**Section VII. Understanding Monitoring Data and Its Implications**

**[Contents]{.underline}**

1.  **Background**

2.  **Purpose**

3.  **Recommendations for specific data uses**

    1.  **Criteria pollutants**

        1.  **NAAQS attainment**

        2.  **Understanding AQI and AIRNow**

    2.  **Air quality characterization for ambient, deposition, and
        visibility data**

        1.  **Basic summary statistics**

        2.  **Trends analysis**

        3.  **Data visualization**

        4.  **References, tools and resources**

    3.  **Putting data into context**

    4.  **Source apportionment**

        1.  **Using emissions inventories**

        2.  **Wind direction analysis**

        3.  **Receptor modeling**

    5.  **Exposure assessment for hazardous air pollutants**

    6.  **Multi-media exposure assessment using deposition monitoring**

4.  **Attachment 1. Excerpts from CFR on NAAQS monitoring**

**Section VII. Understanding Monitoring Data and Its Implications**

1.  **Background**

There are many approaches to data interpretation ranging from a simple
data summary to complex statistical procedures. The range of
possibilities can be overwhelming, but is easily narrowed by asking a
simple question: "Why was monitoring conducted in the first place?" The
appropriate use of monitoring data is intimately linked with the
monitoring objective. Assuming that the monitoring agency has followed
the procedures outlined in previous sections of this document, the
existing or proposed monitoring plan has a purpose in mind and steps
have been taken to ensure that the final data product is adequate for
its intended purpose.

For example, an agency located in a potential high ozone area may set up
an ozone monitor to gauge attainment of the National Ambient Air Quality
Standard (NAAQS). Quality assurance measures must be put in place to
collect reliable data for a 3-year period. If the dataset is found to
meet data quality objectives then it may be used as the basis for NAAQS
attainment designation for the county. If the data are incomplete or
otherwise compromised, then an attainment determination will not be
possible.

Similarly, there are data quality requirements for hazardous air
pollutant (HAP) monitoring which is intended for use in exposure
assessment and health risk interpretation. It is not uncommon for an
agency to collect ambient HAP data only to discover later that the wrong
target compounds were reported or that detection limits were too high to
allow comparing the data against cancer risk benchmarks. These problems
can be minimized by effective planning which identifies the intended use
of monitoring data and delineates specific monitoring quality
objectives.

2.  **Purpose**

The main goal of this section is to help Tribal staff achieve their
monitoring program objectives through an effective use of monitoring
data. We will give an overview of potential data uses and provide links
to relevant guidance documents and examples. Possible data uses include:
determining attainment of NAAQS for criteria pollutants; characterizing
population exposure to HAPs, also known as "air toxics"; assessing air
pollutant trends over time; and attributing source contribution to air
pollution.

Basic data summary and statistical techniques allow the monitoring
agency to effectively communicate project results. These methods are
important for a variety of purposes: to inform Tribal members and other
stakeholders about local air quality; to summarize monitoring results in
a final project report or grant related documents; and to describe prior
air quality findings as part of the justification for new or continued
funding in a grant application.

This section will be useful to an agency that has already collected a
dataset and needs help understanding it. However it may be even more
valuable to a program manager who is in the stages of planning an air
quality study as it will provide a clear understanding of what an
ambient monitoring program can do for them and what questions it can
answer. Depending on the technical capabilities of Tribal staff, some of
the techniques described here may be performed in-house while others may
require partnering with another agency or hiring a contractor.

3.  **Recommendations for specific data uses**

    1.  **Criteria pollutants**

Tribal agencies that conduct ambient air quality monitoring most
frequently collect data for the six common pollutants (also referred to
as \"criteria\" pollutants): carbon monoxide (CO), lead (Pb), nitrogen
dioxide (NO~2~), ozone (O~3~), particulate matter (PM), and sulfur
dioxide (SO~2~). PM falls into two categories: particles smaller than 10
microns in diameter (PM~10~) and particles smaller than 2.5 microns
(PM~2.5~). This section will describe how criteria pollutant monitoring
data may be interpreted and used as part of an air quality management
program.

For more information on sources of criteria pollutants, health and
environmental effects, efforts to help reduce emissions, and other
helpful resources, see:
[www.epa.gov/air/urbanair/6poll.html](http://www.epa.gov/air/urbanair/6poll.html)

1.  **NAAQS attainment**

EPA has set NAAQS standards for the six criteria pollutants. The NAAQS
include both primary and secondary standards. Primary standards set
limits to protect public health, including the health of \"sensitive\"
populations such as asthmatics, children, and the elderly. Secondary
standards set limits to protect public welfare, including protection
against decreased visibility, damage to animals, crops, vegetation, and
buildings. The table below lists the current NAAQS for criteria
pollutants. Units of measure for the standards are parts per million
(ppm) by volume, milligrams per cubic meter of air (mg/m^3^), and
micrograms per cubic meter of air (µg/m^3^).

**Table 1. National Ambient Air Quality Standards**

+--------------+----------------+------------------+-------------------+
| *            | **Primary      | **Averaging      | **Secondary       |
| *Pollutant** | Standards**    | Times**          | Standards**       |
+--------------+----------------+------------------+-------------------+
| Carbon       | 9 ppm (10      | 8-hour^1^        | None              |
| Monoxide     | mg/m^3^)       |                  |                   |
+--------------+----------------+------------------+-------------------+
|              | 35 ppm (40     | 1-hour^1^        | None              |
|              | mg/m^3^)       |                  |                   |
+--------------+----------------+------------------+-------------------+
| Lead         | 1.5 µg/m^3^    | Quarterly        | Same as Primary   |
|              |                | Average          |                   |
+--------------+----------------+------------------+-------------------+
| Nitrogen     | 0.053 ppm\     | Annual           | Same as Primary   |
| Dioxide      | (100 µg/m^3^)  |                  |                   |
|              |                | (Arithmetic      |                   |
|              |                | Mean)            |                   |
+--------------+----------------+------------------+-------------------+
| Particulate  | 50 µg/m^3^     | Annual^2^        | Same as Primary   |
| Matter       |                | (Arith. Mean)    |                   |
| (PM~10~)     |                |                  |                   |
+--------------+----------------+------------------+-------------------+
|              | 150 ug/m^3^    | 24-hour^1^       |                   |
+--------------+----------------+------------------+-------------------+
| Particulate  | 15.0 µg/m^3^   | Annual^3^        | Same as Primary   |
| Matter       |                | (Arith. Mean)    |                   |
| (PM~2.5~)    |                |                  |                   |
+--------------+----------------+------------------+-------------------+
|              | 65 ug/m^3^     | 24-hour^4^       |                   |
+--------------+----------------+------------------+-------------------+
| Ozone        | 0.08 ppm       | 8-hour^5^        | Same as Primary   |
+--------------+----------------+------------------+-------------------+
| Sulfur       | 0.03 ppm       | Annual (Arith.   | \-\-\-\-\-\--     |
| Oxides       |                | Mean)            |                   |
+--------------+----------------+------------------+-------------------+
|              | 0.14 ppm       | 24-hour^1^       | \-\-\-\-\-\--     |
+--------------+----------------+------------------+-------------------+
|              | \-\-\-\-\-\--  | 3-hour^1^        | 0.5 ppm (1,300    |
|              |                |                  | ug/m^3^)          |
+--------------+----------------+------------------+-------------------+

^1^ Not to be exceeded more than once per year.

