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Accurate prediction of PM2.5 concentration is of great significance to environmental protection and public health. Our study takes Nanning urban area, which has unique geographical, climatic and pollution source characteristics, as the object. Based on the dual-time resolution raster data of the China High-resolution and High-quality PM2.5 Dataset (CHAP) from 2012 to 2023, the PM2.5 concentration prediction study is carried out using SARIMA, Prophet and LightGBM models. The study systematically compares the performance of each model from the spatial and temporal dimensions using indicators such as mean square error (MSE), mean absolute error (MAE) and coefficient of determination (R2). The results show that the LightGBM model has a strong ability to mine complex nonlinear relationships, but its stability is poor. The Prophet model has obvious advantages in dealing with seasonality and trend of time series, but it lacks adaptability to complex changes. The SARIMA model is based on time series prediction theory and performs well in some scenarios, but has limitations in dealing with non-stationary data and spatial heterogeneity. Our research provides a multi-dimensional model performance reference for subsequent PM2.5 concentration predictions, helps researchers select models reasonably according to different scenarios and needs, provides new ideas for analyzing concentration change patterns, and promotes the development of related research in the field of environmental science.<\/jats:p>","DOI":"10.3390\/a18030167","type":"journal-article","created":{"date-parts":[[2025,3,14]],"date-time":"2025-03-14T07:02:16Z","timestamp":1741935736000},"page":"167","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Decoding PM2.5 Prediction in Nanning Urban Area, China: Unraveling Model Superiorities and Drawbacks Through SARIMA, Prophet, and LightGBM"],"prefix":"10.3390","volume":"18","author":[{"given":"Minru","family":"Chen","sequence":"first","affiliation":[{"name":"School of Geography and Planning, Nanning Normal University, Nanning 530001, China"},{"name":"Key Laboratory of Environment Change and Resources Use in Beibu Gulf, Ministry of Education, Nanning Normal University, Nanning 530001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8376-1694","authenticated-orcid":false,"given":"Binglin","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Geography and Planning, Nanning Normal University, Nanning 530001, China"},{"name":"Key Laboratory of Environment Change and Resources Use in Beibu Gulf, Ministry of Education, Nanning Normal University, Nanning 530001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingzhi","family":"Liang","sequence":"additional","affiliation":[{"name":"Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nini","family":"Yao","sequence":"additional","affiliation":[{"name":"Department of Architecture and Built Environment, University of Nottingham, Ningbo 315154, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1233","DOI":"10.1016\/S0140-6736(02)11274-8","article-title":"Air pollution and health","volume":"360","author":"Brunekreef","year":"2002","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.pharmthera.2006.06.005","article-title":"The pharmacology of particulate matter air pollution-induced cardiovascular dysfunction","volume":"113","author":"Bai","year":"2007","journal-title":"Pharmacol. Ther."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"107218","DOI":"10.1016\/j.landusepol.2024.107218","article-title":"Carbon surplus or carbon deficit under land use transformation in China?","volume":"143","author":"Li","year":"2024","journal-title":"Land Use Policy"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Mak, H.W.L., and Ng, D.C.Y. (2021). Spatial and socio-classification of traffic pollutant emissions and associated mortality rates in high-density hong kong via improved data analytic approaches. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph18126532"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1016\/j.scitotenv.2017.02.029","article-title":"Sources, health effects and control strategies of indoor fine particulate matter (PM2.5): A review","volume":"586","author":"Li","year":"2017","journal-title":"Sci. Total Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"107533","DOI":"10.1016\/j.eiar.2024.107533","article-title":"How does rapid urban construction land expansion affect the spatial inequalities of ecosystem health in China? Evidence from the country, economic regions and urban agglomerations","volume":"106","author":"Wei","year":"2024","journal-title":"Environ. Impact Assess. Rev."