AQS Reports Guide

The AQS Site ID in the format XX-YYY-ZZZZ where XX is the State FIPS code, YYY is the County FIPS code, and ZZZZ is the site number within the county.

This is the "Parameter Occurrence Code" used to distinguish different instruments that measure the same parameter at the same site. There is no meaning to the POC (e.g. POC 1 does not indicate the primary monitor). For example, the first monitor established to measure carbon monoxide (CO) at a site could have a POC of 1. If an additional monitor were established at the same site to measure CO, that monitor could have a POC of 2. However, if a new instrument were installed to replace the original instrument used as the first monitor, that would be the same monitor and it would still have a POC of 1.

The name of the city where the monitoring site is located. This represents the legal incorporated boundaries of cities and not urban areas.

The name of the county where the monitoring site is located.

The street address giving an approximate location of the site.

The year the data represents.

A three-digit code representing the measurement method. A method code is only unique within a parameter (that is, method 132 for ozone is not the same as method 123 for benzene).

A description of the method codes displayed in the report is available on a page near the end of the report (usually the second to last page).

A Flag indicating the official certification status of the monitor-year, as assigned by the applicable regional office user.

See the AQS code tables for meanings of Certification Flags.

A designation indicating how a summary value is affected by exceptional events. It indicates whether exceptional data exists in the time period being summarized, and whether such exceptional data is included in the summary value.

For summaries of sample measurements, the following Exceptional Data Types are available:

0: No Events. None of the measurement data contributing to the summary has been flagged for exceptional events.

1: All Events Excluded. The summary excludes any measurements that have been flagged for exclusion because of exceptional events. (These measurements are excluded whether or not EPA has concurred with the flagging.)

2: All Events Included. Measurements included in the summary have been flagged for exceptional event exclusion but their data is included. (These measurements are included whether or not EPA has concurred with the flagging.)

5: Concurred Events Excluded. The summary excludes any measurements that have been flagged for exclusion for exceptional events AND the EPA Regional Office has concurred with the flagging.

For any site/monitor and summary time period, either a type 0 summary will exist (no data was flagged), or a type 1, type 2 and type 5 summary will all exist together.

For summaries created from lower-level summaries (e.g. daily summaries created from NAAQS_Average rows):

0: Created only when only lower-level summaries with EDT_ID = 0 exist

1: Created from lower-level summaries with EDT_ID 0 and 1

2: Created from lower-level summaries with EDT_ID 0 and 2

5: Created from lower-level summaries with EDT_ID 0 and 5

The number of observations (samples) taken during the averaging period.

The highest value for the indicated year. This only includes data at the selected duration or standard (e.g., it may be the maximum daily value). For seasonal monitoring, it only includes data during the effective monitoring season.

The second highest value for the indicated year. This only includes data at the selected duration or standard (e.g., it may be the second maximum daily value). For seasonal monitoring, it only includes data during the effective monitoring season.

Maxes for both 1-hour and 8-hour durations are shown on the report.

The number of samples in the summarized sample set that exceed the level of the primary standard. (Only applicable for criteria pollutants.)

Exceedance counts for both 1-hour and 8-hour durations are shown on the report.

The number of observations (samples) taken during the averaging period.

The number of quarterly summaries, with corresponding pollutant standard and exceptional data type, where the summary criterion is met.

The highest value for the indicated year. This only includes data at the selected duration or standard (e.g., it may be the maximum daily value). For seasonal monitoring, it only includes data during the effective monitoring season.

The second highest value for the indicated year. This only includes data at the selected duration or standard (e.g., it may be the second maximum daily value). For seasonal monitoring, it only includes data during the effective monitoring season.

Maxes for both 1-hour and 24-hour durations are shown on the report.

The 99th percentile 1-hour value (the value that 99% of all values are equal to or below).

The number of days where the 24-hour average was above the 24-hour standard.

The Arithmetic Mean calculated in the way that would be comparable to the annual standard.

Calculation method:

\[u = \frac {1}{v} {\sum_{i=1}^v d_i}\]

Where:

$u$ = mean value d_i = valid daily maximum occurring in the effective monitoring season v = count of valid days

The number of required monitoring days in the aggregation period (e.g., year) where the monitoring criteria were met. (Only applicable for criteria pollutants.)

The number of days during the year which the monitor was scheduled to take samples if measurements are required.

The highest value for the indicated year. This only includes data at the selected duration or standard (e.g., it may be the maximum daily value). For seasonal monitoring, it only includes data during the effective monitoring season.

The second highest value for the indicated year. This only includes data at the selected duration or standard (e.g., it may be the second maximum daily value). For seasonal monitoring, it only includes data during the effective monitoring season.

The third highest value for the indicated year. This only includes data at the selected duration or standard (e.g., it may be the third maximum daily value). For seasonal monitoring, it only includes data during the effective monitoring season.

The fourth highest value for the indicated year. This only includes data at the selected duration or standard (e.g., it may be the fourth maximum daily value). For seasonal monitoring, it only includes data during the effective monitoring season.

The number of samples in the summarized sample set that exceed the level of the primary standard. (Only applicable for criteria pollutants.)

The estimated number of days greater than the standard for the year (or quarter if viewing quarterly data). It is computed for specific pollutants when an exceedance has occurred during the year. The underlying assumption is that missing data is just as likely to exceed the standard as reported data.

