Articles | Volume 17, issue 7
https://doi.org/10.5194/gmd-17-2617-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-2617-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model description paper
| 
10 Apr 2024
Model description paper |
| 10 Apr 2024
| 10 Apr 2024
Decision Support System version 1.0 (DSS v1.0) for air quality management in Delhi, India
Gaurav Govardhan
CORRESPONDING AUTHOR
Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, Maharashtra, India
National Centre for Medium-Range Weather Forecasting, Ministry of Earth Sciences, Noida, Uttar Pradesh, India
Sachin D. Ghude
CORRESPONDING AUTHOR
Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, Maharashtra, India
Rajesh Kumar
NSF National Center for Atmospheric Research, Boulder, CO, United States of America
Sumit Sharma
The Energy and Resources Institute, Delhi, India
Preeti Gunwani
India Meteorology Department, Ministry of Earth Sciences, Delhi, India
Chinmay Jena
India Meteorology Department, Ministry of Earth Sciences, Delhi, India
Prafull Yadav
Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, Maharashtra, India
Department of Atmospheric and Space Sciences, Savitribai Phule Pune University, Pune, Maharashtra, India
Shubhangi Ingle
Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, Maharashtra, India
Sreyashi Debnath
Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, Maharashtra, India
Department of Atmospheric and Space Sciences, Savitribai Phule Pune University, Pune, Maharashtra, India
Pooja Pawar
Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, Maharashtra, India
Department of Chemical Technology, Kalinga Institute of Industrial Technology (KIIT), Bhubaneshwar, India
Prodip Acharja
Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, Maharashtra, India
Department of Atmospheric and Space Sciences, Savitribai Phule Pune University, Pune, Maharashtra, India
Rajmal Jat
Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, Maharashtra, India
Gayatry Kalita
Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, Maharashtra, India
Rupal Ambulkar
Indian Institute of Tropical Meteorology, Ministry of Earth Sciences, Pune, Maharashtra, India
Department of Environmental Sciences, Savitribai Phule Pune University, Pune, India
Santosh Kulkarni
Centre for Development of Advanced Computing, Innovation Park, Pune, Maharashtra, India
Akshara Kaginalkar
Centre for Development of Advanced Computing, Innovation Park, Pune, Maharashtra, India
Vijay K. Soni
India Meteorology Department, Ministry of Earth Sciences, Delhi, India
Ravi S. Nanjundiah
Centre for Atmospheric and Oceanic Sciences, Indian Institute of Science, Bengaluru, India
Divecha Centre for Climate Change, Indian Institute of Science, Bengaluru, India
Madhavan Rajeevan
National Centre for Earth Science Studies, Thiruvananthapuram, Kerala, India
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Pooja V. Pawar, Sachin D. Ghude, Gaurav Govardhan, Prodip Acharja, Rachana Kulkarni, Rajesh Kumar, Baerbel Sinha, Vinayak Sinha, Chinmay Jena, Preeti Gunwani, Tapan Kumar Adhya, Eiko Nemitz, and Mark A. Sutton
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Chaman Gul, Shichang Kang, Siva Praveen Puppala, Xiaokang Wu, Cenlin He, Yangyang Xu, Inka Koch, Sher Muhammad, Rajesh Kumar, and Getachew Dubache
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Mathew Sebastian, Sobhan Kumar Kompalli, Vasudevan Anil Kumar, Sandhya Jose, S. Suresh Babu, Govindan Pandithurai, Sachchidanand Singh, Rakesh K. Hooda, Vijay K. Soni, Jeffrey R. Pierce, Ville Vakkari, Eija Asmi, Daniel M. Westervelt, Antti-Pekka Hyvärinen, and Vijay P. Kanawade
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Xinxin Ye, Pargoal Arab, Ravan Ahmadov, Eric James, Georg A. Grell, Bradley Pierce, Aditya Kumar, Paul Makar, Jack Chen, Didier Davignon, Greg R. Carmichael, Gonzalo Ferrada, Jeff McQueen, Jianping Huang, Rajesh Kumar, Louisa Emmons, Farren L. Herron-Thorpe, Mark Parrington, Richard Engelen, Vincent-Henri Peuch, Arlindo da Silva, Amber Soja, Emily Gargulinski, Elizabeth Wiggins, Johnathan W. Hair, Marta Fenn, Taylor Shingler, Shobha Kondragunta, Alexei Lyapustin, Yujie Wang, Brent Holben, David M. Giles, and Pablo E. Saide
Atmos. Chem. Phys., 21, 14427–14469, https://doi.org/10.5194/acp-21-14427-2021, https://doi.org/10.5194/acp-21-14427-2021, 2021
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Wildfire smoke has crucial impacts on air quality, while uncertainties in the numerical forecasts remain significant. We present an evaluation of 12 real-time forecasting systems. Comparison of predicted smoke emissions suggests a large spread in magnitudes, with temporal patterns deviating from satellite detections. The performance for AOD and surface PM2.5 and their discrepancies highlighted the role of accurately represented spatiotemporal emission profiles in improving smoke forecasts.
