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EM: Modeling the provision of air-quality regulation ecosystem service provided by urban green spaces using lichens as ecological indicators (EM-970)
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Expand AllEM Identity and Description
EM Identification
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EM ID
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EM-970 |
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EM Short Name
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Air quality regulation, Lisbon |
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EM Full Name
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Modeling the provision of air-quality regulation ecosystem service provided by urban green spaces using lichens as ecological indicators |
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EM Source or Collection
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None |
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EM Source Document ID
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454 |
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Document Author
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Matos, P., Vieira, J., Rocha, B., Branquinho, C., & Pinho, P. |
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Document Year
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2019 |
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Document Title
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Modeling the provision of air-quality regulation ecosystem service provided by urban green spaces using lichens as ecological indicators |
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Document Status
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Peer reviewed and published |
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Comments on Status
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Published journal manuscript |
Software and Access
| Not applicable | |
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Contact Name
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Pedro Pinho |
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Contact Address
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N/A |
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Contact Email
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ppinho@fc.ul.pt |
EM Description
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Summary Description
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The UN Sustainable Development Goals states that urban air pollution must be tackled to create more inclusive, safe, resilient and sustainable cities. Urban green infrastructures can mitigate air pollution, but a crucial step to use this knowledge into urban management is to quantify how much air-quality regulation can green spaces provide and to understand how the provision of this ecosystem service is affected by other environmental factors. Considering the insufficient number of air quality monitoring stations in cities to monitor the wide range of natural and anthropic sources of pollution with high spatial resolution, ecological indicators of air quality are an alternative cost-effective tool. The aim of this work was to model the supply of air-quality regulation based on urban green spaces characteristics and other environmental factors. For that, we sampled lichen diversity in the centroids of 42 urban green spaces in Lisbon, Portugal. Species richness was the best biodiversity metric responding to air pollution, considering its simplicity and its significative response to the air pollutants concentration data measured in the existent air quality monitoring stations. Using that metric, we then created a model to estimate the supply of air quality regulation provided by green spaces in all green spaces of Lisbon based on the response to the following environmental drivers: the urban green spaces size and its vegetation density. We also used the unexplained variance of this model to map the background air pollution. Overall, we suggest that management should target the smallest urban green spaces by increasing green space size or tree density. The use of ecological indicators, very flexible in space, allow the understanding and the modeling of the provision of air-quality regulation by urban green spaces, and how urban green spaces can be managed to improve air quality and thus improve human well-being and cities resilience. |
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Specific Policy or Decision Context Cited
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None identified |
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Biophysical Context
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Green spaces in Lisbon, Portugal |
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EM Scenario Drivers
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No scenarios presented |
EM Relationship to Other EMs or Applications
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Method Only, Application of Method or Model Run
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Method + Application |
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New or Pre-existing EM?
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New or revised model |
Related EMs (for example, other versions or derivations of this EM) described in ESML
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Document ID for related EM
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None |
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EM ID for related EM
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None |
EM Modeling Approach
EM Relationship to Time
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EM Temporal Extent
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2015-2018 |
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EM Time Dependence
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time-stationary |
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EM Time Reference (Future/Past)
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Not applicable |
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EM Time Continuity
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Not applicable |
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EM Temporal Grain Size Value
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Not applicable |
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EM Temporal Grain Size Unit
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Not applicable |
EM Spatial Extent
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Bounding Type
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Physiographic or ecological |
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Spatial Extent Name
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Urban green spaces in Lisbon |
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Spatial Extent Area (Magnitude)
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100-1000 km^2 |
Spatial Distribution of Computations
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EM Spatial Distribution
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spatially distributed (in at least some cases) |
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Spatial Grain Type
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map scale, for cartographic feature |
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Spatial Grain Size
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N/A |
EM Structure and Computation Approach
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EM Computational Approach
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Analytic |
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EM Determinism
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deterministic |
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Statistical Estimation of EM
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Model Checking Procedures Used
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Model Calibration Reported?
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Yes |
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Model Goodness of Fit Reported?
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Yes |
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Goodness of Fit (metric| value | unit)
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Model Operational Validation Reported?
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No |
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Model Uncertainty Analysis Reported?
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No |
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Model Sensitivity Analysis Reported?
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Unclear |
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Model Sensitivity Analysis Include Interactions?
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Not applicable |
EM Locations, Environments, Ecology
Location of EM Application
Terrestrial location (Classification hierarchy: Continent > Country > U.S. State [United States only])
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Marine location (Classification hierarchy: Realm > Region > Province > Ecoregion)
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| None |
Centroid Lat/Long (Decimal Degree)
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Centroid Latitude
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38.75 |
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Centroid Longitude
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9.8 |
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Centroid Datum
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None provided |
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Centroid Coordinates Status
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Estimated |
Environments and Scales Modeled
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EM Environmental Sub-Class
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Created Greenspace |
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Specific Environment Type
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Green spaces in Lisbon, Portugal |
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EM Ecological Scale
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Ecological scale corresponds to the Environmental Sub-class |
Scale and taxa of organisms modeled
Scale of differentiation of organisms modeled
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EM Organismal Scale
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Guild or Assemblage |
Taxonomic level and name of organisms or groups identified
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EnviroAtlas URL
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| Green Space per Capita |
EM Ecosystem Goods and Services (EGS) potentially modeled, by classification system
CICES v 4.3 - Common International Classification of Ecosystem Services (Section > Division > Group > Class)
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(Environmental Subclass > Ecological End-Product (EEP) > EEP Subclass > EEP Modifier)
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EM Variable Names (and Units)
Predictor
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Driving Variables (and Units)
view details (2 variables)
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Constant or Factor Variables (and Units)
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None |
Intermediate
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Intermediate (Computed) Variables (and Units)
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None |
Response
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Observed Response Variables (and Units)
view details (1 variable)
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Computed Response Variables (and Units)
view details (1 variable)
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