{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T05:26:08Z","timestamp":1775539568212,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,4,4]],"date-time":"2022-04-04T00:00:00Z","timestamp":1649030400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key Research and Development Program of China","award":["2020YFB2103403"],"award-info":[{"award-number":["2020YFB2103403"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>During the last few decades, worsening air quality has been diagnosed in many cities around the world. The accurately prediction of air pollutants, particularly, particulate matter 2.5 (PM2.5) is extremely important for environmental management. A Convolutional Neural Network (CNN) P-CNN model is presented in this paper, which uses seven different pollutant satellite images, such as Aerosol index (AER AI), Methane (CH4), Carbon monoxide (CO), Formaldehyde (HCHO), Nitrogen dioxide (NO2), Ozone (O3) and Sulfur dioxide (SO2), as auxiliary variables to estimate daily average PM2.5 concentrations. This study estimates daily average of PM2.5 concentrations in various cities of Pakistan (Islamabad, Lahore, Peshawar and Karachi) by using satellite images. The dataset contains a total of 2562 images from May-2019 to April-2020. We compare and analyze AlexNet, VGG16, ResNet50 and P-CNN model on every dataset. The accuracy of machine learning models was checked with Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results show that P-CNN is more accurate than other approaches in estimating PM2.5 concentrations from satellite images. This study presents robust model using satellite images, useful for estimating PM2.5 concentrations.<\/jats:p>","DOI":"10.3390\/rs14071735","type":"journal-article","created":{"date-parts":[[2022,4,4]],"date-time":"2022-04-04T09:49:41Z","timestamp":1649065781000},"page":"1735","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Estimation of Ground PM2.5 Concentrations in Pakistan Using Convolutional Neural Network and Multi-Pollutant Satellite Images"],"prefix":"10.3390","volume":"14","author":[{"given":"Maqsood","family":"Ahmed","sequence":"first","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Zemin","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Yonglin","family":"Shen","sequence":"additional","affiliation":[{"name":"National Engineering Research Center of Geographic Information System, China University of Geosciences, Wuhan 430074, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,4]]},"reference":[{"key":"ref_1","first-page":"E69","article-title":"The impact of PM2.5 on the human respiratory system","volume":"8","author":"Xing","year":"2016","journal-title":"J. Thorac. Dis."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1068","DOI":"10.1289\/ehp.7533","article-title":"Air pollution\u2013associated changes in lung function among asthmatic children in Detroit","volume":"113","author":"Lewis","year":"2005","journal-title":"Environ. Health Perspect."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"129","DOI":"10.1016\/j.neulet.2011.06.019","article-title":"No exercise-induced increase in serum BDNF after cycling near a major traffic road","volume":"500","author":"Bos","year":"2011","journal-title":"Neurosci. Lett."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1186\/1476-069X-9-64","article-title":"Subclinical responses in healthy cyclists briefly exposed to traffic-related air pollution: An intervention study","volume":"9","author":"Jacobs","year":"2010","journal-title":"Environ. Health"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"692","DOI":"10.1161\/01.RES.0000243586.99701.cf","article-title":"Environmental cardiology: Studying mechanistic links between pollution and heart disease","volume":"99","author":"Bhatnagar","year":"2006","journal-title":"Circ. Res."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1080\/10590500802494538","article-title":"Airborne particulate matter and human health: Toxicological assessment and importance of size and composition of particles for oxidative damage and carcinogenic mechanisms","volume":"26","author":"Valavanidis","year":"2008","journal-title":"J. Environ. Sci. Health Part C"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"730","DOI":"10.1177\/09720634211050425","article-title":"Deep Transfer Learning-based COVID-19 prediction using Chest X-rays","volume":"23","author":"Kumar","year":"2021","journal-title":"J. Health Manag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"927","DOI":"10.1080\/10473289.1996.10467528","article-title":"Is daily mortality associated specifically with fine particles?","volume":"46","author":"Schwartz","year":"1996","journal-title":"J. Air Waste Manage. Assoc."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1080\/08958370701490551","article-title":"Assessing the role of particulate matter size and composition on gene expression in pulmonary cells","volume":"19","author":"Graff","year":"2007","journal-title":"Inhal. Toxicol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1016\/S0013-9351(89)80012-X","article-title":"Lung function and chronic exposure to air pollution: A cross-sectional analysis of NHANES II","volume":"50","author":"Schwartz","year":"1989","journal-title":"Environ. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1080\/00039896.1991.9937440","article-title":"Pulmonary function and ambient particulate matter: Epidemiological evidence from NHANES I","volume":"46","author":"Chestnut","year":"1991","journal-title":"Arch. Environ. Health Int. J."