Air Quality Information Systems and geoss: Applications to India

Advancement of Information Infrastructure

This aspect of the proposed effort focuses on using and enhancing the GEOSS GCI, and community-based extensions thereof, as defined by the data, information and analytical needs of the India air quality research, management and policy communities. The foundation of this task is the GEOSS Common Infrastructure and the air quality community information infrastructure components developed during the GEOSS Architecture Implementation Pilot – Phase 2 (AIP-2; http://www.ogcnetwork.net/AIP2ERs#AQ) and currently being advanced in AIP-3. The GEOSS Air Quality community has been building entities on top of the GCI to accommodate unique characteristics of air quality data and analysis web services through metadata standards and an associated community catalog (Figure 4). The resulting framework is intended to simplify the process for providers to share their data and provide ways for web applications and decision support systems to connect with the data.

This part of our proposal addresses the task of developing the necessary components for an interoperable network of data and tools to support the air quality science and management efforts in India and connect it with GEOSS. The foundation of this task is the GEOSS Common Infrastructure and the air quality community information infrastructure components developed during the GEOSS Architecture Implementation Pilot – Phase 2 (AIP-2).

Figure 3: An air quality community infrastructure connected with the GEOSS Common Infrastructure

An underlying principle of AIP is the use of Service Oriented Architecture (SOA) with the publish-find-bind protocol, where data services are published to a common registry, search tools allow the finding of those data, and a wide variety of applications and tools can connect to those data for visualization, processing or analysis. Key activities include:

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  Creation of standards-based web service interfaces to satellite, surface and modeled data
  
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  Registration of web services in a GEOSS air quality community catalog and harvesting of the Community Catalog by the GEOSS Clearinghouse.
  
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  Access of services through the GEOSS Clearinghouse with AQ Clients and portals for the use of those services in web applications and decision support systems
  

The publish capability in the information architecture will involve the creation of metadata needed for finding, understanding and using the data. The AQ metadata record developed in AIP-2 is based on the ISO 19115 standard and can be semi-automatically generated based on a combination of metadata extracted from web service descriptions and manually entered information. The generated metadata record is saved into a community catalog that is already registered as a component in the GEOSS Component and Service Registry (CSR). The GEOSS Clearinghouses query the GEOSS CSR for catalogs and then “harvest” the catalogs for their metadata records.

In order to find the data access services in the GEOSS Clearinghouse one has to know what to search for and how to extract the relevant information from the GEOSS Clearinghouse. AIP-2 has helped define this process for AQ-related searches. General search parameters defined by the GEOSS Clearinghouses are used as a first filtering step and then further refined in customized search parameters specific to AQ communities.

Further, subsequent to finding the data for AQ decision-making, there is a need to bind, or use, those data in meaningful applications. The information returned from search results should provide the necessary information to connect those data with processing, visualization or analysis tools of the user’s choice. This aspect of the information infrastructure will be a focus area for the proposed project to define conventions for connecting GEOSS earth observation services with decision support tools.

The end result of this task will be data services that are made available to stakeholders in India through GEOSS along with a capability for users to register data services with GEOSS, and mechanisms to connect those data services for use in decision support systems.

Enhancement of Decision Support Systems

The proposed project intends to leverage existing data and information systems, tools and methods to facilitate the integration of global and local data from multiple sources (surface-based and satellite measurements, models and analysis tools) to allow air quality management groups to better understand the air quality issues in specific regions of India, and conduct analyses, such as emission source identification, exceptional event analyses, forecasting, assessment of air quality scenarios and identification of air pollution hot spots in the country.

To meet the needs of the intended beneficiaries, the information infrastructure should effectively provide information that can meaningfully enhance decision support systems. The project team has extensive experience in developing information and analysis tools in support of air quality science and management, and also has experience in communicating, collaborating and coordinating with related systems. We seek to leverage implemented technologies, best practices, and existing information systems for application in India.

Existing Systems and Community-oriented Efforts

The proposed project relies on and builds upon previous efforts. An important activity is the assessment of existing systems and capabilities along with determination of how they are best applied to the India air quality problem.

