J Losfeld
email: jimmy.losfeld@cea.fr

L Desgranges

Y Pontillon

G Baldinozzi

A simple modeling for gas release during annealing of irradiated nuclear fuel

INTRODUCTION

In irradiated UO2, fission gases accumulate in the fuel and form different reservoirs. The size and location of these reservoirs depend on many parameters, such as burn-up and power history. During an annealing experiment of an irradiated fuel sample, these reservoirs change, interconnect, and under the effect of the thermal load can give rise to a release outside the sample. This corresponding fission gas release (FGR) occurs in successive bursts at different temperature levels. Although the mechanisms to explain this release are not completely identified, the literature suggests that the grain boundaries decohesion is the preferred mechanism. [START_REF] Rest | Fission gas release from UO2 nuclear fuel: A review[END_REF] More specifically, annealing test results on irradiated UO2 sample from work by Noirot et al. [START_REF] Noirot | Fission gas release behaviour of a 103GWd/tHM fuel disc during a 1200°C annealing test[END_REF] and Marcet et al. [START_REF] Marcet | High Burn up Structure Contribution to the Fission Gas Release under Transient Conditions[END_REF] indicates that the release around 1100°C comes preferentially from the HBS zone of the fuel. This temperature level coincides with the last burst of release (Burst around 1200°C in figure 2) highlighted in slow ramp annealing performed in the MERARG furnace on high burn up irradiated fuel samples. [START_REF] Pontillon | Experimental and theoretical investigation of fission gas release from UO2 up to 70 GWd/t under simulated LOCA type conditions: the GASPARD program[END_REF] We aim to determine whether the release kinetics observed at these temperature levels are consistent with a gas reservoir located in the intergranular bubbles of the HBS zone. In this paper, we propose a simplified model of the gas flows of the third burst in order to determine the physical characteristics of the exit channels followed by the fission gas.

Modelling fission gas emptying kintetics

The release of the gas contained in the HBS bubbles takes place through their interconnection to a free surface, as can be seen in the SEM image in Figure 1. We have developed a gas flow model in the fuel during the annealing. It postulates that the gas release during an isothermal plateau at 1200°C corresponds to the equilibrium between overpressure gases reservoirs located in the fuel sample connected to the free surface at atmospheric pressure. We will simplify the gas reservoir corresponding to the intergranular gas bubbles interconnection of the HBS zone as a single reservoir connected by a single channel outside of the fuel as shown in Figure 2. The annexes contain the details of the calculation to obtain this equation.

Experimental data:

We tested our model on an annealing test on a highly irradiated UO2 sample with a temperature ramp of 0.2°C/s from 300 to 1200°C as shown in Figure 3. At 1200°C we maintained an isothermal plateau until the end of the gas release was observed. During the ramp the release describes different bursts that we can interpret as resulting from the successive opening of the release channels due to the increase of the pressure of the associated gas reservoirs. From the 100th minute, we observe the third burst, which we hypothesised to be correlated with the HBS zone of the sample. From the beginning of the 1200°C isothermal plateau, we observe the change of the gas release regime, and its decrease. We will focus on the characteristic decay of the gas release curve from the 125th minute, as highlighted in the insert to Figure 3, when the gas release regime is established, which we will fit with the above-mentioned model.

Dataset fitting

From equation ( 1), the fit involves the following three parameters: λ, τ and c These three parameters are highly dependent, which gives fitting results that are very sensitive to small variations. To overcome this sensitivity and improve the robustness of the fit, we will use the logarithmic derivative method.

The main advantage of using the logarithmic derivative in our fit is to simplify the number of parameters to be fit by simplifying the scale factor λ, which does not interest us here. Thus, the fit will concern the parameters: τ and c

The second benefit of the logarithmic derivative is that it allows us to identify the beginning of the emptying phase, i.e. the inflection point of the emptying curve where we will place 𝒕 𝟎 .

Therefore:

RESULTS

We have applied the simplified gas flow model defined above to the example of emptying kinetics shown in Figure 3. It fits the experimental data accurately as shown in figure 4. The model is well suited to annealing where an isothermal stage at 1200°C is maintained long enough to observe the emptying kinetics. The question now arises as to how to interpret the model. As we do not know how many paths are created during discharge, we reason here on the scale of an equivalent channel, i.e. if the emptying had only taken place in a single path. Our calculations allow us to determine the equivalent permeability (K) of the channel from 𝜏 as follows:

𝐾 = C- µ • L • 𝑉 H 𝑃 3 • 8 𝜋 𝜏 (3)
µ, 𝑃 3 𝑒𝑡 𝜏 are known. 𝑉 H the volume of the reservoir associated with the emptying third burst can be measured experimentally. We know that the gas released by this burst is mainly from the HBS zone, so we will assume that the length of the emptying path will be of the same order of magnitude as the width of the HBS of the sample, i.e. 50 µm. Thus, for the example studied, we determined the equivalent permeability as being equal to 1,28 • 10 78T m². Given our hypotheses, we can interpret this equivalent permeability value as a maximum value. Indeed, the higher the number of emptying channels, the lower the value of the permeability of these channels will be.

To conclude, without going further in terms of interpretation, it is interesting to note that the order of magnitude of equivalent permeability obtained is close to the permeability of porous rocks such as sandstone or granite, studied in geology. [START_REF] Géraud | Perméabilité des roches et loi de Darcy » ULP, centre de géochimie de la surface[END_REF] This encouraging outcome encourages us to improve the model in order to refine the results.

Fig. 1 .

 1 Fig. 1. Post annealing SEM image of the HBS area of an irradiated UO2 fuel sample

Fig. 2 .

 2 Fig. 2. Schematic of the interconnection between the sample's actual (a) and simulated (b) fission gas reservoirs to the free surface.Thus defined, and from the mass balance, Darcy and perfect gas equations, the release kinetics of krypton 85 during the isothermal stage should take the form of equation[START_REF] Rest | Fission gas release from UO2 nuclear fuel: A review[END_REF].

Fig. 3 .

 3 Fig. 3. Example of emptying kinetics observed at the 125th minute on the release curve of krypton 85 during annealing.

Fig. 4 .

 4 Fig. 4. Logarithmic derivative fitting (top) and residuals (bottom) applied to the release kinetics shown in Figure 1.

ANNEXES Model of the fission gas emptying into the fuel

This description consists of a mass balance, Darcy's law and the law of perfect gases: Expression of the density, as a function of the perfect gas constant R=8.31 J.mol-1 .K-1, M [kg/mol] the molar mass of the gas, and T [K] the temperature.

Injecting ( 5) and ( 6) into (4) we obtain:

Isothermal flow:

For isothermal flow, the constant temperature can be simplified on either side of the above expression. Applied to our example it will take this form: In MERARG we measure a quantity of 85Kr atoms relative to the mass of the sample, not a total flux. To simplify and adapt equation 10 to our release data, we will use the following equation to model the release of 85Kr fission gas:


Luís Soares Barbosa

Michael Goedicke

Erich J Neuhold

Ricardo Reis

Steven Furnell

Eunika Mercier-Laurent

Marco Winckler

Rainer Malaka

Robert Nowak

Jerzy Chrząszcz

Stelian Brad

Muhammad Iqbal

Narayanan Kulathuramaiyer

Adnan Shahid Khan

Johari Abdullah

Ashley Edward Roy Soosay

Mohd Muhammad Hamdi Mahmood

Saiful Bahari

IFIP -The International Federation for Information Processing

IFIP was founded in 1960 under the auspices of UNESCO, following the first World Computer Congress held in Paris the previous year. A federation for societies working in information processing, IFIP's aim is two-fold: to support information processing in the countries of its members and to encourage technology transfer to developing nations. As its mission statement clearly states:

IFIP is the global non-profit federation of societies of ICT professionals that aims at achieving a worldwide professional and socially responsible development and application of information and communication technologies.

IFIP is a non-profit-making organization, run almost solely by 2500 volunteers. It operates through a number of technical committees and working groups, which organize events and publications. IFIP's events range from large international open conferences to working conferences and local seminars.

The flagship event is the IFIP World Computer Congress, at which both invited and contributed papers are presented. Contributed papers are rigorously refereed and the rejection rate is high.

As with the Congress, participation in the open conferences is open to all and papers may be invited or submitted. Again, submitted papers are stringently refereed.

The working conferences are structured differently. They are usually run by a working group and attendance is generally smaller and occasionally by invitation only. Their purpose is to create an atmosphere conducive to innovation and development. Refereeing is also rigorous and papers are subjected to extensive group discussion.

Publications arising from IFIP events vary. The papers presented at the IFIP World Computer Congress and at open conferences are published as conference proceedings, while the results of the working conferences are often published as collections of selected and edited papers.

IFIP distinguishes three types of institutional membership: Country Representative Members, Members at Large, and Associate Members. The type of organization that can apply for membership is a wide variety and includes national or international societies of individual computer scientists/ICT professionals, associations or federations of such societies, government institutions/government related organizations, national or international research institutes or consortia, universities, academies of sciences, companies, national or international associations or federations of companies.

More information about this series at https://link.springer.com/bookseries/6102 Robert Nowak • Jerzy Chrząszcz

Preface

The Theory of Inventive Problem Solving (TRIZ) is popular creative thinking and systematic organizing knowledge methodology. It is a scientifically grounded and structured approach for forecasting the evolution of technological systems, and includes numerous tools and methods for product and process innovation, enabling an increase in creative and inventive productivity.

The European TRIZ Association (ETRIA) World Conference TRIZ Future (also known as the International TRIZ Future Conference, TFC) is traditionally a fundamental venue for disseminating information about TRIZ. TFC has reached its 22nd edition (TFC22) which took place in Warsaw, Poland, during September 27-29, 2022. The conference was organized at the Faculty of Electronics and Information Technology, Warsaw University of Technology, in Warsaw, Poland by the Artificial Intelligence Division. The conference motto is 'Systematic Innovation partnerships with AI and IT', which higlights the main idea of this event: to join TRIZ with Information Technology (IT) and Artificial Intelligence (AI).