Ernesto Reyes-Villegas, Upasana Panda, Eoghan Darbyshire, James M. Cash, Rutambhara Joshi, Ben Langford, Chiara F. Di Marco, Neil J. Mullinger, Mohammed S. Alam, Leigh R. Crilley, Daniel J. Rooney, W. Joe F. Acton, Will Drysdale, Eiko Nemitz, Michael Flynn, Aristeidis Voliotis, Gordon McFiggans, Hugh Coe, James Lee, C. Nicholas Hewitt, Mathew R. Heal, Sachin S. Gunthe, Tuhin K. Mandal, Bhola R. Gurjar, Shivani, Ranu Gadi, Siddhartha Singh, Vijay Soni, and James D. Allan
Atmos. Chem. Phys., 21, 11655–11667, https://doi.org/10.5194/acp-21-11655-2021, https://doi.org/10.5194/acp-21-11655-2021, 2021
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Pooja V. Pawar, Sachin D. Ghude, Chinmay Jena, Andrea Móring, Mark A. Sutton, Santosh Kulkarni, Deen Mani Lal, Divya Surendran, Martin Van Damme, Lieven Clarisse, Pierre-François Coheur, Xuejun Liu, Gaurav Govardhan, Wen Xu, Jize Jiang, and Tapan Kumar Adhya
Atmos. Chem. Phys., 21, 6389–6409, https://doi.org/10.5194/acp-21-6389-2021, https://doi.org/10.5194/acp-21-6389-2021, 2021
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In this study, simulations of atmospheric ammonia (NH3) with MOZART-4 and HTAP-v2 are compared with satellite (IASI) and ground-based measurements to understand the spatial and temporal variability of NH3 over two emission hotspot regions of Asia, the IGP and the NCP. Our simulations indicate that the formation of ammonium aerosols is quicker over the NCP than the IGP, leading to smaller NH3 columns over the higher NH3-emitting NCP compared to the IGP region for comparable emissions.
Fernando Chouza, Thierry Leblanc, Mark Brewer, Patrick Wang, Sabino Piazzolla, Gabriele Pfister, Rajesh Kumar, Carl Drews, Simone Tilmes, Louisa Emmons, and Matthew Johnson
Atmos. Chem. Phys., 21, 6129–6153, https://doi.org/10.5194/acp-21-6129-2021, https://doi.org/10.5194/acp-21-6129-2021, 2021
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The tropospheric ozone lidar at the JPL Table Mountain Facility (TMF) was used to investigate the impact of Los Angeles (LA) Basin pollution transport and stratospheric intrusions in the planetary boundary layer on the San Gabriel Mountains. The results of this study indicate a dominant role of the LA Basin pollution on days when high ozone levels were observed at TMF (March–October period).
Cited articles
Banerjee, P., Satheesh, S. K., and Moorthy, K. K.: The unusual severe dust storm of May 2018 over northern India: Genesis, propagation, and associated conditions, J. Geophys. Res.-Atmos., 126, 1–25, https://doi.org/10.1029/2020JD032369, 2021.
Bhardwaj, P., Kumar, R., and Seddon, J.: Interstate transport of carbon monoxide and black carbon over India, Atmos. Environ., 251, 118268, https://doi.org/10.1016/j.atmosenv.2021.118268, 2021.
Bikkina, S., Andersson, A., Kirillova, E. N., Holmstrand, H., Tiwari, S., Srivastava, A. K., Bisht, D. S., and Gustafsson, Ö.: Air quality in megacity Delhi affected by countryside biomass burning, Nat. Sustain., 2, 200–205, https://doi.org/10.1038/s41893-019-0219-0, 2019.
Bray, C. D., Battye, W. H., and Aneja, V. P.: The role of biomass burning agricultural emissions in the Indo-Gangetic Plains on the air quality in New Delhi, India, Atmos. Environ., 218, 116983, https://doi.org/10.1016/j.atmosenv.2019.116983, 2019.
CAQM (Commision for Air Quality Management in the National Capital Region and the adjoining areas): Policy to Curb Air Pollution in the National Capital Region, https://caqm.nic.in/WriteReadData/LINKS/0031dcb806e-8af7-4b38-a9bc-65b91f2704cd.pdf (last access: 8 April 2024), 2022.
Census Of India 2011: Population Projections For India And States 2011–2036, in: Report Of The Technical Group On Population Projections, National Commission On Population Ministry Of Health & Family Welfare Nirman Bhawan, New Delhi – 110011, https://main.mohfw.gov.in/sites/default/files/Population Projection Report 2011-2036 - upload_compressed_0.pdf (last access: 1 April 2024), 2020.
Central Pollution Control Board: Central Control Room for Air Quality Management – All India, Central Pollution Control Board [data set], https://app.cpcbccr.com/ccr/#/caaqm-dashboard-all/caaqm-landing/caaqm-data-repository, last access: 4 April 2024.