^2^ To attain this standard, the 3-year average of the weighted annual
mean PM~10~ concentration at each monitor within an area must not exceed
50 ug/m^3^.

^3^ To attain this standard, the 3-year average of the weighted annual
mean PM~2.5~ concentrations from single or multiple community-oriented
monitors must not exceed 15.0 ug/m^3^.

^4^ To attain this standard, the 3-year average of the 98th percentile
of 24-hour concentrations at each population-oriented monitor within an
area must not exceed 65 ug/m^3^.

^5^ To attain this standard, the 3-year average of the fourth-highest
daily maximum 8-hour average ozone concentrations measured at each
monitor within an area over each year must not exceed 0.08 ppm.

"Designation" is the term EPA use to describe the air quality in a given
area for any of the criteria pollutants. Geographic areas are designated
as "attainment" or "nonattainment" based on ambient air monitoring data
collected in that area and reported to the Air Quality System (AQS)
national database. Tribes and States submit recommendations to the EPA
as to whether or not an area is attaining the NAAQS for a criteria
pollutant. After working with the Tribal or State agencies and
considering the air quality data, EPA officially designates an area as
attainment or nonattainment. If an area is designated as nonattainment
EPA informs the public that the air in the area is unhealthy to breathe,
and states, local and tribal governments must develop and implement
control plans to reduce pollution. A Tribal Implementation Plan (TIP) is
a set of regulatory

programs that a tribe can develop and adopt to help attain or maintain
national air quality standards. Once a nonattainment area meets the
standards and additional redesignation requirements in the CAA \[Section
107(d)(3)(E)\], EPA will designate the area to attainment as a
\"maintenance area.\"

The website listed below provides an unofficial list of Tribes in 8-hour
ozone nonattainment areas as of April 15, 2004. Official nonattainment
boundaries are specified in 40 CFR Part 81.
[www.epa.gov/ozonedesignations/tribaldesig.htm](http://www.epa.gov/ozonedesignations/tribaldesig.htm)

Detailed instructions on how to determine attainment status based on
ambient monitoring data may be found in the Code of Federal Regulations
(CFR) Title 40 Part 50. The relevant passages for each criteria
pollutant is included as Attachment 1 and may also be accessed on the
web at:

<http://ecfr.gpoaccess.gov/cgi/t/text/text-idx?c=ecfr&sid=e18bc4907fc6d399c035b0bd125e238b&tpl=/ecfrbrowse/Title40/40cfr50_main_02.tpl>

2.  **Understanding the Air Quality Index (AQI) and AIRNow**

##### 

The AQI is an index for reporting daily air quality. It tells how clean
or polluted the air is, and what associated health effects might be a
concern for the public. The AQI focuses on health effects that may be
experienced within a few hours or days after breathing polluted air. EPA
calculates the AQI for five of the criteria pollutants: O~3~, PM, CO,
SO~2~, and NO~x~. The AQI scale runs from 0 to 500. The higher the AQI
value, the greater the level of air pollution and the greater the health
concern. For example, an AQI value of 50 represents good air quality
with little potential to affect public health, while an AQI value over
300 represents hazardous air quality.

An AQI value of 100 generally corresponds to the national air quality
standard for the pollutant, which is the level EPA has set to protect
public health. AQI values below 100 are generally thought of as
satisfactory. When AQI values are above 100, air quality is considered
to be unhealthy -- at first for certain sensitive groups of people, then
for everyone as AQI values get higher.

Raw ambient air monitoring data is converted into AQI values using
standard formulas developed by EPA. An AQI value is calculated for each
pollutant in an area. The highest AQI value for the individual
pollutants is the AQI value for that day. For example, if a certain date
had AQI values of 90 for ozone and 88 for sulfur dioxide, the AQI value
would be 90 for the pollutant ozone on that day.

#####  

The purpose of the AQI is to help the public understand what local air
quality means to their health. To make it easier to understand, the AQI
is divided into six categories, each of which corresponds to a different
level of health concern. The six levels of health concern and what they
mean are:

-   **\"Good\"** The AQI value for your community is between 0 and 50.
    Air quality is considered satisfactory, and air pollution poses
    little or no risk.

-   **\"Moderate\"** The AQI for your community is between 51 and 100.
    Air quality is acceptable; however, for some pollutants there may be
    a moderate health concern for a very small number of people. For
    example, people who are unusually sensitive to ozone may experience
    respiratory symptoms.

-   **\"Unhealthy for Sensitive Groups\"** When AQI values are between
    101 and 150, members of sensitive groups may experience health
    effects. This means they are likely to be affected at lower levels
    than the general public. For example, people with lung disease are
    at greater risk from exposure to ozone, while people with either
    lung disease or heart disease are at greater risk from exposure to
    particle pollution. The general public is not likely to be affected
    when the AQI is in this range.

-   **\"Unhealthy\"** Everyone may begin to experience health effects
    when AQI values are between 151 and 200. Members of sensitive groups
    may experience more serious health effects.

-   **\"Very Unhealthy\"** AQI values between 201 and 300 trigger a
    health alert, meaning everyone may experience more serious health
    effects.

-   **\"Hazardous\"** AQI values over 300 trigger health warnings of
    emergency conditions. The entire population is more likely to be
    affected.

#### The AIRNow Web site delivers daily AQI forecasts as well as real-time AQI conditions for over 300 cities across the United States. The EPA developed the AIRNow program together with the National Oceanic and Atmospheric Administration (NOAA), National Park Service (NPS), Tribal, State, and Local agencies to provide the public with easy access to national air quality information. AQI data are presented in maps which were generated based on "real-time" ambient monitoring data using either federal reference or equivalent monitoring techniques or techniques approved by the state, local or tribal monitoring agencies. Although some preliminary data quality assessments are performed, the data as such are not fully verified and validated through the quality assurance procedures monitoring organizations use to officially submit and certify data in AQS. Therefore, data are used on the AIRNow Web site only for the purpose of reporting the AQI. Information on the AIRNow web site is not used to formulate or support regulation, guidance or any other Agency decision or position.

In 2005 there were 13 Tribal monitoring agencies that participated in
AIRNow. Tribes interested in joining the AIRNow network should contact
the appropriate OAQPS staffer, who is currently Richard Wayland.

 

Air quality forecasts and more information about AQI and AIRNow is
available at: [www.airnow.gov](http://www.airnow.gov/)

1.  **Air quality characterization for ambient, deposition, and
    visibility data**

The previous section was focused specifically on criteria pollutants and
interpreting data in terms of the NAAQS rules. Beyond the six criteria
pollutants, however, there are hundreds of other pollutants and indices
that a monitoring agency may wish to address. These non-criteria
pollutants and measures include ambient air toxics, wet/dry deposition,
visibility data, and even biomonitoring of ozone injury to sensitive
plants. These types of data do not have corresponding national air
quality standards that help to guide data summary and interpretation.
Instead, monitoring results should be described using basic summary
statistics. The data may also be visualized using simple graphic
techniques.

1.  **Basic summary statistics**

The first step in summarizing air quality data is to take inventory of
the number of samples collected, the range of measurements, and to
provide related information about the monitoring schedule. It is
important to specify the measurement units of the pollutant. If any of
the samples are below detection limits (called "nondetects") then it
becomes necessary to state the method detection limit (MDL) as well as
the number or percent of samples below the MDL. An example in table form
is shown below.