},{"key":"ref_12","first-page":"1","article-title":"A Review of Domestic and Overseas Research on Air Quality Monitoring Networks Designing","volume":"4","author":"Li","year":"2012","journal-title":"Environ. Monit. China"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Mei, S., Li, H., Fan, J., Zhu, X., and Dyer, C.R. (2014, January 17\u201320). Inferring air pollution by sniffing social media. Proceedings of the 2014 IEEE\/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2014), Beijing, China.","DOI":"10.1109\/ASONAM.2014.6921638"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Murty, R.N., Mainland, G., Rose, I., Chowdhury, A.R., Gosain, A., Bers, J., and Welsh, M. (2008, January 12\u201313). Citysense: An urban-scale wireless sensor network and testbed. Proceedings of the 2008 IEEE Conference on Technologies for Homeland Security, Waltham, MA, USA.","DOI":"10.1109\/THS.2008.4534518"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1049\/iet-wss.2011.0121","article-title":"Efficient sampling and compressive sensing for urban monitoring vehicular sensor networks","volume":"2","author":"Yu","year":"2012","journal-title":"IET Wirel. Sens. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Li, L., Zheng, Y., and Zhang, L. (2014, January 15\u201317). Demonstration abstract: PiMi air box\u2014A cost-effective sensor for participatory indoor quality monitoring. Proceedings of the 13th International Symposium on Information Processing in Sensor Networks, Berlin, Germany.","DOI":"10.1109\/IPSN.2014.6846786"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5880","DOI":"10.1016\/j.atmosenv.2006.03.016","article-title":"Satellite remote sensing of particulate matter and air quality assessment over global cities","volume":"40","author":"Gupta","year":"2006","journal-title":"Atmos. Environ."},{"key":"ref_18","unstructured":"Padayachi, Y.R. (2022, February 27). Satellite Remote Sensing of Particulate Matter and Air Quality Assessment in the Western Cape, South Africa. Available online: https:\/\/ukzn-dspace.ukzn.ac.za."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"617","DOI":"10.1016\/0004-6981(86)90177-0","article-title":"Air pollution detection by satellites: The transport and deposition of air pollutants over oceans","volume":"20","author":"Chung","year":"1986","journal-title":"Atmos. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"959","DOI":"10.1016\/1352-2310(94)00370-Z","article-title":"Black smoke as a surrogate for PM10 in health studies?","volume":"29","author":"Muir","year":"1995","journal-title":"Atmos. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1216","DOI":"10.1039\/b101491i","article-title":"A portable pulsed cavity ring-down transmissometer for measurement of the optical extinction of the atmospheric aerosol","volume":"126","author":"Smith","year":"2001","journal-title":"Analyst"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1126\/science.182.4109.248","article-title":"Air Pollution Monitoring by Advanced Spectroscopic Techniques: A variety of spectroscopic methods are being used to detect air pollutants in the gas phase","volume":"182","author":"Hodgeson","year":"1973","journal-title":"Science"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"22408","DOI":"10.1007\/s11356-016-7812-9","article-title":"Deep learning architecture for air quality predictions","volume":"23","author":"Li","year":"2016","journal-title":"Environ. Sci. Pollut. Res."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Chen, J., Chen, H., Zheng, G., Pan, J.Z., Wu, H., and Zhang, N. (2014, January 7\u201311). Big smog meets web science: Smog disaster analysis based on social media and device data on the web. Proceedings of the 23rd International Conference on World Wide Web, Seoul, Korea.","DOI":"10.1145\/2567948.2576941"},{"key":"ref_25","first-page":"93","article-title":"Characteristic analysis on uneven distribution of air pollution in cities","volume":"27","author":"Liu","year":"2011","journal-title":"Environ. Monit. China"},{"key":"ref_26","first-page":"1097","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_27","first-page":"1030","article-title":"The evaluation of air quality using image quality","volume":"16","author":"Liu","year":"2011","journal-title":"Chin. J. Image Graph."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Wang, H., Yuan, X., Wang, X., Zhang, Y., and Dai, Q. (2014, January 7\u201310). Real-time air quality estimation based on color image processing. Proceedings of the 2014 IEEE Visual Communications and Image Processing Conference, Valletta, Malta.","DOI":"10.1109\/VCIP.2014.7051572"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Ma, H., Fu, H., and Wang, X. (2015, January 5\u20137). Outdoor air quality inference from single image. Proceedings of the International Conference on Multimedia Modeling, Sydney, Australia.","DOI":"10.1007\/978-3-319-14442-9_2"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhang, C., Yan, J., Li, C., Rui, X., Liu, L., and Bie, R. (2016, January 15\u201319). On estimating air pollution from photos using convolutional neural network. Proceedings of the 24th ACM international conference on Multimedia, Amsterdam, The Netherlands.","DOI":"10.1145\/2964284.2967230"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Chakma, A., Vizena, B., Cao, T., Lin, J., and Zhang, J. (2017, January 17\u201320). Image-based air quality analysis using deep convolutional neural network. Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China.","DOI":"10.1109\/ICIP.2017.8297023"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.neunet.2020.10.013","article-title":"PM2.5 concentration modeling and prediction by using temperature-based deep belief network","volume":"133","author":"Xing","year":"2021","journal-title":"Neural Netw."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Song, Y.-Z., Yang, H.-L., Peng, J.-H., Song, Y.