF] Liao | Measurements and computations of electric field intensity and power density[END_REF].

( 1 3 , 1 8 , 2 0 ) ( 2 0 , 2 0 , 2 0 ) ( 2 1 , 2 9 , 3 1 ) ( 3 2

633

The antenna gain of the smartphone is unknown, so 634 we assume an isotropic antenna (i.e., G = 1). In our We found that by considering all countries together, 647 2 As explained in Materials and Methods, we perform the sum in Watt, and because we only measure the RSSI for the operator declared in the SIM card, we multiply each RSSI by the number of operators in the country in a pre-processing phase. Electric field intensity (V/m) Figure 6: The population exposure is orders of magnitude lower than any existing regulation limits for the considered countries. This figure shows the distribution of the estimated electric field produced by cellular antennas at the receiver per country using boxplots, where the middle orange line shows the median, the lower and higher hinges show the first and third quartiles, respectively, and the lower and higher whiskers show a limit of 1.5x the interquartile range from the lower and higher hinges, respectively. The red dot shows the mean. Considering all signals together, we have a median at 0.005 V/m, and a 99 th percentile at 0.18 V/m. only 1% of the scans are above 0.18 V/m. Admittedly, this estimation is a coarse description of reality. We now explore how the different limitations and approximations of our estimation will impact our conclusion. First, as described in Materials and Methods, the maximum cellular RSSI that we can measure is -51 dBm, so measurements above -51 dBm are capped. However, measurements at -51 dBm represent only 1.8% of all measurements, a very small fraction that cannot fundamentally change our conclusions. Second, we apply the same frequency (that of the serving cell) to all cellular measurements in the same exposure scan. Considering that 98% of the frequencies are within [782, 2660] MHz and Equation 2 is linear with f , we have at most a factor of 3.4. Note that this is a very conservative estimate, as the median frequency is 1,745 MHz. Last, in Boussad et al. [START_REF] Boussad | Evalu-1086 ating smartphone accuracy for rssi measure-1087 ments[END_REF], we show, using calibrated measurements in an anechoic chamber, that the average deviation between the real received power at a calibrated isotropic antenna and a smartphone is 2.5 dB. If we translate this offset in Equation 2, we find that it results in a multiplying factor of 10 2.4GHz 5.3GHz

BR AU NL IN ES BE CH GB

Figure 7: The mean exposure is significantly higher when the Tx power is higher in the 2.4 GHz band, but significantly lower in the 5.3 GHz band. The figure shows the distribution of the per-user monthly average exposure using boxplots. The middle orange line shows the median, the lower and higher hinges show the first and third quartiles, respectively, and the lower and higher whiskers show a limit of 1.5x the interquartile range from the lower and higher hinges, respectively. The red dot shows the mean. To compute the significance of the mean, we perform a permutation test (N=1,000,000). The test statistic is the difference of the means for the same frequency band. The two-sided p-value is lower than 0.001 for both bands. taining the Wi-Fi emitting power per country and frequency for the Linux kernel [START_REF]Wireless regulatory database for CRDA[END_REF].

To understand the impact of regulation on exposure, we focus on two frequency bands that include a large enough number of countries using different regulations: 2.4 GHz ([2400, 2483] MHz) and 5.3 GHz ([5250, 5350] MHz). The 2.4 GHz (resp. 5.3 GHz) band represents 76% (resp. 2%, still 37 million measurements) of all Wi-Fi measurements. In the 2.4 GHz band, the maximum transmission power is 36 dBm for Australia, 30 dBm for the USA and Canada, and 20 dBm for all the other considered countries. In the 5.3 GHz band, the maximum transmission power is 24 dBm for Brazil, India, and Canada, 23 dBm for the USA, and 20 dBm for all the other considered countries.