Recent Studies and Experiences in India

In a recent effort by CPCB in India, a nation-wide study was undertaken to address air quality issues in the context of the Auto-Fuel Policy for India (MPNG, 2003)). Seven agencies, namely, IIT Bombay, IIT Kanpur, IIT Madras, ARAI Pune, NEERI Nagpur, NEERI Mumbai and TERI Bangalore undertook a study of source apportionment for the cities of Bangalore, Chennai, Delhi, Kanpur, Mumbai and Pune (CPCB, 2010). The study followed a common methodology for the six cities and the results were used to project future air quality scenarios based on control/management options exercised. A community of researchers, practitioners and government agencies participated in this first ever effort of this scale.

As a follow-up of the 6-city study, an exercise was carried out through collaboration between ISRO, IIT Bombay, and NEERI Mumbai to relate satellite data with the ground data measured for the city of Mumbai. While the spatial and temporal resolutions were limited, the results indicated episodic events that influenced the local measurements on particular days.

In 2004, the annual meeting in IASTA focused on the theme of satellite and remote sensing studies in India. A large body of data is available in the research communities of IITM Pune, ISRO, IIT Delhi, IIT Kanpur, and IMD and several such focused, yet to be integrated with a common multi-dimensional thread, that could provide greater access and a platform for thinking.

More recently, the MoEF, through CPCB has asked the State Pollution Control Boards to prepare an abatement action plan for 88 industrial clusters that are above the critical CEPI. While there is debate on the relevance and effectiveness of using CEPI and the methodology used to arrive at CEPI, the prominent outcome emerging from the debate is the need for comprehensive data sets that are sourced through multiple sources, and even further, the quality and sanctity of the available data. A recent round table co-hosted by Indian Environment Association (members from Industries) and IIT Bombay pointed to the need for community participation in national level decision making processes.

The Ministry of Environment and Forestry, India along with Pune Municipal corporation and USAID/USEPA initiated a "science based Urban air quality management for Pune City" project. C-DAC, NEERI and other stakeholders participated in this project for emission modeling, source apportionment and AERMOD based air quality modeling. C-DAC, in collaboration with USEPA and University of Riverside, has developed a coupled WRF-AERMOD modeling system. (published in Atmospheric Environment). The effort also conducted a future what-if scenario analysis for Pune city (introduction of Rapid transport, change of fuel engines, change of vehicle population, etc) with AERMOD model. C-DAC has developed a grid enabled WRF-AERMOD system with hybrid linux-windows platform.

The recent experiences in India point to the need for a framework that fosters collaboration among organizations in exchanging air pollution data and information from multiple sources. Such collaborative efforts to develop tools that allow integrated data analysis to derive decision ready information shall be highly valuable. Other countries and communities face similar challenges and lessons learned by these other organization can be useful in addressing the Indian challenges. The following section outlines some of the ongoing community-oriented efforts.

Other Related Efforts

Coordination and effective interoperability among air quality systems is a well documented challenge. In the US, an Air Quality Data Summit was convened by EPA to discuss challenges in and approaches to address information and capability sharing among projects and organizations. During the GEO VI Plenary in November 2009, an air quality meeting was organized to discuss similar topics. Members of the proposed project team have been actively engaged in these interoperability efforts and the GEO Decision Support RFP provides an opportunity to address these issues and achieve the interoperability goals of GEOSS within the context of air quality decision support in India. Appendix C describes some of the systems and projects that the project team has been involved with and others that are expected to be part of the collaboration pursued during the project. These only represent a starting point that will be more comprehensively expanded during the proposed effort. For example, a recently initiated project with the goal of air quality forecasting and mapping for Indian cities seems to be a project with which we would gain from collaboration and that might be interested in participating in the IAQ-CoP.

These systems, projects and tools provide various capabilities for information access, visualization, processing and analysis for air quality, each with its unique interfaces and specialties. An objective of the proposed project is to make use of these systems and other related systems within the GEOSS infrastructure that is outlined in the previous section so that data and tools are broadly available for multiple decision support uses. A combination of existing capabilities applied to India would offer a wide-range of decision support capabilities that dynamically use earth observation data distributed across the Web, such as integrated data visualization and analysis over multiple spatial, temporal, and air pollutant dimensions and tools to reconcile observation data and modeled output.

