{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T15:43:00Z","timestamp":1788450180871,"version":"build-2803163510"},"reference-count":48,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFC3701802"],"award-info":[{"award-number":["2022YFC3701802"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52272342"],"award-info":[{"award-number":["52272342"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Environmental Research"],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1016\/j.envres.2024.118190","type":"journal-article","created":{"date-parts":[[2024,1,20]],"date-time":"2024-01-20T23:20:20Z","timestamp":1705792820000},"page":"118190","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":16,"special_numbering":"C","title":["Enhancing vehicular emissions monitoring: A GA-GRU-based soft sensors approach for HDDVs"],"prefix":"10.1016","volume":"247","author":[{"given":"Luoshu","family":"Yang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1686-8969","authenticated-orcid":false,"given":"Yunshan","family":"Ge","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4108-1495","authenticated-orcid":false,"given":"Liqun","family":"Lyu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianwei","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lijun","family":"Hao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hang","family":"Yin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junfang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.envres.2024.118190_bib1","doi-asserted-by":"crossref","DOI":"10.3390\/s22103838","article-title":"A TinyML soft-sensor approach for low-cost detection and monitoring of vehicular emissions","volume":"22","author":"Andrade","year":"2022","journal-title":"Sensors"},{"key":"10.1016\/j.envres.2024.118190_bib2","doi-asserted-by":"crossref","first-page":"3616","DOI":"10.1021\/es903722p","article-title":"On-road emission measurements of reactive nitrogen compounds from three California cities","volume":"44","author":"Bishop","year":"2010","journal-title":"Environ. Sci. Technol."},{"key":"10.1016\/j.envres.2024.118190_bib3","series-title":"Empirical evaluation of gated recurrent neural networks on sequence modeling","author":"Chung","year":"2014"},{"key":"10.1016\/j.envres.2024.118190_bib4","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1023\/A:1022627411411","article-title":"SUPPORT-VECTOR networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"10.1016\/j.envres.2024.118190_bib5","doi-asserted-by":"crossref","first-page":"8653","DOI":"10.1021\/es050360s","article-title":"Measurements of particle number and mass concentrations and size distributions in a tunnel environment","volume":"39","author":"Geller","year":"2005","journal-title":"Environ. Sci. Technol."},{"key":"10.1016\/j.envres.2024.118190_bib6","series-title":"Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow","author":"Geron","year":"2019"},{"key":"10.1016\/j.envres.2024.118190_bib7","doi-asserted-by":"crossref","first-page":"383","DOI":"10.1007\/s00521-006-0062-x","article-title":"Extracting the main patterns of natural time series for long-term neurofuzzy prediction","volume":"16","author":"Gholipour","year":"2007","journal-title":"Neural Comput. Appl."},{"key":"10.1016\/j.envres.2024.118190_bib8","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","volume":"9","author":"Glorot","year":"2010","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.envres.2024.118190_bib9","doi-asserted-by":"crossref","DOI":"10.1016\/j.csite.2023.103813","article-title":"Prediction model of goaf coal temperature based on PSO-GRU deep neural network","volume":"53","author":"Guo","year":"2024","journal-title":"Case Stud. Therm. Eng."