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Li J, Xie Y, Xu J, Zhang C, Wang H, Huang D, Li G, Tian J. Association between greenspace and cancer: evidence from a systematic review and meta-analysis of multiple large cohort studies. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:91140-91157. [PMID: 37474858 DOI: 10.1007/s11356-023-28461-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 06/23/2023] [Indexed: 07/22/2023]
Abstract
Cancer is a chronic disease that seriously endangers human health, and studies on its association with greenspace have been published. We aimed to systematically review the epidemiological evidence and obtain the best available evidence. PubMed, Web of Science, Embase, and Cochrane Library were used as search databases, the time limit was September 12, 2022, and the cited articles were manually supplemented. Two researchers independently performed literature screening and data extraction. We performed a meta-analysis of data using a normalized difference vegetation index (NDVI) as the greenspace measure, providing hazard ratio (HR) and corresponding 95% CI. After standardization of the data, we used a random effects model for pooling. We also assessed the risk of bias for each study and the quality of each evidence body. We identified 10,108 items and included 14 studies from 11 institutions in eight countries. All studies had a low risk of bias. Quantitative analysis of 13 studies found a beneficial association of greenspace with the mortality of lung cancer (pooled HR [95% CI]=0.965 [0.947, 0.983]) and prostate cancer (HR [95% CI]=0.939 [0.898, 0.980]) based on 0.1-unit NDVI increment and a potential beneficial association with the incidence of prostate, lung, and breast cancer. Greenspace had opposite associations with cancer mortality for urban and rural populations. Indirect comparisons did not find statistically significant differences in the effects of greenspace on different cancer outcomes. The evidence body assessment was considered to be "very low." This review indicated potential beneficial associations between greenspace for lung, prostate, and breast cancer outcomes. However, there was a lack of mediation analysis to explore the underlying mechanism of a causal association. Meanwhile, the interstudy heterogeneity was large. Therefore, future studies should consider more accurate exposure assessment and more comprehensive covariate coverage, while focusing on mediating analysis. PROSPERO: CRD42022361068.
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Affiliation(s)
- Jiang Li
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Yafei Xie
- Key Laboratory of Evidence Based Medicine and Knowledge Translation of Gansu Province, Lanzhou University, Lanzhou, 730000, China
- The First Clinical Medical College of Lanzhou University, Lanzhou, 730000, China
| | - Jianguo Xu
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, 730000, China
| | - Chun Zhang
- Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China
| | - Huilin Wang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Danqi Huang
- National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Guoqiang Li
- Key Laboratory of Western China's Environmental Systems (Ministry of Education), College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China
| | - Jinhui Tian
- Key Laboratory of Evidence Based Medicine and Knowledge Translation of Gansu Province, Lanzhou University, Lanzhou, 730000, China.
- Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou, 730000, China.
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Karanja J, Wanyama D, Kiage L. Weighting mechanics and the spatial pattern of composite metrics of heat vulnerability in Atlanta, Georgia, USA. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 812:151432. [PMID: 34748844 DOI: 10.1016/j.scitotenv.2021.151432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 10/20/2021] [Accepted: 10/31/2021] [Indexed: 06/13/2023]
Abstract
This study constructs two biophysical metrics; one based on Land Surface Temperatures (LST) and an integrated spectral index. The latter is an aggregate of Normalized Difference Vegetation Index (NDVI), Normalized Difference Bareness Index (NDBaI), Normalized Difference Water Index (NDWI), and Normalized Difference Built-up Index (NDBI). The goal is to determine how disparate weighting techniques, data transformation approaches, and spatial visualization pathways influence the computation of composite heat metrics. Using composite images made of aggregated images from late May to Early September within Google Earth Engine, we generated four composites by combining biophysical metrics with SoVI using equal and Eigen-based weightings informed by Principal Component Analysis (PCA). We compared equal interval classification, global and local Moran's as pathways for spatial visualization of hotspots. We utilized several data transformation techniques in a Geographic Information System (GIS), including rescaling, reclassification, zonal statistics, and spatial weighting. Mann Kendall and Sen's Slope detected and quantified monotonic trends in each spectral index. The results show that the LST biophysical metric and its composites indicate increased heat susceptibility over time, with disproportionately exposed core metro counties. The integrated spectral index and its proxies showed reduced vulnerability hence not a good proxy for LST. At the same time, the Mann Kendall and Sen's Slope found persistent increases in NDVI and NDWI and decreases in NDBI and NDBaI. However, opposite trends were evident in core city counties. The LST-based composites and spectral indices-based composites varied in the spatial-temporal distribution of hotspots. Disparate weighting mechanics, data transformation techniques, and visualization alternatives influence the magnitude and spatial-temporal distribution of heat hotspots.
