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Outdoor Air Quality - Fine Particulate Matter:
Daily Fine Particulate Matter (PM2.5) (µg/m³)
Years 2003 - 2011

Summary:

  

The Outdoor Air Quality - Fine Particulate Matter data available on CDC WONDER are geographically aggregated daily measures of fine particulate matter in the outdoor air, spanning the years 2003-2011. PM2.5 particles are air pollutants with an aerodynamic diameter less than 2.5 micrometers. Reported measures are the daily measure of fine particulate matter in micrograms per cubic meter (PM2.5) (µg/m³), the number of observations, minimum and maximum range value, and standard deviation. Data are available by place (combined 48 contiguous states plus the District of Columbia, region, division, state, county), time (year, month, day) and specified fine particulate matter (µg/m³) value. County-level and higher data are aggregated from 10 kilometer square spatial resolution grids. Please read Cautions and Limitations section below.

Source:

  

In a study funded by the NASA Applied Sciences Program / Public Health Program (fully cited below), scientists at NASA Marshall Space Flight Center / Universities Space Research Association modified the regional surfacing algorithm of Al-Hamdan et al. (2009) and used it to generate continuous spatial surfaces (grids) of daily PM2.5 for the whole conterminous U.S. for 2003-2011. Two sources of environmental data were used as input to the surfacing algorithm, US Environmental Protection Agency (EPA) Air Quality System (AQS) PM2.5 in-situ data and National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth remotely sensed data. They also identified in a Geographic Information System (GIS) the associated geographic locations of the centroids of the gridded PM2.5 dataset in terms of the counties and states they fall into to enable aggregation to different geographic levels in CDC WONDER. See also Data Source Information.

In WONDER:    You can produce tables, maps, charts, and data extracts. Obtain average fine particulate matter (µg/m³), the number of observations, range, and standard deviation. Select specific criteria to produce cross-tabulated average fine particulate matter (µg/m³)measures. Data are organized into three levels of geographic detail: the 48 contiguous states, state (including multi-state regions and divisions) and county. County-level data are aggregated from 10 kilometer square spatial resolution grids. You can limit and index your data by any and all of the variables.
Contents:    PM2.5 Data Request
Data Source Information
Additional Information
Cautions and Limitations

PM2.5 Data Request

Output:    You can produce tables, maps, charts, and data extracts. Obtain average fine particulate matter (µg/m³), the number of observations, range, and standard deviation. Select specific criteria to produce cross-tabulated fine particulate matter measures. Data are organized into three levels of geographic detail: the 48 contiguous states, state (including multi-state regions and divisions) and county. You can limit and index your data by any and all of the variables.
Variables:    You can limit and index your data by any and all of these variables:
  1. Location: Region, Division, State, County
  2. Year: 2003-2011
  3. Month: January - December, available also as specific "Month, Year" values
  4. Day: Available as specific "Month Day, Year" values, or as Day of Month (1-31) or as Day of Year (1-366)
  5. Fine Particulate Matter (µg/m³): 0 to >=65
How?    The Request screen has sections to guide you through the making a data request as step-by-step process. However, to get your first taste of how the system works, you might want to simply press any Send button, and execute the default data request. The data results for your query appear on the Table screen. After you get your data results, try the Chart and Map screens. Or export your data to a file (tab-delimited line listing) for download to your computer.
For more information, see the Quick Start Guide and the following steps:
  1. Organize table layout
  2. Select location
  3. Select year, month, day
  4. Select values for fine particulate matter
  5. Other options
'By-Variables'    Select variables that serve as keys (indexes) for organizing your data. See "How do I organize my data?" for more information.
Note:   To map your data, you must select at least one geographical location as a "By-Variable" for grouping your data, such as State or County.
Help:    Click on any button labeled "Help", located to the right hand side of the screen at the top of each section. Each control's label, such as the "Location" label next to the Location entry box, is linked to the on-line help for that item.
Send:    Sends your data request to be processed on the CDC WONDER databases. The Send buttons are located on the bottom of the Request page, and also in the upper right corner of each section, for easy access.


Step 1. Organize table layout:
Group Results By:    Select up to five variables that serve as keys for grouping your data. See Group Results By below for hints.
Optional Measures:    The Average fine particulate matter (µg/m³) measures for the specified time and place are reported by default. If checked, these optional measures will also appear in the results table.
  1. Number of observations, how many measurements went into the average daily fine particulate matter statistic
  2. Range or minimum - maximum values for the average fine particulate matter statistic
  3. Standard deviation for this summary statistic
Title:    Enter any desired description to display as a title with your results.

