Estimation of PM2.5 as a Harmful Environmental Hazard in Tehran by Fusion of MODIS Aerosol Products through a Machine Learning Approach

جغرافیا و مخاطرات محیطی

Article 6, Volume 12, Issue 3 - Serial Number 47, September 2023, Pages 101-122   

Document Type: Research Article

DOI: 10.22067/geoeh.2023.79402.1296

Authors

Ali Mirzaei¹; Hossein Bagheri^{* 2}; Mehran Sattari²

¹MA in Reomte Sensing, University of Isfahan, Isfahan, Iran

²Assistant Professor in Geomatics, University of Isfahan, Isfahan, Iran

Abstract

Air pollution is one of the most harmful natural hazards in Tehran metropolitan. Particles with a diameter of less than 2.5 micrometers (PM2.5) as one of the most harmful pollutants have endangered the health of people living in Tehran. One of the PM2.5 estimation techniques is the use of Aerosol Optical Depth (AOD) products derived from satellite observations. Various AOD products are retrieved with different algorithms that do not have the same accuracy and spatial resolution. Due to the differences in many assumptions and approximations adopted in the AOD retrieval process, the generated AOD products involve uncertainties. This issue reduces the accuracy of PM2.5 concentration estimation. The purpose of this study was to investigate the possibility of fusing AOD products obtained from MODIS sensor observations (retrieved by Deep Blue and Dark Target algorithms) to estimate PM2.5 more accurately. First, the performance of different machine learning algorithms in estimating PM2.5 from AOD data was evaluated. As a result, the XGBoost algorithm with the highest performance was selected as the base model for PM2.5 estimation. Then, the AOD products were fused using a weighted averaging based on the retrieval quality of the primary products. Finally, the fused AOD product along with meteorological data were employed to estimate PM2.5 using XGboost. The results demonstrated that the accuracy of PM2.5 estimation from the fused AOD product is better than when the AOD products are used individually (= 0.77, MAE = 7.00 , RMSE = 9.59 ). Thus, the retrieval quality of AOD products will lead to more accurate estimation of PM2.5 in the end.

Keywords

Aerosol Optical Depth; Data Fusion; MODIS; PM2.5; XGBoost

References

Anderson, L. O., Malhi, Y., Aragão, L. E., Ladle, R., Arai, E., Barbier, N., & Phillips, O. (2010). Remote sensing detection of droughts in Amazonian forest canopies. New Phytologist, 187(3), 733-750. doi:https://doi.org/10.1111/j.1469-8137.2010.03355.x

Bagheri, H. (2022). A machine learning-based framework for high resolution mapping of PM2.5 in Tehran, Iran, using MAIAC AOD data. Advances in Space Research, 69(9), 3333-3349. doi:https://doi.org/10.1016/j.asr.2022.02.032

Bagheri, H. (2023). Using deep ensemble forest for high-resolution mapping of PM2. 5 from MODIS MAIAC AOD in Tehran, Iran. Environmental Monitoring and Assessment, 195(3), 377. doi:https://doi.org/10.1007/s10661-023-10951-1

Bagheri, H., Sadeghian, S., & Sadjadi, S. Y. (2014). The Assessment of using an Intelligent Algorithm for the Interpolation of Elevation in the DTM Generation. Photogrammetrie-Fernerkundung-Geoinformation, 197-208. doi:https://doi.org/0.1127/1432-8364/2014/0220

Bagheri, H., Schmitt, M., d’Angelo, P., & Zhu, X. X. (2018). A framework for SAR-optical stereogrammetry over urban areas. ISPRS journal of photogrammetry and remote sensing, 146, 389-408. doi:https://doi.org/10.1016/j.isprsjprs.2018.10.003

Bagheri, H., Schmitt, M., & Zhu, X. X. (2017). Uncertainty assessment and weight map generation for efficient fusion of TanDEM-X and Cartosat-1 DEMs. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42(1/W1)), 433-439. doi:https://doi.org/10.5194/isprs-archives-XLII-1-W1-433-2017

Bagheri, H., Schmitt, M., & Zhu, X. X. (2018). Fusion of TanDEM-X and Cartosat-1 elevation data supported by neural network-predicted weight maps. ISPRS journal of photogrammetry and remote sensing, 144, 285-297. doi:https://doi.org/10.1016/j.isprsjprs.2018.07.007

Bharath Ramsundar & Reza Bosagh Zadeh. (2018). TensorFlow for Deep Learning (Rachel Roumeliotis & Alicia Young Ed. first ed. Vol. 16802 KB).

