{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T16:40:15Z","timestamp":1784997615036,"version":"3.55.0"},"reference-count":93,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2024,8,5]],"date-time":"2024-08-05T00:00:00Z","timestamp":1722816000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Effective air quality monitoring and forecasting are essential for safeguarding public health, protecting the environment, and promoting sustainable development in smart cities. Conventional systems are cloud-based, incur high costs, lack accurate Deep Learning (DL)models for multi-step forecasting, and fail to optimize DL models for fog nodes. To address these challenges, this paper proposes a Fog-enabled Air Quality Monitoring and Prediction (FAQMP) system by integrating the Internet of Things (IoT), Fog Computing (FC), Low-Power Wide-Area Networks (LPWANs), and Deep Learning (DL) for improved accuracy and efficiency in monitoring and forecasting air quality levels. The three-layered FAQMP system includes a low-cost Air Quality Monitoring (AQM) node transmitting data via LoRa to the Fog Computing layer and then the cloud layer for complex processing. The Smart Fog Environmental Gateway (SFEG) in the FC layer introduces efficient Fog Intelligence by employing an optimized lightweight DL-based Sequence-to-Sequence (Seq2Seq) Gated Recurrent Unit (GRU) attention model, enabling real-time processing, accurate forecasting, and timely warnings of dangerous AQI levels while optimizing fog resource usage. Initially, the Seq2Seq GRU Attention model, validated for multi-step forecasting, outperformed the state-of-the-art DL methods with an average RMSE of 5.5576, MAE of 3.4975, MAPE of 19.1991%, R2 of 0.6926, and Theil\u2019s U1 of 0.1325. This model is then made lightweight and optimized using post-training quantization (PTQ), specifically dynamic range quantization, which reduced the model size to less than a quarter of the original, improved execution time by 81.53% while maintaining forecast accuracy. This optimization enables efficient deployment on resource-constrained fog nodes like SFEG by balancing performance and computational efficiency, thereby enhancing the effectiveness of the FAQMP system through efficient Fog Intelligence. The FAQMP system, supported by the EnviroWeb application, provides real-time AQI updates, forecasts, and alerts, aiding the government in proactively addressing pollution concerns, maintaining air quality standards, and fostering a healthier and more sustainable environment.<\/jats:p>","DOI":"10.3390\/s24155069","type":"journal-article","created":{"date-parts":[[2024,8,5]],"date-time":"2024-08-05T15:45:22Z","timestamp":1722872722000},"page":"5069","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Design and Enhancement of a Fog-Enabled Air Quality Monitoring and Prediction System: An Optimized Lightweight Deep Learning Model for a Smart Fog Environmental Gateway"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2966-413X","authenticated-orcid":false,"given":"Divya Bharathi","family":"Pazhanivel","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Coimbatore 641112, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1167-939X","authenticated-orcid":false,"given":"Anantha Narayanan","family":"Velu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Coimbatore 641112, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3763-9583","authenticated-orcid":false,"given":"Bagavathi Sivakumar","family":"Palaniappan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Coimbatore 641112, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"131946","DOI":"10.1016\/j.jclepro.2022.131946","article-title":"Forecasting of Future Greenhouse Gas Emission Trajectory for India Using Energy and Economic Indexes with Various Metaheuristic Algorithms","volume":"360","author":"Soudagar","year":"2022","journal-title":"J. Clean. Prod."},{"key":"ref_2","unstructured":"(2023, January 05). Air Pollution and Health | UNECE. Available online: https:\/\/unece.org\/air-pollution-and-health."