{"title":"埃塞俄比亚亚的斯亚贝巴环境空气污染空间分布统计分析","authors":"Daniel Mulgeta, Butte Gotu, Shibru Temesgen, Merga Belina, Habte Tadesse Likassa, Dejene Tsegaye","doi":"10.1007/s00477-024-02748-6","DOIUrl":null,"url":null,"abstract":"<p>Ambient air pollution has recently emerged as a major global public health issue, causing a variety of negative health impacts even at the lowest measurable concentrations. This study aims to analyze the spatial distribution of ambient air pollution in Addis Ababa, Ethiopia. The study was based on cross-sectional data collected from 21 selected sites within the period of October 13, 2019 to January 26, 2020, and July 5 to October 29, 2021. The spatial distribution of ambient air pollution was analyzed using spatial autocorrelation (Moran’s I and Geary’s C), and the hotspot areas of ambient air pollution were identified using the Ord and Getis statistics after visualizing via the Moran Scatter Plot. The average concentration of ambient air pollution was modeled against the covariates using a spatial lag model. Moran’s I, and Geary’s C, showed that the spatial distribution of ambient air pollution was globally clustered in the study area. Results revealed that Petros, Tekle Haimanot, and Bob Marley Squares, Legehar, Jamo Mikael, Sholla, Megenagna, African Union traffic signal, Stadium, North and East sampling sites of Akaki Kality's metal welding shade were identified as the hotspot sites of both ambient air pollutants. The results showed that temperature, average wind speed, wind direction, road characteristics, and land use characteristics were statistically significantly associated with the ambient air pollution concentrations. Paying attention to reducing ambient air pollution in pollution hotspot areas is recommended by the government and all concerned bodies.</p>","PeriodicalId":21987,"journal":{"name":"Stochastic Environmental Research and Risk Assessment","volume":"33 1","pages":""},"PeriodicalIF":3.9000,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Statistical Analysis of Spatial Distribution of Ambient Air Pollution in Addis Ababa, Ethiopia\",\"authors\":\"Daniel Mulgeta, Butte Gotu, Shibru Temesgen, Merga Belina, Habte Tadesse Likassa, Dejene Tsegaye\",\"doi\":\"10.1007/s00477-024-02748-6\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Ambient air pollution has recently emerged as a major global public health issue, causing a variety of negative health impacts even at the lowest measurable concentrations. This study aims to analyze the spatial distribution of ambient air pollution in Addis Ababa, Ethiopia. The study was based on cross-sectional data collected from 21 selected sites within the period of October 13, 2019 to January 26, 2020, and July 5 to October 29, 2021. The spatial distribution of ambient air pollution was analyzed using spatial autocorrelation (Moran’s I and Geary’s C), and the hotspot areas of ambient air pollution were identified using the Ord and Getis statistics after visualizing via the Moran Scatter Plot. The average concentration of ambient air pollution was modeled against the covariates using a spatial lag model. Moran’s I, and Geary’s C, showed that the spatial distribution of ambient air pollution was globally clustered in the study area. Results revealed that Petros, Tekle Haimanot, and Bob Marley Squares, Legehar, Jamo Mikael, Sholla, Megenagna, African Union traffic signal, Stadium, North and East sampling sites of Akaki Kality's metal welding shade were identified as the hotspot sites of both ambient air pollutants. The results showed that temperature, average wind speed, wind direction, road characteristics, and land use characteristics were statistically significantly associated with the ambient air pollution concentrations. Paying attention to reducing ambient air pollution in pollution hotspot areas is recommended by the government and all concerned bodies.</p>\",\"PeriodicalId\":21987,\"journal\":{\"name\":\"Stochastic Environmental Research and Risk Assessment\",\"volume\":\"33 1\",\"pages\":\"\"},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2024-06-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Stochastic Environmental Research and Risk Assessment\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://doi.org/10.1007/s00477-024-02748-6\",\"RegionNum\":3,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, CIVIL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Stochastic Environmental Research and Risk Assessment","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1007/s00477-024-02748-6","RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, CIVIL","Score":null,"Total":0}
