OPTAIR: Multi-criteria optimization for air quality
Supported by the Austrian Research Foundation FFG,
Project No. 814799 and the Province of Lower Austria
management and emission control
5. Time table and scheduling
The OPTAIR project is designed for a three year duration,
within the framework of the recently extended EUREKA E!3266 WEBAIR.
The basic principle is rapid prototyping, with sequential but heavily
overlapping work packages; a total of 14 work packages with their
respective tasks are foreseen. Experience with international cooperation
in WEBAIR is reflected by minimizing dependency of development
on external inputs, all of which is designed as optional in a modular architecture.
The 14 work packages (WP) of the proposed project are:
1. WP: International project coordination, involving the WEBAIR partners in all 17 partner countries where test applications are planned in principle, and the more closely integrated partners that develop optional components for the OPTAIR proposal.
2. WP: Requirements and documentation: the work package spans the entire project duration, with a gradual progression from requirements to documentation and manuals.
3. WP: End user (DM) participation: maintain user contacts and develop tools for the elicitation of end user inputs to the valuation of impacts.
4. WP: Instruments: a data base of technological, regulatory, planning, and economic instruments with their associated cost functions.
5. WP: Emission modeling: a central component of any emission control strategy and the related air quality modeling is the description of dynamic emissions.
6. WP: Basic simulation models: linkage and integration. Multiple alternative but closely linked models will be used for efficient optimization.
7. WP: Assessment, performance criteria: A key of the multi-criteria optimization is the description of systems performance by scalar variables as the basis for the optimization
8. WP: Optimization framework, satisficing. The primary step in the multi-criteria optimization is the generation of feasible alternatives against the set of constraints defined by the representative stakeholders identified in WP 3 in terms of the criteria from WP 7.
9. WP: Computational framework, cluster and grid computing. Provides the necessary performance to generate sufficiently large samples of feasible solutions for the subsequent reference point methodology
10. WP: Knowledge representation, machine learning and adaptive heuristics. Requires first results and an operational computing environment as well as realistic test cases from WP 8 and 9 to start beyond theoretical preparations.
11. WP: Discrete multi-criteria DSS. The second step in the optimization is a discrete multi-criteria DSS implementing a reference point methodology.
NOT ACTIVE IN PROJECT YEAR 1
12. WP: Uncertainty analysis, robustness: climate change scenarios.
NOT ACTIVE IN PROJECT YEAR 1
13. WP: Test cases: demonstrated improvements: The test cases are the central work package, spanning most of the proposed project duration,
14. WP: Dissemination strategy: planned over the entire duration of the project.
The planned scheduling of the work packages is shown in the table below,
with the first year schedule highlighted:
||Work package name, short description
|WP01||Project coordination and management||1||36
|WP02||Requirements and documentation||1||36
|WP03||End user participation (stakeholders, DM)||1||15
|WP04||Instruments of emission control, cost functions||2||16
|WP05||Dynamic emission modeling incl. dust entrainment||3||19
|WP06||Simulation models: integration, alternative models||4||27
|WP07||Assessment: performance criteria: compliance, economics||7||28
|WP08||Optimization framework, phase I: satisficing||7||30
|WP09||Computational experiments: cluster and grid infrastructure||7||33
|WP10||Knowledge representation, heuristics, GA, machine learning||10||35
|WP11||Optimization framework, phase II: Discrete multi-criteria DSS||-||-
|WP12||Uncertainty analysis, robustness, GCM scenarios||-||-
|WP13||Test cases: implementation, performance, validation||7||36
The workplan for the first project year will see all work packages
with the exception of WP 11 and 12 running or at least started.
WP01: Project coordination and management
This work package spans the entire project duration at a low level of effort, and includes the maintenance of a common project web server as the core of the electronic communication and coordination system between partners to manage and synchronize development and test cases. A major part of the activities will be the management of all common project documents (see also WP 02) in a web based repository with a number of web based management tools.
