How to cite this paper
APA: Molavi-Arabshahi, M., Rashidinia, J & Rezaee, S. (2026). Multiscale wavelet modeling of PM2.5 dynamics in Tehran: A time–frequency framework for urban air quality management. Journal of Future Sustainability, 6(3), 193-210.
Chicago/Turabian: Molavi-Arabshahi, M., Rashidinia, J & Rezaee, S. 2026. "Multiscale wavelet modeling of PM2.5 dynamics in Tehran: A time–frequency framework for urban air quality management." Journal of Future Sustainability 6, no. 3 (2026): 193-210.
AMA: Molavi-Arabshahi, M., Rashidinia, J & Rezaee, S. Multiscale wavelet modeling of PM2.5 dynamics in Tehran: A time–frequency framework for urban air quality management. Journal of Future Sustainability. 2026;6(3):193-210.
References
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Al Yammahi, A., & Aung, Z. A study of nitrogen dioxide (NO2) periodicity over the United Arab Emirates using wave-let analysis. Scientific Reports, 12(1), 18144. https://doi.org/10.1038/s41598-022-21937-3 (2022)
Ariapak, S., Jalalian, A., & Honarjoo, N. Source identification, seasonal and spatial variations of airborne dust trace el-ements pollution in Tehran, the capital of Iran. Urban Climate, 42, 101049. https://doi.org/10.1016/j.uclim.2021.101049 (2022)
Arunab, K. S., & Mathew, A. (2024). Quantifying urban heat island and pollutant nexus: A novel geospatial ap-proach. Sustainable Cities and Society, 101, 105117. https://doi.org/10.1016/j.scs.2023.105117 (2024)
Chavez, M., & Cazelles, B. Detecting dynamic spatial correlation patterns with generalized wavelet coherence and non-stationary surrogate data. Scientific reports, 9(1), 7389. https://doi.org/10.1038/S41598-019-43571-2 (2019)
Cheng, K., He, Y., Zhong, Y., Li, X., Li, S., & Ayitken, M. Source apportionment and health risk assessment of PM2. 5-bound elements on winter pollution days in industrial cities on the northern slope of Tianshan Mountain, Chi-na. Atmospheric Pollution Research, 15(4), 102036. https://doi.org/10.1016/j.apr.2024.102036 (2024)
Chołoniewski, J., Sienkiewicz, J., Dretnik, N., Leban, G., Thelwall, M., & Hołyst, J. A. A calibrated measure to compare fluctuations of different entities across timescales. Scientific reports, 10(1), 20673. https://doi.org/10.1038/S41598-020-77660-4 (2020)
Costagliola, M. A., Marchitto, L., Giuzio, R., Casadei, S., Rossi, T., Lixi, S., & Faedo, D. Non-exhaust particulate emis-sions from road transport vehicles. Energies, 17(16), 4079. https://doi.org/10.3390/en17164079 (2024)
Deshpande, N. A., Tomar, N. A., & Pagare, N. A. Quantitative analysis of air pollution levels and its health implications in Delhi-NCR: A longitudinal study. International Journal of Science and Research Archive, 11, 759-68. https://doi.org/10.30574/ijsra.2024.11.2.0488 (2024)
Dhaka, S. K., Chetna, Kumar, V., Panwar, V., Dimri, A. P., Singh, N., ... & Hayashida, S. PM2. 5 diminution and haze events over Delhi during the COVID-19 lockdown period: an interplay between the baseline pollution and meteorol-ogy. Scientific reports, 10(1), 13442. https://doi.org/10.1038/S41598-020-70179-8 (2020).
