Data Synthesis Reviews
Synthesis Reviews
RECLAIM Network Plus has funded 7 individual reviews to identify the current knowledge in these areas and the biggest knowledge gaps. The reviews cover urban challenges including water flow, air pollution, urban cooling, noise, biodiversity, carbon stocks and sequestration and health, equity and wellbeing.
To view these reviews including papers and presentations, scroll down below.
Water Flow
Dr Thomas Kjeldsen, Reader, University of Bath
Summary - The water flow review aimed to assess recent scientific literature on performance of GBGI in urban environments, focussing on managing excess surface water runoff and peak flow. The review process involved 4 steps. 1): Initial screening: Web of Science search across all 51 GBGI categories yielded 42,000+ original research studies, excluding conference proceedings and reviews. For categories with 1000+ results (private gardens, sports fields, city farms, flood
control channels, wetlands, river/stream, lakes, reservoirs, sea, woodlands, arable agriculture), only studies from the last 5 years were considered for screening, reducing the total to 15,732 studies. 2) Title and abstract screening: Identified 594 potentially relevant studies 3) Data extraction: Reviewed full text of all step 2 studies of which 164 contained relevant data on GBGI impact on hydrological parameters (interception, infiltration, volume reduction and peak flow reduction). 4) Results analysis for volume and peak flow reduction as major hydrologically relevant parameters: Peak flow reduction was quantified in 42 studies (involving 72 individual interventions); Volume reduction was quantified in 35 studies (?? Individual interventions 84) considering reduction in volume (%) when transforming rainfall to runoff.
Most studies focussed on the performance of GBGI interventions during individual rainfall events, with a strong emphasis on high rainfall periods rather than multi-seasonal long-term studies including dry spells.
The reviewed studies included both controlled small-scale indoor and mesocosm studies and field-based studies across various physical dimensions. When assessing GBGI hydrological performance considering the physical scale of interventions is crucial, as for example, response time between rainfall and runoff is closely linked to both scale and substrate medium (e.g. soil type). To enable meaningful comparisons across diverse studies, our review focused on performance indicators quantifiable as percentages, such as the reduction in peak flow. This approach allows for standardized evaluation of GBGI effectiveness, regardless of scale or setting.
In addition to hydrological parameters, the review collected meta data for each intervention, including location, GBGI physical dimensions and operation time, climatic drivers like rainfall intensity and duration. Reporting of these factors varied widely in detail reported and number of rainfall events studied. Several studies noted GBGI performance variations based on rainfall extremity, with decreased effectiveness during more extreme events compared to average rainfall and saturation during periods of frequent rain events. Additionally, several studies emphasised the effect of age and maintenance practices as important factors affecting performance of GBGI. The values reported here represent average performance over time considering variable maintenance practices and do not account for variations across rainfall magnitude and frequency.
Presentation - CLICK HERE
DATA SYNTHESIS REVIEWS
Urban Cooling
Dr Sisay Debele, Research Fellow
Summary - The Heat Review Project aims to comprehensively assess the scientific literature surrounding Green and Blue Green Infrastructures (GBGI), focusing on their direct benefits like urban heat mitigation, as well as associated advantages such as managing natural hazards, societal issues, and enhancing biodiversity. Screening nearly 27,486 papers, 202 were selected for detailed analysis, covering 51 GBGI types classified into 10 main divisions, further dissected into 33 sub-categories concentrating on heat mitigation and related co-benefits.
Efficiency evaluation of GBGI involved measuring mean temperature reduction (∆T) in degrees Celsius (°C), alongside 95% lower confidence (LCI) and upper confidence (UCI) intervals, compared against a reference case without GBGI. The study utilised in-situ monitoring, modeling approaches, and remote sensing techniques, with in-situ studies focusing on air temperature and remote sensing studies on land surface temperature.
Among the 202 papers analyzed, evidence from 101 papers relied on in-situ monitoring, 48 utilized modelling approaches, 32 employed remote sensing for temperature monitoring, and 21 integrated multiple methods. The extracted data included: (a) location (city and country), (b) latitude and longitude coordinates, (c) study types, (d) GBGI categories and subcategories, (e ) daytime, and nighttime cooling efficiency in degrees centigrade, and (f) reference/source information.
