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Article ; Online: Reassessing the role of urban green space in air pollution control.

Venter, Zander S / Hassani, Amirhossein / Stange, Erik / Schneider, Philipp / Castell, Núria

Proceedings of the National Academy of Sciences of the United States of America

2024 Volume 121, Issue 6, Page(s) e2306200121

Abstract: The assumption that vegetation improves air quality is prevalent in scientific, popular, and political discourse. However, experimental and modeling studies show the effect of green space on air pollutant concentrations in urban settings is highly ... ...

Abstract	The assumption that vegetation improves air quality is prevalent in scientific, popular, and political discourse. However, experimental and modeling studies show the effect of green space on air pollutant concentrations in urban settings is highly variable and context specific. We revisited the link between vegetation and air quality using satellite-derived changes of urban green space and air pollutant concentrations from 2,615 established monitoring stations over Europe and the United States. Between 2010 and 2019, stations recorded declines in ambient NO
Language	English
Publishing date	2024-01-29
Publishing country	United States
Document type	Journal Article
ZDB-ID	209104-5
ISSN	1091-6490 ; 0027-8424
ISSN (online)	1091-6490
ISSN	0027-8424
DOI	10.1073/pnas.2306200121
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Article ; Online: Crowdsourced air temperatures contrast satellite measures of the urban heat island and its mechanisms.

Venter, Zander S / Chakraborty, Tirthankar / Lee, Xuhui

Science advances

2021 Volume 7, Issue 22

Abstract: The ubiquitous nature of satellite data has led to an explosion of studies on the surface urban heat island (SUHI). Relatively few have simultaneously used air temperature measurements to compare SUHI with the canopy UHI (CUHI), which is more relevant to ...

Abstract	The ubiquitous nature of satellite data has led to an explosion of studies on the surface urban heat island (SUHI). Relatively few have simultaneously used air temperature measurements to compare SUHI with the canopy UHI (CUHI), which is more relevant to public health. Using crowdsourced citizen weather stations (>50,000) and satellite data over Europe, we estimate the CUHI and SUHI intensity in 342 urban clusters during the 2019 heat wave. Satellites produce a sixfold overestimate of UHI relative to station measurements (mean SUHI 1.45°C; CUHI 0.26°C), with SUHI exceeding CUHI in 96% of cities during daytime and in 80% at night. Using empirical evidence, we confirm the control of aerodynamic roughness on UHI intensity, but find evaporative cooling to have a stronger overall impact during this time period. Our results support urban greening as an effective UHI mitigation strategy and caution against relying on satellite data for urban heat risk assessments.
Language	English
Publishing date	2021-05-26
Publishing country	United States
Document type	Journal Article
ZDB-ID	2810933-8
ISSN	2375-2548 ; 2375-2548
ISSN (online)	2375-2548
ISSN	2375-2548
DOI	10.1126/sciadv.abb9569
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Article ; Online: Environmental justice in a very green city: Spatial inequality in exposure to urban nature, air pollution and heat in Oslo, Norway.

Venter, Zander S / Figari, Helene / Krange, Olve / Gundersen, Vegard

The Science of the total environment

2022 Volume 858, Issue Pt 3, Page(s) 160193

Abstract: Poorer citizens are often more exposed to environmental hazards due to spatial inequalities in the distribution of urban blue-green space. Few cities have managed to prevent spatial and social inequality despite sustainable development strategies like ... ...

