Environmental Management Plan Summary of Analysis of Ambient Air Quality and Emissions around Coastal Gujarat Power Limited Power Plant January 2018 IND: Mundra Ultra Mega Power Project This report has been prepared in response to the Remedial Action Plan for the Project. The views expressed herein do not necessarily represent those of ADB s Board of Directors and Management, and may be preliminary in nature. Your attention is directed to the Terms of Use section of this website. In preparing any country program or strategy, financing any project, or by making any designation of or reference to a particular territory or geographic area in this document, the Asian Development Bank does not intend to make any judgments as to the legal or other status of any territory or area.
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 1
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 2
Non-parametric wind regression (NWR) 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 3
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 Tunda village Main Gate Gate for material transport Road between CGPL and Vandh APL stacks Ash pond CGPL stacks CGPL Coal yard APL Coal yard Wind barrier Vandh village 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. 4
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 5
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 6
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 7