From outdoor to indoor air pollution source apportionment: Answers to ten challenging questions

被引：2

作者：

Saraga, Dikaia ^{[1
]}

Duarte, Regina M. B. O. ^{[2
]}

Manousakas, Manousos-Ioannis ^{[3
]}

Maggos, Thomas ^{[1
]}

Tobler, Anna ^{[4
]}

Querol, Xavier ^{[5
]}

机构：

[1] NCSR Demokritos, Atmospher Chem & Innovat Technol Lab, INRASTES, Athens 15310, Greece

[2] Univ Aveiro, CESAM Ctr Environm & Marine Studies, Dept Chem, P-3810193 Aveiro, Portugal

[3] Paul Scherrer Inst, Lab Atmospher Chem, CH-5232 Villigen, Switzerland

[4] Datalystica Ltd, Parkstr 1, CH-5234 Villigen, Switzerland

[5] CSIC, Inst Environm Assessment & Water Res IDAEA, Barcelona, Spain

来源：

TRAC-TRENDS IN ANALYTICAL CHEMISTRY | 2024年 / 178卷

关键词：

Indoor air quality; Particulate matter; Volatile organic compounds; Online monitoring; Source apportionment; Receptor models; Machine learning; FINE PARTICULATE MATTER; EXPOSURE; TRANSFORMATION; INFILTRATION; PM2.5;

D O I：

10.1016/j.trac.2024.117821

中图分类号：

O65 [分析化学];

学科分类号：

070302 ; 081704 ;

摘要：

Indoor air pollution source apportionment (SA) is an evolving field and remains challenging to adjust the gained knowledge from atmospheric studies to the indoor environment. This paper identifies ten key-questions on indoor SA. Firstly, the scientific challenges of indoor SA are presented, including the differences between indoor and outdoor SA (Q1), the appropriate tracers (Q2), the challenges for indoor organic PM and VOCs apportionment (Q3) and the effect of oxidative reactions on indoor SA (Q4). Afterwards, the advances of indoor air monitoring/analysis are discussed towards an optimized indoor air SA (Q5-6). Q7 and Q8 review the type of receptor models best applying for indoor SA and discuss how the advances in online outdoor SA can benefit indoor SA. Q9 deals with the optimization of the estimation of the outdoor sources' contribution to indoor air. Finally, the challenge of using machine learning techniques for indoor SA is discussed in Q10.

引用

页数：8

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