Calculation method:

\[e = d + \left(\left(\frac{d}{v}\right)\cdot(r-v-a)\right)\]

Where:

\( e = \) estimated days greater than standard
\( d = \) count of primary exceedances
\( v = \) count of valid days
\( r = \) count of required days
\( a = \) count of days assumed less than standard

The number of invalid or missing days in the effective monitoring season whose daily maxima are assumed to be less than or equal to the standard. (Only applicable to 1-hour and 8-hour ozone.)

Calculation method:

A missing or invalid day is assumed to be less than the standard when either of the following conditions exists:

The ratio of recorded sample values that were reported divided by the number of sample values that were scheduled to have been reported for the year.

Calculation method:

\[p = \frac {(v+a)} {n} \cdot 100\]

Where:

\( p = \) observation percent
\( v = \) number of valid days
\( a = \) number of missing days assumed less than standard
\( n = \) number of required days

The number of required monitoring days in the aggregation period (e.g., year) where the monitoring criteria were met. (Only applicable for criteria pollutants.)

Calculation method:

The number of active days within the effective monitoring season when minimum daily criteria were met, i.e., the daily Summary Criteria Met value is ?Y?.

The number of days during the year which the monitor was scheduled to take samples if measurements are required.

Calculation method:

The number of active days within the effective monitoring season.

The max values listed are the daily max values. That is, each day the maximum 8-hour average is determined. This is the daily max value. For the year, the four highest daily max values are listed.

The highest value for the indicated year. This only includes data at the selected duration or standard (e.g., it may be the maximum daily value). For seasonal monitoring, it only includes data during the effective monitoring season.

The second highest value for the indicated year. This only includes data at the selected duration or standard (e.g., it may be the second maximum daily value). For seasonal monitoring, it only includes data during the effective monitoring season.

The third highest value for the indicated year. This only includes data at the selected duration or standard (e.g., it may be the third maximum daily value). For seasonal monitoring, it only includes data during the effective monitoring season.

The fourth highest value for the indicated year. This only includes data at the selected duration or standard (e.g., it may be the fourth maximum daily value). For seasonal monitoring, it only includes data during the effective monitoring season.

The number of samples in the summarized sample set that exceed the level of the primary standard. (Only applicable for criteria pollutants.)

The number of observations (samples) taken during the averaging period.

The number of days during the year which the monitor was scheduled to take samples if measurements are required.

Calculation method:

The number of active days in the year.

The number of required monitoring days in the aggregation period (e.g., year) where the monitoring criteria were met. (Only applicable for criteria pollutants.)

Calculation method:

The number of valid 24-hour block Arithmetic Mean (NAAQS) values within the year.

The ratio of recorded sample values that were reported divided by the number of sample values that were scheduled to have been reported for the year.

Calculation method:

\[p = \frac {v} {n} \cdot 100\]

Where:

\( p = \) observation percent
\( v = \) number of valid days
\( n = \) number of required (e.g., scheduled) days

The highest value for the indicated year. This only includes data at the selected duration or standard (e.g., it may be the maximum daily value). For seasonal monitoring, it only includes data during the effective monitoring season.

The second highest value for the indicated year. This only includes data at the selected duration or standard (e.g., it may be the second maximum daily value). For seasonal monitoring, it only includes data during the effective monitoring season.

The third highest value for the indicated year. This only includes data at the selected duration or standard (e.g., it may be the third maximum daily value). For seasonal monitoring, it only includes data during the effective monitoring season.

The fourth highest value for the indicated year. This only includes data at the selected duration or standard (e.g., it may be the fourth maximum daily value). For seasonal monitoring, it only includes data during the effective monitoring season.

The number of samples in the summarized sample set that exceed the level of the primary standard. (Only applicable for criteria pollutants.)

The estimated number of days greater than the standard for the year (or quarter if viewing quarterly data). It is computed for specific pollutants when an exceedance has occurred during the year. The underlying assumption is that missing data is just as likely to exceed the standard as reported data.

Calculation method:

\[E = {\sum_{q=1}^4 e_q} \quad \text{Where} \quad e_q = \left( \frac {N_q} {m_q} \right) \cdot \sum_{i=1}^{m_q} \left( \frac {v_i} {k_i} \right)\]

Where:

\( E = \) estimated number of exceedances for the year
\( e_q = \) estimated number of exceedances for calendar quarter q
\( q = \) calendar qaurter
\( N_q = \) number of days in quarter q
\( m_q = \) number of strata with samples during quarter q
\( v_i = \) number of observed exceedances in stratum i
\( k_i = \) number of samples in stratum i

Number of scheduled and make-up days that are given credit when determining data completeness for a site. (Note: This may not be the number of values averaged, since "extra samples" may be included in the mean.

Calculation method:

The sum of valued, scheduled sampling days, plus make-ups for missing scheduled days.

Scheduled days are the number of days within the year that were scheduled for sampling, as determined by the EPA-defined calendar for the required collection frequency, and which also fall within the period of operation, as defined by sampling periods.

A make-up day is a sample recorded in the same stratum as, or exactly seven days after, a missing scheduled sample. In both conditions, the make-up sample must occur within the same quarter as the missed sample. A maximum of five make-up samples are allowed per quarter. (References: EPA-454/R-99-008 Guide line on Data Handling Conventions for the PM NAAQS; Memorandum: February 3, 1999 Use of Make-Up Samples to Replace Scheduled PM Samples.)