Abstract	Poorer citizens are often more exposed to environmental hazards due to spatial inequalities in the distribution of urban blue-green space. Few cities have managed to prevent spatial and social inequality despite sustainable development strategies like compact city planning. We explore whether environmental injustice exists in a city where one would least expect to find it: a city with abundant nature, an affluent population governed by a left leaning social democratic city council, and an aggressive densification strategy; Oslo, Norway. Green space was measured with a satellite-derived vegetation index which captures the combined availability of gardens, street trees, parks and forest. Blue space was defined by the proximity of residential areas to the closest lake, river or fjord. We found that poorer city districts, often with greater immigrant populations, have less available blue-green spaces and are disproportionately exposed to hazardous air pollution levels, but not extreme heat compared to wealthier city districts. Citizens living within 100 m of a water body are likely to earn US$ 20,000 more per year than citizens living 500 m away from water, and a US$ 3000 increase in annual income corresponds to a 10 % increase in green space availability. Hazardous air pollution concentrations in the poorest city districts were above levels recommended by the WHO and Oslo municipality. Historical trends showed that districts undergoing population densification coincide with the lowest availability of blue-green space, suggesting that environmental justice has been overlooked in compact city planning policy. Despite Oslo's affluence and egalitarian ideals, the patterns of inequality we observed mirror the city's historical east-west class divide and point to spatial concentration of wealth as a core factor to consider in studies of green segregation. Urban greening initiatives in Oslo and other cities should not take spatial equality for granted, and instead consider socio-economic geographies in their planning process.
MeSH term(s)	Hot Temperature ; Environmental Justice ; Cities ; Air Pollution ; Water
Chemical Substances	Water (059QF0KO0R)
Language	English
Publishing date	2022-11-13
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	121506-1
ISSN	1879-1026 ; 0048-9697
ISSN (online)	1879-1026
ISSN	0048-9697
DOI	10.1016/j.scitotenv.2022.160193
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Article ; Online: Continental-Scale Land Cover Mapping at 10 m Resolution Over Europe (ELC10)

Zander S. Venter / Markus A. K. Sydenham

Remote Sensing, Vol 13, Iss 2301, p

2021 Volume 2301

Abstract: Land cover maps are important tools for quantifying the human footprint on the environment and facilitate reporting and accounting to international agreements addressing the Sustainable Development Goals. Widely used European land cover maps such as ... ...

Abstract	Land cover maps are important tools for quantifying the human footprint on the environment and facilitate reporting and accounting to international agreements addressing the Sustainable Development Goals. Widely used European land cover maps such as CORINE (Coordination of Information on the Environment) are produced at medium spatial resolutions (100 m) and rely on diverse data with complex workflows requiring significant institutional capacity. We present a 10 m resolution land cover map (ELC10) of Europe based on a satellite-driven machine learning workflow that is annually updatable. A random forest classification model was trained on 70K ground-truth points from the LUCAS (Land Use/Cover Area Frame Survey) dataset. Within the Google Earth Engine cloud computing environment, the ELC10 map can be generated from approx. 700 TB of Sentinel imagery within approx. 4 days from a single research user account. The map achieved an overall accuracy of 90% across eight land cover classes and could account for statistical unit land cover proportions within 3.9% (R 2 = 0.83) of the actual value. These accuracies are higher than that of CORINE (100 m) and other 10 m land cover maps including S2GLC and FROM-GLC10. Spectro-temporal metrics that capture the phenology of land cover classes were most important in producing high mapping accuracies. We found that the atmospheric correction of Sentinel-2 and the speckle filtering of Sentinel-1 imagery had a minimal effect on enhancing the classification accuracy (<1%). However, combining optical and radar imagery increased accuracy by 3% compared to Sentinel-2 alone and by 10% compared to Sentinel-1 alone. The addition of auxiliary data (terrain, climate and night-time lights) increased accuracy by an additional 2%. By using the centroid pixels from the LUCAS Copernicus module polygons we increased accuracy by <1%, revealing that random forests are robust against contaminated training data. Furthermore, the model requires very little training data to achieve moderate ...
Keywords	machine learning ; land use ; CORINE ; Sentinel-1 ; sar ; Sentinel-2 ; Science ; Q
Subject code	910
Language	English
Publishing date	2021-06-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Continental-Scale Land Cover Mapping at 10 m Resolution Over Europe (ELC10)

Venter, Zander S. / Sydenham, Markus A. K.