Information Technology (IT) and Artificial Intelligence (AI) achieve many goals. It is worth noting that thoughtlessly comparing artificial intelligence to human intelligence may be a source of misunderstandings and confusion. Computers are only machines that, to some extent, should be viewed as fast-acting calculators that can only perform simple operations on binary numbers. Despite the huge successes, scope and scale of their applications, it is not without reason that computers are sometimes called 'stupid automatons'. It is impossible for any program executed by a computer to become a unit autonomous enough to execute something other than its creator, a computer scientist, originally designed. The success of IT and AI is the success of the creativity of computer scientists, which now needs the support of TRIZ.

After disrupting the traditional format of the TFC 2020 and TFC 2021 conferences, TFC 2022 was provided in a hybrid form, which included seeing each other face-to-face as well as participating online.

Despite the focus on joining TRIZ and IT and AI, other topics typically linked with TRIZ and protagonists of previous TFC editions were still accepted. All 39 papers published in this book were at least triple peer-reviewed by several members of the Scientific Committee, and the authors had the chance to improve their papers based on these reviews before the final decision. The papers were presented during the TRIZ Future Conference 2022, a lively discussion followed the presentation.

August 2022

Robert Nowak Jerzy Chrząszcz

Stelian Brad

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Ebru Dogan
email: ebru.dogan@vedecom.fr

Cécile Barbier
email: cecile.barbier@unige.ch

Estelle Peyrard
email: estelle.peyrard@apf.asso.fr

Public perception of ethical issues concerning automated mobility A focus group study among three road user categories

Keywords: • General and reference → Document types, General conference proceedings conference proceedings, • Human-centered computing → Human computer interaction (HCI), HCI Ethics, Automated vehicle, Transport, Public Acceptance, Vulnerable road users

As the deployment of automated vehicles (AV) progresses, questions about the public acceptance and ethical issues raised by automated mobility emerge. The present focus group study aimed at examining the ethical issues related to the AVs as perceived by road users through two scenarios. Nineteen participants in three groups, namely, drivers, pedestrians, and road users with disabilities, participated in the study. The three major principles that concerned all road user groups were responsibility, human autonomy, and wellbeing. For the road users with disabilities social justice was additionally a paramount issue, while for drivers it was privacy. The scenarios revealed both similarities and discrepancies in worries and expectations of different road user categories.

The deployment of automated vehicles (AV) premises changes in individual and public transport, while raising questions about public acceptance, including ethical issues. The critical situations that require ethical considerations were quickly compared to the "trolley problem" for application of different ethical frameworks [START_REF] François Bonnefon | The social dilemma of autonomous vehicles[END_REF][START_REF] Santoni De Sio | Killing by autonomous vehicles and the legal doctrine of necessity[END_REF][START_REF] Keeling | Legal necessity, pareto efficiency & justified killing in autonomous vehicle collisions[END_REF][START_REF] Leben | A Rawlsian algorithm for autonomous vehicles[END_REF][START_REF] Evans | Ethical decision making in autonomous vehicles: the AVEthics project[END_REF]. This research shows a social dilemma in people's approach to AV. That is, people have conflicting opinions when they think of their interests versus the greater good of society, which paves the way to the ethical challenges at a societal level. While the focus has always been on the behavior of a single AV in a critical situation, scaling up the behavior of the AV to the entire transport system is likely to have consequences for society, beyond the question of which moral code to implement in the AV [START_REF] Mladenovic | Engineering social justice into traffic control for Self-Driving vehicles[END_REF].

Laypeople rely on their intuitions and socio-cognitive constructs, such as trust, value congruency, emotions, and contextual factors in their judgments of moral acceptability of new, complex technology, instead of crunching numbers like experts do [START_REF] Roeser | The role of emotions in judging the moral acceptability of risks[END_REF]. In this respect, people become increasingly concerned about the vulnerability of artificial intelligence technologies to privacy breaches, data protection, consent, and security in general. AV is not an exception to this skepticism. There is a rising public interest in the implications of AV in terms of accessibility and fairness. Nonetheless, a comprehensive analysis showed that the research on the non-tangible implications of automated transportation, such as the implications for social equity and public health, are relatively under-researched [START_REF] Milakis | Policy and society related implications of automated driving: A review of literature and directions for future research[END_REF]. Additionally, while the driver needs and use have been heavily studied, vulnerable road user categories, such as pedestrians, elderly road users, road users with disabilities, children, were overlooked.

The current manuscript reports the results of a focus groups study that was carried out as part of the H2020 project SUaaVE (Supporting acceptance of automated vehicles, 2019-2022) in order to comprehend public perception on the ethical issues raised by AV, targeting different road user groups, namely, drivers, pedestrians, and road users with disabilities (RUD).

METHOD 1.Participants

Nineteen people (6 males) with the mean age of 39-years (SD=6.5) participated in the current study, divided into three groups, namely, drivers (6 people), pedestrians (7 people), and RUD (6 people).

Scenarios

We defined three scenarios related to automated urban mobility for the focus group discussions. Transgression of a traffic rule depicted an AV that has to decide whether or not to comply with a prohibited action (i.e. crossing a continuous lane line) in order to overtake a stranded vehicle blocking the traffic circulation (Figure 1, left).

Changes in urban landscape depicted potential changes in the use of (shared) space, provision of public and private transport services, commuting habits, and air pollution that might occur with the deployment of AV in the urban transport system. Unavoidable accident depicted an imminent impact between the AV and a pedestrian that was initially invisible to the sensors of the vehicle because he was behind a delivery truck (Figure 1, right).

Ethical principles

Six ethical principles had been identified in a previous focus group with experts in philosophy, robotics, urban design, human factors, and legal issues (December 2019): responsibility, autonomy, wellbeing, democracy, justice, and privacy/ data protection.

Procedure

A focus group methodology, supplemented with individual written phases, was adopted. The aim of the individual phases was to collect participants' opinion without the group effect and to anchor their individual point of view to launch the discussions. The aims of the group discussions were to create a (controversial) debate and observe the social influence on the evolution of opinions.

After the introduction and signing of the consent forms, two video clips explaining the AV technology were used as an icebreaker. In Step 1, not reported here, participants expressed their general take on the AV on sticky notes, followed by 10 min of group discussion. In Step 2, we presented participants with the six ethical principles from the expert focus group and defined each of them. Participants, then, wrote on a sticky note the three principles that they considered indispensable to take into account for the deployment of AV, followed by a 25-30 min discussion. In Step 3 on scenarios, participants expressed on sticky notes which ethical issues they considered relevant for the scenario, followed by a group discussion of 50 min in total for three scenarios. Each session lasted 2 hours and the study was conducted the last week of February 2020.

RESULTS

The current manuscript is focused on results of Step 2 and two scenarios in Step 3. We will provide verbatims in italics to exemplify the expressions and opinions of participants.

Step 2: selection of ethical principles

The analysis of the three ethical principles considered absolutely necessary is presented in Table 1 and the mapping of responses are presented in Figure 2.

Responsibility -Who would be held responsible in case of an accident is identified as the most relevant ethical issue by all groups. Participants recognized that there were a lot of stakeholders involved in the AV ecosystem and expressed concerns about the discharge of responsibility by the industrial stakeholders, so that in the end the responsibility falls on the user, as in the case of "the problem of many hands" [START_REF] Nissenbaum | Accountability in a computerized society[END_REF]. "There is no zero accident. So, it boils down to responsibility". "There are too many actors, too many responsibilities. "

Drivers group distinguished legal responsibility and moral responsibility, stating in consensus that they would morally feel responsible for killing or injuring someone, even if they would not be held responsible legally. "If we kill someone, we will have to live with that feeling. It is not only a matter of jail or insurance money. "

The next prominent ethical principle was human autonomy among all groups. The impact of the deployment of AV on autonomy was perceived rather negatively. Drivers mainly emphasized remaining, delegating control, out of the loop, and the diminished of capacity to react after a period of automated driving. Additionally, becoming dependent on technology was a major concern among drivers and pedestrians. "It becomes like mobile phone. I don't take initiative while using phone. What if I start leaving every decision to my car?" "Today we can deactivate [the automated mode], but if tomorrow they produce only fully automated vehicle, what do we do?"

On the positive side, participants recognized that the AV could improve the autonomy of currently immobile road users. In the RUD group, AV is sometimes considered enabling freedom. "For healthy people, the primary concern is "I will lose my autonomy and control". However, for some it may be a source of autonomy to get out of isolation. " "I said autonomy because. . . I will have more freedom. "

Wellbeing was ranked third and expressed on various dimensions, such as gaining time, being able to engage in other activities, comfort and less fatigue during the ride, improved road safety, and environmental wellbeing (regarding pollution and how the battery of the AV is handled), common among all three groups. Nonetheless, pedestrians had some safety concerns about the AV's interactions with pedestrians, such as whether they will be able to understand that the AV has detected them or to anticipate the vehicle's behavior. "Crossing the street may be more stressful due to lack of anticipation [of the AV behavior]". "Since AV is a robot, does it mean that the pedestrian will always be at fault in case of an accident?". Among the RUD, wellbeing, like autonomy, was associated with mobility gains.

Privacy was expressed heavily by the drivers. Drivers stated they had the feeling of being followed and detected all the time even with the current technologies, a situation likely to aggravate with AV, and argued the necessity of the possibility of deleting one's data. Data protection and financial concerns seem to interact with concerns that the insurance companies would use the personal data obtained from the AV to calibrate insurance premium. The RUD group was divided on the question of privacy. For half of the group, holding driver's license, it was a major concern. "Privacy comes first.

