{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T23:38:53Z","timestamp":1792798733467,"version":"4.5.0"},"reference-count":49,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,7,16]],"date-time":"2024-07-16T00:00:00Z","timestamp":1721088000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Science of The Total Environment"],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1016\/j.scitotenv.2024.174804","type":"journal-article","created":{"date-parts":[[2024,7,15]],"date-time":"2024-07-15T13:27:17Z","timestamp":1721050037000},"page":"174804","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":12,"special_numbering":"C","title":["Estimating black carbon levels using machine learning models in high-concentration regions"],"prefix":"10.1016","volume":"948","author":[{"given":"Pratima","family":"Gupta","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pau","family":"Ferrer-Cid","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jose M.","family":"Barcelo-Ordinas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jorge","family":"Garcia-Vidal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vijay Kumar","family":"Soni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mira L.","family":"P\u00f6hlker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ajit","family":"Ahlawat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mar","family":"Viana","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.scitotenv.2024.174804_bb2000","series-title":"The Environmental Cost of Air Pollution Due to Stubble Burning: Evidence from Delhi","first-page":"1","author":"Agarwal","year":"2022"},{"issue":"5","key":"10.1016\/j.scitotenv.2024.174804_bb0005","doi-asserted-by":"crossref","first-page":"628","DOI":"10.1080\/10962247.2015.1006377","article-title":"Exploring the modeling of spatiotemporal variations in ambient air pollution within the land use regression framework: estimation of PM10 concentrations on a daily basis","volume":"65","author":"Alam","year":"2015","journal-title":"J. Air Waste Manage. Assoc."},{"key":"10.1016\/j.scitotenv.2024.174804_bb0010","article-title":"Elemental characteristics and sources apportionment of PM2.5 during the post-monsoon season in Delhi, India","volume":"3","author":"Bangar","year":"2021","journal-title":"Fron. Sustain."},{"issue":"21","key":"10.1016\/j.scitotenv.2024.174804_bb0015","doi-asserted-by":"crossref","first-page":"6749","DOI":"10.1080\/01431161.2010.512943","article-title":"Variation in aerosol black carbon concentration and its emission estimates at the megacity Delhi","volume":"32","author":"Bano","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"10.1016\/j.scitotenv.2024.174804_bb0020","doi-asserted-by":"crossref","first-page":"5380","DOI":"10.1002\/jgrd.50171","article-title":"Bounding the role of black carbon in the climate system: a scientific assessment","volume":"118","author":"Bond","year":"2013","journal-title":"J. Geophys. Res."},{"issue":"10082","key":"10.1016\/j.scitotenv.2024.174804_bb0025","doi-asserted-by":"crossref","first-page":"1907","DOI":"10.1016\/S0140-6736(17)30505-6","article-title":"Estimates and 25-year trends of the global burden of disease attributable to ambient air pollution: an analysis of data from the Global Burden of Diseases Study 2015","volume":"389","author":"Cohen","year":"2017","journal-title":"Lancet"},{"key":"10.1016\/j.scitotenv.2024.174804_bb0030","doi-asserted-by":"crossref","first-page":"3195","DOI":"10.5194\/amt-14-3195-2021","article-title":"Inter-comparison and characterization of 23 Aethalometers under laboratory and ambient air conditions: procedures and unit-to-unit variabilities","volume":"14","author":"Cuesta-Mosquera","year":"2021","journal-title":"Atmos. Meas. Tech."},{"issue":"1","key":"10.1016\/j.scitotenv.2024.174804_bb0035","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-020-70179-8","article-title":"PM2.5 diminution and haze events over Delhi during the COVID-19 lockdown period: an interplay between the baseline pollution and meteorology","volume":"10","author":"Dhaka","year":"2020","journal-title":"Sci. Rep."},{"issue":"24","key":"10.1016\/j.scitotenv.2024.174804_bb0040","first-page":"1753","article-title":"An association between air pollution and mortality in six U.S. cities. N Engl","volume":"329","author":"Dockery","year":"1993","journal-title":"J. Med."},{"key":"10.1016\/j.scitotenv.2024.174804_bb0045","doi-asserted-by":"crossref","DOI":"10.1016\/j.envres.2020.109650","article-title":"Effects of air pollutants on the transmission and severity of respiratory viral infections","volume":"187","author":"Domingo","year":"2020","journal-title":"Environ. Res."