Particulate matter estimation over a semi arid region Jaipur, India using satellite AOD and meteorological parameters

The present study estimates ground-level Respirable Particulate Matter (RSPM) by the combined use of satellite remote sensing Aerosol Optical Depth (AOD) at 550 nm (AODMODIS or MODIS AOD) and ground-based meteorological measurements from April-2010 to March-2014 over Jaipur, semi-arid region in North-western, India. The satellite MODIS Level 2.0 AOD is used in developing multi-regression statistical models to estimate RSPM values over the study area. The relationship between particulate matter (PM) and AOD depends on size distribution, particle composition and vertical profile of aerosols. Thus, for optimal representation of MODIS AOD, the factors like Height of Planetary Boundary Layer (HPBL) and meteorological parameters has been considered in all regression models in the present study as surrogates. The performance of regression models is analyzed on the basis of descriptive statistical measures i.e. Normalised Mean Square Error (NMSE), Correlation (R), Factor of two observations (FA2), and Fractional Bias (FB). The nonlinear multi-regression model (MODEL V) performed better than other models for our study period and region on the basis of statistical analysis (R = 0.80, NMSE = 0.01, FB = 0.0, FA2 = 100). The coefficients obtained from MODEL V were again used over Jodhpur and found to perform better than other models. The study is further extended to find out the Air Quality Index (AQI) category over Jaipur. The average RSPM obtained from Rajasthan Pollution Control Board (RPCB) observations and those of model estimated values come under the “Moderately Polluted” category as per Indian air quality standards.

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Balakrishnan, K., Sankar, S., Parikh, J., Padmavathi, R., Srividya, K., Venugopal, V., Prasad, S., Pandey, V.L., 2002. Daily average exposures to respirable particulate matter from combustion of biomass fuels in rural households of southern India. Environ. Health Persp. 110 (11), 1069–1075.

Ballester, F., Estarlich, M., Iñiguez, C., Llop, S., Ramón, R., Esplugues, A., Lacasaña, M., Rebagliato, M., 2010. Air pollution exposure during pregnancy and reduced birth size: a prospective birth cohort study in Valencia, Spain. Environ. Health 9 (1), 6.

Boyouk, N., Léon, J.F., Delbarre, H., Podvin, T., Deroo, C., 2010. Impact of the mixing boundary layer on the relationship between PM2. 5 and aerosol optical thickness. Atmos. Environ. 44 (2), 271–277.

Chitranshi, S., Sharma, S.P., Dey, S., 2015. Satellite-based estimates of outdoor particulate pollution (PM10) for Agra City in northern India. Air Qual. Atmos. Health 8 (1), 55–65.

Chu, D.A., Kaufman, Y.J., Ichoku, C., Remer, L.A., Tanré, D., Holben, B.N., 2002. Validation of MODIS aerosol optical depth retrieval over land.Geophys. Res. Lett. 29, 1617–1620. https://doi.org/10.1029/2001GL013205.

Dey, S., Girolamo, L.D., Van Donkelaar, A., Tripathi, S.N., Gupta, T., Mohan, M., 2012. Decadal exposure to fine particulate matters (PM 2.5) in the Indian subcontinent using remote sensing data. Remote Sens. Environ. 27, 153–161.

Dominici, F., Peng, R.D., Bell, M.L., Pham, L., McDermott, A., Zeger, S.L., Samet, J.M., 2006. Fine particulate air pollution and hospital admission for cardiovascular and respiratory diseases. JAMA 295 (10), 1127–1134.

Dubovik, O., Holben, B., Eck, T.F., Smirnov, A., Kaufman, Y.J., King, M.D., Tanré, D., Slutsker, I., 2002. Variability of absorption and optical properties of key aerosol types observed in worldwide locations. J. Atmos. Sci. 59 (3), 590–608.

Eck, T.F., Holben, B.N., Reid, J.S., Dubovik, O., Smirnov, A., O'neill, N.T., Slutsker, I., Kinne, S., 1999. Wavelength dependence of the optical depth of biomass burning, urban, and desert dust aerosols. J. Geophys. Res. Atmos. 104 (D24), 31333–31349.

