Assessing Air Quality Dynamics during Short-Period Social Upheaval Events in Quito, Ecuador, Using a Remote Sensing Framework

IF 4.2 2区 地球科学 Q2 ENVIRONMENTAL SCIENCES Remote Sensing Pub Date : 2024-09-16 DOI:10.3390/rs16183436
Cesar Ivan Alvarez, Santiago López, David Vásquez, Dayana Gualotuña
{"title":"Assessing Air Quality Dynamics during Short-Period Social Upheaval Events in Quito, Ecuador, Using a Remote Sensing Framework","authors":"Cesar Ivan Alvarez, Santiago López, David Vásquez, Dayana Gualotuña","doi":"10.3390/rs16183436","DOIUrl":null,"url":null,"abstract":"This study uses a remote sensing approach to investigate air quality fluctuations during two short-period social upheaval events caused by civil protests in 2019 and the COVID-19 pandemic in 2020 in Quito, Ecuador. We used data from the TROPOMI Sentinel-P5 satellite to evaluate the concentrations of two greenhouse gases, namely O3 and NO2. TROPOMI Sentinel-P5 satellite data are becoming essential in air quality monitoring, particularly for countries that lack ground-based monitoring systems. For a better approximation of satellite data with ground data, we related the remotely sensed data using ground station data and Pearson correlation analysis, which revealed a significant association between the two sources (0.43 ≤ r ≤ 0.78). Using paired t-test comparisons, we evaluated the differences in mean gas concentrations at 30 randomly selected intervals to identify significant changes before and after the events. The results indicate noticeable changes in the two gases over the three analysis periods. O3 significantly decreased between September and November 2019 and between March and May 2020, while NO2 significantly increased. NO2 levels decreased by 18% between February and March 2020 across the study area, as indicated by remote sensing data. The geovisualization of remotely sensed data over these periods supports these patterns, suggesting a potential connection with population density. The results show the complexity of drawing global conclusions about the impact of social disruptions on the atmosphere and emphasize the advantages of using remote sensing as an effective framework to address air quality changes over short periods of time. This study also highlights the advantages of a remote sensing approach to monitor atmospheric conditions in countries with limited air quality monitoring infrastructure and provides a valuable approach for the evaluation of short-term alterations in atmospheric conditions due to social disturbance events.","PeriodicalId":48993,"journal":{"name":"Remote Sensing","volume":"38 1","pages":""},"PeriodicalIF":4.2000,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Remote Sensing","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.3390/rs16183436","RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0

Abstract

This study uses a remote sensing approach to investigate air quality fluctuations during two short-period social upheaval events caused by civil protests in 2019 and the COVID-19 pandemic in 2020 in Quito, Ecuador. We used data from the TROPOMI Sentinel-P5 satellite to evaluate the concentrations of two greenhouse gases, namely O3 and NO2. TROPOMI Sentinel-P5 satellite data are becoming essential in air quality monitoring, particularly for countries that lack ground-based monitoring systems. For a better approximation of satellite data with ground data, we related the remotely sensed data using ground station data and Pearson correlation analysis, which revealed a significant association between the two sources (0.43 ≤ r ≤ 0.78). Using paired t-test comparisons, we evaluated the differences in mean gas concentrations at 30 randomly selected intervals to identify significant changes before and after the events. The results indicate noticeable changes in the two gases over the three analysis periods. O3 significantly decreased between September and November 2019 and between March and May 2020, while NO2 significantly increased. NO2 levels decreased by 18% between February and March 2020 across the study area, as indicated by remote sensing data. The geovisualization of remotely sensed data over these periods supports these patterns, suggesting a potential connection with population density. The results show the complexity of drawing global conclusions about the impact of social disruptions on the atmosphere and emphasize the advantages of using remote sensing as an effective framework to address air quality changes over short periods of time. This study also highlights the advantages of a remote sensing approach to monitor atmospheric conditions in countries with limited air quality monitoring infrastructure and provides a valuable approach for the evaluation of short-term alterations in atmospheric conditions due to social disturbance events.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用遥感框架评估厄瓜多尔基多短时期社会动荡事件期间的空气质量动态
本研究采用遥感方法调查了厄瓜多尔基多在2019年民间抗议和2020年COVID-19大流行所引发的两次短周期社会动荡期间的空气质量波动。我们利用 TROPOMI Sentinel-P5 卫星的数据评估了两种温室气体(即臭氧和二氧化氮)的浓度。TROPOMI Sentinel-P5 卫星数据在空气质量监测中变得至关重要,尤其是对于缺乏地面监测系统的国家。为了使卫星数据与地面数据更加接近,我们利用地面站数据和皮尔逊相关分析将遥感数据联系起来,结果显示这两种数据源之间存在显著关联(0.43 ≤ r ≤ 0.78)。通过配对 t 检验比较,我们评估了随机选择的 30 个时间间隔内平均气体浓度的差异,以确定事件发生前后的显著变化。结果表明,这两种气体在三个分析期间发生了明显变化。O3 在 2019 年 9 月至 11 月以及 2020 年 3 月至 5 月期间明显减少,而 NO2 则明显增加。遥感数据显示,2020 年 2 月至 3 月期间,整个研究区域的二氧化氮水平下降了 18%。这些时期遥感数据的地理可视化支持这些模式,表明与人口密度存在潜在联系。研究结果表明了就社会干扰对大气层的影响得出全球性结论的复杂性,并强调了利用遥感技术作为有效框架来应对短时间内空气质量变化的优势。这项研究还强调了在空气质量监测基础设施有限的国家采用遥感方法监测大气状况的优势,并为评估社会干扰事件导致的大气状况短期变化提供了宝贵的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Remote Sensing
Remote Sensing REMOTE SENSING-
CiteScore
8.30
自引率
24.00%
发文量
5435
审稿时长
20.66 days
期刊介绍: Remote Sensing (ISSN 2072-4292) publishes regular research papers, reviews, letters and communications covering all aspects of the remote sensing process, from instrument design and signal processing to the retrieval of geophysical parameters and their application in geosciences. Our aim is to encourage scientists to publish experimental, theoretical and computational results in as much detail as possible so that results can be easily reproduced. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.
期刊最新文献
Mapping Field-Level Maize Yields in Ethiopian Smallholder Systems Using Sentinel-2 Imagery Development of a Background Filtering Algorithm to Improve the Accuracy of Determining Underground Cavities Using Multi-Channel Ground-Penetrating Radar and Deep Learning Enhancing Digital Twins with Human Movement Data: A Comparative Study of Lidar-Based Tracking Methods Development of a UAS-Based Multi-Sensor Deep Learning Model for Predicting Napa Cabbage Fresh Weight and Determining Optimal Harvest Time Mini-Satellite Fucheng 1 SAR: Interferometry to Monitor Mining-Induced Subsidence and Comparative Analysis with Sentinel-1
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1