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Record W2046315956 · doi:10.1115/1.4023176

Review of World Urban Heat Islands: Many Linked to Increased Mortality

2013· article· en· W2046315956 on OpenAlexaboutno aff
Kaufui V. Wong, Andrew Paddon, Alfredo Jimenez

Bibliographic record

VenueJournal of Energy Resources Technology · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsOverheating (electricity)DemographyHeat waveExtreme heatUrban heat islandPopulationGeographyMeteorologyClimate changeEngineeringGeologyOceanography

Abstract

fetched live from OpenAlex

Medical and health researchers have shown that fatalities during heat waves are most commonly due to respiratory and cardiovascular diseases, primarily from heat's negative effect on the cardiovascular system. In an attempt to control one's internal temperature, the body’s natural instinct is to circulate large quantities of blood to the skin. However, to perform this protective measure against overheating actually harms the body by inducing extra strain on the heart. This excess strain has the potential to trigger a cardiac event in those with chronic health problems, such as the elderly, Cui et al. Frumkin showed that the relationship of mortality and temperature creates a J-shaped function, showing a steeper slope at higher temperatures. Records show that more casualties have resulted from heat waves than hurricanes, floods, and tornadoes together. This statistic’s significance is that extreme heat events (EHEs) are becoming more frequent, as shown by Stone et al. Their analysis shows a growth trend of EHEs by 0.20 days/year in U.S. cities between 1956 and 2005, with a 95% confidence interval and uncertainty of ±0.6. This means that there were 10 more days of extreme heat conditions in 2005 than in 1956. Studies held from 1989 to 2000 in 50 U.S. cities recorded a rise of 5.7% in mortality during heat waves. The research of Schifano et al. revealed that Rome’s elderly population endures a higher mortality rate during heat waves, at 8% excess for the 65–74 age group and 15% for above 74. Even more staggering is findings of Dousset et al. on French cities during the 2003 heat wave. Small towns saw an average excess mortality rate of 40%, while Paris witnessed an increase of 141%. During this period, a 0.5 °C increase above the average minimum nighttime temperature doubled the risk of death in the elderly. Heat-related illnesses and mortality rates have slightly decreased since 1980, regardless of the increase in temperatures. Statistics from the U.S. Census state that the U.S. population without air conditioning saw a drop of 32% from 1978 to 2005, resting at 15%. Despite the increase in air conditioning use, a study done by Kalkstein through 2007 proved that the shielding effects of air conditioning reached their terminal effect in the mid-1990s. Kan et al. hypothesize in their study of Shanghai that the significant difference in fatalities from the 1998 and 2003 heat waves was due to the increase in use of air conditioning. Protective factors have mitigated the danger of heat on those vulnerable to it, however projecting forward the heat increment related to sprawl may exceed physiologic adaptation thresholds. It has been studied and reported that urban heat islands (UHI) exist in the following world cities and their countries and/or states: Tel-Aviv, Israel, Newark, NJ, Madrid, Spain, London, UK, Athens, Greece, Taipei, Taiwan, San Juan, Puerto Rico, Osaka, Japan, Hong Kong, China, Beijing, China, Pyongyang, North Korea, Bangkok, Thailand, Manila, Philippines, Ho Chi Minh City, Vietnam, Seoul, South Korea, Muscat, Oman, Singapore, Houston, USA, Shanghai, China, Wroclaw, Poland, Mexico City, Mexico, Arkansas, Atlanta, USA, Buenos Aires, Argentina, Kenya, Brisbane, Australia, Moscow, Russia, Los Angeles, USA, Washington, DC, USA, San Diego, USA, New York, USA, Chicago, USA, Budapest, Hungary, Miami, USA, Istanbul, Turkey, Mumbai, India, Shenzen, China, Thessaloniki, Greece, Rotterdam, Netherlands, Akure, Nigeria, Bucharest, Romania, Birmingham, UK, Bangladesh, and Delhi, India. The strongest being Shanghai, Bangkok, Beijing, Tel-Aviv, and Tokyo with UHI intensities (UHII) of 3.5–7.0, 3.0–8.0, 5.5–10, 10, and 12 °C, respectively. Of the above world cities, Hong Kong, Bangkok, Delhi, Bangladesh, London, Kyoto, Osaka, and Berlin have been linked to increased mortality rates due to the heightened temperatures of nonheat wave periods. Chan et al. studied excess mortalities in cities such as Hong Kong, Bangkok, and Delhi, which currently observe mortality increases ranging from 4.1% to 5.8% per 1 °C over a temperature threshold of approximately 29 °C. Goggins et al. found similar data for the urban area of Bangladesh, which showed an increase of 7.5% in mortality for every 1 °C the mean temperature was above a similar threshold. In the same study, while observing microregions of Montreal portraying heat island characteristics, mortality was found to be 28% higher in heat island zones on days with a mean temperature of 26 °C opposed to 20 °C compared to a 13% increase in colder areas.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.013
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0100.014
Science and technology studies0.0010.000
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.288
Teacher spread0.263 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations154
Published2013
Admission routes1
Has abstractyes

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