Eck, T.F., Holben, B.N., Dubovik, O., Smirnov, A., Slutsker, I., Lobert, J.M., Ramanathan, V., 2001. Column-integrated aerosol optical properties over the Maldives during the northeast monsoon for 1998–2000. J. Geophys. Res. Atmos. 106, 28.

Gupta, P., Christopher, S.A., Wang, J., Gehrig, R., Lee, Y.C., Kumar, N., 2006. Satellite remote sensing of particulate matter and air quality assessment over global cities. Atmos. Environ. 40 (30), 5880–5892.

Gupta, P., Christopher, S.A., 2008. Seven year particulate matter air quality assessment from surface and satellite measurements. Atmos. Chem. Phys. 8 (12), 3311–3324.

Gupta, P., Christopher, S.A., 2009a. Particulate matter air quality assessment using integrated surface, satellite, and meteorological products: multiple regression approach. J. Geophys. Res. Atmos. 114 (D14).

Gupta, P., Christopher, S.A., 2009b. Particulate matter air quality assessment using integrated surface, satellite, and meteorological products: 2. A neural network approach. J. Geophys. Res. Atmos. 114 (D20).

Grgurić, S., Križan, J., Gašparac, G., Antonić, O., Špirić, Z., Mamouri, R., Christodoulou, A., Nisantzi, A., Agapiou, A., Themistocleous, K., Fedra, K., 2014. Relationship between MODIS based aerosol optical depth and PM10 over Croatia. Open Geosci. 6 (1), 2–16.

Hoff, R.M., Christopher, S.A., 2009. Remote sensing of particulate pollution from space: have we reached the promised land? J. Air Waste Manag. Assoc. 59 (6), 645–675.

Holben, B.N., Eck, T.F., Slutsker, I., Tanre, D., Buis, J.P., Setzer, A., Vermote, E., Reagan, J.A., Kaufman, Y.J., Nakajima, T., Lavenu, F., 1998. AERONET—a federated instrument network and data archive for aerosol characterization. Remote Sens. Environ. 66, 1–16.

Holben, B.N., Tanre, D., Smirnov, A., Eck, T.F., Slutsker, I., Abuhassan, N., Newcomb, W.W., Schafer, J.S., Chatenet, B., Lavenu, F., Kaufman, Y.J., 2001. An emerging ground-based aerosol climatology: aerosol optical depth from AERONET. J. Geophys. Res. 106, 12067–12097. http://dx.doi.org/10.1029/2001JD900014. IPCC: Intergovernmental Panel.

Ichoku, C., Kaufman, Y.J., Remer, L.A., Levy, R., 2004. Global aerosol remote sensing from MODIS. Adv. Space. Res. 34 (4), 820–827.

Kanabkaew, T., 2013. Prediction of hourly particulate matter concentrations in Chiangmai, Thailand using MODIS aerosol optical depth and ground-based meteorological data. Environ. Asia 6, 65–70.

Kaufman, Y.J., Tanré, D., Boucher, O., 2002. A satellite view of aerosols in the climate system. Nature 419 (6903), 215 Sep 12.

Kim, K., Lee, K.H., Kim, J.I., Noh, Y., Shin, D.H., Shin, S.K., Lee, D., Kim, J., Kim, Y.J., Song, C.H., 2016. Estimation of surface-level PM concentration from satellite observation taking into account the aerosol vertical profiles and hygroscopicity. Chemo. 143, 32–40.

Koelemeijer, R.B.A., Homan, C.D., Matthijsen, J., 2006. Comparison of spatial and temporal variations of aerosol optical thickness and particulate matter over Europe. Atmos. Environ 40 (27), 5304–5315. https://doi.org/10.1016/j.atmosenv.2006.04. 044.

Kumar, N., Chu, A., Foster, A., 2007. An empirical relationship between PM 2.5 and aerosol optical depth in Delhi Metropolitan. Atmos. Environ. 41, 4492–4503.

