Study On Ambient Air Monitoring, Emission Inventory and Source Apportionment Methodology Frame Work By A. L. Aggarwal

Mission of the Projects Ambient air quality monitoring is being carried out at various cities & towns in the country under the National Air Monitoring Programme (NAMP). The air quality data generated over the years reveal that air quality is deteriorating in many parts of the country, particularly the urban centers. Based on the data, CPCB has identified more than 53 non-attainment cities and towns including 16 major cities recording significantly higher levels of SPM & RSPM. In these cities the problem becomes complex due to multiplicity and complexity of air polluting sources (eg. Industries, automobiles, generator sets, fuel burning, construction activities, etc.). Ambient air quality monitoring is being carried out at various cities & towns in the country under the National Air Monitoring Programme (NAMP). The air quality data generated over the years reveal that air quality is deteriorating in many parts of the country, particularly the urban centers. Based on the data, CPCB has identified more than 53 non-attainment cities and towns including 16 major cities recording significantly higher levels of SPM & RSPM. In these cities the problem becomes complex due to multiplicity and complexity of air polluting sources (eg. Industries, automobiles, generator sets, fuel burning, construction activities, etc.). The recent “ Auto Fuel Policy “ document submitted to Govt. of India by Dr. Mashelkar Committee has identified the knowledge gap in the area of air pollutant’s apportionment. With this view, Oil Companies in India in association with leading scientific & research organizations and automobile industry initiated a scientific solutions to ensure better environment in six select cities of India, which can be replicated to entire country thereafter. The recent “ Auto Fuel Policy “ document submitted to Govt. of India by Dr. Mashelkar Committee has identified the knowledge gap in the area of air pollutant’s apportionment. With this view, Oil Companies in India in association with leading scientific & research organizations and automobile industry initiated a scientific solutions to ensure better environment in six select cities of India, which can be replicated to entire country thereafter.

Role of Air Quality Models in AQ Management System EmissionInventory Air Quality Monitoring Rulemaking & Implementation ModelingInventory Air Quality Modeling Cost/Benefit Analysis, Risk Assessment Control Strategy

Urban Air Aerosol Size Distribution Characteristics

Objectives of the Study: To profile baseline glc of air pollutants and other relevant air toxic levels in different parts of all the project cities, which includes source specific “hot spots” viz. road curbsides, industrial zones etc. To profile baseline glc of air pollutants and other relevant air toxic levels in different parts of all the project cities, which includes source specific “hot spots” viz. road curbsides, industrial zones etc. To develop:”Emission factors” (EF) for different categories of pertinent contributory sources and EF developed should to reflect the local variance in fuel quality, technology, size and vintage of sources, control systems options etc. The factor shall cover both fugitive as well as flue gas emissions. To develop:”Emission factors” (EF) for different categories of pertinent contributory sources and EF developed should to reflect the local variance in fuel quality, technology, size and vintage of sources, control systems options etc. The factor shall cover both fugitive as well as flue gas emissions. To inventories the pollution loads from various sources for their spatial and temporal distribution in the project cites. To inventories the pollution loads from various sources for their spatial and temporal distribution in the project cites. To profile the source emissions characteristics of different sources. To profile the source emissions characteristics of different sources. To conduct source apportionment studies and prioritizes the source categories for evolving mitigation strategies. To conduct source apportionment studies and prioritizes the source categories for evolving mitigation strategies. To assess the impact of sources emission loads on ambient air quality under different management/interventions/control options and draws a roadmap of short term and long term measures as considered appropriate and cost effective to ensure “Cleaner air in urban areas. To assess the impact of sources emission loads on ambient air quality under different management/interventions/control options and draws a roadmap of short term and long term measures as considered appropriate and cost effective to ensure “Cleaner air in urban areas.

Use of Source Dispersion & Receptor Models Dispersion Models  Detailed city emission inventory/Pollution loads analysis  Development cause- impact relationships for different sources  Impact analysis of alternative control strategies  Development of action plan on urban clean air  Apportionment of contribution from major pollution source including fugitive & small scale industrial sector  Identification of secondary aerosols formation  Suitable irrespective of complex terrain and meteorology  Regional /tranboundary /air quality background sources can be identified Receptor Models

Monitored ambient concentrations Onsite meteorological conditions (including: mixing height, stability - IMD/CPCB data base) Emission inventory and source location (+ future changes) on GIS maps  EF developed by ARAI  EF methodology of Pune (US- EPA) study  EF from CPCB/MoEF data base Air dispersion modeling (ISC 3) Source profiling Interpretation of model outputs scenario(s) (dispersion/receptor models) Cost effective air quality management strategies Chemical characterization of PM 10 /VOC sampling Receptor modeling (CMB) Source apportionment PM 10 /VOC using CMB Impact on ambient concentration Proposed Study Framework Model Calibration

Identification of concerned sources Point Area Line Earlier emission inventory experiences Secondary data and primary site surveys Collection of activity levels and sources location data for each source type Emission inventorisation of identified sources Point and Area (secondary data & primary site and activity survey Line (primary traffic survey and secondary vehicular characteristics data) Quality Control checks Data handling and statistical analysis Source wise emission inventorisation & source profiles Scenarios Analysis Emission characterization Point & Area: (published reports/USEPA & Indian experience and primary emission profiling/ monitoring) Line: (ARAI, study & traffic survey data) GIS Mapping of total emission inventory (grid-wise) Point Area Line Proposed Framework on Emission Inventory Source data input files for dispersion and receptor modeling