Castanedo, F. (2013). A review of data fusion techniques. The Scientific World Journal, 2013, 704504. doi:https://doi.org/10.1155/2013/704504

Chang, C.-C., & Lin, C.-J. (2011). LIBSVM: a library for support vector machines. ACM transactions on intelligent systems and technology (TIST), 2(3), 1-27. doi:https://dl.acm.org/ doi/10.1145/ 1961189.1961199

Chen, N., Yang, M., Du, W., & Huang, M. (2021). PM2.5 estimation and spatial-temporal pattern analysis based on the modified support vector regression model and the 1 km resolution MAIAC AOD in Hubei, China. ISPRS International Journal of Geo-Information, 10(1), 31. doi:https://doi.org/10.3390/ijgi10010031

Dark Target aerosol produact user's guid (2020). In: NASA.

Gogikar, P., Tripathy, M. R., Rajagopal, M., Paul, K. K., & Tyagi, B. (2021). PM2.5 estimation using multiple linear regression approach over industrial and non-industrial stations of India. Journal of Ambient Intelligence and Humanized Computing, 12(2), 2975-2991. doi:https://doi.org/10.1007/s12652-020-02457-2

Gupta, P., & Christopher, S. A. (2009). Particulate matter air quality assessment using integrated surface, satellite, and meteorological products: Multiple regression approach. Journal of Geophysical Research: Atmospheres, 114(D14). doi:https://doi.org/10.1029/2008JD011496

Habibi, R., Alesheikh, A. A., Mohammadinia, A., & Sharif, M. (2017). An assessment of spatial pattern characterization of air pollution: A case study of CO and PM2.5 in Tehran, Iran. ISPRS International Journal of Geo-Information, 6(9), 270. doi: https://doi.org/10.3390/ijgi6090270

Hall, D. L., & Llinas, J. (1997). An introduction to multisensor data fusion. Proceedings of the IEEE, 85(1), 6-23. doi:https://doi.org/10.1109/5.554205

Han, B., Ding, H., Ma, Y., & Gong, W. (2017). Improving retrieval accuracy for areosol optical depth by fusiom of MODIS and CALIOP data. Tehnicki vjesnik/Technical Gazette, 24(3). doi:https://doi.org/10.17559/TV-20160429044233

Hsu, N., Jeong, M. J., Bettenhausen, C., Sayer, A., Hansell, R., Seftor, C., . . . Tsay, S. C. (2013). Enhanced Deep Blue aerosol retrieval algorithm: The second generation. Journal of Geophysical Research: Atmospheres, 118(16), 9296. doi:https://doi.org/10.1002/jgrd.50712

Hu, X., Waller, L. A., Lyapustin, A., Wang, Y., Al-Hamdan, M. Z., Crosson, W. L., . . . Puttaswamy, S. J. (2014). Estimating ground-level PM2.5 concentrations in the Southeastern United States using MAIAC AOD retrievals and a two-stage model. Remote Sensing of Environment, 140, 220-232. doi:https://doi.org/10.1016/j.rse.2013.08.032

Huang, C.-J., & Kuo, P.-H. (2018). A deep CNN-LSTM model for particulate matter (PM2. 5) forecasting in smart cities. Sensors, 18(7), 2220. doi:https://doi.org/10.3390/s18072220

Jung, C.-R., Chen, W.-T., & Nakayama, S. F. (2021). A national-scale 1-km resolution PM2.5 estimation model over japan using maiac aod and a two-stage random forest model. Remote Sensing, 13(18), 3657. doi:https://doi.org/10.3390/rs13183657

Khaleghi, B., Khamis, A., Karray, F. O., & Razavi, S. N. (2013). Multisensor data fusion: A review of the state-of-the-art. Information Fusion, 14(1), 28-44. doi:https://doi.org/ 10.1016/ j.inffus. 2011.08.001

Kianian, B., Liu, Y., & Chang, H. H. (2021). Imputing satellite-derived aerosol optical depth using a multi-resolution spatial model and random forest for PM2.5 prediction. Remote Sensing, 13(1), 126. doi:https://doi.org/10.3390/rs13010126

Li, L. (2020). A robust deep learning approach for spatiotemporal estimation of satellite AOD and PM2.5. Remote Sensing, 12(2), 264. doi:https://doi.org/10.3390/rs12020264

Liao, Q., Jin, W., Tao, Y., Qu, J., Li, Y., & Niu, Y. (2020). Health and Economic Loss Assessment of PM2. 5 Pollution during 2015–2017 in Gansu Province, China. International Journal of Environmental Research and Public Health, 17(9), 3253. doi:https:// doi.org/ 10.3390/ ijerph1 7093253

