City – Varanasi, India


Varanasi (formerly Banaras) lies on the banks of the Ganges river in Uttar Pradesh and is center for pilgrimage and tourism in India. It is also famous for several handcrafted goods such as muslin and silk fabrics, perfumes, ivory works, and sculpture, diesel locomotive works and Bharat Heavy Electricals Limited. The Varanasi urban agglomeration consists of seven urban sub-units, with an estimated urban population of about 2.5 million. However, since tourism is a very important sector for Varanasi, with over 3 million domestic and 200,000 foreign tourists visiting annually – there is a significant strain on city infrastructure.

Varanasi generates 650 metric tonnes of solid waste per day – most of which is dumped in landfills. The government has recently announced that waste will be processed in a treatment plant at Karsara. The efficient operation of which still needs to be seen once it begins. The city’s infrastructure is inadequate to keep up with the increase in population and visitors, especially with the solid waste management. Much of the waste is either burnt, or dumped in landfills or the river.

To assess Varanasi’s air quality, we selected 40km x 40km 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

Monitoring

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 Varanasi, there is 1 continuous air monitoring station (CAMS) reporting data for all the criteria pollutants and 2 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 Varanasi is presented below. The global PM2.5 files are available for download and further analysis @ Dalhousie University.

Emissions

We compiled an emissions inventory for the varanasi 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)

 PM2.5PM10BCOCNOxCOVOCSO2CO2
12,100 17,450 3,050 4,850 14,050 134,400 21,850 2,300 1.79
TRAN 2,200 2,300 950700 3,800 30,900 9,100 1500.81
RESI 2,800 2,850 500 1,500 750 43,300 5,650 4000.27
INDU10010050-35035050500.04
DUST950 5,950 -------
WAST 1,800 1,900 150 1,100 50 8,650 1,750 500.01
DGST650700400100 6,150 1,650 150500.28
BRIC 3,600 3,650 1,000 1,450 2,950 49,550 5,150 1,600 0.38

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)

Meteorology

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 Varanasi 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.

 

Windrose Functions for 2013-2016

Windrose Functions Windrose Functions Windrose Functions Windrose Functions

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 78.4 ± 10.3 μg/m3 – nearly 2 times the national standard (40) and more than 7 times the WHO guideline (10)
  • The city requires at least 23 continuous air monitoring stations to statistically, spatially, and temporally, represent the mix of sources and range of pollution in the city (current status – 2 manual and 1 continuous)
  • The modeled source contributions domestic cooking and heating, highlight transport (including on road dust), brick kilns, and open waste burning as the key air pollution sources in the urban area
  • The city has an estimated 31% 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 450 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.

Back to the APnA page.

All the analysis and results are sole responsibility of the authors @ UrbanEmissions.Info. Please send you comments and questions to simair@urbanemissions.info