Application of Systems to Air Quality Characterization in India

The characterization of air quality is an integrative activity that is a precondition for all AQ decision-support activities shown in Figure 1. The characterization of air pollution over India will consist of deriving the spatial, temporal and chemical pattern of air pollutants over the Indian sub-continent. The spatial characterization will utilize to the maximum degree possible the surface-based observations from the monitoring networks in India. This will include the 348 station routine monitoring network. The additional, available monitoring data in India will be identified and their use facilitated by the IAQ CoP. These regional and local monitoring data will be made accessible and registered in GEOSS through the Air Quality Community catalog.

The characterization of air pollution over India will also utilize the global and regional datasets provided by the GEOSS Air Quality Community. An illustrative subset of these data resources is shown in Figure 4. For more details and for access to these datasets see the GEOSS Air Quality Community console (Husar et al, 2010b).

Figure 4. Global and regional datasets provided by the GEOSS Air Quality Community

The digital population data (Population Density) will be useful for estimating pollutant exposure and human health effects. The emission data (Emission Density) from high resolution inventories of anthropogenic emissions will be available as inputs to regional, chemical transport models. These inventories will be augmented with estimates of biomass smoke emissions derived from remotely sensed fire pixels and detected smoke. The global datasets will include light extinction observations derived from surface based visual range observations (Visibility). These daily data are available for over 100 monitoring sites in India since the 1970's. Remote sensing observations will be crucial for establishing the spatial pattern of aerosols (Hoff and Christopher, 2009; Husar, 2010), NO2 and other gases. These daily, global-scale observations will include Aerosol Optical Thickness from the MODIS and MISR sensors as well as Absorbing Aerosol Index, columnar NO2, SO2 and CHCO from the OMI sensor. It is anticipated that the spatial AQ characterization will consist of an iterative process through which the ambient and remotely-sensed observations are reconciled with the emission estimates using suitable chemical transport models.

The temporal characterization of air pollutants will consist of time series analysis of the long term datasets. The diurnal, weekly and seasonal cycle of the pollutants will be characterized along with the synoptic scale variation, including air pollution events from man-made and natural sources. For long-term trends, effort will be made to reconstruct the natural background concentrations and the anthropogenic perturbation over the recent decades. The chemical characterization of the Indian sub-continent will focus on aerosols since the observation of gaseous pollutants is sparse in both time and space.

Locally available emissions data will be compiled to augment an existing emissions database for the Indian subcontinent and the surrounding regions influencing air quality in India. We plan to use regionally relevant emissions inventory data in the use case modeling studies to be identified in the course of the project. Based on Ms. Shankar's previous collaborative research with IIT-Mumbai in modeling the INDOEX observational period of 1999, we have access to a fine-resolution (0.25 degree x 0.25 degree) inventory of emissions collected for several emissions sectors in India, and have the tools available to convert them to CMAQ-ready format. We will include updates in the biofuel and open burning sectors, and growth and control projections to 2006 conditions made recently by Dr. Venkataraman (Cherian et al., 2010; Venkataraman et al., 2006; Venkataraman et al., 2005; Habib et al., 2004) to capture more contemporary impacts of black carbon, which is very important to the region, due to both its air quality and health impacts and its climate impacts.

Air quality modeling output is another component of the integrated air quality characterization analysis. We anticipate running retrospective air quality model simulations and targeted emissions control studies for time periods identified by the India Air Quality Community of Practice. This activity would include enhancements to available analysis tools and their application to evaluate the model results against Earth observations, both surface- and satellite-based. The modeling and analysis results will be made available to the GEOSS Air Quality Community Infrastructure for reuse in other applications.

A series of demonstrations and workshops are planned in order to reach a broad audience and to collect critical feedback on the strengths and weaknesses of the prototype approach, analyses and applications. The demonstrations and workshops will highlight the processes used in the information infrastructure as well as the insights gained from the integrated air quality characterization analyses. The feedback will be used to refine the developed systems in order to provide the most robust and meaningful information systems for the intended beneficiaries to incorporate into their operations.