Chakravarty, K., Vincent, V., Vellore, R., Srivastava, A. K., Rastogi, A., and Soni, V. K.: Revisiting Andhi in northern India: A case study of severe dust-storm over the urban megacity of New Delhi, Urban Climate, 37, 100825, https://doi.org/10.1016/j.uclim.2021.100825, 2021.
Chin, M., Rood, R. B., Lin, S.-J., Müller, J.-F., and Thompson, A. M.: Atmospheric sulfur cycle simulated in the global model GOCART: Model description and global properties, J. Geophys. Res., 105, 24671–24687, https://doi.org/10.1029/2000JD900384, 2000.
Chin, M., Ginoux, P., Kinne, S., Torres, O., Holben, B. N., Duncan, B. N., Martin, R. V., Logan, J. A., Higurashi, A., and Nakajima, T.: Tropospheric aerosol optical thickness from the GOCART model and comparisons with satellite and Sun photometer measurements, J. Atmos. Sci., 59, 461–483, https://doi.org/10.1175/1520-0469(2002)059<0461:TAOTFT>2.0.CO;2, 2002.
Chopra, R.: Environmental degradation in India: causes and consequences, Int. J. Appl. Environ. Sci., 11, 1593–1601, 2016.
Chowdhury, S., Dey, S., Di Girolamo, L., Smith, K. R., Pillarisetti, A., and Lyapustin, A.: Tracking ambient PM2.5 build-up in Delhi national capital region during the dry season over 15 years using a high-resolution (1km) satellite aerosol dataset, Atmos. Environ., 204, 142–150, https://doi.org/10.1016/j.atmosenv.2019.02.029, 2019.
Clough, S. A., Shephard, M. W., Mlawer, E. J., Delamere, J. S., Iacono, M. J., Cady-Pereira, K., Boukabara, S., and Brown, P. D.: Atmospheric radiative transfer modeling: A summary of the AER codes, J. Quant. Spectrosc. Ra, 91, 233–244, https://doi.org/10.1016/j.jqsrt.2004.05.058, 2005.
Colette, A., Rouïl, L., Meleux, F., Lemaire, V., and Raux, B.: Air Control Toolbox (ACT_v1.0): a flexible surrogate model to explore mitigation scenarios in air quality forecasts, Geosci. Model Dev., 15, 1441–1465, https://doi.org/10.5194/gmd-15-1441-2022, 2022.
Cusworth, D. H., Mickley, L. J., Sulprizio, M. P., Liu, T., Marlier, M. E., DeFries, R. S., Guttikunda, S. K., and Gupta, P.: Quantifying the influence of agricultural fires in northwest India on urban air pollution in Delhi, India, Environ. Res. Lett., 13, 044018, https://doi.org/10.1088/1748-9326/aab303, 2018.
Debnath, S., Jena, C., Ghude, S. D., Kumar, R., Govardhan, G., Gunwani, P., Saha, S. K., Hazra, A., and Pokhrel, S.: Simulation of Indian Summer Monsoon Rainfall (ISMR) with fully coupled regional chemistry transport model: A case study for 2017, Atmos. Environ., 268, 118785, https://doi.org/10.1016/j.atmosenv.2021.118785, 2022.
Denby, B. R., Gauss, M., Wind, P., Mu, Q., Grøtting Wærsted, E., Fagerli, H., Valdebenito, A., and Klein, H.: Description of the uEMEP_v5 downscaling approach for the EMEP MSC-W chemistry transport model, Geosci. Model Dev., 13, 6303–6323, https://doi.org/10.5194/gmd-13-6303-2020, 2020.
Ek, M. B., Mitchell, K. E., Lin, Y., Rogers, E., Grunmann, P., Koren, V., Gayno, G., and Tarpley, J. D.: Implementation of Noah land surface model advances in the National Centers for Environmental Prediction operational mesoscale Eta model, J. Geophys. Res.-Atmos., 108, 8851, https://doi.org/10.1029/2002JD003296, 2003.
Emery, C., Liu, Z., Russell, A. G., Odman, M. T., Yarwood, G., and Kumar, N.: Recommendations on statistics and benchmarks to assess photochemical model performance, JAPCA J. Air Waste Ma., 67, 582–598, https://doi.org/10.1080/10962247.2016.1265027,2017.
Emmons, L. K., Walters, S., Hess, P. G., Lamarque, J.-F., Pfister, G. G., Fillmore, D., Granier, C., Guenther, A., Kinnison, D., Laepple, T., Orlando, J., Tie, X., Tyndall, G., Wiedinmyer, C., Baughcum, S. L., and Kloster, S.: Description and evaluation of the Model for Ozone and Related chemical Tracers, version 4 (MOZART-4), Geosci. Model Dev., 3, 43–67, https://doi.org/10.5194/gmd-3-43-2010, 2010.
Gadi, R., Shivani, Sharma, S. K., and Mandal, T. K.: Source apportionment and health risk assessment of organic constituents in fine ambient aerosols (PM2.5): a complete year study over National Capital Region of India, Chemosphere, 221, 583–596, https://doi.org/10.1016/j.chemosphere.2019.01.067, 2019.