Remote Sensing. 2021 June 11, v. 13, no. 12

2021

Abstract	Land cover maps are important tools for quantifying the human footprint on the environment and facilitate reporting and accounting to international agreements addressing the Sustainable Development Goals. Widely used European land cover maps such as CORINE (Coordination of Information on the Environment) are produced at medium spatial resolutions (100 m) and rely on diverse data with complex workflows requiring significant institutional capacity. We present a 10 m resolution land cover map (ELC10) of Europe based on a satellite-driven machine learning workflow that is annually updatable. A random forest classification model was trained on 70K ground-truth points from the LUCAS (Land Use/Cover Area Frame Survey) dataset. Within the Google Earth Engine cloud computing environment, the ELC10 map can be generated from approx. 700 TB of Sentinel imagery within approx. 4 days from a single research user account. The map achieved an overall accuracy of 90% across eight land cover classes and could account for statistical unit land cover proportions within 3.9% (R² = 0.83) of the actual value. These accuracies are higher than that of CORINE (100 m) and other 10 m land cover maps including S2GLC and FROM-GLC10. Spectro-temporal metrics that capture the phenology of land cover classes were most important in producing high mapping accuracies. We found that the atmospheric correction of Sentinel-2 and the speckle filtering of Sentinel-1 imagery had a minimal effect on enhancing the classification accuracy (<1%). However, combining optical and radar imagery increased accuracy by 3% compared to Sentinel-2 alone and by 10% compared to Sentinel-1 alone. The addition of auxiliary data (terrain, climate and night-time lights) increased accuracy by an additional 2%. By using the centroid pixels from the LUCAS Copernicus module polygons we increased accuracy by <1%, revealing that random forests are robust against contaminated training data. Furthermore, the model requires very little training data to achieve moderate accuracies—the difference between 5K and 50K LUCAS points is only 3% (86% vs. 89%). This implies that significantly less resources are necessary for making in situ survey data (such as LUCAS) suitable for satellite-based land cover classification. At 10 m resolution, the ELC10 map can distinguish detailed landscape features like hedgerows and gardens, and therefore holds potential for aerial statistics at the city borough level and monitoring property-level environmental interventions (e.g., tree planting). Due to the reliance on purely satellite-based input data, the ELC10 map can be continuously updated independent of any country-specific geographic datasets.
Keywords	Internet ; climate ; data collection ; humans ; land cover ; land use ; landscapes ; models ; phenology ; radar ; satellites ; statistics ; surveys ; sustainable development ; trees ; Europe
Language	English
Dates of publication	2021-0611
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
ZDB-ID	2513863-7
ISSN	2072-4292
ISSN	2072-4292
DOI	10.3390/rs13122301
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Mapping soil organic carbon stocks and trends with satellite-driven high resolution maps over South Africa.

Venter, Zander S / Hawkins, Heidi-Jayne / Cramer, Michael D / Mills, Anthony J

The Science of the total environment

2021 Volume 771, Page(s) 145384

Abstract: Estimation and monitoring of soil organic carbon (SOC) stocks is important for maintaining soil productivity and meeting climate change mitigation targets. Current global SOC maps do not provide enough detail for landscape-scale decision making, and do ... ...

Abstract	Estimation and monitoring of soil organic carbon (SOC) stocks is important for maintaining soil productivity and meeting climate change mitigation targets. Current global SOC maps do not provide enough detail for landscape-scale decision making, and do not allow for tracking carbon sequestration or loss over time. Using an optical satellite-driven machine learning workflow, we mapped SOC stocks (topsoil; 0 to 30 cm) under natural vegetation (86% of land area) over South Africa at 30 m spatial resolution between 1984 and 2019. We estimate a total topsoil SOC stock of 5.6 Pg C with a median SOC density of 6 kg C m
Language	English
Publishing date	2021-01-27
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	121506-1
ISSN	1879-1026 ; 0048-9697
ISSN (online)	1879-1026
ISSN	0048-9697
DOI	10.1016/j.scitotenv.2021.145384
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Article ; Online: Is green space associated with reduced crime? A national-scale study from the Global South.

Venter, Zander S / Shackleton, Charlie / Faull, Andrew / Lancaster, Lizette / Breetzke, Gregory / Edelstein, Ian

The Science of the total environment

2022 Volume 825, Page(s) 154005

Abstract: Assumptions about the link between green space and crime mitigation are informed by literature that overwhelmingly originates in the Global North. Little is known about the association between green spaces and crime in the Global South. We utilized 10 ... ...

