ارزیابی داده‌های سنجنده مودیس (MODIS) در پایش غلظت آلاینده‌های PM2.5 و PM10 با تأکید بر متغیرهای هواشناسی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 گروه مهندسی آبیاری و آبادانی، دانشکده مهندسی و فناوری کشاورزی، پردیس کشاورزی و منابع طبیعی، دانشگاه تهران

2 استادیار/گروه مهندسی آبیاری و آبادانی، دانشکده مهندسی و فناوری کشاورزی، پردیس کشاورزی و منابع طبیعی دانشگاه تهران

3 استادیار، گروه مهندسی آب دانشگاه ارومیه

چکیده

در این پژوهش با استفاده از داده‌های حاصل از سنجنده مودیس (MODIS) به ارزیابی توانایی داده‌های این سنجنده در برآورد غلظت آلاینده‌های PM2.5 وPM10 در شهر تهران پرداخته شد. برای این منظور از داده‌های شش ایستگاه هواشناسی و آلودگی سنجی زمینی استفاده شد و مدل‌های خطی و غیرخطی برآورد غلظت هواویزها ارائه شد. متغیرهای این مدل‌ها شامل متغیرهای هواشناسی و عمق نوری هواویزها (AOD) مستخرج از محصولات MOD04-L2 سنجنده مودیس ماهواره ترا (TERRA) است. نتایج تحلیل‌های آماری نشان داد مدل رگرسیون خطی که شامل متغیرهای عمق نوری هواویزها، بارش 24 ساعته، میانگین فشار بخار آب و ساعت آفتابی است، در مقیاس کل شهر تهران (75/0R2= و ug/m3 47/7 RMSE=) و ایستگاه‌ها مناسب‌ترین مدل در مقایسه با بقیه مدل‌های به‌دست‌آمده است. در این مدل غلظت PM2.5  با ساعت آفتابی رابطه عکس و با بقیه متغیرها رابطه مستقیم دارد. نتایج نشان داد استفاده از متغیرهای هواشناسی و توجه به پدیده‌های جوی موجب بهبود عملکرد داده‌های سنجندة مودیس در برآورد غلظت آلاینده PM2.5 می‌شود و مدل ارائه‌شده می‌تواند مکمل مناسبی برای ایستگاه‌های زمینی پایش آلودگی هوا در برآورد غلظت هواویزها باشد و نواقص آن‌ها تا حد زیادی برطرف سازد.

کلیدواژه‌ها


عنوان مقاله [English]

Assessment of MODIS Data in Monitoring the Concentrations of PM2.5 and PM10 Pollutants with Emphasis on Meteorological Variables

نویسندگان [English]

  • Saba Hoseini Tabesh 1
  • Zahra Aghashariatmadari 2
  • Somayeh Hejabi 3
1 , Irrigation Engineering Department, Faculty of Agricultural Engineering and Technology, University College of Agriculture and Natural Resources, University of Tehran,
2 Assistant Prof., Irrigation & Reclamation Engrg. Dept. University of Tehran Karaj, Iran.
3 Assistant professor, Department of Water Engineering, Faculty of Agriculture, Urmia University, Urmia, Iran
چکیده [English]

In this study, the function of the MODIS (Moderate Resolution Imaging Spectroradiometer) sensor to estimate the concentration of PM2.5 and PM10 pollutants in Tehran was assessed by using the data obtained from this sensor. In this study, linear and non-linear models for estimating the concentration of aerosols were presented in six meteorological and ground pollution monitoring stations in Tehran province. The results of the models were compared to the ground station observations by using statistical tests and the most appropriate model was elected from the regressions. The developed model (based on aerosol optical depth which was extracted from MOD04-L2 products of TERRA satellite MODIS sensor), 24-hour precipitation, average water vapor pressure, and sunshine) showed high accuracy, very low RMSE, and rather high R2 in Tehran province (R2 = 0.75 and RMSE= 7.47 ug / m3) and stations. In this model, PM2.5 concentration and sunshine hours have a negative correlation, also the positive relationship to other variables is observed. The results demonstrated that utilizing meteorological variables and attention to the prevailing atmospheric phenomena enhance the performance of MODIS sensor data in estimating PM2.5 pollutant concentration. Undesirable as MODIS sensor data might be in terms of certainty and accuracy, they are undoubtedly beneficial considering elimination defect of ground-based pollution monitoring stations in estimating aerosols concentration and complement each other suitably.