Ghude, S. D., Chate, D. M., Jena, C., Beig, G., Kumar, R., Barth, M. C., Pfister, G. G., Fadnavis, S., and Pithani, P.: Premature mortality in India due to PM2.5 and ozone exposure, Geophys. Res. Lett., 43, 4650–4658, https://doi.org/10.1002/2016GL068949, 2016.
Ghude, S. D., Kumar, R., Jena, C., Debnath, S., Kulkarni, R. G., Alessandrini, S., Biswas, M., Kulkrani, S., Pithani, P., Kelkar, S., and Sajjan, V.: Evaluation of PM2.5 forecast using chemical data assimilation in the WRF-Chem model: A novel initiative under the Ministry of Earth Sciences Air Quality Early Warning System for Delhi India, Curr. Sci., 118, 1803–1815, https://doi.org/10.18520/cs/v118/i11/1803-1815, 2020.
Ghude, S. D., Kumar, R., Govardhan, G., Jena, C., Nanjundiah, R. S., and Rajeevan, M.: New Delhi: air-quality warning system cuts peak pollution, Nature, 602, 211–211, https://doi.org/10.1038/d41586-022-00332-y, 2022.
Ginoux, P., Chin, M., Tegen, I., Prospero, J. M., Holben, B., Dubovik, O., and Lin, S.-J.: Sources and distributions of dust aerosols simulated with the GOCART model. J. Geophys. Res., 106, 20255–20273, https://doi.org/10.1029/2000JD000053, 2001.
Govardhan, G.: WRF-Chem-DSS-Model, figshare [software], https://doi.org/10.6084/m9.figshare.21655883.v1, 2022.
Govardhan, G.: User manual for WRF-Chem based DSS for air quality management, figshare [software], https://doi.org/10.6084/m9.figshare.22335424.v1, 2023.
Govardhan, G., Nanjundiah, R. S., Satheesh, S. K., Krishnamoorthy, K., and Kotamarthi, V. R.: Performance of WRF-Chem over Indian region: Comparison with measurements, J. Earth Syst. Sci., 124, 875–896, https://www.ias.ac.in/article/fulltext/jess/124/04/0875-0896 (last access: 4 April 2024), 2015.
Govardhan, G., Satheesh, S. K., Moorthy, K. K., and Nanjundiah, R.: Simulations of black carbon over the Indian region: improvements and implications of diurnality in emissions, Atmos. Chem. Phys., 19, 8229–8241, https://doi.org/10.5194/acp-19-8229-2019, 2019.
Govardhan, G., Ambulkar, R., Kulkarni, S., Vishnoi, A., Yadav, P., Chowdhury, B. A., Khare, M., and Ghude, S. D.: Stubble-burning activities in north-western India in 2021: Contribution to air pollution in Delhi, Heliyon, 9, e16939, https://doi.org/10.1016/J.HELIYON.2023.E16939, 2023.
Govardhan, G. R., Nanjundiah, R. S., Satheesh, S. K., Moorthy, K. K., and Takemura, T.: Inter-comparison and performance evaluation of chemistry transport models over Indian region, Atmos. Environ., 125, 486–504, https://doi.org/10.1016/j.atmosenv.2015.10.065, 2016.
Grell, G. A. and Freitas, S. R.: A scale and aerosol aware stochastic convective parameterization for weather and air quality modeling, Atmos. Chem. Phys., 14, 5233–5250, https://doi.org/10.5194/acp-14-5233-2014, 2014.
Grell, G. A., Peckham, S. E., Schmitz, R., McKeen, S. A., Frost, G., Skamarock, W. C., and Eder, B.: Fully coupled “online” chemistry within the WRF model, Atmos. Environ., 39, 6957–6975, 2005.
Guo, H., Kota, S. H., Chen, K., Sahu, S. K., Hu, J., Ying, Q., Wang, Y., and Zhang, H.: Source contributions and potential reductions to health effects of particulate matter in India, Atmos. Chem. Phys., 18, 15219–15229, https://doi.org/10.5194/acp-18-15219-2018, 2018.
Guo, H., Kota, S. H., Sahu, S. K., and Zhang, H.: Contributions of local and regional sources to PM2.5 and its health effects in north India, Atmos. Environ., 214, 116867, https://doi.org/10.1016/j.atmosenv.2019.116867, 2019.
Guttikunda, S. K. and Goel, R.: Health impacts of particulate pollution in a megacity-Delhi, India, Environ. Dev., 6, 8–20, https://doi.org/10.1016/j.envdev.2012.12.002, 2013.
Guttikunda, S. K. and Gurjar, B. R.: Role of meteorology in seasonality of air pollution in megacity Delhi, India, Environ. Monit. Assess., 184, 3199–3211, https://doi.org/10.1007/s10661-011-2182-8, 2012.
Hama, S., Kumar, P., Alam, M. S., Rooney, D. J., Bloss, W. J., Shi, Z., Harrison, R. M., Crilley, L. R., Khare, M., and Gupta, S. K.: Chemical source profiles of fine particles for five different sources in Delhi, Chemosphere, 274, https://doi.org/10.1016/j.chemosphere.2021.129913, 2021.
