Delhi Air Quality Forecasts – District Average PM2.5 Source Contributions

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Air pollution has emerged as a major challenge in Delhi and the problem becomes more complex due to multiplicity and complexity of the mix of emission sources, such as, industries, automobiles, generator sets, domestic fuel burning, road side dusts, construction activities, etc. To support Delhi’s air pollution management, a modeling framework to simulate pollution movement for the next 72-hours was put together, using meteorology processed through 3D-WRF meteorological model and the GFS meteorological fields and the concentrations simulated through the CAMx chemical transport modeling system.

One question that is repeatedly asked and often unanswered is, what is causing the pollution in the city?

Using the CAMx model, which allows for particulate source apportionment, a series of simulations are conducted every day to answer this question, based on a detailed spatially and temporally resolved emissions inventory. The hourly average source contributions (presented below) is a modeled average for all the 1 km x 1 km grids overlapping in each of the district. A map showing the geographical extent of each of the districts is presented here (along with a kml file for reference and use). The modeling domain considered for this exercise includes 14 districts – 9 in Delhi, 2 in Haryana, and 3 in Uttar Pradesh.

What is included in the 7 clubbed source categories is described below and more information on the emission sources is available here. If you click on any of the source categories in the legend, it will allow you to minus that contribution and see what could be the PM2.5 concentrations without its contribution. If you click on it again, the source will added back to the chart.

The data fields are updated everyday at ~7:00 PM local time.

• TRA.PASS = pollution from passenger vehicles (2Ws, 3Ws, 4Ws, Taxis, and Buses) including vehicle exhaust and associated resuspended dust
• TRA.FRGT= pollution from freight vehicles (heavy and light trucks, and non-road vehicles) including vehicle exhaust and associated resuspended dust
• URB.DUST = pollution from road dust resuspension and construction activities
• RESI = pollution from domestic cooking, space heating, water heating, and lighting
• IND.BK = pollution from industrial activities and brick kilns
• PP.GS = pollution from power plants and in-situ diesel generator sets
• WST.BURN = pollution from open waste burning
• OTHERS = pollution from small and intermittent sources, like fireworks and funeral homes
• OUTSIDE = pollution linked to boundary conditions, in other words, pollution from outside the 80 km x 80 km modeling domain; which is calculated from a simulation over the Indian subcontinent, including the anthropogenic emissions, seasonal fires and dust events (calculated based on the most recent satellite data), and other natural sources

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For more details, visit the Delhi air quality forecasting page. Additional data fields

• summary of monitoring data from local networks as box plots and time series
• Forecasts – animations of hourly wind, temperature, precipitation, and mixing heights (maps)
• Forecasts – daily (24 hour) average concentrations (maps)
• Forecasts – animations of hourly average concentrations (maps)
• Forecasts – hourly time series at the district level
• Forecasts – modeled source contributions to hourly PM2.5 concentrations at the district level