Abstract	Assumptions about the link between green space and crime mitigation are informed by literature that overwhelmingly originates in the Global North. Little is known about the association between green spaces and crime in the Global South. We utilized 10 years of precinct-level crime statistics (n = 1152) over South Africa, a global crime hotspot, to test the hypothesis that green space is associated with reduced crime rates. We found that, after controlling for a number of socio-demographic confounders (unemployment, income, age, education, land use and population density), for every 1% increase in total green space there is a 1.2% (0.7 to 1.7%; 95% confidence interval) decrease in violent crime, and 1.3% (0.8 to 1.8%) decrease in property crime, with no effect on sexual crimes. However, the direction of the association changed for property crimes when exploring the effect of green space characteristics including tree cover and park accessibility. Property crimes increase by 0.4% (0.1 to 0.7%) with a percentage increase in tree cover, and by 0.9% (0.5 to 1.3%) with every kilometer increase in proximity to a public park. Further research, including experimental studies, is needed to better isolate causal mechanisms behind crime-green space associations, especially considering that green space may map to race and income inequality and that there may be more crime reporting in affluent areas. Nevertheless, our results provide a complementary contribution to the evidence from the Global North, highlighting the need for more nuanced definitions of green space and its characteristics when considering links to crime. When viewed in light of the broader suite of ecosystem services provided by green space, our results support urban greening as a major strategy towards achieving just and sustainable cities and towns.
MeSH term(s)	Cities ; Crime ; Ecosystem ; Parks, Recreational ; Trees
Language	English
Publishing date	2022-02-19
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	121506-1
ISSN	1879-1026 ; 0048-9697
ISSN (online)	1879-1026
ISSN	0048-9697
DOI	10.1016/j.scitotenv.2022.154005
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Article: Is green space associated with reduced crime? A national-scale study from the Global South

Venter, Zander S. / Shackleton, Charlie / Faull, Andrew / Lancaster, Lizette / Breetzke, Gregory / Edelstein, Ian

Science of the total environment. 2022 June 15, v. 825

2022

Abstract	Assumptions about the link between green space and crime mitigation are informed by literature that overwhelmingly originates in the Global North. Little is known about the association between green spaces and crime in the Global South. We utilized 10 years of precinct-level crime statistics (n = 1152) over South Africa, a global crime hotspot, to test the hypothesis that green space is associated with reduced crime rates. We found that, after controlling for a number of socio-demographic confounders (unemployment, income, age, education, land use and population density), for every 1% increase in total green space there is a 1.2% (0.7 to 1.7%; 95% confidence interval) decrease in violent crime, and 1.3% (0.8 to 1.8%) decrease in property crime, with no effect on sexual crimes. However, the direction of the association changed for property crimes when exploring the effect of green space characteristics including tree cover and park accessibility. Property crimes increase by 0.4% (0.1 to 0.7%) with a percentage increase in tree cover, and by 0.9% (0.5 to 1.3%) with every kilometer increase in proximity to a public park. Further research, including experimental studies, is needed to better isolate causal mechanisms behind crime-green space associations, especially considering that green space may map to race and income inequality and that there may be more crime reporting in affluent areas. Nevertheless, our results provide a complementary contribution to the evidence from the Global North, highlighting the need for more nuanced definitions of green space and its characteristics when considering links to crime. When viewed in light of the broader suite of ecosystem services provided by green space, our results support urban greening as a major strategy towards achieving just and sustainable cities and towns.
Keywords	confidence interval ; crime ; ecosystems ; education ; environment ; green infrastructure ; income ; land use ; population density ; public parks ; social inequality ; trees ; unemployment ; South Africa
Language	English
Dates of publication	2022-0615
Publishing place	Elsevier B.V.
Document type	Article
ZDB-ID	121506-1
ISSN	1879-1026 ; 0048-9697
ISSN (online)	1879-1026
ISSN	0048-9697
DOI	10.1016/j.scitotenv.2022.154005
Database	NAL-Catalogue (AGRICOLA)

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Article: Global 10 m Land Use Land Cover Datasets: A Comparison of Dynamic World, World Cover and Esri Land Cover

Venter, Zander S. / Barton, David N. / Chakraborty, Tirthankar / Simensen, Trond / Singh, Geethen

Remote Sensing. 2022 Aug. 21, v. 14, no. 16

2022

Abstract: The European Space Agency’s Sentinel satellites have laid the foundation for global land use ... accuracy assessment of Google’s Dynamic World (DW), ESA’s World Cover (WC) and Esri’s Land Cover (Esri ...