},{"key":"10.1016\/j.scitotenv.2024.174804_bb0050","doi-asserted-by":"crossref","first-page":"1965","DOI":"10.5194\/amt-8-1965-2015","article-title":"The \u201cdual-spot\u201d Aethalometer: an improved measurement of aerosol black carbon with real-time loading compensation","volume":"8","author":"Drinovec","year":"2015","journal-title":"Atmos. Meas. Tech."},{"issue":"4","key":"10.1016\/j.scitotenv.2024.174804_bb0055","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1007\/s12647-021-00506-5","article-title":"Variations and source apportionment of PM2.5 and PM10 before and during COVID-19 lockdown phases in Delhi, India","volume":"37","author":"Fatima","year":"2022","journal-title":"MAPAN"},{"issue":"1","key":"10.1016\/j.scitotenv.2024.174804_bb0060","doi-asserted-by":"crossref","first-page":"182","DOI":"10.3390\/s20010182","article-title":"Input-adaptive proxy for black carbon as a virtual sensor","volume":"20","author":"Fung","year":"2019","journal-title":"Sensors (Basel)."},{"key":"10.1016\/j.scitotenv.2024.174804_bb0065","doi-asserted-by":"crossref","DOI":"10.1016\/j.jaerosci.2020.105694","article-title":"Evaluation of white-box versus black-box machine learning models in estimating ambient black carbon concentration","volume":"152","author":"Fung","year":"2021","journal-title":"J. Aerosol Sci."},{"issue":"3","key":"10.1016\/j.scitotenv.2024.174804_bb0070","doi-asserted-by":"crossref","first-page":"1861","DOI":"10.5194\/acp-22-1861-2022","article-title":"Input-adaptive linear mixed-effects model for estimating alveolar lung-deposited surface area (LDSA) using multipollutant datasets","volume":"22","author":"Fung","year":"2022","journal-title":"Atmos. Chem. Phys."},{"key":"10.1016\/j.scitotenv.2024.174804_bb0075","doi-asserted-by":"crossref","DOI":"10.1016\/j.envint.2024.108449","article-title":"Constructing transferable and interpretable machine learning models for black carbon concentrations","volume":"184","author":"Fung","year":"2024","journal-title":"Environ. Int."},{"key":"10.1016\/j.scitotenv.2024.174804_bb0080","doi-asserted-by":"crossref","DOI":"10.1016\/j.uclim.2021.100926","article-title":"Source apportionment of black carbon over Delhi: a case study of extreme biomass burning events and Diwali festival","volume":"39","author":"Goel","year":"2021","journal-title":"Urban Clim."},{"key":"10.1016\/j.scitotenv.2024.174804_bb0085","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.atmosenv.2016.09.063","article-title":"Assessment of particulate matter variation during 2011-2015 over a tropical station Agra","volume":"147","author":"Gogikar","year":"2016","journal-title":"India. Atmos. Environ."},{"issue":"6","key":"10.1016\/j.scitotenv.2024.174804_bb0090","doi-asserted-by":"crossref","first-page":"620","DOI":"10.1080\/10962247.2014.912692","article-title":"Urban air quality management-a review, public health and components of particulate matter: the changing assessment of black carbon","volume":"64","author":"Gulia","year":"2014","journal-title":"J. Air Waste Manage. Assoc."},{"issue":"6","key":"10.1016\/j.scitotenv.2024.174804_bb2005","doi-asserted-by":"crossref","first-page":"620","DOI":"10.1080\/10962247.2014.912692","article-title":"Public health and components of particulate matter: The changing assessment of black carbon","volume":"64","author":"Grahame","year":"2014","journal-title":"J. Air Waste Manage. Assoc."},{"key":"10.1016\/j.scitotenv.2024.174804_bb0095","doi-asserted-by":"crossref","first-page":"599","DOI":"10.1007\/s10666-015-9446-6","article-title":"Assessment of urban air quality around a heritage site using AERMOD: a case study of Amritsar City","volume":"20","author":"Gulia","year":"2015","journal-title":"India. Environ. Model Ass."},{"key":"10.1016\/j.scitotenv.2024.174804_bb0100","doi-asserted-by":"crossref","first-page":"1082","DOI":"10.1007\/s00376-017-6234-z","article-title":"Measurements and characterization of black carbon in ambient air of city of Taj over Indo-Gangetic: seasonal variation and meteorological influence","volume":"34","author":"Gupta","year":"2017","journal-title":"Adv. Atmos. Sci."