Hodzic, A. and Jimenez, J. L.: Modeling anthropogenically controlled secondary organic aerosols in a megacity: a simplified framework for global and climate models, Geosci. Model Dev., 4, 901–917, https://doi.org/10.5194/gmd-4-901-2011, 2011.
Hong, S. Y. and Lim, J. O. J.: The WRF single-moment 6-class microphysics scheme (WSM6), Asia-Pac. J. Atmos. Sci., 42, 129–151, 2006.
Iacono, M. J., Mlawer, E. J., Clough, S. A., and Morcrette, J.-J.: Impact of an improved longwave radiation model, rrtm, on the energy budget and thermodynamic properties of the ncar community climate model ccm3, J. Geophys. Res., 105, 14873–14890, https://doi.org/10.1029/2000JD900091, 2000.
Iacono, M. J., Delamere, J. S., Mlawer, E. J., Shephard, M. W., Clough, S. A., and Collins, W. D.: Radiative forcing by long-lived greenhouse gases: Calculations with the aer radiative transfer models, J. Geophys. Res., 113, 1–8, https://doi.org/10.1029/2008JD009944, 2008.
Jain, S., Sharma, S. K., Vijayan, N., and Mandal, T. K.: Seasonal characteristics of aerosols (PM2.5 and PM10) and their source apportionment using PMF: a four year study over Delhi, India, Environ. Pollut., 262, 114337, https://doi.org/10.1016/j.envpol.2020.114337, 2020.
Janssens-Maenhout, G., Crippa, M., Guizzardi, D., Dentener, F., Muntean, M., Pouliot, G., Keating, T., Zhang, Q., Kurokawa, J., Wankmüller, R., Denier van der Gon, H., Kuenen, J. J. P., Klimont, Z., Frost, G., Darras, S., Koffi, B., and Li, M.: HTAP_v2.2: a mosaic of regional and global emission grid maps for 2008 and 2010 to study hemispheric transport of air pollution, Atmos. Chem. Phys., 15, 11411–11432, https://doi.org/10.5194/acp-15-11411-2015, 2015.
Jena, C., Ghude, S. D., Kulkarni, R., Debnath, S., Kumar, R., Soni, V. K., Acharja, P., Kulkarni, S. H., Khare, M., Kaginalkar, A. J., Chate, D. M., Ali, K., Nanjundiah, R. S., and Rajeevan, M. N.: Evaluating the sensitivity of fine particulate matter (PM2.5) simulations to chemical mechanism in Delhi, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2020-673, 2020.
Jena, C., Ghude, S. D., Kumar, R., Debnath, S., Govardhan, G., Soni, V. K., Kulkarni, S. H., Beig, G., Nanjundiah, R. S., and Rajeevan, M.: Performance of high resolution (400 m) PM2.5 forecast over Delhi, Sci. Rep.-UK, 11, 4104, https://www.nature.com/articles/s41598-021-83467-8 (last access: 4 April 2024), 2021.
Jiménez, P. A., Dudhia, J., González-Rouco, J. F., Navarro, J., Montávez, J. P., and García-Bustamante, E.: A revised scheme for the WRF surface layer formulation, Mon. Weather Rev., 140, 898–918, https://doi.org/10.1175/MWR-D-11-00056.1, 2012.
Kaiser, J. W., Heil, A., Andreae, M. O., Benedetti, A., Chubarova, N., Jones, L., Morcrette, J.-J., Razinger, M., Schultz, M. G., Suttie, M., and van der Werf, G. R.: Biomass burning emissions estimated with a global fire assimilation system based on observed fire radiative power, Biogeosciences, 9, 527–554, https://doi.org/10.5194/bg-9-527-2012, 2012.
Kanawade, V. P., Srivastava, A. K., Ram, K., Asmi, E., Vakkari, V., Soni, V. K., Varaprasad, V., and Sarangi, C.: What caused severe air pollution episode of November 2016 in New Delhi?, Atmos. Environ., 222, 117125, https://doi.org/10.1016/j.atmosenv.2019.117125, 2020.
Kulkarni, S. H., Ghude, S. D., Jena, C., Karumuri, R. K., Sinha, B., Sinha, V., Kumar, R., Soni, V. K., and Khare, M.: How Much Does Large-Scale Crop Residue Burning Affect the Air Quality in Delhi?, Environ. Sci. Technol., 54, 4790–4799, https://doi.org/10.1021/ACS.EST.0C00329, 2020.
Kumar, A., Hakkim, H., Sinha, B., and Sinha, V.: Gridded 1 km × 1 km emission inventory for paddy stubble burning emissions over north-west India constrained by measured emission factors of 77 VOCs and district-wise crop yield data, Sci. Total Environ., 789, 148064, https://doi.org/10.1016/j.scitotenv.2021.148064, 2021.
Kumar, P., Gulia, S., Harrison, R. M., and Khare, M.: The influence of odd–even car trial on fine and coarse particles in Delhi, Environ. Pollut., 225, 20–30, https://doi.org/10.1016/j.envpol.2017.03.017, 2017.
