Ecosystem modeling can help decision making regarding planting of urban trees for climate change mitigation and air pollution reduction. Algorithms and models that link the properties of plant functional types, species groups, or single species to their impact on specific ecosystem services have been developed. However, these models require a considerable effort for initialization that is inherently related to uncertainties originating from the high diversity of plant species in urban areas. We therefore suggest a new automated method to be used with the i-Tree Eco model to derive light competition for individual trees and investigate the importance of this property. Since competition depends also on the species, which is difficult to determine from increasingly used remote sensing methodologies, we also investigate the impact of uncertain tree species classification on the ecosystem services by comparing a species-specific inventory determined by field observation with a genus-specific categorization and a model initialization for the dominant deciduous and evergreen species only. Our results show how the simulation of competition affects the determination of carbon sequestration, leaf area, and related ecosystem services and that the proposed method provides a tool for improving estimations. Misclassifications of tree species can lead to large deviations in estimates of ecosystem impacts, particularly concerning biogenic volatile compound emissions. In our test case, monoterpene emissions almost doubled and isoprene emissions decreased to less than 10% when species were estimated to belong only to either two groups instead of being determined by species or genus. It is discussed that this uncertainty of emission estimates propagates further uncertainty in the estimation of potential ozone formation. Overall, we show the importance of using an individual light competition approach and explicitly parameterizing all ecosystem functions at the species-specific level.

With increasing realization that particles in the air are a major health risk in urban areas, strengthening particle deposition is discussed as a means to air-pollution mitigation. Particles are deposited physically on leaves and thus the process depends on leaf area and surface properties, which change throughout the year. Current state-of-the-art modeling accounts for these changes only by altering leaf longevity, which may be selected by vegetation type and geographic location. Particle removal also depends on weather conditions, which determine deposition and resuspension but generally do not consider properties that are specific to species or plant type. In this study, we modeled < 2.5 µm-diameter particulate-matter (PM 2.5) deposition, resuspension, and removal from urban trees along a latitudinal gradient (Berlin, Munich, Rome) while comparing coniferous with broadleaf (deciduous and evergreen) tree types. Accordingly, we re-implemented the removal functionality from the i-Tree Eco model, investigated the uncertainty connected with parameterizations, and evaluated the efficiency of pollution mitigation depending on city conditions. We found that distinguishing deposition velocities between conifers and broadleaves is important for model results, i.e., because the removal efficiency of conifers is larger. Because of the higher wind speed, modeled PM 2.5 deposition from conifers is especially large in Berlin compared to Munich and Rome. Extended periods without significant precipitation decrease the amount of PM 2.5 removal because particles that are not occasionally washed from the leaves or needles are increasingly resuspended into the air. The model predicted this effect particularly during the long summer periods in Rome with only very little precipitation and may be responsible for less-efficient net removal from urban trees under climate change. Our analysis shows that the range of uncertainty in particle removal is large and that parameters have to be adjusted at least for major tree types if not only the species level. Furthermore, evergreen trees (broadleaved as well as coniferous) are predicted to be more effective at particle removal in northern regions than in Mediterranean cities, which is unexpected given the higher number of evergreens in southern cities. We discuss to what degree the effect of current PM 2.5 abundance can be mitigated by species selection and which model improvements are needed.

Extremely high temperatures, which negatively affect the human health and plant performances, are becoming more frequent in cities. Urban green infrastructure, particularly trees, can mitigate this issue through cooling due to transpiration, and shading. Temperature regulation by trees depends on feedbacks among the climate, water supply, and plant physiology. However, in contrast to forest or general ecosystem models, most current urban tree models still lack basic processes, such as the consideration of soil water limitation, or have not been evaluated sufficiently. In this study, we present a new model that couples the soil water balance with energy calculations to assess the physiological responses and microclimate effects of a common urban street-tree species (Tilia cordata Mill.) on temperature regulation. We contrast two urban sites in Munich, Germany, with different degree of surface sealing at which microclimate and transpiration had been measured. Simulations indicate that differences in wind speed and soil water supply can be made responsible for the differences in transpiration. Nevertheless, the calculation of the overall energy balance showed that the shading effect, which depends on the leaf area index and canopy cover, contributes the most to the temperature reduction at midday. Finally, we demonstrate that the consideration of soil water availability for stomatal conductance has realistic impacts on the calculation of gaseous pollutant uptake (e.g., ozone). In conclusion, the presented model has demonstrated its ability to quantify two major ecosystem services (temperature mitigation and air pollution removal) consistently in dependence on meteorological and site conditions.

Trees and urban forests remove particulate matter (PM) from the air through the deposition of particles on the leaf surface, thus helping to improve air quality and reduce respiratory problems in urban areas. Leaf deposited PM, in turn, is either resuspended back into the atmosphere, washed off during rain events or transported to the ground with litterfall. The net amount of PM removed depends on crown and leaf characteristics, air pollution concentration, and weather conditions, such as wind speed and precipitation. Many existing deposition models, such as i-Tree Eco, calculate PM2.5 removal using a uniform deposition velocity function and resuspension rate for all tree species, which vary based on leaf area and wind speed. However, model results are seldom validated with experimental data. In this study, we compared i-Tree Eco calculations of PM2.5 deposition with fluxes determined by eddy covariance assessments (canopy scale) and particulate matter accumulated on leaves derived from measurements of vacuum/filtration technique as well as scanning electron microscopy combined with energy-dispersive X-ray spectroscopy (leaf scale). These investigations were carried out at the Capodimonte Royal Forest in Naples. Modeled and measured fluxes showed good overall agreement, demonstrating that net deposition mostly happened in the first part of the day when atmospheric PM concentration is higher, followed by high resuspension rates in the second part of the day, corresponding with increased wind speeds. The sensitivity analysis of the model parameters showed that a better representation of PM deposition fluxes could be achieved with adjusted deposition velocities. It is also likely that the standard assumption of a complete removal of particulate matter, after precipitation events that exceed the water storage capacity of the canopy (Ps), should be reconsidered to better account for specific leaf traits. These results represent the first validation of i-Tree Eco PM removal with experimental data and are a starting point for improving the model parametrization and the estimate of particulate matter removed by urban trees.

According to projects and practices that the Italian botanists and ecologists are carrying out for bringing "more nature in the city", new insights for a factual integration between ecological perspectives and more consolidated aesthetic and agronomic approaches to the sustainable planning and management of urban green areas are provided.Keywords Ecosystem services, Human well-being, Green infrastructure, Urban green areas, Urban biodiversity.

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deposition in the modeled urban area would hardly change, indicating that the service of air pollution removal would not be degraded. These results may help selecting urban tree species in future greening programs.