Nagpur, India – Emissions and Concentrations

An emissions inventory for the Greater Nagpur region was developed for gases – sulfur dioxide (SO2), nitrogen oxides (NOx), carbon monoxide (CO), non-methane volatile organic compounds (NMVOCs), and 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. The SIM-air family of tools were customized 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. Since, there are many factors which influence the changes in a city’s social, economic, landuse, urban, and industrial layout, the growth rates assumed should be considered as an estimate only. Given the air quality status in the city, reflected in the monitoring feeds, we used these estimates to evaluate the likely trend in city’s total emissions, their likely impact on the ambient particulate matter (PM) concentrations, and health impacts through 2030.

Particulate Matter (less than 2.5 μm) (PM2.5)

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Sulfur Dioxide (SO2)

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Nitrogen Oxides (NOx)

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This inventory is based on the local activity and fuel consumption estimates for the selected urban airshed and does not include natural emission sources (like dust storms, lightning, and sea salt) and seasonal open (agricultural and forest) fires; which is included in a regional scale simulations. These emission sources are accounted in the concentration calculation as an external (also know as boundary or long-range) contribution to the city’s air quality (more explanation on this below).

The emission sectors represented in the baseline projections are the following (a) TRAN = transport sector emissions, includes on-road (passenger and freight) and aviation activities (b) RESI = residential sector, includes cooking, heating, and lighting activities (c) INDU = industrial sector, includes light and heavy duty sectors (d) DUST = resuspended dust from on-road activities and construction activities (e) WAST = open waste burning (f) DGST = diesel generator sets to supplement any load shedding in the region and (g) BRIC = brick kiln sector, which is carved out of the industrial emissions, because of its significant contribution to the city’s air quality.

Key observations from the emissions inventory:

  • heavy industry is a large component of the emissions – four coal-base thermal power plants, one large cement plant; and small iron and steel processing units. The largest of the power plants is closer to the Northern boundary, with a generation capacity of 1280 MW
  • light industry is spread across to the urban airshed; with majority of them using coal as their primary energy source
  • brick kilns are also located closer to the Northern boundary; the mix of fixed chimney and clamp style kilns is 50-50; with coal as the primary fuel for baking bricks
  • winter time heating requirements are minimal; households with access to LPG in the urban areas is more than 80% and in the rural areas is more than 50%
  • the fuel for vehicles is BS-IV grade
  • minus the heavy industry in the urban airshed, waste burning, vehicle exhaust, dust, and coal use in the light industry are key emission sources

The emissions inventory is also maintained on a GIS platform and spatially segregated at 0.01° grid resolution in longitudes and latitudes (equivalent of 1 km), for further use in atmospheric dispersion modeling. For the Greater Nagpur region, this domain extends 40 grids in longitude and 40 grids in latitude. We used spatial proxies to allocate these emissions for each sector to the grid. The schematics of the gridding procedure are presented below.

Satellite-based PM2.5 over India
In case of the transport sector, we used grid based population density, road density (defined as number of km per grid), and commercial activity like industries, brick kilns, hotels, hospitals, apartment complexes, cinemas, telecom tower density, and markets, to distribute emissions on feeder, arterial, and main roads. Emissions from industries were allocated to the respective industrial estates and brick kiln emissions were directly assigned to their respective clusters. The domestic sector and garbage burning emissions are distributed based on the population density. For convenience, only the gridded emissions for PM2.5 are presented below. The same are available for other pollutants – PM10, SO2, NOx, CO and VOCs; suitable for use in an atmospheric dispersion model.

Particulate Matter (less than 2.5 μm) (PM2.5)

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Since, the inventory is based on bottom-up activity data in the city and secondary information on emission factors in India (and elsewhere), the overall estimation has an uncertainty of ±20-30%. In the transport sector, the largest margin is in vehicle km traveled and vehicle age distribution with an uncertainty of ±20% for passenger, public, and freight transport vehicles. The silt loading, responsible for road dust resuspension, has an uncertainty of ±25%, owing to continuing domestic construction and road maintenance works, which also vary with the road type. In the brick manufacturing sector, the production rates which we assumed constant per kiln, has an uncertainty of ±20%. The data on fuel for cooking and heating in the domestic sector is based on national census surveys with an uncertainty of ±25%. Though lower in total emissions, open waste burning along the roads and at the landfills has the largest uncertainty of ±50%. The fuel consumption data for the in-situ generator sets is based load shedding rates, has an uncertainty of ±30%.

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