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https://doi.org/10.5194/acp-2021-854Preprint. Discussion started: 20 October 2021c Author(s) 2021. CC BY 4.0 License.Opinion: Insights into updating Ambient Air Quality Directive(2008/50EC)Joel Kuula1, Hilkka Timonen1, Jarkko V. Niemi2, Hanna E. Manninen2, Topi Rönkkö3, Tareq Hussein4,Pak Lun Fung4, Sasu Tarkoma5, Mikko Laakso6, Erkka Saukko7, Aino Ovaska4, Markku Kulmala4, Ari5 Karppinen1, Lasse Johansson1, Tuukka Petäjä41Atmospheric Composition Research, Finnish Meteorological Institute, Helsinki, 00560, HelsinkiHelsinki Region Environmental Services Authority HSY, Helsinki, 00240, Helsinki, Finland3Aerosol Physics Laboratory, Physics Unit, Tampere University, Tampere, 33100, Finland4Institute for Atmospheric and Earth System Research (INAR/Physics), University of Helsinki, Helsinki, 00560, Finland5Department of Computer Science, University of Helsinki, Helsinki, 00560, Finland6Vaisala Oyj, Vantaa, 01670, Finland7Pegasor Oy, Tampere, 33100, Finland210Correspondence to: Joel Kuula ([email protected])Abstract. As the evidence for the adverse health effects of air pollution continues to increase, World Health Organization15(WHO) recently published its latest edition of the Global Air Quality Guidelines. Although not legally binding, theguidelines aim to provide a framework in which policymakers can combat air pollution by formulating evidence-based airquality management strategies. In the light of this, European Union has stated its intent to revise the current Ambient AirQuality Directive (2008/50/EC) to resemble closer to that of the newly published WHO guidelines. This article provides aninformed opinion on selected features of the air quality directive that we believe would benefit from a reassessment. The20selected features include discussion about 1) air quality sensors as a part of hierarchical observation network, 2) number ofminimum sampling points and their siting criteria, and 3) new target air pollution parameters for future consideration.1 BackgroundAir pollution continues to be one of the top-ranking mortality risk factors in the global burden on disease analysis, anddifferent estimates have linked air pollution to 3.3-9 million premature deaths globally (Burnett et al., 2018; Lelieveld et al.,252015; World Health Organization, 2021). Air pollution consists of both gas- and particle-phased components, whichoriginate from a variety of different anthropogenic and natural sources. Typical anthropogenic sources are related tocombustion processes such as vehicular exhaust emissions and residential wood burning whereas natural sources include, forinstance, wildfires and volcano eruptions (e.g. Aguilera et al., 2021; Rönkkö et al., 2017). From the health effects point ofview, particulate matter (PM2.5 and PM10) is the most detrimental to human health, although trace gases such as NO2 and30O3 also have a contribution (European Environment Agency, 2020). Besides regulated mass-based PM2.5 and PM10parameters, typically evaluated non-regulated particle metrics also include particle number and surface area-based1

https://doi.org/10.5194/acp-2021-854Preprint. Discussion started: 20 October 2021c Author(s) 2021. CC BY 4.0 License.concentrations, particle size distributions as well as black carbon or elemental carbon (BC/EC) (e.g. de Jesus et al., 2019; Wu& Boor, 2020). Exposure to air pollution occurs both indoors and outdoors although people spend most of their time indoors(World Health Organization, 2006).35To combat poor outdoor air quality, World Health Organization (WHO) has produced a series of guidelines to supportpolicymakers in setting air quality management strategies. Although being neither standards nor legally binding criteria, theguidelines are based on expert evaluation of scientific evidence and are thus a valuable source of information. The earliestguidelines were published in 1987 and the subsequent revisions in 2000 and 2006. The newest edition, which was published40in 2021, represents the most up-to-date understanding of air pollution and its impacts on human lives (World HealthOrganization, 2021). In the light of this, the European Union has stated its intent to revise the current Ambient Air QualityDirective 2008/50/EC (AAQD, European Council, 2008) to resemble closer to that of the new WHO guidelines. The purposeof this article is to provide an expert opinion on how the AAQD should be developed taking into account the latesttechnological and scientific advances in air quality monitoring.452 Air quality sensors as a part of hierarchical observation networkIn the current