Scaling features in high-concentrations PM 2.5 evolution: the Ignored factor affecting scarlet fever incidence.

Autor: Shi K; College of Environmental Sciences and Engineering, China West Normal University, Nanchong, Sichuan, China.; Key Laboratory of Nanchong City of Ecological Environment Protection and Pollution Prevention in Jialing River Basin, China West Normal University, Nanchong, China., Liu C; College of Environmental Sciences and Engineering, China West Normal University, Nanchong, Sichuan, China. liuchunqiong@cwnu.edu.cn.; Key Laboratory of Nanchong City of Ecological Environment Protection and Pollution Prevention in Jialing River Basin, China West Normal University, Nanchong, China. liuchunqiong@cwnu.edu.cn., Zhong X; College of Mathematics and Statistics, Jishou University, Jishou, Hunan, China. zhongxinyu1019@163.com.
Jazyk: angličtina
Zdroj: Environmental geochemistry and health [Environ Geochem Health] 2024 Jun 07; Vol. 46 (7), pp. 217. Date of Electronic Publication: 2024 Jun 07.
DOI: 10.1007/s10653-024-01989-2
Abstrakt: As an acute respiratory disease, scarlet fever has great harm to public health. Some evidence indicates that the time distribution pattern of heavy PM 2.5 pollution occurrence may have an impact on health risks. This study aims to reveal the relation between scaling features in high-concentrations PM 2.5 (HC-PM 2.5 ) evolution and scarlet fever incidence (SFI). Based on the data of Hong Kong from 2012 to 2019, fractal box-counting dimension (D) is introduced to capture the scaling features of HC-PM 2.5 . It has been found that index D can quantify the time distribution of HC-PM 2.5 , and lower D values indicate more cluster distribution of HC-PM 2.5 . Moreover, scale-invariance in HC-PM 2.5 at different time scales has been discovered, which indicates that HC-PM 2.5 occurrence is not random but follows a typical power-law distribution. Next, the exposure-response relationship between SFI and scale-invariance in HC-PM 2.5 is explored by Distributed lag non-linear model, in conjunction with meteorological factors. It has been discovered that scale-invariance in HC-PM 2.5 has a nonlinear effect on SFI. Low and moderate D values of HC-PM 2.5 are identified as risk factors for SFI at small time-scale. Moreover, relative risk shows a decreasing trend with the increase of exposure time. These results suggest that exposure to short-term clustered HC-PM 2.5 makes individual more prone to SFI than exposure to long-term uniform HC-PM 2.5 . This means that individuals in slightly-polluted regions may face a greater risk of SFI, once the PM 2.5 concentration keeps rising. In the future, it is expected that the relative risk of scarlet fever for a specific region can be estimated based on the quantitative analysis of scaling features in high-concentrations PM 2.5 evolution.
(© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)
Databáze: MEDLINE
Externí odkaz: