The guide on the Methodology is targeted at air quality and urban green infrastructure experts and the research community. The paper was used at training events of the CLAIRO project organized for professionals and university students. At these events, the feedbacks on the methodology was positive both thanks to experts and students. A Manual, a shortened version of the Methodology was also published for non-specialist audiences. These documents lend a hand to cities to design similar green infrastructure interventions and to track their effectiveness. The English versions were published in early 2022.
Factors to consider when designing greenery for pollutant capture
The Manual and in more details the Methodology showcase the basic principles for planting greenery for pollutant capture. The adoption of these design principles can help cities in their efforts to effectively contribute to air quality improvements in selected urban neighbourhoods.
The first step during planning is to assess whether the proposed vegetation will thrive in local environmental conditions. Plants can effectively filter air pollutants only as long as they remain sustainably healthy. To ensure plant longevity, the vegetation must be adapted to the local environmental conditions of the selected site, such as microclimate, topography, soil, air pollution and in many cases to unfavourable artificial conditions in an urban setting (heat, drought, limited availability of soil and moisture, or soil salinity). At a site with poor air quality the sensitivity of specific plant species to air pollution should also be considered.
After a careful pre-selection, plants should be prioritized depending on their ability to capture pollutants.
Foliage longevity is an important aspect influencing the filtering ability of plants. Evergreen species can act as year-round filters, as opposed to deciduous trees, which lose their leaves during winter. Experiments have shown a significantly higher capture of pollutants on the surface of evergreen trees throughout the year compared to deciduous trees. Among deciduous trees, the ones that are preferable are those which exhibit longer in-leaf season. At a given location the seasonal variability of pollution concentrations should also be assessed and during planning it should be ensured that the green area is maximized at periods with high pollutant concentrations.
The size and the shape of the leaf also influences pollution capture. The species with smaller leaves seem to be more effective in trapping particles than the ones with larger leaves. This might be explained by the higher perimeter-surface area ratio of smaller leaves. Accordingly, the needles of conifers seem to support more effectively deposition of particulates than the broader leaves of deciduous species. In addition, complex leaf shapes exhibit greater potential for particulate capture than simple, elliptical ones, as these provide lower resistance to airflow.
The characteristics of the leaf surface also matters. Smooth leaf surfaces are less effective at capturing pollutants than rough, hairy or waxy leaf surfaces.
Not surprisingly, mature trees with their extensive foliage are significantly more efficient in removing pollutants compared to low vegetation. The density of the foliage is a key factor as it actually determines the vegetation area available for deposition. Generally, the larger the surface area of the vegetation per unit of area, the greater the capture of pollutants.
In an urban setting the sensitivity of plant species to air pollution and salination of soil also need to be taken into account when planning for green infrastructure interventions.
It should be noted though that greenery, despite its ability to capture air pollutants, can also be a source of pollution. Trees and other plants produce volatile organic compounds, which when exposed to sunlight react with nitrogen oxides emitted mainly from vehicles and industrial sources to form ozone, that can lead to the formation of ground-level smog. In addition, many tree species produce pollen, which can cause allergic reactions. When planning urban greenery in the vicinity of residential areas, it is wise to avoid planting tree species that produce highly allergenic pollen, such as birch, ash, oak, and elm, or those ones that emit significant amount of volatile organic compounds, such as black locust, poplar or plane tree.
In accordance with the above principles, as it is described in the paper on Methodology, continuously connected tree communities were designed in Ostrava that were differentiated to be adapted to the various habitat types. The new vegetation has two tree layers and a shrub layer in order to maximize the canopy density and thus its filtration efficiency. Species with increased resistance to air pollution were preferred and exclusively domestic plant species were selected.
Since the two target sites are located next to an industrial area, the newly planted trees are exposed to air pollution and other environmental extremes, such as soil contaminated with heavy metals. Under CLAIRO plants are treated as part of an experiment with specific preparations that contain plant hormones to increase their tolerance to air pollutants and contamination. Additional care is necessary to maintain the functions of the new greenery on a permanent basis through improving their basic physiological parameters. The paper on the Methodology provides details of the innovative treatment of the plants.
Air quality monitoring supports design, modelling and tracking change
The guide on the Methodology highlights that air quality monitoring was essential in supporting the design of greenery, and the development of models of pollution captured by urban vegetation. The air quality measurements are also relevant for tracking development relating to air quality improvements over a longer time period and for assessing the effectiveness of the planted greenery in filtering pollutants.
Sensor technology was used for air quality monitoring under CLAIRO in Ostrava. Modular sensor networks that allow real-time simultaneous measurement of gas pollutants and particulate matter in the air were deployed in 2019 at all three target areas in Ostrava, Radvanice and Bartovice. Continuous measurements have started since September 2019.