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Yuan, L., Li, H., Fu, S., and Zhang, Z. (2022). Learning Behavior Evaluation Model and Teaching Strategy Innovation by Social Media Network Following Learning Psychology. Front. Psychol., 13.","DOI":"10.3389\/fpsyg.2022.843428"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"eabd4049","DOI":"10.1126\/sciadv.abd4049","article-title":"Air Pollution and COVID-19 Mortality in the United States: Strengths and Limitations of an Ecological Regression Analysis","volume":"6","author":"Wu","year":"2020","journal-title":"Sci. Adv."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Pekdogan, T., Udri\u0219tioiu, M.T., Yildizhan, H., and Ameen, A. (2024). From Local Issues to Global Impacts: Evidence of Air Pollution for Romania and Turkey. Sensors, 24.","DOI":"10.3390\/s24041320"},{"key":"ref_6","unstructured":"Georgiev, D. (2024, January 13). Internet of Things Statistics, Facts & Predictions [2024\u2019s Update]. Available online: https:\/\/review42.com\/resources\/internet-of-things-stats\/."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Bharathi, P.D., Ananthanarayanan, V., and Sivakumar, P.B. (2019). Fog Computing-Based Environmental Monitoring Using Nordic Thingy: 52 and Raspberry Pi. Smart Innovation, Systems and Technologies, Springer.","DOI":"10.1007\/978-981-13-8406-6_27"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Losada, M., Cort\u00e9s, A., Irizar, A., Cejudo, J., and P\u00e9rez, A. (2020). A Flexible Fog Computing Design for Low-Power Consumption and Low Latency Applications. Electronics, 10.","DOI":"10.3390\/electronics10010057"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1002\/spe.2766","article-title":"How to Place Your Apps in the Fog: State of the Art and Open Challenges","volume":"50","author":"Brogi","year":"2019","journal-title":"Softw. Pract. Exp."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Daraghmi, Y.-A., Daraghmi, E.Y., Daraghma, R., Fouchal, H., and Ayaida, M. (2022). Edge\u2013Fog\u2013Cloud Computing Hierarchy for Improving Performance and Security of NB-IoT-Based Health Monitoring Systems. Sensors, 22.","DOI":"10.3390\/s22228646"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"5080","DOI":"10.1109\/JIOT.2019.2896311","article-title":"FOGPLAN: A Lightweight QoS-Aware Dynamic Fog Service Provisioning Framework","volume":"6","author":"Yousefpour","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"101035","DOI":"10.1016\/j.iot.2023.101035","article-title":"Edge AI for Internet of Energy: Challenges and Perspectives","volume":"25","author":"Himeur","year":"2024","journal-title":"Internet Things"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Peruzzi, G., and Pozzebon, A. (2022). Combining LoRaWAN and NB-IoT for Edge-to-Cloud Low Power Connectivity Leveraging on Fog Computing. Appl. Sci., 12.","DOI":"10.3390\/app12031497"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Fraga-Lamas, P., Celaya-Echarri, M., Lopez-Iturri, P., Castedo, L., Azpilicueta, L., Aguirre, E., Su\u00e1rez-Albela, M., Falcone, F., and Fern\u00e1ndez-Caram\u00e9s, T.M. (2019). Design and Experimental Validation of a LoRaWAN Fog Computing Based Architecture for IoT Enabled Smart Campus Applications. Sensors, 19.","DOI":"10.3390\/s19153287"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"5621","DOI":"10.3233\/JIFS-212713","article-title":"Fog Computing Enabled Air Quality Monitoring and Prediction Leveraging Deep Learning in IoT","volume":"43","author":"Bharathi","year":"2022","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_16","unstructured":"(2023, January 18). National Air Quality Index. Available online: https:\/\/cpcb.nic.in\/displaypdf.php?id=bmF0aW9uYWwtYWlyLXF1YWxpdHktaW5kZXgvQWJvdXRfQVFJLnBkZg==."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2499","DOI":"10.1007\/s11277-019-06535-3","article-title":"A Comprehensive Review of Wireless Sensor Networks Based Air Pollution Monitoring Systems","volume":"108","author":"Grace","year":"2019","journal-title":"Wirel. Pers. Commun."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"5577","DOI":"10.1109\/JIOT.2019.2903821","article-title":"Internet of Things Mobile\u2013Air Pollution Monitoring System (IoT-Mobair)","volume":"6","author":"Dhingra","year":"2019","journal-title":"IEEE Internet Things J."