Jaipur, the capital city of Rajasthan, India, grapples with substantial air pollution challenges attributed to rapid urbanization,
industrialization, and increased vehicular traffic. Major pollutants, including particulate matter (PM10 and PM2.5), nitrogen dioxide (NO2),
sulphur dioxide (SO2), and ozone (O3), consistently surpass national air quality standards. The city's geographical location, surrounded by
arid and semi-arid regions, amplifies pollutant dispersion, resulting in poor air quality. Seasonal variations, particularly dust storms in
pre-monsoon months, further contribute to deteriorating air conditions. The Respirable Particulate Matter (RSPM) in the city of Jaipur,
exhibits a persistent upward trend over the last decade, particularly during the summer and winter seasons, and this elevated particulate
pollution poses a substantial risk to public health, potentially causing irreversible damage, with gaseous pollution considered a lesser
threat in the region. The imperative need for early warning systems in Jaipur arises to predict upcoming air pollution episodes, allowing
policymakers to proactively inform the public and provide guidance, while also necessitating specific information on local emission sources
for effective pollution abatement strategies.
Building upon the success of the initial Air Quality Early Warning System implemented in Delhi, IITM has expanded its forecasting capabilities
to include Jaipur and developed a state-of-the-art air quality early warning forecasting system for Jaipur and its surrounding areas.
The comprehensive modeling framework utilizes the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), seamlessly
integrating the three-dimensional variational (3DVAR) framework from the community Gridpoint Statistical Interpolation (GSI) system.
The system operates through two domain runs, with the outer domain covering the entire Indian subcontinent at a horizontal grid spacing
of 10 kms. The resulting output is then subjected to dynamical downscaling, refining the resolution for Jaipur and its vicinity to a
horizontal grid spacing of 2 kms. This forecasting configuration integrates a data assimilation facility adept at incorporating satellite
data for Aerosol Optical Depth (AOD) retrievals, along with surface observational data related to particulate matter. Furthermore,
the authorities need detailed information on emission sources causing upcoming air pollution events and seek solutions to mitigate the
impact of forecasted events. In line with these needs, a Decision Support System (DSS) is integrated into the system. The DSS provides
insights into the source apportionment of particulate pollution in the city. It recognizes and measures the contributions made by
different regions and sectors to the total pollution burden. Moreover, the DSS aids in making decisions about air quality management
by evaluating how specific interventions at the source level could affect predicted air pollution events. Effective management of air
quality, facilitated by the DSS, is critical for Jaipur, especially considering its status as a favoured tourist destination.
Objectives of the early warning and decision support system:
(i) Establish a 72-hour lead-time high-resolution air quality forecast system for Jaipur.
(ii)Evaluate contributions from the local, regional, and distant sources impacting PM2.5 pollution in Jaipur,
crucial for pinpointing origins and planning focused mitigation strategies.
(iii)Analyze the effectiveness of the possible emission-source-level interventions in order to avoid the forecasted air
pollution events in Jaipur.
The system is designed to assist the Rajasthan State Pollution Control Board (RSPCB),
empowering them to take proactive measures in minimizing exposure to deteriorating air quality conditions.
The early warning and decision support systems aim to notify Jaipur about upcoming air pollution episodes and provide a
daily breakdown of the main contributors to particulate matter pollution for the next five days. This information is crucial
for effective air-quality management in the city. The system also enables policymakers to assess the effectiveness of source-level
interventions before implementation, facilitating science-based decisions to manage air quality. Thus, the improved air quality
resulting from these measures will reduce the risks of mortality and morbidity associated with acute exposure to air pollution in
the region.