Liu, N., Zou, B., Feng, H., Wang, W., Tang, Y., & Liang, Y. (2019). Evaluation and comparison of multiangle implementation of the atmospheric correction algorithm, Dark Target, and Deep Blue aerosol products over China. Atmospheric Chemistry and Physics, 19(12), 8243-8268. doi:https://doi.org/10.5194/acp-19-8243-2019

Luo, H., Guan, Q., Lin, J., Wang, Q., Yang, L., Tan, Z., & Wang, N. (2020). Air pollution characteristics and human health risks in key cities of northwest China. Journal of Environmental Management, 269, 110791. doi:https:// doi.org/ 10.1016/ j.jenvman. 2020. 110791

Meng, T., Jing, X., Yan, Z., & Pedrycz, W. (2020). A survey on machine learning for data fusion. Information Fusion, 57, 115-129. doi:https://doi.org/10.1016/j.inffus.2019.12.001

Mirzaei, A., Bagheri, H., & Sattari, M. (2023). Data level and decision level fusion of satellite multi-sensor AOD retrievals for improving PM2. 5 estimations, a study on Tehran. Earth Science Informatics, 16(1), 753-771. doi:http://dx.doi.org/10.48550/arXiv.2302.10278

Nabavi, S. O., Haimberger, L., & Abbasi, E. (2019). Assessing PM2.5 concentrations in Tehran, Iran, from space using MAIAC, deep blue, and dark target AOD and machine learning algorithms. Atmospheric Pollution Research, 10(3), 889-903. doi:https:// doi.org/ 10.1016/ j.apr.2018.12.017

Ni, X., Cao, C., Zhou, Y., Cui, X., & P Singh, R. (2018). Spatio-temporal pattern estimation of PM2. 5 in Beijing-Tianjin-Hebei Region based on MODIS AOD and meteorological data using the back propagation neural network. Atmosphere, 9(3), 105. doi:https:// doi.org/ 10.3390/atmos9030105

Pashayi, M., & Satari, M. (2022). Improvement of spatial-temporal resolution of aerosol profile by using multi-source satellite data over the Persian Gulf. Atmospheric Environment, 119410. doi:https://doi.org/10.1016/j.atmosenv.2022.119410

Popov, S., Morozov, S., & Babenko, A. (2019). Neural oblivious decision ensembles for deep learning on tabular data. arXiv preprint arXiv:1909.06312. doi:https://doi.org/ 10.48550/ arXiv.1909.06312

Qi, Y., Li, Q., Karimian, H., & Liu, D. (2019). A hybrid model for spatiotemporal forecasting of PM2.5 based on graph convolutional neural network and long short-term memory. Science of the Total Environment, 664, 1-10. doi:https://doi.org/10.1016/j.scitotenv.2019.01.333

Remer, L., Mattoo, S., Levy, R., & Munchak, L. (2013). MODIS 3 km aerosol product: algorithm and global perspective. Atmospheric Measurement Techniques, 6(7), 1829-1844. doi:https://doi.org/10.5194/amt-6-1829-2013

Sayer, A., Munchak, L., Hsu, N., Levy, R., Bettenhausen, C., & Jeong, M. J. (2014). MODIS Collection 6 aerosol products: Comparison between Aqua's Deep Blue, Dark Target, and “merged” data sets, and usage recommendations. Journal of Geophysical Research: Atmospheres, 119(24), 13,965-913,989. doi:https://doi.org/10.1002/2014JD022453

Shwartz-Ziv, R., & Armon, A. (2022). Tabular data: Deep learning is not all you need. Information Fusion, 81, 84-90. doi:https://doi.org/10.1016/j.inffus.2021.11.011

Singer, A., Zobeck, T., Poberezsky, L., & Argaman, E. (2003). The PM10 and PM2.5 dust generation potential of soils/sediments in the Southern Aral Sea Basin, Uzbekistan. Journal of Arid Environments, 54(4), 705-728. doi:https://doi.org/10.1006/jare.2002.1084

Stafoggia, M., Bellander, T., Bucci, S., Davoli, M., De Hoogh, K., De'Donato, F., . . . Renzi, M. (2019). Estimation of daily PM10 and PM2.5 concentrations in Italy, 2013–2015, using a spatiotemporal land-use random-forest model. Environment International, 124, 170-179. doi:https://doi.org/10.1016/j.envint.2019.01.016

Tang, Q., Bo, Y., & Zhu, Y. (2016). Spatiotemporal fusion of multiple‐satellite aerosol optical depth (AOD) products using Bayesian maximum entropy method. Journal of Geophysical Research: Atmospheres, 121(8), 4034-4048. doi:https://doi.org/10.1002/2015JD024571