Du, J., Hu, M., & Zhang, W. Missing data problem in the monitoring system: A review. IEEE Sensors Journal, 20(23), 13984-13998. https://doi.org/10.1109/JSEN.2020.3009265
Ehteshami-Moinabadi, M., & Nasiri, S. A. (2023). critique review and update of the earthquake surface fault rupture hazard in the northern zone of Tehran metropolis, Iran. In EGU General Assembly Conference Abstracts (pp. EGU-3749). https://doi.org/10.5194/egusphere-egu23-3749
Franzke, C. L., Barbosa, S., Blender, R., Fredriksen, H. B., Laepple, T., Lambert, F., ... & Yuan, N. (2020). The structure of climate variability across scales. Reviews of Geophysics, 58(2), e2019RG000657. https://doi.org/10.1029/2019RG000657
Fuladlu, K., & Altan, H. (2021). Examining land surface temperature and relations with the major air pollutants: A re-mote sensing research in case of Tehran. Urban Climate, 39, 100958. https://doi.org/10.1016/j.uclim.2021.100958
Gozuyilmaz, S., & Kundakcioglu, O. E. (2021). Mathematical optimization for time series decomposition. OR Spec-trum, 43(3), 733-758. https://doi.org/10.1007/S00291-021-00637-W
Guo, Q., He, Z., & Wang, Z. (2023). Simulating daily PM2. 5 concentrations using wavelet analysis and artificial neural network with remote sensing and surface observation data. Chemosphere, 340, 139886. https://doi.org/10.1016/j.chemosphere.2023.139886
Kan, X., Liu, X., Zhou, Z., Zhang, Y., Zhu, L., Sian, K. T. C. L. K., & Liu, Q. (2023). Analysis of spatiotemporal varia-tion and influencing factors of PM2. 5 in China based on multisource data. Sustainability, 15(19), 14656. https://doi.org/10.3390/su151914656
Kermani, M., Asadgol, Z., Gholami, M., Jafari, A. J., Shahsavani, A., Goodarzi, B., & Arfaeinia, H. (2021). Occurrence, spatial distribution, seasonal variations, potential sources, and inhalation-based health risk assessment of organ-ic/inorganic pollutants in ambient air of Tehran. Environmental geochemistry and health, 43(5), 1983-2006. https://doi.org/10.1007/S10653-020-00779-W
Khoshakhlagh, A. H., Mohammadzadeh, M., & Morais, S. (2023). Air quality in Tehran, Iran: Spatio-temporal charac-teristics, human health effects, economic costs and recommendations for good practice. Atmospheric Environment: X, 19, 100222. https://doi.org/10.1016/j.aeaoa.2023.100222
Koue, J., Shimadera, H., Matsuo, T., & Kondo, A. (2018). Numerical analysis of sensitivity of structure of the stratifi-cation in Lake Biwa, Japan by changing meteorological elements. Water, 10(10), 1492. https://doi.org/10.3390/w10101492
Lausier, A. M., & Jain, S. (2018). Overlooked trends in observed global annual precipitation reveal underestimated risks. Scientific reports, 8(1), 16746. https://doi.org/10.1038/S41598-018-34993-5
Liang, Q., Zhang, X., Miao, Y., & Liu, S. (2024). Multi-scale meteorological impact on PM2. 5 pollution in Tangshan, Northern China. Toxics, 12(9), 685. https://doi.org/10.3390/toxics12090685
Li, X., Li, H., Lu, H., Jensen, C. S., Pandey, V., & Markl, V. (2023). Missing value imputation for multi-attribute sensor data streams via message propagation. Proceedings of the VLDB Endowment, 17(3), 345-358. https://doi.org/10.14778/3632093.3632100
Liu, B., Jin, Y., & Li, C. (2021). Analysis and prediction of air quality in Nanjing from autumn 2018 to summer 2019 using PCR–SVR–ARMA combined model. Scientific reports, 11(1), 348. https://doi.org/10.1038/S41598-020-79462-0
Molavi-Arabshahi, M., Azizpour, J., Nikan, O., Naderi Beni, A., & Lopes, A. M. (2022). Investigation and analysis of sea surface temperature and precipitation of the southern caspian sea using wavelet analysis. Axioms, 12(1), 10. https://doi.org/10.3390/axioms12010010