Group Results By...

Select up to five variables that serve as keys for grouping your data. For example, you could select to group (summarize, stratify, index) your data by State and by County.

How?    See "How do I organize my data?" for more information.

Hints:   

  1. About charts:
    You cannot make charts when your data has more than two By-Variables.
  2. About maps:
    To make a map, you must request data with a geographic location variable, such as Region, as the first "By-Variable" in the "Group Results By" box. Send your data request, then click the Map tab when you get the results.

Average Fine Particulate Matter (µg/m³)

The average daily fine particulate matter summary measure is the mean value of all of the daily outdoor air fine particulate matter (PM2.5) (µg/m³) values that met the selected query criteria. For example, if results are grouped by Year and limited to the South Region for years 2003-2011, then the first row shows the value 12.35 (µg/m³) as the average fine particulate matter value for year 2003 in the South Region. This summary measure is the mean of the daily values recorded for fine particulate matter (PM2.5) (µg/m³) estimates of the 10 kilometer square spatial resolution geographic-area grids in the selected time and place.

How? This summary measures is reported by default for all queries.

Notes:

  • The average measurement is the mean value of all of the daily values that met the selected criteria for time and place.
  • This dataset contains daily fine particulate matter (PM2.5) (µg/m³) estimates of outdoor air quality. PM2.5 particles are air pollutants with an aerodynamic diameter less than 2.5 micrometers. The values are summarized to the county-level, from 10 kilometer square spatial area grids covering the 48 contiguous United states (not including Alaska and Hawaii) plus the District of Columbia.
  • Fine particulate matter estimates are recorded for 10 kilometer square spatial resolution grids in the selected area. PM2.5 estimates from each grid are assigned to the county where the grid centroid is located. WONDER shows the summary the grid-level estimates into counties and larger areas. The county locations are from the 1999-2000 Federal Information Processing (FIPS) code set.


Number of Observations

The number of observations is the number of daily fine particulate matter values that produced the summary measure. For example, for January 1, 2008, there were 80,650 fine particulate matter values averaged to produce the national average fine particulate matter value of 8.15 (µg/m³). The observations are the daily fine particulate matter values recorded for the 10 kilometer square spatial resolution grids in the selected time and place.

How? Select the checkbox labeled "# of Observations" under the "Optional Measures" heading in section 1. Organize table layout on the Request Form.

Notes:

  • The number of observations is the total number of daily fine particulate matter values recorded for the 10 kilometer square spatial resolution grids in the selected time and place.
  • This dataset contains daily average fine particulate matter estimates. The values are summarized to the county-level, from 10 kilometer square spatial area grids covering the 48 contiguous United states (not including Alaska and Hawaii) plus the District of Columbia.
  • MODIS fine particulate matter measurements are recorded for 10 kilometer square spatial resolution grids in the selected area. Measurements from each grid are assigned to the county where the grid centroid is located. WONDER shows the summary the grid-level estimates into counties and larger areas. The county locations are from the 1999-2000 Federal Information Processing (FIPS) code set.


Range

The range shows the minimum and maximum measurements for the criteria that defines the cell in the table, from all of the grid-level estimates that contribute to the spatial average daily measurement. For example, when data are grouped by year and limited to the District of Columbia, for the 732 grid values of daily fine particulate matter in year 2008, the lowest value was 3.00 and the highest value was 34.50 (µg/m³).

How? Select the checkbox labeled "Range" under the "Optional Measures" heading in section 1. Organize table layout on the Request Form.

Notes:

  • The range shows the lowest and the highest values in the set that defines the average fine particulate matter measurement in the table cell, in the format: (minimum - maximum). Daily values are recorded for 10 kilometer square geographic-area grids in the selected time and place.
  • This dataset contains daily average fine particulate matter estimates. The values are summarized to the county-level, from 10 kilometer square spatial area grids covering the 48 contiguous United states (not including Alaska and Hawaii) plus the District of Columbia.
  • Fine particulate matter estimates are recorded for 10 kilometer square spatial resolution grids in the selected area. Measurements from each grid are assigned to the county where the grid centroid is located. WONDER shows the summary the grid-level measurements into counties and larger areas. The county locations are from the 1999-2000 Federal Information Processing (FIPS) code set.


Standard Deviation

The standard deviation shows the variance from the average or mean value that occurs in the grid-level values that meet the place and time criteria that define the table cell. A low standard deviation value is close to the mean, and a high standard deviation value indicates the data are spread of large range of values. For example, for the District of Columbia in 2008, for the 732 values of daily fine particulate matter, which ranged from 3.00 - 34.50 (µg/m³), the standard deviation from the average value of 12.40 (µg/m³) was 6.21 (µg/m³).

How? Select the checkbox labeled "Standard Deviation" under the "Optional Measures" heading in section 1. Organize table layout on the Request Form.

Notes:

  • The standard deviation shows the variance from the average or mean value that occurs in the grid-level values in the set that defines the average fine particulate matter measurement in the table cell. Daily values are recorded for 10 kilometer square geographic-area grids in the selected time and place.
  • This dataset contains daily average fine particulate matter estimates. The values are summarized to the county-level, from 10 kilometer square spatial area grids covering the 48 contiguous United states (not including Alaska and Hawaii) plus the District of Columbia.
  • Fine particulate matter measurements are recorded for 10 kilometer square spatial resolution grids in the selected area. Measurements from each grid are assigned to the county where the grid centroid is located. WONDER shows the summary the grid-level measurements into counties and larger areas. The county locations are from the 1999-2000 Federal Information Processing (FIPS) code set.


Step 2. Select location:

Select the place of interest:
  1. Location:   the 48 contiguous United States plus District of Columbia by Region, Division, State, and County

Location

Data are available for the United States by Region, Division, State, County. Select the location(s) for the query. Any number of locations can be specified here.

How? Hints:
  • The default is all values (the United States).
  • The Advanced mode let you easily pick several items from different parts of the list. Items are not selected until you click the "Move" button in Advanced mode. You may also enter values by hand, one code per line, in the Advanced mode. Use the Finder to see the correct code format. For example, 05 is the Arkansas state code.
  • The "plus" symbol, "+" indicates that you can open the item, to see more items below it.
  • The results to a search are shown in blue, and indicated by ">".

Region

Regions are multi-state groups. For regional data, you can group by Region, or you can select any combination of individual regions.
How?   Notes:  
  • The Regions are identified by both name and codes in data extracts.
  • The United States is split into 4 regions: Northeast, Midwest, South and West. The states that comprise each region are shown below.

State abbreviation and name   FIPS code
________________________________________ 
Northeast Region:
CT    Connecticut               09
ME    Maine                     23
MA    Massachusetts             25
NH    New Hampshire             33
NJ    New Jersey                34
NY    New York                  36
PA    Pennsylvania              42
RI    Rhode Island              44
VT    Vermont                   50
________________________________________
Midwest Region:
IL    Illinois                  17
IN    Indiana                   18
IA    Iowa                      19
KS    Kansas                    20
MI    Michigan                  26
MN    Minnesota                 27
MO    Missouri                  29
NE    Nebraska                  31
ND    North Dakota              38
OH    Ohio                      39
SD    South Dakota              46
WI    Wisconsin                 55
________________________________________ 
South Region:
AL    Alabama                   01
AR    Arkansas                  05
DE    Delaware                  10
DC    District of Columbia      11
FL    Florida                   12
GA    Georgia                   13
KY    Kentucky                  21
LA    Louisiana                 22
MD    Maryland                  24
MS    Mississippi               28
NC    North Carolina            37
OK    Oklahoma                  40
SC    South Carolina            45
TN    Tennessee                 47
TX    Texas                     48
VA    Virginia                  51
WV    West Virginia             54
________________________________________ 
West Region:
AK    Alaska *                 02
AZ    Arizona                  04
CA    California               06
CO    Colorado                 08
HI    Hawaii *                 15
ID    Idaho                    16
MT    Montana                  30
NV    Nevada                   32
NM    New Mexico               35
OR    Oregon                   41
UT    Utah                     49
WA    Washington               53
WY    Wyoming                  56
________________________________________ 

 * Alaska and Hawaii are not included in these data.


Division

Divisions are multi-state groups, sub-sets of Regions. For division-level data, you can group by Division, or select any combination of individual divisions.
How?   Notes:  


State

For state level data, you can select any combination of individual states. Or group by State and leave the Location Finder selection at the default (all locations or the 48 United States and the District of Columbia).
How?   Notes:  
  • The states and the District of Columbia are identified by both state name and Standard Federal Information Processing (FIPS) codes in data extracts. See About FIPS Codes below.