Tsai, T.-C., Jeng, Y.-J., Chu, D. A., Chen, J.-P., & Chang, S.-C. (2011). Analysis of the relationship between MODIS aerosol optical depth and particulate matter from 2006 to 2008. Atmospheric Environment, 45(27), 4777-4788. doi:https:// doi.org/ 10.1016/ j.atmosenv. 2009.10. 006

Wang, Y., Yuan, Q., Shen, H., Zheng, L., & Zhang, L. (2020). Investigating multiple aerosol optical depth products from MODIS and VIIRS over Asia: Evaluation, comparison, and merging. Atmospheric Environment, 230, 117548. doi:https:// doi.org/ 10.1016/ j.atmosenv. 2020. 117548

Wang, Z., Chen, L., Tao, J., Zhang, Y., & Su, L. (2010). Satellite-based estimation of regional particulate matter (PM) in Beijing using vertical-and-RH correcting method. Remote Sensing of Environment, 114(1), 50-63. doi:https://doi.org/10.1016/j.rse.2009.08.009

Wei, X., Chang, N.-B., Bai, K., & Gao, W. (2020). Satellite remote sensing of aerosol optical depth: Advances, challenges, and perspectives. Critical Reviews in Environmental Science and Technology, 50(16), 1640-1725. doi:https://doi.org/10.1080/10643389.2019.1665944

Xia, X., Zhao, B., Zhang, T., Wang, L., Gu, Y., Liou, K.-N., . . . Huang, Y. (2021). Satellite-Derived Aerosol Optical Depth Fusion Combining Active and Passive Remote Sensing Based on Bayesian Maximum Entropy. IEEE Transactions on Geoscience and Remote Sensing, 60, 1-13. doi:https://doi.org/10.1109/TGRS.2021.3051799

Xiao, Q., Chang, H. H., Geng, G., & Liu, Y. (2018). An ensemble machine-learning model to predict historical PM2.5 concentrations in China from satellite data. Environmental science & technology, 52(22), 13260-13269. doi:https://doi.org/10.1021/acs.est.8b02917

Xu, H., Guang, J., Xue, Y., De Leeuw, G., Che, Y., Guo, J., . . . Wang, T. (2015). A consistent aerosol optical depth (AOD) dataset over mainland China by integration of several AOD products. Atmospheric Environment, 114, 48-56. doi:https://doi.org/ 10.1016/ j.atmosenv. 2015. 05.023

Xu, H., Xue, Y., Guang, J., Li, Y., Yang, L., Hou, T., . . . Chen, Z. (2012). A semi-empirical optical data fusion technique for merging aerosol optical depth over China. Paper presented at the 2012 IEEE International Geoscience and Remote Sensing Symposium.

Yang, Q., Yuan, Q., Li, T., Shen, H., & Zhang, L. (2017). The relationships between PM2.5 and meteorological factors in China: seasonal and regional variations. International Journal of Environmental Research and Public Health, 14(12), 1510. doi:https://doi.org/ 10.3390%2 Fijerph14121510

You, W., Zang, Z., Zhang, L., Li, Y., Pan, X., & Wang, W. (2016). National-scale estimates of ground-level PM2.5 concentration in China using geographically weighted regression based on 3 km resolution MODIS AOD. Remote Sensing, 8(3), 184. doi:https:// doi.org/ 10.3390/rs8030184

Zamani Joharestani, M., Cao, C., Ni, X., Bashir, B., & Talebiesfandarani, S. (2019). PM2.5 prediction based on random forest, XGBoost, and deep learning using multisource remote sensing data. Atmosphere, 10(7), 373. doi:https://doi.org/10.3390/atmos10070373

Zhao, C., Wang, Q., Ban, J., Liu, Z., Zhang, Y., Ma, R., . . . Li, T. (2020). Estimating the daily PM2.5 concentration in the Beijing-Tianjin-Hebei region using a random forest model with a 0.01°× 0.01° spatial resolution. Environment International, 134, 105297. doi:https:// doi.org/ 10.1016/j.envint.2019.105297

Zhu, X. X., Hu, J., Qiu, C., Shi, Y., Kang, J., Mou, L., . . . Huang, R. (2020). So2Sat LCZ42: A benchmark data set for the classification of global local climate zones [Software and Data Sets]. IEEE Geoscience and Remote Sensing Magazine, 8(3), 76-89. doi:https://d oi.org/ 10.1109/ MGRS.2020.2964708