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  1. Article ; Online: Book Review

    Michael Bräuer

    Raumforschung und Raumordnung, Vol 78, Iss

    Hönes, Ernst-Rainer (2018): Entstehung des städtebaulichen Denkmalschutzes Worms: Wernersche Verlagsgesellschaft. 901 Seiten

    2020  Volume 2

    Keywords Book Review ; Cities. Urban geography ; GF125 ; Urbanization. City and country ; HT361-384
    Language German
    Publishing date 2020-04-01T00:00:00Z
    Publisher oekom verlag GmbH
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Towards healthy school neighbourhoods

    Niloofar Shoari / Sean Beevers / Michael Brauer / Marta Blangiardo

    Environment International, Vol 165, Iss , Pp 107286- (2022)

    A baseline analysis in Greater London

    2022  

    Abstract: Creating healthy environments around schools is important to promote healthy childhood development and is a critical component of public health. In this paper we present a tool to characterize exposure to multiple urban environment features within 400 m ( ...

    Abstract Creating healthy environments around schools is important to promote healthy childhood development and is a critical component of public health. In this paper we present a tool to characterize exposure to multiple urban environment features within 400 m (5–10 min walking distance) of schools in Greater London. We modelled joint exposure to air pollution (NO2 and PM2.5), access to public greenspace, food environment, and road safety for 2,929 schools, employing a Bayesian non-parametric approach based on the Dirichlet Process Mixture modelling. We identified 12 latent clusters of schools with similar exposure profiles and observed some spatial clustering patterns. Socioeconomic and ethnicity disparities were manifested with respect to exposure profiles. Specifically, three clusters (containing 645 schools) showed the highest joint exposure to air pollution, poor food environment, and unsafe roads and were characterized with high deprivation. The neighbourhood of the most deprived cluster of schools had a median of 2.5 ha greenspace, 29.0 µg/m3 of NO2, 19.3 µg/m3 of PM2.5, 20 fast food retailers, and five child pedestrian crashes over a three-year period. The neighbourhood of the least deprived cluster of schools had a median of 21.8 ha greenspace, 15.6 µg/m3 of NO2, 15.1 µg/m3 of PM2.5, 2 fast food retailers, and one child pedestrian crash over a three-year period. To have a school-level understanding of exposure levels, we then benchmarked schools based on the probability of exceeding the median exposure to various features of interest. Our study accounts for multiple exposures, enabling us to highlight spatial distribution of exposure profile clusters, and to identify predominant exposure to urban environment features for each cluster of schools. Our findings can help relevant stakeholders, such as schools and public health authorities, to compare schools based on their exposure levels, prioritize interventions, and design local policies that target the schools most in need.
    Keywords Air quality ; Greenspace ; Food environment ; Pedestrian child crash ; School exposure ; Bayesian nonparametrics ; Environmental sciences ; GE1-350
    Subject code 380 ; 333
    Language English
    Publishing date 2022-07-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Assessing Trade-Offs and Optimal Ranges of Density for Life Expectancy and 12 Causes of Mortality in Metro Vancouver, Canada, 1990–2016

    Jessica Yu / Paul Gustafson / Martino Tran / Michael Brauer

    International Journal of Environmental Research and Public Health, Vol 19, Iss 2900, p

    2022  Volume 2900

    Abstract: Background: Understanding and managing the impacts of population growth and densification are important steps for sustainable development. This study sought to evaluate the health trade-offs associated with increasing densification and to identify the ... ...