The highest value for the indicated year. This only includes data at the selected duration or standard (e.g., it may be the maximum daily value). For seasonal monitoring, it only includes data during the effective monitoring season.

The second highest value for the indicated year. This only includes data at the selected duration or standard (e.g., it may be the second maximum daily value). For seasonal monitoring, it only includes data during the effective monitoring season.

The third highest value for the indicated year. This only includes data at the selected duration or standard (e.g., it may be the third maximum daily value). For seasonal monitoring, it only includes data during the effective monitoring season.

The fourth highest value for the indicated year. This only includes data at the selected duration or standard (e.g., it may be the fourth maximum daily value). For seasonal monitoring, it only includes data during the effective monitoring season.

The sample value in the summarized sample set where 98% of the values in that set are less than or equal to it. (For ozone, based on valid daily maxima; for PM2.5, based on seasonal and non-seasonal algorithms.)

Note, for seasonal PM 2.5 sampling, there is an alternate method for computing the 98th percentile that weights the seasons based on the number of samples.

The weighted arithmetic mean for the sample values in the summarized sample set. (Only applicable to PM10 and PM2.5)

Calculation method:

\[Mean_{wtd} = \frac{\sum_{i=1}^{q} u_i} {q}\]

Where:

\( Mean_{wtd} = \) weighted arithmetic mean
\( q = \) number of active quarters
\( i = \) quarter
\( u_i = \) arithmetic mean of samples in quarter i of appropriate exceptional data type

The name or description assigned in AQS to the parameter measured by the monitor. Parameters may be pollutants or non-pollutants (e.g., wind speed).

The name of the agency assigned as Primary Quality Assurance Organization for the monitor reporting data.

An indicator (Y or N) as to whether the listed pollutant is subject to the requirements of 40 CFR Part 50 Appendix A.

The year the data represents.

A numerical identifier (with leading zero) for the EPA Region where the monitoring site resides.

The two letter abbreviation for the state.

The AQS Site ID in the format XX-YYY-ZZZZ where XX is the State FIPS code, YYY is the County FIPS code, and ZZZZ is the site number within the county.

This is the "Parameter Occurrence Code" used to distinguish different instruments that measure the same parameter at the same site. There is no meaning to the POC (e.g. POC 1 does not indicate the primary monitor). For example, the first monitor established to measure carbon monoxide (CO) at a site could have a POC of 1. If an additional monitor were established at the same site to measure CO, that monitor could have a POC of 2. However, if a new instrument were installed to replace the original instrument used as the first monitor, that would be the same monitor and it would still have a POC of 1.

An administrative or regulatory classification for the monitor.

See the "Notes" page at the beginning of the report for the meanings of the abbreviations.

The greatest of: January 1 of the summary year; Earliest date falling within a sample period for the monitor and summary year; Earliest date falling within the PQAO assignment for the monitor and summary year.

The earliest of: December 31 of the summary year; Latest date falling within a sample period for the monitor and summary year; Latest date falling within the PQAO assignment for the monitor and summary year; Calendar date that the summary is retrieved.

The number of intervals between the begin and end date that require a one point quality control check.

The number of two week periods falling completely within a sample period and the PQAO assignment for the monitor for the summary year.

The number of intervals between the begin and end date in which a one point quality control check was reported.

The 1-point QC completeness data is evaluated in the following manner: * Count the number of checks in each 14-day interval starting with the Jan 1-14 interval. For each 14-day interval, multiple checks will only count as one. * Divide the total number of checks in #1 by 26

For certification, a green Y is > 75%. That means a monitoring organization could miss six 14-day intervals (meaning a check past the 14-day interval) and still get a green Y. For a yellow flag, they could miss nine 14-day intervals and get a warning. Missing ten 14-day intervals will elicit an N flag.

The 90% Upper bound on Coefficient of Variance. This is often used as the precision estimate for one point quality control checks. Only valid observations are used in this calculation.

Calculation method:

\[CV = \sqrt{ \frac { n \cdot {\left( {\sum_{i=1}^n d_i^2 } \right)} - {\left( {\sum_{i=1}^n d_i } \right)^2} } {2n \cdot (n-1)} } \cdot \sqrt { \frac {n-1} { \chi_{0.1, n-1}^2} } \]

Where:

\(CV = \) the coefficient of variation
\(d = \) the percent difference for any given audit
\(n = \) the number of audits examined
\({ \chi_{0.1, n-1}^2} = \) the 10th percentile of a chi-squared distribution with n-1 degrees of freedom

Calculation method:

\[d = \frac { \left( Y - X \right) } {X} \cdot 100\]

Where:

\( d = \) percent difference
\( X = \) indicated (measured) concentration or flow rate
\( Y = \) known (true) concentration or flow rate

The quality control bias estimator is an upper bound on the mean absolute value of the percent differences of regular and assessment samples.