The use of sensor technology is a suitable mean for undertaking air quality monitoring in this context. The technology based on small and easily transferable sensors is flexible and quite affordable. The installation of sensors requires much lower initial investment compared to standard monitoring stations, and on top, their operation cost is particularly low.
With the help of a sensor network detailed data can be gathered from areas of interest that are not covered by stationary monitoring stations, and maps can be developed on pollutant distribution with high spatial resolution.
First of all, pollutant concentrations and meteorological parameters measured by sensors lend support for planning, enabling the optimization of tree and shrub planting for filtering air pollutants. Besides, spatial data on pollutant concentrations and meteorological data can be used for modelling the capture of pollutants by urban greenery and for assessing to what extent the vegetation contributes to air quality improvements. The resulting models give a clearer picture of the actual air pollution and allow the estimation of the future improvement of air quality. The modelling based on accurate measurements allows to determine the vegetation area that is able to absorb the required amounts of various substances emitted by any source of air pollution.
Although the sensors enable rapid measurement, their data cannot be used for compliance monitoring since the attempt to miniaturize the devices resulted in their reduced reliability and accuracy. Therefore, it is stressed by the paper on Methodology that calibration of the sensors is essential. This can be accomplished by performing comparative measurements using the stations of the state reference network. During this the sensor units are placed in one location near stationary monitoring stations of the reference network and are frequently calibrated before deployment.
Within the networks in Ostrava sensor boxes were installed, each one including several sensors. Some boxes contain sensors for measuring meteorological parameters, such as wind speed and direction, temperature, humidity and global radiation. The sensor boxes are solar panel-powered and also contain integrated batteries enabling autonomous operation.
The sensors allow fast, short-term measurements of both organic and inorganic substances. They measure a large number of values, capturing data at very short intervals. The values are recorded every 10 seconds, and the average concentrations are sent in data packets every 5 minutes.
A total of 19 sensor units and one reference system were installed in Ostrava under CLAIRO, enabling the measurement of concentrations of particulate matter, nitrogen dioxide, and ozone. Data from the sensors are collected in an integrated data logger and transmitted to the database of a central web portal. The very extensive database makes it possible to monitor long-term changes in pollutant concentrations in relation to the ability of greenery to absorb airborne pollutants.
A model facilitating the prediction of future capture
A model of capture of air pollutants was developed under CLAIRO to enable the accurate quantification of the positive effects of greenery on air quality. The guide on the Methodology provides a detailed description on how modelling was done.
The measurements of the sensor networks provide essential input data for the model. During the quantification of capture, the dry deposition of the various pollutants is estimated. Dry deposition is a process that removes airborne materials from the atmosphere and deposits them on a surface due to gravity during periods without precipitation. Atmospheric deposition significantly reduces the amount of pollutants in the air. In the model the concentration of compounds and their deposition rate were used to estimate the dry deposition of various air pollutants. Concentration values were provided by sensors.
Deposition rate can be calculated from the resistances to pollution transport linked to the deposition process, which can roughly be described by three simplified steps. During the first step pollutants are carried from the lower level of the atmosphere to the so called 'boundary layer' that is surrounding each object. During the second step transport takes place across the boundary layer, which is a very thin layer of still air enveloping an object. Finally, the pollutant interacts with the surface. Accordingly, deposition rate during the various steps can be determined by aerodynamic resistance, boundary layer resistance, and surface resistance. Aerodynamic resistance and boundary resistance are calculated from micrometeorological data accessed through monitoring. Surface resistance depends on the nature and the area of the surface. Generally, the slowest step determines the overall rate of the deposition process.
For the quantification of capture by greenery, apart from the estimation of dry deposition, the calculation of the leaf area index (LAI) that determines the amount of leaf area in a canopy is also necessary.
Regarding original vegetation, for the determination of the leaf area index, the tree species were inventoried, their height was measured using a digital altimeter and their crown diameter was also measured. The health status of the plant was finally assessed as it directly influences leaf area.
In contrast, for the proposed greenery the leaf area index was estimated based on the height of the future vegetation, the average crown area and species composition. Standard figures were used for establishing the height of trees and shrubs. The spatial arrangement of the vegetation was defined and georeferenced using planting plan drawings.
During the modelling work, separate models were developed for the existing vegetation, the designed new greenery, and the planted greenery. First a hypothetical model was created, which was tested and improved by continuous measurements of pollutant concentration levels though an iterative process. Building on the separate models a final innovative complex model was developed that includes predictions for future capture of air pollution by the planted greenery. The opportunity for accurate predictions is vital, since decades are required for a new plantation to become a mature forest, which can effectively filter pollutants. As such, the full potential of the new greenery will not be seen immediately at project closure.
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