    Abstract Background: Understanding and managing the impacts of population growth and densification are important steps for sustainable development. This study sought to evaluate the health trade-offs associated with increasing densification and to identify the optimal balance of neighbourhood densification for health. Methods: We linked population density with a 27-year mortality dataset in Metro Vancouver that includes census-tract levels of life expectancy (LE), cause-specific mortalities, and area-level deprivation. We applied two methods: (1) difference-in-differences (DID) models to study the impacts of densification changes from the early 1990s on changes in mortality over a 27-year period; and (2) smoothed cubic splines to identify thresholds of densification at which mortality rates accelerated. Results: At densities above ~9400 persons per km 2 , LE began to decrease more rapidly. By cause, densification was linked to decreased mortality for major causes of mortality in the region, such as cardiovascular diseases, neoplasms, and diabetes. Greater inequality with increasing density was observed for causes such as human immunodeficiency virus and acquired immunodeficiency syndrome (HIV/AIDS), sexually transmitted infections, and self-harm and interpersonal violence. Conclusions: Areas with higher population densities generally have lower rates of mortality from the major causes, but these environments are also associated with higher relative inequality from largely preventable causes of death.
    Keywords density ; mortality ; urban planning ; life expectancy ; cause-specific mortality ; urban health ; Medicine ; R
    Subject code 310
    Language English
    Publishing date 2022-03-01T00:00:00Z
    Publisher MDPI AG
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Article ; Online: A global spatial-temporal land use regression model for nitrogen dioxide air pollution

    Andrew Larkin / Susan Anenberg / Daniel L. Goldberg / Arash Mohegh / Michael Brauer / Perry Hystad

    Frontiers in Environmental Science, Vol

    2023  Volume 11

    Abstract: Introduction: The World Health Organization (WHO) recently revised its health guidelines for Nitrogen dioxide (NO2) air pollution, reducing the annual mean NO2 level to 10 μg/m3 (5.3 ppb) and the 24-h mean to 25 μg/m3 (13.3 ppb). NO2 is a pollutant of ... ...

    Abstract Introduction: The World Health Organization (WHO) recently revised its health guidelines for Nitrogen dioxide (NO2) air pollution, reducing the annual mean NO2 level to 10 μg/m3 (5.3 ppb) and the 24-h mean to 25 μg/m3 (13.3 ppb). NO2 is a pollutant of global concern, but it is also a criteria air pollutant that varies spatiotemporally at fine resolutions due to its relatively short lifetime (~hours). Current models have limited ability to capture both temporal and spatial NO2 variation and none are available with global coverage. Land use regression (LUR) models that incorporate timevarying predictors (e.g., meteorology and satellite NO2 measures) and land use characteristics (e.g., road density, emission sources) have significant potential to address this need.Methods: We created a daily Land use regression model with 50 × 50 m2 spatial resolution using 5.7 million daily air monitor averages collected from 8,250 monitor locations.Results: In cross-validation, the model captured 47%, 59%, and 63% of daily, monthly, and annual global NO2 variation. Daily, monthly, and annual root mean square error were 6.8, 5.0, and 4.4 ppb and absolute bias were 46%, 30%, and 21%, respectively. The final model has 11 variables, including road density and built environments with fine (30 m or less) spatial resolution and meteorological and satellite data with daily temporal resolution. Major roads and satellite-based estimates of NO2 were consistently the strongest predictors of NO2 measurements in all regions.Discussion: Daily model estimates from 2005–2019 are available and can be used for global risk assessments and health studies, particularly in countries without NO2 monitoring.
    Keywords NO2 ; land use regression (LUR) ; global ; daily ; air pollution ; Environmental sciences ; GE1-350
    Subject code 333
    Language English
    Publishing date 2023-04-01T00:00:00Z
    Publisher Frontiers Media S.A.
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Article ; Online: The role of cities in reducing the cardiovascular impacts of environmental pollution in low- and middle-income countries

    Jill Baumgartner / Michael Brauer / Majid Ezzati

    BMC Medicine, Vol 18, Iss 1, Pp 1-

    2020  Volume 11

    Abstract: Abstract Background As low- and middle-income countries urbanize and industrialize, they must also cope with pollution emitted from diverse sources. Main text Strong and consistent evidence associates exposure to air pollution and lead with increased ... ...

