ArticleEnvironmental science and pollution research international2019
Using the seasonal FGM(1,1) model to predict the air quality indicators in Xingtai and Handan.
Article in Environmental science and pollution research international, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- COVID-19 lockdowns and air quality: Evidence from grey spatiotemporal forecasts.Socio-economic planning sciences · 2022Article
- Forecasting seasonal electricity generation in European countries under Covid-19-induced lockdown using fractional grey prediction models and machine learning methods.Applied energy · 2021Article
- Assessment of the air pollution emission reduction effect of the coal substitution policy in China: an improved grey modelling approach.Environmental science and pollution research international · 2020Article
- An improved gray Bernoulli model for estimating the relationship between economic growth and pollution emissions.Environmental science and pollution research international · 2020Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
The air pollution problem in Xingtai and Handan is the focus of public attention. The seasonal gray model with fractional order accumulation is proposed to predict the quarterly concentrations of PM
Indexed as
Identifiers
30877543What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.