Calculation method:

\[|bias| = AB + t_{0.95, n-1} \cdot \frac {AS} {\sqrt{n}} \]

Where:

\(\vert bias \vert = \) the bias estimate
\(AB = \) the mean of the absolute values of the percent differences
\(AS = \) the standard deviation of the absolute value of the percent differences
\(n = \) the number of audits examined
\(t_{0.95, n-1} = \) the 95th quantile of a t-distribution with n-1 degrees of freedom

Calculation method:

\[AB = \frac {1} {n} \cdot {\sum_{i=1}^n \vert d_i \vert } \]

Calculation method:

\[AS = \sqrt { \frac { ({n \cdot \sum_{i=1}^n {\vert d_i \vert}^2}) - ({\sum_{i=1}^n {\vert d_i \vert}})^2 } {n \cdot (n-1)} } \]

Sign (+, -, or +/-) of the bias of the percent differences.

A sign will be determined by rank ordering the percent differences of the QC check samples from a given site for a particular assessment interval.

Calculate the 25th and 75th percentiles of the percent differences for each site. The absolute bias upper bound should be flagged as positive if both percentiles are positive and negative if both percentiles are negative. If one is positive and the other is negative, the upper bound would not be flagged and +/- will be displayed.

The name or description assigned in AQS to the parameter measured by the monitor. Parameters may be pollutants or non-pollutants (e.g., wind speed).

The name of the agency assigned as Primary Quality Assurance Organization for the monitor reporting data.

An indicator (Y or N) as to whether the listed pollutant is subject to the requirements of 40 CFR Part 50 Appendix A.

The year the data represents.

A numerical identifier (with leading zero) for the EPA Region where the monitoring site resides.

The two letter abbreviation for the state.

The AQS Site ID in the format XX-YYY-ZZZZ where XX is the State FIPS code, YYY is the County FIPS code, and ZZZZ is the site number within the county.

This is the "Parameter Occurrence Code" used to distinguish different instruments that measure the same parameter at the same site. There is no meaning to the POC (e.g. POC 1 does not indicate the primary monitor). For example, the first monitor established to measure carbon monoxide (CO) at a site could have a POC of 1. If an additional monitor were established at the same site to measure CO, that monitor could have a POC of 2. However, if a new instrument were installed to replace the original instrument used as the first monitor, that would be the same monitor and it would still have a POC of 1.

An administrative or regulatory classification for the monitor.

The greatest of: January 1 of the summary year; Earliest date falling within a sample period for the monitor and summary year; Earliest date falling within the PQAO assignment for the monitor and summary year.

The earliest of: December 31 of the summary year; Latest date falling within a sample period for the monitor and summary year; Latest date falling within the PQAO assignment for the monitor and summary year; Calendar date that the summary is retrieved.

The average of the percent differences for all pairs taken at each of the audit levels. Levels 1-5 are displayed on the first row, and levels 6-10 are displayed on the second row (i.e., level 6 is immediately below level 1).

Calculation method:

\[d = \frac { \left( Y - X \right) } {X} \cdot 100\]

Where:

\( d = \) percent difference
\( X = \) indicated (measured) concentration or flow rate
\( Y = \) known (true) concentration or flow rate

The number of audits performed in each calendar quarter.

An indicator of whether the Annual Performance Audit reporting requirement was met. A monitor has met the conditions if (a) an audit was performed at least once per year and (b) the audit was performed, at a minimum, within three consecutive levels (1,2,3 or 2,3,4 or 3,4,5) within each of those years.

The lower probability limit (95% confidence) of the compared percent differences. This is calculated for quality assurance assessment results.

Calculation method:

\[l = D - \left( S \cdot 1.96 \right)\]

Where:

\( l = \) lower 95% confidence limit
\( D = \) mean value
\( S = \) standard deviation of the percent differences

The upper probability limit (95% confidence) of the compared percent differences.

Calculation method:

\[u = D + \left( S \cdot 1.96 \right)\]

Where:

\( u = \) upper 95% confidence limit
\( D = \) mean value
\( S = \) standard deviation of the percent differences

The percent of audits that are between the upper and lower confidence limits. This value is rounded to a whole number.

The name or description assigned in AQS to the parameter measured by the monitor. Parameters may be pollutants or non-pollutants (e.g., wind speed).

The name of the agency assigned as Primary Quality Assurance Organization for the monitor reporting data.

An indicator (Y or N) as to whether the listed pollutant is subject to the requirements of 40 CFR Part 50 Appendix A.

The year the data represents.

A numerical identifier (with leading zero) for the EPA Region where the monitoring site resides.

The two letter abbreviation for the state.

The AQS Site ID in the format XX-YYY-ZZZZ where XX is the State FIPS code, YYY is the County FIPS code, and ZZZZ is the site number within the county.

This is the "Parameter Occurrence Code" used to distinguish different instruments that measure the same parameter at the same site. There is no meaning to the POC (e.g. POC 1 does not indicate the primary monitor). For example, the first monitor established to measure carbon monoxide (CO) at a site could have a POC of 1. If an additional monitor were established at the same site to measure CO, that monitor could have a POC of 2. However, if a new instrument were installed to replace the original instrument used as the first monitor, that would be the same monitor and it would still have a POC of 1.

An administrative or regulatory classification for the monitor.

See the "Notes" page at the beginning of the report for the meanings of the abbreviations.

The greatest of: January 1 of the summary year; Earliest date falling within a sample period for the monitor and summary year; Earliest date falling within the PQAO assignment for the monitor and summary year.

The earliest of: December 31 of the summary year; Latest date falling within a sample period for the monitor and summary year; Latest date falling within the PQAO assignment for the monitor and summary year; Calendar date that the summary is retrieved.