Abstract	The European Space Agency’s Sentinel satellites have laid the foundation for global land use land cover (LULC) mapping with unprecedented detail at 10 m resolution. We present a cross-comparison and accuracy assessment of Google’s Dynamic World (DW), ESA’s World Cover (WC) and Esri’s Land Cover (Esri) products for the first time in order to inform the adoption and application of these maps going forward. For the year 2020, the three global LULC maps show strong spatial correspondence (i.e., near-equal area estimates) for water, built area, trees and crop LULC classes. However, relative to one another, WC is biased towards over-estimating grass cover, Esri towards shrub and scrub cover and DW towards snow and ice. Using global ground truth data with a minimum mapping unit of 250 m², we found that Esri had the highest overall accuracy (75%) compared to DW (72%) and WC (65%). Across all global maps, water was the most accurately mapped class (92%), followed by built area (83%), tree cover (81%) and crops (78%), particularly in biomes characterized by temperate and boreal forests. The classes with the lowest accuracies, particularly in the tundra biome, included shrub and scrub (47%), grass (34%), bare ground (57%) and flooded vegetation (53%). When using European ground truth data from LUCAS (Land Use/Cover Area Frame Survey) with a minimum mapping unit of <100 m², we found that WC had the highest accuracy (71%) compared to DW (66%) and Esri (63%), highlighting the ability of WC to resolve landscape elements with more detail compared to DW and Esri. Although not analyzed in our study, we discuss the relative advantages of DW due to its frequent and near real-time data delivery of both categorical predictions and class probability scores. We recommend that the use of global LULC products should involve critical evaluation of their suitability with respect to the application purpose, such as aggregate changes in ecosystem accounting versus site-specific change detection in monitoring, considering trade-offs between thematic resolution, global versus. local accuracy, class-specific biases and whether change analysis is necessary. We also emphasize the importance of not estimating areas from pixel-counting alone but adopting best practices in design-based inference and area estimation that quantify uncertainty for a given study area.
Keywords	data collection ; ecosystems ; grasses ; ice ; land cover ; land use ; land use and land cover maps ; landscapes ; shrublands ; shrubs ; snow ; surveys ; trees ; tundra ; uncertainty
Language	English
Dates of publication	2022-0821
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
ZDB-ID	2513863-7
ISSN	2072-4292
ISSN	2072-4292
DOI	10.3390/rs14164101
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Air pollution declines during COVID-19 lockdowns mitigate the global health burden.

Venter, Zander S / Aunan, Kristin / Chowdhury, Sourangsu / Lelieveld, Jos

Environmental research

2020 Volume 192, Page(s) 110403

Abstract: The lockdown response to COVID-19 has resulted in an unprecedented reduction in global economic activity and associated air pollutant levels, especially from a decline in land transportation. We utilized a network of >10,000 air quality stations ... ...

Abstract	The lockdown response to COVID-19 has resulted in an unprecedented reduction in global economic activity and associated air pollutant levels, especially from a decline in land transportation. We utilized a network of >10,000 air quality stations distributed over 34 countries during lockdown dates up until 15 May 2020 to obtain lockdown related anomalies for nitrogen dioxide, ozone and particulate matter smaller than 2.5 μm in diameter (PM
MeSH term(s)	Air Pollutants/analysis ; Air Pollution/analysis ; Air Pollution/prevention & control ; COVID-19 ; Child ; China/epidemiology ; Global Health ; Humans ; India ; Pandemics ; Particulate Matter/analysis ; SARS-CoV-2
Chemical Substances	Air Pollutants ; Particulate Matter
Keywords	covid19
Language	English
Publishing date	2020-11-02
Publishing country	Netherlands
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	205699-9
ISSN	1096-0953 ; 0013-9351
ISSN (online)	1096-0953
ISSN	0013-9351
DOI	10.1016/j.envres.2020.110403
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