    Abstract Abstract Background As low- and middle-income countries urbanize and industrialize, they must also cope with pollution emitted from diverse sources. Main text Strong and consistent evidence associates exposure to air pollution and lead with increased risk of cardiovascular disease occurrence and death. Further, increasing evidence, mostly from high-income countries, indicates that exposure to noise and to both high and low temperatures may also increase cardiovascular risk. There is considerably less research on the cardiovascular impacts of environmental conditions in low- and middle-income countries (LMICs), where the levels of pollution are often higher and the types and sources of pollution markedly different from those in higher-income settings. However, as such evidence gathers, actions to reduce exposures to pollution in low- and middle-income countries are warranted, not least because such exposures are very high. Cities, where pollution, populations, and other cardiovascular risk factors are most concentrated, may be best suited to reduce the cardiovascular burden in LMICs by applying environmental standards and policies to mitigate pollution and by implementing interventions that target the most vulnerable. The physical environment of cities can be improved though municipal processes, including infrastructure development, energy and transportation planning, and public health actions. Local regulations can incentivize or inhibit the polluting behaviors of industries and individuals. Environmental monitoring can be combined with public health warning systems and publicly available exposure maps to inform residents of environmental hazards and encourage the adoption of pollution-avoiding behaviors. Targeted individual or neighborhood interventions that identify and treat high-risk populations (e.g., lead mitigation, portable air cleaners, and preventative medications) can also be leveraged in the very near term. Research will play a key role in evaluating whether these approaches achieve their intended ...
    Keywords Developing countries ; Heavy metals ; Household air pollution ; Inequalities ; Noise ; Urban ; Medicine ; R
    Subject code 333
    Language English
    Publishing date 2020-02-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Article ; Online: Remote sensing metrics to assess exposure to residential greenness in epidemiological studies

    Maya Sadeh / Michael Brauer / Rachel Dankner / Nir Fulman / Alexandra Chudnovsky

    Environment International, Vol 146, Iss , Pp 106270- (2021)

    A population case study from the Eastern Mediterranean

    2021  

    Abstract: Introduction/aims: Application of remote sensing-based metrics of exposure to vegetation in epidemiological studies of residential greenness is typically limited to several standard products. The Normalized Difference Vegetation Index (NDVI) is the most ... ...

    Abstract Introduction/aims: Application of remote sensing-based metrics of exposure to vegetation in epidemiological studies of residential greenness is typically limited to several standard products. The Normalized Difference Vegetation Index (NDVI) is the most widely used, but its precision varies with vegetation density and soil color/moisture. In areas with heterogeneous vegetation cover, the Soil-adjusted Vegetation Index (SAVI) corrects for soil brightness. Linear Spectral Unmixing (LSU), measures the relative contribution of different land covers, and estimates percent of each over a unit area. We compared the precision of NDVI, SAVI and LSU for quantifying residential greenness in areas with high spatial heterogeneity in vegetation cover. Methods: NDVI, SAVI, and LSU in a 300 m radius surrounding homes of 3,188 cardiac patients living in Israel (Eastern Mediterranean) were derived from Landsat 30 m spatial resolution imagery. Metrics were compared to assess shifts in exposure quartiles and differences in vegetation detection as a function of overall greenness, climatic zones, and population density, using NDVI as the reference method. Results: For the entire population, the dispersion (SD) of the vegetation values detected was 60% higher when greenness was measured using LSU compared to NDVI: mean (SD) NDVI: 0.17 (0.05), LSU (%): 0.23 (0.08), SAVI: 0.12 (0.03). Importantly, with an increase in population density, the sensitivity of LSU, compared to NDVI, doubled: There was a 95% difference between the LSU and NDVI interquartile range in the highest population density quartile vs 47% in the lowest quartile. Compared to NDVI, exposures estimated by LSU resulted in 21% of patients changing exposure quartiles. In urban areas, the shift in exposure quartile depended on land cover characteristics. An upward shift occurred in dense urban areas, while no shift occurred in high and low vegetated urban areas. Conclusions: LSU was shown to outperform the commonly used NDVI in terms of accuracy and variability, especially in ...
    Keywords Normalized difference vegetation index (NDVI) ; Linear spectral unmixing ; Exposure assessment ; Residential greenness ; Epidemiological studies ; Spectral mixture analysis ; Environmental sciences ; GE1-350
    Subject code 333 ; 910
    Language English
    Publishing date 2021-01-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Article ; Online: A picture tells a thousand…exposures