County

County-level data are available for the United States and the District of Columbia. For county level data, you can select any combination of individual counties, or group by County. Leave the Location Finder selection at the default (all locations or the 48 United States and the District of Columbia).
How?   Notes:  
  • The county coded represents the spatial average of data observations from 1 kilometer square spatial resolution grids. Grids are coded to the county that includes the grid centroid.
  • The counties and the District of Columbia are identified by both county name and Standard Federal Information Processing (FIPS) codes in data extracts. The county locations are from the 1999-2000 Federal Information Processing (FIPS) code set.
  • About FIPS Codes:   The FIPS State and county codes were established by the National Bureau of Standards, U.S. Department of Commerce in 1968. This standard set of codes provides names and codes for counties and county equivalents of the 50 States of the United States and the District of Columbia. Counties are considered to be the "first order subdivisions" of each State, regardless of their local designation (county, parish, borough, census area). Washington, D.C.; the consolidated government of Columbus City, Georgia; the independent cities of the States of Maryland, Missouri, Nevada, and Virginia; and the census areas and boroughs of Alaska are identified as county equivalents. The system is standard throughout the Federal Government. The State codes are ascending, two-digit numbers; the county codes are ascending three-digit numbers. For both the State and county codes, space has been left for new States or counties. Some changes in the FIPS codes and county boundaries have occurred since 1968. See Location Updates for information on how these changes affect the data.
  • About County Changes:   Comparable measures may be misleading for counties with changing boundaries. See Location Updates for information on how these changes affect the data. Due to boundary changes, data are available for some counties for a limited period of time.
    The following county-level constraints apply to the data:
    • Alaska: data for Alaska are not included in this NLDAS average daily air temperature collection.
    • Colorado:
    • Florida:
      • Dade county, Florida (FIPS code 12025) - data are shown for the Dade county code value 12025, also the location of Miami, Florida.
    • Hawaii: data for Hawaii are not included in this NLDAS average daily air temperature collection.


Step 3. Select year, month and day:
Select the time period for the query. Any number of dates can be specified here. Choose between these three controls:
  1. The Date Range control is selected at first by default, with the full time period of available data shown in the range controls. Use this control to select a continuous span of time, such as May 25, 2010 - September 25, 2010.
  2. Individual date fields allow you to separately limit or filter data for Year (2003-2011), Month (January - December), and Day of Month (1-31) or Day of Year (1-366). Use this control to select a specific point in time, such as the average measure for the months of June, July and August.
  3. The Aggregate Date Finder lets you select specific, discrete year/month/day dates, such as January 1, 2003.
How?
  • Click a round button to switch between Date Range, Individual Date Fields or Aggregate Dates.
  • Hints for the Date Range fields:
    • Click the down-arrow to the right of each field to open the drop-down list, then click on your selection in the list.
    • Click the blue counter-clockwise swoop image to the right of the date range fields to reset the entries to the default values (all).
  • Hints for the Individual Date Fields:
    • To select more than one value in the list, press down the Ctrl key on your keyboard while you click your left Mouse button.
    • To select a range of contiguous values, press down the Shift key on your keyboard while you click and drag your left Mouse button.
  • Hints for the Aggregate Date Finder:
    • See "How do I use a Finder?" for more information on using the Aggregate Date Finder.
    • See Finder Tool help for more hints.
    • The default for the Aggregate Date Finder is all values (2003/01/01 - 2011/12/31).
    • The Advanced mode let you easily pick several items from different parts of the list. Items are not selected until you click the "Move" button in Advanced mode. You may also enter values by hand, one code per line, in the Advanced mode. The code format is YYYY/MM/DD. For example, 2003/09/01 is September 1, 2003.
    • The "plus" symbol, "+" indicates that you can open the item, to see more items below it.
    • The results to a search are shown in blue, and indicated by ">".

Dates

Select a range of continuous time. The default is the full time-period found in the data, 2003 - 2011.
How?  
  • Click a round button to indicate Date Range Fields.
  • Click the down-arrow to the right of each field to open the drop-down list, then click on your selection in the list.
  • Click the blue counter-clockwise swoop image to the right of the range fields to reset the entries to the default values (all).

Year

Select All Years or any number of individual years (2003 - 2011). For example, select year 2008 to get the average daily measures for 2008, as well as the number of observations and the minimum and maximum measures in 2008.
How?  
  • Click a round button to indicate Individual Date Fields.
  • Click your left Mouse button on any desired single option in the list box.
  • Press the Ctrl key while clicking your left Mouse button to make multiple selections.
  • Press the Shift key while clicking your left Mouse button and dragging the selection for a range of values.

