Prediction of surface ozone episodes using clusters based generalized linear mixed effects models in Houston–Galveston–Brazoria area, Texas

Sun, Wei; Palazoğlu, Ahmet; Singh, Amrik; Zhang, Hao; Wang, Qi; Zhao, Zemeng; Cao, Ding

doi:10.5094/apr.2015.029

Cited by 20 publications

(7 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Following the same, moving average approach was applied to determine the cumulative lag effect of air pollutants and their association with meteorological factors (Kim et al, 2019;Qiu et al, 2020;Rojas-roa and Rodríguezvillamizar, 2019;Zhang et al, 2020;Zhu et al, 2018Zhu et al, , 2019. So, Generalised additive model (GAM) with Gaussian distribution was utilised to connect the infected rate due to COVID-19 and air pollutants or meteorological parameters (Chuang et al, 2011;Gao et al, 2019;Ravindra et al, 2019;Sun et al, 2015;Tong et al, 2018;Yoon, 2019;Zhang and Batterman, 2010). The model also used to estimate the correlations between moving average concentrations of air pollutants and meteorological factors at Lag0-7, Lag0-14 and Lag0-21 with daily reported COVID-19 infected cases in India (Charles et al, 2020;Ge et al, 2017;Hao et al, 2019;Liang et al, 2020a;Lin et al, 2013;Jiandong Yang et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

Significance between Air pollutants, Meteorological Factors and COVID-19 Infections: Probable Evidences in India

Sahoo

2020

Preprint

View full text Add to dashboard Cite

SARS-CoV-2 (Coronavirus) disease represents the causative agent with a potentially fatal risk which is having great global human health concern. Earlier studies suggested that air pollutants and meteorological factors were considered as the risk factors for acute respiratory infection, which carries harmful pathogens and affects the immunity. The study intended to explore the correlation between air pollutants, meteorological factors and the daily reported infection cases caused by novel coronavirus in India. The daily positive infected cases, air pollution and meteorological factors in 288 districts were collected from January 30, 2020 to April 23, 2020 in India. Speraman’s correlation and generalised additive model were applied to investigate the correlations of four air pollutants (PM2.5, PM10, NO2 and SO2) and eight meteorological factors (Temp, DTR, RH, AH, AP, RF, WS and WD) with COVID-19 infected cases. The study indicated that a 10 µg/m3 increase during (Lag0-14) in PM2.5, PM10 and NO2 was resulted in 2.21% (95%CI: 1.13 to 3.29), 2.67% (95% CI: 0.33 to 5.01) and 4.56 (95% CI: 2.22 to 6.90) increase in daily counts of COVID 19 infected cases respectively. However, only 1 unit increase in meteorological factor levels in case of daily mean temperature and DTR during (Lag0-14) associated with 3.78% (95%CI: 1.81 to 5.75) and 1.82% (95% CI: -1.74 to 5.38) rise of COVID-19 infected cases respectively. In addition, SO2 and relative humidity were negatively associated with COVID-19 infected cases at Lag0-14 with decrease of 7.23% (95% CI: -10.99 to -3.47) and 1.11% (95% CI: -3.45 to 1.23) for SO2 and for relative humidity respectively. The study recommended that there is significant relationship between air pollutants and meteorological factors with COVID-19 infected cases, which substantially explain the effect of national lockdown and suggested positive implications for control and prevention of the spread of SARS-CoV-2 disease.

show abstract

Section: Discussionmentioning

confidence: 99%

Significance between Air pollutants, Meteorological Factors and COVID-19 Infections: Probable Evidences in India

Sahoo

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…This is a major advantage in the case of repeated measurements or hierarchical data, as it allows for accurate predictions despite the presence of correlated errors [15]. In various scientific domains, including ecology and atmospheric pollution research, LMM have shown high predictive performance and large applicability domains [16][17][18]. To our knowledge, LMMs have never been applied to predict the transfer of chemicals from food packaging materials into food.…”

Section: Introductionmentioning

confidence: 99%

A regression-based model to predict chemical migration from packaging to food

Douziech

Benítez‐López

Ernstoff

et al. 2019

J Expo Sci Environ Epidemiol

View full text Add to dashboard Cite

Article 25fa pilot End User AgreementThis publication is distributed under the terms of Article 25fa of the Dutch Copyright Act (Auteurswet) with explicit consent by the author. Dutch law entitles the maker of a short scientific work funded either wholly or partially by Dutch public funds to make that work publicly available for no consideration following a reasonable period of time after the work was first published, provided that clear reference is made to the source of the first publication of the work.This publication is distributed under The Association of Universities in the Netherlands (VSNU) 'Article 25fa implementation' pilot project. In this pilot research outputs of researchers employed by Dutch Universities that comply with the legal requirements of Article 25fa of the Dutch Copyright Act are distributed online and free of cost or other barriers in institutional repositories. Research outputs are distributed six months after their first online publication in the original published version and with proper attribution to the source of the original publication.You are permitted to download and use the publication for personal purposes. All rights remain with the author(s) and/or copyrights owner(s) of this work. Any use of the publication other than authorised under this licence or copyright law is prohibited.If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website.

show abstract

“…Several studies have been proposed for the task of predicting ozone levels [5], [6]. Such methods utilized the clustering techniques in order to group the spots that have a high level of ozone.…”

Section: Introductionmentioning

confidence: 99%

An Agglomerative Hierarchical Clustering with Association Rules for Discovering Climate Change Patterns

Sammour

Othman

Muda

et al. 2019

ijacsa

View full text Add to dashboard Cite

Ozone analysis is the process of identifying meaningful patterns that would facilitate the prediction of future trends. One of the common techniques that have been used for ozone analysis is the clustering technique. Clustering is one of the popular methods which contribute a significant knowledge for time series data mining by aggregating similar data in specific groups. However, identifying significant patterns regarding the ground-level ozone is quite a challenging task especially after applying the clustering task. This paper presents a pattern discovery for ground-level ozone using a proposed method known as an Agglomerative Hierarchical Clustering with Dynamic Time Warping (DTW) as a distance measure on which the patterns have been extracted using the Apriori Association Rules (AAR) algorithm. The experiment is conducted on a Malaysian Ozone dataset collected from Putrajaya for year 2006. The experiment result shows 20 pattern influences on high ozone with a high confident (1.00). However, it can be classified into four meaningful patterns; more high temperature with low nitrogen oxide, nitrogen oxide and nitrogen dioxide high, nitrogen oxide with carbon oxide high, and carbon oxide high. These patterns help in decision making to plan the amount of carbon oxide and nitrogen oxide to be reduced in order to avoid the high ozone surface.

show abstract

Prediction of surface ozone episodes using clusters based generalized linear mixed effects models in Houston–Galveston–Brazoria area, Texas

Cited by 20 publications

References 36 publications

Significance between Air pollutants, Meteorological Factors and COVID-19 Infections: Probable Evidences in India

Significance between Air pollutants, Meteorological Factors and COVID-19 Infections: Probable Evidences in India

A regression-based model to predict chemical migration from packaging to food

An Agglomerative Hierarchical Clustering with Association Rules for Discovering Climate Change Patterns

Contact Info

Product

Resources

About