**Table 2. Example of monitoring data summary**

  --------------- ------------ ------------ ----------- ---------- --------- --------- --------- -----------
  **Pollutant**   **Sampling   **Sampling   **Total     **Unit**   **MDL**   **Min.    **Max.    **Percent
                  schedule**   period**     samples**                        value**   value**   samples
                                                                                                 \<MDL**

  Benzene         1-in-6 days  Jan. 2003 -- 107         ppbC       0.01      0.02      1.3       0
                               Dec. 2004                                                         

  1,3-butadiene   1-in-6 days  Jan. 2003 -- 107         ppbC       0.01      Below MDL 0.8       78%
                               Dec. 2004                                                         

  Arsenic         Monthly      Jan. 2003 -- 18          ug/m^3^    0.002     Below MDL 0.012     65%
  (PM~10~)                     June. 2004                                                        

  Cadmium         Monthly      Jan. 2003 -- 18          ug/m^3^    0.01      Below MDL 0.02      21%
  (PM~10~)                     June. 2004                                                        

  Lead (PM~10~)   Monthly      Jan. 2003 -- 18          ug/m^3^    0.005     0.008     0.31      0
                               June. 2004                                                        
  --------------- ------------ ------------ ----------- ---------- --------- --------- --------- -----------

If any of the monitored pollutants has nondetect values then this issue
must be resolved before moving on to data analysis. EPA recommends that
nondetects be replaced with a value equal to half of the detection limit
for a given pollutant (1/2 MDL). In the example above, the detection
limit for 1,3-butadiene is 0.01 ppbC, thus nondetects should be replaced
with 0.005 ppbC throughout the dataset. These substituted values are
then used alongside "real" values in calculating summary statistics. We
do not advise deleting nondetects from the dataset because this will
cause an upward bias in the results; similarly nondetects should not be
replaced by zeroes because this biases the results downward.

Note that a high percentage of nondetects results in less reliable data
summary statistics. Although there is no definite cut-off, a pollutant
with greater than 50% nondetects should be treated with care and one
with more than 80% nondetects may be removed from further analysis.
Depending on the importance of a specific pollutant to the monitoring
study, the data analyst has a few choices: state that the pollutant has
a very high rate of nondetects and remove it from the data analysis;
include the pollutant and point out potential problems related to
nondetects; include the pollutant and use advanced statistical
techniques developed for datasets with a high rate of nondetects.

Statistical techniques that may be useful in handling datasets with a
high rate of nondetects are described in the following article:

Less than obvious -- statistical treatment of data below the detection
limit, Dennis R. Helsel (USGS), Environmental Science and Technology,
Vol. 24, No. 12, 1990.

The rest of this section will describe ways to summarize basic
characteristics of the dataset using common statistical measures. Some
useful examples include: measures of central tendency, such as the mean
or median; measures of relative standing, such as percentiles; measures
of dispersion, such as range, variance, standard deviation, coefficient
of variation, or interquartile range; measures of distribution symmetry
or shape; and measures of association between two or more variables,
such as correlation. These measures can then be used for description and
communication about the dataset.

The definitions and procedures outlined in parts 3.2.1 and 3.2.2. are
primarily taken from the EPA document "Guidance for Data Quality
Assessment -- Practical Methods for Data Analysis"

> (EPA/600/R-96/084) which is available in full at this website:

[www.epa.gov/quality/qs-docs/g9-final.pdf](http://www.epa.gov/quality/qs-docs/g9-final.pdf)

This section provides mathematical formulas that allow the user to
calculate descriptive statistics using a simple calculator or computer
spreadsheet program. Data analysts that are interested in continuing on
to do advanced statistical procedures may consider investing in a
statistical software package and attending training sessions to practice
using them. The end of this section provides resources for those
interested in learning more.

[Central tendency]{.underline}

The most common estimates for central tendency in environmental data are
the mean and median. The ***mean*** may be considered to be the "center
of gravity" of the dataset. It is calculated as a basic arithmetic
average. The ***median*** is the value which falls directly in the
middle of the data when the measurements are ranked in order from
smallest to largest. Thus ½ of the data are smaller than the sample
median and ½ of the data are larger than the sample median. Unlike the
mean, the median is not influenced by a small number of extreme values.

**Formula 1. Measuring central tendency**

![](media/image1.wmf){width="5.374305555555556in"
height="2.5229166666666667in"}

[Relative Standing (Percentiles)]{.underline}

It may be useful to know the relative position of one or several
observations in relation to all of the observations. Percentiles are one
such measure of relative standing that may also be useful for
summarizing data. A percentile is the data value that is greater than or
equal to a given percentage of the data values. For example the data
point which is the 25^th^ percentile is greater than or equal to 25% of
the data values and is less than or equal to 75%. Important percentiles
usually reviewed are the quartiles of the data: the 25^th^, 50th, and
75^th^ percentiles. The 50^th^ percentile is also called the sample
median (previously described), and the 25^th^ and 75^th^ percentiles are
used to estimate the dispersion of a data set (next section).

**Formula 2. Calculating percentiles**

![](media/image2.wmf){width="5.252777777777778in"
height="2.102777777777778in"}

[Measures of Dispersion]{.underline}

Measures of central tendency are more meaningful if accompanied by
information on how the data spread out from the center. Measures of
dispersion in a data set include the range, variance, sample standard
deviation, coefficient of variation, and the interquartile range. These
measures are all described below and formulas provided.

The easiest measure of dispersion to compute is the sample ***range***.
For small samples, the range is easy to interpret and may adequately
represent the dispersion of the data. For large samples, the range is
not very informative because it only considers (and therefore is greatly
influenced) by extreme values.

The sample ***variance*** measures the dispersion from the mean of a
data set. A large sample variance implies that there is a large spread
among the data so that the data are not clustered around the mean. A
small sample variance implies that there is little spread among the data
so that most of the data are near the mean. The sample variance is
affected by extreme values and by a large number of nondetects. The
sample standard deviation is the square root of the sample variance and
has the same unit of measure as the data.

The ***coefficient of variation (CV)*** is a unitless measure that
allows the comparison of dispersion across several sets of data. The CV
is often used in environmental applications because variability
(expressed as a standard deviation) is often proportional to the mean.

When extreme values are present, the ***interquartile range*** may be
more representative of the dispersion of the data than the standard
deviation. This statistical quantity does not depend on extreme values
and is therefore useful when the data include a large number of
nondetects.

**Formula 3. Calculating measures of dispersion**

![](media/image3.wmf){width="5.372916666666667in"
height="3.0833333333333335in"}

2.  **Trends analysis**

EPA uses trend analysis to assess year-to-year changes in ambient air
quality and pollutant emissions. Annual Trends Reports are EPA\'s
\"report card\" on the status of air quality and emission reductions.
Annual trends reports and special studies dating back to 1994 are
available at:
[www.epa.gov/airtrends/reports.html](http://www.epa.gov/airtrends/reports.html).
Some methods used in these reports will be useful to Tribal agencies.

The data analyst needs a reasonably long and complete dataset to be able
to distinguish a genuine trend from other kinds of data variability. A
suspected trend in data may not be a "real" trend, but a function of
data variation caused by weather conditions or other factors. For
example, because higher temperatures cause more formation of some
pollutants, like ozone and formaldehyde, a year with warmer temperatures
may have higher concentrations of these pollutants, regardless of any
possible changes in precursor emissions. Thus the measurements in one
year may be higher than the previous year, but we cannot reliably say
that there is an upward trend in ambient concentrations.

