Air pollution removal by urban trees and shrubs in the United States

A modeling study using hourly meteorological and pollution concentration data from across the coterminous United States demonstrates that urban trees remove large amounts of air pollution that consequently improve urban air quality. Pollution removal (O₃, PM₁₀, NO₂, SO₂, CO) varied among cities with total annual air pollution removal by US urban trees estimated at 711,000 metric tons ($3.8 billion value). Pollution removal is only one of various ways that urban trees affect air quality. Integrated studies of tree effects on air pollution reveal that management of urban tree canopy cover could be a viable strategy to improve air quality and help meet clean air standards.     

Institutionalizing urban forestry as a “biotechnology” to improve environmental quality

Urban forests can provide multiple environmental benefits. As urban areas expand, the role of urban vegetation in improving environmental quality will increase in importance. Quantification of these benefits has revealed that urban forests can significantly improve air quality. As a result, national air quality regulations are now willing to potentially credit tree planting as means to improve air quality. Similarly, quantification of other environmental benefits of urban trees (e.g., water quality improvement, carbon sequestration) could provide for urban vegetation to be incorporated in other programs/regulations designed to improve environmental quality.     

Application of artificial intelligence for tree risk assessment optimization in Itanhaém – São Paulo,Brazil

Tree risk assessment consists of the identification of a set of defects that may affect the stability of the tree, leading to a possible collapse or failure, either of the whole plant or a part of it; and also, the identification of the targets to be reached and the side effects caused by any eventual failure. Although this is a necessary practice, the large number of variables involved in this evaluation makes the analysis time-consuming. Thus, this research aimed to optimize the tree risk assessment by creating a new protocol with the three visual assessment methods common variables and generating a new protocol applied to trees of species frequently used in urban afforestation worldwide: Terminalia catappa, Ficus Benjamin and Delonix regia. Altogether 36 variables were used for tree risk visual assessment applied in the evaluation of 230 trees located in the urban forest in Itanhaém - São Paulo - Brazil. The data collection was carried out using a smartphone and a data spreadsheet created in ODK collect app, facilitating data storage and processing. To identify the variables with the greatest possibility to determine the risk of falling, artificial intelligence was used through the Decision Tree algorithm (C4.5) in the WEKA software. The results showed that, from the 36 variables evaluated, 14 were enough to determine the risk of tree falling, with 73% hit rate in the tree risk classification. It is concluded that the use of artificial intelligence was essential in detecting tree problems in order to redirect management practices.     

Effect of street trees on microclimate and air pollution in a tropical city

One of the fastest growing cities in India, Bangalore is facing challenges of urban microclimate change and increasing levels of air pollution. This paper assesses the impact of street trees in mitigating these issues. At twenty locations in the city, we compare segments of roads with and without trees, assessing the relationship of environmental differences with the presence or absence of street tree cover. Street segments with trees had on average lower temperature, humidity and pollution, with afternoon ambient air temperatures lower by as much as 5.6 °C, road surface temperatures lower by as much as 27.5 °C, and SO₂ levels reduced by as much as 65%. Suspended Particulate Matter (SPM) levels were very high on exposed roads, with 50% of the roads showing levels approaching twice the permissible limits, while 80% of the street segments with trees had SPM levels within prescribed limits. In an era of exacerbated urbanization and climate change, tropical cities such as Bangalore will have to face some of the worst impacts including air pollution and microclimatic alterations. The information generated in this study can help appropriately assess the environmental benefits provided by urban trees, providing useful inputs for urban planners.     