The fact that we are tracked, that they know everything, were we go, with whom. . . We are not free." "Where do the data go? To the car manufacturer?" For the other half, not holding driver's license, on the contrary, privacy was not a major issue.

Social justice was predominantly prioritized by the RUD group and not mentioned at all by the drivers. For the RUD, social justice seems to have two dimensions. The first one is the role of AV in the improvement of mobility, for instance in access to work and education, since disabilities often impede the use of personal car or the access to public transport. The other dimension is the social and economic inequalities to purchase AV. Vehicles with specifications for RUD are already more expensive than regular vehicles. Hence, for the RUD group AV could exacerbate already existing social and economic inequalities. There was a consensus that the deployment of AV especially in public transport should bear in mind inclusiveness to improve the condition of RUD. "AV is supposed to contribute to social equity to improve the conditions of everybody, not only one [privileged] part of the society. We can imagine AV in public transport, in addition to personal use. "

Although democracy was mentioned by three people in total, it did not come up during the discussions.

2.2

Step 3: Scenarios 2.2.1 Scenario 1: Transgression of a traffic rule. The main inquiries of this scenario were what participants thought about permitting an AV to transgress a traffic rule and whether it would be acceptable to design a machine that could violate the law. We grouped the answers written on the sticky notes in Table 2.

The striking difference among the three groups at first glance is the drivers' higher tolerance for the transgression of the traffic rules in contrast to the pedestrian and RUD groups' insistence on the impermissibility of a transgression. However, drivers insisted that this should be exceptional, for justified reasons, and still within the regulatory framework. Hence, paradoxically, drivers would want the violation of law to be defined within the law to avoid infringement of liability.

When the possibility of letting the AV learn these exceptional situations via machine learning algorithms instead of regulatory bodies defining them individually was raised, one participant said "I want the vehicle still to be a vehicle, not a person. A vehicle does not think!". Thus, we see a worry about anthropomorphism attributed to AV. Two other participants, also in the driver group, expressed Few participants in drivers and pedestrians groups proposed that a human decides vehicle's behavior. The possibilities of asking the driver or a remote operator to take over control of the AV were evoked.

One participant in the RUD group rejected transgression of traffic rules because it may result in people behaving irresponsibly, for instance, consuming more alcohol than the legal limit knowing that the AV would be able to handle any situation on the road. The AV could be considered to leave room for complacency.

Two common concerns raised in all groups were the extent to which we can keep defining exceptional cases and the impact on the non-automated mobility as a result of adopting the traffic rules to accommodate AV.

Scenario 2: Urban mobility.

The interest of this scenario were how people imagined future cities with AV and what kind of compromises were considered acceptable. As in the first scenario, we grouped participants' responses in Table 3.

The most prominent opinion expressed among all user groups was that the AV should be riding on a dedicated lane segregated from the rest of the road users. In the pedestrian group, this was practically the only response. The main motivation behind this suggestion was concerns for safety and efficiency (e.g. the AV being blocked due to uncourteous behaviors of pedestrians or non-AVs). In a previous focus group with experts, an expert on urban planning expressed that such segregation would mean regression of the gains that have been done in terms of shared space and giving priority to non-motorized road users in the urban cityscape, and would be unfair to non-AV users. In the current non-expert group, we observe a dominant concern for safety overriding other issues related to equitable and fair sharing of space. The governance of the co-existence of AV with other road users is a primary ethical issue to be tackled.

Among drivers and RUD, there was also recognition of a negative impact of dedicating space to AV on the accessibility of other road users and non-AVs. Hence, although not expressed as such, fairness and social inclusion preoccupies the minds, more precisely minds of drivers and one RUD in the current sample.

The pedestrian group diverged from their initial position in favor of segregated space for safety and converged on equity by supporting the integration of the AV in public transport (some of them already tried several demo cases of automated shuttles) or car-sharing scheme, instead of private vehicles. This seemed to be an acceptable compromise to justify a dedicated lane for collective use rather than individual use and to improve equity. "An AV does not have priority over other road users. It should bring more equity [for all]. "

In the RUD group, we observed a similar pattern of convergence on shared use, either by public transport or car-sharing, for the benefit of everyone. Additionally, RUD argued that their use of space is not always straightforward in order to maneuver the wheelchair. Moreover, they expressed the need to take into account the people in wheelchairs in the design of new shared spaces with AV to facilitate accessibility.

Scenario 3: Unavoidable accident.

The interest of this scenario were the kind of sacrifice that participants were willing to accept and the ethics chart that they considered appropriate for AV. A general ethics chart for AV has not been retained during the discussions among participants. As in the previous scenarios, we grouped participants' responses in Table 4.

All groups had a consensus on giving priority to the protection of the pedestrian because, unlike the pedestrian, the driver is protected by the vehicle itself and by the passive safety systems. This is in line with the results of the famous Moral Machine project [e.g. 1]. Vulnerability of the victim at the target of the morally questionable act seems to determine the decision of permissibility.

One of the issues in the driver group was the possibility of letting the driver define the ethics settings of the vehicle as "they are the ones who have to live with the consequences". This is aligned with the view that AV becomes a moral proxy of its user, instead of a moral agent, and thus, should reflect the user's ethical preferences [START_REF] Sandberg | What do Cars Think of Trolley Problems: Ethics for Autonomous Cars[END_REF].

Two additional possibilities of risk mitigation and random decision were raised in the pedestrian group. The reason behind the risk mitigation argument was that "the situation is so complex that there will always be something that we will fail to take into account while programming. Hence, random [decision] would be the only fair way".

With respect to risk mitigation, the argument was that "we should take into account both. We do not settle saying beforehand that we absolutely protect one or the other".

DISCUSSION

The current article aims at reporting the results of three focus group sessions consisting of drivers, pedestrians, and road users with disabilities on the ethical issues raised by the deployment of AV conducted as part of the H2020 project SUaaVE.

The primary concern for an ethically aligned AV was responsibility among all road user groups. The large number of stakeholders involved in the design and deployment of automated transport blurs the lines of responsibility, leaving gaps for accountability, a phenomenon known as "problem of many hands" [START_REF] Nissenbaum | Accountability in a computerized society[END_REF]. Interestingly, drivers were preoccupied also by the moral consequences of the AV actions, which is a new dimension to the usual emphasis on legal responsibility and accountability. We observed clear distinctions among currently mobile and immobile road users in their reflections about human autonomy, wellbeing, and social justice. While all road user groups acknowledged the potential positive and negative impact of the AV on human autonomy and wellbeing, drivers were mainly concerned with loss of control and agency. RUD, to the contrary, reflected on the possibility of gaining autonomy and mobility. As RUD experience the challenges and injustice in the current transport system combined with economic precarity [START_REF] Epting | Automated vehicles and transportation justice[END_REF], they seem to be more sensitive to and knowledgeable about the implications new technologies for social justice and equity. This group was indeed the only one prioritizing social justice at Step 2. In fact, the issues related to human autonomy, wellbeing, and social justice become a question of accessibility, that is, access to basic rights and services for the road users currently limited in their mobility.

The similar points raised by different road user groups provide information to technology developers and policy makers about the potential bottlenecks for moral acceptability of AV technology. However, we also observe divergences among the different groups in their reasoning and prioritization of different principles. An inclusive approach to make sure that these diverging points are addressed in design and policy is of particular importance to ensure public acceptance.

The analysis of individual scenarios revealed interesting results. In the transgression of a traffic rule scenario, we observed a difference between the drivers and non-drivers. While drivers considered transgression permissible, non-drivers considered it impermissible. This may be related to the concerns about an anthropomorphic AV. The fact that a vehicle should remain as a machine and not be equipped with judgment faculties and feelings, like humans, has been raised on several occasions. In fact, the opacity of AIbased technologies and the transparency of the decision making process constitute a large item in several internationally recognized recommendations, such as European Commission's report on Trustworthy AI 1 .

The second scenario on the changes in the urban environment with the deployment of AV revealed a strong support for a separate lane for AV for the sakes of safety and efficiency. This result is counter-intuitive, given that the urban design is moving in the direction of shared space and co-existence of different road users. While the risks associated with the uncertainties about the AV may have led people to prefer a separate space for safety, counterarguments highlighted the unfairness of such exclusive use of space. Nonetheless, during the discussions we observed a distinction between the personal and collective use of AV, in favor of privileges for collective use to promote equity.

The last scenario on the unavoidable accident was also the most familiar one to the participants due to the large media coverage of the Moral Machine project. A clear pattern in favor of a Kantian principle of now harming an innocent [e.g. 12] emerged in all groups by the protection of the pedestrian. The vulnerability of the pedestrian seems to have contributed to participants' decision. In fact, perceived vulnerability underlies different moral emotions pertaining to protection and caring tendency [START_REF] Anton | Perceived vulnerability as a common basis of moral emotions[END_REF]. Hence, it is possible that moral emotions mediate the permissibility of the action. Finally, an ethics policy based on claim mitigation has been studied by [START_REF] Evans | Ethical decision making in autonomous vehicles: the AVEthics project[END_REF][START_REF] Dogan | A claim-mitigation approach to the ethical decisions of automated vehicles: a study on public acceptability[END_REF]. A study on the public acceptability of AV's decision making strategies showed that people considered a claim mitigation approach more fair and moral than absolute protection of one or the road user or a random decision process.

Overall, the ethical issues related to the deployment of AV were intertwined with public acceptability of this new technology among all road user groups in the current study. While the uncertainties about the technology underlie some worries about negative impacts, the potential benefits of the automated transport are also appreciated, provided the deployment addresses the needs and expectations of all road users.

