Urban-Hazard Risk Analysis: Mapping of Heat-Related Risks in the Elderly in Major Italian Cities

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Background

Short-term impacts of high temperatures on the elderly are well known. Even though Italy has the highest proportion of elderly citizens in Europe, there is a lack of information on spatial heat-related elderly risks.

Objectives

Development of high-resolution, heat-related urban risk maps regarding the elderly population (≥65).

Methods

A long time-series (2001–2013) of remote sensing MODIS data, averaged over the summer period for eleven major Italian cities, were downscaled to obtain high spatial resolution (100 m) daytime and night-time land surface temperatures (LST). LST was estimated pixel-wise by applying two statistical model approaches: 1) the Linear Regression Model (LRM); 2) the Generalized Additive Model (GAM). Total and elderly population density data were extracted from the Joint Research Centre population grid (100 m) from the 2001 census (Eurostat source), and processed together using “Crichton’s Risk Triangle” hazard-risk methodology for obtaining a Heat-related Elderly Risk Index (HERI).

Results

The GAM procedure allowed for improved daytime and night-time LST estimations compared to the LRM approach. High-resolution maps of daytime and night-time HERI levels were developed for inland and coastal cities. Urban areas with the hazardous HERI level (very high risk) were not necessarily characterized by the highest temperatures. The hazardous HERI level was generally localized to encompass the city-centre in inland cities and the inner area in coastal cities. The two most dangerous HERI levels were greater in the coastal rather than inland cities.

Conclusions

This study shows the great potential of combining geospatial technologies and spatial demographic characteristics within a simple and flexible framework in order to provide high-resolution urban mapping of daytime and night-time HERI. In this way, potential areas for intervention are immediately identified with up-to-street level details. This information could support public health operators and facilitate coordination for heat-related emergencies.

Related collections

Most cited references 15

Record: found
Abstract: found
Article: not found

Strong contributions of local background climate to urban heat islands.

Lei Zhao, Xuhui Lee, Ronald Smith … (2014)

The urban heat island (UHI), a common phenomenon in which surface temperatures are higher in urban areas than in surrounding rural areas, represents one of the most significant human-induced changes to Earth's surface climate. Even though they are localized hotspots in the landscape, UHIs have a profound impact on the lives of urban residents, who comprise more than half of the world's population. A barrier to UHI mitigation is the lack of quantitative attribution of the various contributions to UHI intensity (expressed as the temperature difference between urban and rural areas, ΔT). A common perception is that reduction in evaporative cooling in urban land is the dominant driver of ΔT (ref. 5). Here we use a climate model to show that, for cities across North America, geographic variations in daytime ΔT are largely explained by variations in the efficiency with which urban and rural areas convect heat to the lower atmosphere. If urban areas are aerodynamically smoother than surrounding rural areas, urban heat dissipation is relatively less efficient and urban warming occurs (and vice versa). This convection effect depends on the local background climate, increasing daytime ΔT by 3.0 ± 0.3 kelvin (mean and standard error) in humid climates but decreasing ΔT by 1.5 ± 0.2 kelvin in dry climates. In the humid eastern United States, there is evidence of higher ΔT in drier years. These relationships imply that UHIs will exacerbate heatwave stress on human health in wet climates where high temperature effects are already compounded by high air humidity and in drier years when positive temperature anomalies may be reinforced by a precipitation-temperature feedback. Our results support albedo management as a viable means of reducing ΔT on large scales.

0 comments Cited 287 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

Surface urban heat island across 419 global big cities.

Shushi Peng, Shilong Piao, Philippe Ciais … (2012)

Urban heat island is among the most evident aspects of human impacts on the earth system. Here we assess the diurnal and seasonal variation of surface urban heat island intensity (SUHII) defined as the surface temperature difference between urban area and suburban area measured from the MODIS. Differences in SUHII are analyzed across 419 global big cities, and we assess several potential biophysical and socio-economic driving factors. Across the big cities, we show that the average annual daytime SUHII (1.5 ± 1.2 °C) is higher than the annual nighttime SUHII (1.1 ± 0.5 °C) (P < 0.001). But no correlation is found between daytime and nighttime SUHII across big cities (P = 0.84), suggesting different driving mechanisms between day and night. The distribution of nighttime SUHII correlates positively with the difference in albedo and nighttime light between urban area and suburban area, while the distribution of daytime SUHII correlates negatively across cities with the difference of vegetation cover and activity between urban and suburban areas. Our results emphasize the key role of vegetation feedbacks in attenuating SUHII of big cities during the day, in particular during the growing season, further highlighting that increasing urban vegetation cover could be one effective way to mitigate the urban heat island effect.

0 comments Cited 280 times – based on 0 reviews      Review now

Bookmark

Record: found
Abstract: found
Article: not found

International study of temperature, heat and urban mortality: the 'ISOTHURM' project.