},{"key":"10.1016\/j.envres.2024.118190_bib10","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"10.1016\/j.envres.2024.118190_bib11","first-page":"424","article-title":"Ieee, binary taylor diagrams: an efficient implementation of taylor expansion diagrams","author":"Hooshmand","year":"2005"},{"key":"10.1016\/j.envres.2024.118190_bib12","doi-asserted-by":"crossref","DOI":"10.1016\/j.socl.2020.100009","article-title":"Foreign exchange currency rate prediction using a GRU-LSTM hybrid network","volume":"3","author":"Islam","year":"2021","journal-title":"Soft Computing Letters"},{"key":"10.1016\/j.envres.2024.118190_bib13","doi-asserted-by":"crossref","first-page":"17862","DOI":"10.1007\/s11356-018-1983-5","article-title":"Analysis of the repeatability of the exhaust pollutants emission research results for cold and hot starts under controlled driving cycle conditions","volume":"25","author":"Jaworski","year":"2018","journal-title":"Environ. Sci. Pollut. Control Ser."},{"key":"10.1016\/j.envres.2024.118190_bib14","doi-asserted-by":"crossref","DOI":"10.1016\/j.envres.2019.109071","article-title":"Particulate emissions from L-Category vehicles towards Euro 5","volume":"182","author":"Kontses","year":"2020","journal-title":"Environ. Res."},{"key":"10.1016\/j.envres.2024.118190_bib15","doi-asserted-by":"crossref","first-page":"19411","DOI":"10.1109\/JSEN.2022.3201706","article-title":"Noise-tolerant Co-trained semisupervised soft sensor model for industrial process","volume":"22","author":"Lei","year":"2022","journal-title":"IEEE Sensor. J."},{"key":"10.1016\/j.envres.2024.118190_bib16","doi-asserted-by":"crossref","first-page":"2203","DOI":"10.1111\/gcb.16584","article-title":"Crop residue return sustains global soil ecological stoichiometry balance","volume":"29","author":"Liu","year":"2023","journal-title":"Global Change Biol."},{"key":"10.1016\/j.envres.2024.118190_bib17","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2023.129750","article-title":"Quantifying changes and trends of NO3 concentrations and concentration-discharge relationships in a complex, heavily managed, drought-sensitive river system","volume":"622","author":"Liu","year":"2023","journal-title":"J. Hydrol."},{"key":"10.1016\/j.envres.2024.118190_bib18","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.jprocont.2020.03.012","article-title":"Rebooting data-driven soft-sensors in process industries: a review of kernel methods","volume":"89","author":"Liu","year":"2020","journal-title":"J. Process Control"},{"key":"10.1016\/j.envres.2024.118190_bib19","doi-asserted-by":"crossref","DOI":"10.1016\/j.envres.2023.116396","article-title":"NOx emission deterioration in modern heavy-duty diesel vehicles based on long-term real driving measurements","volume":"232","author":"Lyu","year":"2023","journal-title":"Environ. Res."},{"key":"10.1016\/j.envres.2024.118190_bib20","series-title":"Limits and Measurementmethods for Emissions Fromdiesel Fuelled Heavy-Dutyvehicles","year":"2018"},{"key":"10.1016\/j.envres.2024.118190_bib21","series-title":"China Mobile SourceEnvironmental ManagementAnnual Report","year":"2022"},{"key":"10.1016\/j.envres.2024.118190_bib22","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1016\/j.cjche.2016.07.005","article-title":"Dynamic soft sensor development based on Gaussian mixture regression for fermentation processes","volume":"25","author":"Mei","year":"2017","journal-title":"Chin. J. Chem. Eng."},{"key":"10.1016\/j.envres.2024.118190_bib23","doi-asserted-by":"crossref","DOI":"10.3389\/fnbot.2013.00021","article-title":"Gradient boosting machines, a tutorial","volume":"7","author":"Natekin","year":"2013","journal-title":"Front. Neurorob."},{"key":"10.1016\/j.envres.2024.118190_bib24","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.atmosenv.2016.09.021","article-title":"A Portable Emissions Measurement System (PEMS) study of NOx and primary NO2 emissions from Euro 6 diesel passenger cars and comparison with COPERT emission factors","volume":"145","author":"O'Driscoll","year":"2016","journal-title":"Atmos. Environ."},{"key":"10.1016\/j.envres.2024.118190_bib25","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.106780","article-title":"Prediction of total dissolved solids, based on optimization of new hybrid SVM models","volume":"126","author":"Pourhosseini","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.envres.2024.118190_bib26","doi-asserted-by":"crossref","DOI":"10.1016\/j.jclepro.2023.139859","article-title":"Prediction of instantaneous particle number for light-duty gasoline vehicles under real driving conditions based on ensemble learning","volume":"434","author":"Qiao","year":"2024","journal-title":"J. Clean. Prod."