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Tu, J., Inthavong, K., Ahmadi, G., Tu, J., Inthavong, K., and Ahmadi, G. (2013). Case studies in the human respiratory system. Computational Fluid and Particle Dynamics in the Human Respiratory System, Springer.","DOI":"10.1007\/978-94-007-4488-2"},{"key":"ref_8","first-page":"E69","article-title":"The impact of PM25 on the human respiratory system","volume":"8","author":"Xing","year":"2016","journal-title":"J. Thorac. Dis."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Li, R.L., Ho, Y.C., Luo, C.W., Lee, S.S., and Kuan, Y.H. (2019). Influence of PM2.5 exposure level on the association between Alzheimer\u2019s disease and allergic rhinitis: A national population-based cohort study. Int. J. Environ. Res. Public Health, 16.","DOI":"10.3390\/ijerph16183357"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Toczylowski, K., Wietlicka-Piszcz, M., Grabowska, M., and Sulik, A. (2021). Cumulative effects of particulate matter pollution and meteorological variables on the risk of influenza-like illness. Viruses, 13.","DOI":"10.1101\/2021.01.18.21250031"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1186\/s12940-016-0115-2","article-title":"Impact of ambient fine particulate matter (PM2.5) exposure on the risk of influenza-like-illness: A time-series analysis in Beijing, China","volume":"15","author":"Feng","year":"2016","journal-title":"Environ. Health"},{"key":"ref_12","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","author":"Gao","year":"2018","journal-title":"Atmos. Res."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"127008","DOI":"10.1289\/EHP3668","article-title":"Effects of indoor and ambient black carbon and PM2.5 on pulmonary function among individuals with COPD","volume":"126","author":"Hart","year":"2018","journal-title":"EHP"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2893","DOI":"10.1016\/j.atmosenv.2009.03.009","article-title":"Seasonal and diurnal variations of ambient PM2.5 concentration in urban and rural environments in Beijing","volume":"43","author":"Zhao","year":"2009","journal-title":"Atmos. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Miller, L., and Xu, X. (2018). Ambient PM2.5 human health effects\u2014Findings in China and research directions. Atmosphere, 9.","DOI":"10.3390\/atmos9110424"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"100603","DOI":"10.1016\/j.envadv.2024.100603","article-title":"An update on adverse health effects from exposure to PM2.5","volume":"18","author":"Sangkham","year":"2024","journal-title":"Environ. Adv."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"107883","DOI":"10.1016\/j.resconrec.2024.107883","article-title":"Trade-offs and synergies pattern evolution of ecosystem structure-resilience-activity-services (SRAS) in the Belt and Road Initiative region","volume":"211","author":"Wei","year":"2024","journal-title":"Resour. Conserv. Recycl."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liu, B., Li, Y., Wang, L., Zhang, L., Qiao, F., Nan, P., Ji, D., Hu, B., Xia, Z., and Lou, Z. (2024). Evaluating the effects of meteorology and emission changes on ozone in different regions over China based on machine learning. Atmos. Pollut. Res., 102354.","DOI":"10.1016\/j.apr.2024.102354"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Mak, H.W.L., Laughner, J.L., Fung, J.C.H., Zhu, Q., and Cohen, R.C. (2018). Improved Satellite Retrieval of Tropospheric NO2 Column Density via Updating of Air Mass Factor (AMF): Case Study of Southern China. Remote Sens., 10.","DOI":"10.20944\/preprints201810.0213.v1"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"176667","DOI":"10.1016\/j.scitotenv.2024.176667","article-title":"How do meteorological conditions impact the effectiveness of various traffic measures on NOx concentrations in a real hot-spot?","volume":"954","author":"Santiago","year":"2024","journal-title":"Sci. Total Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"148575","DOI":"10.1016\/j.scitotenv.2021.148575","article-title":"Removing the effects of meteorological factors on changes in nitrogen dioxide and ozone concentrations in China from 2013 to 2020","volume":"793","author":"Lin","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"117410","DOI":"10.1016\/j.atmosenv.2020.117410","article-title":"Observation of PM2.5 using a combination of satellite remote sensing and low-cost sensor network in Siberian urban areas with limited reference monitoring","volume":"227","author":"Lin","year":"2020","journal-title":"Atmos. Environ."