Although it is tempting to calculate trends based on two or three years
of data, more years are needed to calculate a meaningful trend. For
dispersion modeling, meteorologists recommend using between 3 and 5
years of data just to assess the "baseline" condition. With this in
mind, we suggest the following:

-   1 to 3 years of monitoring data -- do not use for trend analysis

-   4 to 5 years of monitoring data -- consider trend results to be
    "preliminary"

-   6 or more years of monitoring data -- adequate dataset for trend
    analysis

If there are enough years of data available, then it is also important
to confirm that each year has adequate data completeness. Multi-year
trends can only be calculated if each year has a valid summary statistic
(e.g. annual mean) based on sufficient data. The issue of data
completeness was previously discussed in the section on data quality
objectives and data validation.

Assuming that there are enough complete years of monitoring, then an
annual air quality statistic can be determined for each individual year
of air monitoring and then a trend may be evaluated for multiple years.
The trend statistic can be one of those described in Section 3.2.1 (such
as the annual average or 90^th^ percentile) or a NAAQS design value as
described in Section 3.1.1 (such as the PM~2.5~ 24-hour maximum). This
section will explain how to quantify and visualize trends in monitoring
data.

[Percent change]{.underline}

Actual ambient concentrations have little meaning to the general public
and a change in concentration (for example a "0.04 ppb decline over 8
years") is even more abstract. For this reason EPA most often explains
trends in terms of a percent change over time. The example below shows
the trend calculation and graph format most widely used in trend
reports. The main trend statistic is the combined annual average of
multiple monitoring sites. The figure also shows the 90^th^ and 10^th^
percentiles of all site averages for each year.

**Figure 1. CO air quality, 1983-2002, based on annual second maximum
8-hr average**

![](media/image4.png){width="3.8756944444444446in"
height="2.6354166666666665in"}

**Formula 4. Percent change over multiple years of monitoring**

$$P = \begin{array}{r}
\left\lbrack \underline{\left( C_{F} - C_{S} \right)} \middle| \right\rbrack \\

\end{array}$$

Where C~S~ = concentration at the start; C~F~ = concentration at the
finish of the time period. A positive P indicates an upward trend and a
negative value indicates a downward trend.

In the carbon monoxide example above,

$$P = \begin{array}{r}
\left( \underline{2\text{.}9\text{ppm} - 8\text{.}2\text{ppm}} \middle| \right) \\

\end{array}$$

There are some important pitfalls to the percent change approach to
trends. The main concern is that if the dataset has strong year-to-year
variability, then the existence of a positive or negative trend is
dependent on which years are chosen as the start and finish. The figure
below shows national annual total manganese emissions as reported to the
Toxics Release Inventory (TRI). The percent change in emissions between
1988 and 2003 is --26%. However, the downward trend is magnified if we
look only at 1989 through 1997 (--88%); the trend is relatively flat if
the time period is 1993--1999 (--2%); the trend reversed if we look at
1991 to 1999 (+19%). For this reason it is preferable to use the percent
change method for a dataset with smooth trends. Highly variable datasets
should be evaluated by using a moving average (described below) or with
a more rigorous statistical method, such as linear regression or using
non-parametric methods.

**Figure 2. Example of dataset with variable year-to-year data**

![](media/image5.wmf){width="3.9993055555555554in"
height="2.6069444444444443in"}

[National Air Toxics Trend Site (NATTS) method]{.underline}

Trends within the new NATTS network are figured based on six (6) years
of annual average concentrations for key HAPs, specifically benzene,
1,3-butadiene, arsenic, chromium, acrolein, and formaldehyde. The trend
is calculated by finding the percent difference between the mean of the
first three annual concentrations and the mean of the last three annual
concentrations. This is a variation on the percent change method
described above.

**Formula 5. NATTS trend method**

First the annual average concentration (Xi) is found for each year i =
1, 2, 3, 4, 5 & 6. Then the mean (X) for the first three years and the
mean (Y) for years 4 through 6 is calculated:

![](media/image6.wmf){width="2.8756944444444446in"
height="0.46944444444444444in"}

The downward trend (T) is the percent decrease from the first 3-year
period to the second.

![](media/image7.wmf){width="1.125in" height="0.4875in"}

According to the DQOs for the NATTS program, a trend of at least 15% is
considered a significant decrease. A tribal agency may wish to adopt
this protocol for their own monitoring program or adapt it as needed
through consultation with quality assurance experts. The Quality
Assurance Guidance Document for the NATTS program is available at:

[www.epa.gov/ttn/amtic/files/ambient/airtox/nattsqapp.pdf](http://www.epa.gov/ttn/amtic/files/ambient/airtox/nattsqapp.pdf)

[Moving average]{.underline}

A dataset with considerable year-to-year variability may be smoothed out
by calculating a moving average. Instead at looking at annual averages
that rise and fall with each year, we look at 3-year, 4-year, or 5-year
averages which are less subject to variability swings.

The example below shows data graphed first as annual averages and then
as a moving 3-year average. The first figure shows annual averages from
1997 to 2005; the second shows the combined average for 1997 through
1999, then 1998-2000, 1999-2001, and so forth. The downward trend in
data is more evident in the second figure.

> [Year]{.underline} [Mercury wet deposition (µg/m^2^)]{.underline}
>
> 1997 12.8
>
> 1998 13.7
>
> 1999 11.9
>
> 2000 12.0
>
> 2001 13.1
>
> 2002 10.8
>
> 2003 11.7
>
> 2004 11.1
>
> 2005 9.6

**Figure 3. Data trend presented with annual average and moving
average**

![](media/image8.wmf){width="3.9479166666666665in"
height="2.0416666666666665in"}

![](media/image9.wmf){width="3.9479166666666665in" height="2.125in"}

1.  **Data visualization**

Simple graphing techniques are useful to describe the dataset and
communicate monitoring results. Graphs can be used to identify patterns
and trends in the data. Graphical representations include displays of
individual data points, statistical quantities, temporal data, spatial
data, and two or more variables.

Detailed instructions on how to produce these graphics are provided in
Section 2 of the previously mentioned "Guidance for Data Quality
Assessment -- Practical Methods for Data Analysis", available at:
[www.epa.gov/quality/qs-docs/g9-final.pdf](http://www.epa.gov/quality/qs-docs/g9-final.pdf)

[Histogram/Frequency Plots]{.underline}

Two of the oldest methods for summarizing data distributions are the
frequency plot and the histogram. Both the histogram and the frequency
plot use the same basic principles to display the data: dividing the
data range into units, counting the number of points within the units,
and displaying the data as the height or area within a bar graph.

**Figure 5. Example of a frequency plot**

![](media/image10.wmf){width="2.498611111111111in"
height="1.7798611111111111in"}

[Box and Whisker Plot]{.underline}

A box and whisker plot or box plot is a schematic diagram useful for
visualizing important statistical quantities of the data. A box and
whiskers plot is composed of a central box divided by a line and two
lines extending out from the box called whiskers. The length of the
central box indicates the spread of the bulk of the data (the
inter-quartile range, 25^th^ to 75^th^ percentile) while the length of
the whiskers show how stretched the tails of the distribution are. The
sample median is displayed as a line through the box and the sample mean
is displayed using a '+' sign. Any unusually small or large data points
are displayed by a '\*' on the plot.

