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Record W4317178398 · doi:10.1289/isee.2022.p-0678

Field measurements of indoor and community air quality in rural Beijing before, during, and after the COVID-19 lockdown

2022· article· en· W4317178398 on OpenAlexaff
Xiaoying Li, Xiang Zhang, Talia Sternbach, Collin Brehmer, Jill Baumgartner, Christopher Barrington‐Leigh, Sam Harper, Brian E. Robinson, Guofeng Shen, Shu Tao, Yuanxun Zhang, Ellison Carter

Bibliographic record

VenueISEE Conference Abstracts · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality Monitoring and Forecasting
Canadian institutionsMcGill University
Fundersnot available
KeywordsBeijingEnvironmental scienceCoronavirus disease 2019 (COVID-19)Environmental healthIndoor air qualityAir quality indexOutbreakGeographyChinaMeteorologyEnvironmental engineeringMedicine

Abstract

fetched live from OpenAlex

Background and aims The outbreak of the coronavirus (COVID-19) initiated a global prevention response to curb the spread of the virus, including a series of actions to reduce human mobility. Previous studies report reductions in outdoor PM₂.₅ associated with COVID-19 lockdown in many countries and regions. Few studies assessed the impacts of COVID-19 on local air quality in environments with diverse socioeconomic and household energy use patterns. We evaluated whether indoor and community PM₂.₅ in homes with different energy use patterns in rural Beijing, China differed before, during, and after the lockdown. Methods We deployed low-cost PM₂.₅ sensors (Plantower), calibrated with co-located filter-based PM₂.₅, to measure indoor and community air quality in 147 homes from 30 villages in Beijing in January-April, 2022. We apply mixed-effects models to assess the impact of the COVID-19 lockdown on indoor PM₂.₅ and used the random component superposition model (RCSM) to estimate the contributions of indoor and outdoor sources to indoor PM₂.₅. Results Community pollution was higher during the lockdown period (61 ± 47 μg/m³) compared with before (45 ± 35 μg/m³) and after (47 ± 37 μg/m³) the lockdown. However, we did not observe higher indoor PM₂.₅ during the lockdown (during vs. before: 98 ± 86 vs. 96 ± 83 μg/m³). Indoor-generated PM₂.₅ was lowest in homes using clean energy exclusively for heating and without smokers, and did not change significantly during the lockdown compared with homes using solid fuels. Conclusions Indoor air quality did not worsen during the COVID-19 lockdown in our rural Beijing sites, though community PM₂.₅ was higher during the lockdown. Indoor-generated PM₂.₅ in homes using clean energy exclusively for heating was low and stable, while decreased during the lockdown in homes using solid fuel, which may be due to less solid fuel burning for heating because outdoor temperatures warmed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.072
GPT teacher head0.309
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2022
Admission routes1
Has abstractyes

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