Spatiotemporal analysis of atmospheric methane concentrations and key influencing factors using machine learning in the Middle East (2010–2021)

IF 3.8 Q2 ENVIRONMENTAL SCIENCES

Remote Sensing Applications-Society and Environment Pub Date : 2024-11-20 DOI:10.1016/j.rsase.2024.101406

Seyed Mohsen Mousavi

{"title":"Spatiotemporal analysis of atmospheric methane concentrations and key influencing factors using machine learning in the Middle East (2010–2021)","authors":"Seyed Mohsen Mousavi","doi":"10.1016/j.rsase.2024.101406","DOIUrl":null,"url":null,"abstract":"<div><div>Methane (CH4) is a potent greenhouse gas that significantly impacts climate change due to its rising atmospheric concentrations. Hence, it is crucial to comprehend the spatial and temporal fluctuations in atmospheric CH4 concentration (XCH4) at both national and international levels. This study investigates the correlation between atmospheric XCH4 concentrations (XCH4) and key influencing factors to identify the primary sources and sinks of CH4 across the Middle East (ME). Initially, XCH4 data from the GOSAT satellite, covering the period from 2010 to 2021, were employed to generate spatiotemporal distribution maps of XCH4 across the ME region. Subsequently, the study investigated the single and simultaneous relationship between XCH4 and relevant environmental factors, such as vegetation, temperature, precipitation, and others, across different months using correlation analysis and the Permutation Feature Importance (PFI) method to identify the key factors influencing XCH4 variations. The results reveal significant spatial and temporal variations in XCH4 concentrations, with higher levels detected in the central and southern regions of the ME during the summer months. The results also highlight the presence of both peak positive and negative correlations with temperature and moisture during winter months. Additionally, both precipitation and vegetation demonstrated negative correlations with XCH4, especially during the winter and plant-growing seasons. According to the PFI results, temperature emerged as the most significant factor, accounting for over 40% of the variance in XCH4 concentrations during summer. At the same time, anthropogenic activities exerted minimal influence on these patterns. This comprehensive spatiotemporal analysis provides crucial insights into the variation of CH4 and its primary drivers in this climatically vulnerable region. Identifying emission patterns can support the development of targeted mitigation policies to curb the future rise of CH4.</div></div>","PeriodicalId":53227,"journal":{"name":"Remote Sensing Applications-Society and Environment","volume":"37 ","pages":"Article 101406"},"PeriodicalIF":3.8000,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Remote Sensing Applications-Society and Environment","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2352938524002702","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}

引用次数: 0

Abstract

Methane (CH₄) is a potent greenhouse gas that significantly impacts climate change due to its rising atmospheric concentrations. Hence, it is crucial to comprehend the spatial and temporal fluctuations in atmospheric CH₄ concentration (XCH₄) at both national and international levels. This study investigates the correlation between atmospheric XCH₄ concentrations (XCH₄) and key influencing factors to identify the primary sources and sinks of CH₄ across the Middle East (ME). Initially, XCH₄ data from the GOSAT satellite, covering the period from 2010 to 2021, were employed to generate spatiotemporal distribution maps of XCH₄ across the ME region. Subsequently, the study investigated the single and simultaneous relationship between XCH₄ and relevant environmental factors, such as vegetation, temperature, precipitation, and others, across different months using correlation analysis and the Permutation Feature Importance (PFI) method to identify the key factors influencing XCH₄ variations. The results reveal significant spatial and temporal variations in XCH₄ concentrations, with higher levels detected in the central and southern regions of the ME during the summer months. The results also highlight the presence of both peak positive and negative correlations with temperature and moisture during winter months. Additionally, both precipitation and vegetation demonstrated negative correlations with XCH₄, especially during the winter and plant-growing seasons. According to the PFI results, temperature emerged as the most significant factor, accounting for over 40% of the variance in XCH₄ concentrations during summer. At the same time, anthropogenic activities exerted minimal influence on these patterns. This comprehensive spatiotemporal analysis provides crucial insights into the variation of CH₄ and its primary drivers in this climatically vulnerable region. Identifying emission patterns can support the development of targeted mitigation policies to curb the future rise of CH₄.

查看原文本刊更多论文

利用机器学习对中东地区大气甲烷浓度和主要影响因素进行时空分析（2010-2021 年）

甲烷（CH4）是一种强效温室气体，由于其在大气中的浓度不断上升，对气候变化产生了重大影响。因此，了解国家和国际层面大气中 CH4 浓度（XCH4）的时空波动至关重要。本研究调查了大气中 XCH4 浓度（XCH4）与主要影响因素之间的相关性，以确定中东（ME）地区 CH4 的主要来源和吸收汇。首先，利用 GOSAT 卫星提供的 XCH4 数据生成整个中东地区 XCH4 的时空分布图，时间跨度为 2010 年至 2021 年。随后，研究利用相关性分析和排列特征重要性（PFI）方法，研究了不同月份 XCH4 与植被、温度、降水等相关环境因素之间的单一和同步关系，以确定影响 XCH4 变化的关键因素。结果表明，XCH4浓度存在明显的时空变化，在夏季，地中海中部和南部地区的浓度水平较高。结果还显示，在冬季月份，XCH4 浓度与温度和湿度之间存在正相关和负相关的峰值。此外，降水和植被与 XCH4 呈负相关，尤其是在冬季和植物生长季节。根据 PFI 结果，温度是最重要的因素，占夏季 XCH4 浓度变异的 40% 以上。同时，人为活动对这些模式的影响微乎其微。这项全面的时空分析提供了有关这一气候脆弱地区甲烷变化及其主要驱动因素的重要见解。确定排放模式有助于制定有针对性的减缓政策，以遏制未来甲烷的上升。

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分钟内获得全文求助全文

来源期刊

Remote Sensing Applications-Society and Environment Multiple-

CiteScore

8.00

自引率

8.50%

发文量

204

审稿时长

65 days

期刊介绍： The journal ''Remote Sensing Applications: Society and Environment'' (RSASE) focuses on remote sensing studies that address specific topics with an emphasis on environmental and societal issues - regional / local studies with global significance. Subjects are encouraged to have an interdisciplinary approach and include, but are not limited by: " -Global and climate change studies addressing the impact of increasing concentrations of greenhouse gases, CO2 emission, carbon balance and carbon mitigation, energy system on social and environmental systems -Ecological and environmental issues including biodiversity, ecosystem dynamics, land degradation, atmospheric and water pollution, urban footprint, ecosystem management and natural hazards (e.g. earthquakes, typhoons, floods, landslides) -Natural resource studies including land-use in general, biomass estimation, forests, agricultural land, plantation, soils, coral reefs, wetland and water resources -Agriculture, food production systems and food security outcomes -Socio-economic issues including urban systems, urban growth, public health, epidemics, land-use transition and land use conflicts -Oceanography and coastal zone studies, including sea level rise projections, coastlines changes and the ocean-land interface -Regional challenges for remote sensing application techniques, monitoring and analysis, such as cloud screening and atmospheric correction for tropical regions -Interdisciplinary studies combining remote sensing, household survey data, field measurements and models to address environmental, societal and sustainability issues -Quantitative and qualitative analysis that documents the impact of using remote sensing studies in social, political, environmental or economic systems