AAQD documentation, air quality observations comprise of four modes (Table 1) as follows: 1) fixedmeasurements, 2) indicative measurements, 3) modelling, and 4) objective estimation. In the current hierarchy theuncertainties are shown in a descending order from the most stringent and accurate to the least stringent and accurate. Thetwo most accurate modes – fixed and indicative measurements – are based on the actual measurements whereas the two least50accurate modes – modelling and objective estimation – are based on mathematical and expert estimations of theconcentrations. The main weakness of the measurement-based observation modes is their expensiveness and laboriousness;establishing and maintaining a measurement station, especially a fixed measurement-classified station, requires considerableresources, leading to a scarcity of stations and poor spatial coverage of measurements. Modelling and objective estimationobservation modes require far less continuous effort and can cover essentially any spatial domain but provide potentially less55reliable, mathematically derived data. The hierarchical observation framework, in which different tier-level observationmodes can be used in parallel to complement each other’s strengths and weaknesses, is well justified; the authorityconducting monitoring may use a combination of techniques that best fits its needs and resources.Currently, the devices used for measurement-based online observations are almost exclusively limited to fixed measurement60types. This is at least partly due to the long and costly process of acquiring a device type approval, which is mandatory ifregulatory measurements are to be made. Type approval means that the measurement device adheres to the specifications setforth in standard DIN EN 15267, and that the adherence to this standard has been verified by an accredited laboratory (TÜVRheinland). Because the type approval process is the same and the cost is the same for both fixed and indicative2

https://doi.org/10.5194/acp-2021-854Preprint. Discussion started: 20 October 2021c Author(s) 2021. CC BY 4.0 License.measurement devices, there is little incentive for a company to pursue other than the more stringent and accurate fixed65measurement classification. This is problematic, when considering the recent emergence and proven usefulness of air qualitysensors (e.g. Petäjä et al., 2021). Although sensors have no formal definition, they are typically perceived as small,standalone devices, which are easy to use and easy to deploy within the city infrastructure due to their wirelesscommunication features. Furthermore, their cost is usually a fraction (e.g. 5000 EUR) of a conventional fixedmeasurement monitor. Air quality sensors and their respective performances have been studied intensively for several years70(e.g. Alfano et al., 2020), and they have been used successfully in several voluntary, non-regulatory applications, such as inthe wildfire smoke map provided by the U.S. EPA (https://fire.airnow.gov). The consensus within the research community isthat, while sensors are unlikely to be a direct replacement to the established fixed measurement monitors, their capability tocost-efficiently complement existing monitoring networks as indicative measurement devices is a novel and valuable feature(Peltier, 2020). However, the current formulation and procedure for device type approval does not facilitate extensive75integration of sensors into regulatory air quality management strategies, and a new testing protocol is needed if this is to bechanged.Although the testing protocol should be less exhaustive for the companies to apply the device type approval with a lowerthreshold, it is equally important that no major compromises are being made with respect to the quality criteria of the testing80protocol; the sensors should still be able to measure target pollutants with an adequate certainty over a certain, for example1-year timespan (Kaduwela & Wexler, 2021). Additionally, an important point of focus is the sensor intra-unit precision.Distinguishing sensor imprecisions from the true spatial variability of pollutant concentrations is paramount as one of themost useful applications for sensors is the ultra-dense monitoring networks (Popoola et al., 2018). In practice, if theuncertainty of a measurement from one node to another is too large, identifying and assessing the local concentration85hotspots and emission sources will not be possible. When considering dense monitoring networks alone, it could be arguedthat the absolute sensor accuracy is secondary to its intra-unit precision.When testing