**Figure 6. Example of a box and whisker plot**

![](media/image11.wmf){width="0.7951388888888888in"
height="1.511111111111111in"}

[Scatter Plot]{.underline}

For data sets consisting of paired observations where two or more
variables are measured for each sampling point, a scatter plot is one of
the most powerful tools for analyzing the relationship between two or
more variables. A scatter plot clearly shows the relationship between
two variables. Both potential outliers from a single variable and
potential outliers from the paired variables may be identified on this
plot. A scatter plot also displays the correlation between the two
variables. Scatter plots of highly linearly correlated variables cluster
compactly around a straight line.

**Figure 7. Example of a scatter plot**

![](media/image12.wmf){width="3.376388888888889in"
height="2.196527777777778in"}

[Time Plot]{.underline}

One of the simplest plots to generate that provides a large amount of
information is a time plot. A time plot is a plot of the data over time.
This plot makes it easy to identify large-scale and small-scale trends
over time. Small-scale trends show up on a time plot as fluctuations in
smaller time periods. For example, ozone levels over the course of one
day typically rise until the afternoon, then decrease, and this process
is repeated every day. An example of a large-scale trend is a multi-year
decrease in air pollution resulting from effective air quality control
programs. For example, the annual average concentration of NO~x~ at a
particular monitoring site may decline over the course of several years
as a result of emissions controls at local industries and the
introduction of cleaner cars.

**Figure 8. Example of a time plot**

![](media/image13.wmf){width="4.248611111111111in"
height="2.134027777777778in"}

2.  **References, tools, and resources**

## [EPA courses]{.underline}

EPA\'s Air Pollution Training Institute (APTI) primarily provides
technical air pollution training to state, tribal, and local air
pollution professionals, although others may benefit from this training.
The curriculum is available in classroom, telecourse, self-instruction,
and web-based formats. A few potentially useful courses are described
below.

## 

***Introduction to Environmental Statistics***

This series of online lectures was developed for USEPA by the University
of Illinois at Chicago School of Public Health, Environmental and
Occupational Health Sciences Division. No registration is required to
access the archived lectures. The lectures are available at this
website:

[www.epa.gov/air/oaqps/eog/envirostats/index.html](http://www.epa.gov/air/oaqps/eog/envirostats/index.html)

-   Module 1: Interpreting Your Monitoring Data

-   Module 2: Sampling and Analytical Limitations & Sample Detection
    Limits

-   Module 3: Quality Assurance Quality Control

```{=html}
<!-- -->
```
-   Module 4: Analysis of Trends

-   Module 5: Language of Data Graphing

-   Module 6: Censored Values and Extreme Values

-   Module 7: Fundamentals of Trajectory Analysis

## 

## *Introduction to Environmental Statistics - SI:473B*

This course introduces the student to the basic concepts of statistical
analysis. The course was designed for students with little formal
education in statistics who must apply statistical techniques to analyze
environmental data. The package has seven modules, a workbook, and a VHS
format video tape. The workbook and video tape are mailed to the student
by EPA, but it is necessary to acquire one of the recommended companion
texts.

## Course information is available at: [**www.epa.gov/air/oaqps/eog/catalog/si473b.html**](http://www.epa.gov/air/oaqps/eog/catalog/si473b.html)

# *Training Courses on Quality Assurance and Quality Control Activities*

EPA Quality Staff develops a variety of traditional training courses on
quality assurance (QA) and quality control (QC) activities and the EPA
quality system. Two subject area of particular interest are
"Interpreting monitoring data" and "Introduction to Data Quality
Assessment". Materials are available at:
[www.epa.gov/quality/trcourse.html](http://www.epa.gov/quality/trcourse.html)

[Other tools and resources]{.underline}

Statistics books:

-   Basic Statistical Methods for Engineers and Scientists

Adam Neville, John Kennedy, International Textbook Company (out of
print)

-   Probability and Statistics for Engineers, Irwin Miller, John Freund,
    Prentice Hall

-   Statistics Concepts and Applications, David Anderson, Dennis J.
    Sweeney, Thomas A. Williams, West

-   Exploring Statistics -- A Modern Introduction, Larry J. Kitchens,
    West

-   Engineering Statistics, Robert V. Hogg, Johannes Ledolter, Macmillan

-   Introduction to Statistical Thinking, E.A. Maxwell, Prentice Hall

-   Statistical Analysis for Decision Making, Morris Hamburg, Harcourt
    Brace Jovanovich

EPA has a website for Quality-Related Resources which contains links to
other sources of information on quality systems available on the web:

[www.epa.gov/quality/qa_links.html](http://www.epa.gov/quality/qa_links.html)

Examples of tools and resources available through the EPA quality
resources page:

-   DataPlot (National Institutes of Science and Technology) is a free,
    public-domain, multi-platform software system for scientific
    visualization, statistical analysis, and non-linear modeling.
    [www.itl.nist.gov/div898/software/dataplot.html/](http://www.itl.nist.gov/div898/software/dataplot.html/)

-   StatPages.Net (by John C. Pezzullo) contains links to online
    calculators, free statistical software, online statistics books,
    tutorials, and related resources.\
    [members.aol.com/johnp71/javastat.html](http://members.aol.com/johnp71/javastat.html)

-   Statistics Calculators (UCLA Department of Statistics) includes
    calculators for statistical graphs, power calculations, sample size
    calculations, etc.
    [calculators.stat.ucla.edu/](http://calculators.stat.ucla.edu/)

-   Guide to Statistical Software (George Mason University) provides a
    comparison of commercially available statistical software.
    [www.galaxy.gmu.edu/papers/astr1.html](http://www.galaxy.gmu.edu/papers/astr1.html)

    1.  **Putting monitoring data into context**

[Criteria pollutants]{.underline}

In addition to determining NAAQS attainment and AQI values, Tribal
monitoring agencies may benefit from putting their monitoring data into
a broader context. There are a few ways to do this. The Tribe may look
up data for the same pollutant at other monitoring sites located in the
same State or region to see how the values compare. It may also be
helpful to look at a nation-wide summary of data or a list of
nonattainment areas. A broader context may also be obtained by learning
about national trends in air quality data.

EPA's AirData website provides access to air pollution data for the
entire US as submitted to AQS. AirData produces reports and maps of air
pollution data based on user-specified queries. For example a Tribal
agency located in Arizona may wish to look up last year's ozone data for
all monitoring sites in the State. The link below is the interface where
the user selects the geographic area for the data search. Subsequent web
pages narrow the search to the desired pollutant, year, and report
format.
[www.epa.gov/air/data/geosel.html](http://www.epa.gov/air/data/geosel.html)

EPA's Air Explorer is a collection of user-friendly visualization tools
for air quality analysts. The tools generate maps, graphs, and data
tables based on criteria pollutant data reported to AQS. This is a
developmental site. Based on user feedback, EPA is continually improving
the existing tools and developing new
ones.[www.epa.gov/mxplorer/index.htm](http://www.epa.gov/mxplorer/index.htm)

EPA's "Green Book" lists all nonattainment areas in the US. The user can
access a variety of maps and reports for each criteria pollutant at this
web site:
[www.epa.gov/air/oaqps/greenbk/](http://www.epa.gov/air/oaqps/greenbk/)

EPA tracks air pollution trends using two main indicators: ambient air
monitoring data and pollutant emissions. EPA estimates nationwide
emissions of criteria pollutants and air toxics based on many factors,
including actual measurements, levels of industrial activity, fuel
consumption, vehicles miles traveled, and other estimates of activities
that cause pollution.

