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Record W4320085100 · doi:10.3126/njst.v21i1.49916

A Short-Term Measurement of PM2.5 Concentration During the COVID-19 Lockdown Period in Kathmandu Valley

2022· article· en· W4320085100 on OpenAlexfundno aff
Pawan Kumar Neupane, Sunil Babu Shrestha, Dipesh Rupakheti, Dev Raj Joshi, Tista Prasai Joshi

Bibliographic record

VenueNepal Journal of Science and Technology · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicCOVID-19 impact on air quality
Canadian institutionsnot available
FundersNepal Academy of Science and TechnologyInternational Development Research Centre
KeywordsAir quality indexEnvironmental scienceParticulatesCoronavirus disease 2019 (COVID-19)Air pollution2019-20 coronavirus outbreakEnvironmental healthMeteorologyEnvironmental protectionGeographyMedicine

Abstract

fetched live from OpenAlex

The Government of Nepal implemented a nationwide lockdown from 24 March 2020 to 21 July 2020 to control the person-to-person transmission of COVID-19. This study was conducted in a trafficintensified area of Kathmandu valley, where vehicular movement represents one of the main sources of air pollution. Hence, this study was intended to quantify the concentration of particulate matter (PM2.5) for 11 hours of daytime from 23 April to 20 May 2020. It was also to evaluate the influences of lockdown on air quality. PM2.5 was observed using HAZ-Dust, Environmental Particulate Air Monitor in the 18 different traffic sites of the Kathmandu valley. During the lockdown period, a substantially low mean concentration of PM2.5 ranging from 3.69±1.78 µg/ m3 to 7.58±3.98 µg/m3 was recorded in Kathmandu valley, which reflected improved air quality due to the cessation of vehicular activities. Therefore, the study outcome suggests that controlling the existing vehicular activities and promoting energy-efficient vehicles like electric vehicles in specific locations in the city will improve air quality and benefit public health.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.380
Threshold uncertainty score0.762

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.030
GPT teacher head0.294
Teacher spread0.264 · 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 teacher head, 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

Citations1
Published2022
Admission routes1
Has abstractyes

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