Increasing the Use of Earth Science Data and Models in Air Quality Management

Figures & data

Figure 1. Percentage of respondents who reported involvement with the specified activities in their current position.

Table 1. Mean and standard deviation of responses to the question “Which air quality management issues are active challenges for your agency?” with 1 = not an issue, 2 = somewhat challenging, 3 = more challenging, and 4 = major challenge, and percentage of respondents who included the item in response to the question “For which three air quality management issues does your agency have the greatest need for improved scientific or technical information?”

Issue	Degree of challenge	Top three needs for improved information (rank)
1. Ozone NAAQS attainment or maintenance	3.39 ± 0.87	49.6% (1)
2. Emissions from transportation/mobile sources	3.14 ± 0.85	31.3% (3)
3. Interstate transport of air pollution	3.07 ± 0.93	38.3% (2)
4. Particulate matter NAAQS attainment or maintenance	2.96 ± 1.00	26.1% (4)
5. Emissions of greenhouse gases	2.86 ± 0.99	14.8% (9)
6. Attribution of uncontrollable sources to exceptional events	2.80 ± 0.98	20.9% (5)
7. Fires and smoke management	2.72 ± 1.01	20.0% (6)
8. Regional haze or visibility	2.68 ± 0.86	8.7% (11)
9. Emissions of hazardous air pollutants	2.63 ± 0.94	13.0% (10)
10. Emissions from power plants	2.52 ± 1.05	8.7% (11)
11. International transport of air pollution	2.51 ± 1.07	18.3% (7)
12. Emissions from oil and gas activity	2.22 ± 1.15	18.3% (7)
13. Nitrogen deposition and ecosystem impacts	2.21 ± 0.96	7.8% (13)

Table 2. Mean and standard deviation of responses to the question “In order to carry out your professional responsibilities, which types of scientific or technical information do you use?” with 1 = never, 2 = infrequently, 3 = occasionally, 4 = more often, and 5 = frequently, and percentage of respondents who included each item in response to the question “Of the types of scientific or technical resources you use, which three have the greatest need for improvement?”

Tools	Frequency of use	Top three needs for improvement (rank)
1. Surface monitoring data	4.45 ± 0.94	9.6% (10)
2. Emissions inventories	4.45 ± 0.86	48.7% (2)
3. Air quality models	4.41 ± 0.90	56.5% (1)
4. Air quality forecasts	3.92 ± 1.26	16.5% (5)
5. Meteorological forecasts	3.89 ± 1.27	13.0% (8)
6. Assessments of emissions trends	3.82 ± 1.04	13.9% (6)
7. Source emissions reports	3.60 ± 1.24	0.9% (14)
8. Satellite data	3.27 ± 1.14	19.1% (4)
9. Estimates of interstate or international transport	3.23 ± 1.20	32.2% (3)
10. Information on costs and effectiveness of control measures	3.17 ± 1.20	13.9% (6)
11. Transportation plans and projections	3.08 ± 1.11	9.6% (10)
12. Health effects information	3.06 ± 1.05	10.4% (9)
13. Source inspection reports	3.01 ± 1.44	0.9% (14)
14. Aircraft data and other measurements from special field campaigns	2.29 ± 1.06	9.6% (10)
15. Personal exposure monitoring data	2.08 ± 0.99	6.1% (13)

Table 3. Percentage of responses to the question “For what purpose, if any, are the following technical resources used within your agency?”

Resources	Public information	Control strategy development	Other	Not used
Satellite measurements of aerosol optical depth (AOD)	27.7%	15.4%	21.5%	35.4%
Satellite measurements of trace gases (NO2, CO, HCHO, O3)	19.6%	19.6%	17.9%	42.9%
Satellite measurements of land use, vegetation, or land cover	25.0%	25.0%	20.5%	29.5%
Satellite measurements of cloud cover or radiation	32.1%	7.1%	21.4%	39.3%
Satellite observations of smoke plumes	46.3%	16.8%	28.4%	8.4%
Model estimates of North American boundary conditions or North American background	25.5%	36.4%	5.5%	32.7%
NOAA’s National Air Quality Forecasting Capability	61.1%	15.6%	14.4%	8.9%

Figure 2. Percentage of respondents who indicated that each issue remains a barrier to use of satellite data by their organization, for respondents who indicated that their agency had some involvement with AQAST and those whose agency did not.

Figure 3. Mean and standard deviation of responses to the request “Please rate your agency’s experience working with AQAST researchers on the following dimensions” with 1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, and 5 = strongly agree (N = 53).