Figure 7 shows that in the 2.4 GHz band, a Tx power of 20 dBm leads to significantly lower exposure than a Tx power higher than 30 dBm. Therefore, this regulation clearly impacts population exposure. Surprisingly, when we observe the exposure for the 5.3 GHz band, we have the opposite result: a Tx power of 20 dBm leads to significantly higher exposure than a Tx power over 23 dBm.

We can explain this seemingly contradictory result. Unlike regulations for cellular, regulations for Wi-Fi limit the Tx power; therefore, it is not surprising to see that Tx power impacts population exposure. When the difference in Tx power is large (a minimum of 10 dB between the two groups in the 2.4 GHz band), the Tx power dominates the other factors that affect population exposure. However, when the difference in the Tx power is small (a maximum of 4 dB for the 5.3 GHz band), other factors dominate the population's exposure. Indeed, as the attenuation increases with the frequency (see Equation 1), a small 4 dB difference in the Tx power will have a marginal impact on the total exposure compared to, for instance, the deployment and density of Wi-Fi access points per country.

In summary, the impact of Wi-Fi regulation on population exposure depends not only on the Tx power, but also on the frequency bands. It is worth noting that the goal of this regulation is to limit interference rather than population exposure.

The population is most exposed at home

User location is also a factor that may affect personal exposure. In the following, we focus on two location categories: at-home and out-of-home. The rationale is that, according to the results reported in the previous sections, Wi-Fi is the greatest contributor to total exposure. We hypothesize that users are more exposed at home because most users have Wi-Fi at home 3 and are closer to their router than would be the case in other environments. The goal of this section is to explore the difference between at-home and out-of-home exposure.

To cluster measurements according to the user location, we need users with a large enough number of measurements to identify the home location; we call them dense users. More precisely, when we compute the per-user monthly average exposure, we only keep users with at least 14 days of data in that month and at least 80% hourly sampling density. To calculate sampling density, we count the number of hours between the first and last day we see a user in a given month. An 80% hourly sampling density means that the user has at least one exposure scan for 80% of the counted hours. In our entire dataset, we have 22,907 dense users, which is 9% of all users.

Finally, we use the DBSCAN algorithm [START_REF] Ester | A density-based algorithm for discovering clus-1107 ters in large spatial databases with noise[END_REF] ( = 100 meters, minPts = 24, distance = haversine) on the GPS coordinates of the dense users for each month, independently. We label the cluster that most frequently appears between 10PM and 8AM as the home cluster. All the other clusters are labeled "out-of-home". Therefore, out-of-home gathers together all other indoor and outdoor locations, including those frequented for work, transportation, etc.

Figure 8 shows that users at home are significantly less exposed to cellular radiation. The main reason is that cellular antennas are outside, so walls attenuate the radiation. Conversely, exposure to Wi-Fi is more important at home than out-of-home. Here, the increased adoption of Wi-Fi technology at home 3 According to the US Census Bureau, 81% of USA households had internet access in 2016 [START_REF] Bureau | Computer and Internet Use in 1096 the United States[END_REF]. In 2019, more than 80% of the households in the European countries included in our study had internet access, with 83% coverage in France and 98% in the Netherlands) [START_REF][END_REF]. The mean exposure is significantly lower at home for cellular (-1.19 dB) and higher at home for Wi-Fi (+1.55 dB). This figure shows the distribution of the per-user monthly average exposure for dense users when they are at home (in green) and out-of-home (in blue) for Bluetooth, Cellular, and Wi-Fi sources. In the boxplots, the middle orange line shows the median, the lower and higher hinges show the first and third quartiles, respectively, and the lower and higher whiskers show a limit of 1.5x the interquartile range from the lower and higher hinges, respectively. The red dots and labels show the mean exposure. We performed a permutation test (N=1,000,000) between at-home and out-of-home for each of the three types of sources. We obtained a two-sided p < 0.001 for Wi-Fi and Cellular, and a two-sided p = 0.09 for Bluetooth.