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Affiliation(s)
- Joseph Karanja
- Department of Geosciences, Georgia State University, 34 Peachtree Center Avenue, Atlanta, GA 30302, USA
| | - Dan Wanyama
- Department of Geography, Environment and, Spatial Sciences, Michigan State University, East Lansing, MI, USA; Remote Sensing and GIS Research and Outreach Services, Michigan State University, East Lansing, MI, USA
| | - Lawrence Kiage
- Department of Geosciences, Georgia State University, 34 Peachtree Center Avenue, Atlanta, GA 30302, USA.
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Gutiérrez‐Avila I, Arfer KB, Wong S, Rush J, Kloog I, Just AC. A spatiotemporal reconstruction of daily ambient temperature using satellite data in the Megalopolis of Central Mexico from 2003 to 2019. INTERNATIONAL JOURNAL OF CLIMATOLOGY : A JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY 2021; 41:4095-4111. [PMID: 34248276 PMCID: PMC8251982 DOI: 10.1002/joc.7060] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 01/31/2021] [Accepted: 02/13/2021] [Indexed: 05/05/2023]
Abstract
While weather stations generally capture near-surface ambient air temperature (Ta) at a high temporal resolution to calculate daily values (i.e., daily minimum, mean, and maximum Ta), their fixed locations can limit their spatial coverage and resolution even in densely populated urban areas. As a result, data from weather stations alone may be inadequate for Ta-related epidemiology particularly when the stations are not located in the areas of interest for human exposure assessment. To address this limitation in the Megalopolis of Central Mexico (MCM), we developed the first spatiotemporally resolved hybrid satellite-based land use regression Ta model for the region, home to nearly 30 million people and includes Mexico City and seven more metropolitan areas. Our model predicted daily minimum, mean, and maximum Ta for the years 2003-2019. We used data from 120 weather stations and Land Surface Temperature (LST) data from NASA's MODIS instruments on the Aqua and Terra satellites on a 1 × 1 km grid. We generated a satellite-hybrid mixed-effects model for each year, regressing Ta measurements against land use terms, day-specific random intercepts, and fixed and random LST slopes. We assessed model performance using 10-fold cross-validation at withheld stations. Across all years, the root-mean-square error ranged from 0.92 to 1.92 K and the R 2 ranged from .78 to .95. To demonstrate the utility of our model for health research, we evaluated the total number of days in the year 2010 when residents ≥65 years old were exposed to Ta extremes (above 30°C or below 5°C). Our model provides much needed high-quality Ta estimates for epidemiology studies in the MCM region.
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Affiliation(s)
- Iván Gutiérrez‐Avila
- Department of Environmental Medicine and Public HealthIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Kodi B. Arfer
- Department of Environmental Medicine and Public HealthIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Sandy Wong
- Department of GeographyFlorida State University (FSU)TallahasseeFloridaUSA
| | - Johnathan Rush
- Department of Environmental Medicine and Public HealthIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
| | - Itai Kloog
- Department of Geography and Environmental DevelopmentBen‐Gurion University of the NegevBeershebaIsrael
| | - Allan C. Just
- Department of Environmental Medicine and Public HealthIcahn School of Medicine at Mount SinaiNew YorkNew YorkUSA
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Urban Spatial Patterns and Heat Exposure in the Mediterranean City of Tel Aviv. ATMOSPHERE 2020. [DOI: 10.3390/atmos11090963] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
This study aims to examine the effect of urban spatial patterns on heat exposure in the city of Tel Aviv using multiple methodologies, Local Climate Zones (LCZ), meteorological measurements, and remote sensing. A Local Climate Zone map of Tel Aviv was created using Geographic Information System (GIS), and satellite images were used to identify the spatial patterns of the urban heat island (UHI). Climatic variables were measured by fixed meteorological stations and by mobile cross-section. Surface and wall temperatures were obtained by satellite images and a hand-held infrared camera. Meteorological measurements at a height of 2 m showed that during midday the city is ~3.6 °C warmer than the surrounding rural area. The cooling effect of parks was evident only during the hot hours of the day (9:00–17:00). Land Surface Temperature in the southern part of the city was hotter by ~7–9 °C compared to the northern part due to lack of urban vegetation. Hot spots were found in compact midrise forms (LCZ 2) that are not ideal from the climatological perspective. Whereas compact low-rise forms (LCZ 3) were less heat vulnerable. The results of this study suggest that climatologists can provide planners and architects with scientific insight into the causes of and solutions for urban climatic heat exposure.