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Laskar, M.R., Sen, P.K., and Mandal, S.K.D. (2019, January 25\u201328). An IoT-Based e-Health System Integrated With Wireless Sensor Network and Air Pollution Index. Proceedings of the 2019 Second International Conference on Advanced Computational and Communication Paradigms (ICACCP), Gangtok, India.","DOI":"10.1109\/ICACCP.2019.8882985"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Alam, S.S., Islam, A.J., Hasan, M., Rafid, M.N.M., Chakma, N., and Imtiaz, N. (2018, January 13\u201315). Design and Development of a Low-Cost IoT Based Environmental Pollution Monitoring System. Proceedings of the 2018 4th International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT), Dhaka, Bangladesh.","DOI":"10.1109\/CEEICT.2018.8628053"},{"key":"ref_21","first-page":"151","article-title":"Design of a Low-Cost Air Quality Monitoring System Using Arduino and ThingSpeak","volume":"70","author":"Kelechi","year":"2022","journal-title":"Comput. Mater. Contin. Comput. Mater. Contin."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1016\/j.procs.2023.01.008","article-title":"AIRO: Development of an Intelligent IoT-Based Air Quality Monitoring Solution for Urban Areas","volume":"218","author":"Kumar","year":"2023","journal-title":"Procedia Comput. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Bobulski, J., Szymoniak, S., and Pasternak, K. (2024). An IoT System for Air Pollution Monitoring with Safe Data Transmission. Sensors, 24.","DOI":"10.3390\/s24020445"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Kairuz-Cabrera, D., Hernandez-Rodriguez, V., Schalm, O., Laguardia, A.M., Laso, P.M., and S\u00e1nchez, D.A. (2024). Development of a Unified IoT Platform for Assessing Meteorological and Air Quality Data in a Tropical Environment. Sensors, 24.","DOI":"10.3390\/s24092729"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"112574","DOI":"10.1016\/j.envres.2021.112574","article-title":"IoT Enabled Environmental Toxicology for Air Pollution Monitoring Using AI Techniques","volume":"205","author":"Asha","year":"2022","journal-title":"Environ. Res."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Arroyo, P., G\u00f3mez-Su\u00e1rez, J., Su\u00e1rez, J.I., and Lozano, J. (2021). Low-Cost Air Quality Measurement System Based on Electrochemical and PM Sensors with Cloud Connection. Sensors, 21.","DOI":"10.3390\/s21186228"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"235","DOI":"10.1007\/s10661-022-10857-4","article-title":"A Markov Chain\u2013Based IoT System for Monitoring and Analysis of Urban Air Quality","volume":"195","author":"Barthwal","year":"2022","journal-title":"Environ. Monit. Assess."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Samad, A., Kieser, J., Chourdakis, I., and Vogt, U. (2024). Developing a Cloud-Based Air Quality Monitoring Platform Using Low-Cost Sensors. Sensors, 24.","DOI":"10.3390\/s24030945"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Binsy, M.S., and Sampath, N. (2018). User Configurable and Portable Air Pollution Monitoring System for Smart Cities Using IoT. International Conference on Computer Networks and Communication Technologies, Springer. Lecture Notes on Data Engineering and Communications Technologies.","DOI":"10.1007\/978-981-10-8681-6_32"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"135948","DOI":"10.1016\/j.chemosphere.2022.135948","article-title":"Wearable System for Outdoor Air Quality Monitoring in a WSN with Cloud Computing: Design, Validation and Deployment","volume":"307","author":"Arroyo","year":"2022","journal-title":"Chemosphere"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"111879","DOI":"10.1016\/j.knosys.2024.111879","article-title":"Efficient Calibration of Cost-Efficient Particulate Matter Sensors Using Machine Learning and Time-Series Alignment","volume":"295","author":"Koziel","year":"2024","journal-title":"Knowl.