WP02: Requirements and documentation
The initial set of technical and functional specifications is gradually replaced with progressing implementation turned into the corresponding reference and user manual pages, implemented as on-line hypertext documents integrated in the overall system.
WP03: End user participation (stakeholders, DM)
economic valuation as a central component in the multi-criteria optimization
requires direct involvement of representative users,
decision makers, stakeholders, major actors to obtain these
unavoidably subjective values. At the same time, finding a compromise solution
from multi-criteria alternatives requires a preference structure
that is also subjective. The basic tools are a stakeholder data base
to manage the necessary contacts in each test case,
and on-line questionnaires on issues, objectives, criteria,
constraints that define preference structures, as well as
procedure for effective stakeholder involvement.
WP04: Instruments of emission control, cost functions:
While the data base is generic, and will support the selection and
automatic download of instruments into the optimization scenarios,
the specific parameters have to be set at the level of the individual scenario.
Instruments can be defined:
Cost functions will be represented by piecewise linear approximations
for a very high degree of flexibility in representing economies of scale
but also diminishing returns for most solutions, and include
- By domain, globally (with, however, potentially different effects
on individual sources or source classes);
- By emission source class or category, e.g., industry, households, traffic.
- By geographical or administrative groupings
- By individual emissions sources.
- investment costs (either annualized or computed as NPV from total investment, project life time, and discount rate); and
- operating costs, that may be time or activity dependent.
WP05: Dynamic emission modeling incl. dust entrainment
The emission data used for air quality modeling and emission control optimization
are either based on direct measurements (for major point sources),
or generated from activity and fuel/technology data,
where each of the parameters used to estimate emissions is subject to modification
by any one of the emission control instruments.
For the specific case of VOCs, fuel and technology specific
speciation of chemicals will be applied.
For biogenic emissions, land use/land cover (vegetation) maps
will be used together with the basic meteorological parameters
of temperature, humidity, and solar radiation.
The most complex emission estimates are for particulates not originating
from combustion processes: dust entrainment by wind will be generated
by a separate dynamic emission model that uses:
For the calibration/validation of the dust entrainment model,
on-line PM monitoring data as well as passive samplers will be used.
- Detailed short-term high resolution wind fields derived from
MM5 meteorological simulations, The proposed method will use a frequency
distribution of bottom layer wind speeds around the average value
estimated by the model to account for the non-linearities of the
wind driven entrainment (exponential threshold function);
- Soil characteristics, in particular size distribution and specific weights;
- Soil moisture as generated by MM5;
- Economic activities such as construction and mining, trucking.
WP06: Simulation models: integration, alternative models
According to the proposed methodology of multiple models to
improve optimization performance, the underlying detailed air quality simulation
models are grouped into two sets according to the processes covered:
- Conservative substances SO2, NOx, PM10/2.5;
these will be represented by the regulatory Gaussian model system AERMOD,
and the dynamic 3D nested grid Eulerian model CAMx;
due to the restrictions of the Gaussian approach
(assuming homogeneous meteorological fields) the geographic scope
of applicability is limited; for larger areas, several parallel
AERMOD domains will be used corresponding to one CAMx master domain with nested sub-domains.
- Photochemical processes: O3, NOx: represented by the photochemical
box model PBM and the detailed, dynamic 3D Eulerian model CAMx,
that offers alternative chemical mechanism for the photochemical reaction kinetics.
WP07: Assessment: performance criteria: compliance, economics
A key of the multi-criteria optimization is the description of
systems performance by scalar variables as the basis for the optimization.
These must be extracted, aggregated or integrated from the dynamic,
spatially distributed, multi-parameter models results and directly
address regulatory targets (compliance), policy objectives beyond
compliance, public health and environmental impacts,
as well as economic (cost) criteria. Cost functions will be based on
piecewise linear approximation, and include annualized (NPV) investment
and activity proportional operating costs (e.g., Bromley, 1995; McAllister, 1980).
See also: the EEA Core Set of Indicators: http://themes.eea.europa.eu/IMS/CSI.
