Disproportionate exposure to surface-urban heat islands across vulnerable populations in Lima city, Peru

Dados Bibliográficos

ID15545705
AutoresEdson J Ascencio (0000-0002-8340-9236, Universidad Peruana Cayetano Heredia), Antony Barja (0000-0001-5921-2858, Universidad Peruana Cayetano Heredia), Tarik Benmarhnia (0000-0002-4018-3089, University of California San Diego), Gabriel Carrasco‐Escobar (0000-0002-6945-0419, Universidad Peruana Cayetano Heredia, autor correspondente)
Ano2023
Volume18
Fascículo7
Páginas074001-074001
Data de publicação2023-06-08
Peer ReviewedSim
Open AccessSim
TipoARTICLE
PeriódicoEnvironmental Research Letters (JOURNAL)
Identificadores do periódicoISSN: 1748-9326 • E-ISSN: 1748-9326
EditoraIOP Publishing (PUBLISHER • GB)
DOI10.1088/1748-9326/acdca9
OpenAlexW4379791081
IdiomaEN
Citações recebidas2
Referências citadas67

Climate change constitutes an unprecedented challenge for public health and one of its main direct effects are extreme temperatures. It varies between intra-urban areas and this difference is called surface urban heat island (SUHI) effect. We aimed to assess SUHI distribution among socioeconomic levels in Lima, Peru by conducting a cross-sectional study at the block-level. The mean land surface temperature (LST) from 2017 to 2021 were estimated using the TIRS sensor (Landsat-8 satellite [0.5 km scale]) and extracted to block level. SUHI was calculated based on the difference on mean LST values (2017–2021) per block and the lowest LST registered in a block. Socioeconomic data were obtained from the 2017 Peruvian census. A principal component analysis was performed to construct a socioeconomic index and a mixture analysis based on quantile g-computation was conducted to estimate the joint and specific effects of socioeconomic variables on SUHI. A total of 69 618 blocks were included in the analysis. In the Metropolitan Lima area, the mean SUHI estimation per block was 6.44 (SD = 1.44) Celsius degrees. We found that blocks with high socioeconomic status (SES) showed a decreased exposure to SUHI, compared to those blocks where the low SES were predominant ( p -value < 0.001) and that there is a significant SUHI exposure variation ( p -value < 0.001) between predominant ethnicities per block (Non-White, Afro-American, and White ethnicities). The mixture analysis showed that the overall mixture effect estimates on SUHI was −1.01 (effect on SUHI of increasing simultaneously every socioeconomic variable by one quantile). Our study highlighted that populations with low SES are more likely to be exposed to higher levels of SUHI compared to those who have a higher SES and illustrates the importance to consider SES inequalities when designing urban adaptation strategies aiming at reducing exposure to SUHI

Geography · Physical geography · Population · Quantile · Socioeconomic status · Socioeconomics · Statistics · Climate Change and Health Impacts · Environmental Science · Mathematics · Urban Green Space and Health · Urban Heat Island Mitigation · Demography