List of Project Stakeholders  Ministry of Environment & Forest  Central Pollution Control Boards  Respective State Pollution Control Board  Local city Authorities: Municipalities, Traffic & development Authorities etc.  Participating Institutes : NEERI, TERI, ARAI, IIT Mumbai, IIT Kanpur, Madras Universities  SIAM & Auto Sector Industries  Oil Sector industries  Ministry of Heavy Industry

Current Status of the Project  TOR of Study reviewed and new study plans evolved.  IOC signed NOC wells NEERI, TERI, ARAI  Studies started in Delhi  Studies will state in Bangalore & Pune in MOU are 2005  Study center done for Bombay, Chennai, Kanpur  ARAI Conducted Emission Factor for auto and fuel motion designed by fuel & Auto sector  Source Profiling study proposed in programm

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Air Quality Management Process Ambient Monitoring & Standard Ambient Air Quality Data Emissions Inventory Modeling Control Strategy

Chemical Mass Balance (CMB) Model Quantifies contributions from chemically distinct source-types rather than contributions from individual emitters Quantifies contributions from chemically distinct source-types rather than contributions from individual emitters Performs tests on ambient data and source profiles which tell how well source-type contributions can be resolved from each other Performs tests on ambient data and source profiles which tell how well source-type contributions can be resolved from each other Different particle size fractions can be accommodated Different particle size fractions can be accommodated

Emission Estimation Methods Continuous emission monitors or source tests Continuous emission monitors or source tests Emission factor * Activity level Emission factor * Activity level Material balance Material balance Emission estimation model Emission estimation model

CMB : Overview Receptor modeling uses chemical and physical characteristics of collected air samples, along with statistical techniques to determine likely proportional source-type responsibility. Receptor modeling uses chemical and physical characteristics of collected air samples, along with statistical techniques to determine likely proportional source-type responsibility.

CMB : Advantages Do not need extensive emissions inventory as with dispersion modeling Do not need extensive emissions inventory as with dispersion modeling Do not need extensive, long-term meteorological monitoring network Do not need extensive, long-term meteorological monitoring network Can perform necessary sampling with inexpensive, portable samplers in a short period of time Can perform necessary sampling with inexpensive, portable samplers in a short period of time Use direct, fingerprinting approach instead of relying on meteorological and dispersion models Use direct, fingerprinting approach instead of relying on meteorological and dispersion models

CMB : Requirements Significant laboratory analytical expertise is required (x-ray fluorescence, ion chromatography, colorimetry, microbalance) Significant laboratory analytical expertise is required (x-ray fluorescence, ion chromatography, colorimetry, microbalance) Experience in using statistical grouping techniques (e.g., factor analysis, principal components analysis, and/or chemical mass balance- CMB) Experience in using statistical grouping techniques (e.g., factor analysis, principal components analysis, and/or chemical mass balance- CMB) Ideally, have some samples from actual, local source types that will enable more accurate fingerprinting of ambient portions of particulates Ideally, have some samples from actual, local source types that will enable more accurate fingerprinting of ambient portions of particulates

CMB : Analysis Basic source-receptor relationships can be estimated by statistical techniques such as factor analysis or principal components analysis Basic source-receptor relationships can be estimated by statistical techniques such as factor analysis or principal components analysis CMB is a formalized software package that provides more options and accuracy for analysis CMB is a formalized software package that provides more options and accuracy for analysis

CMB Formulation C i = F i1 S 1 +F i2 S 2 + … + F ij S j i=1..I J=1..j C i = F i1 S 1 +F i2 S 2 + … + F ij S j i=1..I J=1..j Where C i = concentration of species I measured at a receptor site F ij = fraction of species i in emissions from source j source j S j = estimate of the contribution of source j I = number of chemical species J = number of source types

Major CMB Assumptions Sufficient data to determine excessive pollutant levels Sufficient data to determine excessive pollutant levels Samples may be or have been chemically speciated Samples may be or have been chemically speciated Potential source contributors identified Potential source contributors identified Source profiles measured or approximated Source profiles measured or approximated More receptor species than source types More receptor species than source types

Validate/Evaluate CMB Use different source profiles and note changes Use different source profiles and note changes Identify and characterize missing sources Identify and characterize missing sources Measure additional species at source and receptor Measure additional species at source and receptor Stratify samples by meteorological type Stratify samples by meteorological type Test for effect with biased data Test for effect with biased data If use in concert with a dispersion model, compare results and refine model inputs If use in concert with a dispersion model, compare results and refine model inputs

An effective air quality monitoring specific to objective Monitors the right things Monitors the right things Monitors frequently enough Monitors frequently enough Monitors in the right places Monitors in the right places Ensures data are acquired, processed, and stored quickly Ensures data are acquired, processed, and stored quickly Ensures high data quality: Develop QA/QC Protocoles Ensures high data quality: Develop QA/QC Protocoles Does valuable data analysis – answers question “what do the data mean?” Does valuable data analysis – answers question “what do the data mean?”