Bluetooth

is a reasonable explanation. We computed how many 779 hours (per month) each dense user is connected to a 780 Wi-Fi source at home and out-of-home. We found 781 that half of the users (median) are connected 91% of 782 the time at home, and 29% of the time out-of-home. 783 Finally, we found that the difference of exposure to 784 Bluetooth between at-home and out-of-home is not 785 significant.

786

In summary, user location has a significant impact 787 on exposure. In particular, users are more exposed 788 to Wi-Fi at home. As they are largely connected to 789 Wi-Fi at home, we further conclude that personal Wi-790 Fi routers are the most significant factor in at-home 791 exposure. Understanding the potential human health impacts 794 of exposure to radio frequencies is a long journey. An 795 important challenge in performing sound epidemio-796 logical studies is the complexity of characterizing the 797 real exposure of the population. The methods and 798 dataset we present here offer the first analysis of the 799 evolution of radio frequency exposure at populationscale for 13 countries over four years. This change of paradigm from previous small-scale studies has direct consequences for the current debate on population exposure and the impact of this exposure on health.

The Council of Europe, following the principle of precaution, has called for an As Low As Reasonably Achievable (ALARA) rule [START_REF] Assembly | The potential dangers of elec-1110 tromagnetic fields and their effect on the 1111 environment[END_REF]. In line with this principle, one proposal is to reduce exposure levels as low as 0.6 V/m and even 0.2 V/m in the medium term. The debate currently includes proponents, who see ALARA as a necessary drastic reduction to curb the current level of exposure, and cellular operators, who oppose ALARA by arguing that it would impede the deployment of communication infrastructure, and thus, eventually, access. We reveal that for the vast majority of the population, exposure is already below the lowest ALARA level. However, reducing the current regulation levels would still benefit the small fraction of the population that is currently more exposed than recommended by the ALARA rule.

  350

436 2 . 3 .

 23 Personal exposure definition and calculation 437

  tify in the following. i) First, for all computations based on an exposure scan (as defined in Materials and Methods), we consider the sum of the received power in Watt of all signals in this scan. Computing the sum is relevant because an exposure scan is atomic in terms of time, so it represents all the signals simultaneously exposing an individual. ii) Second, we average the exposure scans of each user per month. This gives a per-user monthly average exposure. The rationale of computing per-user monthly averages is to prevent users with a large number of measurements from biasing the monthly average. iii) Third, for each country, we group the per-user monthly average exposures. When a user has been in different countries for a given month, we compute one monthly average exposure per country. Then, we compute the mean of these per-user monthly average exposures to obtain a monthly average exposure per country. Finally, we obtain the yearly average exposure by computing the mean of the monthly average exposure per country. Computing the yearly average exposure this way

1 .

 1 World-wide sustained growth of radio exposure 492 is primarily driven by WiFi 493

Figure 1 :

 1 Figure1: The total exposure of the population has been multiplied by 2.3 in 4 years. For each year, we take the yearly average exposure as given in Table2, convert it to Watt, compute the mean for all 13 countries, and convert it back to dBm. The bars represent a 95% confidence interval for the mean using empirical bootstrap resampling with replacement (N=1,000) on the yearly average exposure per country. Plots are shifted horizontally to avoid confidence interval overlap. An increase of 3 dB results in the doubling of the exposure.

Figure 2 :

 2 Figure2: A large number of sources in the vicinity marginally increases individual exposure. The figure represents the distribution of all the exposure scans in Bluetooth (top) and Wi-Fi (bottom) when there is a given number of (Bluetooth or Wi-Fi) sources in the scan (the boxplot convention is the following: the middle orange line shows the median, the lower and higher hinges show the first and third quartiles, respectively, and the lower and higher whiskers show a limit of 1.5x the interquartile range from the lower and higher hinges, respectively). For instance, the last box in the top figure represents the sum of the received power in Bluetooth for exposure scans with exactly 20 detected Bluetooth sources. We observe that beyond 4 to 5 sources in the vicinity, any additional sources marginally change the individual exposure.