For EPA\'s most recent evaluation of air pollution trends, see:
[www.epa.gov/airtrends/](http://www.epa.gov/airtrends/)

The Visibility Information Exchange Web System (VIEWS) is an online
exchange of air quality data, research, and ideas designed to understand
the effects of air pollution on visibility in support of the Regional
Haze Rule. <http://vista.cira.colostate.edu/views>

[Air toxics, deposition, and other monitoring data]{.underline}

Air toxics monitoring data from other sites in the U.S., as submitted to
AQS, may be accessed through the AirData website. Additionally, some
materials are available from nation-wide air toxics data analyses. The
following links may be useful for putting toxics data into context:

-   Geographic Variability in Air Toxics

[www.epa.gov/ttn/amtic/files/ambient/airtox/2005%20workshop/spatial.pdf](http://www.epa.gov/ttn/amtic/files/ambient/airtox/2005 workshop/spatial.pdf)

-   Temporal Trends in Air Toxics
    [www.epa.gov/ttn/amtic/files/ambient/airtox/2005%20workshop/temporal.pdf](http://www.epa.gov/ttn/amtic/files/ambient/airtox/2005 workshop/temporal.pdf)

The Clean Air Status and Trends Network (CASTNET) is the nation\'s
primary source for data on dry acidic deposition and rural, ground-level
ozone. CASTNET consists of over 80 sites across the eastern and western
United States and is cooperatively operated and funded with the National
Park Service. Data are available for download for ambient air pollutants
and wet/dry deposition at this site:
[www.epa.gov/castnet/data.html](http://www.epa.gov/castnet/data.html)

The National Atmospheric Deposition Program/National Trends Network
(NADP/NTN) is a nationwide network of precipitation monitoring sites.
The network includes over 200 monitoring sites, including those operated
by 9 Tribal agencies. Data are available for download at:
<http://nadp.sws.uiuc.edu/sites/ntnmap.asp>

The Mercury Deposition Network (MDN) is intended to develop a national
database of weekly concentrations of total mercury in precipitation and
the seasonal and annual flux of total mercury in wet deposition.
<http://nadp.sws.uiuc.edu/sites/mdnmap.asp>

EPA established the National Dioxin Air Monitoring Network (NDAMN) to is
determine the temporal and geographical variability of atmospheric CDDs,
CDFs and coplanar PCBs at rural and nonimpacted locations throughout the
United States. Summary reports are available:

[www.epa.gov/glnpo/monitoring/air2/bio_toxics.html](http://www.epa.gov/glnpo/monitoring/air2/bio_toxics.html)

The Integrated Atmospheric Deposition Network (IADN) was established by
the United States and Canada for conducting air and precipitation
monitoring in the Great Lakes Basin. PAHs, PCBs, and organochlorine
compounds are measured in air and precipitation samples in the U.S. and
Canada. See:
[www.msc.ec.gc.ca/iadn/index_e.html](http://www.msc.ec.gc.ca/iadn/index_e.html)

The USDA Forest Service, Forest Health Monitoring (FHM) is a national
program designed to determine the status, changes, and trends in
indicators of forest condition on an annual basis. The FHM program uses
data from ground plots and surveys, aerial surveys, and other biotic and
abiotic data sources and develops analytical approaches to address
forest health issues that affect the sustainability of forest
ecosystems. See: <http://fhm.fs.fed.us/>

The USDA - Forest Service\'s Forest Inventory and Analysis (FIA) uses
biomonitoring to monitor the potential impact of tropospheric ozone
(smog) on forests. This program involves using bioindicator plants to
detect and quantify ozone stress in the forest environment. A nationwide
network of ozone biomonitoring sites has been established across the
forested landscape. Each year these sites are evaluated for the amount
and severity of ozone injury on sensitive plants. The foliar injury data
is used to monitor changes in relative air quality over time and to
examine relationships between ozone stress and tree health. See:
[www.fiaozone.net/](http://www.fiaozone.net/)

1.  **Source apportionment**

In the absence of air monitoring, pollutant emissions data may be used
to help characterize air quality. This approach is discussed in more
detail elsewhere in this document. In this Section we focus on
evaluating air monitoring data in the context of emissions sources that
may be impacting the monitor site. Different approaches are described to
identify pollution sources and to estimate their potential to affect
local air quality.

1.  **Using emissions inventories**

If monitoring data show that a specific pollutant is exceeding air
quality standards, or is otherwise causing concern, then emission
inventories can identify potential sources. Summary emission data are
most easily accessed on EPA's website:
[www.epa.gov/air/data](http://www.epa.gov/air/data). By searching in the
state or county of interest in AirData, the user can access the National
Emission Inventory (NEI) for data on point, nonpoint, and mobile
sources. NEI contains information about sources that emit criteria air
pollutants and their precursors, and hazardous air pollutants. The
AirData website generates reports based on facility-specific and county
aggregate emissions data.

Tribal agencies may want to find more detailed information or identify
smaller sources that might not be included in the NEI. They can contact
their respective state environmental agency to get more detailed
emissions data.

2.  **Wind direction analysis**

Meteorological data collected at an air monitoring site can be used to
further interpret pollutant measurements and potential impact from
emissions sources. Wind data can be summarized over a year or multiple
years to show prevailing wind direction at a given site. A diagram
called a "wind rose" characterizes the wind conditions over time. The
example below is a wind rose produced by the Texas Commission on
Environmental Quality for Wichita Falls. The figure shows the frequency
of winds coming from each direction, broken out by wind speed category.

**Figure 9. Wichita Falls, Texas wind rose**

![](media/image14.png){width="3.0in" height="2.25in"}

If a major pollution source is identified near Tribal land, then a wind
rose can show whether the air monitor site is likely to be downwind of
the emission source on an occasional or frequent basis. An industrial
facility that is predominantly upwind of Tribal land is more likely to
impact the air monitor than a facility that is generally downwind. A
wind rose program called WRPLOT View is available from Lakes
Environmental Software for free download at this site:
[www.weblakes.com/lakewrpl.html](http://www.weblakes.com/lakewrpl.html)

To investigate further, the data analyst may conduct a wind-direction
analysis. This approach requires finding daily wind data that is
reported concurrently with air sampling events. Monitoring data may be
divided into "high pollution days" and "low pollution days", and the
meteorological data consulted to see whether higher concentrations occur
on days when the winds come from a certain direction. Alternatively,
each hourly or daily pollutant measurement can be divided into one of 16
categories according to the predominant wind sector (north,
north-northeast, northeast, east-northeast, etc.) and the average
concentrations for all sectors compared with one another.

Tribal agencies may generate wind roses or conduct wind-direction data
analysis using their own on-site meteorological data. If the Tribe does
not have a met station, data may potentially be obtained from other
agencies that have a nearby meteorological station, for example another
environmental or meteorological agency. In some cases, the data may be
downloaded from AQS. Technical staff at the state agency may have some
insights about how to locate data from other sources. Historic wind data
for many communities (useful for wind roses) is available for free
download at this website: [www.webmet.com](http://www.webmet.com/).
Recent meteorological data is considerably more difficult to find and
often must be purchased from a private company.