-Based Syst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"114529","DOI":"10.1016\/j.measurement.2024.114529","article-title":"Field Calibration of Low-Cost Particulate Matter Sensors Using Artificial Neural Networks and Affine Response Correction","volume":"230","author":"Koziel","year":"2024","journal-title":"Measurement"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Lai, X., Yang, T., Wang, Z., and Chen, P. (2019). IoT Implementation of Kalman Filter to Improve Accuracy of Air Quality Monitoring and Prediction. Appl. Sci., 9.","DOI":"10.3390\/app9091831"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2923","DOI":"10.1007\/s40747-021-00476-w","article-title":"An IoT Enabled System for Enhanced Air Quality Monitoring and Prediction on the Edge","volume":"7","author":"Moursi","year":"2021","journal-title":"Complex Intell. Syst."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1070","DOI":"10.1007\/s11036-020-01620-5","article-title":"The Implementation of a Cloud-Edge Computing Architecture Using OpenStack and Kubernetes for Air Quality Monitoring Application","volume":"26","author":"Kristiani","year":"2020","journal-title":"J. Spec. Top. Mob. Netw. Appl. Mob. Netw. Appl."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"103172","DOI":"10.1016\/j.micpro.2020.103172","article-title":"Intelligent Based Novel Embedded System Based IoT Enabled Air Pollution Monitoring System","volume":"77","author":"Senthilkumar","year":"2020","journal-title":"Microprocess. Microsyst."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Santos, J., Leroux, P., Wauters, T., Volckaert, B., and De Turck, F. (2018, January 23\u201327). Anomaly Detection for Smart City Applications over 5G Low Power Wide Area Networks. Proceedings of the NOMS 2018\u20142018 IEEE\/IFIP Network Operations and Management Symposium, Taipei, Taiwan.","DOI":"10.1109\/NOMS.2018.8406257"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"100540","DOI":"10.1016\/j.iot.2022.100540","article-title":"LoRaWAN-Based IoT System Implementation for Long-Range Outdoor Air Quality Monitoring","volume":"19","author":"Jabbar","year":"2022","journal-title":"Internet Things"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"012005","DOI":"10.1088\/1757-899X\/955\/1\/012005","article-title":"IoT Enabled Environmental Air Pollution Monitoring and Rerouting System Using Machine Learning Algorithms","volume":"955","author":"Moses","year":"2020","journal-title":"IOP Conf. Series. Mater. Sci. Eng."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Manalu, I.P., Silalahi, S.M., Wowiling, G.I., Sigiro, M.M.T., Zalukhu, R.P., and Nababan, P.K. (2023, January 10\u201311). Lora Communication Design and Performance Test (Case Study: Air Quality Monitoring System). Proceedings of the 2023 International Conference of Computer Science and Information Technology (ICOSNIKOM), Binjia, Indonesia.","DOI":"10.1109\/ICoSNIKOM60230.2023.10364454"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Nalakurthi, N.V.S.R., Abimbola, I., Ahmed, T., Anton, I., Riaz, K., Ibrahim, Q., Banerjee, A., Tiwari, A., and Gharbia, S. (2024). Challenges and Opportunities in Calibrating Low-Cost Environmental Sensors. Sensors, 24.","DOI":"10.3390\/s24113650"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"38","DOI":"10.34172\/ajehe.2023.5376","article-title":"Predictive Modeling for Forecasting Air Quality Index (AQI) Using Time Series Analysis","volume":"10","author":"Pant","year":"2023","journal-title":"Avicenna J. Environ. Health Eng."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"8049504","DOI":"10.1155\/2020\/8049504","article-title":"A Machine Learning Approach to Predict Air Quality in California","volume":"2020","author":"Castelli","year":"2020","journal-title":"Complexity"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2057","DOI":"10.1016\/j.procs.2020.04.221","article-title":"Forecasting Air Pollution Particulate Matter (PM2.5) Using Machine Learning Regression Models","volume":"171","author":"Doreswamy","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Liang, Y.-C., Maimury, Y., Chen, A.H.