Figure 2

 2 Figure2shows the relationship between individ-

549Figure 3 :

 3 Figure 3:The most exposing source is the primary driver of individual exposure. This figure represents the distribution of the percentage contribution of the top five exposure sources in all exposure scans, with Bluetooth in green and Wi-Fi in blue (the boxplot convention is the following: the middle line shows the median, the lower and higher hinges show the first and third quartiles, respectively, and the lower and higher whiskers show a limit of 1.5x the interquartile range from the lower and higher hinges, respectively). For instance, the first green box shows the distribution of the contribution of the most exposing Bluetooth source to the sum of the exposure of all Bluetooth sources for each exposure scan. We observe that for 75% of the exposure scans (containing at least one Bluetooth measurement), the most exposing Bluetooth source represents at least 56% of the entire Bluetooth exposure.

Figure 4 :

 4 Figure4: By switching off connected Wi-Fi sources and bounded Bluetooth devices, 50% of the users can reduce their exposure by 3.1 dB, and 25% of the users can reduce it by at least 10 dB. This figure shows the distribution of the individual exposure reduction for each user when we remove connected Wi-Fi sources and bounded Bluetooth devices. In red, we show the median and in blue, the 75 th percentile. For each user and month, we first compute the per-user monthly average exposure. Then, for each user and month, we collect all connected Wi-Fi sources and bounded Bluetooth devices, and we re-compute the per-user monthly average exposure by removing all collected connected sources and bounded devices from the exposure scans. Finally, we compute the difference between the per-user monthly average exposure in each case. The result is the distribution shown in this figure for each user. Note that in some rare cases, the difference can be negative. This can occur when an exposure scan contains only one connected source. By removing connected sources, we change the number of samples on which we average. As a result, a user with only a few samples could end up with a higher average without connected sources. In this figure, we drop users with a negative gain; they represent 0.92% of all users.

578 3 . 3 .

 33 Impact of regulation on personal exposure 579 Electromagnetic field emissions are regulated, 580 which means that both the spectrum used and the 581 emitting power per frequency band are fixed by a 582 regulatory authority. The types of cellular and Wi-583 Fi sources we explore in this paper are regulated on 584 a country-specific basis. Therefore, the maximum 585 emitting power per frequency band is not uniform 586 in the top 13 countries we consider. By contrast, 587 Bluetooth uses the same emitting power in all the 588 countries we consider. We explore next how cellular 589 and Wi-Fi regulation impacts the received power.

  590

Figure 5 :

 5 Figure 5: We observe no correlation between regulation limits and exposure. This figure shows the correlation between the exposure and a regulation limit triplet for the three cellular technologies we measure, 2G, 3G, and 4G (the boxplot convention is the following: the middle orange line shows the median, the lower and higher hinges show the first and third quartiles, respectively, and the lower and higher whiskers show a limit of 1.5x the interquartile range from the lower and higher hinges, respectively). Here is the association between regulation limit triplets and countries:[START_REF] Sagar | Radiofrequency electromagnetic field exposure in everyday microenvironments in europe: A systematic literature review[END_REF][START_REF] Ramirez-Vazquez | Personal exposure assessment to wi-fi radiofrequency electromagnetic fields in mexican microenvironments[END_REF][START_REF] Gallastegi | Children's exposure assessment of ra-972 diofrequency fields: Comparison between spot 973 and personal measurements[END_REF] is for IN;[START_REF] Gallastegi | Children's exposure assessment of ra-972 diofrequency fields: Comparison between spot 973 and personal measurements[END_REF][START_REF] Gallastegi | Children's exposure assessment of ra-972 diofrequency fields: Comparison between spot 973 and personal measurements[END_REF][START_REF] Gallastegi | Children's exposure assessment of ra-972 diofrequency fields: Comparison between spot 973 and personal measurements[END_REF] is for IT;[START_REF] Aerts | As-976 sessment of long-term spatio-temporal radiofre-977 quency electromagnetic field exposure[END_REF][START_REF] Mazloum | Xmobisenseplus: An updated application for the assessment of human exposure to rf-emfs[END_REF][START_REF]OpenStreetMap Nominatim[END_REF] is for BE;[START_REF] Timezonefinder | [END_REF][START_REF] Liao | Measurements and computations of electric field intensity and power density[END_REF][START_REF] Bureau | Computer and Internet Use in 1096 the United States[END_REF] is for CA; (41, 58, 61) is for FR, DE, GB, CH, ES, NL, AU, BR; (47, 61, 61) is for US.