Figure 1 :

 1 Figure 1: Illustration of the scenario of transgression of a traffic rule (left) and unavoidable accident (right)

Figure 2 :

 2 Figure 2: Mapping responses in Step 2

Table 1 :

 1 The analysis of the top 3 ethical principles partitioned among the three groups

		Wellbeing	Autonomy	Privacy	Justice	Democracy	Responsibility
	Drivers	3	5	4	-	-	6
	Pedestrians	2	4	1	1	2	5
	RUD	3	5	-	5	1	5

Table 2 :

 2 Opinions on the transgression scenario across three groups

	Drivers	Pedestrians	RUD

Table 3 :

 3 Opinions on the urban scenario across three groups

	Drivers	Pedestrians	RUD

Table 4 :

 4 Opinions on the unavoidable accident scenario across three groups

	Drivers	Pedestrians	RUD

https://ec.europa.eu/digital-single-market/en/news/ethics-guidelines-trustworthyai

ACKNOWLEDGMENTS

This research was conducted as part of the SUaaVE project "Supporting acceptance of automated vehicles". Grant sponsor: European Union's H2020 Research and Innovation Programme. Grant no: 814999.


domain: sde sdu sdu.ocean

text:
Xiaoxia Shang
email: xiaoxia.shang@fmi.fi

Antti Lipponen

Maria Filioglou

Anu-Maija Sundström

Mark Parrington

Virginie Buchard

Anton S Darmenov

Ellsworth J Welton

Eleni Marinou

Vassilis Amiridis

Michael Sicard

Alejandro Rodríguez-Gómez

Mika Komppula

Tero Mielonen

Monitoring biomass burning aerosol transport using CALIOP observations and reanalysis models: a Canadian wildfire event in 2019

In May-June 2019, smoke plumes from wildfires in Alberta, Canada, were advected all the way to Europe. To analyze the evolution of the plumes and to estimate the amount of smoke aerosols transported to Europe, retrievals from the spaceborne lidar CALIOP (Cloud-Aerosol LIdar with Orthogonal Polarization) were used. The plumes were located with the help of a trajectory analysis, and the masses of smoke aerosols were retrieved from the CALIOP observations. The accuracy of the CALIOP mass retrievals was compared with the accuracy of ground-based lidars/ceilometer near the source in North America and after the long-range transport in Europe. Overall, CALIOP and the ground-based lidars/ceilometer produced comparable results. Over North America the CALIOP layer mean mass was 30 % smaller than the ground-based estimates, whereas over southern Europe that difference varied between 12 % and 43 %. Finally, the CALIOP mass retrievals were compared with simulated aerosol concentrations from two reanalysis models: MERRA-2 (Modern-Era Retrospective analysis for Research and Applications, Version 2) and CAMS (Copernicus Atmospheric Monitoring System). The simulated total column aerosol optical depths (AODs) and the total column mass concentration of smoke agreed quite well with CALIOP observations, but the comparison of the layer mass concentration of smoke showed significant discrepancies. The amount of smoke aerosols in the model simulations was consistently smaller than in the CALIOP retrievals. These results highlight the limitations of such models and more specifically their limitation to reproduce properly the smoke vertical distribution. They indicate that CALIOP is a useful tool monitoring smoke plumes over secluded areas, whereas reanalysis models have difficulties in representing the aerosol mass in these plumes. This study shows the advantages of spaceborne aerosol lidars, e.g., being of paramount importance to monitor smoke plumes, and reveals the urgent need of future lidar missions in space.

Introduction

Boreal wildfires are common, with their location, intensity, and extent to vary from year to year. Moreover, it is expected that future fire activity will increase as a result of global warming [START_REF] Descals | Unprecedented fire activity above the Arctic Circle linked to rising temperatures[END_REF]. The characteristics of wildfires and their emissions depend on the properties of the fuel available, meteorological conditions, and burning conditions. There are more high-intensity crown fires in North American forests than in Eurasia, where lower-intensity surface fires are common. The North American fires tend to spread faster, burn longer, and emit smoke higher into the atmosphere than the Eurasian fires (Rogers et al., 2015).

Wildfires emit extensive amounts of carbonaceous aerosols, such as organic carbon (OC) and black carbon (BC), into the atmosphere. The emissions from boreal fires are particularly interesting as they are located in the vicinity of the pristine Arctic and can be transported there. Our knowledge of carbonaceous aerosols in the atmosphere depends heavily on model results as there is a lack of globalscale observations. For the concentration of BC, models agree with each other within a factor of 2 in Europe and North America. However, the models underestimate the concentration of BC at the surface in the Arctic by 1 or 2 orders of magnitude. Consequently, there is little confidence in quantifying the present-day distribution and burden of carbonaceous aerosol components (IPCC, 2023). Therefore, observational constraints are urgently needed.

Boreal wildfires occur every year in both North America and Eurasia. They are mainly ignited by people or lightning and their extent depends on proximity to populated areas, availability of wildfire mitigation resources, and meteorological conditions. As climate warms twice as fast in the boreal region as on average, the potential for wildfires and the consequent emissions will increase in the future [START_REF] Descals | Unprecedented fire activity above the Arctic Circle linked to rising temperatures[END_REF]. Several studies have been published regarding the long-range transport of smoke from boreal wildfires using a wide range of methods, such as remote sensing, in situ observations, and model simulations. For example, Markowicz et al. (2016a, b) analyzed the optical properties of Canadian smoke plumes that reached central Europe in July 2013 and the European Arctic in July 2015, respectively. [START_REF] Sicard | Ground/space, passive/active remote sensing observations coupled with particle dispersion modelling to understand the inter-continental transport of wildfire smoke plumes[END_REF] reported the horizontal and vertical transport of a smoke plume from North America to Europe during the 2017 record-breaking burning season. [START_REF] Johnson | Long-range transport of Siberian biomass burning emissions to North America during FIREX-AQ[END_REF] used aerosol and trace gas data from a synergy of remote-sensing and in situ observations, as well as model simulations, to assess the impact of emissions from Siberian wildfires on atmospheric chemical composition and air quality in North America. Boreal smoke plumes have also been studied widely using lidar observations, as, for example, Shang et al. (2021) and the references therein illustrate. Shang et al. (2021) analyzed a smoke plume that reached Kuopio, Finland, on 4-6 June 2019 and found that well-calibrated ceilometers can be used to monitor smoke plumes and their aerosol mass con-centrations. Furthermore, they reported that the global reanalysis model MERRA-2 [START_REF] Buchard | The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part II: Evaluation and Case Studies[END_REF][START_REF] Randles | The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part I: System Description and Data Assimilation Evaluation[END_REF] had some difficulties in reproducing the smoke plumes.

As the abovementioned studies show, boreal fires are studied extensively. However, significant knowledge gaps still remain, especially related to the properties and amount of the smoke aerosol transported over long distances to pristine regions. Due to the vastness of the boreal region, it cannot be covered with advanced ground-based observations or flight campaigns. For secluded areas, spaceborne remote sensing has the potential to provide useful information with a reasonable temporal resolution. Furthermore, although passive satellite observations of aerosols are typically limited to their optical properties, with lidar measurements we are also able to estimate the mass of the aerosol layers (Shang et al., 2021).

To study the evolution of smoke aerosols during longrange transport, we analyzed the smoke plumes originating from Alberta, Canada, at the end of May 2019. We used trajectory analysis to locate the plumes and retrievals from the spaceborne lidar Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) to study the properties of the smoke layers, concentrating on the mass of the aerosols. The smoke plumes were transported across North America, the North Atlantic, and Europe. We compared the accuracy of the mass estimates derived from the CALIOP data products with the accuracy of the ground-based lidars or ceilometer near the sources in North America and after the long-range transport in Europe. Finally, we compared the mass observations with simulated aerosol concentrations from two reanalysis models, MERRA-2 [START_REF] Gelaro | The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2)[END_REF] and CAMS (Inness et al., 2019), and found clear discrepancies.

Materials and methods

May-June 2019 fire event in Canada

Starting at the end of May 2019, extensive wildfires occurred in northern Canada, producing massive amounts of smoke that got transported to Europe. This study concentrates on fires that occurred in Alberta at two sub-regions (longitudes 114.9 to 113.9 • W and latitudes 55.5 to 56.5 • N, longitudes 118.2 to 117.0 • W and latitudes 56.8 to 58.7 • N, Fig. 1). These regions were selected as back-trajectory analysis indicated that they were the main sources of the smoke plumes that reached Europe at the beginning of June. During that time, Alberta had above-average fire danger conditions due to severe drought. Three major wildfires, the largest being the Chuckegg Creek wildfire, ignited during May and burned throughout the summer. Overall, in Alberta there were fewer fires in 2019 than the 10-year average, but the burned area of 883 414 ha was significantly higher than the 10-year average of 242 660 ha (https://www.ciffc.ca/sites/default/ files/2020-07/Canada%20Report%202019.pdf, last access: 22 August 2023). To define the exact locations and intensity of fires, the MODIS Collection 6 Level-2 active fire product was used [START_REF] Giglio | The collection 6 MODIS active fire detection algorithm and fire products[END_REF]. The fire radiative power (FRP) observations from both Aqua and Terra satellites were considered.

Aerosol observations

CALIOP

The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite [START_REF] Winker | Overview of the CALIPSO Mission and CALIOP Data Processing Algorithms[END_REF][START_REF] Winker | The CALIPSO Mission: A Global 3D View of Aerosols and Clouds[END_REF] has been providing observations on aerosols and clouds since https://doi.org/10.5194/acp-24-1329-2024 Atmos. Chem. Phys., 24, 1329-1344, 2024

June 2006. CALIPSO carries the Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) instrument, which measures the vertical structure of the atmosphere at two wavelengths: at 532 nm where it has two channels that are orthogonally polarized and at 1064 nm where it measures the total backscattered signal [START_REF] Hunt | CALIPSO Lidar Description and Performance Assessment[END_REF].

