ArticleInternational journal of environmental research and public health2020
The Functional Spatio-Temporal Statistical Model with Application to O
Article in International journal of environmental research and public health, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, with rapid industrialization and massive energy consumption, ground-level ozone ( O 3 ) has become one of the most severe air pollutants. In this paper, we propose a functional spatio-temporal statistical model to analyze air quality data. Firstly, since the pollutant data from the monitoring network usually have a strong spatial and temporal correlation, the spatio-temporal statistical model is a reasonable method to reveal spatial correlation structure and temporal dynamic mechanism in data. Secondly, effects from the covariates are introduced to explore the formation mechanism of ozone pollution. Thirdly, considering the obvious diurnal pattern of ozone data, we explore the diurnal cycle of O 3 pollution using the functional data analysis approach. The spatio-temporal model shows great applicational potential by comparison with other models. With application to O 3 pollution data of 36 stations in Beijing, China, we give explanations of the covariate effects on ozone pollution, such as other pollutants and meteorological variables, and meanwhile we discuss the diurnal cycle of ozone pollution.
Indexed as
Identifiers
What 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.