Analysis shown that botanical gardens, wetlands, green walls, street trees, and vegetated balconies showcased the most efficient air cooling, with temperature reductions spanning from 3.8°C to 5.0°C. These findings hold substantial significance for policymakers and urban planners, offering insights to prioritize effective interventions for mitigating urban overheating, addressing research gaps, and promoting community resilience.
Paper - Kumar, P., Debele, S., Khalili, S., Halios, C.H., Sahani, J., Aghamohammadi, N., Andrade, M.F.A, Athanassiadou, M., Bhui, K., Calvillo, N., Cao, S.J., Coulon, F., Edmondson, J.E., Fletcher, D., Freitas, E.D., Guo, H., Hort, M.C., Katti, M., Kjeldsen, T.R., Lehmann, S., Locosselli, G.M., Malham, S.K., Morawska, L., Parajuli, R., Rogers, C.D.F, Yao, R., Wang, F., Wenk, J., & Jones, L., 2024. Urban heat mitigation by green and blue infrastructure: a review of drivers, effectiveness, and future needs. The Innovation 5(2), 100588.
Presentation - CLICK HERE
Air Pollution
Abhijith Valappil
Summary - Green-blue-grey infrastructure (GBGI) offers environmental benefits in urban areas, yet its impact on air pollution is under-researched, and the literature is fragmented. This review evaluates quantitative studies on GBGI's capability to mitigate air pollution, compares their specific pollutant removal processes, and identifies areas for further investigation. Of the 51 GBGI types reviewed, only 22 provided quantitative pollution reduction data. Street trees and mixed-GBGI are the most studied GBGIs, with efficacy influenced by wind, GBGI type, vegetation characteristics, and urban morphology. Negative percentages denote worsening air quality, while positive percentages reflect improvement.
The 22 different GBGI types, grouped into eight main categories, provide an average reduction in air pollution of 16±21%. Substantial reductions are shown by linear features (23±21%), parks (22±34%), constructed GI (14±25%), and other non-sealed urban areas (14±20%). Individual GBGIs that reduce air pollutants include woodlands (21±38%), hedges (14±25%), green walls (14±27%), shrubland (12±20%), green roofs (13±23%), parks (9±36%), and mixed-GBGI (7±23%). On average, GBGI reduced PM1, PM2.5, PM10, UFP, and BC by 13±21%, 11±25%, 7±42%, 27±27%, and 16±41%, respectively. GBGI also lowered gaseous pollutants CO, O3, and NOx by 10±21%, 7±21%, and 12±36%, respectively. Linear features (e.g., street trees and hedges) and constructed features (e.g., green walls) can impact local air quality positively or negatively, depending on the configuration and density of the built environment. Street trees generally showed adverse effects in street canyons and beneficial outcomes in open-road conditions.
Climate change could worsen air pollution problems and impact GBGI effectiveness by shifting climate zones. In Europe and China, climate shifts are anticipated to affect eight of the 22 GBGIs, with the rest expected to remain resilient. Despite GBGI's potential to enhance air quality, the meta-analysis highlights the need for a standardized reporting structure to enable meaningful comparisons and effectively integrate findings into urban pollution and climate strategies.
Presentation - CLICK HERE
Noise
Dr David Fletcher, Research Associate, UKCEH
Summary - The Noise Review Project aimed to comprehensively assess the scientific literature surrounding Green and Blue Green Infrastructure (GBGI), focusing on their direct benefit of noise mitigation. Screening more than 6,913 papers, 209 were selected for detailed analysis, covering 51 GBGI types classified into 10 main divisions. From this 209, just 21 publications contained data that fulfilled the requirements, i.e. quantitative data relating to the impacts of any of the 51 GBGI types on noise, where the study design included some sort of control, or reference scenario, and the GBGI extent or size was suitably quantified. The noise mitigation effect was quantified in several of different ways, including decibels, A-weighted decibels, random incidence sound absorption coefficient (A-weighted frequencies), and % attenuation based in incident noise.
Extracted data also included: (a) location (city and country), (b) latitude and longitude coordinates, (c) study types, (d) GBGI categories and subcategories.
Within the 21 publications from which data were extracted, six GBGI types were represented: Park, Footpath (as green/blue corridor), Green wall, Green Verge, Woodland, Hedge. These GBGI were studied sometimes in situ (16) and sometimes in laboratories (5 - including anechoic chambers) and sometimes modelled (1). The study locations span 14 different countries, across four continents.
