Introduction

This site describes our preliminary design of a Comprehensive Air Modeling/Optimization System (CAMOS). Our goal is to provide a simulation system to guide and assist decision makers in developing cost-effective emission reduction strategies to improve air quality in a region.

Air Pollution Historical Challenge

Air pollution problems have been historically addressed by establishing air quality standards, i.e., ambient concentration levels that should not be exceeded. In regions where these standards were exceeded, measures were taken to reduce local emissions, often 1) by establishing emission standards, i.e., maximum emission rates at each point source or industrial facility; and 2) imposing emission standards on automobiles and trucks.

This approach has produced some positive results, especially for reducing industrial air pollution emissions in the Western countries. For example, ambient concentrations of SO2, CO and lead in the US had declined dramatically. However, this approach has required enormous public costs of study, design, implementation, and enforcement of regulations, plus the costs carried by businesses and industries to comply. Therefore, several questions should be addressed:

  1. Were benefits greater than costs?
  2. Were air quality improvement plans designed to maximize benefits or minimize costs?
  3. Could we have applied better cost-benefit planning and achieved better results?
  4. Can we use cost-benefit optimization in the future?

The history of air pollution abatement and control is clear: advanced computer simulation/optimization techniques have never really been used as a principal guide to plan actions of governments and agencies with:

  • maximization of benefits (with fixed costs) or
  • minimization of costs (with fixed benefits)

Instead, the actions of governments have focused on:

  1. air quality standards (that should not be exceeded, but often are) verified by air quality
    2. measurements, even though air monitoring is costly and we cannot measure all pollutants in all locations;
  2. emission standards, which are not always easy to control;
  3. enforcement, often partial and selective.

Government actions have also been often uneven. For example, for decades, more emphasis was given to "criteria" pollutants instead of air toxics. Also, in general, limited efforts were spent for indoor air pollution problems, even though people spend most of the time indoor

Some Data on Cost-Benefits

Example of Costs:

Example of Benefits:

According to a 1997 EPA Report to Congress (http://www.epa.gov/oar/caa/40th_highlights.html), the first 20 years of Clean Air Act programs, from 1970 - 1990, led to the prevention in the year 1990 of:
  • 205,000 premature deaths
  • 672,000 cases of chronic bronchitis
  • 21,000 cases of heart disease
  • 843,000 asthma attacks
  • 189,000 cardiovascular hospitalizations
  • 10.4 million lost I.Q. points in children - from lead reductions
  • 18 million child respiratory illnesses

Our Cost-Benefit Optimization Approach

We believe that computer simulation/optimization techniques offer a tool for optimal planning that should play a key role in the future. This is particularly true for emerging countries, e.g., China, with

  • rapid industrialization
  • distressing deterioration of air quality, especially in major cities

Only this approach assures cost-effectiveness where, for every investment allocated to improve air quality, the efforts are channeled in the right directions, i.e., those that produce maximum benefit.

Difficulties

Cost-benefits optimization techniques have been used in many scientific fields, including environmental sciences. However, their practical use in air pollution has been very limited because of:

  • the difficulty in correctly simulating air pollution phenomena at large scales
  • the non-linearity of the relationship between emissions of primary pollutants and concentrations of secondary pollutants, such as ozone and a fraction of PM2.5
  • the fact that many decision makers are uncomfortable with delegating important, strategic planning decisions to computer programs

Our Conceptual Design

We envision the development of a series of interacting software modules that the user can access through a user-friendly GUI on a PC Microsoft Windows-based computer platform:

  • The software system will be installed on our own servers and made available to authorized users as a web-Application
  • We call it Comprehensive Air Modeling/Optimization System (CAMOS)
  • Authorized users will be able to access the system with user name/password at camos.co (under development)

Documents and Presentations