The number of intervals between the begin and end date that require a one point quality control check.

The number of observations (samples) taken during the averaging period.

The number of observations (samples) taken during the averaging period.

The relative difference between the known and measured concentrations, expressed as a percentage.

Calculation method:

\[d = \frac { \left( Y - X \right) } {X} \cdot 100\]

Where:

\( d = \) percent difference
\( X = \) indicated (measured) concentration or flow rate
\( Y = \) known (true) concentration or flow rate

The ratio of recorded sample values that were reported divided by the number of sample values that were scheduled to have been reported for the year.

The quality control bias estimator is an upper bound on the mean absolute value of the percent differences of regular and assessment samples.

Calculation method:

\[|bias| = AB + t_{0.95, n-1} \cdot \frac {AS} {\sqrt{n}} \]

Where:

\(\vert bias \vert = \) the bias estimate
\(AB = \) the mean of the absolute values of the percent differences
\(AS = \) the standard deviation of the absolute value of the percent differences
\(n = \) the number of audits examined
\(t_{0.95, n-1} = \) the 95th quantile of a t-distribution with n-1 degrees of freedom

Calculation method:

\[AB = \frac {1} {n} \cdot {\sum_{i=1}^n \vert d_i \vert } \]

Calculation method:

\[AS = \sqrt { \frac { ({n \cdot \sum_{i=1}^n {\vert d_i \vert}^2}) - ({\sum_{i=1}^n {\vert d_i \vert}})^2 } {n \cdot (n-1)} } \]

Sign (+, -, or +/-) of the bias of the percent differences.

A sign will be determined by rank ordering the percent differences of the QC check samples from a given site for a particular assessment interval.

Calculate the 25th and 75th percentiles of the percent differences for each site. The absolute bias upper bound should be flagged as positive if both percentiles are positive and negative if both percentiles are negative. If one is positive and the other is negative, the upper bound would not be flagged and +/- will be displayed.

The name or description assigned in AQS to the parameter measured by the monitor. Parameters may be pollutants or non-pollutants (e.g., wind speed).

The name of the agency assigned as Primary Quality Assurance Organization for the monitor reporting data.

An indicator (Y or N) as to whether the listed pollutant is subject to the requirements of 40 CFR Part 50 Appendix A.

The year the data represents.

A numerical identifier (with leading zero) for the EPA Region where the monitoring site resides.

The two letter abbreviation for the state.

The AQS Site ID in the format XX-YYY-ZZZZ where XX is the State FIPS code, YYY is the County FIPS code, and ZZZZ is the site number within the county.

This is the "Parameter Occurrence Code" used to distinguish different instruments that measure the same parameter at the same site. There is no meaning to the POC (e.g. POC 1 does not indicate the primary monitor). For example, the first monitor established to measure carbon monoxide (CO) at a site could have a POC of 1. If an additional monitor were established at the same site to measure CO, that monitor could have a POC of 2. However, if a new instrument were installed to replace the original instrument used as the first monitor, that would be the same monitor and it would still have a POC of 1.

An administrative or regulatory classification for the monitor.

See the "Notes" page at the beginning of the report for the meanings of the abbreviations.

The greatest of: January 1 of the summary year; Earliest date falling within a sample period for the monitor and summary year; Earliest date falling within the PQAO assignment for the monitor and summary year.

The earliest of: December 31 of the summary year; Latest date falling within a sample period for the monitor and summary year; Latest date falling within the PQAO assignment for the monitor and summary year; Calendar date that the summary is retrieved.

The number of intervals between the begin and end date that require a one-point quality control check.

The number of two week periods falling completely within a sample period and the PQAO assignment for the monitor for the summary year.

The number distinct quarters in which an evaluation was performed.

The ratio of actual evaluations to required evaluations. If sampler operates <9 months at least 1 is expected. If operated >9 months two audits expected.

This field is valued at 100% if there were 3 or 4 audits in 3 or 4 distinct quarters; or if there were 2 audits during the year separated by at least 5 months.

This field is valued at 50% if there were 2 audits during the year not separated by at least 5 months.

The number of audits performed in each calendar quarter.

The average of the Percent Differences (d).

Calculate the average of the values determined using the equation below for each audit.

Calculation method:

\[d = \frac { \left( Y - X \right) } {X} \cdot 100\]

Where:

\( d = \) percent difference
\( X = \) indicated (measured) concentration or flow rate
\( Y = \) known (true) concentration or flow rate

The lower probability limit (95% confidence) of the compared percent differences. This is calculated for quality assurance assessment results.

Calculation method:

\[l = D - \left( S \cdot 1.96 \right)\]

Where:

\( l = \) lower 95% confidence limit
\( D = \) mean value
\( S = \) standard deviation of the percent differences

The upper probability limit (95% confidence) of the compared percent differences.

Calculation method:

\[u = D + \left( S \cdot 1.96 \right)\]

Where:

\( u = \) upper 95% confidence limit
\( D = \) mean value
\( S = \) standard deviation of the percent differences

The percent of audits that are between the upper and lower confidence limits. This value is rounded to a whole number.

The name or description assigned in AQS to the parameter measured by the monitor. Parameters may be pollutants or non-pollutants (e.g., wind speed).

The name of the agency assigned as Primary Quality Assurance Organization for the monitor reporting data.