Month

Select All Months or any number of individual months (January - December). For example, select June, July and August to get the average, minimum and maximum measures for the summer months, and the number of observations.
How?  
  • Click a round button to indicate Individual Date Fields.
  • Click a round button to indicate Day of Month Fields.
  • Click your left Mouse button on any desired single option in the list box.
  • Press the Ctrl key while clicking your left Mouse button to make multiple selections.
  • Press the Shift key while clicking your left Mouse button and dragging the selection for a range of values.

Day of Month

Select All Days or any number of individual days (1 - 31).
How?  
  • Click a round button to indicate Individual Date Fields.
  • Click a round button to indicate Day of Month Fields.
  • Click your left Mouse button on any desired single option in the list box.
  • Press the Ctrl key while clicking your left Mouse button to make multiple selections.
  • Press the Shift key while clicking your left Mouse button and dragging the selection for a range of values.
Notes:  
Data are reported as "Missing" when the selections in the Individual Date Fields are not found in the data. For example, if data are limited to Day of Month values (29, 30, 31) for All Years in the month of February, then data are only reported for February 29 in year 2004 and year 2008. If the query also shows the data grouped by "Day of Month" and shows zero-value rows, then you see "Missing" in the cells for days 30 and 31.


Step 4. Select values:
Limit your data to the selected average daily fine particulate matter (PM2.5) values (µg/m³). PM2.5 particles are air pollutants with an aerodynamic diameter less than 2.5 micrometers. The average daily values in the 2003-2011 data set range from 0 to >=65 (µg/m³).


Fine Particulate Matter (µg/m³)

Limit your data to the selected fine particulate matter values. Select All Values or any combination of the individual values in the list box.

How?  
  • Click a round button to switch between Ranges or Lists, in Section 4.
  • Hints for the fine particulate matter Range fields:
    • Leave the box blank to use the default value for the threshold, which is displayed below each box inside parenthesis.
    • Type in your desired range of sunlight values, use whole numbers. For example, to limit data to all values above 60 (µg/m³), you would type 60 in the lower range box and leave the upper range box blank.
    • Click the blue counter-clockwise swoop image to the right of the date range fields to reset the entries to the default values (all).
  • Hints for the List:
    • Click your left Mouse button on any desired single option in the list box.
    • To select more than one value in the list, press down the Ctrl key on your keyboard while you click your left Mouse button. You can select specific, distinct values that are not contiguous in the list.
    • To select a range of contiguous values, press down the Shift key on your keyboard while you click and drag your left Mouse button.

Notes:  

  • Note that the values in the list box are the range of average daily values available in the data.
  • Values are a spatial average of the daily fine particulate matter (PM2.5) values recorded for each 10 kilometer square spatial resolution grid, aggregated at the county level.
  • PM2.5 particles are air pollutants with an aerodynamic diameter less than 2.5 micrometers. The average daily values in the 2003-2011 data set range from 0 to >=65 micrograms per cubic meter (µg/m³).
  • About the upper threshold:
    A threshold of 65 micrograms per cubic meter (µg/m³) was included as part of the Bspline model parameters. A threshold is needed since the Bspline is a fitting technique and not an interpolating technique. When fitting a sparse dataset, the fitted values can reach very high levels near the geographic boundaries. In this data set, values above a threshold of 65 (µg/m³) are truncated, or set to a maximum value of 65, because this value was the Environmental Protection Agency (EPA) National Ambient Air Quality Standard (NAAQS) for PM2.5 (98th percentile, averaged over 3 years) at the time this algorithm was developed.


Step 5. Other options:
Export Results:    If checked query results are exported to a local file. More information on how to import this file into other applications can be found here.
How? See "How do I use a checkbox?"
Show Totals:    If checked totals and sub-totals will appear in the results table.
How? See "How do I use a checkbox?"
Show Zero Values:    If checked, rows containing zero counts are included in the results table. If unchecked, zero count rows are not included.
How? See "How do I use a checkbox?"
Precision:    Select the precision for rate calculations. When the rate calculated for a small numerator (incidence count) is zero, you may increase the precision to reveal the rate by showing more numbers to the right of the decimal point.
How? See "How do I select items from the list box?"
Data Access Timeout:    This value specifies the maximum time to wait for the data access for a query to complete. If the data access takes too long to complete, a message will be displayed and you can increase the timeout or simplify your request. If you can't complete a request using the maximum timeout, contact user support and we will try to run a custom data request for you.
How? See "How do I select items from the list box?"

