{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T05:20:14Z","timestamp":1777958414383,"version":"3.51.4"},"reference-count":42,"publisher":"Emerald","issue":"4","license":[{"start":{"date-parts":[[2021,2,11]],"date-time":"2021-02-11T00:00:00Z","timestamp":1613001600000},"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>Aiming to address the forecasting dilemma of seasonal air quality, the authors design the novel self-adaptive seasonal adjustment factor to extract the seasonal fluctuation information about the air quality index. Based on the novel self-adaptive seasonal adjustment factor, the novel seasonal grey forecasting models are established to predict the air quality in China.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Design\/methodology\/approach<\/jats:title><jats:p>This paper constructs a novel self-adaptive seasonal adjustment factor for quantifying the seasonal difference information of air quality. The novel self-adaptive seasonal adjustment factor reflects the periodic fluctuations of air quality. Therefore, it is employed to optimize the data generation of three conventional grey models, consisting of the<jats:italic>GM<\/jats:italic>(1,1) model, the discrete grey model and the fractional-order grey model. Then three novel self-adaptive seasonal grey forecasting models, including the self-adaptive seasonal<jats:italic>GM<\/jats:italic>(1,1) model (<jats:italic>SAGM<\/jats:italic>(1,1)), the self-adaptive seasonal discrete grey model (<jats:italic>SADGM<\/jats:italic>(1,1)) and the self-adaptive seasonal fractional-order grey model (<jats:italic>SAFGM<\/jats:italic>(1,1)), are put forward for prognosticating the air quality of all provinces in China .<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Findings<\/jats:title><jats:p>The experiment results confirm that the novel self-adaptive seasonal adjustment factors promote the precision of the conventional grey models remarkably. Simultaneously, compared with three non-seasonal grey forecasting models and the<jats:italic>SARIMA<\/jats:italic>model, the performance of self-adaptive seasonal grey forecasting models is outstanding, which indicates that they capture the seasonal changes of air quality more efficiently.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Research limitations\/implications<\/jats:title><jats:p>Since air quality is affected by various factors, subsequent research may consider including meteorological conditions, pollutant emissions and other factors to perfect the self-adaptive seasonal grey models.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Practical implications<\/jats:title><jats:p>Given the problematic air pollution situation in China, timely and accurate air quality forecasting technology is exceptionally crucial for mitigating their adverse effects on the environment and human health. The paper proposes three self-adaptive seasonal grey forecasting models to forecast the air quality index of all provinces in China, which improves the adaptability of conventional grey models and provides more efficient prediction tools for air quality.<\/jats:p><\/jats:sec><jats:sec><jats:title content-type=\"abstract-subheading\">Originality\/value<\/jats:title><jats:p>The self-adaptive seasonal adjustment factors are constructed to characterize the seasonal fluctuations of air quality index. Three novel self-adaptive seasonal grey forecasting models are established for prognosticating the air quality of all provinces in China. The robustness of the proposed grey models is reinforced by integrating the seasonal irregularity. The proposed methods acquire better forecasting precisions compared with the non-seasonal grey models and the<jats:italic>SARIMA<\/jats:italic>model.<\/jats:p><\/jats:sec>","DOI":"10.1108\/gs-06-2020-0081","type":"journal-article","created":{"date-parts":[[2021,2,13]],"date-time":"2021-02-13T10:27:17Z","timestamp":1613212037000},"page":"596-618","source":"Crossref","is-referenced-by-count":20,"title":["Forecasting air quality in China using novel self-adaptive seasonal grey forecasting models"],"prefix":"10.1108","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4809-4830","authenticated-orcid":false,"given":"Xiaoyue","family":"Zhu","sequence":"first","affiliation":[]},{"given":"Yaoguo","family":"Dang","sequence":"additional","affiliation":[]},{"given":"Song","family":"Ding","sequence":"additional","affiliation":[]}],"member":"140","published-online":{"date-parts":[[2021,2,11]]},"reference":[{"key":"key2021101806271904900_ref001","first-page":"115","article-title":"Health impacts assessment due to PM2.5, PM10, and NO2 exposure in National Capital Territory (NCT)","volume":"6","year":"2020","journal-title":"Pollution"},{"key":"key2021101806271904900_ref002","doi-asserted-by":"crossref","first-page":"739","DOI":"10.1016\/j.jclepro.2019.03.253","article-title":"Hourly PM2.5 concentration forecast using stacked auto-encoder model with emphasis on seasonality","volume":"224","year":"2019","journal-title":"Journal of Cleaner Production"},{"key":"key2021101806271904900_ref003","article-title":"Effect modification of the short-term effects of air pollution on morbidity by season: a systematic review and meta-analysis","volume":"716","year":"2020","journal-title":"The Science of the Total Environment"},{"key":"key2021101806271904900_ref004","volume-title":"Time Series Analysis: Forecasting and Control","year":"2008"},{"key":"key2021101806271904900_ref005","doi-asserted-by":"crossref","first-page":"393","DOI":"10.1016\/j.energy.2018.08.040","article-title":"A novel self-adapting intelligent grey model for forecasting China's natural-gas demand","volume":"162","year":"2018","journal-title":"Energy"},{"key":"key2021101806271904900_ref006","article-title":"Estimating Chinese energy-related CO2 emissions by employing a novel discrete grey prediction model","volume":"259","year":"2020","journal-title":"Journal of Cleaner Production"},{"key":"key2021101806271904900_ref007","article-title":"A novel adaptive discrete grey model with time-varying parameters for long-term photovoltaic power generation forecasting","volume":"227","year":"2021","journal-title":"Energy Conversion and Management"},{"key":"key2021101806271904900_ref008","doi-asserted-by":"crossref","first-page":"626","DOI":"10.1111\/resp.13583","article-title":"Ambient air pollution in China","volume":"24","year":"2019","journal-title":"Respirology"},{"key":"key2021101806271904900_ref009","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.atmosenv.2015.02.030","article-title":"Artificial neural networks forecasting of PM2.5 pollution using air mass trajectory-based geographic model and wavelet transformation","volume":"107","year":"2015","journal-title":"Atmospheric Environment"},{"key":"key2021101806271904900_ref010","doi-asserted-by":"crossref","first-page":"1005","DOI":"10.1016\/j.jclepro.2019.05.319","article-title":"Recurrent neural network and random forest for analysis and accurate forecast of atmospheric pollutants: a case study in Hangzhou, China","volume":"231","year":"2019","journal-title":"Journal of Cleaner Production"},{"key":"key2021101806271904900_ref011","first-page":"893","article-title":"Forecasting air quality index using an ensemble of artificial neural networks and regression models","volume":"28","year":"2019","journal-title":"Journal of Intelligent Systems"},{"key":"key2021101806271904900_ref012","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1016\/j.scitotenv.2015.01.005","article-title":"Health impacts and economic losses assessment of the 2013 severe haze event in Beijing area","volume":"511","year":"2015","journal-title":"The Science of the Total Environment"},{"key":"key2021101806271904900_ref013","first-page":"1045","article-title":"Time series analysis and forecasting for air pollution in small urban area: a SARIMA and factor analysis approach","volume":"28","year":"2013","journal-title":"Stochastic Environmental Research and Risk Assessment"},{"key":"key2021101806271904900_ref014","unstructured":"Greenstone M. and Fan, C.Q. 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