Najafzadeh, F., Mohammadzadeh, A., Ghorbanian, A., & Jamali, .S (2021). Spatial and temporal analysis of surface ur-ban heat island and thermal comfort using landsat satellite images between 1989 and 2019: A case study in Teh-ran. Remote Sensing, 13(21), 4469. https://doi.org/10.3390/rs13214469 (2021)
Ojha, N., Sharma, A., Kumar, M., Girach, I., Ansari, T. U., Sharma, S. K., ... & Gunthe, S. S. (2020). On the widespread enhancement in fine particulate matter across the Indo-Gangetic Plain towards winter. Scientific reports, 10(1), 5862. https://doi.org/10.1038/S41598-020-62710-8
Peng, Y., Wang, H., Zhang, X., Liu, Z., Zhang, W., Li, S., ... & Che, H. (2023). Superimposed effects of typical local cir-culations driven by mountainous topography and aerosol–radiation interaction on heavy haze in the Beijing–Tianjin–Hebei central and southern plains in winter. Atmospheric Chemistry and Physics, 23(14), 8325-8339. https://doi.org/10.5194/acp-23-8325-2023
Polanco-Martínez, J. M., Fernández-Macho, J., & Medina-Elizalde, M. (2020). Dynamic wavelet correlation analysis for multivariate climate time series. Scientific Reports, 10(1), 21277. https://doi.org/10.1038/S41598-020-77767-8
Sirisha, U. M., Belavagi, M. C., & Attigeri, G. (2020). Profit prediction using ARIMA, SARIMA and LSTM models in time series forecasting: A comparison. Ieee Access, 10, 124715-124727. https://doi.org/10.1109/ACCESS.2022.3224938
Song, Y., Wu, H., Dai, Q., Liu, X., Zhang, Y., & Feng, Y. (2024). Differentiating periodic drivers of air quality changes: A two‐step decomposition approach integrating machine learning and wavelet analysis. Journal of Geophysical Re-search: Atmospheres, 129(7), e2023JD039658. https://doi.org/10.1029/2023JD039658
Tary, J. B., Herrera, R. H., & Van Der Baan, M. (2018). Analysis of time-varying signals using continuous wavelet and synchrosqueezed transforms. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engi-neering Sciences, 376(2126), 20170254. https://doi.org/10.1098/rsta.2017.0254
Twigg, M. M., Di Marco, C. F., McGhee, E. A., Braban, C. F., Nemitz, E., Brown, R. J., ... & Butterfield, D. M. (2023). The potential of high temporal resolution automatic measurements of PM2. 5 composition as an alternative to the filter-based manual method used in routine monitoring. Atmospheric Environment, 315, 120148. https://doi.org/10.1016/j.atmosenv.2023.120148
Vassallo, M., Cultrera, G., Di Giulio, G., Cara, F., & Milana, G. (2022). Peak frequency changes from HV spectral ratios in central Italy: Effects of strong motions and seasonality over 12 years of observations. Journal of Geophysical Re-search: Solid Earth, 127(5), e2021JB023848. https://doi.org/10.1029/2021JB023848 (2022)
Wan, Y., Chen, Y., Sim, K. Y. H., Wu, L., Geng, X., & Chau, K. (2019). Precise temporal localization of sudden onsets in audio signals using the wavelet approach. In Audio Engineering Society Convention 147. Audio Engineering Soci-ety.
Wang, P., Hu, T., Gao, F., Wu, R., Guo, W., & Zhu, X. (2022). A hybrid data-driven framework for spatiotemporal traf-fic flow data imputation. IEEE Internet of Things Journal, 9(17), 16343-16352. https://doi.org/10.1109/JIOT.2022.3151238
Wilson, R. E., Eckley, I. A., Nunes, M. A., & Park, T. (2021). A wavelet-based approach for imputation in nonstationary multivariate time series. Statistics and Computing, 31(2), 18. https://doi.org/10.1007/S11222-021-09998-2
Wu, S., Yao, J., Wang, Y., & Zhao, W. (2023). Influencing factors of PM2. 5 concentration in the typical urban agglom-erations in China based on wavelet perspective. Environmental Research, 237, 116641. https://doi.org/10.1016/j.envres.2023.116641
