| Autor: |
Govardhan, Gaurav, Ghude, Sachin D., Kumar, Rajesh, Sharma, Sumit, Gunwani, Preeti, Jena, Chinmay, Yadav, Prafull, Ingle, Shubhangi, Debnath, Sreyashi, Pawar, Pooja, Acharja, Prodip, Jat, Rajmal, Kalita, Gayatry, Ambulkar, Rupal, Kulkarni, Santosh, Kaginalkar, Akshara, Soni, Vijay K., Nanjundiah, Ravi S., Rajeevan, Madhavan |
| Předmět: |
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| Zdroj: |
Geoscientific Model Development Discussions; 4/12/2023, p1-30, 30p |
| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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