Kumar, N., Chu, A., Foster, A., 2008. Remote sensing of ambient particles in Delhi and its environs: estimation and validation. Int. J. Remote Sens. 29, 3383–3405.

Kumar, N., Chu, A.D., Foster, A.D., Peters, T., Willis, R., 2011. Satellite remote sensing for developing time and space resolved estimates of ambient particulate in Cleveland, OH. Aerosol Sci. Technol. 45, 1090–1108. http://dx.doi.org/10.1080/02786826. 2011.581256.

Lee, H.J., Liu, Y., Coull, B.A., Schwartz, J., Koutrakis, P., 2011. A novel calibration approach of MODIS AOD data to predict PM2. 5 concentrations. Atmos. Chem. Phys. 11 (15), 7991.

Levy, R.C., Remer, L.A., Mattoo, S., Vermote, E.F., Kaufman, Y.J., 2007. Second-generation operational algorithm: retrieval of aerosol properties over land from inversion of Moderate Resolution Imaging Spectroradiometer spectral reflectance. J. Geophys. Res. 112, D13. https://doi.org/10.1029/2006JD007811.

Li, H., Guo, B., Han, M., Tian, M., Zhang, J., 2015. Particulate matters pollution characteristic and the correlation between PM (PM2. 5, PM10) and meteorological factors during the summer in Shijiazhuang. J. Environ. Prot. 6 (05), 457.

Liu, Y., Franklin, M., Kahn, R., Koutrakis, P., 2007. Using aerosol optical thickness to predict ground-level PM 2.5 concentrations in the St. Louis area: a comparison between MISR and MODIS. Remote Sens. Environ. 107, 33–44.

Liu, Y., Sarnat, J.A., Kilaru, V., Jacob, D.J., Koutrakis, P., 2005. Estimating ground-level PM2.5 eastern United States using satellite remote sensing. Environ. Sci. Technol. 39, 3269–3278.

Mao, X., Shen, T., Feng, X., 2017. Prediction of hourly ground-level PM 2.5 concentrations 3 days in advance using neural networks with satellite data in eastern China. Atmos. Poll. Res. https://doi.org/10.1016/j.apr.2017.04.002.

Munir, S., 2016. Modelling the non-linear association of particulate matter (PM10) with meteorological parameters and other air pollutants—a case study in Makkah. Arab. J. Geosci. 9 (1), 1–13.

Paciorek, C.J., Liu, Y., Moreno-Macias, H., Kondragunta, S., 2008. Spatiotemporal associations between GOES aerosol optical depth retrievals and ground-level PM2. 5. Environ. Sci. Tech. 42, 5800–5806. https://doi.org/10.1021/es703181j.

Park, M.E., Song, C.H., Park, R.S., Lee, J., Kim, J., Lee, S., Woo, J.H., Carmichael, G.R., Eck, T.F., Holben, B.N., Lee, S.S., 2014. New approach to monitor transboundary particulate pollution over Northeast Asia. Atmos. Chem. Phys. 14 (2), 659–674.

Payra, S., Soni, M., Kumar, A., Prakash, D., Verma, S., 2015. Intercomparison of aerosol optical thickness derived from MODIS and in situ ground datasets over Jaipur, a semiarid zone in India. Environ. Sci. Tech. 49 (15), 9237–9246.

Pope, C.A., Brook, R.D., Burnett, R.T., Dockery, D.W., 2011. How is cardiovascular disease mortality risk affected by duration and intensity of fine particulate matter exposure? An integration of the epidemiologic evidence. Air Qual. Atmos. Health 4, 5–14.

Prakash, D., Payra, S., Verma, S., Soni, M., 2013. Aerosol particle behavior during Dust Storm and Diwali over an urban location in north western India. Nat. Hazards 69 (3), 1767–1779.

Remer, L.A., Kaufman, Y.J., Tanré, D., Mattoo, S., Chu, D.A., Martins, J.V., Li, R.R., Ichoku, C., Levy, R.C., Kleidman, R.G., Eck, T.F., 2005. The MODIS aerosol algorithm, products, and validation. J. Atmos. Sci. 62, 947–973. https://doi.org/10.1175/ JAS3385.1.