Anthony J McMichael, Paul Wilkinson, R Sari Kovats … (2008)

This study describes heat- and cold-related mortality in 12 urban populations in low- and middle-income countries, thereby extending knowledge of how diverse populations, in non-OECD countries, respond to temperature extremes. The cities were: Delhi, Monterrey, Mexico City, Chiang Mai, Bangkok, Salvador, São Paulo, Santiago, Cape Town, Ljubljana, Bucharest and Sofia. For each city, daily mortality was examined in relation to ambient temperature using autoregressive Poisson models (2- to 5-year series) adjusted for season, relative humidity, air pollution, day of week and public holidays. Most cities showed a U-shaped temperature-mortality relationship, with clear evidence of increasing death rates at colder temperatures in all cities except Ljubljana, Salvador and Delhi and with increasing heat in all cities except Chiang Mai and Cape Town. Estimates of the temperature threshold below which cold-related mortality began to increase ranged from 15 degrees C to 29 degrees C; the threshold for heat-related deaths ranged from 16 degrees C to 31 degrees C. Heat thresholds were generally higher in cities with warmer climates, while cold thresholds were unrelated to climate. Urban populations, in diverse geographic settings, experience increases in mortality due to both high and low temperatures. The effects of heat and cold vary depending on climate and non-climate factors such as the population disease profile and age structure. Although such populations will undergo some adaptation to increasing temperatures, many are likely to have substantial vulnerability to climate change. Additional research is needed to elucidate vulnerability within populations.

0 comments Cited 202 times – based on 0 reviews      Review now

Bookmark

All references

Author and article information

Contributors

Koustuv Dalal: Role: Academic Editor

Journal

Journal ID (nlm-ta): PLoS One

Journal ID (iso-abbrev): PLoS ONE

Journal ID (publisher-id): plos

Journal ID (pmc): plosone

Title: PLoS ONE

Publisher: Public Library of Science (San Francisco, CA USA )

ISSN (Electronic): 1932-6203

Publication date (Electronic): 18 May 2015

Publication date Collection: 2015

Volume: 10

Issue: 5

Electronic Location Identifier: e0127277

Affiliations

[1 ]Institute of Biometeorology, National Research Council, Florence, Italy

[2 ]Interdepartmental Centre of Bioclimatology, University of Florence, Florence, Italy

[3 ]Fondazione per il Clima e la Sostenibilità, Florence, Italy

[4 ]Department of Agrifood Production and Environmental Sciences, University of Florence, Florence, Italy

[5 ]Clinica Medica e Cardiologia, University of Florence, Florence, Italy

Örebro University, SWEDEN

Author notes

Competing Interests: The authors have declared that no competing interests exist.

Conceived and designed the experiments: MM AC BG GG. Performed the experiments: MM AC BG GG PT. Analyzed the data: MM AC BG GG PT. Contributed reagents/materials/analysis tools: MM AC BG GG PT VDS. Wrote the paper: MM AC BG GG PT VDS SO GFG.

* E-mail: marco.morabito@ 123456unifi.it

Article

Publisher ID: PONE-D-14-52382

DOI: 10.1371/journal.pone.0127277

PMC ID: 4436225

PubMed ID: 25985204

SO-VID: fb72b2c5-4323-443a-82de-5b71abcacac5

License:

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

History

Date received : 21 November 2014

Date accepted : 13 April 2015

Page count

Figures: 5, Tables: 6, Pages: 18

Funding

This study was supported and funded by the Regional MeteoSalute Project, Regional Health System of Tuscany and the Smart HealthyENV Project “Smart Monitoring Integrated System for a Healthy Urban ENVironment in Smart Cities”, POR CReO FESR 2007/2013.

Custom metadata

Data Availability Demonstrative work-flow, data and code functions built-up for this work are available on github.com/meteosalute/mapheatrisk. Furthermore, MODIS satellite data products were obtained on https://lpdaac.usgs.gov/. The ESA (European Space Agency) Globcover land cover dataset available on http://due.esrin.esa.int/globcover was used. The population density data was extracted from the JRC (Joint Research Centre) population grid (version 5) available on http://www.eea.europa.eu/data-and-maps/data/population-density-disaggregated-with-corine-land-cover-2000-2.

Urban-Hazard Risk Analysis: Mapping of Heat-Related Risks in the Elderly in Major Italian Cities

Read this article at

Abstract

Background

Objectives

Methods

Results

Conclusions

Related collections

PLOS Climate

Most cited references 15

Strong contributions of local background climate to urban heat islands.

Surface urban heat island across 419 global big cities.

International study of temperature, heat and urban mortality: the 'ISOTHURM' project.

Author and article information

Contributors

Journal

Affiliations

Author notes

Article

History

Page count

Funding

Categories

Custom metadata

Comments

Comment on this article

Similar content 96

Cited by 23

Most referenced authors 224