An indicator (Y or N) as to whether the listed pollutant is subject to the requirements of 40 CFR Part 50 Appendix A.

The year the data represents.

A three-digit code representing the measurement method. A method code is only unique within a parameter (that is, method 132 for ozone is not the same as method 123 for benzene).

A numerical identifier (with leading zero) for the EPA Region where the monitoring site resides.

The two letter abbreviation for the state.

The number of collocated audits required. Calculated for PM as 15% of total sites. Calculated for Lead as If there are 5 or fewer lead sites in the PQAO measuring the parameter in a given year then 4 Lead (PM10 or TSP) collocated audits are required; if there more than 5 lead sites in the PQAO within the year, the number required is 6.

This number only includes sites that meet the selection criteria entered when the report was created and that are within the sort group of: method and PQAO.

The number of sites (within the group) that require collocated monitors.

The number of sites (within the group) that have collocated monitors.

The percentage of sites that meet their collocation requirements, based on the previous two fields.

The number of collocated audits that are required to be performed. Determined as CEIL(0.2 * Monitor_Count)

The number of collocated data pairs reported to AQS.

The number of valid collocated value pairs recorded during the time period. For a single assessment, the value will be 1 if the pair is valid and 0 if it is not. The "valid" collocated observations are those where the primary and collocated measurement values are both equal to or above the limits identified in 40 CFR Part 58 Appendix A 4(c). These are the values that are used for precision and bias calculations (e.g. CV UB).

The ratio of # Obs to # Obs Req.

The 90% Upper bound on Coefficient of Variance. This is often used as the precision estimate for one point quality control checks. Only valid observations are used in this calculation.

Calculation method:

\[CV = \sqrt{ \frac { n \cdot {\left( {\sum_{i=1}^n d_i^2 } \right)} - {\left( {\sum_{i=1}^n d_i } \right)^2} } {2n \cdot (n-1)} } \cdot \sqrt { \frac {n-1} { \chi_{0.1, n-1}^2} } \]

Where:

\(CV = \) the coefficient of variation
\(d = \) the percent difference for any given audit
\(n = \) the number of audits examined
\({ \chi_{0.1, n-1}^2} = \) the 10th percentile of a chi-squared distribution with n-1 degrees of freedom

Calculation method:

\[d = \frac { \left( Y - X \right) } {X} \cdot 100\]

Where:

\( d = \) percent difference
\( X = \) indicated (measured) concentration or flow rate
\( Y = \) known (true) concentration or flow rate

The name or description assigned in AQS to the parameter measured by the monitor. Parameters may be pollutants or non-pollutants (e.g., wind speed).

The name of the agency assigned as Primary Quality Assurance Organization for the monitor reporting data.

An indicator (Y or N) as to whether the listed pollutant is subject to the requirements of 40 CFR Part 50 Appendix A.

The year the data represents.

A numerical identifier (with leading zero) for the EPA Region where the monitoring site resides.

The two letter abbreviation for the state.

The number of collocated audits required. Calculated for PM as 15% of total sites. Calculated for Lead as If there are 5 or fewer lead sites in the PQAO measuring the parameter in a given year then 4 Lead (PM10 or TSP) collocated audits are required; if there more than 5 lead sites in the PQAO within the year, the number required is 6.

This number only includes sites that meet the selection criteria entered when the report was created and that are within the sort group of: method and PQAO.

The number of PEP (performance evaluation program) assessment observations that are required to meet regulatory obligations.

The number of PEP (performance evaluation program) assessment observations that were reported to AQS.

The number of Collocated Performance Evaluation Program (PEP) audits required to be collected. This value is only computed for SUMMARY rows, where applicable.

Lead Only: Number of Collocated PEP Audits Collected

The percentage of required observations (or scheduled days) made for the given assessment time period.

The quality control bias estimator is an upper bound on the mean absolute value of the percent differences of regular and assessment samples.

Calculation method:

\[|bias| = AB + t_{0.95, n-1} \cdot \frac {AS} {\sqrt{n}} \]

Where:

\(\vert bias \vert = \) the bias estimate
\(AB = \) the mean of the absolute values of the percent differences
\(AS = \) the standard deviation of the absolute value of the percent differences
\(n = \) the number of audits examined
\(t_{0.95, n-1} = \) the 95th quantile of a t-distribution with n-1 degrees of freedom

Calculation method:

\[AB = \frac {1} {n} \cdot {\sum_{i=1}^n \vert d_i \vert } \]

Calculation method:

\[AS = \sqrt { \frac { ({n \cdot \sum_{i=1}^n {\vert d_i \vert}^2}) - ({\sum_{i=1}^n {\vert d_i \vert}})^2 } {n \cdot (n-1)} } \]

Sign (+, -, or +/-) of the bias of the percent differences.

A sign will be determined by rank ordering the percent differences of the QC check samples from a given site for a particular assessment interval.

Calculate the 25th and 75th percentiles of the percent differences for each site. The absolute bias upper bound should be flagged as positive if both percentiles are positive and negative if both percentiles are negative. If one is positive and the other is negative, the upper bound would not be flagged and +/- will be displayed.

The lower probability limit (95% confidence) of the compared percent differences. This is calculated for quality assurance assessment results.