Figure 6

 6 Figure6shows the distribution of the measured

  646

2 . 5 10 ≈ 1 . 3 .

 2513 By combining the two main

  sources of error, the actual exposure in V/m could 672 be 4.4 times higher than what we report in Figure6, 673 which is still orders of magnitude lower than the most 674 restrictive regulation limits in the countries we con-675 sider.676In summary, 99% of our exposure scans report a 677 cellular exposure lower than 0.18 V/m (corrected to 678 0.79 V/m if we take into account the multiplying fac-679 tor of 4.4, corresponding to a worst-case estimate sce-680 nario), which is orders of magnitude lower than any 681 regulation limits in the considered countries.

682 3 . 3 . 2 .

 332 WiFi regulation 683 Wi-Fi is a generic term that gathers together a 684 large number of standards covering a wide spectrum 685 of frequencies in the 2.4 GHz and 5 GHz bands. For 686 Wi-Fi, the goal of regulation is to reduce interference 687 by limiting the maximum transmission power. This 688 limit might be different for each country and each 689 frequency. Getting a consolidated view of the vari-690 ous international regulations on Wi-Fi is tricky. For 691 this purpose, we rely on the efforts of J. W. Linville 692 and S. Forshee, who maintain a consolidated file con-693

Figure 8 :

 8 Figure8: The mean exposure is significantly lower at home for cellular (-1.19 dB) and higher at home for Wi-Fi (+1.55 dB). This figure shows the distribution of the per-user monthly average exposure for dense users when they are at home (in green) and out-of-home (in blue) for Bluetooth, Cellular, and Wi-Fi sources. In the boxplots, the middle orange line shows the median, the lower and higher hinges show the first and third quartiles, respectively, and the lower and higher whiskers show a limit of 1.5x the interquartile range from the lower and higher hinges, respectively. The red dots and labels show the mean exposure. We performed a permutation test (N=1,000,000) between at-home and out-of-home for each of the three types of sources. We obtained a two-sided p < 0.001 for Wi-Fi and Cellular, and a two-sided p = 0.09 for Bluetooth.

  Our work also fundamentally changes the debate on frequency exposure, currently heavily centered on the regulation of cellular operators. Not only do we show that Wi-Fi is by far the largest contributor to population exposure, but also that a few sets of sources, namely those used by individuals and those present at home, are the key contributors. Offering tools for individuals to prevent unnecessary exposure at home, or working on technology that automatically reduces exposure are just some examples of short and medium term ways to expand the precautionary principle. Such approaches have not yet received the attention that they deserve. Beyond these direct implications, we envision our work and dataset providing a foundation for future epidemiological studies. CRediT authorship contribution statement Y. Boussad: Conceptualization, Data curation, Formal Analysis, Investigation, Methodology, Software, Validation, Visualization, Writing original draft, Writing review & editing. X. Chen: Formal Analysis, Methodology, Software, Validation, Visualization, Writing original draft, Writing re-843 view & editing. A. Legout: Conceptualization, 844 Data curation, Formal Analysis, Funding acquisi-845 tion, Investigation, Methodology, Project adminis-846 tration, Resources, Software, Supervision, Valida-847 tion, Writing original draft, Writing review & editing. 848 A. Chaintreau: Conceptualization, Formal Analy-849 sis, Methodology, Resources, Supervision, Validation, 850 Writing original draft, Writing review & editing. W. 851 Dabbous: Writing original draft, Writing review & 852 editing. et al., Comparison of radiofrequency electromag-908 netic field exposure levels in different everyday 909 microenvironments in an international context, 910 Environment international 114 (2018) 297-306. 911 [11] M. Velghe, W. Joseph, S. Debouvere, R. Am-912 inzadeh, L. Martens, A. Thielens, Characteri-913 sation of spatial and temporal variability of rf-914 emf exposure levels in urban environments in 915 flanders, belgium, Environmental research 175