Data Source Information

Data Sources:   

In a study funded by the NASA Applied Sciences Program / Public Health Program (fully cited below), scientists at NASA Marshall Space Flight Center / Universities Space Research Association modified the regional surfacing algorithm of Al-Hamdan et al. (2009) and used it to generate continuous spatial surfaces (grids) of daily PM2.5 for the whole conterminous U.S. for 2003-2011. Two sources of environmental data were used as input to the surfacing algorithm, US EPA AQS PM2.5 in-situ data and NASA MODIS aerosol optical depth remotely sensed data. They also identified in a Geographic Information System (GIS) the associated geographic locations of the centroids of the gridded PM2.5 dataset in terms of the counties and states they fall into to enable aggregation to different geographic levels in CDC WONDER

To learn more about the methods and source of these data, please reference:


Additional Information

Suggested Data Source Citations:   

Daily Fine Particulate Matter (PM2.5) (µg/m³) for years 2003-2011 on CDC WONDER Online Database, released 2012.

The suggested citation including the original series for the data is shown below each table, chart or map.

Contact:    For data questions that are not addressed in this document, e-mail mohammad.alhamdan@nasa.gov or bill.crosson@nasa.gov
Acknowledgements:    This work was part of a collaborative study funded by the NASA Applied Sciences Program/Public Health Program (grant# NNX09AV81G), whose team members are:
  • Dr. Leslie McClure (PI), School of Public Health, University of Alabama at Birmingham
  • Dr. Mohammad Al-Hamdan (Co-PI), Universities Space Research Association, NASA Marshall Space Flight Center
  • Dr. William Crosson (Co-I), Universities Space Research Association, NASA Marshall Space Flight Center
  • Ms. Sigrid Economou (Collaborator), Centers for Disease Control and Prevention
  • Mr. Maurice Estes, Jr. (Co-I), Universities Space Research Association, NASA Marshall Space Flight Center
  • Ms. Sue Estes (Co-I), Universities Space Research Association, NASA Marshall Space Flight Center
  • Mr. Mark Puckett (Collaborator), Centers for Disease Control and Prevention
  • Dr. Dale Quattrochi, Jr. (Co-I), Earth Science Office, NASA Marshall Space Flight Center
Other Topics:    Reference the following topics:
Cautions and Limitations
Locations: About County Level Changes
Contact for Data Questions
Suggested Citation


Cautions and Limitations

  1. Users are cautioned from averaging daily PM2.5 data over long time periods (annual and longer) and presenting such means alone as that may convey an incomplete and sometimes misleading picture by ignoring variability on daily and seasonal time scales.
  2. "Because the MODIS aerosol retrieval algorithm is based on dark surface pixels in the near-infrared at 2.1 µm, the difference in the surface reflectance in different regions and seasons can cause the difference in the accuracy of aerosol optical depth (AOD) retrievals. Over a high-reflectance surface, the retrieved AODs are less accurate. The eastern United States, which is covered with green vegetation, has low reflectance during the warm seasons, whereas the western United States is mostly rocky and desert, which has high reflectance. During the winter, early spring, and late fall, the vegetation in the east turns reddish and the surface reflectance increases. This is one reason that the western United States has lower correlation between AOD and PM2.5 than the eastern United States and that the high correlations are usually observed in summer and fall. It also has been noted in previous papers (Engel-Cox et al., 2004, Engel-Cox et al., 2006, Hutchison et al., 2008) that AOD in the west arises more from smoke events, which may be transported more aloft than the high-AOD pollution events in the east. High AOD-PM2.5 correlations are usually found in areas with high variations of particulate matter. Because the intercepts in the regression relation are not zero, the PM2.5 with low values cannot be detected by AOD and small variations in PM2.5 should not introduce corresponding AOD variations. Pollution events are mostly found over the eastern United States and cover a large area during warm seasons caused by smoke or anthropogenic air pollution. It can be seen that the average PM2.5 has a maximum value over regions 1-7 during summer and a minimum value during winter. This may be another reason to explain the observed seasonal pattern over the east." (Zhang et al., 2009; pages 1360-1361).
  3. "One of the limitations of the PM2.5 surface-fitting model is the difficulty of incorporating meteorological factors such as wind velocity, which affects the advection of particulates from point sources within the region; the mixing height of the atmosphere, which significantly affects the environmental hazard distribution (Joffre et al. 2001, Gupta et al. 2006, Paciorek et al. 2008, Al-Hamdan, et al., 2009); and relative humidity (RH), which significantly affects the size of the hygroscopic particles and the light extinction efficiency. The MODIS column measurements used in this study could be combined in future work with profile information provided by satellites like the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) to provide a more robust remotely-sensed dataset. Also, new algorithms designed to provide better AOD retrievals in regions of sparse vegetation such as (Hsu et al. 2004) could be applied to improve PM2.5 estimates in the western United States." (Al-Hamdan et al., 2014; page 96).
References cited in the Cautions and Limitations section:

  1. Al-Hamdan, M.; Crosson, W.; Limaye, A.; Rickman, D.; Quattrochi, D.; Estes, M.; Qualters, J.; Sinclair, A.; Tolsma, D.; Adeniyi, K.; Niskar, A.; 2009. Methods for Characterizing Fine Particulate Matter Using Ground Observations and Satellite Remote-Sensing Data: Potential Use for Environmental Public Health Surveillance. Journal of the Air & Waste Management Association; 59, 865-881. (More information here and here).
  2. Al-Hamdan, M.; Crosson, W.; Economou, S.; Estes, M.; Estes, S.; Hemmings, S.; Kent, S.; Puckett, M.; Quattrochi, D.; Rickman, D., Wade, G.; McClure, L. 2014. Environmental Public Health Applications Using Remotely Sensed Data. Geocarto International; 29, 85-98. (More information).
  3. Engel-Cox, J.; Holloman, C.H.; Coutant, B.W.; Hoff, R.M. 2004. Qualitative and Quantitative Evaluation of MODIS Satellite Sensor Data for Regional and Urban Scale Air Quality; Atmospheric Environment; 38, 2495-2509. (More information).
  4. Engel-Cox, J.; Hoff, R.; Rogers, R.; Dimmick, F.; Rush, A.; Szykman, J.; al-Saadi, J.; Chu, D.; Zell, E. 2006. Integrating Lidar and Satellite Optical Depth with Ambient Monitoring for 3-Dimensional Particulate Characterization; Atmospheric Environment; 40, 8056-8067. (More information).
  5. Gupta P.; Christopher S.; Wang J.; Gehrig R.; Lee Y.; Kumar R.; 2006. Satellite Remote Sensing of Particulate Matter and Air Quality over Global Cities. Atmospheric Environment; 40, 5880-5892 (More information).
  6. Hsu, N.C.; Tsay, S.C.; King, M.D.; Herman, J.R.; 2004. Aerosol Properties over Bright-Reflecting Source Regions. IEEE Transactions on Geoscience and Remote Sensing; 42, 557-569. (More information and manuscript).
  7. Hutchison, K.D.; Faruqui, S.J.; Smith, S. 2008. Improving Correlations between MODIS Aerosol Optical Thickness and Ground-Based PM2.5 Observations through 3D Spatial Analyses; Atmospheric Environment; 42, 530-543. (More information
  8. Joffre KA, Kukkonen SM, Bremer P. 2001. Evaluation of Inversion Strengths and Mixing Heights during Extremely Stable Atmospheric Stratification. International Journal of Environment and Pollution; 16, 1-6. (More information and manuscript).
  9. Paciorek C.J.; Liu Y.; Moreno-Macias H.; Kondragunta S.; 2008. Spatio-Temporal Associations between GOES Aerosol Optical Depth Retrievals and Ground-Level PM2.5. Environmental Science & Technology; 42, 5800-5806. (More information).
  10. Zhang, H.; Hoff, R.M.; Engel Cox, J.A.; 2009. The relation between Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth and PM2.5 over the United States: a geographical comparison by EPA regions. Journal of the Air & Waste Management Association; 59, 1358 1369. (More information).


Location Updates: notes about specific county-level changes in boundaries and codes

Comparable measures may be misleading for counties with changing boundaries. The data collection may lag behind some Federal Information Processing (FIPS) location code changes. Some places, such as independent cities and New York City boroughs are included as unique locations in the data. Some county and census tract area (CA) locations are not included, instead the data are associated with a neighboring county or the previous location name and FIPS code. The list below of county-level changes is organized alphabetically by state name and then county name.