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Hough I, Just AC, Zhou B, Dorman M, Lepeule J, Kloog I. A multi-resolution air temperature model for France from MODIS and Landsat thermal data. ENVIRONMENTAL RESEARCH 2020; 183:109244. [PMID: 32097815 PMCID: PMC7167357 DOI: 10.1016/j.envres.2020.109244] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2019] [Revised: 01/17/2020] [Accepted: 02/07/2020] [Indexed: 06/10/2023]
Abstract
Understanding and managing the health effects of ambient temperature (Ta) in a warming, urbanizing world requires spatially- and temporally-resolved Ta at high resolutions. This is challenging in a large area like France which includes highly variable topography, rural areas with few weather stations, and heterogeneous urban areas where Ta can vary at fine spatial scales. We have modeled daily Ta from 2000 to 2016 at a base resolution of 1 km2 across continental France and at a 200 × 200 m2 resolution over large urban areas. For each day we predict three Ta measures: minimum (Tmin), mean (Tmean), and maximum (Tmax). We start by using linear mixed models to calibrate daily Ta observations from weather stations with remotely sensed MODIS land surface temperature (LST) and other spatial predictors (e.g. NDVI, elevation) on a 1 km2 grid. We fill gaps where LST is missing (e.g. due to cloud cover) with additional mixed models that capture the relationship between predicted Ta at each location and observed Ta at nearby weather stations. The resulting 1 km Ta models perform very well, with ten-fold cross-validated R2 of 0.92, 0.97, and 0.95, mean absolute error (MAE) of 1.4 °C, 0.9 °C, and 1.4 °C, and root mean square error (RMSE) of 1.9 °C, 1.3 °C, and 1.8 °C (Tmin, Tmean, and Tmax, respectively) for the initial calibration stage. To increase the spatial resolution over large urban areas, we train random forest and extreme gradient boosting models to predict the residuals (R) of the 1 km Ta predictions on a 200 × 200 m2 grid. In this stage we replace MODIS LST and NDVI with composited top-of-atmosphere brightness temperature and NDVI from the Landsat 5, 7, and 8 satellites. We use a generalized additive model to ensemble the random forest and extreme gradient boosting predictions with weights that vary spatially and by the magnitude of the predicted residual. The 200 m models also perform well, with ten-fold cross-validated R2 of 0.79, 0.79, and 0.85, MAE of 0.4, 0.3, and 0.3, and RMSE of 0.6, 0.4, and 0.5 (Rmin, Rmean, and Rmax, respectively). Our model will reduce bias in epidemiological studies in France by improving Ta exposure assessment in both urban and rural areas, and our methodology demonstrates that MODIS and Landsat thermal data can be used to generate gap-free timeseries of daily minimum, maximum, and mean Ta at a 200 × 200 m2 spatial resolution.