  3 (from -34.6 dBm in 2017 to -508 31 dBm in 2020) over the four-year period. The trend 509

		2017		2018			2019			2020
	Country Mean	95%CI	Mean	95%CI	Change Mean	95%CI	Change Mean	95%CI	Change
	BR	-39.4 [-41.1, -38.1] -36.3 [-39.1, -34.1] +105% -34.4 [-37.3, -32.4] +56% -32.0 [-33.1, -31.0] +71%
	AU	-34.2 [-37.4, -31.5] -34.1 [-36.5, -32.1] +2%	-31.0 [-34.0, -28.4] +104% -31.1 [-31.9, -30.4] -3%
	NL	-39.1 [-41.9, -36.9] -37.1 [-39.6, -34.9] +57% -36.3 [-38.7, -34.3] +19% -33.6 [-35.3, -32.1] +87%
	IN	-29.8 [-35.1, -26.3] -27.6 [-37.0, -23.6] +64% -32.2 [-33.8, -30.9] -65%	-30.6 [-32.2, -29.4] +46%
	ES	-37.4 [-40.2, -35.1] -35.4 [-37.6, -33.6] +60% -32.9 [-34.6, -31.7] +77% -31.6 [-32.9, -30.5] +35%
	BE	-40.7 [-42.0, -39.7] -35.9 [-37.7, -34.3] +204% -35.4 [-36.5, -34.4] +13% -32.5 [-33.8, -31.5] +91%
	CH	-31.6 [-33.4, -30.2] -32.9 [-34.4, -31.7] -25%	-33.1 [-34.9, -31.6] -6%	-32.6 [-34.3, -31.2] +13%
	GB	-39.2 [-41.0, -37.7] -34.7 [-36.8, -32.9] +182% -32.7 [-35.1, -30.6] +60% -30.9 [-32.3, -29.8] +49%
	CA	-35.6 [-37.8, -33.8] -32.3 [-33.5, -31.0] +112% -31.9 [-33.3, -30.6] +9%	-29.2 [-30.1, -28.3] +89%
	DE	-36.6 [-37.5, -35.9] -36.9 [-38.4, -35.8] -7%	-32.8 [-34.8, -31.3] +158% -32.1 [-33.0, -31.0] +19%
	IT	-33.8 [-38.4, -30.7] -33.9 [-35.3, -32.7] -2%	-33.3 [-34.1, -32.4] +16% -32.1 [-33.1, -31.4] +30%
	US	-33.5 [-34.9, -32.0] -30.5 [-31.2, -29.9] +98% -29.8 [-31.0, -28.5] +18% -27.3 [-28.3, -26.4] +76%
	FR	-33.5 [-34.1, -33.0] -33.0 [-33.8, -32.2] +14% -33.3 [-33.9, -32.7] -7%	-31.8 [-32.2, -31.4] +42%

Table 2 :

 2

Most likely, the issue comes from the modem associated with the Exynos SoC, but we only have access to the SoC name from the Android API.

860

This work has been supported by the French gov-861 ernment, through the UCAJEDI Investments in the 862 Future project managed by the National Research 863 Agency (ANR) with the reference number ANR-15-864 IDEX-01.

Conflicts of Interest:

854

The authors declare no conflict of interest.