  1. Alaska boroughs and census areas:
    Data are not available for the state of Alaska, nor Alaskan boroughs and census areas.
  2. Colorado: Broomfield county
    Broomfield county, Colorado (FIPS code 08014) was created effective November 15, 2001 from parts of four counties: Adams, Boulder, Jefferson, and Weld. Data are not available for Broomfield county. Data are aggregated within the previous boundaries of adjacent Adams, Boulder, Jefferson, and Weld counties.
  3. Florida: Dade county and Miami city
    Dade county, Florida (FIPS code 12025) was renamed Miami-Dade County and its FIPS code changed to 12086, effective November 13, 1997. The previous label and code, Dade county (FIPS code 12025), are used here.
  4. Maryland: Baltimore city and Baltimore county
    The independent city of Baltimore, Maryland has been treated as a county. Data are reported separately for Baltimore city (FIPS code 24510) and Baltimore county (FIPS code 24005).
  5. Missouri:
    1. St. Genevieve county, Missouri
      In order to achieve alphabetical consistency, the FIPS code for St. Genevieve, Missouri was changed in 1979 from 29193 to 29186. The new code (29186) is used here.
    2. St. Louis city and St. Louis county, Missouri
      The independent city of St. Louis, Missouri has been treated as a county. Data are reported separately for St. Louis city (FIPS code 29510) and St. Louis county (FIPS code 29189).
  6. Nevada: Carson City
    The independent city of Carson City, Nevada (FIPS code 32510) has been treated as a county. Data are shown separately from the adjacent counties for Carson City, Nevada.
  7. New York: New York City boroughs
    The five boroughs of New York City have been treated as counties and maintained as separate entities.

    Borough County FIPS Code
    Bronx Bronx 36005
    Brooklyn Kings 36047
    Manhattan New York 36061
    Queens Queens 36081
    Staten Island   Richmond   36085

  8. Virginia independent cities:
    1. Alleghany, Virginia
      Alleghany, Virginia (FIPS code 51005) and Clifton Forge city, Virginia (FIPS code 51560) are reported separately.
    2. Clifton Forge city, Virginia
      On July 1, 2001, Clifton Forge city, Virginia (FIPS code 51560), formerly an independent city, merged with Alleghany county (FIPS code 51005). However, data for Clifton Forge city are reported separately from Alleghany county, Virginia (FIPS code 51005) for all years.
    3. Nansemond city, Virginia
      Nansemond city, Virginia (FIPS code 51123) has been part of the independent city of Suffolk, VA (FIPS code 51800) since 1979. For all years, data for Nansemond are aggregated and reported with those for Suffolk city.
    4. Table of Virginia independent cities and counties
      The Virginia independent cities are treated as counties and appear on the data with the following FIPS codes:

         Independent City	       County
      

      Name	     FIPS code	 Name       FIPS code   
      
      Alexandria	 51510	 Arlington	51013
      Bedford		 51515	 Bedford	51019
      Bristol		 51520	 Washington	51191
      Buena Vista	 51530	 Rockbridge	51163
      Charlottesville	 51540	 Albemarle 	51003
      Chesapeake	 51550	
      Clifton Forge	 51560	 Alleghany 	51005 
      Colonial Heights 51570   Chesterfield 	51041
      Covington	 51580   Alleghany 	51005
      Danville	 51590   Pittsylvania 	51143
      Emporia		 51595   Greensville	51081
      Fairfax		 51600   Fairfax	51059
      Falls Church	 51610   Fairfax 	51059
      Franklin	 51620   Southampton	51175
      Fredericksburg	 51630   Spotsylvania	51177
      Galax		 51640   Grayson 	51077          
      Hampton		 51650
      Harrisonburg	 51660	 Rockingham	51165
      Hopewell	 51670	 Prince George	51149
      Lexington	 51678	 Rockbridge	51163
      Lynchburg	 51680	 Campbell 	51031
      Manassas	 51683	 Prince William 51153
      Manassas Park	 51685	 Prince William	51153
      Martinsville	 51690	 Henry	 	51089
      Newport News	 51700
      Norfolk		 51710
      Norton		 51720	 Wise	 	51195
      Petersburg	 51730	 Dinwiddie	51053
      Poquoson	 51735	 York	 	51199
      Portsmouth	 51740	 Norfolk city	51710
      Radford		 51750	 Montgomery	51121
      Richmond	 51760	 Henrico 	51087
      Roanoke		 51770	 Roanoke 	51161
      Salem		 51775	 Roanoke 	51161
      Staunton	 51790	 Augusta 	51015
      Suffolk		 51800
      Virginia Beach	 51810
      Waynesboro	 51820	 Augusta 	51015
      Williamsburg	 51830	 James City	51095
      Winchester	 51840	 Frederick	51069
      




This page last reviewed: Monday, January 25, 2016
This information is provided as technical reference material. Please contact us at cwus@cdc.gov to request a simple text version of this document.
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