Kumar, R., Ghude, S. D., Biswas, M., Jena, C., Alessandrini, S., Debnath, S., Kulkarni, S., Sperati, S., Soni, V. K., Nanjundiah, R. S., and Rajeevan, M.: Enhancing accuracy of air quality and temperature forecasts during paddy crop residue burning season in Delhi via chemical data assimilation, J. Geophys. Res.-Atmos., 125, 1–16, https://doi.org/10.1029/2020JD033019, 2020.
Lalchandani, V., Kumar, V., Tobler, A., M. Thamban, N., Mishra, S., Slowik, J. G., Bhattu, D., Rai, P., Satish, R., Ganguly, D., Tiwari, S., Rastogi, N., Tiwari, S., Močnik, G., Prévôt, A. S. H., and Tripathi, S. N.: Real-time characterization and source apportionment of fine particulate matter in the Delhi megacity area during late winter, Sci. Total Environ., 770, 145324, https://doi.org/10.1016/j.scitotenv.2021.145324, 2021.
Li, R., Tao, M., Zhang, M., Chen, L., Wang, L., Wang, Y., He, X., Wei, L., Mei, X., and Wang, J.: Application potential of satellite thermal anomaly products in updating industrial emission inventory, Geophys. Res. Lett., 48, 1–7, https://doi.org/10.1029/2021GL092997, 2021.
Liu, T., Mickley, L. J., Singh, S., Jain, M., Defries, R. S., and Marlier, M. E.: Crop residue burning practices across north India inferred from household survey data: Bridging gaps in satellite observations, Atmos. Environ., 8, 100091, https://doi.org/10.1016/j.aeaoa.2020.100091, 2020.
Liu, Y., Hu, C., Zhan, W., Sun, C., Murch, B., and Ma, L.: Identifying industrial heat sources using time-series of the VIIRS Night fire product with an object-oriented approach, Remote Sens. Environ., 204, 347–365, https://doi.org/10.1016/j.rse.2017.10.019, 2018.
Liu, Z., Liu, Q., Lin, H.-C., Schwartz, C. S., Lee, Y.-H., and Wang, T.: Three-dimensional variational assimilation of MODIS aerosol optical depth: Implementation and application to a dust storm over East Asia, J. Geophys. Res., 116, 1–19, https://doi.org/10.1029/2011JD016159, 2011.
Meng, X., Zhang, K., Pang, K., and Xiang, X.: Characterization of spatio-temporal distribution of vehicle emissions using web-based real-time traffic data, Sci. Total Environ., 709, 136227, https://doi.org/10.1016/j.scitotenv.2019.136227, 2020.
Meteosim: World Most Polluted Cities – Meteosim (WWW Document), Meteosim, https://www.meteosim.com/en/world-most-polluted-citie/ (last access: 5 April 2024), 2019.
Mlawer, E. J., Taubman, S. J., Brown, P. D., Iacono, M. J., and Clough, S. A.: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave, J. Geophys. Res.-Atmos., 102, 16663–16682, https://doi.org/10.1029/97JD00237, 1997.
Molina, M. J. and Molina, L. T.: Megacities and atmospheric pollution, J. Air Waste Manage., 54, 644–680, https://doi.org/10.1080/10473289.2004.10470936, 2004.
Mukherjee, T., Vinoj, V., Midya, S. K., Puppala, S. P., and Adhikary, B.: Numerical simulations of different sectoral contributions to post-monsoon pollution over Delhi, Heliyon, 6, e03548, https://doi.org/10.1016/j.heliyon.2020.e03548, 2020.
Mukhopadhyay, P., Prasad, V. S., Krishna, R., Deshpande, M., Ganai, M., Tirkey, S., Sarkar, S., Goswami, T., Johny, C. J., Roy, K., Mahakur, M., Durai V. R., and Rajeevan, M.: Performance of a very high-resolution global forecast system model (GFS T1534) at 12.5 km over the Indian region during the 2016–2017 monsoon seasons, J. Earth Syst. Sci., 128, 1–18, https://doi.org/10.1007/s12040-019-1186-6, 2019.
Nair, M., Bherwani, H., Kumar, S., Gulia, S., Goyal, S., and Kumar, R.: Assessment of contribution of agricultural residue burning on air quality of Delhi using remote sensing and modelling tools, Atmos. Environ., 230, 117504, https://doi.org/10.1016/j.atmosenv.2020.117504, 2020.
Nakanishi, M. and Niino, H.: An Improved Mellor–Yamada Level-3 Model: Its Numerical Stability and Application to a Regional Prediction of Advection Fog, Bound. Lay. Meteorol., 119, 397–407, https://doi.org/10.1007/s10546-005-9030-8, 2006.
Niu, G. Y., Yang, Z. L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., Kumar, A., Manning, K., Niyogi, D., Rosero, E., and Tewari, M.: The community Noah land surface model with multi parameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements, J. Geophys. Res.-Atmos., 116, 1–19, https://doi.org/10.1029/2010JD015140, 2011.