If long-range transport of pollutants is a concern then another approach
to consider is the HYSPLIT (HYbrid Single-Particle Lagrangian Integrated
Trajectory) model developed by NOAA. Hysplit can be used to map a back
trajectory which shows where an air parcel passed before reaching the
air monitor. The example below shows the back trajectory of different
air parcels that may have reached a monitor in western Michigan over a
24-hour period. The air parcels originated in several different states
(Iowa, Missouri, Illinois and Indiana) however they all passed through
northern Illinois before reaching the monitor in Michigan. If this
particular date had been a high ozone day, then the Hysplit results
would suggest that precursor pollutants from the greater Chicago area
contributed to ozone formation. Hysplit can be downloaded for free or
used on-line on the NOAA website:
[www.arl.noaa.gov/ready/hysplit4.html](http://www.arl.noaa.gov/ready/hysplit4.html)

**Figure 10. Example of Hysplit backward wind trajectory**

![](media/image15.png){width="3.375in" height="2.5097222222222224in"}

3.  **Receptor modeling**

Receptor models are mathematical or statistical procedures for
identifying and quantifying the sources of air pollutants at a receptor
(air monitor) location. Unlike dispersion models, receptor models do not
use pollutant emissions, meteorological data and chemical transformation
mechanisms to estimate the contribution of sources to receptor
concentrations. Instead, receptor models use the chemical and physical
characteristics of gases and particles measured at source and receptor
to both identify the presence of and to quantify source contributions to
receptor concentrations. These models are used as part of State
Implementation Plans (SIPs) for identifying sources contributing to air
quality problems. The EPA has developed the Chemical Mass Balance (CMB)
and UNMIX models as well as the Positive Matrix Factorization (PMF)
method for use in air quality management. CMB fully apportions receptor
concentrations to chemically distinct source-types depending upon the
source profile database, while UNMIX and PMF internally generate source
profiles from the ambient data.

Receptor models have been used most often to identify the sources of
PM~2.5~ and ozone. PM~2.5~ modeling requires a speciated dataset
(carbon, ions, and trace elements) whereas ozone precursor modeling
calls for speciated VOC data. Receptor models require some training and
experience to operate effectively. Monitoring agencies may prefer to
hire a contractor or partner with another agency or university to get
this work done. EPA approved models and supporting materials are
available at:
[www.epa.gov/scram001/receptorindex.htm](http://www.epa.gov/scram001/receptorindex.htm)

2.  **Exposure assessment for hazardous air pollutants**

Air toxics, or HAPs, are those pollutants that are known or suspected to
cause cancer or other serious health effects, such as reproductive
effects or birth defects, or adverse environmental effects. Examples of
toxic air pollutants include benzene, which is found in gasoline;
perchlorethlyene, which is emitted from some dry cleaning facilities;
and methylene chloride, which is used as a solvent and paint stripper by
a number of industries. Other listed air toxics include dioxin,
asbestos, toluene, and metals such as cadmium, mercury, chromium, and
lead compounds.

Links to more information about air toxics are provided below.

-   EPA's Health Effects Notebook provides fact sheets about the 188
    HAPs:
    [www.epa.gov/ttn/atw/hlthef/hapindex.html](http://www.epa.gov/ttn/atw/hlthef/hapindex.html)

-   Air Pollution and Health Risk

> [www.epa.gov/ttn/atw/3_90_022.html](http://www.epa.gov/ttn/atw/3_90_022.html)

-   Evaluating Exposures to Toxic Air Pollutants: A Citizen\'s Guide

> [www.epa.gov/ttn/atw/3_90_023.html](http://www.epa.gov/ttn/atw/3_90_023.html)

-   Risk Assessment for Toxic Air Pollutants: A Citizen\'s Guide

> [www.epa.gov/ttn/atw/3_90_024.html](http://www.epa.gov/ttn/atw/3_90_024.html)

Risk assessment is a tool used by environmental specialists to estimate
the increased risk of health problems in people who are exposed to
different amounts of toxic substances over a long period of time. The
risk assessment process has four steps, which are described below.

-   *Hazard assessment* -- what health problems are caused by the
    pollutant?

-   *Dose-response assessment* -- what are the health problems at
    different exposures?

-   *Exposure assessment* -- how much of the pollutant do exposed people
    inhale?

-   *Risk characterization* -- what is the extra risk of health problems
    in the exposed population?

Air toxics monitoring data may be used in the exposure assessment step
of risk assessment. If sufficient data exist for the pollutants of
concern, then monitoring data may be used instead of, or in addition to,
dispersion modeling outputs.

Using monitoring data in a risk assessment can be a very complex
process, requiring assistance from a statistician and toxicologist,
among other specialists. To decide whether a full-blown risk assessment
is warranted, EPA Region 4 scientists developed a screening procedure
that can help monitoring staff do a preliminary evaluation of their air
toxics data. The document is available at this website:

A Preliminary Risk-Based Screening Approach for Air Toxics Monitoring
Data Sets

[www.epa.gov/region4/air/airtoxic/athera1.htm](http://www.epa.gov/region4/air/airtoxic/athera1.htm)

EPA has also developed a Community Air Screening How-to Manual for use
by groups that includes non-technical community residents and leaders as
well as technical experts. The Manual describes a process that uses
input from a wide variety of community stakeholders.

[www.epa.gov/oppt/cahp/howto.html](http://www.epa.gov/oppt/cahp/howto.html)

If the Tribal agency has determined that a complete technical risk
assessment is needed, then EPA guidance documents are available for use
by Tribal staff or contractors. The Air Toxics Risk Assessment Reference
Library is located at:
[www.epa.gov/ttn/fera/risk_atra_main.html](http://www.epa.gov/ttn/fera/risk_atra_main.html).
The library provides information on the fundamental principles of
risk-based assessment for air toxics and how to apply those principles
in different settings as well as strategies for reducing risk at the
local level.

EPA has compiled dose-response data for air toxics for use in risk
assessments, including values for long-term (chronic) inhalation
short-term (acute) inhalation exposures. This information is regularly
updated as new information becomes available about the toxicity of
specific HAPs. The dose-response values provided at this site are
recommended by EPA as the most appropriate for use in air toxics risk
assessment.
[www.epa.gov/ttn/atw/toxsource/summary.html](http://www.epa.gov/ttn/atw/toxsource/summary.html)

1.  **Multi-media risk assessment using ambient and deposition
    monitoring data**

For a limited subset of HAPs, it is important to consider deposition
from air to soil, vegetation, or water bodies. Many studies indicate
that some pollutants emitted into the atmosphere (e.g., mercury) are
passed to humans or wildlife through non-inhalation pathways. An example
would be an air pollutant depositing from the air onto the soil,
followed by ingestion of the soil by people or by other living things in
an ecosystem. These air pollutants typically are persistent in the
environment, have a strong tendency to bioaccumulate, and exhibit
moderate to high toxicity.

