{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,11]],"date-time":"2026-05-11T18:57:21Z","timestamp":1778525841818,"version":"3.51.4"},"reference-count":47,"publisher":"Emerald","issue":"4","license":[{"start":{"date-parts":[[2020,12,22]],"date-time":"2020-12-22T00:00:00Z","timestamp":1608595200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.emerald.com\/insight\/site-policies"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["GS"],"published-print":{"date-parts":[[2021,10,19]]},"abstract":"<jats:sec><jats:title content-type=\"abstract-subheading\">Purpose<\/jats:title><jats:p>Smog seriously affects the ecological environment and poses a threat to public health. Therefore, smog control has become a key task in China, which requires reliable prediction.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>This paper establishes a novel time-lag GM(1,N) model based on interval grey number sequences. Firstly, calculating kernel and degree of greyness of the interval grey number sequence respectively. Then, establishing the time-lag GM(1,N) model of kernel and degree of greyness sequences respectively to obtain their values after determining the time-lag parameters of two models. Finally, the upper and lower bounds of interval grey number sequences are obtained by restoring the values of kernel and degree of greyness.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>In order to verify the validity and practicability of the model, the monthly concentrations of PM2.5, SO2 and NO2 in Beijing during August 2017 to September 2018 are selected to establish the time-lag GM(1,3) model for kernel and degree of greyness sequences respectively. Compared with three existing models, the proposed model in this paper has better simulation accuracy. Therefore, the novel model is applied to forecast monthly PM2.5 concentration for October to December 2018 in Beijing and provides a reference basis for the government to formulate smog control policies.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Practical implications<\/jats:title><jats:p>The proposed model can simulate and forecast system characteristic data with the time-lag effect more accurately, which shows that the time-lag GM(1,N) model proposed in this paper is practical and effective.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>Based on interval grey number sequences, the traditional GM(1,N) model neglects the time-lag effect of driving terms, hence this paper introduces the time-lag parameters into driving terms of the traditional GM(1,N) model and proposes a novel time-lag GM(1,N) model.<\/jats:p><\/jats:sec>","DOI":"10.1108\/gs-02-2020-0025","type":"journal-article","created":{"date-parts":[[2020,12,21]],"date-time":"2020-12-21T03:15:10Z","timestamp":1608520510000},"page":"754-778","source":"Crossref","is-referenced-by-count":10,"title":["Forecasting smog in Beijing using a novel time-lag GM(1,N) model based on interval grey number sequences"],"prefix":"10.1108","volume":"11","author":[{"given":"Jia","family":"Shi","sequence":"first","affiliation":[]},{"given":"Pingping","family":"Xiong","sequence":"additional","affiliation":[]},{"given":"Yingjie","family":"Yang","sequence":"additional","affiliation":[]},{"given":"Beichen","family":"Quan","sequence":"additional","affiliation":[]}],"member":"140","published-online":{"date-parts":[[2020,12,22]]},"reference":[{"key":"key2021101806272710200_ref001","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/j.atmosenv.2018.10.029","article-title":"Smog analysis and its effect on reported ocular surface diseases: a case study of 2016 smog event of Lahore","volume":"198","year":"2019","journal-title":"Atmospheric Environment"},{"issue":"4","key":"key2021101806272710200_ref002","doi-asserted-by":"crossref","first-page":"652","DOI":"10.1016\/j.apr.2016.12.014","article-title":"Recursive neural network model for analysis and forecast of PM10 and PM2.5","volume":"8","year":"2017","journal-title":"Atmospheric Pollution Research"},{"issue":"06","key":"key2021101806272710200_ref003","first-page":"1147","article-title":"An optimized grey prediction model of interval grey numbers based on residual corrections","volume":"33","year":"2018","journal-title":"Control and Decision"},{"issue":"9","key":"key2021101806272710200_ref004","first-page":"1672","article-title":"Grey multivariable discrete time-lag model based on driving information control term and its application","volume":"32","year":"2017","journal-title":"Control and Decision"},{"key":"key2021101806272710200_ref005","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1016\/j.cie.2018.11.016","article-title":"A novel discrete grey multivariable model and its application in forecasting the output value of China\u2019s high-tech industries","volume":"127","year":"2019","journal-title":"Computers and Industrial Engineering"},{"issue":"11","key":"key2021101806272710200_ref006","first-page":"1997","article-title":"Research on multivariate discrete grey prediction model based on time-lag effect","volume":"32","year":"2017","journal-title":"Control and Decision"},{"key":"key2021101806272710200_ref007","first-page":"105","article-title":"A novel grey model based on the trends of driving factors and its application","volume":"30","year":"2018","journal-title":"Journal of Grey System"},{"key":"key2021101806272710200_ref008","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.atmosres.2018.04.027","article-title":"Microscopic morphology and seasonal variation of health effect arising from heavy metals in PM2.5 and PM10: one-year measurement in a densely populated area of urban Beijing","volume":"212","year":"2018","journal-title":"Atmospheric Research"},{"issue":"17","key":"key2021101806272710200_ref009","doi-asserted-by":"crossref","first-page":"8923","DOI":"10.1016\/j.amc.2013.03.018","article-title":"A SVM-based regression model to study the air quality at local scale in Oviedo urban area (Northern Spain): a case study","volume":"219","year":"2013","journal-title":"Applied Mathematics and Computation"},{"key":"key2021101806272710200_ref010","doi-asserted-by":"crossref","first-page":"743","DOI":"10.1016\/j.jclepro.2017.10.195","article-title":"How harmful is air pollution to economic development? 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