},{"key":"10.1016\/j.scitotenv.2024.174804_bb0105","doi-asserted-by":"crossref","first-page":"2081","DOI":"10.1007\/s10653-019-00461-w","article-title":"Exposure to respirable and fine dust particle over North-Central India: chemical characterization, source interpretation, and health risk analysis","volume":"42","author":"Gupta","year":"2020","journal-title":"Environ. Geochem. Health"},{"key":"10.1016\/j.scitotenv.2024.174804_bb0110","doi-asserted-by":"crossref","first-page":"3507","DOI":"10.1007\/s10653-022-01430-6","article-title":"COVID-19-associated 2020 lockdown: a study on atmospheric black carbon fall impact on human health","volume":"45","author":"Gupta","year":"2023","journal-title":"Environ. Geochem. Health"},{"key":"10.1016\/j.scitotenv.2024.174804_bb0115","doi-asserted-by":"crossref","first-page":"3199","DOI":"10.1007\/s10661-011-2182-8","article-title":"Role of meteorology in seasonality of air pollution in megacity Delhi","volume":"184","author":"Guttikunda","year":"2012","journal-title":"India. Environ. Monit. Assess."},{"key":"10.1016\/j.scitotenv.2024.174804_bb2010","doi-asserted-by":"crossref","DOI":"10.3390\/su15054209","article-title":"What Is Polluting Delhi\u2019s Air? A Review from 1990 to 2022","volume":"15","author":"Guttikunda","year":"2023","journal-title":"Sustainability"},{"key":"10.1016\/j.scitotenv.2024.174804_bb0120","article-title":"Air pollution climate change and human helath in Indian cities: a brief review","volume":"3","author":"Kaur","year":"2021","journal-title":"Front. Sustan."},{"key":"10.1016\/j.scitotenv.2024.174804_bb0125","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1023\/B:EMAS.0000003555.36394.17","article-title":"Spatial and temporal variation of heavy metals in atmospheric aerosol of Delhi","volume":"90","author":"Khillare","year":"2004","journal-title":"Environ. Monit. Assess."},{"key":"10.1016\/j.scitotenv.2024.174804_bb0130","doi-asserted-by":"crossref","DOI":"10.1016\/j.scitotenv.2020.138060","article-title":"Evaluation of spatial and temporal heterogeneity of black carbon aerosol mass concentration over India using three-year measurements from IMD BC observation network","volume":"723","author":"Kumar","year":"2020","journal-title":"Sci. of The Tot. Environ."},{"key":"10.1016\/j.scitotenv.2024.174804_bb0135","article-title":"Estimating black carbon levels with proxy variables and low-cost sensors","author":"Liu","year":"2024","journal-title":"IEEE Internet Things J."},{"issue":"3","key":"10.1016\/j.scitotenv.2024.174804_bb0140","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1175\/JTECH-D-11-00162.1","article-title":"Study of MPLNET-derived aerosol climatology over Kanpur, India, and validation of CALIPSO level 2 version 3 backscatter and extinction products","volume":"29","author":"Misra","year":"2012","journal-title":"J. Atmos. Ocean. Technol."},{"key":"10.1016\/j.scitotenv.2024.174804_bb0145","doi-asserted-by":"crossref","first-page":"13609","DOI":"10.5194\/acp-21-13609-2021","article-title":"In situ ozone production is highly sensitive to volatile organic compounds in Delhi, India","volume":"21","author":"Nelson","year":"2021","journal-title":"Atmos. Chem. Phys."},{"key":"10.1016\/j.scitotenv.2024.174804_bb0150","series-title":"Proceedings of the 1st International Workshop on Advances in Environmental Sensing Systems for Smart Cities","first-page":"1","article-title":"Robust proxy sensor model for estimating black carbon concentrations using low-cost sensors","author":"Paredes-Ahumada","year":"2023"},{"key":"10.1016\/j.scitotenv.2024.174804_bb0155","series-title":"Proceedings of the International Conference on Embedded Wireless Systems and Networks","first-page":"1","article-title":"Black carbon proxy sensor model for air quality IoT monitoring networks","author":"Paredes-Ahumada","year":"2023"},{"key":"10.1016\/j.scitotenv.2024.174804_bb0160","doi-asserted-by":"crossref","DOI":"10.1016\/j.atmosenv.2023.119734","article-title":"Black carbon in contrasting environments in India: temporal variability, source apportionment and radiative forcing","volume":"302","author":"Romshoo","year":"2023","journal-title":"Atmos. Environ."},{"key":"10.1016\/j.scitotenv.2024.174804_bb0165","article-title":"Improving the predictions of black carbon (BC) optical properties at various aging stages using a machine-learning-based approach","author":"Romshoo","year":"2023","journal-title":"EGUsphere"},{"key":"10.1016\/j.scitotenv.2024.174804_bb0170","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1038\/s41612-024-00634-0","article-title":"Significant contribution of fractal morphology to aerosol light absorption in polluted environments dominated by black carbon (BC)","volume":"7","author":"Romshoo","year":"2024","journal-title":"npj. Clim Atmos. Sci."},{"key":"10.1016\/j.scitotenv.2024.174804_bb0175","doi-asserted-by":"crossref","DOI":"10.1016\/j.envres.2022.113269","article-title":"Non-linear models for black carbon exposure modelling using air pollution datasets","author":"Rovira","year":"2022","journal-title":"Environ. Res."