Mapping leaf area of urban greenery using aerial LiDAR and ground-based measurements in Gothenburg,Sweden

Leaf area of urban vegetation is an important ecological characteristic, influencing urban climate through shading and transpiration cooling and air quality through air pollutant deposition. Accurate estimates of leaf area over large areas are fundamental to model such processes. The aim of this study was to explore if an aerial LiDAR dataset acquired to create a high resolution digital terrain model could be used to map effective leaf area index (L_e) and to assess the L_e variation in a high latitude urban area, here represented by the city of Gothenburg, Sweden. L_e was estimated from LiDAR data using a Beer-Lambert law based approach and compared to ground-based measurements with hemispherical photography and the Plant Canopy Analyser LAI-2200. Even though the LiDAR dataset was not optimized for L_e mapping, the comparison with hemispherical photography showed good agreement (r² = 0.72, RMSE = 0.97) for urban parks and woodlands. Leaf area density of single trees, estimated from LiDAR and LAI-2200, did not show as good agreement (r² = 0.53, RMSE = 0.49). L_e in 10 m resolution covering most of Gothenburg municipality ranged from 0 to 14 (0.3% of the values >7) with an average L_e of 3.5 in deciduous forests and 1.2 in urban built-up areas. When L_e was averaged over larger scales there was a high correlation with canopy cover (r² = 0.97 in 1 × 1 km² scale) implying that at this scale L_e is rather homogenous. However, when L_e was averaged only over the vegetated parts, differences in L_e became clear. Detailed study of L_e in seven urban green areas with different amount and type of greenery showed a large variation in L_e, ranging from average L_e of 0.9 in a residential area to 4.1 in an urban woodland. The use of LiDAR data has the potential to considerably increase information of forest structure in the urban environment.     

Transforming the economic value of hillside housing—A case study of Seoul,South Korea

This study examines transforming real estate value of urban hillside for residential developments, drawing on two-fold panel analyses: a random-effect panel modeling and a growth-curve panel modeling, in Seoul, South Korea from 1990 to 2015. The results reveal the reduced gap between the land values in hillside and low-lying residential sites as moving toward 2015, evidenced by the rapidly growing rate of increase of land value in the former, whereby the parabolic curvature of the land value trajectory increased from 0.00152 to 0.00156, compared to that of the latter, whereby the equivalent to that decreased from 0.00224 to 0.00214. The results also suggest that the negative effect of higher elevation has decreased to approximately zero (−0.625% per 10-m increment) and projected to turn positive, in hillside residential sites, while the equivalent to that in the low-lying residential sites has been exacerbated (−3.786% per 10-m increment). By helping the understanding of the past, current and future hillside housing development—how the hillside sites have been and will be reevaluated by passage of time, this study will provide practical lessons in planning urban housing and natural amenity resources.     

Visitors’ and residents’ perceptions of urban forests for leisure in Washington D.C

While there is an increasing number of studies on the experience of urban forests, few have examined the similarities and differences between first time visitors, repeat visitors, and local residents in their perceptions of urban forests for leisure. This study fills this research gap using Analysis of Covariance (ANCOVA) and Structural Equation Modeling (SEM), based on data collected on-site from a year-long survey of 1090 participants in Washington D.C. Results indicate that participants are generally positive on urban forests’ value for leisure and are highly satisfied with their leisure experience in the city, with “beliefs in parks and gardens” and “beliefs in street trees” being assessed the highest and lowest, respectively. The study also finds that interaction effects do exist between gender, season, location, and past experience, suggesting respondents’ visiting experience with urban forests is multidimensional, contextual, and situational. Specifically, the older the respondents are, the more positive their responses would be. A location with diverse and dense urban forests is more likely to stimulate positive perceptions, particularly for first timers. In addition, residents tend to focus on recreational aspects of urban forests as opposed to visitors, especially repeaters who are more likely to value the visual and aesthetic aspects of urban forests. This is not only evidenced by the ANCOVA results whereas fall and summer, the best seasons for pursuing outdoor activities, are highly positively perceived by residents, but also by the SEM analyses which show that street trees significantly contribute to repeaters’ satisfaction via their perceptions of “leisure value of urban forests” while no such relationship exists for first timers and residents. Urban parks and gardens are found to significantly and positively contribute to respondents’ visiting experience, which, in turn, leads to satisfaction for each respondent group. Finally, research limitations and theoretical, methodological, and managerial implications are discussed.     