In this study both the profile and layer products were utilized to locate smoke layers and estimate their optical properties. From CALIPSO lidar data, the Level 2 V4.20 product was used (which is the latest version of the data available at the time of data analyzing). The data were downloaded from the Atmospheric Sciences Data Center (ASDC), located at the National Aeronautics and Space Administration (NASA) Langley Research Center (LaRC). The CALIOP aerosol classification algorithms were used for the identification of the smoke layers. The CALIOP tropospheric aerosol subtype classification (Kim et al., 2018) uses integrated attenuated backscatter coefficient, depolarization ratio, surface type, altitude, and location to assign the layer to one of the following aerosol classes: clean continental, clean marine, dust, dusty marine, polluted continental/smoke, polluted dust, or elevated smoke (https://www-calipso.larc.nasa.gov/resources/calipso_ users_guide/data_summaries/vfm/index_v420.php, last access: 22 August 2023). The depolarization ratio is enhanced if the particles are nonspherical; thus, it can be used to distinguish between, for example, dust and spherical aerosols.

In order to analyze only the most trustworthy observations, several quality control products were considered, and only nighttime measurements were used. The first criterion was that the cloud-aerosol discrimination (CAD) score had to be between -100 and -70, indicating high confidence of aerosol layers. Furthermore, only aerosol bins with the extinction quality control (QC) flags of 0, 1, 16, and 18 were used to ensure the good quality of the retrievals (Kim et al., 2018). In CALIOP observations, smoke particles can be classified as "polluted continental/smoke" or "elevated smoke". In this study, only elevated smoke was considered since the focus is on fire-emitted aerosols. The separation between polluted continental and smoke aerosols is not possible in the polluted continental/smoke class.

Ground-based lidars and ceilometer

In order to evaluate the usability of CALIOP observations in the monitoring of aerosol mass in smoke plumes, we utilized open-access data from several ground-based lidar/ceilometer networks. The NASA Micro-Pulse Lidar Network (MPLNET) has been continuously providing aerosol and cloud properties, as well as the planetary boundary layer structures, at over 80 sites worldwide since 2000 [START_REF] Welton | Global monitoring of clouds and aerosols using a network of micropulse lidar systems[END_REF]. Currently, the observations from the polarized Micro-Pulse Lidar instruments are processed using the MPLNET Version 3 system (Welton et al., 2018).

More information on MPLNET and data products is available on their website (https://mplnet.gsfc.nasa.gov, last access: 22 August 2023). For the European-based comparisons, besides MPLNET, observations submitted to the EU-funded Cloudnet project [START_REF] Illingworth | Cloudnet: Continuous Evaluation of Cloud Profiles in Seven Operational Models Using Ground-Based Observations[END_REF] and the polarization lidar network PollyNET (Baars et al., 2016) have been utilized. Cloudnet enumerates 17 permanent and numerous campaign stations across Europe, and observations are routinely collected, processed, visualized, and distributed through the ACTRIS Cloudnet data portal (http://cloudnet.fmi.fi, last access: 22 August 2023). PollyNET (https://polly.tropos.de/, last access: 22 August 2023) is utilizing the capabilities from multiwavelength polarization Raman lidars of type Polly [START_REF] Althausen | Portable Raman Lidar Polly XT for Automated Profiling of Aerosol Backscatter, Extinction, and Depolarization[END_REF] [START_REF] Kampouri | Investigation of Volcanic Emissions in the Mediterranean: "The Etna-Antikythera Connection[END_REF].

Models

MERRA-2 reanalysis aerosols

The Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) is NASA's global reanalysis model [START_REF] Gelaro | The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2)[END_REF]. It includes meteorological variables, as well as aerosols and trace gases. MERRA-2 assimilates bias-corrected aerosol optical depth (AOD) at 550 nm from various sources, including satellite-based MODIS aerosol products [START_REF] Buchard | The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part II: Evaluation and Case Studies[END_REF][START_REF] Randles | The MERRA-2 Aerosol Reanalysis, 1980 Onward. Part I: System Description and Data Assimilation Evaluation[END_REF].

The spatial resolution of MERRA-2 is 0.5 • × 0.625 • , and it has 72 pressure levels. MERRA-2 provides data with 1 or 3 h instantaneous or time-averaged time steps, depending on the variable, and data are available since 1980. In MERRA-2, aerosol output is written out every 3 h and it produces vertical profiles of mass mixing ratios for five different aerosol species: dust, sea salt, black carbon, organic carbon, and sulfate. The daily emissions of biomass burning used in MERRA-2 come from the Quick Fire Emission Dataset (QFED) version 2.4-r6 [START_REF] Darmenov | The Quick Fire Emissions Dataset (QFED): Documentation of versions 2.1, 2[END_REF] (after 2010). Based on the FRP, the QFED implements the cloud correction method from the Global Fire Assimilation System (GFAS; Kaiser et al., 2012) and applies a sophisticated treatment of emissions from non-observed land areas [START_REF] Darmenov | The Quick Fire Emissions Dataset (QFED): Documentation of versions 2.1, 2[END_REF].

In this study, we used the mass mixing ratios from the MERRA-2 assimilated aerosol mixing ratio data product inst3_3d_aer_Nv (GMAO, 2015a). We used linear interpola-tion of MERRA-2 aerosol profile data to spatially collocate the model data with CALIOP profiles. For temporal collocation, we used the nearest time step of the MERRA-2 aerosol data to match with CALIOP overpass.

CAMS reanalysis aerosols

The Copernicus Atmospheric Monitoring System (CAMS) Atmospheric Composition Reanalysis 4 (EAC4) is run by the European Centre for Medium-Range Weather Forecasts (ECMWF) (Inness et al., 2019). It consists of meteorological variables and also aerosol and trace gas information. The CAMS reanalysis assimilates total AOD observations, including satellite data from MODIS (Rémy et al., 2019). The spatial resolution of CAMS reanalysis data products is 0.75 • × 0.75 • with 60 model levels. The CAMS reanalysis provides aerosol data every 3 h, and data are available since 2003. The CAMS reanalysis provides vertical profiles of mass mixing ratios for five different aerosol species: organic matter, black carbon, sea salt, dust, and sulfate. Daily biomass burning emissions derive from the GFAS (Flemming et al., 2015(Flemming et al., , 2017)), which is based on the combined MODIS observations of FRP from Terra and Aqua (Kaiser et al., 2012).

The collocation of CAMS reanalysis aerosol profiles was carried out similarly as for MERRA-2 -linear interpolation for spatial collocation and the closest time instant for temporal collocation.

Daily fire injection height information was available using the CAMS GFAS which assimilates FRP observations and meteorological conditions simulated from the ECMWF operational weather forecast. In particular, the daily injection height (mean altitude of maximum injection and altitude of plume top) is estimated by the Plume Rise Model (PRM) and IS4FIRES (Sofiev et al., 2012). The GFAS injection height calculations are not used in the reanalysis, but they were considered for the determination of the initial heights for the trajectories.

Trajectory model

In this study, we computed the air parcel trajectories using a custom Lagrangian trajectory model that moved the air parcels according to the MERRA-2 wind fields. The eastward and northward wind and vertical pressure velocity components from the MERRA-2 assimilated meteorological fields data product inst3_3d_asm_Nv were used (GMAO, 2015b). To get the wind components for the exact location and time of the air parcel, we linearly interpolated the MERRA-2 wind field information. We used a 5 min time step and simple forward Euler method in our trajectory computations. The vertical pressure level of the air parcel was converted to heights using MERRA-2 pressure profile information. In the trajectory model, the air parcels were restricted from going below the surface elevation. When reaching the surface elevation, the air parcels were moved along the wind components at the surface level. The trajectory model only computed the transport of the air parcels and did not simulate any other processes such as mixing, chemical transformation, or deposition of particles.

Methods

Estimation of mass from lidar signals

It is possible to estimate the particle mass concentration profile (m) from the vertical profile of lidar-derived backscatter coefficient (β), together with the particle mass density (ρ), the volume-to-extinction conversion factor (c v ), and the type-dependent lidar ratio (LR); see Eq. ( 1). The method described in Shang et al. (2021) was applied here to estimate the mass concentrations based on the measured backscatter coefficients at 532 nm or converted to 532 nm using the corresponding backscatter-related Ångström exponent (BAE); see Eq. ( 2).

m = ρ • c v (λ = 532) • β (λ = 532) • LR (λ = 532),
(1)

β (λ = 532) = β meas (λ = 1064) • (532/1064) -BAE .
(2)

The conversion factor at 532 nm of 0.16 ± 0.01 × 10 -6 m for the fresh and medium-fresh smoke (i.e., less than 2 d) (or 0.13 ± 0.01 × 10 -6 m for aged smoke) and a particle density of 1.3 g cm -3 were used for the biomass burning particles (Ansmann et al., 2021). Following Ansmann et al. ( 2021), we assume uncertainties of 10 % and 20 % in the conversion factor and smoke mass density. Using ground-based multiwavelength lidar measurements, the lidar ratio at 532 nm and the backscatter-related Ångström exponent between 532 and 1064 nm were derived as 71 ± 5 sr and 2.2 ± 0.3, respectively, for the smoke plumes during the same wildfire event as in this study (Shang et al., 2021). For the backscatter coefficient retrievals, we used relative uncertainties of 10 %, 15 %, and 25 % for ground-based lidar, ceilometer, and spaceborne lidar, respectively. These values were taken from Shang et al. (2021) and Ansmann et al. (2021). The Ångström value of 2.2 was applied to convert the ceilometer-measured backscatter coefficients at 1064 to 532 nm (Eq. 2), resulting in a relative uncertainty of 24 % on the converted backscatter coefficients. This study employed a lidar ratio at 532 nm of 70 sr, which was the value used for the aerosol subtype of elevated smoke in CALIOP version 4 (Kim et al., 2018). The lidar ratios at 532 nm for ground-based lidars are measured with a typical relative uncertainty of about 20 %, which can also be assumed for the 532 nm CALIOP lidar ratio for elevated smoke (the uncertainty is 70 ± 16 sr in CALIPSO V4 lidar data). More details of the uncertainties in the CALIPSO products can be found in [START_REF] Young | The Retrieval of Profiles of Particulate Extinction from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) Data: Uncertainty and Error Sensitivity Analyses[END_REF]Young et al. ( , 2018)). Applying the law of error propagation to Eq. ( 1) with the abovementioned uncertainties, we expected an overall uncertainty in the mass concentration estimates of 32 % for ground-based lidar and 40 % for ceilometer and CALIOP.

