Air Quality Analysis for Kanpur, India

India APnA City ProgramKanpur is one of the largest industrial towns in North India, on the the Indo-Gangetic plains, and the largest city in the state of Uttar Pradesh, with an urban population of over 3.5 million. Kanpur supports the largest textile and leather processing sectors in the region. Kanpur briefly attempted to put in place a public-private partnership system for the management of its solid waste. Initially the project seemed to function for about 4 years before differences between the government and the private company led to a breakdown of the arrangement, leading to one of the major causes of air, water, and solid waste management issues in the city.

In 2011, the CPCB released a National summary report on “Air Quality Monitoring, Emission Inventory and Source Apportionment Study” based on monitoring data from six cities (Delhi, Mumbai, Kanpur, Pune, Chennai and Bangalore). According to the report, in Delhi and Kanpur, the monitoring data at almost all locations and in all seasons were higher than the prescribed standards. Among the major causes of air pollution in Kanpur are industrial sector, vehicles, road dust and domestic cooking. The industrial sector is the biggest cause of air pollution in Kanpur (out of all the six cities).

To assess Kanpur’s air quality, we selected 40km x 30km 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 Kanpur, there is 1 continuous air monitoring station (CAMS) reporting data for all the criteria pollutants and 8 manual stations reporting data on PM10, SO2, and NO2.



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 Kanpur is presented below. The global PM2.5 files are available for download and further analysis @ Dalhousie University.


We compiled an emissions inventory for the Kanpur 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). Below is the gridded PM2.5 emissions and the total (shares by sector) emissions.

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 3,100 3,300 1,300 1,000 4,150 51,150 17,700 2001.22
RESI 4,900 4,950 900 2,700 1,100 75,650 9,450 6000.25
INDU 21,450 23,000 2,000 250 11,100 9,700 3,200 8500.45
DUST 1,400 8,850 -------
WAST 1,350 1,400 10080050 6,500 1,300 500.01
DGST800850450150 7,500 2,000 2001000.34
BRIC 1,550 1,550 400650850 21,000 2,250 6500.13
34,550 43,900 5,150 5,550 24,750 166,000 34,100 2,450 2.39

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 Kanpur 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


  • Modeled urban average ambient PM2.5 concentration is 114.1 ± 25.6 μg/m3 – nearly 3 times the national standard (40) and more than 11 times the WHO guideline (10)
  • The city requires at least 27 continuous air monitoring stations to statistically, spatially, and temporally, represent the mix of sources and range of pollution in the city (current status – 8 manual and 1 continuous)
  • The modeled source contributions highlight transport (including on road dust), domestic cooking and heating, industries (small and medium), and open waste burning as the key air pollution sources in the urban area
  • The city has an estimated 23% of the ambient annual PM2.5 pollution (in 2015) originating outside the urban airshed, which strongly suggests that air pollution control policies in the Indo-Gangetic plain need a regional outlook
  • 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 share of emissions from residential cooking and lighting is expected to decrease with a greater share of LPG, residential electrification, and increasing urbanization. However, biomass and coal burning to provide warmth in the winter will still be an issue
  • By 2030, the vehicle exhaust emissions are expected to remain constant, if and only if, Bharat 6 fuel standards are introduced nationally in 2020, as recommended by the Auto Fuel Policy
  • The 125 brick kilns in the urban airshed (and more outside) 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
  • Most of the small and the medium industry needs an energy efficiency management plan to address the emissions from coal, heavy fuel oil, and gas combustion or shift towards using electricity
  • Open waste burning is dispersed across the city and 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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