},{"key":"10.1016\/j.envres.2024.118190_bib27","doi-asserted-by":"crossref","DOI":"10.1016\/j.envint.2022.107241","article-title":"Long-term exposure to air pollution and mortality in a Danish nationwide administrative cohort study: beyond mortality from cardiopulmonary disease and lung cancer","volume":"164","author":"So","year":"2022","journal-title":"Environ. Int."},{"key":"10.1016\/j.envres.2024.118190_bib28","doi-asserted-by":"crossref","first-page":"1729","DOI":"10.1016\/j.jclepro.2015.03.035","article-title":"Appraisal of artificial neural networks to the emission analysis and prediction of CO 2 , soot, and NOx of n-heptane fueled engine","author":"Taghavifar","year":"2016","journal-title":"J. Clean. Prod."},{"key":"10.1016\/j.envres.2024.118190_bib29","doi-asserted-by":"crossref","DOI":"10.1115\/1.4052793","article-title":"Neural network soft sensors for gasoline engine exhaust emission estimation","volume":"144","author":"Tan","year":"2022","journal-title":"JOURNAL OF ENERGY RESOURCES TECHNOLOGY-TRANSACTIONS OF THE ASME"},{"key":"10.1016\/j.envres.2024.118190_bib30","doi-asserted-by":"crossref","DOI":"10.1016\/j.fuel.2023.130594","article-title":"A hybrid GA-ANN and correlation approach to developing a laminar burning velocity prediction model for isooctane\/blends-air mixtures","volume":"360","author":"Udaybhanu","year":"2024","journal-title":"Fuel"},{"key":"10.1016\/j.envres.2024.118190_bib31","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2022.123611","article-title":"Comparison and evaluation of advanced machine learning methods for performance and emissions prediction of a gasoline Wankel rotary engine","volume":"248","author":"Wang","year":"2022","journal-title":"Energy"},{"key":"10.1016\/j.envres.2024.118190_bib32","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2022.125961","article-title":"Multi-objective optimization of a hydrogen-fueled Wankel rotary engine based on machine learning and genetic algorithm","volume":"263","author":"Wang","year":"2023","journal-title":"Energy"},{"key":"10.1016\/j.envres.2024.118190_bib33","doi-asserted-by":"crossref","first-page":"133074","DOI":"10.1016\/j.jhazmat.2023.133074","article-title":"Soft sensor for predicting indoor PM2.5 concentration in subway with adaptive boosting deep learning model","volume":"465","author":"Wang","year":"2023","journal-title":"J. Hazard Mater."},{"key":"10.1016\/j.envres.2024.118190_bib34","doi-asserted-by":"crossref","DOI":"10.1016\/j.ast.2022.107566","article-title":"Numerical study of back-propagation suppression and intake loss in an air-breathing pulse detonation engine","volume":"126","author":"Wang","year":"2022","journal-title":"Aero. Sci. Technol."},{"key":"10.1016\/j.envres.2024.118190_bib35","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.jprocont.2015.01.003","article-title":"Comparison of variable selection methods for PLS-based soft sensor modeling","volume":"26","author":"Wang","year":"2015","journal-title":"J. Process Control"},{"key":"10.1016\/j.envres.2024.118190_bib36","doi-asserted-by":"crossref","DOI":"10.1016\/j.envint.2021.106977","article-title":"Super-learner model realizes the transient prediction of CO2 and NOx of diesel trucks: model development, evaluation and interpretation","volume":"158","author":"Wei","year":"2022","journal-title":"Environ. Int."},{"key":"10.1016\/j.envres.2024.118190_bib37","doi-asserted-by":"crossref","DOI":"10.3390\/s19163540","article-title":"Automatic and fast recognition of on-road high-emitting vehicles using an optical remote sensing system","volume":"19","author":"Xie","year":"2019","journal-title":"Sensors"},{"key":"10.1016\/j.envres.2024.118190_bib38","doi-asserted-by":"crossref","first-page":"2506","DOI":"10.1002\/joc.6359","article-title":"Short communication comments on 'DISO: a rethink of Taylor diagram'","volume":"40","author":"Xu","year":"2020","journal-title":"Int. J. Climatol."