-L., and Juarez, J.R.C. (2020). Machine Learning-Based Prediction of Air Quality. Appl. Sci., 10.","DOI":"10.3390\/app10249151"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Zhu, D., Cai, C., Yang, T., and Zhou, X. (2018). A Machine Learning Approach for Air Quality Prediction: Model Regularization and Optimization. Big Data Cogn. Comput., 2.","DOI":"10.3390\/bdcc2010005"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"3520815","DOI":"10.1109\/TIM.2021.3091511","article-title":"Integrated Multiple Directed Attention-Based Deep Learning for Improved Air Pollution Forecasting","volume":"70","author":"Dairi","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Jiao, Y., Wang, Z., and Zhang, Y. (2019, January 24\u201326). Prediction of Air Quality Index Based on LSTM. Proceedings of the 2019 IEEE 8th Joint International Information Technology and Artificial Intelligence Conference (ITAIC), Chongqing, China.","DOI":"10.1109\/ITAIC.2019.8785602"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"6003804","DOI":"10.1109\/LSENS.2023.3290144","article-title":"Air Quality Forecasting Using the GRU Model Based on Multiple Sensors Nodes","volume":"7","author":"Wang","year":"2023","journal-title":"IEEE Sens. Lett."},{"key":"ref_50","first-page":"18","article-title":"Air Quality Prediction in Visakhapatnam with LSTM Based Recurrent Neural Networks","volume":"11","author":"Rao","year":"2019","journal-title":"Int. J. Intell. Syst. Appl."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.procs.2020.03.036","article-title":"Air Quality Forecasting Using LSTM RNN and Wireless Sensor Networks","volume":"170","author":"Belavadi","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"118422","DOI":"10.1016\/j.eswa.2022.118422","article-title":"An Air Quality Prediction Model Based on Improved Vanilla LSTM with Multichannel Input and Multiroute Output","volume":"211","author":"Fang","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_53","first-page":"118972","article-title":"Pollutant Specific Optimal Deep Learning and Statistical Model Building for Air Quality Forecasting","volume":"301","author":"Middya","year":"2022","journal-title":"Soc. Sci. Res. Netw."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1394","DOI":"10.1016\/j.procs.2018.05.068","article-title":"DeepAirNet: Applying Recurrent Networks for Air Quality Prediction","volume":"132","author":"Athira","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"101045","DOI":"10.1016\/j.apr.2021.03.008","article-title":"Ensemble Multifeatured Deep Learning Models for Air Quality Forecasting","volume":"12","author":"Lin","year":"2021","journal-title":"Atmos. Pollut. Res."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"106957","DOI":"10.1016\/j.asoc.2020.106957","article-title":"Intelligent Modeling Strategies for Forecasting Air Quality Time Series: A Review","volume":"102","author":"Liu","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"120404","DOI":"10.1016\/j.envpol.2022.120404","article-title":"Air Quality Index Prediction Using an Effective Hybrid Deep Learning Model","volume":"315","author":"Sarkar","year":"2022","journal-title":"Environ. Pollut."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Huang, C.-J., and Kuo, P.-H. (2018). A Deep CNN-LSTM Model for Particulate Matter (PM2.5) Forecasting in Smart Cities. Sensors, 18.","DOI":"10.3390\/s18072220"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"119111","DOI":"10.1016\/j.atmosenv.2022.119111","article-title":"Novel Hybrid Deep Learning Model for Satellite Based PM10 Forecasting in the Most Polluted Australian Hotspots","volume":"279","author":"Sharma","year":"2022","journal-title":"Atmos. Environ."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"15073","DOI":"10.1007\/s00521-021-06082-8","article-title":"Efficient PM2.5 Forecasting Using Geographical Correlation Based on Integrated Deep Learning Algorithms","volume":"33","author":"Yeo","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"50755","DOI":"10.1109\/ACCESS.2022.3173734","article-title":"Revealing Influence of Meteorological Conditions on Air Quality Prediction Using Explainable Deep Learning","volume":"10","author":"Yang","year":"2022","journal-title":"IEEE Access"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"1451","DOI":"10.1016\/j.apr.2020.05.015","article-title":"An LSTM-Based Aggregated Model for Air Pollution Forecasting","volume":"11","author":"Chang","year":"2020","journal-title":"Atmos. Pollut. Res."