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"118159","DOI":"10.1016\/j.envpol.2021.118159","article-title":"Estimating monthly PM2.5 concentrations from satellite remote sensing data, meteorological variables, and land use data using ensemble statistical modeling and a random forest approach","volume":"291","author":"Chen","year":"2021","journal-title":"Environ. Pollut."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"e2020JD033599","DOI":"10.1029\/2020JD033599","article-title":"Improved modeling of spatiotemporal variations of fine particulate matter using a three-dimensional variational data fusion method","volume":"126","author":"Zhang","year":"2021","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhang, L., and Tang, N. (2024). PM2.5 Pollution and Monitoring. Field Work and Laboratory Experiments in Integrated Environmental Sciences, Springer Nature.","DOI":"10.1007\/978-981-99-6532-8_2"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"243","DOI":"10.33003\/fjs-2024-0803-2505","article-title":"Advancements and Innovations in PM2.5 Monitoring: A Comprehensive Review of Emerging Technologies","volume":"8","author":"Onaiwu","year":"2024","journal-title":"Fudma J. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/0960-1686(93)90245-T","article-title":"The DRI thermal\/optical reflectance carbon analysis system: Description, evaluation and applications","volume":"27","author":"Chow","year":"1993","journal-title":"Atmos. Environ."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2930582","DOI":"10.1155\/2024\/2930582","article-title":"Enhancing PM2.5 Measurement Accuracy: Insights from Environmental Factors and BAM-Light Scattering Device Correlation","volume":"2024","author":"Kim","year":"2024","journal-title":"Indoor Air"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"751","DOI":"10.1007\/s00477-009-0361-8","article-title":"ARIMA forecasting of ambient air pollutants (O3, NO, NO2, and CO)","volume":"24","author":"Kumar","year":"2010","journal-title":"Stoch. Environ. Res. Risk Assess."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1559","DOI":"10.1016\/j.scitotenv.2010.12.040","article-title":"Evaluation of a multiple regression model for the forecasting of the concentrations of NOx and PM10 in Athens and Helsinki","volume":"409","author":"Vlachogianni","year":"2011","journal-title":"Sci. Total Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"100400","DOI":"10.1016\/j.ese.2024.100400","article-title":"Deep-learning architecture for PM2.5 concentration prediction: A review","volume":"21","author":"Zhou","year":"2024","journal-title":"Environ. Sci. Ecotechnol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.eswa.2004.08.009","article-title":"An application of support vector machines in bankruptcy prediction model","volume":"28","author":"Shin","year":"2005","journal-title":"Expert Syst. Appl."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"120396","DOI":"10.1016\/j.atmosenv.2024.120396","article-title":"Predicting PM2.5 levels and exceedance days using machine learning methods","volume":"323","author":"Gao","year":"2024","journal-title":"Atmos. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"700","DOI":"10.1016\/j.apr.2017.01.004","article-title":"Control chart and Six sigma based algorithms for identification of outliers in experimental data, with an application to particulate matter PM10","volume":"8","year":"2017","journal-title":"Atmos. Pollut. Res."