    Scott Weichenthal / Marianne Hatzopoulou / Michael Brauer

    Environment International, Vol 122, Iss , Pp 3-

    Opportunities and challenges of deep learning image analyses in exposure science and environmental epidemiology

    2019  Volume 10

    Abstract: Background: Artificial intelligence (AI) is revolutionizing our world, with applications ranging from medicine to engineering. Objectives: Here we discuss the promise, challenges, and probable data sources needed to apply AI in the fields of exposure ... ...

    Abstract Background: Artificial intelligence (AI) is revolutionizing our world, with applications ranging from medicine to engineering. Objectives: Here we discuss the promise, challenges, and probable data sources needed to apply AI in the fields of exposure science and environmental health. In particular, we focus on the use of deep convolutional neural networks to estimate environmental exposures using images and other complementary data sources such as cell phone mobility and social media information. Discussion: Characterizing the health impacts of multiple spatially-correlated exposures remains a challenge in environmental epidemiology. A shift toward integrated measures that simultaneously capture multiple aspects of the urban built environment could improve efficiency and provide important insights into how our collective environments influence population health. The widespread adoption of AI in exposure science is on the frontier. This will likely result in new ways of understanding environmental impacts on health and may allow for analyses to be efficiently scaled for broad coverage. Image-based convolutional neural networks may also offer a cost-effective means of estimating local environmental exposures in low and middle-income countries where monitoring and surveillance infrastructure is limited. However, suitable databases must first be assembled to train and evaluate these models and these novel approaches should be complemented with traditional exposure metrics. Conclusions: The promise of deep learning in environmental health is great and will complement existing measurements for data-rich settings and could enhance the resolution and accuracy of estimates in data poor scenarios. Interdisciplinary partnerships will be needed to fully realize this potential.
    Keywords Environmental sciences ; GE1-350
    Subject code 333
    Language English
    Publishing date 2019-01-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: Road proximity, air pollution, noise, green space and neurologic disease incidence

    Weiran Yuchi / Hind Sbihi / Hugh Davies / Lillian Tamburic / Michael Brauer

    Environmental Health, Vol 19, Iss 1, Pp 1-

    a population-based cohort study

    2020  Volume 15

    Abstract: Abstract Background Emerging evidence links road proximity and air pollution with cognitive impairment. Joint effects of noise and greenness have not been evaluated. We investigated associations between road proximity and exposures to air pollution, and ... ...

    Abstract Abstract Background Emerging evidence links road proximity and air pollution with cognitive impairment. Joint effects of noise and greenness have not been evaluated. We investigated associations between road proximity and exposures to air pollution, and joint effects of noise and greenness, on non-Alzheimer’s dementia, Parkinson’s and Alzheimer’s disease and multiple sclerosis within a population-based cohort. Methods We assembled administrative health database cohorts of 45–84 year old residents (N ~ 678,000) of Metro Vancouver, Canada. Cox proportional hazards models were built to assess associations between exposures and non-Alzheimer’s dementia and Parkinson’s disease. Given reduced case numbers, associations with Alzheimer’s disease and multiple sclerosis were evaluated in nested case-control analyses by conditional logistic regression. Results Road proximity was associated with all outcomes (e.g. non-Alzheimer’s dementia hazard ratio: 1.14, [95% confidence interval: 1.07–1.20], for living < 50 m from a major road or < 150 m from a highway). Air pollutants were associated with incidence of Parkinson’s disease and non-Alzheimer’s dementia (e.g. Parkinson’s disease hazard ratios of 1.09 [1.02–1.16], 1.03 [0.97–1.08], 1.12 [1.05–1.20] per interquartile increase in fine particulate matter, Black Carbon, and nitrogen dioxide) but not Alzheimer’s disease or multiple sclerosis. Noise was not associated with any outcomes while associations with greenness suggested protective effects for Parkinson’s disease and non-Alzheimer’s dementia. Conclusions Road proximity was associated with incidence of non-Alzheimer’s dementia, Parkinson’s disease, Alzheimer’s disease and multiple sclerosis. This association may be partially mediated by air pollution, whereas noise exposure did not affect associations. There was some evidence of protective effects of greenness.
    Keywords Road proximity ; Air pollution ; Greenness ; Noise ; Neurological disorders ; Population-based ; Industrial medicine. Industrial hygiene ; RC963-969 ; Public aspects of medicine ; RA1-1270
    Subject code 333
    Language English
    Publishing date 2020-01-01T00:00:00Z
    Publisher BMC
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Article ; Online: Correction