and evaluating sensors, the manufacturer should make clear whether the data generated by a sensor is based onan actual measurement of the pollutant itself or whether it is based on the combination of, for example, machine learning and90secondary data (Li et al., 2020). This is important to ensure transparency. If the sensor relies heavily on data post-processingand advanced conversion methods, the data should be treated as modelling data. In our view, this is because the data thenresembles closer to a mathematical prediction rather than an independent measurement. It can be debated where exactly theboundary between a measurement and a prediction is; some empirical corrections may be justifiable and reasonable(Schneider et al., 2019). However, as multiple studies have shown that sensors may entail detrimental flaws (Castell et al.,952017; Giordano et al., 2021), it is very important to distinguish between a real and valid observation and, for example, an AIbased extrapolation of the originally insufficient and inaccurate data.3

https://doi.org/10.5194/acp-2021-854Preprint. Discussion started: 20 October 2021c Author(s) 2021. CC BY 4.0 License.The authors are aware of the technical specification CEN/TC 264/WG 42 - Ambient air – Air quality sensors being underdevelopment, and publication of this specification is awaited with interest.1003 Determining the minimum amount of sampling pointsIn the European Union, the Member States define their zones and agglomerates in which air quality monitoring takes place.Typically, the division between areas follows the municipality and city boarders, although joint efforts, where neighbouringmunicipalities or cities conduct air quality monitoring together, are also possible. In general, the minimum number of fixedsampling points for a specific area is then determined according to the population of that area, as shown in Table 2. As105population is the sole factor determining the minimum number of sampling points, this may lead to cases where the numberof minimum sampling points between two vastly different zoners or agglomerates is strikingly similar. For example, theFinnish Lapland – one of the monitoring zones in Finland – covers an area of approximately 100 000 km2 and has 175 000inhabitants, whereas the Helsinki Metropolitan area – also a monitoring zone in Finland – covers an area of 1 500 km2 andhas 1.2 million inhabitants. According to Table 2, if the maximum concentrations are between the upper and lower110assessment thresholds, the Lapland monitoring zone requires a total of 2 sampling points (1 per 87 500 inhabitants) whereasthe Helsinki Metropolitan area requires 5 sampling points (1 per 240 000 inhabitants). In our view, the proportions of therequired sampling points are imbalanced, and the difference in required sampling points should be greater between the twoareas. A more sophisticated approach would be to connect the minimum number of sampling points to the population l.jrc.ec.europa.eu/ghs pop2019.php). Implementation of this connection could be done in a multitude of ways,and it should be ensured that the scaling is appropriate; on average, the number of sampling points should increase ratherthan decrease. Taking population density into account the determination of the amount of observation points would trackmore closely the real exposure citizens are subjected to and thus the directive would better fulfil its main purpose ofprotecting human health.1204 Siting criteria of sampling pointsIn the current AAQD, the definitions and instructions regarding the siting of sampling points are in some parts poorlyformulated. This has been outlined exceptionally well by Maiheu & Janssen (2019) in their report Assessing the spatialrepresentativeness of AQ sampling points. Some examples of the poor formulation include the use of vague terms such as“some metres away” and “edge of major junction”, which both can be interpreted in a multitude of ways. We propose here125that rather than trying to define the ultimate set of all-encompassing siting guidelines, it is worth considering to what extentit would be appropriate to rely on the expertise and judgement of the local air quality authorities themselves. The potentialabuse of liberty with respect to artificially improving air quality by situating the stations to only low concentration areas is a4