کلیدواژه‌ها [English]

  • PM2.5
  • PM10
  • MODIS sensor
  • Meteorological Variables
  • Aerosol Depth of Atmosphere
Adams, M. D. and Kanaroglou, P. S. (2016). Mapping real-time air pollution health risk for environmental management: Combining mobile and stationary air pollution monitoring with neural network models. Journal of environmental management, 168, 133-141.‏ https://doi.org/10.1016/j.jenvman.2015.12.012.
Al-Saadi, J., Szykman, J., Pierce, R. B., Kittaka, C., Neil, D., Chu, D. A., Remer, L., Gumley, L., Prins, E., Weinstock, L., MacDonald, C., Wayland, R., Dimmick, F., & Fishman, J. (2005). Improving National Air Quality Forecasts with Satellite Aerosol Observations, Bulletin of the American Meteorological Society, 86(9), 1249-1262. Retrieved Sep 6, 2021, from https://journals.ametsoc.org/view/journals/bams/86/9/bams-86-9-1249.xml
Asl, S. Z., Farid, A., & Choi, Y. S. (2019). Assessment of CALIOP and MODIS aerosol products over Iran to explore air quality. Theoretical and Applied Climatology, 137(1-2), 117-131. https://doi.org/10.1007/s00704-018-2555-9.
Bilal, M., Nichol, J. E. and Spak, S. N. (2017). A new approach for estimation of fine particulate concentrations using satellite aerosol optical depth and binning of meteorological variables. Aerosol Air Qual. Res11, 356-367. https://doi.org/10.4209/aaqr.2016.03.0097.
Cao, H., Amiraslani, F., Liu, J., & Zhou, N. (2015). Identification of dust storm source areas in West Asia using multiple environmental datasets. Science of the Total Environment, 502, 224-235. https://doi.org/10.1016/j.scitotenv.2014.09.025.
Chatterjee S, Price B. 1977. Regression Analysis by Example. Wiley: New York, NY.
Chelani, A. B. (2019). Estimating PM2. 5 concentrations from satellite derived aerosol optical depth and meteorological variables using a combination model. Atmospheric Pollution Research10(3), 847-857. https://doi.org/10.1007/s00376-020-0009-7.
Clarke, A. D., Collins, W. G., Rasch, P. J., Kapustin, V. N., Moore, K., Howell, S. and Fuelberg, H. E. (2001). Dust and pollution transport on global scales: Aerosol measurements and model predictions. Journal of Geophysical Research: Atmospheres106(D23), 32555-32569. https://doi.org/10.1029/2000JD900842.
Faraji, M. and Nadi, S. (2018). Assessment of aerosol optical depth of MODIS sensor data by using PM2.5 meteorological data in urban area. In proceeding of 3th spatial data of technology of engineering. Khaje Nasir Toosi University of technology, Tehran. (In Fasi)
Gupta, P., Christopher, S. A., Wang, J., Gehrig, R., Lee, Y. and Kumar, N. (2006). Satellite remote sensing of particulate matter and air quality assessment over global cities. Atmospheric Environment, 40(30), 5880-5892. https://doi.org/10.1016/j.atmosenv.2006.03.016.
Hadjimitsis, D. G., Clayton, C. R. I. and Hope, V. S. (2004). An assessment of the effectiveness of atmospheric correction algorithms through the remote sensing of some reservoirs. International Journal of Remote Sensing25(18), 3651-3674. https://doi.org/10.1080/01431160310001647993.
Holben, B. N., Tanre, D., Smirnov, A., Eck, T. F., Slutsker, I., Abuhassan, N., ...  and Kaufman, Y. J. (2001). An emerging ground‐based aerosol climatology: Aerosol optical depth from AERONET. Journal of Geophysical Research: Atmospheres106(D11), 12067-12097. https://doi.org/10.1029/2001JD900014.
Lee, H. J., Chatfield, R. B. and Strawa, A. W. (2016). Enhancing the applicability of satellite remote sensing for PM2. 5 estimation using MODIS deep blue AOD and land use regression in California, United States. Environmental Science & Technology, 50(12), 6546-6555.‏ https://doi.org/10.1021/acs.est.6b01438.
Levy, R. (2019). Dark Target Aerosol Retrieval Algorithm. https://darktarget.gsfc.nasa.gov.
Li, S., Joseph, E. and Min, Q. (2016). Remote sensing of ground-level PM2. 5 combining AOD and backscattering profile. Remote Sensing of Environment, 183, 120-128. https://doi.org/10.1016/j.rse.2016.05.025.
Lin, C., Li, Y., Yuan, Z., Lau, A. K., Li, C. and Fung, J. C. (2015). Using satellite remote sensing data to estimate the high-resolution distribution of ground-level PM2. 5. Remote Sensing of Environment, 156, 117-128.‏ https://doi.org/10.1021/es5009399.
Liu, Z., Vaughan, M., Winker, D., Kittaka, C., Getzewich, B., Kuehn, R., ... and Hostetler, C. (2009). The CALIPSO lidar cloud and aerosol discrimination: Version 2 algorithm and initial assessment of performance. Journal of Atmospheric and Oceanic Technology26(7), 1198-1213. https://doi.org/10.1175/2009JTECHA1229.1