},{"key":"10.1016\/j.scitotenv.2024.174804_bb0180","doi-asserted-by":"crossref","unstructured":"Safai, P.D., Kewat, S., Praveen, P.S., Rao, P.S.P., Momin,G.A., Ali, K. and Devara, P.C.S., 2007. Seasonal variation of black carbon aerosols over tropical urban city of Pune, India. Atmos. Environ. 41, 2699\u20132709. doi:https:\/\/doi.org\/10.1016\/j.atmosenv.2006.11.044.","DOI":"10.1016\/j.atmosenv.2006.11.044"},{"key":"10.1016\/j.scitotenv.2024.174804_bb2015","article-title":"Machine learning techniques to predict atmospheric black carbon in a tropical coastal environment","volume":"34","author":"Satpathy","year":"2024","journal-title":"Rem. Sens. Appli.Soc. and Environ."},{"key":"10.1016\/j.scitotenv.2024.174804_bb0190","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1007\/s11869-023-01456-4","article-title":"Application of dynamic spatiotemporal modeling to predict urban traffic\u2013related air pollution changes","volume":"17","author":"Shogrkhodaei","year":"2023","journal-title":"Air Qual. Atmos. Health"},{"key":"10.1016\/j.scitotenv.2024.174804_bb0195","first-page":"125","article-title":"Diurnal and seasonal variation of black carbon and PM2.5 over New Delhi, India: influence of meteorology","author":"Tiwari","year":"2013","journal-title":"Atmos. Res."},{"issue":"6","key":"10.1016\/j.scitotenv.2024.174804_bb0200","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1016\/j.apr.2018.04.008","article-title":"Pollution concentrations in Delhi India during winter 2015\u201316: a case study of an odd-even vehicle strategy","volume":"9","author":"Tiwari","year":"2018","journal-title":"Atmos. Poll. Res."},{"key":"10.1016\/j.scitotenv.2024.174804_bb2020","doi-asserted-by":"crossref","first-page":"1454","DOI":"10.1126\/science.1104359","article-title":"Residential biofuels in south Asia: Carbonaceous aerosol emissions and climate impacts","volume":"307","author":"Venkataraman","year":"2005","journal-title":"Science"},{"issue":"512\u2013513","key":"10.1016\/j.scitotenv.2024.174804_bb0205","first-page":"133","article-title":"Chemical speciation and source apportionment of fine particulate matter in Santiago","volume":"5","author":"Villalobos","year":"2013","journal-title":"Chile. Sci. Total Environ."},{"key":"10.1016\/j.scitotenv.2024.174804_bb0210","doi-asserted-by":"crossref","first-page":"1445","DOI":"10.1016\/S0021-8502(03)00359-8","article-title":"Absorption of light by soot particles: determination of the absorption coefficient by means of aethalometers","volume":"34","author":"Weingartner","year":"2003","journal-title":"J. Aerosol Sci."},{"issue":"22","key":"10.1016\/j.scitotenv.2024.174804_bb0215","doi-asserted-by":"crossref","first-page":"4976","DOI":"10.3390\/app9224976","article-title":"Bayesian proxy modelling for estimating black carbon concentrations using white-box and black-box models","volume":"9","author":"Zaidan","year":"2019","journal-title":"Appl. Sci."},{"issue":"22","key":"10.1016\/j.scitotenv.2024.174804_bb0220","doi-asserted-by":"crossref","first-page":"13638","DOI":"10.1109\/JSEN.2020.3010316","article-title":"Intelligent calibration and virtual sensing for integrated low-cost air quality sensors","volume":"20","author":"Zaidan","year":"2020","journal-title":"IEEE Sensors J."},{"key":"10.1016\/j.scitotenv.2024.174804_bb0225","doi-asserted-by":"crossref","first-page":"119","DOI":"10.3390\/atmos12010119","article-title":"Temporal variability of equivalent black carbon components in atmospheric air in Southern Poland","volume":"12","author":"Zio\u0142a","year":"2021","journal-title":"Atmosphere"}],"container-title":["Science of The Total Environment"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0048969724049532?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0048969724049532?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T08:57:52Z","timestamp":1752569872000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0048969724049532"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10]]},"references-count":49,"alternative-id":["S0048969724049532"],"URL":"https:\/\/doi.org\/10.1016\/j.scitotenv.2024.174804","relation":{},"ISSN":["0048-9697"],"issn-type":[{"value":"0048-9697","type":"print"}],"subject":[],"published":{"date-parts":[[2024,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Estimating black carbon levels using machine learning models in high-concentration regions","name":"articletitle","label":"Article Title"},{"value":"Science of The Total Environment","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.scitotenv.2024.174804","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"174804"}}