Application of a Combined Measurement and Modeling Method to Quantify Windblown Dust Emissions from the Exposed Playa at Mono Lake, California

Figures & data

Figure 1. A solar-powered TEOM monitors hourly PM₁₀ concentrations at Mono Lake, CA.

Figure 2. Process flow diagram for the Dust ID method.

Figure 3. PM₁₀, sand flux, and meteorological monitoring network.

Figure 4. Diagram of a Cox sand catcher.

Figure 5. Sand flux site and dust monitoring equipment at Mono Lake, CA.

Figure 6. Daily average sand flux for the study period.

Figure 7. Daily average sand flux and downwind PM₁₀ concentration.

Figure 8. Hourly and seasonal K-factors for the study period.

Figure 9. Hourly average PM₁₀ emissions versus wind speed using the Dust ID method and the portable wind tunnel emissions algorithm.

Table 1. Hourly model performance statistics July 2009 to June 2010

Statistic	Observed (O)	Predicted (P)
Number (P + O > 150)	266	266
Number > 150	225	205
Mean (μg/m^3)	3,013	3,086
SD (μg/m^3)	7,396	7,289
Median (μg/m^3)	623	639
Maximum (μg/m^3)	65,113	57,438
98th percentile (μg/m^3)	29,852	30,035
Geometric mean (μg/m^3)	761	608
Geometric SD	1.57	1.91
Paired Statistics
Factor-of-2	60.2%
Arithmetic r	0.88
Geometric r	0.84
Slope	0.89
R ^2	0.77
Notes: Hourly model performance statistics were screened to look at hours when winds were from the direction of the study area toward the monitor site. Hourly data pairs are displayed in Figure 10.

Figure 10. Hourly modeled versus monitored PM₁₀ concentrations. Corresponding model performance statistics are shown in Table 1.

Figure 11. Comparison of hourly modeled and monitored PM₁₀ concentrations for the November 20, 2009, dust event.