[1] Özarslan H., Uluocak İ. 2026. ―CNN–LSTM and CNN–GRU based estimation of NOx conversion efficiency in diesel
engine exhaust system‖. Fuel. 405:136807.
[2] Wang S., Zhang Y. 2025. ―An attention-based CNN model integrating observational and simulation data for high
resolution spatial estimation of urban air quality‖. Atmos. Environ. 340:120921.
[3] Xia S., Zhang R., Zhang L., Wang T., Wang W. 2025. ―Multi-dimensional distribution prediction of PM2.5 concentration
in urban residential areas using CNN‖. Build. Environ. 267:112167.
[4] Illescas-Martinez F., Garcia L., Garcia-Sanchez A.J., Asorey-Cacheda R., Garcia-Haro J. 2025. ―Air quality forecasting in
non-monitored urban areas using machine and deep learning models.‖Expert Syst. Appl. 284:127749.
[5] Lei K., Wang M., Wang M., Liu Q., Zhang F., Xing M., et al. 2025. ―SHAP-explainable PSO–CNN–BiLSTM for 6-hour
prediction of urban PM2.5 and O3 concentrations.‖Atmos. Pollut. Res. 102705.
[6] Salman A.K., Choi Y., Singh D., Kayastha S.G., Dimri R., Park J. 2025. ―Temporal CNN-based 72-h ozone forecasting in
South Korea: Explainability and uncertainty quantification.‖Atmos. Environ. 343:120987.
[7] Attanayake G., Senarathna M., Bergin M., Carlson D., Bhave P.V., Bowatte G., Harischandra N. 2025. ―An RF–CNN
pipeline for predicting PM2.5 concentration in Sri Lanka.‖J. Hazard. Mater. Adv. 19:100782.
[8] Rajan S.P., Rahul R., Jegan T., Arafath S.Y. 2025. ―Air quality analysis and prediction in Tamil Nadu using ANN and
CNN.‖Proc. Int. Conf. Adv. Comput. Technol. (ICoACT). 1–6.
[9] Naz F., Fahim M., Cheema A.A., McNiven B.D.E., Cao T.V., Hunter R., Duong T.Q. 2025. ―Air quality and healthy
ageing: Predictive modeling of pollutants using CNN–Quantum LSTM.‖IEEE Access. 13:94212–94223.
[10] Yu Y., Li V.O.K., Lam J.C.K., Chan K., Zhang Q. 2025. ―CTDI: CNN–Transformer-based spatial–temporal missing air
pollution data imputation.‖IEEE Trans. Big Data. 11(5):2443–2456.
[11] Marpaung Y.P.L., Nuha H.H., Oktaria D., Sailellah H. 2024. ―Air pollution forecasting using integrated weather stations
and CNN algorithm.‖Proc. Int. Conf. Data Sci. Appl. (ICoDSA). 178–182.
[12] Kow P.Y., Hsu C.Y., Sun W., Wang Y.T., Chang L.C., Chang F.J. 2025. ―A CNN–Transformer framework for air
quality forecasting to support aeolian dust management.‖Adv. Eng. Inform. 68:103758.
[13] Ahmad N., Kumar V. 2025. ―Spatio-temporal forecasting using a hybrid BiGRU–1DCNN model for PM2.5 in Delhi,
India.‖Water Air Soil Pollut. 236(7):459.
[14] Wang J., Li X., Jin L., Li J., Sun Q., Wang H. 2022. ―An air quality index prediction model based on CNN–ILSTM.‖Sci.
Rep. 12(1):8373.
[15] Chen Y., Chen X., Xu A., Sun Q., Peng X. 2022. ―A hybrid CNN–Transformer model for ozone concentration
prediction.‖Air Qual. Atmos. Health. 15(9):1533–1546.
[16] Zhang B., Zhang H., Zhao G., Lian J. 2020. ―PM2.5 prediction using autoencoder and Bi-LSTM neural
networks.‖Environ. Model. Softw. 124:104600.
[17] Tsokov S., Lazarova M., Aleksieva-Petrova A. 2022. ―A hybrid spatiotemporal deep model based on CNN and LSTM
for air pollution prediction.‖Sustainability. 14(9):5104.
[18] Lu Y., Li K. 2023. ―Multistation collaborative prediction of air pollutants using CNN–BiLSTM.‖Environ. Sci. Pollut.
Res. 30:92417–92435.
[19] Zhang Q., Han Y., Li V.O.K., Lam J.C.K. 2022. ―Deep-AIR: A hybrid CNN–LSTM framework for fine-grained air
pollution estimation‖. IEEE Access. 10:55818–55841.
[20] Faraji M., Nadi S., Ghaffarpasand O., Homayoni S., Downey K. 2022. ―An integrated 3D CNN–GRU method for short
term PM2.5 prediction.‖Sci. Total Environ. 834:155324.
[21] Cui B., Liu M., Li S., Jin Z., Zeng Y., Lin X. 2023. ―Deep learning methods for PM2.5 prediction: Transformer vs CNN
LSTM–attention.‖Atmos. Pollut. Res. 14(9):101833.
[22] Rabie R., Asghari M., Nosrati H., Emami Niri M., Karimi S. 2024. ―Spatially resolved AQI prediction with a CNN
BiLSTM framework.‖Sustain. Cities Soc. 109:105537.
[23] Elbaz K., Shaban W.M., Zhou A., Shen S.L. 2023. ―Real-time image-based air quality forecasting using 3D-CNN with
attention.‖Chemosphere. 333:138867.
[24] Goudarzi M., Karimian A., Maleki A., Khosravi H. 2021. ―Air pollution prediction using a CNN–LSTM deep learning
model.‖Environ. Sci. Pollut. Res. 28(31):43526–43539.
[25] Abbood A.A., Mansour R.F., Kadry S. 2022. ―Deep learning-based CNN–LSTM model for air pollution prediction in
Baghdad.‖Appl. Sci. 12(19):9736.
[26] Mohammadi M., Al-Fuqaha A., Guizani M., Oh J. 2020. ―Deep learning for air pollution forecasting using CNN
LSTM.‖Sustainability. 12(20):8627.
[27] Gong Y.P. 2023. ―Air pollution prediction using deep learning models: A case study of Zhoushan.‖Sci. Rep.
13(1):12458.
[28] Guo Z., Yang C., Wang D., Liu H. 2023. ―RF–CNN–GRU: A hybrid model for indoor air quality prediction.‖Process
Saf. Environ. Prot. 171:934–945.
[29] Alnowaiser K., Alarfaj A.A., Alabdulqader E.A., Umer M., Cascone L., Alankar B. 2024. ―An IoT-enabled stacked
voting-based CNN model for air quality prediction.‖Comput. Electr. Eng. 115:109222.
[30] Wang J., Jin L., Li X., He S., Huang M., Wang H. 2022. CNN–AGU: ―A novel hybrid deep learning model for AQI
prediction.‖IEEE Access. 10:94918–94932.
[31] Putri T.H., Caraka R.E., Toharudin T., Kim Y., Chen R.C., Gio P.U., et al. 2024. ―Fine-tuning CNN–LSTM and CONV
LSTM models for PM2.5 nowcasting in Jakarta.‖IEEE Access. 12:15543–15557.
[32] Kataria A., Puri V. 2022. ―CNN–LSTM–BOA hybrid with IoT sensors for AQI prediction.‖Procedia Comput. Sci.
215:1247–1256.
