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Indoor Chemistry
Open accessAbstract
This review aims to encapsulate the importance, ubiquity, and complexity of indoor chemistry. We discuss the many sources of indoor air pollutants and summarize their chemical reactions in the air and on surfaces. We also summarize some of the known impacts of human occupants, who act as sources and sinks of indoor chemicals, and whose activities (e.g., cooking, cleaning, smoking) can lead to extremely high pollutant concentrations. As we begin to use increasingly sensitive and selective instrumentation indoors, we are learning more about chemistry in this relatively understudied environment.
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Metadata
- Authors
- Weschler, C. J., & Carslaw, N.
- Venue
- Environmental Science & Technology
- Publisher
- American Chemical Society (ACS)
- Published
- 2018-02-05
- Volume
- 52
- Issue
- 5
- Pages
- 2419-2428
- ISSN
- 0013-936X (ISSN-L)
- 1520-5851
- OpenAlex
- W4255621398
- Cited by
- 293
- References
- 108
- FWCI
- 13.84
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