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"2412","DOI":"10.1109\/TKDE.2019.2954510","article-title":"Deep Air Quality Forecasting Using Hybrid Deep Learning Framework","volume":"33","author":"Du","year":"2021","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"121285","DOI":"10.1016\/j.jclepro.2020.121285","article-title":"Seamless Integration of Convolutional and Back-Propagation Neural Networks for Regional Multi-Step-Ahead PM2.5 Forecasting","volume":"261","author":"Kow","year":"2020","journal-title":"J. Clean. Prod."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"102720","DOI":"10.1016\/j.scs.2021.102720","article-title":"A Deep Learning Approach for Prediction of Air Quality Index in a Metropolitan City","volume":"67","author":"Janarthanan","year":"2021","journal-title":"Sustain. Cities Soc."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"14199","DOI":"10.1007\/s00521-021-06067-7","article-title":"Intelligent Forecaster of Concentrations (PM2.5, PM10, NO2, CO, O3, SO2) Caused Air Pollution (IFCsAP)","volume":"33","author":"Alkaim","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"14765","DOI":"10.1109\/ACCESS.2021.3052429","article-title":"Uncertainty-Aware Deep Learning Architectures for Highly Dynamic Air Quality Prediction","volume":"9","author":"Mokhtari","year":"2021","journal-title":"IEEE Access"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"101543","DOI":"10.1016\/j.apr.2022.101543","article-title":"Air Quality Prediction Using Spatio-Temporal Deep Learning","volume":"13","author":"Hu","year":"2022","journal-title":"Atmos. Pollut. Res."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Feng, H., and Zhang, X. (2023). A Novel Encoder-Decoder Model Based on Autoformer for Air Quality Index Prediction. PLoS ONE, 18.","DOI":"10.1371\/journal.pone.0284293"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"144507","DOI":"10.1016\/j.scitotenv.2020.144507","article-title":"A Novel Encoder-Decoder Model Based on Read-First LSTM for Air Pollutant Prediction","volume":"765","author":"Zhang","year":"2021","journal-title":"Sci. Total Environ."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.ins.2021.01.037","article-title":"An Autoencoder Wavelet Based Deep Neural Network with Attention Mechanism for Multi-Step Prediction of Plant Growth","volume":"560","author":"Alhnaity","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"3306","DOI":"10.1109\/TNNLS.2020.3015929","article-title":"Dual Attention-Based Encoder\u2013Decoder: A Customized Sequence-to-Sequence Learning for Soft Sensor Development","volume":"32","author":"Feng","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Chen, Z., Yu, H., Geng, Y.-A., Li, Q., and Zhang, Y. (2020, January 10\u201313). EvaNet: An Extreme Value Attention Network for Long-Term Air Quality Prediction. Proceedings of the 2020 IEEE International Conference on Big Data (Big Data), Atlanta, GA, USA.","DOI":"10.1109\/BigData50022.2020.9378094"},{"key":"ref_74","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_75","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1016\/j.neucom.2019.12.118","article-title":"Multivariate Time Series Forecasting via Attention-Based Encoder\u2013Decoder Framework","volume":"388","author":"Du","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"117917","DOI":"10.1016\/j.atmosenv.2020.117917","article-title":"Real-Time Hourly Ozone Prediction System for Yangtze River Delta Area Using Attention Based on a Sequence to Sequence Model","volume":"244","author":"Jia","year":"2021","journal-title":"Atmos. Environ."},{"key":"ref_77","unstructured":"(2023, February 10). Blogs. Available online: https:\/\/community.intel.com\/t5\/Blogs\/ct-p\/blogs\/ai-inference-at-scale#gs.6ojv36."