Yousefian, F., Faridi, S., Azimi, F., Aghaei, M., Shamsipour, M., Yaghmaeian, K., & Hassanvand, M. S. (2020). Tem-poral variations of ambient air pollutants and meteorological influences on their concentrations in Tehran during 2012–2017. Scientific reports, 10(1), 292. https://doi.org/10.1038/S41598-019-56578-6
Zhang, L., Liu, J., Feng, Y., Wu, P., & He, P. (2023). PM2. 5 concentration prediction using weighted CEEMDAN and improved LSTM neural network. Environmental Science and Pollution Research, 30(30), 75104-75115. https://doi.org/10.1007/s11356-023-27630-w
Zhang, X., Chen, X., Yue, Y., Wang, S., Zhao, B., Huang, X., ... & Wang, J. (2023). Ecological study on global health ef-fects due to source-specific ambient fine particulate matter exposure. Environmental Science & Technology, 57(3), 1278-1291. http://doi.org/10.1021/acs.est.2c06752
Zhou, Q., Nizamani, M. M., Zhang, H. Y., & Zhang, H. L. (2023). The air we breathe: An In-depth analysis of PM2. 5 pollution in 1312 cities from 2000 to 2020. Environmental Science and Pollution Research, 30(41), 93900-93915. https://doi.org/10.1007/s11356-023-29043-1
Zou, Z., Cheng, C., & Shen, S. (2021). The complex nonlinear coupling causal patterns between PM2. 5 and meteorolog-ical factors in Tibetan plateau: a case study in xining. IEEE Access, 9, 150373-150382. https://doi.org/10.1109/ACCESS.2021.3123455
Almaliki, A. H., Derdour, A., & Ali, E. Air Quality Index (AQI) Prediction in Holy Makkah based on machine learning methods. Sustainability, 15(17), 13168. https://doi.org/10.3390/su151713168 (2023)
Al Yammahi, A., & Aung, Z. A study of nitrogen dioxide (NO2) periodicity over the United Arab Emirates using wave-let analysis. Scientific Reports, 12(1), 18144. https://doi.org/10.1038/s41598-022-21937-3 (2022)
Ariapak, S., Jalalian, A., & Honarjoo, N. Source identification, seasonal and spatial variations of airborne dust trace el-ements pollution in Tehran, the capital of Iran. Urban Climate, 42, 101049. https://doi.org/10.1016/j.uclim.2021.101049 (2022)
Arunab, K. S., & Mathew, A. (2024). Quantifying urban heat island and pollutant nexus: A novel geospatial ap-proach. Sustainable Cities and Society, 101, 105117. https://doi.org/10.1016/j.scs.2023.105117 (2024)
Chavez, M., & Cazelles, B. Detecting dynamic spatial correlation patterns with generalized wavelet coherence and non-stationary surrogate data. Scientific reports, 9(1), 7389. https://doi.org/10.1038/S41598-019-43571-2 (2019)
Cheng, K., He, Y., Zhong, Y., Li, X., Li, S., & Ayitken, M. Source apportionment and health risk assessment of PM2. 5-bound elements on winter pollution days in industrial cities on the northern slope of Tianshan Mountain, Chi-na. Atmospheric Pollution Research, 15(4), 102036. https://doi.org/10.1016/j.apr.2024.102036 (2024)
Chołoniewski, J., Sienkiewicz, J., Dretnik, N., Leban, G., Thelwall, M., & Hołyst, J. A. A calibrated measure to compare fluctuations of different entities across timescales. Scientific reports, 10(1), 20673. https://doi.org/10.1038/S41598-020-77660-4 (2020)
Costagliola, M. A., Marchitto, L., Giuzio, R., Casadei, S., Rossi, T., Lixi, S., & Faedo, D. Non-exhaust particulate emis-sions from road transport vehicles. Energies, 17(16), 4079. https://doi.org/10.3390/en17164079 (2024)
Deshpande, N. A., Tomar, N. A., & Pagare, N. A. Quantitative analysis of air pollution levels and its health implications in Delhi-NCR: A longitudinal study. International Journal of Science and Research Archive, 11, 759-68. https://doi.org/10.30574/ijsra.2024.11.2.0488 (2024)
Dhaka, S. K., Chetna, Kumar, V., Panwar, V., Dimri, A. P., Singh, N., ... & Hayashida, S. PM2. 5 diminution and haze events over Delhi during the COVID-19 lockdown period: an interplay between the baseline pollution and meteorol-ogy. Scientific reports, 10(1), 13442. https://doi.org/10.1038/S41598-020-70179-8 (2020).