    Rakesh Ghosh / Kate Causey / Katrin Burkart / Sarah Wozniak / Aaron Cohen / Michael Brauer

    PLoS Medicine, Vol 18, Iss

    Ambient and household PM2.5 pollution and adverse perinatal outcomes: A meta-regression and analysis of attributable global burden for 204 countries and territories

    2021  Volume 11

    Keywords Medicine ; R
    Language English
    Publishing date 2021-11-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Article ; Online: Ambient and household PM2.5 pollution and adverse perinatal outcomes

    Rakesh Ghosh / Kate Causey / Katrin Burkart / Sara Wozniak / Aaron Cohen / Michael Brauer

    PLoS Medicine, Vol 18, Iss 9, p e

    A meta-regression and analysis of attributable global burden for 204 countries and territories.

    2021  Volume 1003718

    Abstract: Background Particulate matter <2.5 micrometer (PM2.5) is associated with adverse perinatal outcomes, but the impact on disease burden mediated by this pathway has not previously been included in the Global Burden of Disease (GBD), Mortality, Injuries, ... ...

    Abstract Background Particulate matter <2.5 micrometer (PM2.5) is associated with adverse perinatal outcomes, but the impact on disease burden mediated by this pathway has not previously been included in the Global Burden of Disease (GBD), Mortality, Injuries, and Risk Factors studies. We estimated the global burden of low birth weight (LBW) and preterm birth (PTB) and impacts on reduced birth weight and gestational age (GA), attributable to ambient and household PM2.5 pollution in 2019. Methods and findings We searched PubMed, Embase, and Web of Science for peer-reviewed articles in English. Study quality was assessed using 2 tools: (1) Agency for Healthcare Research and Quality checklist; and (2) National Institute of Environmental Health Sciences (NIEHS) risk of bias questions. We conducted a meta-regression (MR) to quantify the risk of PM2.5 on birth weight and GA. The MR, based on a systematic review (SR) of articles published through April 4, 2021, and resulting uncertainty intervals (UIs) accounted for unexplained between-study heterogeneity. Separate nonlinear relationships relating exposure to risk were generated for each outcome and applied in the burden estimation. The MR included 44, 40, and 40 birth weight, LBW, and PTB studies, respectively. Majority of the studies were of retrospective cohort design and primarily from North America, Europe, and Australia. A few recent studies were from China, India, sub-Saharan Africa, and South America. Pooled estimates indicated 22 grams (95% UI: 12, 32) lower birth weight, 11% greater risk of LBW (1.11, 95% UI: 1.07, 1.16), and 12% greater risk of PTB (1.12, 95% UI: 1.06, 1.19), per 10 μg/m3 increment in ambient PM2.5. We estimated a global population-weighted mean lowering of 89 grams (95% UI: 88, 89) of birth weight and 3.4 weeks (95% UI: 3.4, 3.4) of GA in 2019, attributable to total PM2.5. Globally, an estimated 15.6% (95% UI: 15.6, 15.7) of all LBW and 35.7% (95% UI: 35.6, 35.9) of all PTB infants were attributable to total PM2.5, equivalent to 2,761,720 (95% ...
    Keywords Medicine ; R
    Subject code 333
    Language English
    Publishing date 2021-09-01T00:00:00Z
    Publisher Public Library of Science (PLoS)
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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