Selection of trajectories

There were 6480 trajectories generated, considered to be originated from a single day (0 to 24 h), from all initial heights (0 to 7.5 km), and from both wildfire sub-regions (nine initial spots in each, Fig. 1). The dominant air mass pathway was determined by the trajectory frequencies, which were calculated via the bivariate bin counts in two steps: the latitude and longitude trajectory frequencies were calculated based on 1 • × 1 • pixels, whereas the altitude and time trajectory frequencies were calculated based on 500 m × 1 h pixels. The pixels with an occurrence frequency above the median value were selected, referring to the most possible air mass transportation. Only the trajectories included in the predefined pixels were kept (less than 10 % of the total trajectories). These screened trajectories were used to define fourdimensional hypercubes, with a doubled resolution as previously used for the frequency pixels, considering the model uncertainties. Each hypercube has eight values of four variables (i.e., the edge values of latitude, longitude, altitude, and time). Next, the CALIOP-derived smoke layers (after the quality control; see Sect. 2.2.1) were automatically selected using these 4-D hypercubes to ensure that they are on the dominant air mass pathway. The uncertainties due to trajectory computations, wind data, and temporal and spatial collocation cause uncertainty in the estimates of the dominant air mass pathway. However, we estimate these uncertainties to be small and to not significantly affect the results of our study.

Results and discussion

Comparison of CALIOP retrievals with ground-based lidars and ceilometer

Among the automatically selected CALIOP smoke layers, four cases were intensively analyzed when nearby groundbased lidar/ceilometer observations were available.

Case 1: GSFC (MPLNET). On 3 June 2019, some lofted layers were detected by the ground-based lidar at GSFC station (https://mplnet.gsfc.nasa.gov/data?v=V3&s=GSFC& t=20190603, last access: 22 August 2023). Version 3 Level 1.5 aerosol data are only available after 09:40 UTC after the quality control. The closest CALIPSO track was passed at about 07:18 UTC. From the CALIPSO VFM (Vertical Feature Mask) product (Fig. 2, first row, panel b), elevated smoke layers were detected at about 7-10 km for longitudes from -75.5 to -74.5 • E, whereas dust layers were identified below at about 6 km. These two layers were also detected by the ground-based lidar (Fig. 2, first row, panel c). Particle backscatter coefficient of the smoke layer derived from the ground-based lidar is slightly higher than the one from CALIOP. Consequently, the layer mean mass derived from the ground-based lidar is 30 % higher than the CALIOP estimate. The MPLNET and CALIOP layer mean masses are 12.2 ± 4.5 and 8.5 ± 4.6 µg m -3 , respectively. Note that there is about 2.5 h difference between these two observations; thus, the observed smoke layers could be from different parts of the plume.

Case 2: Granada (Cloudnet). On 6 June continuous lofted layers were detected by CALIOP for longitudes from -7 to -4.5 • E and for altitudes 5-8 km (Fig. 2, second row, panel b). CALIPSO classified these layers as elevated smoke or polluted dust. Faint lofted layers were also visible in the morning from the near-realtime non-screened attenuated backscatter coefficients of the CHM15k ceilometer at Granada (https://cloudnet.fmi.fi/ file/155df385-c4b1-4cd0-b746-bab2afb31355, last access: 22 August 2023). The smoke layer (at ∼ 5 to 7 km) was well detected by both instruments with a good agreement (Fig. 2, second row, panel c). The ceilometer-derived backscatter coefficients (1 h time-averaged centered on the CALIOP profile) of the smoke layer measured at 1064 nm were used to estimate the layer mass concentrations following the method presented in Sect. 2.4.1, resulting in a layer-mean value of 6.6 ± 3.7 µg m -3 compared to 8.3 ± 3.0 µg m -3 from CALIOP. Thus, in this comparison the CALIOP retrieval produces 26 % larger aerosol mass for the smoke layer.

Case 3: Barcelona (MPLNET). Only two smoke layers were identified by the CALIPSO on 8 June for the selected sector (Fig. 2, third row, panel b). The one closer to the Barcelona station was selected in this section, ranging from about 3.5-5 km (a CALIOP horizontal averaging of 20 km was applied). The CALIOP ALay products of this smoke layer show an AOD of ∼ 0.05 and an estimated particulate depolarization ratio of ∼ 0.02 at 532 nm. Several layers up to 6 km were detected in the morning on that day by the ground-based lidar at Barcelona station (https://mplnet.gsfc.nasa.gov/data?v=V3& s=Barcelona&t=20190608, last access: 22 August 2023). The atmosphere was quite inhomogeneous; thus, 1 min ground-based lidar profiles were used to compare with CALIOP-derived backscatter coefficient (Fig. 2, third row, panel c), showing lower values in the smoke layer. Due to the lower backscatter coefficient, the ground-based estimate for layer mean mass (4.4 ± 1.6 µg m -3 ) is 43 % smaller than the CALIOP estimate (6.3 ± 2.1 µg m -3 ). The groundbased lidar-derived particle depolarization ratio of this layer is ∼ 0.03 ± 0.01. There is a 4 min difference between the two measurements.

Case 4: Antikythera (PollyNET). Faint smoke layers were identified by CALIOP for the considered sector on 8 June (Fig. 2, fourth row, panel b). The horizontal resolutions of 80 km were required to detect these layers due to the small aerosol amount. The ground-based lidar at PANGEA observatory in Antikythera also observed the lofted layers (https://polly.tropos.de/datavis/location/38/19/ 1?dates=[2019-06-08T00:00:00,2019-06-09T00:00:00], last access: 22 August 2023). The backscatter coefficients derived from the ground-based lidar (1 h time-averaged) and CALIOP show good consistency (Fig. 2, fourth row, panel c), with about half an hour time difference. Two smoke layers were at about 4.5 and 7.5 km, having AODs at 532 nm of about 0.017 and 0.014, respectively, and estimated particulate depolarization ratios of ∼ 0.06 (CALIOP ALay products). The ground-based lidar observed a thicker layer compared to CALIOP for the upper one. Some of the layers could not be fully detected by CALIOP, e.g., the thin aerosol layers with low concentrations, as well as the boarders of layers where there are fewer aerosols. The estimate for the layer mean mass from the ground-based lidar (4.1 ± 1.3 µg m -3 ) is 12 % larger than the CALIOP-retrieved estimate (3.6 ± 1.3 µg m -3 ). Good agreements were also found for the aerosols inside the boundary layer (i.e., below 3 km), which were classified as dust and dusty marine by CALIOP.

Analysis of the Alberta plume event

During the wildfire event, highest fire emissions were detected on 29-31 May (Fig. 1). Following the method described in Sect. 2.4.2, the CALIOP smoke layers on the dominant air mass pathway originating from the same day were automatically selected. Their location and time-height information are shown in Fig. 3. In total, 1336 smoke layers were selected, related to 1194 CALIOP 5 km profiles. Considering the different horizontal averaging lengths applied in the CALIOP detection scheme, there are 259 unique detections, among which the layer numbers with horizontal averaging of 5, 20, and 80 km are 75, 417, and 844, respectively. The largest number of auto-selected layers was found to be originating from 29 May; thus, more intense investigation was performed for these layers, and the trajectory frequencies from 29 May were used as the colored background in Fig. 3. Smoke layers observed on the same day were grouped, and were related to the age of the smoke particles, for the illustration of the evolution of the particle properties during the transportation (Fig. 4). The CALIPSO Level-2 5 km aerosol layer (ALay) products are used here. Note that the optical depths were summed up (denoted as AOD here) in case there are several smoke layers detected in the same profile (Fig. 4a). In the ALay product, aerosol layers are detected using a "nested multigrid averaging scheme" [START_REF] Vaughan | Fully Automated Detection of Cloud and Aerosol Layers in the CALIPSO Lidar Measurements[END_REF], which may produce vertically overlapping layers. In such cases, CALIPSO Level-2 5 km aerosol profile (APro) products were used instead to calculate the AODs. The same method was also applied later for the column mass calculations to avoid the layer overlapping issue. A clear tendency of AOD and mass decreasing can be seen in Fig. 4a-b. The median values of AOD (or layer-mean mass concentration) decreased from 0.25 (56 µg m -3 ) for the fresh smoke (∼ 1 d aged) to 0.013 (4.5 µg m -3 ) for the aged smoke after the long-range transportation (∼ 10 d aged). The higher decreasing rate was found at the beginning of transportation. Slight increases in AODs and mass concentrations were observed in the middle of the transportation over ocean; these smoke layers were probably a mixture of smoke particles originating from several fire source days (as shown by overlapped symbols in Fig. 3, e.g., CALIPSO track 06-02T04-57). Taylor et al. (2014) reported the mass concentrations of organic aerosol and black carbon to be in the same range for smoke plumes of ∼ 1-2 d after passing over the fires source, unaffected by the wet deposition (precipitation). The estimated particulate depolarization ratio at 532 nm of the smoke layers slightly increased during the transportation, with median values from 0.03 to 0.06 (Fig. 4c). There is no clear tendency for the relative humidity (Fig. 4d). A decreasing tendency was also found for the total attenuated backscatter color ratio (the ratio of attenuated backscatters at 1064 and 532 nm), which is an independent quantity not being used in the subtyping algorithm in the troposphere [START_REF] Omar | The CALIPSO Automated Aerosol Classification and Lidar Ratio Selection Algorithm[END_REF]. Nevertheless, this parameter is modulated by the scattering ratio and is therefore not a direct indicator of particle size.

