Near-Real-Time Emission Characterization for Major Industrial Sectors Using Multisatellite and Base-Year Emission Inventories

IF 8.8 2区 环境科学与生态学 Q1 ENGINEERING, ENVIRONMENTAL
Jiashu Ye, Huilin Liu, Tong Wu, Weiwen Chen, Zhijiong Huang, Manni Zhu, Yuanqian Xu, Zhuangmin Zhong, Duohong Chen and Junyu Zheng*, 
{"title":"Near-Real-Time Emission Characterization for Major Industrial Sectors Using Multisatellite and Base-Year Emission Inventories","authors":"Jiashu Ye,&nbsp;Huilin Liu,&nbsp;Tong Wu,&nbsp;Weiwen Chen,&nbsp;Zhijiong Huang,&nbsp;Manni Zhu,&nbsp;Yuanqian Xu,&nbsp;Zhuangmin Zhong,&nbsp;Duohong Chen and Junyu Zheng*,&nbsp;","doi":"10.1021/acs.estlett.5c00462","DOIUrl":null,"url":null,"abstract":"<p >Near-real-time (NRT) characterization of industrial emissions is crucial for tracking dynamic emission patterns and informing timely regulatory responses. However, existing methods rely heavily on continuous emission monitoring systems (CEMS), which are often limited in data availability, coverage and data quality. This study introduces a novel approach that integrates multisatellite fire radiative power (FRP) observations with a base-year emission inventory (BY-EI) to improve source identification and emission estimation. Compared to previous methods, our multisatellite strategy increased the number of identified industrial point sources by 38%. Fire radiative energy (FRE)-emission response models were developed by linking satellite-derived FRE with emission data from identified sources. These models enabled daily emission estimates with strong performance, especially for SO<sub>2</sub> (R<sup>2</sup> = 0.895) and NO<sub><i>x</i></sub> (R<sup>2</sup> = 0.855). For sources without direct FRP detection, sectoral FRE-based temporal profiles captured emission variability effectively (Pearson’s <i>r</i> &gt; 0.6, mean square errors (MSE) at 10<sup>–5</sup> level). This approach successfully identified high-emission industrial sectors, including cement, ceramics, and steel industries, expanding the scope of detectable sources while reducing reliance on CEMS data. The findings provide a new framework for NRT industrial emission characterization, supporting the timely updating of industrial emissions and refined pollution control strategies and decision-making.</p>","PeriodicalId":37,"journal":{"name":"Environmental Science & Technology Letters Environ.","volume":"12 8","pages":"982–989"},"PeriodicalIF":8.8000,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Science & Technology Letters Environ.","FirstCategoryId":"1","ListUrlMain":"https://pubs.acs.org/doi/10.1021/acs.estlett.5c00462","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ENVIRONMENTAL","Score":null,"Total":0}
引用次数: 0

Abstract

Near-real-time (NRT) characterization of industrial emissions is crucial for tracking dynamic emission patterns and informing timely regulatory responses. However, existing methods rely heavily on continuous emission monitoring systems (CEMS), which are often limited in data availability, coverage and data quality. This study introduces a novel approach that integrates multisatellite fire radiative power (FRP) observations with a base-year emission inventory (BY-EI) to improve source identification and emission estimation. Compared to previous methods, our multisatellite strategy increased the number of identified industrial point sources by 38%. Fire radiative energy (FRE)-emission response models were developed by linking satellite-derived FRE with emission data from identified sources. These models enabled daily emission estimates with strong performance, especially for SO2 (R2 = 0.895) and NOx (R2 = 0.855). For sources without direct FRP detection, sectoral FRE-based temporal profiles captured emission variability effectively (Pearson’s r > 0.6, mean square errors (MSE) at 10–5 level). This approach successfully identified high-emission industrial sectors, including cement, ceramics, and steel industries, expanding the scope of detectable sources while reducing reliance on CEMS data. The findings provide a new framework for NRT industrial emission characterization, supporting the timely updating of industrial emissions and refined pollution control strategies and decision-making.

Abstract Image

基于多卫星和基准年排放清单的主要工业部门近实时排放表征
工业排放的近实时(NRT)特征对于跟踪动态排放模式和及时通知监管响应至关重要。然而,现有的方法严重依赖于连续排放监测系统(CEMS),这些系统在数据的可得性、覆盖范围和数据质量方面往往受到限制。本研究引入了一种将多卫星火辐射功率(FRP)观测与基准年排放清单(BY-EI)相结合的新方法,以改进源识别和排放估算。与以前的方法相比,我们的多卫星战略使确定的工业点源数量增加了38%。通过将卫星获得的火辐射能(FRE)与确定来源的发射数据联系起来,建立了火辐射能(FRE)发射响应模型。这些模型使每日排放估算具有很强的性能,特别是二氧化硫(R2 = 0.895)和氮氧化物(R2 = 0.855)。对于没有直接FRP检测的源,基于部门fred的时间曲线有效地捕获了排放变异性(Pearson’s r >;0.6,均方误差(MSE)在10-5水平)。该方法成功地确定了高排放工业部门,包括水泥、陶瓷和钢铁行业,扩大了可检测源的范围,同时减少了对CEMS数据的依赖。研究结果为NRT工业排放表征提供了新的框架,为工业排放和精细污染控制策略的及时更新和决策提供了支持。
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
Environmental Science & Technology Letters Environ.
Environmental Science & Technology Letters Environ. ENGINEERING, ENVIRONMENTALENVIRONMENTAL SC-ENVIRONMENTAL SCIENCES
CiteScore
17.90
自引率
3.70%
发文量
163
期刊介绍: Environmental Science & Technology Letters serves as an international forum for brief communications on experimental or theoretical results of exceptional timeliness in all aspects of environmental science, both pure and applied. Published as soon as accepted, these communications are summarized in monthly issues. Additionally, the journal features short reviews on emerging topics in environmental science and technology.
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信