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Affiliation(s)
- Ian Hough
- Univ. Grenoble Alpes, Inserm, CNRS, IAB, Site Sante, Allée des Alpes, 38700, La Tronche, France; Department of Geography and Environmental Development, Ben-Gurion University of the Negev, P.O.B. 653, Be'er Sheva, Israel.
| | - Allan C Just
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Place, New York, NY 10029-5674, USA
| | - Bin Zhou
- Department of Geography and Environmental Development, Ben-Gurion University of the Negev, P.O.B. 653, Be'er Sheva, Israel
| | - Michael Dorman
- Department of Geography and Environmental Development, Ben-Gurion University of the Negev, P.O.B. 653, Be'er Sheva, Israel
| | - Johanna Lepeule
- Univ. Grenoble Alpes, Inserm, CNRS, IAB, Site Sante, Allée des Alpes, 38700, La Tronche, France
| | - Itai Kloog
- Department of Geography and Environmental Development, Ben-Gurion University of the Negev, P.O.B. 653, Be'er Sheva, Israel
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The Effect of NDVI Time Series Density Derived from Spatiotemporal Fusion of Multisource Remote Sensing Data on Crop Classification Accuracy. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2019. [DOI: 10.3390/ijgi8110502] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Remote sensing data with high spatial and temporal resolutions can help to improve the accuracy of the estimation of crop planting acreage, and contribute to the formulation and management of agricultural policies. Therefore, it is important to determine whether multisource sensors can obtain high spatial and temporal resolution remote sensing data for the target sensor with the help of the spatiotemporal fusion method. In this study, we employed three different sensor datasets to obtain one normalized difference vegetation index (NDVI) time series dataset with a 5.8-m spatial resolution using a spatial and temporal adaptive reflectance fusion model (STARFM). We studied the effectiveness of using multisource remote sensing data to extract crop classifications and analyzed whether the increase in the NDVI time series density could significantly improve the accuracy of the crop classification. The results indicated that multisource sensor data could be used for crop classification after spatiotemporal fusion and that the data source was not limited by the sensor platform. With the increase in the number of NDVI phases, the classification accuracy of the support vector machine (SVM) and the random forest (RF) classifier gradually improved. If the added NDVI phases were not in the optimal time period for wheat recognition, the classification accuracy was not greatly improved. Under the same conditions, the classification accuracy of the RF classifier was higher than that of the SVM. In addition, this study can serve as a good reference for the selection of the optimal time range for base image pairs in the spatiotemporal fusion method for high accuracy mapping of crops, and help avoid excessive data collection and processing.
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Abstract
PURPOSE OF REVIEW Low, high, extreme, and variable temperatures have been linked to multiple adverse health outcomes, particularly among the elderly and children. Recent models incorporating satellite remote sensing data have mitigated several limitations of previous studies, improving exposure assessment. This review focuses on these new temperature exposure models and their application in epidemiological studies. RECENT FINDINGS Satellite observations of land surface temperature have been used to model air temperature across large spatial areas at high spatiotemporal resolutions. These models enable exposure assessment of entire populations and have been shown to reduce error in exposure estimates, thus mitigating downward bias in health effect estimates. SUMMARY Satellite-based models improve our understanding of spatiotemporal variation in temperature and the associated health effects. Further research should focus on improving the resolution of these models, especially in urban areas, and increasing their use in epidemiological studies of direct temperature exposure and vector-borne diseases.
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A Bayesian Kriging Regression Method to Estimate Air Temperature Using Remote Sensing Data. REMOTE SENSING 2019. [DOI: 10.3390/rs11070767] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Surface air temperature (Ta) is an important physical quantity, usually measured at ground weather station networks. Measured Ta data is inadequate to characterize the complex spatial patterns of Ta field due to low density and unevenness of the networks. Remote sensing can provide satellite imagery with large scale spatial coverage and fine resolution. Estimating spatially continuous Ta by integrating ground measurements and satellite data is an active research area. A variety of methods have been proposed and applied in this area. However, the existing studies primarily focused on daily Ta and failed to quantify uncertainties in model parameter and estimated results. In this paper, a Bayesian Kriging regression (BKR) method is proposed to model and estimate monthly Ta using satellite-derived land surface temperature (LST) as the only input. The BKR is a spatial statistical model with the capacity to quantify uncertainties via Bayesian inference. The BKR method was applied to estimate monthly maximum air temperature (Tmax) and minimum air temperature (Tmin) over the conterminous United States in 2015. An exploratory analysis shows a strong relationship between LST and Ta at the monthly scale, indicating LST has the great potential to estimate monthly Ta. 10-fold cross-validation approach was adopted to compare the predictive performance of the BKR method with the linear regression method over the whole region and the urban areas of the contiguous United States. For the whole region, the results show that the BKR method achieves a competitively better performance with averaged RMSE values 1 . 23 K for Tmax and 1 . 20 K for Tmin, which are also lower than previous studies on estimation of monthly Ta. In the urban areas, the cross-validation demonstrates similar results with averaged RMSE values 1 . 21 K for Tmax and 1 . 27 K for Tmin. Posterior samples for model parameters and estimated Ta were obtained and used to analyze uncertainties in the model parameters and estimated Ta. The BKR method provides a promising way to estimate Ta with competitively predictive performance and to quantify model uncertainties at the same time.
