City – Coimbatore, India


Coimbatore is the second largest city after Chennai in the state of Tamil Nadu and is a center of textile and cotton industry, manufacturing, poultry farming, education, information technology and health care in the state. It is one of the fastest growing cities in India, housing more than 25,000 small, medium and large industries. The city was once a haven for those seeking retirement as it is situated on the banks of the Noyyal river and is surrounded by the western ghats. The city is administered by the Coimbatore Municipal Corporation with an estimated population of 2.5 million.

With several small and medium scale industries, a booming construction sector and an increase in road transport both for private use and for servicing industry and product distribution, Coimbatore has multiple sectors that are responsible for its air quality.

To assess Coimbatore’s air quality, we selected 50km x 50km 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 Coimbatore, there are 3 manual stations reporting data on PM10, SO2, and NO2 and no continuous air monitoring stations.

 

 


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 Coimbatore 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 coimbatore 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)

 PM2.5PM10BCOCNOxCOVOCSO2CO2
14,100 23,900 3,500 3,050 25,150 113,100 28,200 2,650 3.13
TRAN 3,000 3,150 1,150 1,000 4,950 61,850 20,200 2501.41
RESI700700200350400 8,550 1,050 1500.41
INDU 6,050 7,400 1,550 400 15,450 21,250 4,050 1,700 1.05
DUST 1,600 9,750 -------
WAST 1,400 1,500 10085050 6,750 1,350 500.01
DGST35040020050 3,450 900100500.16
BRIC 1,000 1,000 300400850 13,800 1,450 4500.10

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 Coimbatore 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 19.4 ± 4.0 μg/m3 – is well below the national standard (40) and ~2 times the WHO guideline (10)
  • On an annual average, the city benefits significantly from the string westerlies and monsoons, in keeping the pollution levels low
  • The city requires at least 19 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 transport (including on road dust), industries (large cement plants), and open waste burning as the key air pollution sources in the urban areas
  • While the the contribution of sources outside the urban airshed is an estimated 33% of the ambient annual PM2.5 pollution (in 2015), mostly stemming from large industries, brick kilns, and agricultural activities, n absolute terms, this is a equivalent to background concentrations
  • Inside the city, there is a growing need 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 remain constant, if and only if, Bharat 6 fuel standards are introduced nationally in 2020, as recommended by the Auto Fuel Policy
  • 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
  • The 120 brick kilns in the urban airshed 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
  • The cement manufacturing plants need to practice and enforce stricter environmental standards for all the criteria pollutants to reduce their share of influence on urban air quality
  • 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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