Page 266 - Fundamentals of Air Pollution

References 227

or the geometric standard deviation, indicates the range of data around
the value selected to represent the data set.
Raw data must be analyzed and transformed into a format useful for
specific purposes. Summary tables, graphs, and geographic distributions
are some of the formats used for data display. Air quality information often
consists of a large body of data collected at a variety of locations and over
different seasons. Table 15-3 shows the tabular format used by the California
Air Resources Board to reduce ozone hourly measurements to a format
which shows information about compliance with air quality standards (6).
The format has location, maximum values, annual means, and number of
occurrences of hourly values above a given concentration as a function of
the month of the year. One can quickly determine which areas are violating
a standard, at what time of the year elevated concentrations are occurring,
and the number of good data points collected.
Pollutant concentration maps may be constructed as shown in Fig. 15-5
(14). In this example, elevated levels of ambient particulate matter are
associated with population centers. For a given geographic area, isopleths,
lines showing equal concentrations of a pollutant, are drawn on a map.
Regions of high concentration are quickly identified. Further action may
be taken to determine the cause, such as review of emission inventories
of additional sampling.

REFERENCES

1. Code of Federal Regulations, Title 40, Part 58, Ambient Air Quality Surveillance, Appendix
D—Network Design for State and Local Air Monitoring Stations (SLAMS). U.S. Govern-
ment Printing Office, Washington, DC, July 1992, pp. 158-172.
2. Harrison, R. M., and Young, R. J. (eds.), "Handbook of Air Pollution Analysis," 2nd ed.
Chapman & Hall, London, 1986.
3. U.S. Environmental Protection Agency, "Optimum Site Exposure Criteria for SO 2Monitor-
ing," EPA 450/3-77-13. Office for Quality Planning and Standards, Research Triangle
Park, NC, 1977.
4. Smith, D. G., and Egan, B. A., Design of monitoring networks to meet multiple criteria,
in "Quality Assurance in Air Pollution Measurement" (E. D. Frederick, ed.). Air Pollution
Control Association, Pittsburgh, 1979, pp. 139-150.
5. Houghland, E. S., Air quality monitoring network design by analytical techniques III, in
"Quality Assurance in Air Pollution Measurement" (E. D. Frederick, ed.). Air Pollution
Control Association, Pittsburgh, 1979, pp. 181-187.
6. California Air Resources Board, "Summary of 1991 Air Quality Data, Gaseous and Particu-
late Pollutants." California Air Resources Board, Sacramento, 1991.
7. Wilson, W. E., Jr., Atmos. Environ. 12, 537-547 (1978).
8. Alfodi, T. T., Satellite remote sensing for smoke plume definition, in Proceedings of the
4th Joint Conference on Sensing of Environmental Pollutants. American Chemical Society,
Washington, DC, 1978, pp. 258-261.
9. Browell, E. V., Lidar remote sensing of tropospheric pollutants and trace gases, in Proceed-