Air Quality Scenario Players
The scenario player is built as an educational tool (see all tools here). The concept is the “pollution-control” game — assigning a value to “accountability” — how much reduction do we require from each of the contributing sectors, to reach a common goal for the airshed. This tool helps visualize not only the change required in each of the sectors, but also the relative importance of the sectors in the overall air pollution picture (before and after).
In this illustration, sector names can be edited as per users preference. User can enter values only for 2 or 4 sectors or all ten sectors. We limited the number of sectors to 10 — for more flexibility, use the excel file.
The two inputs into the tool are: the average concentration for the airshed (ug/m3), and the results from a source apportionment study (in % – note that the sum of entered %’s must be 100%). In this example, it is assumed that the management problem here is for PM2.5 pollution. How, the concept is adaptable for any of the pollutants (SO2, NO2, Ozone, CO, and NH3), as long as the two inputs are available.
What is source apportionment
Building an effective air pollution control plan requires information of the contribution of various sources. This is not an easy or a quick process, as it involves many steps from field experiments, to laboratory analysis, to (statistical and predictive) modeling, and some related to linking the results to pollution control plans. We structured a primer on the steps of source apportionment, explaining the process using 2 approaches to support an informed air quality management plan. You can download and/or browse the individual pages here.
Some background notes on pollution source apportionment is explained in the following tables.
- What is particulate matter (frequently asked questions)
- Pros and cons of top-down (receptor-based) and bottom-up (emissions-based) methods
- Information and decisions required to conduct top-down (receptor-based) source apportionment
- Laboratory techniques for chemical analysis of filter samples
- Elemental markers for various sources detected during chemical analysis
- Comparison of model requirements, strengths, and limitations of receptor models