Calculation method:

\[l = D - \left( S \cdot 1.96 \right)\]

Where:

\( l = \) lower 95% confidence limit
\( D = \) mean value
\( S = \) standard deviation of the percent differences

The upper probability limit (95% confidence) of the compared percent differences.

Calculation method:

\[u = D + \left( S \cdot 1.96 \right)\]

Where:

\( u = \) upper 95% confidence limit
\( D = \) mean value
\( S = \) standard deviation of the percent differences

The name of the agency assigned as Primary Quality Assurance Organization for the monitor reporting data.

The year the data represents.

A numerical identifier (with leading zero) for the EPA Region where the monitoring site resides.

The two letter abbreviation for the state.

The AQS code corresponding to the parameter measured by the monitor.

The Agency Code of the laboratory performing the audits.

The breakdown by year and quarter of the ration of expected audits versus audits reported.

The quality control bias estimator is an upper bound on the mean absolute value of the percent differences of regular and assessment samples.

Calculation method:

\[|bias| = AB + t_{0.95, n-1} \cdot \frac {AS} {\sqrt{n}} \]

Where:

\(\vert bias \vert = \) the bias estimate
\(AB = \) the mean of the absolute values of the percent differences
\(AS = \) the standard deviation of the absolute value of the percent differences
\(n = \) the number of audits examined
\(t_{0.95, n-1} = \) the 95th quantile of a t-distribution with n-1 degrees of freedom

Calculation method:

\[AB = \frac {1} {n} \cdot {\sum_{i=1}^n \vert d_i \vert } \]

Calculation method:

\[AS = \sqrt { \frac { ({n \cdot \sum_{i=1}^n {\vert d_i \vert}^2}) - ({\sum_{i=1}^n {\vert d_i \vert}})^2 } {n \cdot (n-1)} } \]

Sign (+, -, or +/-) of the bias of the percent differences.

A sign will be determined by rank ordering the percent differences of the QC check samples from a given site for a particular assessment interval.

Calculate the 25th and 75th percentiles of the percent differences for each site. The absolute bias upper bound should be flagged as positive if both percentiles are positive and negative if both percentiles are negative. If one is positive and the other is negative, the upper bound would not be flagged and +/- will be displayed.

The lower probability limit (95% confidence) of the compared percent differences. This is calculated for quality assurance assessment results.

Calculation method:

\[l = D - \left( S \cdot 1.96 \right)\]

Where:

\( l = \) lower 95% confidence limit
\( D = \) mean value
\( S = \) standard deviation of the percent differences

The upper probability limit (95% confidence) of the compared percent differences.

Calculation method:

\[u = D + \left( S \cdot 1.96 \right)\]

Where:

\( u = \) upper 95% confidence limit
\( D = \) mean value
\( S = \) standard deviation of the percent differences

Year for which the data is being certified. If the from of the design value is three years, data for the certification year and the two prior years will be considered.

The name of the agency assigned as "Certifying" for the monitor reporting data.

This section contains one row summarizing the information for each pollutant for the Certifying Agency contained in the report.

This section contains one row summarizing the information for each Primary Quality Assurance Organization affiliated with a monitor listed that has a PQAO that matches the one listed.

This section contains one row for each monitor that AQS recommends against certification.

The name or description assigned in AQS to the parameter measured by the monitor. Parameters may be pollutants or non-pollutants (e.g., wind speed).

The AQS code corresponding to the parameter measured by the monitor.

The number of monitors for that pollutant evaluated by the report.

Out of those monitors evaluated for the pollutant, the number that pass all of the AQS automated checks and the system recommends the certification request be concurred by the regional office.

Out of those monitors evaluated for the pollutant, the number that do not pass all of the AQS automated checks and the system recommends the certification request be not be concurred by the regional office.

The name of the agency assigned as Primary Quality Assurance Organization for the monitor reporting data.

The code representing the Primary Quality Assurance Organization for this monitor.

The date of the most recent Technical Systems Audit of the PQAO.

The code representing the Primary Quality Assurance Organization for this monitor.

The AQS code corresponding to the parameter measured by the monitor.

The AQS Site ID in the format XX-YYY-ZZZZ where XX is the State FIPS code, YYY is the County FIPS code, and ZZZZ is the site number within the county.

This is the "Parameter Occurrence Code" used to distinguish different instruments that measure the same parameter at the same site. There is no meaning to the POC (e.g. POC 1 does not indicate the primary monitor). For example, the first monitor established to measure carbon monoxide (CO) at a site could have a POC of 1. If an additional monitor were established at the same site to measure CO, that monitor could have a POC of 2. However, if a new instrument were installed to replace the original instrument used as the first monitor, that would be the same monitor and it would still have a POC of 1.

AQS Recommended Certificaiton Flag based on the completeness of the data and the results of the QA assessments.

Certification Flag value requested by the certifying agency to override the AQS recommended value (there should be an accompanying explanation in the State Comment field of AQS).

The AQS automated check causing the system to recommended against certification for the monitor.

Year for which the data is being certified. If the from of the design value is three years, data for the certification year and the two prior years will be considered.

The name of the agency assigned as "Certifying" for the monitor reporting data.

The name or description assigned in AQS to the parameter measured by the monitor. Parameters may be pollutants or non-pollutants (e.g., wind speed).

The body of the report is also organized by PQAO, which is listed in a sub-header.