No clear tendency was found for the particulate backscatter color ratio. Smoke layer altitudes were increased at the beginning, then they split into two air mass pathways (e.g., on 1 June). Layer heights decreased a bit over the oceans, then they climbed again over Europe (Fig. 3). The depths of these smoke layers range from 0.30 to 1.44 km with a mean value of 0.68 ± 0.26 km.

For better illustration, one profile from the CALIPSO APro products was selected on each day so as to present the time evolution using the vertical profiles of backscatter coefficients (Fig. 5). The profile locations are given in Fig. 3, whereas the smoke layer AODs are given in Fig. 4a. The CALIPSO algorithm applied different horizontal averaging regarding the signal-to-noise ratio of the aerosol layer.

Comparison of observed and simulated smoke layers

As was discussed in Sect. 3.1 and shown in Fig. 2, mass retrievals from CALIOP have overlapping error bars in all cases with ground-based lidar/ceilometer retrievals; in three cases, the CALIOP means lie within the ground-based error bars and vice versa. To evaluate if models could also capture these plumes, the CALIOP retrievals were compared with aerosol mass concentration profiles from the reanalysis models MERRA-2 and CAMS. These comparisons are presented in Fig. 2e and f. Based on these case studies, MERRA-2 appears to have higher aerosol concentrations than CAMS at the altitudes of the smoke layers. Consequently, MERRA-2 seems to agree better with the CALIOP mass retrievals. However, when the contribution of different aerosol types is considered, it is clear that MERRA-2 produces more comparable aerosol concentrations because it is overestimating the contribution of dust. Recently, Li et al. (2023) reported a quantitative evaluation analysis, showing that the dust products from MERRA-2 reanalysis have higher column concen- trations than the satellite-based component retrievals, with relative differences of about 20 % to 70 %. The comparison at GSFC implies that CAMS is better at simulating lofted smoke layers near the source regions as it includes elevated OC concentrations at higher altitudes. However, the elevated OC concentrations are located between 1 and 8 km, whereas the CALIOP retrieval shows that the smoke plume was mainly above 8 km. Overall, these case studies indicate that reanalysis models have difficulties in capturing the location and properties of long-range-transported smoke. In fact, the difficulties in representing the aerosol altitude is generally true of most models, as stated in Das et al. (2017), Zhong et al. (2022), and references therein.

In order to obtain a more complete picture of the performance of these models, a comparison using all the smoke layers within the transported plume was carried out. As a first step, the total column was considered. The CALIOP profiles with the presence of layers which did not fulfill the QC were excluded, reducing the profile number to 622.

Simulated and observed total column AODs were compared with each other. The CALIOP AOD is calculated only from tropospheric aerosols and based on the analysis of CALIOP aerosol layer and profile products, as the stratospheric contribution is insignificant during the studied cases.

These comparisons are presented in the first row of Fig. 6 as scatterplots and of Fig. 7 as the histograms of the differences. CAMS and MERRA-2 exhibit good agreement with their AOD values. However, when compared with the CALIOP observations, clear differences emerge. CALIOP AODs tend to be smaller than the simulated values; even though the values are positively correlated, there is significant variability. Consequently, the correlation coefficients are only 0.63 and 0.61 for CAMS and MERRA-2, respectively. The fact that bias in reanalysis AOD is unavoidable must be noted, as stated by several studies [START_REF] Mukkavilli | Assessment of atmospheric aerosols from two reanalysis products over Australia[END_REF][START_REF] Song | Diurnal and seasonal variability of PM 2.5 and AOD in North China plain: Comparison of MERRA-2 products and ground measurements[END_REF][START_REF] Salamalikis | Ground-based validation of aerosol optical depth from CAMS reanalysis project: An uncertainty input on direct normal irradiance under cloud-free conditions[END_REF][START_REF] Gueymard | Worldwide validation of CAMS and MERRA-2 reanalysis aerosol optical depth products using 15 years of AERONET observations[END_REF]. Furthermore, some aerosol layers could not be fully detected by CALIOP due to the weak signal-to-noise ratio as discussed in Sect. 3.1 and in Thorsen et al. (2017).

The column mass concentrations of smoke were also compared, shown as the second row in Figs. 6 and7. The CALIOP elevated smoke layers were combined in each profile to estimate the smoke mass concentrations. Furthermore, only OC and BC masses were considered in the calculation of the simulated smoke masses. High correlation was found for the simulated smoke column mass concentrations for CAMS and MERRA-2. However, CAMS simulated higher smoke concentrations than MERRA-2. When compared with https://doi.org/10.5194/acp-24-1329-2024 Atmos. Chem. Phys., 24, 1329-1344, 2024 CALIOP products, the correlation coefficients are quite similar as the ones for AODs, demonstrating that the smoke aerosols are dominant in the column. These column smoke mass concentrations were also presented in Fig. 8 to illustrate the smoke transportation. Mass burden decrease can be clearly seen from west (closer to the source) to east (far from the source), corresponding to the fresh-to-old-aged smoke particles. The models exhibit a clearer contrast between the continents than CALIOP, which indicates that the smoke aerosols in the models could be removed too efficiently.

The accuracy of the simulated smoke layers was compared in a similar fashion using the integrated aerosol mass of the layers. As the models might have the smoke layers at slightly different altitudes than in the observations, the collocation criteria were relaxed by assuming that the thickness of the simulated layer was 3 times larger than in the observations. As an example, if CALIOP had observed a 1 km thick smoke layer at the altitude of 4 km, the simulated layer was assumed to be centered at the same altitude but its thickness was set to be 3 km. These comparisons are presented as the third row in Figs. 6 and7. In this comparison, the models do not agree so well with each other. Moreover, the modeled concentrations are lower than the CALIOP-based concentrations. These results highlight the difficulty in simulating biomass burning plumes: both of these reanalysis models have difficulties in reproducing the location, altitude, layer depth, and aerosol concentration of the plumes.

Conclusions

In May and June 2019, smoke plumes from Canadian wildfires were advected all the way (across North America and the North Atlantic) to Europe. To analyze the evolution of the plumes and to estimate the amount of smoke aerosols transported to Europe, retrievals from the spaceborne lidar CALIOP were used. Mass retrievals from CALIOP were in good agreement with retrievals from ground-based lidars, independently from the distance to the source. Over North America, the CALIOP layer mean mass concentration was 30 % smaller than the ground-based estimate (with about 2.5 h time gap), whereas over southern Europe that difference varied between 12 % and 43 %. These comparisons indicate that CALIOP data, as well as current (e.g., DQ2 of CNSA; Dai et al., 2023; The analysis of aerosol mass concentrations over North America and Europe showed that less than one-tenth of the emitted mass survived the transport over the Atlantic Ocean. This information is valuable for evaluating transport efficiency in atmospheric models. The comparisons with reanalysis models MERRA-2 and CAMS showed that both models have difficulties in representing the aerosol mass of the studied smoke plumes, especially when the aerosol composition is taken into account. For example, the aerosol mass pro-Atmos. Chem. Phys., 24, 1329-1344, 2024 https://doi.org/10.5194/acp-24-1329-2024 files in MERRA-2 matched quite well with the smoke layers observed with CALIOP, but most of the mass in the simulation originated from dust not organic or black carbon as one would expect for smoke plumes. Consequently, reanalysis simulations should be taken cautiously in the analysis of long-range-transported smoke in the boreal region.

These findings indicate that in order to estimate the transport and deposition of smoke aerosols to remote and pristine regions, high-quality observations are still needed. Passive satellite instruments can provide extensive spatial coverage, but they are incapable of accurately providing the altitude of the elevated plumes, and their accuracy starts to suffer over polar regions because of non-optimal measurement https://doi.org/10.5194/acp-24-1329-2024 Atmos. Chem. Phys., 24, 1329-1344, 2024 geometry. Spaceborne lidars are limited in spatial coverage, but the coverage improves closer to the poles. Furthermore, lidars can provide information on the vertical location and extent of the aerosol plumes, which is invaluable for impact studies and model development. The increasing wildfire activity produces a complicated global layering of smoke subtypes (fresh to aged), with emitted plumes from various stages from the burning phase (which impact the emissions). Even within the category of "smoke", the properties of the aerosols can vary widely, especially when they linger in the Northern Hemisphere for weeks to months. Besides, other aerosols may also be mixed in the smoke plumes. For example, the Raikoke volcanic eruption in 2019 occurred only about 2 weeks after the Alberta plume event analyzed in this study; mixtures of smoke and volcanic plumes were present in the atmosphere for many weeks. These increasingly complex problems affect nearly the entire Northern Hemisphere every year. That demonstrates the need to continue deploying spaceborne lidars and ground-based lidar and ceilometer networks, especially those with enhanced capabilities which provide more accurate results and the ability to retrieve microphysical properties of the layers. As the CALIPSO science mission ended on 1 August 2023, there currently is an observational gap during the absence of the upcoming spaceborne lidar missions. This study points to the urgent need for future lidar missions in space, as well as the need of near real-time open-access provision of spaceborne lidar measurements. In addition, it would be good to study in the future how operational aerosol forecasts rather than reanalyses perform for long-range transport, including for the boreal regions and the Arctic.

Figure 1 .

 1 Figure 1. Overview of the Canadian wildfire event. Top: the fire radiative power on 29, 30, and 31 May 2019 are shown by different symbols. Middle: the TROPOMI observations of CO (carbon monoxide) on 3 June 2019. Bottom: CAMS daily total fire radiative power (GFASv1.2) for Alberta from May to June 2019, compared with the 2003-2018 mean daily total.