WP08: Optimization framework, phase I: satisficing
The primary step in the multi-criteria optimization is the generation
of feasible alternatives against the set of constraints defined by
the representative stakeholders identified in WP 3 in terms of the
criteria from WP 7. The basic optimization framework will consist of:
- The interfaces and language to describe and configure an
optimization scenario in terms of model domain, time periods,
meteorology, pollutants and constraints;
- The management of the underlying models and input data required,
with emphasis on computational performance (see WP 10 below).
- An open, modular interface to control and configure the
strategy for the generation of alternatives (sets of decision variables).
WP09: Computational experiments: cluster and grid infrastructure
Provides the necessary performance to generate sufficiently large samples
of feasible solutions for the subsequent reference point methodology
can also, or in addition, be based on brute computational power.
The most promising solution are based on parallel and cluster
computing, since the problem is perfectly task parallel
at a very high level of granularity (individual optimization scenarios)
which leads to a prefect scalability of parallel implementations
with near linear scaling performance. One of the approaches to be
tested in this work package is distributed or GRID computing
utilizing the distributed computational resources of the WEBAIR consortium.
WP10: Knowledge representation, heuristics, GA, machine learning
Adaptive heuristics are designed to increase the efficiency of
computational experiments for the optimization.
The three main strategies are:
- Analysing the cross variance, correlation or contingency of instruments,
we can identify groups that together have a higher probability
to lead to successful model runs.
These groupings (as synthetic allele) can be systematically exploited
with standard genetic algorithms, while retaining their interdependency.
- Machine learning algorithms like ID3/ID5R (Quinlan 1979),
or simulated neural networks can be used to identify efficient search strategies.
They provide guidance for local heuristic search around any
random test case by modifying the a priori probabilities
of instruments given the pattern of constraints violations.
- Domain specific heuristics are a "simpler" version of the same principle,
but defined a priori by domain experts rather than extracted a posteriori
from the observed system behaviour: using first order production rules,
we can formulate strategies that will guide the design of a new
computational experiment based on the observed pattern of performance
and/or violation of constraints.
WP11: Optimization, phase II: Discrete multi-criteria DSS:
not active in year 1.
WP12: Uncertainty analysis, robustness, GCM scenarios:
not active in year 1.
WP13: Test cases: implementation, performance, validation
They provide the real world data and feedback on all the
methods and tools planned. At the same time, they provide
the possibility to establish, and in fact measure, the effectiveness
of the optimization process by comparing baseline scenarios with
efficient solutions. The test cases will take advantage of the
ongoing case studies in the WEBAIR E! 3266 project. The primary test cases will be:
Other WEBAIR case studies will be added as the respective national projects
receive initial or continuation funding and can start,
Gdansk, three city area, Poland; the Republic of Lithuania, national scale,
possibly with one urban domain for Vilnius and surroundings;
Merida, Venezuela; and Manisa, Turkey.
- The Republic of Cyprus, national scale system with several nested grid city
domains including Limassol and its harbor area;
- Seoul and Gyeonggi-do, South Korea, with one nested megacity domain for the Seoul metropolitan area.
- The Greater Tehran Area, Iran.
Activities in WP13 also include validation of the system
overall performance as compared to the requirements formulated in WP2;
the first step will include the definition of appropriate test metrics
and the implementation of corresponding monitoring and analysis tools
related to the on-line implementation but also model performance
as such (e.g., against the requirements in Council Directive 1999/30/EC, Appendix VIII).
WP14: Dissemination activities
Dissemination strategy of OPTAIR is based on
The work package will start with a detailed dissemination plan
that will be continuously updated during the course of the project.
Building on the contacts from WP 3 (stakeholder data base),
a number of information products (Newsletter, project flier)
will be developed and distributed to the stakeholders
in each of the partner countries and beyond, in parallel with traditional
scientific dissemination strategies involving technical/scientific
publications and conference presentations.
- Extensive use of the Internet as the very implementation medium of the system;
- The contacts, networks, and distribution channels of the WEBAIR project partners in 17 countries.