Air Quality Analysis for Dehradun, India

India APnA City ProgramDehradun, is the capital of the state Uttarakhand, carved out of the state of Uttar Pradesh in 2000. The city capital lies in the Doon Valley, in the foothills of the Himalayas between the river Ganges on the east and the river Yamuna on the west, with an estimated urban population of 1.2 million. The city was developed as a getaway from the hot summers of the plains and hosts several institutions such as the Indian Military Academy, ITBP Academy & Indira Gandhi National Forest Academy (IGNFA), Zoological Survey of India (ZSI), Forest Research Institute (FRI) among several others.

Dehradun has increased its population and construction and industry is rapidly growing in the region. The resulting increase in pollution from industrial, domestic, construction and transport activities is responsible for its worsening air quality. According to the WHO study, Dehradun is ranked the 30th most polluted city. The geographical and meteorological conditions do not allow for easy dispersal of pollution.

To assess Dehradun’s air quality, we selected 40km x 20km domain. This domain is further segregated into 1km grids, to study the spatial variations in the emission and the pollution loads.

Monitoring Emissions Meteorology Dispersion References


We present below a summary of the ambient monitoring data available under the National Ambient Monitoring Program (NAMP), operated and maintained by the Central Pollution Control Board (CPCB, New Delhi, India). In Dehradun, there are 3 manual stations reporting data on PM10, SO2, and NO2 and no continuous air monitoring stations (CAMS).



Satellite Data Derived Surface PM2.5 Concentrations:

The results of satellite data derived concentrations are useful for evaluating annual trends in pollution levels and are not a proxy for on-ground monitoring networks. This data is estimated using satellite feeds and global chemical transport models. Satellites are not measuring one location all the time, instead, a combination of satellites provide a cache of measurements that are interpreted using global chemical transport models (GEOS-Chem) to represent the vertical mix of pollution and estimate ground-based concentrations with the help of previous ground-based measurements. The global transport models rely on gridded emission estimates for multiple sectors to establish a relationship with satellite observations over multiple years. These databases were also used to study the global burden of disease, which estimated air pollution as the top 10 causes of premature mortality and morbidity in India. A summary of PM2.5 concentrations from this exercise, for the city of Dehradun is presented below. The global PM2.5 files are available for download and further analysis @ Dalhousie University.


We compiled an emissions inventory for the dehradun region for the following pollutants – sulfur dioxide (SO2), nitrogen oxides (NOx), carbon monoxide (CO), non-methane volatile organic compounds (NMVOCs), carbon dioxide (CO2); and particulate matter (PM) in four bins (a) coarse PM with size fraction between 2.5 and 10 μm (b) fine PM with size fraction less than 2.5 μm (c) black carbon (BC) and (d) organic carbon (OC), for year 2015 and projected to 2030.

We customized the SIM-air family of tools to fit the base information collated from the central pollution control board, state pollution control board, census bureau, national sample survey office, ministry of road transport and highways, annual survey of industries, central electrical authority, ministry of heavy industries, municipal waste management, geographical information systems, meteorological department, and publications from academic and non-governmental institutions.

This emissions inventory is based on the available local activity and fuel consumption estimates for the selected urban airshed (presented in the grid above) and does not include natural emission sources (like dust storms, lightning) and seasonal open (agricultural and forest) fires; which can only be included in a regional scale simulation. These emission sources are accounted in the concentration calculation as an external (also known as boundary or long-range) contribution to the city’s air quality.

The emissions inventory was then spatially segregated at a 0.01° grid resolution in longitude and latitude (equivalent of 1 km) to create a spatial map of emissions for each pollutant (PM2.5, PM10, SO2, NOx, CO and VOCs). The gridded PM2.5 emissions and the total (shares by sector) emissions are presented below.

Gridded PM2.5 Emissions (2015)

Emissions Inventory

Total PM2.5 Emissions by Sector 2015-2030

Emissions Inventory Emissions Inventory Emissions Inventory

Total Estimated Emissions by Sector for 2015 (units – mil.tons/year for CO2 and tons/year for the rest)

TRAN 1,450 1,550 650450 2,450 18,900 5,700 1000.57
RESI950 1,000 200450250 11,250 1,200 3000.18
INDU30030020050 1,750 2,300 4502000.16
DUST500 3,100 -------
WAST 1,100 1,200 10065050 5,400 1,100 500.01
DGST30030015050 2,650 70050500.12
4,650 7,500 1,300 1,700 7,200 39,450 8,600 7501.04

TRAN = transport emissions from road, rail, aviation, and shipping (for coastal cities); RESI = residential emissions from cooking, heating, and lighting activities; INDU = industrial emissions from small, medium, and heavy industries (including power generation); DUST = dust emissions from road re-suspension and construction activities; WAST = open waste burning emissions; DGST = diesel generator set emissions; BRIC = brick kiln emissions (not included in the industrial emissions)


We processed the NCEP Reanalysis global meteorological fields from 2010 to 2016 through the 3D-WRF meteorological model. A summary of the data for year 2015, averaged for Dehradun is presented below. Download the processed data which includes information on year, month, day, hour, precipitation (mm/hour), mixing height (m), temperature (C), wind speed (m/sec), and wind direction (degrees) – key parameters which determine the intensity of dispersion of emissions.


Dispersion Modeling

We calculated the ambient PM2.5 concentrations and the source contributions, using gridded emissions inventory, 3D meteorological data (from WRF), and the CAMx regional chemical transport model. The model simulates concentrations at 0.01° grid resolution and sector contributions, which include contributions from primary emissions, secondary sources via chemical reactions, and long range transport via boundary conditions (represented as “outside” in the pie graph below).

PM2.5 Source Contributions Ambient PM2.5 Concentrations PM2.5 Source Contributions

Findings and Recommendations

  • Modeled urban average ambient PM2.5 concentration is 51.2 ± 9.1 μg/m3 – is above the national standard (40) and more than 5 times the WHO guideline (10)
  • The city requires at least 13 continuous air monitoring stations to statistically, spatially, and temporally, represent the mix of sources and range of pollution in the city (current status – 3 manual and 0 continuous)
  • The modeled source contributions highlight domestic cooking and heating, transport (including on road dust), and open waste burning as the key air pollution sources in the urban areas
  • By 2030, the share of emissions from residential cooking and lighting is expected to decrease with a greater share of LPG, residential electrification, and increasing urbanization, however the need for heating due to low temperatures in the winter months, is expected to keep the share of biomass burning emissions high
  • The contribution of sources outside the urban airshed to an estimated 42% of the ambient annual PM2.5 pollution (in 2015). This contribution is mostly stemming large (metal and non-metal processing) industries, and brick kilns located outside the urban airshed, and towards the state of Uttar Pradesh
  • With increasing tourism opportunities in the region, the share of transport related emissions is expected to increase and the city needs to aggressively promote public and non-motorized transport as part of the city’s urban development plan, along with the improvement of the road infrastructure to reduce on-road dust re-suspension
  • By 2030, the vehicle exhaust emissions are expected to see some decline, if and only if, Bharat 6 fuel standards are introduced nationally in 2020, as recommended by the Auto Fuel Policy
  • Majority of the brick kilns in the region are outside the selected urban airshed and are fueled mostly by coal, agri-waste, and other biomass. These kilns can benefit from a technology upgrade from the current fixed chimney and clamp style baking to (for example) zig-zag, in order to improve their overall energy efficiency
  • Open waste burning is dispersed across the city and increasing with the demand for more tourist opportunities. This requires stricter regulations for addressing the issue, as the city generates ever more garbage, with limited capacity to sort and dispose of it.

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