Parde, A. N., Dhangar, N. G., Nivdange, S., Ghude, S. D., Pithani, P., Jena, C., Lal, D. M., and Gopalakrishnan, V.: The analysis of pre-monsoon dust storm over Delhi using ground-based observations, Nat. Hazards, 112, 829–844, https://doi.org/10.1007/s11069-022-05207-z, 2022.
Parkhi, N., Chate, D., Ghude, S. D., Peshin, S., Mahajan, A., Srinivas, R., Surendran, D., Ali, K., Singh, S., Trimbake, H., and Beig, G.: Large inter annual variation in air quality during the annual festival “Diwali” in an Indian megacity, J. Environ. Sci., 43, 265–272, https://doi.org/10.1016/j.jes.2015.08.015, 2016.
Pawar, P. V., Ghude, S. D., Govardhan, G., Acharja, P., Kulkarni, R., Kumar, R., Sinha, B., Sinha, V., Jena, C., Gunwani, P., Adhya, T. K., Nemitz, E., and Sutton, M. A.: Chloride (HCl / Cl−) dominates inorganic aerosol formation from ammonia in the Indo-Gangetic Plain during winter: modeling and comparison with observations, Atmos. Chem. Phys., 23, 41–59, https://doi.org/10.5194/acp-23-41-2023, 2023.
Rai, P., Furger, M., el Haddad, I., Kumar, V., Wang, L., Singh, A., Dixit, K., Bhattu, D., Petit, J. E., Ganguly, D., Rastogi, N., Baltensperger, U., Tripathi, S. N., Slowik, J. G., and Prévôt, A. S. H.: Real-time measurement and source apportionment of elements in Delhi's atmosphere, Sci. Total Environ., 742, 140332, https://doi.org/10.1016/j.scitotenv.2020.140332, 2020.
Riahi, K., Van Vuuren, D. P., Kriegler, E., Edmonds, J., O'neill, B. C., Fujimori, S., Bauer, N., Calvin, K., Dellink, R., Fricko, O., and Lutz, W.: The shared socioeconomic pathways and their energy, land use, and greenhouse gas emissions implications: an overview, Global Environ. Chang., 42, 153–168, https://doi.org/10.1016/j.gloenvcha.2016.05.009, 2017.
Roebber, P. J.: Visualizing multiple measures of forecast quality, Weather Forecast, 24, 601–608, https://doi.org/10.1175/2008WAF2222159.1, 2009.
Roozitalab, B., Carmichael, G. R., and Guttikunda, S. K.: Improving regional air quality predictions in the Indo-Gangetic Plain – case study of an intensive pollution episode in November 2017, Atmos. Chem. Phys., 21, 2837–2860, https://doi.org/10.5194/acp-21-2837-2021, 2021.
Sahu, S. K., Mangaraj, P., and Beig, G.: Decadal growth in emission load of major air pollutants in Delhi, Earth Syst. Sci. Data, 15, 3183–3202, https://doi.org/10.5194/essd-15-3183-2023, 2023.
Saini, P. and Sharma, M.: Cause and Age-specific premature mortality attributable to PM2.5 Exposure: An analysis for Million-Plus Indian cities, Sci. Total Environ., 710, 135230, https://doi.org/10.1016/j.scitotenv.2019.135230, 2020.
Saxena, P., Srivastava, A., Verma, S., Singh, L., and Sonwani, S.: Analysis of atmospheric pollutants during fireworks festival “`Diwali” at a residential site Delhi in India, in: Energy, Environment, and Sustainability, Springer, 91–105, https://doi.org/10.1007/978-981-15-0540-9_4, 2020.
Schroeder, W., Oliva, P., Giglio, L., and Csiszar, I. A.: The New VIIRS 375 m active fire detection data product: algorithm description and initial assessment, Remote Sens Environ., 143, 85–96, https://doi.org/10.1016/j.rse.2013.12.008, 2014.
Sengupta, A., Govardhan, G., Debnath, S., Yadav, P., Kulkarni, S. H., Parde, A. N., Lonkar, P., Dhangar, N., Gunwani, P., Wagh, S., Nivdange, S., Jena, C., Kumar, R., and Ghude, S. D.: Probing into the wintertime meteorology and particulate matter (PM2.5 and PM10) forecast over Delhi, Atmos. Pollut. Res., 13, 101426, https://doi.org/10.1016/j.apr.2022.101426, 2022.
Sharma, S. K. and Mandal, T. K.: Chemical composition of fine mode particulate matter (PM2.5) in an urban area of Delhi, India and its source apportionment, Urban Climate, 21, 106–122, https://doi.org/10.1016/j.uclim.2017.05.009, 2017.
Sharma, S. K., Sharma, A., Saxena, M., Choudhary, N., Masiwal, R., Mandal, T. K., and Sharma, C.: Chemical characterization and source apportionment of aerosol at an urban area of Central Delhi, India, Atmos. Pollut. Res., 7, 110–121, https://doi.org/10.1016/j.apr.2015.08.002, 2016.