  FRP describes the radiant energy released per time unit by burning, indicating the intensity of fires. Satellite-based carbon monoxide (CO) observations are a good proxy for assessing long-range transport of fire-related emissions. The TROPOMI (https://www.tropomi.eu/, last access: 22 August 2023) observations of CO on 3 June 2019, presented in Fig. 1, illustrate clearly that the emissions from the fires in Alberta were transported all the way to Europe. The Alberta May 2019 wildfires have been used in many studies on newly developed methods for satellite-based applications of wildfire monitoring. Ban et al. (2020) and Zhang et al. (2021) developed machine-learning-based methods to monitor wildfire progression using spaceborne syn-thetic aperture radars and optical instruments. Both studies use the Alberta wildfires in their method evaluation. Furthermore, Tymstra et al. (2021) characterized the weather conditions for large Canadian spring wildfires including May 2019, whereas Whitman et al. (2022) studied the climaterelated changes of wildfires in Alberta. Both Tymstra et al. (2021) and Whitman et al. (2022) concluded that climate change would make the wildfires larger and more intense in this region in the future.

Figure 2 .

 2 Figure 2. Comparison of vertical profiles of observations and simulations. Four sites with nearby CALIPSO overpasses, from top to down: GSFC (MPLNET), Washington DC, USA; Granada (Cloudnet), Spain; and Barcelona (MPLNET), Spain; Antikythera (PollyNET), Greece. (a) Ground-based site location, CALIPSO track, and selected CALIOP aerosol profile (APro) case location. (b) CALIPSO Level 2 Vertical Feature Mask (VFM) product with granule (yyyy-mm-ddTHH-MM-SS) given. (c) Backscatter coefficients at 532 nm from CALIOP (Cal.) Level-2 5 km APro product (corresponding to dashed line in panel b), with elevated smoke in black and other aerosol types in brown, uncertainties are given in gray. Backscatter coefficients from ground-based (Grd.) lidar or ceilometer at 532/1064/355 nm are also shown, with the time-averaging window given on the top. (d) Mass concentrations of the smoke layers, estimated from CALIOP or ground-based lidar/ceilometer. (e-f) Mass concentrations of different components (OC -organic carbon, BC -black carbon, DU -dust, SS -sea salt, SU -sulfate) from MERRA-2 or CAMS models.

Figure 3 .

 3 Figure 3. Trajectory frequency plots (29 May as source) with automatically selected CALIOP smoke layers (from fire sources originating on 29, 30, or 31 May shown by circles (top) and dashed lines (bottom) with different colors sizes). The corresponding CALIPSO granule information is given as "mm-ddTHH-MM" on the top, with color scales showing from earlier (darker) to later (lighter) dates. Red cross: CALIOP APro profile cases used in Fig. 5. Two sub-regions of fire source areas are given in red rectangles on the top-left in the top figure.

Figure 4 .

 4 Figure 4. Statistical properties of smoke layers originating from the Alberta plume event on 29 May 2019. CALIPSO Level-2 5 km ALay products were used. In case of overlapping layers the vertical, 5 km APro products were used to calculate the AODs. Used layer numbers (or profile numbers in panel a) in each boxplot are given on the top. AODs of the smoke layers used in Fig. 5 are shown as black squares in panel (a).

Figure 5 .

 5 Figure 5. Profile examples. One profile was selected each day (only originated from one day source). CALIPSO Level-2 5 km APro products were used. The CALIPSO granule and profile time are given on top. Backscatter coefficients the elevated smoke type (or other aerosol types) are shown in black (or blue), with the uncertainties given in gray. The horizontal averaging applied is in green.

Figure 6 .

 6 Figure 6. Comparison of observed and simulated products for locations where CALIOP detected smoke layers; (a-c) total column AOD (tropospheric AODs from CALIOP); (d-f) column mass concentration of black carbon (BC) and organic carbon (OC) of CAMS and MERRA-2, and summed layer mass concentration of CALIOP elevated smoke layers; (g-i) layer mass concentrations of CALIOP elevated smoke layers and layer mass concentrations (three times the depth of the CALIOP layers) of BC and OC of CAMS and MERRA-2.

Figure 7 .

 7 Figure 7. Similar with Fig. 6 but shown by histograms of the differences of observed and simulated products.

Figure 8 .

 8 Figure 8. Column mass concentrations of smoke using CALIOP elevated smoke layers, black carbon (BC), and organic carbon (OC) from CAMS, as well as BC and OC from MERRA-2.

  to establish aerosol climatology. Observations from four stations were used in this study, which will be presented in Sect. 3.1: GSFC (NASA Goddard Space Flight Center) station (38.993 • N, 76.840 • W; 50 m a.m.s.l., Washington DC, USA) and Barcelona station (41.386 • N, 2.117 • E; 125 m a.m.s.l., Spain) of MPLNET; Granada station (37.164 • N, 3.605 • W; 680 m a.m.s.l., Spain), part of Cloudnet (Cazorla Cabrera and Alados-Arboledas, 2023); and Antikythera station (35.86 • N, 23.31 • E; 193 m a.m.s.l., Greece), member of PollyNET

  https://www.cnsa.gov.cn/, last access: 22 August 2023) and upcoming (e.g., EarthCARE, https://earth. esa.int/eogateway/missions/earthcare, last access: 22 August 2023; Aeolus-2, https://www.esa.int/ESA_Multimedia/ Images/2022/10/Aeolus-2_Value_of_Information, last access: 22 August 2023; AOS, https://aos.gsfc.nasa.gov/home. htm, last access: 22 August 2023) satellite missions with the capability to provide lidar measurements of backscatter coefficient and depolarization ratio, could be used to estimate aerosol masses in remote regions where ground-based observations are not available.

Atmos. Chem. Phys., 24, 1329-1344, 2024 https://doi.org/10.5194/acp-24-1329-2024

https://doi.org/10.5194/acp-24-1329-2024 Atmos. Chem. Phys., 24, 1329-1344, 2024

Acknowledgements. The authors acknowledge support from the Academy of Finland and the Atmosphere and Climate Competence Center (ACCC) Flagship. We acknowledge the support of the MPLNET project by the NASA Radiation Sciences Program and Earth Observing System. This research was supported by data and services obtained from the PANhellenic Geophysical Observatory of Antikythera (PANGEA) of the National Observatory of Athens (NOA). We acknowledge ACTRIS, University of Granada, and the Finnish Meteorological Institute for providing the data set, which is available for download from https://cloudnet.fmi.fi/. We acknowledge PollyNET for the data collection, calibration, processing, and dissemination. We thank M_Map for the open-source code for the map plots used in this paper (Pawlowicz, 2020). The authors wish to acknowledge CSC -IT Center for Science, Finland, for computational resources.

Code availability. A code example of the trajectory computations is available: https://gist.github.com/anttilipp/ 29f2cb56d99a054e1aa0fc5bcc1d8622 (last access: 22 August 2023; https://doi.org/10.5281/zenodo.10567637, Lipponen, 2024). The open-source code M_Map for the map plots used in this paper is available online (https://www.eoas.ubc.ca/~rich/map.html, Pawlowicz, 2020). Data availability. The CALIPSO data were obtained from the NASA Langley Research Center Atmospheric Science Data Center (https://subset.larc.nasa.gov/calipso/, NASA Langley Research Center Atmospheric Science Data Center, 2023). MERRA-2 data are available at the Modeling and Assimilation Data and Information Services Center (MDISC) https://disc.gsfc.nasa.gov/datasets?project=MERRA-2 (NASA Goddard Earth Sciences , GES), managed by the NASA Goddard Earth Sciences (GES) Data and Information Services Center (DISC). The CAMS data are available at the Atmosphere Data Store at https://ads.atmosphere.copernicus.eu/ cdsapp#!/dataset/cams-global-atmospheric-composition-forecasts (Atmosphere Data Store, 2023). MPLNET data are available at https://mplnet.gsfc.nasa.gov/download_tool/ (MPLNET, 2023). Visualization of lidar products of PollyNET are available at https://polly.tropos.de/datavis/location/38/19/1?dates= [2019-06-08T00:00:00,2019-06-09T00:00:00] PollyNET, 2023; lidar data are available upon request. The ceilometer data used in this study are generated by the Aerosol, Clouds and Trace Gases Research Infrastructure (ACTRIS) and are available from the ACTRIS Data Centre using the following link: https://hdl.handle.net/21.12132/1.155df385c4b14cd0 (Cazorla Cabrera and Alados-Arboledas, 2023).

Author contributions. XS: conceptualization, methodology, software, formal analysis, writing (original draft), visualization. AL: conceptualization, software, formal analysis, writing (original draft). MF: conceptualization, writing (original draft). AMS: conceptualization, writing (original draft). MP: data curation, writing (review and editing). VB: data curation, writing (review and editing). ASD: data curation, writing (review and editing). EJW: data curation, writing (review and editing). EM: data curation, writing (review and editing). VA: data curation, writing (review and editing). MS: data curation, writing (review and editing). ARG: data curation, writing (review and editing). MK: data curation, writing (review and editing). TM: conceptualization, project administration, writing (original draft).

Competing interests.

The contact author has declared that none of the authors has any competing interests.

Disclaimer. Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors.

Financial support. This research has been supported by the Academy of Finland (grant nos. 339885 and 337552), the European Union (grant no. 101079201), the Hellenic Foundation for Research and Innovation (grant nos. 3995 and 07222), Horizon Europe (grant no. 101086690), Agencia Estatal de Investigación (grant no. PID2019-103886RB-I00), H2020 Environment (grant nos. 871115 and 101008004), and H2020 Excellent Science (grant no. 778349).

Review statement. This paper was edited by Eduardo Landulfo and reviewed by two anonymous referees.