Due to various inconsistencies, including the metrics used to quantify impact (several types used, and frequencies measure were not always quantified), the descriptions and quantifications of the GBGI and the omission of important factors (e.g. noise source and receiver heights, or thickness or height, of vegetation), a synthesis of results, where impact is averaged by GBGI type, was not possible.
Presentation - CLICK HERE
Biodiversity
Dr Diana Bowler, Interdisciplinary Ecologist, UKCEH
Summary - The biodiversity review mapped the evidence-base for the importance of different GBGI types for supporting insect biodiversity. We searched for studies that measured insect species richness or abundance within 9 broad GBGI types, leading to a screening 3,519 studies. Based on a set of pre-defined inclusion criteria, 202 of these were deemed relevant. Studies were usually excluded because they focused on the negative effects of urbanization (comparing urban habitat with rural habitat) rather than any potential positive effects of GBGI (comparing different types of GBGI or GBGI versus non-green urban).
Most studies examined multiple GBGI types (e.g., parks and gardens) (29%), non-sealed land covers (e.g., grassland or woodland) (16%) or constructed GI (e.g., green roofs) (13%) using an observational study design (83%) rather than an experimental study design (17%). Moreover, most of these studies focused on the effects of local or landscape characteristics of the GBGI (64%, of which 31 tested the role of both), or differences between GBGI types (24%). Several studies focused on the effects of specific GBGI management e.g., mowing regime (13%) or differences between native and non-native vegetation (4%) for supporting insect biodiversity. Few studies compared insect biodiversity within the GBGI with an urban built control (6%).
Terrestrial insects rather than freshwater insects were the focus of most studies (90%)– but we note that there is a large body of literature on river restoration in urban areas that was not fully captured by our review and could contain relevant knowledge. Most studies collected data on multiple insect groups (39%), but there were also targeted studies on pollinators (15%) and pests/natural enemies (5%) as well as a range of other insect taxonomic families.
The evidence-based can be used to inform the design features of GBGI for supporting insects. Key local attributes of the GBGI include its size or area; the amount of native vegetation; the management; the total amount of plant or flower cover as well as its diversity; and the amount of tree cover. Key landscape attributes of the GBGI include its degree of isolation or connectivity with other GBGI and the cover of different land uses in the surrounding areas of the GBGI. The same GBGI can support different amounts of insect biodiversity depending on its placement in the urban matrix.
The studies also show that different insect groups will benefit from different design features, so that the types and characteristics of GBGI that benefit one insect group do not necessarily lead to the same benefits for other insect groups. For instance, a diversity of flowers within a GBGI will mostly benefit pollinators and flower-visiting insects, such as bees. Further research would help understand how spatial planning for heterogeneous GBGI could maximise the benefits across different insect groups.
Finally, the review highlights some of the core challenges in summarising the direct benefits of GBGI for insect biodiversity, in both absolute and relative terms. This is because the benefits to insect biodiversity will critically depend on the attributes and placement of the GBGI. Moreover, the insect biodiversity that can be supported by a GBGI will vary across the world according to the regional species pool and abundance. Predictions of the absolute number of insects supported by GBGI need to be tailored for specific contexts. Assessment of the relative benefits of GBGI, i.e., % change in local insect biodiversity due to GBGI, was hindered by a lack of appropriate counterfactual in most studies. This is because most biodiversity studies only collect data from within GBGI – primarily because few insects are expected in the urban built environment without GBGI. Without a counterfactual, however, the success of a specific GBGI cannot be estimated and disentangled from the range of other geographic and sampling factors affecting the number of insects collected. We recommend further modelling work using the available data to explore relevant counterfactuals for quantifying the benefits of GBGI for insect biodiversity.
Presentation - CLICK HERE
Carbon Stocks and Sequestration
Dr Jill Edmondson, Senior Lecturer, University of Sheffield
Summary - The dataset under discussion is a comprehensive compilation of carbon stock observations, meticulously organized to facilitate research and analysis across various geographic and environmental contexts. The primary objective of this dataset is to categorize and standardize carbon data based on multiple parameters, making it easier for researchers to compare carbon stocks across diverse studies, environments, and methodologies.