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Kawichai, S., Sripan, P., Rerkasem, A., Rerkasem, K., and Srisukkham, W. (2025). Long-term retrospective predicted concentration of PM2.5 in upper northern Thailand using machine learning. Atmosphere, 13.","DOI":"10.3390\/toxics13030170"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"e8391","DOI":"10.1002\/cpe.8391","article-title":"PM2.5 Concentration Prediction Using CNN-LSTM Model Based on Multi-Feature Fusion","volume":"37","author":"Wang","year":"2025","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"997","DOI":"10.1016\/j.envpol.2017.08.114","article-title":"Long short-term memory neural network for air pollutant concentration predictions: Method development and evaluation","volume":"231","author":"Li","year":"2017","journal-title":"Environ. Pollut."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"808","DOI":"10.1016\/j.scitotenv.2019.05.288","article-title":"A novel optimal-hybrid model for daily air quality index prediction considering air pollutant factors","volume":"683","author":"Wu","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"012009","DOI":"10.1088\/1742-6596\/2555\/1\/012009","article-title":"Combined Prediction Model of PM2. 5 Concentration Based on Wavelet Transform and LSTM","volume":"2555","author":"Hu","year":"2023","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.trd.2008.10.004","article-title":"Prediction of hourly air pollutant concentrations near urban arterials using artificial neural network approach","volume":"14","author":"Cai","year":"2009","journal-title":"Transp. Res. Part D Transp. Environ."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"131285","DOI":"10.1016\/j.chemosphere.2021.131285","article-title":"Forecasting PM2.5 concentration using artificial neural network and its health effects in Ahvaz, Iran","volume":"283","author":"Goudarzi","year":"2021","journal-title":"Chemosphere"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"714","DOI":"10.1016\/j.envpol.2017.10.123","article-title":"Temporal-spatial characteristics and source apportionment of PM2.5 as well as its associated chemical species in the Beijng-Tianjin-Hebei region of China","volume":"233","author":"Gao","year":"2018","journal-title":"Environ. Pollut."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"01002","DOI":"10.1051\/e3sconf\/202337901002","article-title":"Impact assessment of biomass burning in Southeast Asia to 2019 annual average PM2.5 concentration in Thailand using atmospheric chemical transport model","volume":"379","author":"Chantaraprachoom","year":"2023","journal-title":"E3S Web Conf."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"102067","DOI":"10.1016\/j.ecoinf.2023.102067","article-title":"Spatio-temporal air quality analysis and PM2.5 prediction over Hyderabad City, India using artificial intelligence techniques","volume":"76","author":"Gokul","year":"2023","journal-title":"Ecol. Inform."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"135715","DOI":"10.1016\/j.scitotenv.2019.135715","article-title":"Quantification of primary and secondary sources to PM2.5 using an improved source regional apportionment method in an industrial city, China","volume":"706","author":"Hao","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"125478","DOI":"10.1007\/s11356-023-31109-z","article-title":"Passive air sampling of VOCs, O3, NO2, and SO2 in the large industrial city of Ulsan, South Korea: Spatial-temporal variations, source identification, and ozone formation potential","volume":"30","author":"Kim","year":"2023","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1417","DOI":"10.1016\/j.envpol.2018.08.029","article-title":"Evaluation of machine learning techniques with multiple remote sensing datasets in estimating monthly concentrations of ground-level PM2.5","volume":"242","author":"Xu","year":"2018","journal-title":"Environ. Pollut."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Zaman, N.A.F.K., Kanniah, K.D., Kaskaoutis, D.G., and Latif, M.T. (2021). Evaluation of machine learning models for estimating PM2.5 concentrations across Malaysia. Appl. Sci., 11.","DOI":"10.3390\/app11167326"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Ma, X., Chen, T., Ge, R., Xv, F., Cui, C., and Li, J. (2023). Prediction of PM2.5 concentration using spatiotemporal data with machine learning models. Atmosphere, 14.","DOI":"10.3390\/atmos14101517"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Giovannini, L., Ferrero, E., Karl, T., Rotach, M.W., Staquet, C., Trini Castelli, S., and Zardi, D. (2020). Atmospheric pollutant dispersion over complex terrain: Challenges and needs for improving air quality measurements and modeling. Atmosphere, 11.","DOI":"10.3390\/atmos11060646"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Just, A.C., De Carli, M.M., Shtein, A., Dorman, M., Lyapustin, A., and Kloog, I. (2018). Correcting measurement error in satellite aerosol optical depth with machine learning for modeling PM2.5 in the Northeastern USA. Remote Sens., 10.","DOI":"10.3390\/rs10050803"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"e2023EA002911","DOI":"10.1029\/2023EA002911","article-title":"Predicting PM2.5 concentrations across USA using machine learning","volume":"10","author":"Vignesh","year":"2023","journal-title":"Earth Space Sci."