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Rosenfeld A, Dorman M, Schwartz J, Novack V, Just AC, Kloog I. Estimating daily minimum, maximum, and mean near surface air temperature using hybrid satellite models across Israel. ENVIRONMENTAL RESEARCH 2017; 159:297-312. [PMID: 28837902 DOI: 10.1016/j.envres.2017.08.017] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2017] [Revised: 08/07/2017] [Accepted: 08/08/2017] [Indexed: 05/21/2023]
Abstract
Meteorological stations measure air temperature (Ta) accurately with high temporal resolution, but usually suffer from limited spatial resolution due to their sparse distribution across rural, undeveloped or less populated areas. Remote sensing satellite-based measurements provide daily surface temperature (Ts) data in high spatial and temporal resolution and can improve the estimation of daily Ta. In this study we developed spatiotemporally resolved models which allow us to predict three daily parameters: Ta Max (day time), 24h mean, and Ta Min (night time) on a fine 1km grid across the state of Israel. We used and compared both the Aqua and Terra MODIS satellites. We used linear mixed effect models, IDW (inverse distance weighted) interpolations and thin plate splines (using a smooth nonparametric function of longitude and latitude) to first calibrate between Ts and Ta in those locations where we have available data for both and used that calibration to fill in neighboring cells without surface monitors or missing Ts. Out-of-sample ten-fold cross validation (CV) was used to quantify the accuracy of our predictions. Our model performance was excellent for both days with and without available Ts observations for both Aqua and Terra (CV Aqua R2 results for min 0.966, mean 0.986, and max 0.967; CV Terra R2 results for min 0.965, mean 0.987, and max 0.968). Our research shows that daily min, mean and max Ta can be reliably predicted using daily MODIS Ts data even across Israel, with high accuracy even for days without Ta or Ts data. These predictions can be used as three separate Ta exposures in epidemiology studies for better diurnal exposure assessment.
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Affiliation(s)
- Adar Rosenfeld
- Department of Geography and Environmental Development, Ben-Gurion University of the Negev, P.O.B. 653, Beer Sheva, Israel
| | - Michael Dorman
- Department of Geography and Environmental Development, Ben-Gurion University of the Negev, P.O.B. 653, Beer Sheva, Israel
| | - Joel Schwartz
- Department of Environmental Health, Harvard T. H. Chan School of Public Health, Cambridge, MA, USA
| | - Victor Novack
- Clinical Research Center, Soroka University Medical Center, Beer Sheva, Israel
| | - Allan C Just
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Itai Kloog
- Department of Geography and Environmental Development, Ben-Gurion University of the Negev, P.O.B. 653, Beer Sheva, Israel.
