Field measurements of indoor and community air quality in rural Beijing before, during, and after the COVID-19 lockdown
Why this work is in the frame
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Bibliographic record
Abstract
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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it