Paciorek, C. J. and Liu, Y. (2009). Limitations of remotely sensed aerosol as a spatial proxy for fine particulate matter. Environmental health perspectives117(6), 904-909. http://dx.doi.org/10.1289/ehp.0800360.
Pahlavan, A. Pahlavan, R. and Esmaeli, A. (2014). Estimating PM10 and PM2.5 in Tehran mega city using MODIS data of Terra and Aqua satellites . In: Proceedings of the first International Congress on Application of advanced models of spatial analysis (remote sensing and GIS) in land management, 24-25 Oct.  Azad University, Yazd, Iran, pp.125138.(In Farsi)
Pranesha, T. S. and Kamra, A. K. (1997). Scavenging of aerosol particles by large water drops: 3. Washout coefficients, half‐lives, and rainfall depths. Journal of Geophysical Research: Atmospheres, 102(D20), 23947-23953.‏ http://dx.doi.org/10.1029/97JD01835.
Qorbani Salkhord R, Mobasheri MR, Rahimzadehgan M. (2012) A Fast Method for Assessment of PM10 Concentration Using MODIS Images, a Case Study in Tehran. Hakim Research Journal,15(2):166-177. (In Farsi)
     Qorbani Salkhord, R., Mobasheri,M. and Rahimzadehgan,M. (2012). Assessment of the MODIS Data Ability in Quantitative and Qualitative Analysis of Air Quality in Urban Area, Journal of Climate Research, 1(3), 61. (In Farsi)
Rangzan, K., Zarasvandi, A., Abdolkhani, A. and Mojaradi, B. (2014). Modeling of Air Pollution using MODIS Data: Khouzestan Dust storm. Journal of Advanced Applied Geology, 4(4), 38-45.
Rees, D.G. (1989). Essential statistics.2nd Edn., Chapman and Hall, London.
Sathe, Y., Kulkarni, S., Gupta, P., Kaginalkar, A., Islam, S. and Gargava, P. (2019). Application of Moderate Resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth (AOD) and Weather Research Forecasting (WRF) model meteorological data for assessment of fine particulate matter (PM2. 5) over India. Atmospheric Pollution Research, 10(2), 418-434.‏
Sotoudeheian, S., and Arhami, M. (2017). Using linear mixed effect model to estimate ground-level PM2.5: case study for Tehran. Iranian Journal of Health and Environment. 10 (2), 213-224. http://ijhe.tums.ac.ir/article-1-5871-fa.html.(In Farsi)
Sun, Y. (2018). Vertical structures of physical and chemical properties of urban boundary layer and formation mechanisms of atmospheric pollution. Chinese Science Bulletin63(14), 1374-1389.‏ https://10.1360/N972018-00258.
Tian, J. and Chen, D. (2010). Spectral, spatial, and temporal sensitivity of correlating MODIS aerosol optical depth with ground-based fine particulate matter (PM2. 5) across southern Ontario. Canadian Journal of Remote Sensing, 36(2), 119-128. https://10.5589/m10-033.
Tsai, T. C., Jeng, Y. J., Chu, D. A., Chen, J. P. and Chang, S. C. (2011). Analysis of the relationship between MODIS aerosol optical depth and particulate matter from 2006 to 2008. Atmospheric Environment45(27), 4777-4788. https://10.1016/j.atmosenv.2009.10.006.
Walton, H. A., Anderson, H. R., Mills, I. C., Katsouyanni, E., Atkinson, R., Brunekreef, B., Cohen, A., Forastiere, F., Hurley, F., Krewski, D., & Krzyzanowski, M. (2015). Quantifying the health impacts of ambient air pollutants: recommendations of a WHO/Europe project. International Journal of Public Health. https://doi.org/10.1007/s00038-015-0690-y
You, W., Zang, Z., Zhang, L. et al. (2016). Estimating national-scale ground-level PM25 concentration in China using geographically weighted regression based on MODIS and MISR AOD. Environ Sci Pollut Res, 23(9), 8327–8338. https://doi.org/10.1007/s11356-015-6027-9.
Zheng, J., Che, W., Zheng, Z., Chen, L. and Zhong, L. (2013). Analysis of Spatial and Temporal Variability of PM10 Concentrations Using MODIS Aerosol Optical Thickness in the Pearl River Delta Region, China. Aerosol Air Qual. Res. 13: 862-876. https://doi.org/10.4209/aaqr.2012.09.0234.
Zhou, L., Chen, X. and Tian, X. (2018). The impact of fine particulate matter (PM2. 5) on China's agricultural production from 2001 to 2010. Journal of Cleaner Production, 178, 133-141. https://doi.org/10.1016/j.jclepro.2017.12.204.
Zieger, P., Weingartner, E., Henzing, J., Moerman, M., de Leeuw, G., Mikkilä, J., Ehn, M., Petäjä, T., Clémer, K., van Roozendael, M., Yilmaz, S., Frieß, U., Irie, H., Wagner, T., Shaiganfar, R., Beirle, S., Apituley, A., Wilson, K. and Baltensperger, U. (2011). Comparison of ambient aerosol extinction coefficients obtained from in-situ, MAX-DOAS and LIDAR measurements at Cabauw, Atmos. Chem. Phys., 11, 2603–2624, https://doi.org/10.5194/acp-11-2603-2011.