Submitted as: model description paper
12 Apr 2023
Submitted as: model description paper |  | 12 Apr 2023
Status: this preprint is currently under review for the journal GMD.

Decision Support System version 1.0 (DSS v1.0) for air quality management in Delhi, India

Gaurav Govardhan, Sachin D. Ghude, Rajesh Kumar, Sumit Sharma, Preeti Gunwani, Chinmay Jena, Prafull Yadav, Shubhangi Ingle, Sreyashi Debnath, Pooja Pawar, Prodip Acharja, Rajmal Jat, Gayatry Kalita, Rupal Ambulkar, Santosh Kulkarni, Akshara Kaginalkar, Vijay K. Soni, Ravi S. Nanjundiah, and Madhavan Rajeevan

Abstract. This paper discusses the newly developed Decision Support System version 1.0 (DSS v1.0) for air quality management activities in Delhi, India. In addition to standard air quality forecasts, DSS provides the contribution of Delhi, its surrounding districts, and stubble-burning fires in the neighboring states of Punjab and Haryana to the PM2.5 load in Delhi. DSS also quantifies the effects of local and neighborhood emission-source-level interventions on the pollution load in Delhi. The DSS-simulated Air Quality Index for the post-monsoon and winter seasons of 2021–22 shows high accuracy (up to 80 %) and a very low false alarm ratio (~20 %) from Day 1 to Day 5 of the forecasts, especially when the ambient AQI is > 300. During the post-monsoon season (winter season), emissions from Delhi, the rest of the NCR districts, biomass-burning activities, and all other remaining regions on average contribute 34.4 % (33.4 %), 31 % (40.2 %), 7.3 % (0.1 %), and 27.3 % (26.4 %), respectively, to PM2.5 load in Delhi. During peak pollution events (stubble-burning periods), however, the contribution from sources within Delhi (farm fires in Punjab-Haryana) could reach 65 % (69 %). According to DSS, a 20 % (40 %) reduction in anthropogenic emissions across all NCR districts would result in a 12 % (24 %) reduction in PM2.5 in Delhi on a seasonal mean basis. DSS is a critical tool for policymakers because it provides such information daily through a single simulation with a plethora of emission reduction scenarios.

Gaurav Govardhan et al.

Status: open (until 07 Jun 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-300', Tabish Ansari, 12 May 2023 reply
  • RC2: 'Comment on gmd-2022-300', Anonymous Referee #2, 20 May 2023 reply

Gaurav Govardhan et al.

Gaurav Govardhan et al.


Total article views: 451 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
355 87 9 451 24 3 2
  • HTML: 355
  • PDF: 87
  • XML: 9
  • Total: 451
  • Supplement: 24
  • BibTeX: 3
  • EndNote: 2
Views and downloads (calculated since 12 Apr 2023)
Cumulative views and downloads (calculated since 12 Apr 2023)

Viewed (geographical distribution)

Total article views: 433 (including HTML, PDF, and XML) Thereof 433 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 05 Jun 2023
Short summary
This newly developed air quality forecasting framework 'Decision Support System' 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 emissions in Delhi and the surrounding districts would result in a 24 % reduction in Delhi's pollution.