IVL Swedish Environmental Research Institute

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  • 1.
    Gustafsson, Malin S.M.
    et al.
    IVL Swedish Environmental Research Institute.
    Lindén, Jenny
    IVL Swedish Environmental Research Institute.
    Johansson, Emelie M.M.
    IVL Swedish Environmental Research Institute.
    Watne, Ågot K.
    IVL Swedish Environmental Research Institute.
    Pleijel, Håkan
    Göteborgs Universitet.
    Air pollution removal with urban greenery – Introducing the Vegetation Impact Dynamic Assessment model (VIDA)2024In: Atmospheric Environment, ISSN 1352-2310, E-ISSN 1873-2844, Vol. 323, p. 120397-120397, article id 120397Article in journal (Refereed)
    Abstract [en]

    Urban greenery is identified as a potential tool in air pollution mitigation. However, the impact is still debated. This paper introduces the innovative VIDA (Vegetation Impact Dynamic Assessment) model, specifically designed to quantify air pollution removal through deposition on vegetation. The VIDA model offers an advanced representation of vegetation that could be integrated into urban air quality dispersion models in the future. Furthermore, the model serves as a valuable tool for exploring the intricate interactions among deposition, resuspension, and wash-off processes, as well as understanding how meteorological conditions and various leaf traits influence these processes.The current version of the model focuses on particulate matter (PM) and encompasses a range of processes, including deposition on vegetation surfaces, encapsulation within the waxy cuticle, wind-driven resuspension, and wash-off.

    Additionally, the model takes into consideration dynamic changes in PM concentrations on the leaf surface over time, incorporating factors such as PM size fractions, meteorological conditions, and leaf characteristics. This comprehensive approach allows for the evaluation of various species or species groups based on their distinct traits. The VIDA model effectively reproduces measured data, yet continued evaluation remains crucial as new data emerges. Notably, challenges are encountered due to data scarcity and the absence of standardized methods for characterizing vegetation traits. Addressing these challenges and refining the representation of wash-off process will enhance the VIDA model's utility in predicting the dynamic relationship between vegetation and air quality. The introduction of VIDA provides a significant advancement in modelling air pollution removal by deposition to vegetation at a relevant local scale and enables inclusion of urban greenery as tool in urban planning for air pollution mitigation. 

  • 2.
    Gustafsson, Malin
    et al.
    IVL Swedish Environmental Research Institute.
    Watne, Ågot
    IVL Swedish Environmental Research Institute.
    Fridén, Håkan
    IVL Swedish Environmental Research Institute.
    Hållbar datadriven kustzonsplanering och förvaltning2024Report (Other academic)
    Abstract [en]

    Coastal marine ecosystems all over the world are under threat due to human activities and climate change. The Maritime Spatial Planning Framework Directive (2014/89/EU) states that maritime spatial planning should support and facilitate the sustainable growth of offshore activities such as fishing, shipping, and aquaculture, while preserving, protecting, and enhancing our marine environments. To succeed, data, good knowledge and careful planning of our complex marine ecosystems are required. Models can be effective tools for coastal zone planning and management, as they enable scenario simulations and non-invasive experiments. Access to good quality data both as input to models and for model validation is essential to ensure high quality model results. However, it is often a challenge to find good data. 

    The goal of this synthesis was to evaluate opportunities and shortcomings in how data, models and planning tools are used today and how they could be used in the future. As part of this, we have compiled, described, and evaluated different models, planning tools and data sources. We have also investigated whether there are new technologies that could help fill the gaps and shortcomings identified. For example, can data and models be combined and used to facilitate coastal zone planning and management? 

    The results from this synthesis show that the current inventory and mapping of species and flora in the coastal zone is insufficient, to serve as a basis for reliable planning. There are many available models and planning tools adapted for the coastal zone, but in most cases basic input data are missing, such as spatial data on where different species and biotopes are located as well as bathymetry. If the underlying data for the models or planning tools is deficient, there is a high risk that the results will be misinterpreted or overinterpreted. 

    In addition to traditional inventory, new techniques and methods should be explored. This may involve combining, for example, environmental DNA, machine learning techniques, modelling, and measurements with new low-cost sensors to acquire the data of spatial distribution of species and biotopes that are missing. 

    Furthermore, the results from this study show that there are great opportunities in sharing and reusing data in coastal zone planning and management. There are today several data portals where data is shared, however, it can be difficult to find the right data due to insufficient metadata. A system for data management and sharing is needed. In particular, we want to highlight the value of working with linked data and persistent identifiers. In order to preserve our coastal ecosystems and enable sustainable growth of offshore activities, we must gather expertise on for example environment and digitization from both authorities, researchers and business practitioners. This will require an overarching long-term investment that includes the development of new methods and techniques as well as data management. 