Du, J., Hu, M., & Zhang, W. Missing data problem in the monitoring system: A review. IEEE Sensors Journal, 20(23), 13984-13998. https://doi.org/10.1109/JSEN.2020.3009265
Ehteshami-Moinabadi, M., & Nasiri, S. A. (2023). critique review and update of the earthquake surface fault rupture hazard in the northern zone of Tehran metropolis, Iran. In EGU General Assembly Conference Abstracts (pp. EGU-3749). https://doi.org/10.5194/egusphere-egu23-3749
Franzke, C. L., Barbosa, S., Blender, R., Fredriksen, H. B., Laepple, T., Lambert, F., ... & Yuan, N. (2020). The structure of climate variability across scales. Reviews of Geophysics, 58(2), e2019RG000657. https://doi.org/10.1029/2019RG000657
Fuladlu, K., & Altan, H. (2021). Examining land surface temperature and relations with the major air pollutants: A re-mote sensing research in case of Tehran. Urban Climate, 39, 100958. https://doi.org/10.1016/j.uclim.2021.100958
Gozuyilmaz, S., & Kundakcioglu, O. E. (2021). Mathematical optimization for time series decomposition. OR Spec-trum, 43(3), 733-758. https://doi.org/10.1007/S00291-021-00637-W
Guo, Q., He, Z., & Wang, Z. (2023). Simulating daily PM2. 5 concentrations using wavelet analysis and artificial neural network with remote sensing and surface observation data. Chemosphere, 340, 139886. https://doi.org/10.1016/j.chemosphere.2023.139886
Kan, X., Liu, X., Zhou, Z., Zhang, Y., Zhu, L., Sian, K. T. C. L. K., & Liu, Q. (2023). Analysis of spatiotemporal varia-tion and influencing factors of PM2. 5 in China based on multisource data. Sustainability, 15(19), 14656. https://doi.org/10.3390/su151914656
Kermani, M., Asadgol, Z., Gholami, M., Jafari, A. J., Shahsavani, A., Goodarzi, B., & Arfaeinia, H. (2021). Occurrence, spatial distribution, seasonal variations, potential sources, and inhalation-based health risk assessment of organ-ic/inorganic pollutants in ambient air of Tehran. Environmental geochemistry and health, 43(5), 1983-2006. https://doi.org/10.1007/S10653-020-00779-W
Khoshakhlagh, A. H., Mohammadzadeh, M., & Morais, S. (2023). Air quality in Tehran, Iran: Spatio-temporal charac-teristics, human health effects, economic costs and recommendations for good practice. Atmospheric Environment: X, 19, 100222. https://doi.org/10.1016/j.aeaoa.2023.100222
Koue, J., Shimadera, H., Matsuo, T., & Kondo, A. (2018). Numerical analysis of sensitivity of structure of the stratifi-cation in Lake Biwa, Japan by changing meteorological elements. Water, 10(10), 1492. https://doi.org/10.3390/w10101492
Lausier, A. M., & Jain, S. (2018). Overlooked trends in observed global annual precipitation reveal underestimated risks. Scientific reports, 8(1), 16746. https://doi.org/10.1038/S41598-018-34993-5
Liang, Q., Zhang, X., Miao, Y., & Liu, S. (2024). Multi-scale meteorological impact on PM2. 5 pollution in Tangshan, Northern China. Toxics, 12(9), 685. https://doi.org/10.3390/toxics12090685
Li, X., Li, H., Lu, H., Jensen, C. S., Pandey, V., & Markl, V. (2023). Missing value imputation for multi-attribute sensor data streams via message propagation. Proceedings of the VLDB Endowment, 17(3), 345-358. https://doi.org/10.14778/3632093.3632100
Liu, B., Jin, Y., & Li, C. (2021). Analysis and prediction of air quality in Nanjing from autumn 2018 to summer 2019 using PCR–SVR–ARMA combined model. Scientific reports, 11(1), 348. https://doi.org/10.1038/S41598-020-79462-0
Molavi-Arabshahi, M., Azizpour, J., Nikan, O., Naderi Beni, A., & Lopes, A. M. (2022). Investigation and analysis of sea surface temperature and precipitation of the southern caspian sea using wavelet analysis. Axioms, 12(1), 10. https://doi.org/10.3390/axioms12010010
Najafzadeh, F., Mohammadzadeh, A., Ghorbanian, A., & Jamali, .S (2021). Spatial and temporal analysis of surface ur-ban heat island and thermal comfort using landsat satellite images between 1989 and 2019: A case study in Teh-ran. Remote Sensing, 13(21), 4469. https://doi.org/10.3390/rs13214469 (2021)
Ojha, N., Sharma, A., Kumar, M., Girach, I., Ansari, T. U., Sharma, S. K., ... & Gunthe, S. S. (2020). On the widespread enhancement in fine particulate matter across the Indo-Gangetic Plain towards winter. Scientific reports, 10(1), 5862. https://doi.org/10.1038/S41598-020-62710-8