https://doi.org/10.5194/acp-2021-854Preprint. Discussion started: 20 October 2021c Author(s) 2021. CC BY 4.0 License.real problem, and some boundary conditions are necessary. But in reality, practicalities such as availability of power andlocal regulations have a significant impact on the final site location. As the technology evolves, sensors may eventually play130a key role in solving this dilemma, but as of yet, the clear and well-defined siting criteria and practical challenges related tothe deployment of monitors are in odds with one another. With respect to solving the problem of “spatial representativeness”and prioritising siting criteria, measurements at two different sites instead of only one solves these problems in many cases;there is no need to make decision between two equally worthy measurement locations if the amount of sampling pointsallows both locations to be covered. Fundamentally, the necessity of siting criteria arises from the scarcity of measurements,135and the hierarchical monitoring approach is the right tool to address it. This further underlines the need and utility of densesensor networks.5 New target parameters for future considerationThe primary motivation of ambient air quality monitoring is the protection of human health. Epidemiological studies haveshown that particulate matter is the dominant pollutant causing morbidity and premature deaths (European Environment140Agency, 2020); however, it is still unclear to some degree, what the explicit mechanisms driving the adverse health effectsare (Fiordelisi et al., 2017). Mass-based parameters PM2.5 and PM10 have been used as target parameters due to the clearevidence of their association with adverse health effects (Burnett et al., 2018). Ultrafine particles have also been considered afactor, but the current literature suggests that besides their short-term association with inflammatory and cardiovascularchanges, there is insufficient evidence showing that they drive negative health effects independently of other particulate145matter constituents (Ohlwein et al., 2019). Exposure to black carbon also entails negative health effects, and it has beenshown to be an important detrimental component of PM2.5 (Yang et al., 2019). Additionally, the climatic effects of blackcarbon further underline its importance regarding air quality monitoring (Bond et al., 2013). Perhaps an often-overlookedparameter is pollen, which causes irritation symptoms to as many people as one in four in Europe each year (Bauchau &Durham, 2004). Pollen originates from plants, and besides reducing the exposure to it by personal behavioural changes, it is150questionable what type of mitigation measures could be taken to reduce its concentrations. Nevertheless, out of all theparticulate matter parameters and constituents, the adverse health effects of pollen are the most visible and evident inpeople’s daily lives. Similarly to sensor technology, measurement techniques for pollen have advanced, and monitorscapable of online concentration measurement and species identification are currently available in the markets. Lastly, themonitoring of ambient aerosol parameters that are included in European emission regulations could significantly improve155both the understanding of emission sources and the emission mitigation actions; for instance, regulatory emissionmeasurements made for vehicles in road traffic include the measurement of exhaust solid particle number, which is currentlynot monitored from traffic-influenced urban air.5

https://doi.org/10.5194/acp-2021-854Preprint. Discussion started: 20 October 2021c Author(s) 2021. CC BY 4.0 License.Monitoring additional particulate parameters would undoubtedly be useful from the scientific point of view, and the newly160published WHO guidelines supports this as it recommends monitoring of ultrafine particles (UFP) and BC/EC (World HealthOrganization, 2021). However, monitoring of the additional parameters would require new and extensive standardizationwith respect to the new parameter-specific instrumentation and expensive investments by the monitoring bodies to these newinstruments. Therefore, it may be more appropriate to suggest that the scientific community monitors these additionalparameters with supporting, research-type Supersite stations, which remain outside of the regulatory monitoring framework.165Naturally, air quality authorities capable of conducting more extensive, voluntary-based monitoring are encouraged to do so,and initiating the preparation for instrument standardization would be useful regardless of the direction in which the AAQDis headed in the future. As illustrated in Fig. 1, the idea of a Supersite station is to support and complement existingregulatory monitoring and to provide novel insights into air quality (e.g. Kulmala et al., 2021). The instrumentation couldcover measurements for the additional