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  • 3. Hassani, Amirhossein
    et al.
    Castell, Núria
    Watne, Ågot K.
    IVL Swedish Environmental Research Institute.
    Schneider, Philipp
    Citizen-operated mobile low-cost sensors for urban PM2.5 monitoring: field calibration, uncertainty estimation, and application2023In: Sustainable cities and society, ISSN 2210-6707, Vol. 95, p. 104607-104607, article id 104607Article in journal (Refereed)
    Abstract [en]

    Research communities, engagement campaigns, and administrative agents are increasingly valuing low-cost air-quality monitoring technologies, despite data quality concerns. Mobile low-cost sensors have already been used for delivering a spatial representation of pollutant concentrations, though less attention is given to their uncertainty quantification. Here, we perform static/on-bike inter-comparison tests to assess the performance of the Snifferbike sensor kit in measuring outdoor PM2.5 (Particulate Matter < 2.5 μm). We build a network of citizen-operated Snifferbike sensors in Kristiansand, Norway, and calibrate the measurements using Machine

    Learning techniques to estimate the concentrations of PM2.5 along the city roads. We also propose a method to estimate the minimum number of PM2.5 measurements required per road segment to assure data representativeness. The co-location of three Snifferbike kits (Sensirion SPS30) at the monitoring station showed a RMSD of 7.55 μg m−3. We approximate that one km h−1 increase in the speed of the bikes will add 0.03 - 0.04 μg m−3 to the Standard Deviation of the Snifferbike PM2.5 measurements. We estimate that at least 27 measurements per road segment are required (50 m here) if the data are sufficiently dispersed over time. We recommend calibrating the mobile sensors when they coincide with reference monitoring stations.

  • 4. Hassani, Amirhossein
    et al.
    Castell, Núria
    Watne, Ågot K.
    IVL Swedish Environmental Research Institute.
    Schneider, Philipp
    Citizen-operated mobile low-cost sensors for urban PM2.5 monitoring: field calibration, uncertainty estimation, and application2023In: Sustainable cities and society, ISSN 2210-6707, Vol. 95, p. 104607-104607, article id 104607Article in journal (Refereed)
    Download full text (pdf)
    fulltext
  • 5.
    Lindén, Jenny
    et al.
    IVL Swedish Environmental Research Institute.
    Gustafsson, Malin
    IVL Swedish Environmental Research Institute.
    Uddling, Johan
    GU.
    Watne, Ågot
    IVL Swedish Environmental Research Institute.
    Pleijel, Håkan
    GU.
    Air pollution removal through deposition on urban vegetation: The importance of vegetation characteristics2023In: Urban Forestry & Urban Greening, ISSN 1618-8667, E-ISSN 1610-8167, Vol. 81, p. 127843-127843, article id 127843Article in journal (Refereed)
    Abstract [en]

    Urban vegetation has the potential to improve air quality as it promotes pollutant deposition and retention.Urban air quality models often include the effect vegetation have on pollution dispersion, however, processesinvolved in pollution removal by vegetation are often excluded or simplified and does not consider differentvegetation characteristics. In this systematic review, we analyze the influence of the large interspecies variationin vegetation characteristics to identify the key factors affecting the removal of the major urban pollutants,particulate matter (PM) and nitrogen dioxide (NO2) from the air through vegetation deposition.

    The aim is toidentify key processes needed to represent vegetation characteristics in urban air quality modelling assessments.We show that PM is mainly deposited to the leaf surface, and thus representation of characteristics affectingthe aerodynamics from canopy down to leaf surface are important, such as branch/shoot complexity and leafsize, leaf surface roughness and hairiness. In addition, characteristics affecting PM retention capacity, resuspensionand wash-off, include leaf surface roughness, hairiness and wax content. NO2 is mainly depositedthrough stomatal uptake, and thus stomatal conductance and its responses to environmental conditions are keyfactors. These include response to solar radiation, vapour pressure deficit and soil moisture.Representation of these vegetation characteristics in urban air quality models could greatly improve ourability to optimize the type and species of urban vegetation from an air quality perspective.

  • 6.
    Watne, Ågot K.
    et al.
    IVL Swedish Environmental Research Institute.
    Linden, Jenny
    IVL Swedish Environmental Research Institute.
    Willhelmsson, Jens
    IVL Swedish Environmental Research Institute.
    Fridén, Håkan
    IVL Swedish Environmental Research Institute.
    Gustafsson, Malin
    IVL Swedish Environmental Research Institute.
    Castell, Nuria
    Tackling Data Quality When Using Low-Cost Air Quality Sensors in Citizen Science Projects2021In: Frontiers in Environmental Science, E-ISSN 2296-665X, Vol. 9Article in journal (Refereed)
    Abstract [en]

    Using low-cost air quality sensors (LCS) in citizen science projects opens many possibilities.LCS can provide an opportunity for the citizens to collect and contributewith their own air qualitydata. However, low data quality is often an issue when using LCS and with it a risk of unrealisticexpectations of a higher degree of empowerment than what is possible. If the data quality andintended use of the data is not harmonized, conclusionsmay be drawn on the wrong basis anddata can be rendered unusable.

    Ensuring high data quality is demanding in terms of labor andresources. The expertise, sensor performance assessment, post-processing, as well as thegeneral workload required will depend strongly on the purpose and intended use of the airquality data. It is therefore a balancing act to ensure that the data quality is high enough for thespecific purpose, while minimizing the validation effort. The aim of this perspective paper is toincrease awareness of data quality issues and provide strategies to minimizing labor intensityand expenses while maintaining adequate QA/QC for robust applications of LCS in citizenscience projects.

    We believe that air quality measurements performed by citizens can be betterutilized with increased awareness about data quality and measurement requirements, incombination with improved metadata collection. Well-documented metadata can not onlyincrease the value and usefulness for the actors collecting the data, but it also the foundation forassessment of potential integration of the data collected by citizens in a broader perspective.

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