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一种基于模糊逻辑的模型,用于检测城市居民区热浪期间空气污染物热点。

A Fuzzy-Based Model to Detect Hotspots of Air Pollutants During Heatwaves in Urban Settlements.

作者信息

Cardone Barbara, Di Martino Ferdinando, Mauriello Cristiano, Miraglia Vittorio

机构信息

Department of Architecture, University of Naples Federico II, Via Toledo 402, 80134 Napoli, Italy.

Center for Interdepartmental Research "Alberto Calza Bini", University of Naples Federico II, Via Toledo 402, 80134 Napoli, Italy.

出版信息

Sensors (Basel). 2025 Mar 28;25(7):2160. doi: 10.3390/s25072160.

DOI:10.3390/s25072160
PMID:40218673
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11990986/
Abstract

High concentrations of pollutants in urban areas generate cardiovascular and respiratory problems in citizens; these are aggravated by the persistence of summer heatwaves. For this reason, in this research, we propose a fuzzy-based method for detecting air pollutant hotspots and determining critical urban areas for air pollution during heatwaves. After acquiring the pollutant concentration values recorded by monitoring stations during heatwaves, a spatial interpolation method is applied to obtain the distribution of the pollutant concentration during heatwaves and, subsequently, a fuzzification process is performed to determine urban hotspots in which the pollutant concentration assumes critical values. Finally, the critical urban areas are determined, consisting of the areas within hotspots with a high population density exposed to health risks. The method was implemented in a GIS platform and tested on an urban study area in the Lombardy region, Italy, to determine the urban areas with high criticality during the heatwaves that occurred in the summer months of 2024. The test results show that the method can provide valid support for decision makers and local administrators when evaluating which urban areas are most critical for the population due to the high rate of air pollution during heatwaves.

摘要

城市地区的高浓度污染物会给市民带来心血管和呼吸系统问题;夏季热浪的持续存在会使这些问题更加严重。因此,在本研究中,我们提出了一种基于模糊逻辑的方法,用于检测空气污染物热点区域,并确定热浪期间空气污染的关键城市区域。在获取热浪期间监测站记录的污染物浓度值后,应用空间插值方法来获得热浪期间污染物浓度的分布,随后进行模糊化处理,以确定污染物浓度达到临界值的城市热点区域。最后,确定关键城市区域,这些区域由热点区域内人口密度高且面临健康风险的区域组成。该方法在地理信息系统(GIS)平台上实现,并在意大利伦巴第大区的一个城市研究区域进行了测试,以确定2024年夏季热浪期间具有高临界性的城市区域。测试结果表明,该方法在评估哪些城市区域因热浪期间的高空气污染率而对居民最为关键时,能够为决策者和地方管理人员提供有效的支持。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/621a/11990986/cf955af362d5/sensors-25-02160-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/621a/11990986/b0b5cbdcea55/sensors-25-02160-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/621a/11990986/f5638c14eb23/sensors-25-02160-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/621a/11990986/4d29f54f8370/sensors-25-02160-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/621a/11990986/78c8161bc7be/sensors-25-02160-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/621a/11990986/f9c3c6a3c01c/sensors-25-02160-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/621a/11990986/c0e6f62172f1/sensors-25-02160-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/621a/11990986/0ec042d3b4bc/sensors-25-02160-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/621a/11990986/a3c04719b035/sensors-25-02160-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/621a/11990986/cf955af362d5/sensors-25-02160-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/621a/11990986/b0b5cbdcea55/sensors-25-02160-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/621a/11990986/f5638c14eb23/sensors-25-02160-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/621a/11990986/4d29f54f8370/sensors-25-02160-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/621a/11990986/78c8161bc7be/sensors-25-02160-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/621a/11990986/f9c3c6a3c01c/sensors-25-02160-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/621a/11990986/c0e6f62172f1/sensors-25-02160-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/621a/11990986/0ec042d3b4bc/sensors-25-02160-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/621a/11990986/a3c04719b035/sensors-25-02160-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/621a/11990986/cf955af362d5/sensors-25-02160-g009.jpg

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本文引用的文献

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