Peng, Y., Wang, H., Zhang, X., Liu, Z., Zhang, W., Li, S., ... & Che, H. (2023). Superimposed effects of typical local cir-culations driven by mountainous topography and aerosol–radiation interaction on heavy haze in the Beijing–Tianjin–Hebei central and southern plains in winter. Atmospheric Chemistry and Physics, 23(14), 8325-8339. https://doi.org/10.5194/acp-23-8325-2023
Polanco-Martínez, J. M., Fernández-Macho, J., & Medina-Elizalde, M. (2020). Dynamic wavelet correlation analysis for multivariate climate time series. Scientific Reports, 10(1), 21277. https://doi.org/10.1038/S41598-020-77767-8
Sirisha, U. M., Belavagi, M. C., & Attigeri, G. (2020). Profit prediction using ARIMA, SARIMA and LSTM models in time series forecasting: A comparison. Ieee Access, 10, 124715-124727. https://doi.org/10.1109/ACCESS.2022.3224938
Song, Y., Wu, H., Dai, Q., Liu, X., Zhang, Y., & Feng, Y. (2024). Differentiating periodic drivers of air quality changes: A two‐step decomposition approach integrating machine learning and wavelet analysis. Journal of Geophysical Re-search: Atmospheres, 129(7), e2023JD039658. https://doi.org/10.1029/2023JD039658
Tary, J. B., Herrera, R. H., & Van Der Baan, M. (2018). Analysis of time-varying signals using continuous wavelet and synchrosqueezed transforms. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engi-neering Sciences, 376(2126), 20170254. https://doi.org/10.1098/rsta.2017.0254
Twigg, M. M., Di Marco, C. F., McGhee, E. A., Braban, C. F., Nemitz, E., Brown, R. J., ... & Butterfield, D. M. (2023). The potential of high temporal resolution automatic measurements of PM2. 5 composition as an alternative to the filter-based manual method used in routine monitoring. Atmospheric Environment, 315, 120148. https://doi.org/10.1016/j.atmosenv.2023.120148
Vassallo, M., Cultrera, G., Di Giulio, G., Cara, F., & Milana, G. (2022). Peak frequency changes from HV spectral ratios in central Italy: Effects of strong motions and seasonality over 12 years of observations. Journal of Geophysical Re-search: Solid Earth, 127(5), e2021JB023848. https://doi.org/10.1029/2021JB023848 (2022)
Wan, Y., Chen, Y., Sim, K. Y. H., Wu, L., Geng, X., & Chau, K. (2019). Precise temporal localization of sudden onsets in audio signals using the wavelet approach. In Audio Engineering Society Convention 147. Audio Engineering Soci-ety.
Wang, P., Hu, T., Gao, F., Wu, R., Guo, W., & Zhu, X. (2022). A hybrid data-driven framework for spatiotemporal traf-fic flow data imputation. IEEE Internet of Things Journal, 9(17), 16343-16352. https://doi.org/10.1109/JIOT.2022.3151238
Wilson, R. E., Eckley, I. A., Nunes, M. A., & Park, T. (2021). A wavelet-based approach for imputation in nonstationary multivariate time series. Statistics and Computing, 31(2), 18. https://doi.org/10.1007/S11222-021-09998-2
Wu, S., Yao, J., Wang, Y., & Zhao, W. (2023). Influencing factors of PM2. 5 concentration in the typical urban agglom-erations in China based on wavelet perspective. Environmental Research, 237, 116641. https://doi.org/10.1016/j.envres.2023.116641
Yousefian, F., Faridi, S., Azimi, F., Aghaei, M., Shamsipour, M., Yaghmaeian, K., & Hassanvand, M. S. (2020). Tem-poral variations of ambient air pollutants and meteorological influences on their concentrations in Tehran during 2012–2017. Scientific reports, 10(1), 292. https://doi.org/10.1038/S41598-019-56578-6
Zhang, L., Liu, J., Feng, Y., Wu, P., & He, P. (2023). PM2. 5 concentration prediction using weighted CEEMDAN and improved LSTM neural network. Environmental Science and Pollution Research, 30(30), 75104-75115. https://doi.org/10.1007/s11356-023-27630-w
Zhang, X., Chen, X., Yue, Y., Wang, S., Zhao, B., Huang, X., ... & Wang, J. (2023). Ecological study on global health ef-fects due to source-specific ambient fine particulate matter exposure. Environmental Science & Technology, 57(3), 1278-1291. http://doi.org/10.1021/acs.est.2c06752
Zhou, Q., Nizamani, M. M., Zhang, H. Y., & Zhang, H. L. (2023). The air we breathe: An In-depth analysis of PM2. 5 pollution in 1312 cities from 2000 to 2020. Environmental Science and Pollution Research, 30(41), 93900-93915. https://doi.org/10.1007/s11356-023-29043-1
Zou, Z., Cheng, C., & Shen, S. (2021). The complex nonlinear coupling causal patterns between PM2. 5 and meteorolog-ical factors in Tibetan plateau: a case study in xining. IEEE Access, 9, 150373-150382. https://doi.org/10.1109/ACCESS.2021.3123455