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Markevych I, Schoierer J, Hartig T, Chudnovsky A, Hystad P, Dzhambov AM, de Vries S, Triguero-Mas M, Brauer M, Nieuwenhuijsen MJ, Lupp G, Richardson EA, Astell-Burt T, Dimitrova D, Feng X, Sadeh M, Standl M, Heinrich J, Fuertes E. Exploring pathways linking greenspace to health: Theoretical and methodological guidance. ENVIRONMENTAL RESEARCH 2017; 158:301-317. [PMID: 28672128 DOI: 10.1016/j.envres.2017.06.028] [Citation(s) in RCA: 962] [Impact Index Per Article: 137.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/16/2017] [Revised: 06/22/2017] [Accepted: 06/23/2017] [Indexed: 05/18/2023]
Abstract
BACKGROUND In a rapidly urbanizing world, many people have little contact with natural environments, which may affect health and well-being. Existing reviews generally conclude that residential greenspace is beneficial to health. However, the processes generating these benefits and how they can be best promoted remain unclear. OBJECTIVES During an Expert Workshop held in September 2016, the evidence linking greenspace and health was reviewed from a transdisciplinary standpoint, with a particular focus on potential underlying biopsychosocial pathways and how these can be explored and organized to support policy-relevant population health research. DISCUSSIONS Potential pathways linking greenspace to health are here presented in three domains, which emphasize three general functions of greenspace: reducing harm (e.g. reducing exposure to air pollution, noise and heat), restoring capacities (e.g. attention restoration and physiological stress recovery) and building capacities (e.g. encouraging physical activity and facilitating social cohesion). Interrelations between among the three domains are also noted. Among several recommendations, future studies should: use greenspace and behavioural measures that are relevant to hypothesized pathways; include assessment of presence, access and use of greenspace; use longitudinal, interventional and (quasi)experimental study designs to assess causation; and include low and middle income countries given their absence in the existing literature. Cultural, climatic, geographic and other contextual factors also need further consideration. CONCLUSIONS While the existing evidence affirms beneficial impacts of greenspace on health, much remains to be learned about the specific pathways and functional form of such relationships, and how these may vary by context, population groups and health outcomes. This Report provides guidance for further epidemiological research with the goal of creating new evidence upon which to develop policy recommendations.
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Affiliation(s)
- Iana Markevych
- Institute for Occupational, Social, and Environmental Medicine, Ludwig-Maximilians-University of Munich, Munich, Germany; Institute of Epidemiology I, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany.
| | - Julia Schoierer
- Institute for Occupational, Social, and Environmental Medicine, Ludwig-Maximilians-University of Munich, Munich, Germany
| | - Terry Hartig
- Institute for Housing and Urban Research, Uppsala University, Uppsala, Sweden
| | - Alexandra Chudnovsky
- AIRO Lab, Department of Geography and Human Environment, School of Geosciences, Tel-Aviv University, Tel-Aviv, Israel
| | - Perry Hystad
- College of Public Health and Human Sciences, Oregon State University, Corvallis, OR, USA
| | - Angel M Dzhambov
- Department of Hygiene and Ecomedicine, Faculty of Public Health, Medical University of Plovdiv, Plovdiv, Bulgaria
| | - Sjerp de Vries
- Wageningen University & Research, Environmental Research, Wageningen, The Netherlands
| | - Margarita Triguero-Mas
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
| | - Michael Brauer
- School of Population and Public Health, University of British Columbia, Vancouver, British Columbia, Canada
| | - Mark J Nieuwenhuijsen
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
| | - Gerd Lupp
- Strategic Landscape Planning and Management, Technical University of Munich, Munich, Germany
| | - Elizabeth A Richardson
- Centre for Research on Environment, Society and Health (CRESH), University of Edinburgh, Edinburgh, Scotland, UK
| | - Thomas Astell-Burt
- Population Wellbeing and Environment Research Lab (PowerLab), Faculty of Social Sciences, University of Wollongong, Wollongong, Australia; Early Start, University of Wollongong, Faculty of Social Sciences, University of Wollongong, Wollongong, Australia
| | - Donka Dimitrova
- Department of Health Management and Healthcare Economics, Faculty of Public Health, Medical University of Plovdiv, Plovdiv, Bulgaria
| | - Xiaoqi Feng
- Population Wellbeing and Environment Research Lab (PowerLab), Faculty of Social Sciences, University of Wollongong, Wollongong, Australia; Early Start, University of Wollongong, Faculty of Social Sciences, University of Wollongong, Wollongong, Australia
| | - Maya Sadeh
- School of Public Health, Tel-Aviv University, Tel-Aviv, Israel
| | - Marie Standl
- Institute of Epidemiology I, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Joachim Heinrich
- Institute for Occupational, Social, and Environmental Medicine, Ludwig-Maximilians-University of Munich, Munich, Germany; Institute of Epidemiology I, Helmholtz Zentrum München - German Research Center for Environmental Health, Neuherberg, Germany
| | - Elaine Fuertes
- ISGlobal, Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain; Universitat Pompeu Fabra (UPF), Barcelona, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Barcelona, Spain
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