parameters described previously as well as for the more advanced parameters such as170chemical composition of particles. Moreover, due to the extensive measurement arsenal, new observation technologies couldbe benchmarked and calibrated rapidly. The more in-depth data generated by the Supersites would enable scientists andpolicymakers to assess the needs and directions of future air quality regulation.6 Summary and OutlookAccurate and reliable fixed measurement monitors are, and should remain, the backbone of regulatory air quality monitoring.175Nevertheless, technological development of air quality sensors is advancing rapidly. The full benefit of the newobservational capacities of small sensors can be obtained when a hierarchical observation system is considered (e.g. Hari etal., 2016). This would allow verification of the sensor network data quality against the higher quality observation sites in thevicinity. The hierarchical network would allow science-based development and rapid deployment of novel air quality devicesas soon as they become available.180Instead of determining the siting criteria to match all the possible urban environments, more trust should be placed uponlocal air quality expert analysis, and in some cases applying multiple sensors can be the way forward. Furthermore, thepopulation density should be considered as the basis for determining the required amount of air quality observations. Thiswill lead to an optimized air quality monitoring network providing more representative data in terms of population exposure.185At the same time, a specific focus should be placed upon constraining the intra-sensor variability to ensure that the truespatial variability of concentrations and sensor imprecision are not mixed with each other.Monitoring of additional air quality parameters, such as ultrafine particles and black carbon, would be beneficial in terms ofbetter understanding air pollution, and research-type Supersite stations operated by scientific community or, where possible,190local air quality authorities could provide the platform for such monitoring. While supporting and complementing regulatory6

https://doi.org/10.5194/acp-2021-854Preprint. Discussion started: 20 October 2021c Author(s) 2021. CC BY 4.0 License.observation network, the more in-depth data generated by the Supersites could help in navigating the future air qualitydirective requirements.Data availabilityNo data sets were used in this article.195Author contributionsJ.K, H.T., and T.P. drafted the preliminary version of the article. All authors contributed to the ideation, commenting,writing, and editing of the text. J.K. finalised the manuscript.Competing interestsMarkku Kulmala and Tuukka Petäjä are members of the editorial board of Atmospheric Chemistry and Physics. The peer200review process was guided by an independent editor, and the authors have also no other competing interests to declare.Financial supportThis work is funded under the European Union's Horizon 2020 research and innovation programme, Grant agreement No101036245 project RI-URBANS (Research Infrastructures Services Reinforcing Air Quality Monitoring Capacities inEuropean Urban & Industrial AreaS),205Business Finland and participating companies via CITYZER (3021/31/2015,2883/31/2015) and BC Footprint (528/31/2019, 530/31/2019) projects, European Regional Development Fund: Urbaninnovative actions initiative (HOPE; Healthy Outdoor Premises for Everyone, project no: UIA03-240), regional innovationsand experimentations funds AIKO, governed by the Helsinki-Uusimaa Regional Council (project HAQT, AIKO014),Academy of Finland Flagship funding (grant no. 337549, 337551, 337552).References210Aguilera, R., Corringham, T., Gershunov, A. and Benmarhnia, T.: Wildfire smoke impacts respiratory health more than fineparticles from other sources: observational evidence from Southern California, Nat. Commun., 12(1), 1493,doi:10.1038/s41467-021-21708-0, 2021.Alfano, B., Barretta, L., Giudice, A. Del, De Vito, S., Francia, G. Di, Esposito, E., Formisano, F., Massera, E., Miglietta, M.L. and Polichetti, T.: A review of low-cost particulate matter sensors from the developers’ perspectives, Sensors215(Switzerland), 20(23), 1–56, doi:10.3390/s20236819, 2020.7

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1 Opinion: Insights into updating Ambient Air Quality Directive (2008/50EC) Joel Kuula 1, Hilkka Timonen 1, Jarkko V. Niemi 2, Hanna E. Manninen 2, Topi Rönkkö 3, Tareq Hussein 4, Pak Lun Fung 4, Sasu Tarkoma 5, Mikko Laakso 6, Erkka Saukko 7, Aino Ovaska 4, Markku Kulmala 4, Ari 5 Karppinen 1, Lasse Johansson