A citywide survey of the pine processionary moth Thaumetopoea pityocampa spatial distribution in Orléans (France)

There is a growing recognition that urban trees provide various valuable benefits and services such as enhanced human wellbeing. However, they also have a cost in terms of public health either directly (allergies) or by harboring species representing health risk for humans. This paper focuses on such a forest insect species, the pine processionary moth Thaumetopoea pityocampa. Its caterpillars develop gregariously during winter in a conspicuous silk nest in coniferous hosts. When disturbed, the larvae release urticating hairs that cause human or animal serious health problems. The purpose of our survey was to (1) inventory all individual trees belonging to potential host species and estimate the density of T. pityocampa (2) assess the spatial pattern of the insect population at the city scale. We conducted an exhaustive inventory of potential coniferous host trees in five municipalities (ca. 5000 ha) in the north of Orléans, France. Each tree was identified, geo-referenced and the number of moth nests it hosted was counted. A total of 9321 urban trees representing 11 coniferous taxonomic units were investigated. The distribution of T. pityocampa exhibited a marked spatial structure citywide. Geostatistics allowed to draw risk maps revealing strong patchiness. We provide the first estimate of T. pityocampa host tree preference in an urban context and found that Pinus nigra, P. pinaster and P. sylvestris were the most attacked trees. We also report numerous cases of T. pityocampa occurrence on the exotic ornamental Himalayan cedar Cedrus deodara. The management implications of our findings are two-fold: (1) risk maps constitute a useful framework for communication and public information, and can help developing control strategies; (2) some species frequently used for ornamental purposes are poor quality hosts regarding T. pityocampa and should therefore be preferred in public place usually frequented by vulnerable people (schools, nurseries, hospitals).     

The social and economic value of cultural ecosystem services provided by urban forests in North America: A review and suggestions for future research

With the majority of the world’s human population now living in cities, urban forests provide an increasingly important range of ecosystem services, from improved air quality and climate change adaptation to better public health outcomes and increased tourism revenues. The importance of these ecosystem services in urban environments, and the central role that cities play in the lives of people around the world, have motivated various attempts to quantify the value of ecosystem services provided by urban forests. This paper reviews existing research in the fields of urban forestry, economics, sociology, and health on the value of urban ecosystem services, with a focus on cultural services, a category of ecosystem services that is of key importance to human well-being but that has suffered from a lack of empirical research. The review identified 38 studies that examined the value of mixed vegetation, 31 studies that examined the value of trees, and 43 studies that examined the value of green spaces. Psychological health is the most-studied ecosystem service category, with most research in this area focusing on the services of mixed vegetation. Social health, community economic development, and tourism are the least-studied, with most research in these areas focusing on mixed vegetation and trees. Multiple metrics were used to quantify the value of urban greenery within each ecosystem service category but only 11 metrics were assigned a monetary value. Gaps in the literature that present strong opportunities for future research include: the value of urban forests for improving social health, equitable access to ecosystem services, the impact of urban forests on community economic development, and economic valuation and green exposure metrics. We hope that this review stimulates future research in the areas highlighted and that municipalities consider including evaluations of a broad range of ecosystem services during land use planning and budgeting processes.     

Updating urban forest inventories: An example of the DISMUT model

Canberra is a unique city in Australia where the trees on public land that dominate the urban forest were planned for at the city's inception. In the mid-1990s, a 100% census of street and park trees was completed, and together with simple health, growth and yield models, this database formed the basis of a decision information system to support the management of the urban trees – DISMUT. The accuracy of the models was evaluated in a study in 2005 where models to predict total tree height were found to be unbiased and precise, tree crown dimension were under-estimated for small trees, and tree health was over-estimated. The over-estimate of health may be due to the relatively poor rainfall conditions over the past 10 years while the biases in crown dimension predictions are more likely due to a too simple model form. However, the existence of DISMUT predictions over all streets and parks in Canberra means that statistically efficient two-phase sampling approaches can be used to correct for any bias in the mean estimates of tree numbers and size, and also to predict the mean value of other environmental, economic or social parameters of interest that are correlated to tree size.