Shivani, Gadi, R., Sharma, S. K., and Mandal, T. K.: Seasonal variation, source apportionment and source attributed health risk of fine carbonaceous aerosols over National Capital Region, India, Chemosphere, 237, 124500, https://doi.org/10.1016/j.chemosphere.2019.124500, 2019.
Singh, D. P., Gadi, R., Mandal, T. K., Dixit, C. K., Singh, K., Saud, T., Singh, N., and Gupta, P. K.: Study of temporal variation in ambient air quality during Diwali festival in India, Environ. Monit. Assess., 169, 1–13, https://doi.org/10.1007/s10661-009-1145-9, 2010.
Sud, S. and Iyengar, S.: A Conceptual Review of the Odd-Even Policy on Delhi's Urban Environment, Artha Journal of Social Sciences, 15, 87–116, https://doi.org/10.12724/ajss.39.6, 2016.
TERI and ARAI: Source apportionment of PM2.5 and PM10 concentrations of Delhi NCR for identification of major sources, TERI and ARAI, https://www.teriin.org/sites/default/files/2018-08/Report_SA_AQM-Delhi-NCR_0.pdf (last access: 26 December 2022), 2018.
Tiwari, S., Bisht, D. S., Srivastava, A. K., Pipal, A. S., Taneja, A., Srivastava, M. K., and Attri, S. D.: Variability in atmospheric particulates and meteorological effects on their mass concentrations over Delhi, India, Atmos. Res., 145, 45–56, https://doi.org/10.1016/j.atmosres.2014.03.027, 2014.
Tiwari, S., Thomas, A., Rao, P., Chate, D. M., Soni, V. K., Singh, S., Ghude, S. D., Singh, D., and Hopke, P. K.: Pollution concentrations in Delhi India during winter 2015–16: A case study of an odd-even vehicle strategy, Atmos. Pollut. Res., 9, 1137–1145, https://doi.org/10.1016/j.apr.2018.04.008, 2018.
Tobler, A., Bhattu, D., Canonaco, F., Lalchandani, V., Shukla, A., Thamban, N. M., Mishra, S., Srivastava, A. K., Bisht, D. S., Tiwari, S., Singh, S., Močnik, G., Baltensperger, U., Tripathi, S. N., Slowik, J. G., and Prévôt, A. S. H.: Chemical characterization of PM2.5 and source apportionment of organic aerosol in New Delhi, India, Sci. Total Environ., 745, 1–12, https://doi.org/10.1016/j.scitotenv.2020.140924, 2020.
Vadrevu, K. P., Lasko, K., Giglio, L., and Justice, C.: Vegetation fires, absorbing aerosols and smoke plume characteristics in diverse biomass burning regions of Asia, Environ. Res. Lett., 10, 105003, https://doi.org/10.1088/1748-9326/10/10/105003, 2015.
Wiedinmyer, C., Akagi, S. K., Yokelson, R. J., Emmons, L. K., Al-Saadi, J. A., Orlando, J. J., and Soja, A. J.: The Fire INventory from NCAR (FINN): a high resolution global model to estimate the emissions from open burning, Geosci. Model Dev., 4, 625–641, https://doi.org/10.5194/gmd-4-625-2011, 2011.
Yadav, A., Behera, S. N., Nagar, P. K., and Sharma, M.: Spatio-seasonal concentrations, source apportionment and assessment of associated human health risks of PM2.5-bound polycyclic aromatic hydrocarbons in delhi, india, Aerosol Air Qual. Res., 20, 2805–2825, https://doi.org/10.4209/aaqr.2020.04.0182, 2020.
Yadav, S., Tripathi, S. N., and Rupakheti, M.: Current status of source apportionment of ambient aerosols in India, Atmos. Environ., 274, 118987, https://doi.org/10.1016/j.atmosenv.2022.118987, 2022.
Zhang, P., Yuan, C., Sun, Q., Liu, A., You, S., Li, X., Zhang, Y., Jiao, X., Sun, D., Sun, M., Liu, M., and Lun, F.: Satellite-based detection and characterization of industrial heat sources in China, Environ. Sci. Technol., 53, 11031–11042, https://doi.org/10.1021/acs.est.9b02643, 2019.
Zhang, X., Han, L., Wei, H., Tan, X., Zhou, W., Li, W., and Qian, Y.: Linking urbanization and air quality together: A review and a perspective on the future sustainable urban development, J. Clean. Prod., 346, 130988, https://doi.org/10.1016/j.jclepro.2022.130988, 2022.
Short summary
A newly developed air quality forecasting framework, Decision Support System (DSS), for air quality management in Delhi, India, provides source attribution with numerous emission reduction scenarios besides forecasts. DSS shows that during post-monsoon and winter seasons, Delhi and its neighboring districts contribute to 30 %–40 % each to pollution in Delhi. On average, a 40 % reduction in the emissions in Delhi and the surrounding districts would result in a 24 % reduction in Delhi's pollution.
A newly developed air quality forecasting framework, Decision Support System (DSS), for air...