The dataset includes several key columns, each serving a specific purpose in organizing the data. The carbon. Type column categorizes the type of carbon observed in each entry, according to the classifications used in the original studies. This categorization helps users understand the nature of the carbon measured, whether it be organic, inorganic, or another specified type. Complementing this, the substrate.depth.cm field records the depth category of the soil or sediment where the carbon was observed, measured in centimetres. In cases where the observation pertains to vegetation rather than soil or sediment, this field is marked as "NA" (Not Applicable).
To ensure traceability and transparency, the dataset includes a reference column, which provides the title of the research paper or study from which each carbon observation was derived. Additionally, a URL column offers a direct link to these references, enabling users to quickly access the source material for further investigation or verification.
Geographic information is also well-documented within the dataset. The country column indicates the specific country where each observation was made, with "multiple" used for studies spanning several countries. Similarly, the continent column specifies the continent associated with the observations, using "global" for studies that are transcontinental. For urban-focused studies, the urban.name field identifies the urban area from which observations were taken, with "multiple" indicating more than one urban area. The lon and lat columns provide the longitude and latitude of the urban area, respectively, though these fields are marked as "NA" for studies covering multiple areas.
The dataset also includes columns that categorize and describe the nature of the studies and observations. The GBGI category and GBGI columns classify the observations based on the Global Biogeochemical Inventory (GBGI) system, offering a standardized framework for data grouping. The substrate column identifies the medium in which the carbon was measured—whether in soil, vegetation, or sediment—providing essential context for understanding the environmental conditions of each observation. The study. Type column further contextualizes the data by indicating the type of study (e.g., observational, experimental) from which the observations were taken.
Finally, the dataset includes detailed information on how the carbon stock values were derived. The depth. From and depth.to columns specify the range of soil or sediment depths associated with each observation, with "NA" used for vegetation. The mean or median column indicates whether the carbon stock value is based on a mean or median, and the original or derived stock column explains whether the carbon stock was directly reported in the study or derived from other measurements. Additionally, the stock. Units’ column ensures consistency by noting that all carbon stock values are reported in kilograms per square meter. This detailed and well-structured dataset is designed to be a valuable resource for researchers, enabling them to explore and analyse carbon stock data across different studies, environments, and geographic locations with ease and precision.
Presentation - CLICK HERE
Health, Equity and Wellbeing
Dr Nerea Calvillo, Associate Professor, University of Warwick
Summary - The Wellbeing Review Project aimed to comprehensively assess the scientific literature surrounding Green and Blue Green Infrastructure (GBGI), focusing on their direct benefit to human wellbeing. The search resulted in 272 papers, with a challenging heterogeneity of scales and measurements. Six scales, represented in 26 papers, were the most consistent. They measure a person’s physical state changes (Systolic and Systolic Blood Pressure, Hart Rate) and mood state changes (PANAS and POMS scales) following exposure to a GBGI. Most papers discuss woodlands, with a few representing green walls, schoolyards, private gardens, street trees, grass, wetlands, sea, lake and mixed environments.
On average, exposure to GBGIs were found to reduce physical stress (Diastolic Blood Pressure by ≃38.09%, Systolic Blood Pressure ≃36.73%, Heart Rate ≃37.49%) and the momentary mood changes after exposure were positive (with a reduction of negative feelings by ≃13.21%, an increase of positive feelings by ≃11.19% and POMS Total Disturbance by ≃42.39%). Woodlands specifically have a more pronounced effect, reducing stress around 10% more than the average GBIs (Diastolic Blood Pressure by 48.72%, Systolic Blood Pressure by ≃44.18%, Heart Rate by ≃48.01%). However, no significant difference was found on mood changes (reduction of negative feelings by ≃17.56%, an increase of positive feelings by ≃11.59%, and POMS Total Disturbance by ≃56.72%). For waterbodies (comprising lakes, seas, and wetlands), the average reductions were small (Diastolic Blood Pressure by ≃6.12%, Heart Rate by ≃0.57%), and negative feelings by ≃8.96%).
At this stage, the extreme heterogeneity in the data suggests that further work is needed to standardize the scales and methods used across studies. Additionally, the lack of representation of certain GBGIs needs to be studied further, as woodlands appear to be overrepresented in comparison, potentially misrepresenting the results. Finally, it’s important to keep in mind that the scales included reflect short-term mood and physical changes. These can contribute to wellbeing, but the stability of these improvements cannot be assumed.
Presentation - CLICK HERE