Sinha, P.R., Gupta, P., Kaskaoutis, D.G., Sahu, L.K., Nagendra, N., Manchanda, R.K., Kumar, Y.B., Sreenivasan, S., 2015. Estimation of particulate matter from satelliteand ground-based observations over Hyderabad, India. Int. J. Remote Sens. 36 (24), 6192–6213.

Smirnov, A., Holben, B.N., Eck, T.F., Dubovik, O., Slutsker, I., 2000. Cloud-screening and quality control algorithms for the AERONET database. Remote Sens. Environ. 73 (3), 337–349.

Song, W., Jia, H., Huang, J., Zhang, Y., 2014. A satellite-based geographically weighted regression model for regional PM 2.5 estimation over the Pearl River Delta region in China. Remote Sens. Environ. 154, 1–7.

Soni, M., Payra, S., Sinha, P., Verma, S., 2014. A performance evaluation of WRF model using different physical parameterization scheme during winter season over a semiarid region, India. Int. J. Earth. Atmosl Sci. 1 (3), 104–114.

Sotoudeheian, S., Arhami, M., 2014. Estimating ground-level PM 10 using satellite remote sensing and ground-based meteorological measurements over Tehran. J. Environ. Health Sci. Eng. 12 (1), 122.

Tian, J., Chen, D., 2010. A semi-empirical model for predicting hourly ground-level fine particulate matter (PM 2.5) concentration in southern Ontario from satellite remote sensing and ground-based meteorological measurements. Remote Sens. Environ. 114 (2), 221–229.

Trang, N.H., Tripathi, N.K., 2014. Spatial correlation analysis between particulate matter 10 (PM10) hazard and respiratory diseases in Chiang Mai Province, Thailand., Int Arch Photogramm. Remote Sens. Spat. Inf. Sci. XL-8, 185–191. http://dx.doi.org/10. 5194/isprsarchives-XL-8-185-2014.

Verma, S., Payra, S., Gautam, R., Prakash, D., Soni, M., Holben, B., Bell, S., 2013. Dust events and their influence on aerosol optical properties over Jaipur in Northwestern India Environ. Monit. Assess. 185 (9), 7327–7342.

Van Donkelaar, A., Martin, R.V., Brauer, M., Kahn, R., Levy, R., Verduzco, C., Villeneuve, P.J., 2010. Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth: development and application. Environ. Health Persp. 118, 847–855. https://doi.org/10.1289/ehp.0901623.

Wang, J., Ogawa, S., 2015. Effects of meteorological conditions on PM2. 5 concentrations in Nagasaki, Japan. Int. J. Environ. Res. Public Health 12 (8), 9089–9101.

Wu, Y., Guo, J., Zhang, X., Tian, X., Zhang, J., Wang, Y., Duan, J., Li, X., 2012. Synergy of satellite and ground based observations in estimation of particulate matter in eastern China. Sci. Total. Environ. 433, 20–30. https://doi.org/10.1016/j.scitotenv.2012.06. 033.

Yap, X.Q., Hashim, M., 2013. A robust calibration approach for PM10 prediction from MODIS aerosol optical depth. Atmos. Chem. Phys. 13, 3517–3526. https://doi.org/ 10.5194/acp-13-3517-2013.

You, W., Zang, Z., Zhang, L., Li, Z., Chen, D., Zhang, G., 2015. Estimating ground-level PM10 concentration in northwestern China using geographically weighted regression based on satellite AOD combined with CALIPSO and MODIS fire count. Remote Sens. Environ. 168, 276–285.

Zhang, Z.H., Hu, M.G., Ren, J., Zhang, Z.Y., Christakos, G., Wang, J.F., 2017. Probabilistic assessment of high concentrations of particulate matter (PM 10) in Beijing, China. Atmos. Poll. Res. https://doi.org/10.1016/j.apr.2017.04.006.

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