中山大学地理科学与规划学院,广东 广州 510006
郑楷灿(2003年生),男;研究方向:地理信息科学;E-mail:zhengkc@mail2.sysu.edu.cn
廖威林(1990年生),男;研究方向:城市气候、气候变化;E-mail:liaoweilin@mail.sysu.edu.cn
纸质出版日期:2024-07-25,
网络出版日期:2024-04-24,
收稿日期:2024-02-20,
录用日期:2024-03-26
移动端阅览
郑楷灿,廖威林.基于因子分析的广东省各区县热浪抵抗力分析[J].中山大学学报(自然科学版)(中英文),2024,63(04):1-8.
ZHENG Kaican,LIAO Weilin.Analysis of heatwave resistance in the districts and counties of Guangdong province based on factor analysis[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2024,63(04):1-8.
郑楷灿,廖威林.基于因子分析的广东省各区县热浪抵抗力分析[J].中山大学学报(自然科学版)(中英文),2024,63(04):1-8. DOI: 10.13471/j.cnki.acta.snus.ZR20240054.
ZHENG Kaican,LIAO Weilin.Analysis of heatwave resistance in the districts and counties of Guangdong province based on factor analysis[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2024,63(04):1-8. DOI: 10.13471/j.cnki.acta.snus.ZR20240054.
在全球气候变暖背景下,热浪事件发生的频率不断增加,给人居环境和人类健康带来了不利影响。已有研究表明,自然因素和社会经济因素均会影响热浪事件的强度。因此,综合考虑自然、社会和经济指标来反映对热浪事件的抵抗力尤为重要。本研究聚焦于广东省各区县,选取了涉及以上3个方面的指标,使用因子分析方法提取到3个公因子,分别为个体抵抗因子、公共抵抗因子和自然抵抗因子,其累计贡献率达到90.029%,能够较为准确地反映区县尺度下热浪事件的抵抗能力。结果表明,热浪抵抗力在广东省内存在明显的不平衡性,抵抗能力强的区县集中在珠三角地区。根据因子得分进行特征组合,可将广东省内各区县分为5类,分别是抵抗力脆弱区、个体抵抗力提高区、公共抵抗力提高区、自然抵抗力提高区和抵抗力强劲区。大部分区县在热浪抵抗力上既存在优势方面,也存在不足之处。本研究针对不同类别的区县提出相应建议,以期为缓解热浪事件影响提供科学依据。
Under the backdrop of global warming, the frequency of heatwave events has been increasing, adversely affecting the living environment and human health. Existing studies suggest that both natural and socio-economic factors influence the intensity of heatwave events. Therefore, it is particularly important to consider a comprehensive set of natural, social, and economic indicators to assess resistance to heatwaves. This study selects indicators related to the three aforementioned aspects through factor analysis concerning the districts and counties of Guangdong Province. Three common factors are extracted: individual resistance factor, public resistance factor, and natural resistance factor, which have a cumulative contribution rate reaching 90.029% and can accurately reflect the resistance capacity to heatwave events at the county scale. The results show a significant imbalance in heatwave resistance within Guangdong Province,with districts having strong resistance concentrated in the Pearl River Delta region. Based on the factor scores, the districts and counties in Guangdong Province can be divided into five categories: vulnerable resistance zone, individual resistance enhancement zone, public resistance enhancement zone, natural resistance enhancement zone, and strong resistance zone. Most districts and counties have both strengths and weaknesses in their resistance to heatwaves. The study provides specific suggestions for different categories of districts and counties, hoping to offer a scientific basis for mitigating the impacts of heatwave events.
因子分析县级尺度热浪抵抗力多源数据
factor analysiscounty level scaleheat wave resistancemultisource data
陈俊梁,林影,史欢欢,2020. 长三角地区乡村振兴发展水平综合评价研究[J]. 华东经济管理,34(3):16-22.
郭岩,陈文斌,2021. 基于因子分析法的地方政府重视生态文明建设程度评价研究 ——以黑龙江省为例[J]. 生态经济,37(12):218-223.
何苗,徐永明,莫亚萍,等,2023. 基于多源遥感数据的北京市高温热浪风险综合评估[J]. 地理科学,43(7):1270-1280.
何晓群,2008. 多元统计分析[M]. 2版. 北京:中国人民大学出版社:192-205.
欧阳骅,戴作元,2000. 中暑的发病机理及其预防措施[J]. 解放军预防医学杂志,(2):149-151.
潘梅竹,许慧慧,东春阳,等,2018. 2013—2016年上海市居民中暑死亡病例的发病特征[J]. 环境与职业医学,35(9):825-829.
薛倩,谢苗苗,郭强,等,2020. 地理学视角下城市高温热浪脆弱性评估研究进展[J]. 地理科学进展,39(4):685-694.
袁萌,2019. 广东省县域经济差异及其影响因素研究[D].广州: 暨南大学.
翟盘茂,余荣,周佰铨,等,2017. 1.5℃增暖对全球和区域影响的研究进展[J]. 气候变化研究进展,13(5):465-472.
张宏远,毛泽见,朱国军,2020. 基于因子分析的长三角中心城市创新力研究[J]. 南京工业大学学报(社会科学版),19(6):99-110+112.
张平,延军平,李英杰,等,2018. 1960—2015年两广地区夏季高温热浪变化特征[J]. 浙江大学学报(理学版),45(1):73-81.
周泽炯,2010. 基于因子分析的县域经济竞争力研究——以安徽县域经济为例[J]. 经济体制改革,(3):148-151.
EBI K L,CAPON A,BERRY P,et al,2021. Hot weather and heat extremes: Health risks[J]. Lancet,398(10301): 698-708.
HU K J,YANG X C,ZHONG J M,et al,2017. Spatially explicit mapping of heat health risk utilizing environmental and socioeconomic data[J]. Environ Sci Technol,51(3): 1498-1507.
JI J S,XI D,HUANG C R,2023. Building resilience in heatwaves[J]. Nat Med,29(7): 1613-1614.
LIAO W L,WANG D G,LIU X P,et al,2017. Estimated influence of urbanization on surface warming in Eastern China using time-varying land use data[J]. Int J Climatol,37(7): 3197-3208.
MURTINOVÁ V,GALLAY I,OLAH B,2022. Mitigating effect of urban green spaces on surface urban heat island during summer period on an example of a medium size town of Zvolen,Slovakia[J]. Remote Sens,14(18): 4492.
PERKINS S E,ALEXANDER L V,NAIRN J R,2012. Increasing frequency,intensity and duration of observed global heatwaves and warm spells[J]. Geophys Res Lett,39(20): L20714.
ROMANELLO M,Di NAPOLI C,GREEN C,2023. The 2023 report of the Lancet Countdown on health and climate change: the imperative for a health-centred response in a world facing irreversible harms[J].Lancet,402(10419): 2346-2394.
WANG J,CHEN Y,LIAO W L,et al,2021. Anthropogenic emissions and urbanization increase risk of compound hot extremes in cities[J]. Nat Clim Change,11: 1084-1089.
YANG J,HUANG X,2021. The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019[J]. Earth Syst Sci Data,13(8): 3907-3925.
YU L X,LIU Y,LIU T X,et al,2020. Impact of recent vegetation greening on temperature and precipitation over China[J]. Agric For Meteorol, 295: 108197.
YU S Y,KONG X S,WANG Q,et al,2023. A new approach of Robustness-Resistance-Recovery(3Rs) to assessing flood resilience: A case study in Dongting Lake Basin[J]. Landsc Urban Plan,230: 104605.
0
浏览量
112
下载量
0
CSCD
关联资源
相关文章
相关作者
相关机构