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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 2021–2022 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 air quality index (AQI) is > 300. During the post-monsoon season (winter season), emissions from Delhi, the rest of the National Capital Region (NCR)'s 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 the PM2.5 load in Delhi. During peak pollution events (stubble-burning periods or wintertime), however, the contribution from the main sources (farm fires in Punjab–Haryana or local sources within Delhi) 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]

The presence of persistent heavy fog in northern India during winter creates hazardous situations for transportation systems and disrupts the lives of about 400 million people. The meteorological factors responsible for its genesis and predictability are not yet completely understood in this region. Given its high potential for socioeconomic impact, there is a pressing need for extensive research that understands the inherently complex nature of the phenomena through field observations and modeling exercises. WiFEX is a first-of-its-kind multi-institutional initiative dealing with intensive ground-based measurement campaigns for developing a suitable fog forecasting capability under the aegis of the smart cities mission of India. Measuring campaigns were conducted during the 2015–20 winters at the Indira Gandhi International Airport, New Delhi, covering more than 90 dense fog events. The field experiments involved extensive suites of in situ instruments and gathered simultaneous observations of micrometeorological conditions, radiative fluxes, turbulence, droplet/aerosol microphysics, aerosol optical properties, fog water chemistry, and vertical thermodynamical structure to describe the environmental stability in which fog develops. An operational modeling framework, the WRF Model, was set up to provide fog predictions during the measurement campaign. These field observations helped to interpret the strengths and deficiencies in the numerical modeling framework. Four scientific objectives were pursued: (i) the life cycle of optically thin and thick fog, (ii) microphysical properties in the polluted boundary layer, (iii) fog water chemistry, gas–aerosol partitioning during the fog life cycle, and (iv) numerical prediction of fog. This paper presents an overview of WiFEX and a synthesis of selected observational and modeling analyses/findings related to the abovementioned scientific topics. [ABSTRACT FROM AUTHOR]

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]

Air quality has become one of the most important environmental concerns for Delhi, India. In this perspective, we have developed a high-resolution air quality prediction system for Delhi based on chemical data assimilation in the chemical transport model -- Weather Research and Forecasting with Chemistry (WRF-Chem). The data assimilation system was applied to improve the PM2.5 forecast via assimilation of MODIS aerosol optical depth retrievals using threedimensional variational data analysis scheme. Near real-time MODIS fire count data were applied simultaneously to adjust the fire-emission inputs of chemical species before the assimilation cycle. Carbon monoxide (CO) emissions from biomass burning, anthropogenic emissions, and CO inflow from the domain boundaries were tagged to understand the contribution of local and non-local emission sources. We achieved significant improvements for surface PM2.5 forecast with joint adjustment of initial conditions and fire emissions. [ABSTRACT FROM AUTHOR]