A variety of computer models are available to describe the multimedia
transport and fate of pollutants released to the atmosphere. EPA
developed TRIM.FaTE, a model that can estimate pollutant concentrations
in multiple environmental media and biota, for use in ecological risk
assessment. The model is available at:
[www.epa.gov/ttn/fera/trim_fate.html](http://www.epa.gov/ttn/fera/trim_fate.html)

Other multi-media models are available here:
[www.epa.gov/ttn/fera/multi_related.html](http://www.epa.gov/ttn/fera/multi_related.html)

4.  **Attachment 1. Excerpts from CFR on NAAQS monitoring**

**O~3~ (8-Hour)**

§ 50.10 National 8-hour primary and secondary ambient air quality
standards for ozone.

\(a\) The level of the national 8-hour primary and secondary ambient air
quality standards for ozone, measured by a reference method based on
appendix D to this part and designated in accordance with part 53 of
this chapter, is 0.08 parts per million (ppm), daily maximum 8-hour
average.

\(b\) The 8-hour primary and secondary ozone ambient air quality
standards are met at an ambient air quality monitoring site when the
average of the annual fourth-highest daily maximum 8-hour average ozone
concentration is less than or equal to 0.08 ppm, as determined in
accordance with appendix I to this part.

**PM~10~**

§ 50.6 National primary and secondary ambient air quality standards for
PM~10~.

\(a\) The level of the national primary and secondary 24-hour ambient
air quality standards for particulate matter is 150 micrograms per cubic
meter (µg/m 3 ), 24-hour average concentration. The standards are
attained when the expected number of days per calendar year with a
24-hour average concentration above 150 µg/m 3 , as determined in
accordance with appendix K to this part, is equal to or less than one.

\(b\) The level of the national primary and secondary annual standards
for particulate matter is 50 micrograms per cubic meter (µg/m 3 ),
annual arithmetic mean. The standards are attained when the expected
annual arithmetic mean concentration, as determined in accordance with
appendix K to this part, is less than or equal to 50 µg/m 3 .

\(c\) For the purpose of determining attainment of the primary and
secondary standards, particulate matter shall be measured in the ambient
air as PM10 (particles with an aerodynamic diameter less than or equal
to a nominal 10 micrometers) by:

\(1\) A reference method based on appendix J and designated in
accordance with part 53 of this chapter, or

\(2\) An equivalent method designated in accordance with part 53 of this
chapter.

**PM~2.5~**

§ 50.7 National primary and secondary ambient air quality standards for
PM2.5.

\(a\) The national primary and secondary ambient air quality standards
for particulate matter are 15.0 micrograms per cubic meter (µg/m3)
annual arithmetic mean concentration, and 65 µg/m3 24-hour average
concentration measured in the ambient air as PM2.5 (particles with an
aerodynamic diameter less than or equal to a nominal 2.5 micrometers) by
either:

\(1\) A reference method based on appendix L of this part and designated
in accordance with part 53 of this chapter; or

\(2\) An equivalent method designated in accordance with part 53 of this
chapter.

\(b\) The annual primary and secondary PM2.5 standards are met when the
annual arithmetic mean concentration, as determined in accordance with
appendix N of this part, is less than or equal to 15.0 micrograms per
cubic meter.

> \(c\) The 24-hour primary and secondary PM2.5 standards are met when
> the 98th percentile 24-hour concentration, as determined in accordance
> with appendix N of this part, is less than or equal to 65 micrograms
> per cubic meter.

**Pb**

§ 50.12 National primary and secondary ambient air quality standards for
lead.

> National primary and secondary ambient air quality standards for lead
> and its compounds, measured as elemental lead by a reference method
> based on appendix G to this part, or by an equivalent method, are: 1.5
> micrograms per cubic meter, maximum arithmetic mean averaged over a
> calendar quarter.

**NO~2~**

40 CFR § 50.11 National primary and secondary ambient air quality
standards for nitrogen dioxide.

\(a\) The level of the national primary ambient air quality standard for
nitrogen dioxide is 0.053 parts per million (100 micrograms per cubic
meter), annual arithmetic mean concentration.

\(b\) The level of national secondary ambient air quality standard for
nitrogen dioxide is 0.053 parts per million (100 micrograms per cubic
meter), annual arithmetic mean concentration.

\(c\) The levels of the standards shall be measured by:

\(1\) A reference method based on appendix F and designated in
accordance with part 53 of this chapter, or

\(2\) An equivalent method designated in accordance with part 53 of this
chapter.

> \(d\) The standards are attained when the annual arithmetic mean
> concentration in a calendar year is less than or equal to 0.053 ppm,
> rounded to three decimal places (fractional parts equal to or greater
> than 0.0005 ppm must be rounded up). To demonstrate attainment, an
> annual mean must be based upon hourly data that are at least 75
> percent complete or upon data derived from manual methods that are at
> least 75 percent complete for the scheduled sampling days in each
> calendar quarter.

**SO~2~**

40 CFR § 50.4 National primary ambient air quality standards for sulfur
oxides (sulfur dioxide).

\(a\) The level of the annual standard is 0.030 parts per million (ppm),
not to be exceeded in a calendar year. The annual arithmetic mean shall
be rounded to three decimal places (fractional parts equal to or greater
than 0.0005 ppm shall be rounded up).

\(b\) The level of the 24-hour standard is 0.14 parts per million (ppm),
not to be exceeded more than once per calendar year. The 24-hour
averages shall be determined from successive nonoverlapping 24-hour
blocks starting at midnight each calendar day and shall be rounded to
two decimal places (fractional parts equal to or greater than 0.005 ppm
shall be rounded up).

\(c\) Sulfur oxides shall be measured in the ambient air as sulfur
dioxide by the reference method described in appendix A to this part or
by an equivalent method designated in accordance with part 53 of this
chapter.

\(d\) To demonstrate attainment, the annual arithmetic mean and the
second-highest 24-hour averages must be based upon hourly data that are
at least 75 percent complete in each calendar quarter. A 24-hour block
average shall be considered valid if at least 75 percent of the hourly
averages for the 24-hour period are available. In the event that only
18, 19, 20, 21, 22, or 23 hourly averages are available, the 24-hour
block average shall be computed as the sum of the available hourly
averages using 18, 19, etc. as the divisor. If fewer than 18 hourly
averages are available, but the 24-hour average would exceed the level
of the standard when zeros are substituted for the missing values,
subject to the rounding rule of paragraph (b) of this section, then this
shall be considered a valid 24-hour average. In this case, the 24-hour
block average shall be computed as the sum of the available hourly
averages divided by 24.

**CO**

40 CFR § 50.8 National primary ambient air quality standards for carbon
monoxide.

\(a\) The national primary ambient air quality standards for carbon
monoxide are:

\(1\) 9 parts per million (10 milligrams per cubic meter) for an 8-hour
average concentration not to be exceeded more than once per year and

\(2\) 35 parts per million (40 milligrams per cubic meter) for a 1-hour
average concentration not to be exceeded more than once per year.

\(b\) The levels of carbon monoxide in the ambient air shall be measured
by:

\(1\) A reference method based on appendix C and designated in
accordance with part 53 of this chapter, or

\(2\) An equivalent method designated in accordance with part 53 of this
chapter.

\(c\) An 8-hour average shall be considered valid if at least 75 percent
of the hourly average for the 8-hour period are available. In the event
that only six (or seven) hourly averages are available, the 8-hour
average shall be computed on the basis of the hours available using six
(or seven) as the divisor.

\(d\) When summarizing data for comparision with the standards, averages
shall be stated to one decimal place. Comparison of the data with the
levels of the standards in parts per million shall be made in terms of
integers with fractional parts of 0.5 or greater rounding up.