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"152616","DOI":"10.1109\/ACCESS.2021.3126854","article-title":"KNN-SC: Novel spectral clustering algorithm using k-nearest neighbors","volume":"9","author":"Kim","year":"2021","journal-title":"IEEE Access"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1007\/s10661-023-11045-8","article-title":"Forecasting PM2.5 concentrations using statistical modeling for Bengaluru and Delhi regions","volume":"195","author":"Agarwal","year":"2023","journal-title":"Environ. Monit. Assess."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"18319","DOI":"10.1007\/s10489-022-04418-y","article-title":"A novel spatiotemporal multigraph convolutional network for air pollution prediction","volume":"53","author":"Chen","year":"2023","journal-title":"Appl. Intell."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Zaini, N.A., Ean, L.W., Ahmed, A.N., Abdul Malek, M., and Chow, M.F. (2022). PM2.5 forecasting for an urban area based on deep learning and decomposition method. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-21769-1"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"38155","DOI":"10.1007\/s11356-020-09855-1","article-title":"An ensemble learning based hybrid model and framework for air pollution forecasting","volume":"27","author":"Chang","year":"2020","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_58","unstructured":"Thundiyil, S., Picone, J., and McKenzie, S. (2025, January 01). Transformer Architectures in Time Series Analysis: A Review. Available online: https:\/\/isip.piconepress.com\/courses\/temple\/ece_8110\/lectures\/2024_00_spring\/lecture_36a.pdf."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"125342","DOI":"10.1016\/j.envpol.2024.125342","article-title":"High-resolution spatiotemporal prediction of PM2.5 concentration based on mobile monitoring and deep learning","volume":"364","author":"Wang","year":"2025","journal-title":"Environ. Pollut."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Al-qaness, M.A., Dahou, A., Ewees, A.A., Abualigah, L., Huai, J., Abd Elaziz, M., and Helmi, A.M. (2023). ResInformer: Residual transformer-based artificial time-series forecasting model for PM2.5 concentration in three major Chinese cities. Mathematics, 11.","DOI":"10.3390\/math11020476"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Li, S., Xie, G., Ren, J., Guo, L., Yang, Y., and Xu, X. (2020). Urban PM2.5 concentration prediction via attention-based, CNN\u2013LSTM. Appl. Sci., 10.","DOI":"10.3390\/app10061953"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Kim, H.S., Han, K.M., Yu, J., Youn, N., and Choi, T. (2025). Development of a Hybrid Attention Transformer for Daily PM2.5 Predictions in Seoul. Atmosphere, 16.","DOI":"10.3390\/atmos16010037"},{"key":"ref_63","unstructured":"Box, G.E., Jenkins, G.M., Reinsel, G.C., and Ljung, G.M. (2015). Time Series Analysis:Forecasting and Control, Wiley."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1080\/00031305.2017.1380080","article-title":"Forecasting at scale","volume":"72","author":"Taylor","year":"2018","journal-title":"Am. Stat."},{"key":"ref_65","unstructured":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. Adv. Neural Inf. Process. Syst., 30, Available online: https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2017\/file\/6449f44a102fde848669bdd9eb6b76fa-Paper.pdf."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1411","DOI":"10.1007\/s11869-020-00895-7","article-title":"A novel hybrid ensemble model for hourly PM2.5 forecasting using multiple neural networks: A case study in China","volume":"13","author":"Liu","year":"2020","journal-title":"Air Qual. Atmos. Health"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"174611","DOI":"10.1016\/j.scitotenv.2024.174611","article-title":"Responses of fine particulate matter (PM2.5) air quality to future climate, land use, and emission changes: Insights from modeling across shared socioeconomic pathways","volume":"948","author":"Bhattarai","year":"2024","journal-title":"Sci. Total Environ."