引用次数: 0
摘要
环境空气污染近来已成为一个重大的全球公共卫生问题,即使在可测量的最低浓度下也会对健康造成各种负面影响。本研究旨在分析埃塞俄比亚亚的斯亚贝巴环境空气污染的空间分布。研究基于从 2019 年 10 月 13 日至 2020 年 1 月 26 日和 2021 年 7 月 5 日至 10 月 29 日期间从 21 个选定地点收集的横截面数据。利用空间自相关性(Moran's I 和 Geary's C)分析了环境空气污染的空间分布,并通过 Moran 散点图直观显示后,利用 Ord 和 Getis 统计法确定了环境空气污染的热点区域。利用空间滞后模型对环境空气污染的平均浓度与协变量进行建模。Moran's I 和 Geary's C 表明,环境空气污染的空间分布在研究区域内呈总体集群状。结果显示,Petros、Tekle Haimanot 和 Bob Marley 广场、Legehar、Jamo Mikael、Sholla、Megenagna、非洲联盟交通信号灯、体育场、Akaki Kality 金属焊接阴凉处的北部和东部采样点被确定为两种环境空气污染的热点地点。结果表明,气温、平均风速、风向、道路特征和土地利用特征与环境空气污染浓度有显著的统计学关联。建议政府和所有相关机构关注减少污染热点地区的环境空气污染。
Statistical Analysis of Spatial Distribution of Ambient Air Pollution in Addis Ababa, Ethiopia
Ambient air pollution has recently emerged as a major global public health issue, causing a variety of negative health impacts even at the lowest measurable concentrations. This study aims to analyze the spatial distribution of ambient air pollution in Addis Ababa, Ethiopia. The study was based on cross-sectional data collected from 21 selected sites within the period of October 13, 2019 to January 26, 2020, and July 5 to October 29, 2021. The spatial distribution of ambient air pollution was analyzed using spatial autocorrelation (Moran’s I and Geary’s C), and the hotspot areas of ambient air pollution were identified using the Ord and Getis statistics after visualizing via the Moran Scatter Plot. The average concentration of ambient air pollution was modeled against the covariates using a spatial lag model. Moran’s I, and Geary’s C, showed that the spatial distribution of ambient air pollution was globally clustered in the study area. Results revealed that Petros, Tekle Haimanot, and Bob Marley Squares, Legehar, Jamo Mikael, Sholla, Megenagna, African Union traffic signal, Stadium, North and East sampling sites of Akaki Kality's metal welding shade were identified as the hotspot sites of both ambient air pollutants. The results showed that temperature, average wind speed, wind direction, road characteristics, and land use characteristics were statistically significantly associated with the ambient air pollution concentrations. Paying attention to reducing ambient air pollution in pollution hotspot areas is recommended by the government and all concerned bodies.
期刊介绍:
Stochastic Environmental Research and Risk Assessment (SERRA) will publish research papers, reviews and technical notes on stochastic and probabilistic approaches to environmental sciences and engineering, including interactions of earth and atmospheric environments with people and ecosystems. The basic idea is to bring together research papers on stochastic modelling in various fields of environmental sciences and to provide an interdisciplinary forum for the exchange of ideas, for communicating on issues that cut across disciplinary barriers, and for the dissemination of stochastic techniques used in different fields to the community of interested researchers. Original contributions will be considered dealing with modelling (theoretical and computational), measurements and instrumentation in one or more of the following topical areas:
- Spatiotemporal analysis and mapping of natural processes.
- Enviroinformatics.
- Environmental risk assessment, reliability analysis and decision making.
- Surface and subsurface hydrology and hydraulics.
- Multiphase porous media domains and contaminant transport modelling.
- Hazardous waste site characterization.
- Stochastic turbulence and random hydrodynamic fields.
- Chaotic and fractal systems.
- Random waves and seafloor morphology.
- Stochastic atmospheric and climate processes.
- Air pollution and quality assessment research.
- Modern geostatistics.
- Mechanisms of pollutant formation, emission, exposure and absorption.
- Physical, chemical and biological analysis of human exposure from single and multiple media and routes; control and protection.
- Bioinformatics.
- Probabilistic methods in ecology and population biology.
- Epidemiological investigations.
- Models using stochastic differential equations stochastic or partial differential equations.
- Hazardous waste site characterization.