Environmental Management Plan

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Summary of Analysis of Ambient Air Quality and Emissions around Coastal Gujarat Power Limited Power Plant January 2018

IND: Mundra Ultra Mega Power Project

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Summary

Advanced analysis of Ambient Air Quality, Stack Emissions and

Metrological Parameters within 10 km radius of CGPL, Mundra, India

Coastal Gujarat Power Ltd. (CGPL) is a privately owned ultra-mega thermal power plant

(4000MW) located near Mundra port of Kutch district in the State of Gujarat, India.

CGPL started operations incrementally from March, 2012 and became fully operational in

March, 2013 with 4150 MW capacity. Being a coal based thermal power plant, emissions of

particulate matter (PM10 & PM2.5) and gaseous pollutants viz. SO2 and NOx were important to

be looked. Both point (stack) and non-point (fugitive) emission sources were considered.

The principal objective of the study was assessment of CGPL’s contribution in ambient PM10

concentration within 10 km radius and in specific to the villages in the vicinity.

Figure 1 shows the flowchart of the methodology developed. As shown in the flowchart,

application of the ‘suit’ of tools (field observations, discussions with CGPL, data analytics and

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modelling) was juxtaposed with each other to arrive at the final conclusion i.e. relative

contribution of PM10 by CGPL.

To begin with, a site visit was carried out. An examination of the satellite imagery was also

done.

Ambient Air Quality (AAQ) monitoring data consisting SO2, NOX and PM10 values from 10

manual and 1 automatic station, stack emission data, and meteorological data was provided by

CGPL over 7 years (2007-2015). See Figure 2. This AAQ data was processed for missing

values, outliers and normality. Basic statistical indicators were computed like annual means,

standard deviation, and frequency histograms. Similarly, the continuously recorded stack

emission data was analysed. The meteorological data collected at the automatic air quality

monitoring station was processed to generate wind roses, including a persistent wind rose, over

three years and for 4 seasons each year.

In addition to above, advanced statistics were applied consisting the following:

• Inter-parameter correlation (Correlation between any 2 AAQ parameter e.g. PM10 and

NOx)

• Inter-station correlation (Correlation between any 2 monitoring stations for a AAQ

parameter)

• Non-parametric Wind Regression (NWR) that helps in identifying the location of

dominant emission sources

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Figure 1: Flowchart of methodology to determine the pollutant contribution of CGPL within

10 km radius

Figure 2: Satellite imagery of the CGPL Air-shed and location of all 10 monitoring stations

• Non-parametric

wind regression

(NWR)

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Major point and area sources of emissions from CGPL were identified during the field visit.

The major sources of emissions were as follows (See Figure 3):

• Stack emissions from CGPL

• Coal Yard emissions from CGPL

• Stack emissions from Adani Power Ltd (APL) (another 4620 MW power plant located

close to CGPL)

• Coal Yard emissions from APL

• Movement of vehicle between CGPL and Vandh village

• Other localized sources of fugitive emissions

Figure 3: Satellite image (Google maps) highlighting major emission sources

Data on the above emissions was compiled, processed and estimated to the extent possible for

the fugitives or area sources.

Dispersion is an effective way to speculate emission influence or relative contribution of

emissions to the monitoring sites. In this regard, relevant advanced Gaussian plume dispersion

models were reviewed and ADMS model developed by Cambridge Environmental Research

Consultants (CERC), was found to be appropriate for the project objective.

Ash pond

CGPL Coal

yard

APL Coal yard

APL stacks

CGPL stacks

Road between CGPL

and Vandh

Tunda village

Vandh village

Main Gate

Gate for material

transport

Wind barrier

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Application of ADMS in this study included modelling of point (stack) as well as fugitive (coal

yard) emission sources for SO2, NOx and PM10 emissions. Incremental impact of SO2 and NOx

emissions was found to be negligible across the 10 km airshed. As regards incremental impact

of PM10 the results showed that coal yards of CGPL and APL are major contributors of the

ambient PM10 at Vandh and Tunda villages and not the stack or elevated emissions.

Tables 1 and 2 show the contribution of CGPL and APL in percentage of seasonal and annual

mean of the measured ambient PM10 concentration as derived through application of ADMS

model at Vandh village and Tunda stations respectively. These estimates should not be

interpreted as the final judgement on the exact contribution of CGPL because of limitations on

data and the model and should be considered as a guide to take necessary control measures.

Table 1 Percentage contribution of CGPL operations to seasonal and annual mean of

ambient PM10 at Vandh station

Period % contribution CGPL % contribution APL

Winter 36.1 4.7

Summer 14.3 13.9

Post monsoon 45.3 7.9

Annual 32.2 8.5

Table 2 Percentage contribution of CGPL operations to seasonal and annual mean of

ambient PM10 at Tunda station

Period % contribution CGPL % contribution APL

Winter 28.0 9.1

Summer 6.9 2.2

Post monsoon 34.8 10.1

Annual 23.8 7.4

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The key observations and recommendations from this study are:

• Location of Vandh village makes it vulnerable to PM10 emissions from the fugitive

emission sources like CGPL coal yard, APL coal yard and coal conveyor belt of CGPL

and construction activities for the Adani solar PV manufacturing power plant

• In the area in 10 km radius of the plant has a virtually flat terrain, scanty natural

vegetation, dominated by agricultural activity and relatively high wind velocities. These

anthropological, geological, and meteorological aspects have a high potential of dust

re-suspension and particulate transport into the air shed.

• Application of Inter-parameter and Inter-station correlation suggested that for PM10

fugitive emissions dominant relative to stack emissions - especially regional transport

and local re-suspension. Therefore, for PM10 in the study area of 10 km radius, a

dominant role is played by local fugitive emission, regional transport, and resuspension

rather than stack or elevated emissions. The application of NWR showed that emissions

PM10 from the Coal yard dominate the nearby receptors such as Vandh village.

• In this study, application of NWR was found to be useful for source diagnostic. To

apply NWR at Vandh and CGPL Main Gate, a higher monitoring frequency (hourly)

for ambient PM10 needs to be followed. This may be best done by conducting a high

frequency monitoring campaign for a period of 3 months of the winter (i.e. October-

December) along with meteorological observations. This exercise will help in

understanding the impact of CGPL’s emission vis-e-vis other sources such as APL’s

coal yard and emissions due to movement of vehicles. Further, such a study will also

help in the assessment of the emission reduction measures undertaken by CGPL around

the Coal Yard.

• Proper maintenance and calibration of the online stack emission monitoring system

should be carried out given the significant downtime of the present instrumentation. It

is very important that CGPL takes this recommendation on priority

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• CGPL should ensure timely maintenance of the present automatic air quality

monitoring instrumentation (including meteorological instruments) to ensure low

downtimes and better quality

• A third party annual audit should be considered by agencies such as National

Environmental Engineering Research Institute (NEERI)

• CGPL’s Department of Environment should submit quarterly report containing basic

analysis of AAQ and meteorological data to Asian Development Bank (ADB) based on

a recommended pro forma