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Andrade, P., Silva, I., Silva, M., Flores, T., Cassiano, J., and Costa, D.G. (2022). A TinyML Soft-Sensor Approach for Low-Cost Detection and Monitoring of Vehicular Emissions. Sensors, 22.","DOI":"10.3390\/s22103838"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Maccantelli, F., Peruzzi, G., and Pozzebon, A. (2023, January 18\u201320). Traffic Level Monitoring in Urban Scenarios with Virtual Sensing Techniques Enabled by Embedded Machine Learning. Proceedings of the 2023 IEEE Sensors Applications Symposium (SAS), Ottawa, ON, Canada.","DOI":"10.1109\/SAS58821.2023.10254088"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.neucom.2021.04.141","article-title":"Bringing AI to Edge: From Deep Learning\u2019s Perspective","volume":"485","author":"Liu","year":"2022","journal-title":"Neurocomputing 1507"},{"key":"ref_81","unstructured":"(2023, June 15). Post-Training Quantization. Available online: https:\/\/www.tensorflow.org\/lite\/performance\/post_training_quantization."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Lee, H., Lee, N., and Lee, S. (2022). A Method of Deep Learning Model Optimization for Image Classification on Edge Device. Sensors, 22.","DOI":"10.3390\/s22197344"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1109\/JPROC.2022.3226481","article-title":"Efficient Acceleration of Deep Learning Inference on Resource-Constrained Edge Devices: A Review","volume":"111","author":"Shuvo","year":"2023","journal-title":"Proc. IEEE"},{"key":"ref_84","first-page":"91","article-title":"Recurrent Neural Networks for Edge Intelligence","volume":"54","author":"Lalapura","year":"2021","journal-title":"ACM Comput. Surv."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Merenda, M., Porcaro, C., and Iero, D. (2020). Edge Machine Learning for AI-Enabled IoT Devices: A Review. Sensors, 20.","DOI":"10.3390\/s20092533"},{"key":"ref_86","first-page":"6866","article-title":"Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI","volume":"1","author":"Yao","year":"2022","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_87","unstructured":"Polino, A., Pascanu, R., and Alistarh, D. (2018). Model Compression via Distillation and Quantization. arXiv."},{"key":"ref_88","unstructured":"(2023, February 06). MQ136 Datasheet. Available online: https:\/\/pdf1.alldatasheet.com\/datasheet-pdf\/view\/1131997\/hanwei\/mq-136.html."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Makhija, J., Nakkeeran, M., and Narayanan, V.A. (2021). Detection of Vehicle Emissions Through Green IoT for Pollution Control. Advances in Automation, Signal Processing, Instrumentation, and Control, Springer. Lecture Notes in Electrical Engineering.","DOI":"10.1007\/978-981-15-8221-9_76"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Gorospe, J., Mulero, R., Arbelaitz, O., Muguerza, J., and Ant\u00f3n, M.\u00c1. (2021). A Generalization Performance Study Using Deep Learning Networks in Embedded Systems. Sensors, 21.","DOI":"10.3390\/s21041031"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"101418","DOI":"10.1016\/j.uclim.2023.101418","article-title":"Predicting next Hour Fine Particulate Matter (PM2.5) in the Istanbul Metropolitan City Using Deep Learning Algorithms with Time Windowing Strategy","volume":"48","author":"Eren","year":"2023","journal-title":"Urban Clim."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"350","DOI":"10.1016\/j.isatra.2021.08.030","article-title":"Novel Double-Layer Bidirectional LSTM Network with Improved Attention Mechanism for Predicting Energy Consumption","volume":"127","author":"He","year":"2022","journal-title":"ISA Trans."},{"key":"ref_93","unstructured":"(2023, January 23). Central Control Room for Air Quality Management\u2014All India, CPCB, Available online: https:\/\/airquality.cpcb.gov.in\/ccr\/#\/caaqm-dashboard-all\/caaqm-landing\/caaqm-data-repository."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/15\/5069\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:30:24Z","timestamp":1760110224000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/15\/5069"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,5]]},"references-count":93,"journal-issue":{"issue":"15","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["s24155069"],"URL":"https:\/\/doi.org\/10.3390\/s24155069","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,5]]}}}