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"126493","DOI":"10.1016\/j.jclepro.2021.126493","article-title":"High-resolution prediction of the spatial distribution of PM2.5 concentrations in China using a long short-term memory model","volume":"297","author":"Wang","year":"2021","journal-title":"J. Clean. Prod."},{"key":"ref_69","first-page":"334","article-title":"Improved ANN model for PM2.5 concentration prediction in urban areas","volume":"30","author":"Zhang","year":"2015","journal-title":"J. Environ. Sci."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"1017","DOI":"10.1289\/ehp.1206010","article-title":"A national prediction model for PM2.5 component exposures and measurement error\u2013corrected health effect inference","volume":"121","author":"Bergen","year":"2013","journal-title":"Environ. Health Perspect."},{"key":"ref_71","unstructured":"Wei, J., and Li, Z. (2023). ChinaHighPM2.5: High-Resolution and High-Quality Ground-Level PM2.5 Dataset for China (2000\u20132023), National Tibetan Plateau\/Third Pole Environment Data Center."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.cities.2017.07.023","article-title":"Nanning\u2013Perils and promise of a frontier city","volume":"72","author":"Wang","year":"2018","journal-title":"Cities"},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Huang, H., Huang, S., He, S., Lu, Y., and Deng, S. (2024). Healthy city evaluation based on factor analysis\u2014Taking cities in the Guangxi Zhuang Autonomous Region as an example. PLoS ONE, 19.","DOI":"10.1371\/journal.pone.0306344"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/j.apr.2017.07.005","article-title":"Characteristics of mass concentration, chemical composition, source apportionment of PM2.5 and PM10 and health risk assessment in the emerging megacity in China","volume":"9","author":"Jiang","year":"2018","journal-title":"Atmos. Pollut. Res."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"7519","DOI":"10.5194\/acp-9-7519-2009","article-title":"Observational study of influence of aerosol hygroscopic growth on scattering coefficient over rural area near Beijing mega-city","volume":"9","author":"Pan","year":"2009","journal-title":"Atmos. Chem. Phys."},{"key":"ref_76","unstructured":"Asia, E. (2025, January 01). Cost-Effectiveness of Harm Reduction Interventions in Guangxi Zhuang Autonomous Region, China. Available online: https:\/\/documentos.bancomundial.org\/es\/publication\/documents-reports\/documentdetail\/379171468214511184\/china-cost-effectiveness-of-harm-reduction-interventions-in-guangxi-zhuang-autonomous-region-china."},{"key":"ref_77","unstructured":"Dama, F., and Sinoquet, C. (2021). Time series analysis and modeling to forecast: A survey. arXiv."},{"key":"ref_78","unstructured":"Rafferty, G. (2021). Forecasting Time Series Data with Facebook Prophet: Build, Improve, and Optimize Time Series Forecasting Models Using the Advanced Forecasting Tool, Packt Publishing Ltd."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"70434","DOI":"10.1109\/ACCESS.2024.3402092","article-title":"A Review on Machine Learning Methods for Customer Churn Prediction and Recommendations for Business Practitioners","volume":"12","author":"Manzoor","year":"2024","journal-title":"IEEE Access"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"103206","DOI":"10.1016\/j.habitatint.2024.103206","article-title":"Sustainable development of urban agglomerations around lakes in China: Achieving SDGs by regulating Ecosystem Service Supply and Demand through New-type Urbanization","volume":"153","author":"Li","year":"2024","journal-title":"Habitat Int."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/3\/167\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:53:35Z","timestamp":1760028815000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/18\/3\/167"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,14]]},"references-count":80,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["a18030167"],"URL":"https:\/\/doi.org\/10.3390\/a18030167","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,14]]}}}