-R., Sun, Q., and Li, Y. (2015). Estimating PM2.5 concentrations in Xi\u2019an City using a generalized additive model with multi-source monitoring data. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0142149"},{"key":"ref_34","unstructured":"(2022, February 13). Sentinel Sentinel-Hub. Available online: https:\/\/apps.sentinel-hub.com\/."},{"key":"ref_35","unstructured":"(2022, February 22). AirNow Air Quality Data, Available online: https:\/\/www.airnow.gov\/."},{"key":"ref_36","first-page":"1","article-title":"Handwritten digit recognition with a back-propagation network","volume":"2","author":"LeCun","year":"1989","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1049\/ip-vis:19941301","article-title":"Original approach for the localisation of objects in images","volume":"141","author":"Vaillant","year":"1994","journal-title":"IEE Proc.-Vis. Image Signal Process"},{"key":"ref_38","unstructured":"Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., and LeCun, Y. (2013). Overfeat: Integrated recognition, localization and detection using convolutional networks. arXiv."},{"key":"ref_39","first-page":"901","article-title":"A convolutional neural network hand tracker","volume":"1","author":"Nowlan","year":"1995","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1109\/72.554195","article-title":"Face recognition: A convolutional neural-network approach","volume":"8","author":"Lawrence","year":"1997","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"627","DOI":"10.1109\/TPAMI.2016.2578328","article-title":"Object instance segmentation and fine-grained localization using hypercolumns","volume":"39","author":"Hariharan","year":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1408","DOI":"10.1109\/TPAMI.2004.97","article-title":"Convolutional face finder: A neural architecture for fast and robust face detection","volume":"26","author":"Garcia","year":"2004","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1038\/14819","article-title":"Hierarchical models of object recognition in cortex","volume":"2","author":"Riesenhuber","year":"1999","journal-title":"Nat. Neurosci."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Ciregan, D., Meier, U., and Schmidhuber, J. (2012, January 16\u201321). Multi-column deep neural networks for image classification. Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA.","DOI":"10.1109\/CVPR.2012.6248110"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1016\/j.egyr.2020.12.034","article-title":"Optimal long-term prediction of Taiwan\u2019s transport energy by convolutional neural network and wildebeest herd optimizer","volume":"7","author":"Yao","year":"2021","journal-title":"Energy Rep."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1520","DOI":"10.1016\/j.molp.2015.06.005","article-title":"A versatile phenotyping system and analytics platform reveals diverse temporal responses to water availability in Setaria","volume":"8","author":"Fahlgren","year":"2015","journal-title":"Mol. Plant"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Li, Y., Huang, J., and Luo, J. (2015, January 19\u201321). Using user generated online photos to estimate and monitor air pollution in major cities. Proceedings of the 7th International Conference on Internet Multimedia Computing and Service, Zhangjiajie, China.","DOI":"10.1145\/2808492.2808564"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"138178","DOI":"10.1016\/j.scitotenv.2020.138178","article-title":"A deep learning and image-based model for air quality estimation","volume":"724","author":"Zhang","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"119962","DOI":"10.1016\/j.jclepro.2020.119962","article-title":"Development of a new framework to identify pathways from socioeconomic development to environmental pollution","volume":"253","author":"Wang","year":"2020","journal-title":"J. Clean. Prod."},{"key":"ref_50","unstructured":"Pakistan, U. (2022, January 11). Available online: https:\/\/www.pk.undp.org\/content\/pakistan\/en\/home\/library\/development_policy\/dap-vol7-issue2-environmental-sustainability-in-pakistan.html."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1016\/j.cities.2017.09.014","article-title":"Lahore, Pakistan\u2013Urbanization challenges and opportunities","volume":"72","author":"Rana","year":"2018","journal-title":"Cities"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"174","DOI":"10.4172\/2469-4134.1000174","article-title":"Monitoring of land use\/land cover changes and urban sprawl in Peshawar City in Khyber Pakhtunkhwa: An application of geo-information techniques using of multi-temporal satellite data","volume":"5","author":"Raziq","year":"2016","journal-title":"J. Remote Sens. GIS"},{"key":"ref_53","first-page":"137","article-title":"Spatiotemporal analysis of urban sprawl and its contributions to climate and environment of Peshawar using remote sensing and GIS techniques","volume":"8","author":"Mehmood","year":"2016","journal-title":"J. Geogr. Inf. Syst."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"354","DOI":"10.1016\/j.envint.2018.08.026","article-title":"Global estimation of exposure to fine particulate matter (PM2.5) from household air pollution","volume":"120","author":"Shupler","year":"2018","journal-title":"Environ. Int."},{"key":"ref_55","unstructured":"(2022, January 15). IQAir Air Quality in Lahore. Available online: https:\/\/www.iqair.com\/pakistan\/punjab\/lahore."},{"key":"ref_56","unstructured":"(2022, January 03). IQair IQAIR. Available online: https:\/\/www.iqair.com\/world-air-quality-ranking."},{"key":"ref_57","first-page":"100063","article-title":"Estimating PM2.5 from photographs","volume":"5","author":"Pudasaini","year":"2020","journal-title":"Atmos. Environ. 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