},{"key":"10.1016\/j.envres.2024.118190_bib39","doi-asserted-by":"crossref","first-page":"4237","DOI":"10.1109\/TIE.2016.2622668","article-title":"A data-driven soft sensor modeling method based on deep learning and its application","volume":"64","author":"Yan","year":"2017","journal-title":"IEEE Trans. Ind. Electron."},{"key":"10.1016\/j.envres.2024.118190_bib40","series-title":"Nature-Inspired Optimization Algorithms","first-page":"91","article-title":"Chapter 6 - genetic algorithms","author":"Yang","year":"2021"},{"key":"10.1016\/j.envres.2024.118190_bib41","article-title":"A novel long short-term memory artificial neural network (LSTM)-based soft-sensor to monitor and forecast wastewater treatment performance","volume":"54","author":"Yu","year":"2023","journal-title":"J. Water Proc. Eng."},{"key":"10.1016\/j.envres.2024.118190_bib42","first-page":"17","article-title":"Recent progress in three-dimensional flexible physical sensors","volume":"13","author":"Zhang","year":"2022","journal-title":"Int. J. Soc. Netw. Min."},{"key":"10.1016\/j.envres.2024.118190_bib43","doi-asserted-by":"crossref","DOI":"10.1016\/j.csite.2023.103445","article-title":"Analysis of energy consumption prediction for office buildings based on GA-BP and BP algorithm","volume":"50","author":"Zhang","year":"2023","journal-title":"Case Stud. Therm. Eng."},{"key":"10.1016\/j.envres.2024.118190_bib44","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2023.118965","article-title":"Adaptive proximal SGD based on new estimating sequences for sparser ERM","volume":"638","author":"Zhang","year":"2023","journal-title":"Inf. Sci."},{"issue":"1\u20136","key":"10.1016\/j.envres.2024.118190_bib45","first-page":"24","article-title":"Kernel-based support vector machines","volume":"44","author":"Zhong-jin","year":"2008","journal-title":"Computer Engineering and Application"},{"key":"10.1016\/j.envres.2024.118190_bib46","doi-asserted-by":"crossref","first-page":"20034","DOI":"10.1007\/s11356-020-11870-1","article-title":"NO2 catalytic formation, consumption, and efflux in various types of diesel particulate filter","volume":"28","author":"Zhong","year":"2021","journal-title":"Environ. Sci. Pollut. Control Ser."},{"key":"10.1016\/j.envres.2024.118190_bib47","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.106847","article-title":"A soft sensor modeling framework embedded with domain knowledge based on spatio-temporal deep LSTM for process industry","volume":"126","author":"Zhou","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.envres.2024.118190_bib48","doi-asserted-by":"crossref","first-page":"5726","DOI":"10.1002\/joc.7149","article-title":"Decompositions of Taylor diagram and DISO performance criteria","volume":"41","author":"Zhou","year":"2021","journal-title":"Int. J. Climatol."}],"container-title":["Environmental Research"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S001393512400094X?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S001393512400094X?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,12,26]],"date-time":"2025-12-26T05:04:21Z","timestamp":1766725461000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S001393512400094X"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4]]},"references-count":48,"alternative-id":["S001393512400094X"],"URL":"https:\/\/doi.org\/10.1016\/j.envres.2024.118190","relation":{},"ISSN":["0013-9351"],"issn-type":[{"value":"0013-9351","type":"print"}],"subject":[],"published":{"date-parts":[[2024,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Enhancing vehicular emissions monitoring: A GA-GRU-based soft sensors approach for HDDVs","name":"articletitle","label":"Article Title"},{"value":"Environmental Research","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.envres.2024.118190","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Elsevier Inc. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"118190"}}