The name of the agency assigned as Primary Quality Assurance Organization for the monitor reporting data.

Date the Quality Assurance Project Plan was approved.

Number of Monitor Level NPAP Assessments submitted where at least 3 levels meet bias criteria

National Performance Audit Program Bias Estimate.

Status (Y/N) of NPAP PQAO-Level Summary

The AQS Site ID in the format XX-YYY-ZZZZ where XX is the State FIPS code, YYY is the County FIPS code, and ZZZZ is the site number within the county.

This is the "Parameter Occurrence Code" used to distinguish different instruments that measure the same parameter at the same site. There is no meaning to the POC (e.g. POC 1 does not indicate the primary monitor). For example, the first monitor established to measure carbon monoxide (CO) at a site could have a POC of 1. If an additional monitor were established at the same site to measure CO, that monitor could have a POC of 2. However, if a new instrument were installed to replace the original instrument used as the first monitor, that would be the same monitor and it would still have a POC of 1.

An administrative or regulatory classification for the monitor.

The measure of central tendency obtained from the sum of the observed pollutant data values or National Ambient Air Quality Standards (NAAQS) averages in the yearly data set divided by the number of values that comprise the sum for the yearly data set. For criteria pollutants, the sum of values only adds the values with the appropriate flagging and concurrence for the exceptional data type.

The lowest sample value recorded during the aggregation period.

The maximum value at the given duration for the aggregation period.

The number of samples during the year that exceeded the primary air quality standard.

Number of sample measurements with Stat/CR finding but without "validated value" qualifier flag

Annual percent completeness. The ratio of the number of reported samples to the number of scheduled samples (or assessments).

The monitor precision estimate is made by evaluating the Coefficient of Variation (CV) of all samples taken at a monitor over a given time.

The bias estimate made by evaluating the mean of the absolute values of the percent differences. (40 CFR Part 58, Appendix A defines bias as the systematic or persistent distortion of a measurement process which causes errors in one direction.)

In this case, it is the combined bias estimate for the 1-Point Quality Assurance assessments conducted at the monitor during the year.

Annual percent completeness. The ratio of the number of reported samples to the number of scheduled samples (or assessments).

The 1-point QC completeness data is evaluated in the following manner: * Count the number of checks in each 14-day interval starting with the Jan 1-14 interval. For each 14-day interval, multiple checks will only count as one. * Divide the total number of checks in #1 by 26

For certification, a green Y is > 75%. That means a monitoring organization could miss six 14-day intervals (meaning a check past the 14-day interval) and still get a green Y. For a yellow flag, they could miss nine 14-day intervals and get a warning. Missing ten 14-day intervals will elicit an N flag.

The bias estimate made by evaluating the mean of the absolute values of the percent differences. (40 CFR Part 58, Appendix A defines bias as the systematic or persistent distortion of a measurement process which causes errors in one direction.)

In this case, it is the combined bias estimate for the Annual Performance Audit assessments conducted at the monitor during the year.

Annual percent completeness. The ratio of the number of reported samples to the number of scheduled samples (or assessments).

For gaseous pollutants, this field will always be null

The bias estimate made by evaluating the mean of the absolute values of the percent differences. (40 CFR Part 58, Appendix A defines bias as the systematic or persistent distortion of a measurement process which causes errors in one direction.)

In this case, it is the combined bias estimate for the National Performance Audit Program assessments conducted at the monitor during the year.

For gaseous pollutants, this field will always be Y

Status (Y/N) of QAPP meeting in certification criteria.

The QAPP Approval Field is based on QAPP approval dates supplied from the monitoring organizations to the EPA Regions. (Note, the QAPP approval date appears in the header of the Data Certification Report.)

If the QAPP approval date is less than 5 years from the date the report is run, the entry will be a green Y. If the approval date is more than 5 years in the past, the entry will be a red N.

This criteria was established in a 2017 policy memo "EPA Review of Monitoring Organizations QAPP’s for Critical Criteria Conformance" at https://www.epa.gov/sites/production/files/2017-10/documents/qappmemo.pdf.

AQS Recommended Certificaiton Flag based on the completeness of the data and the results of the QA assessments.

Certification Flag value requested by the certifying agency to override the AQS recommended value (there should be an accompanying explanation in the State Comment field of AQS).

Each row may be followed by comments relating to the certification of a particular monitor.

Comment provided by certifying agency about why the requested flag is different than the AQS recommended value

Comment Provided by EPA Regional office about why the requested difference.

Year for which the data is being certified. If the from of the design value is three years, data for the certification year and the two prior years will be considered.

The name of the agency assigned as "Certifying" for the monitor reporting data.

The name or description assigned in AQS to the parameter measured by the monitor. Parameters may be pollutants or non-pollutants (e.g., wind speed).

The name of the agency assigned as Primary Quality Assurance Organization for the monitor reporting data.

Date the Quality Assurance Project Plan was approved.

A three-digit code representing the measurement method. A method code is only unique within a parameter (that is, method 132 for ozone is not the same as method 123 for benzene).

The number of sites within the network that were active in the year.

The number of collocated audits required. Calculated for PM as 15% of total sites. Calculated for Lead as If there are 5 or fewer lead sites in the PQAO measuring the parameter in a given year then 4 Lead (PM10 or TSP) collocated audits are required; if there more than 5 lead sites in the PQAO within the year, the number required is 6.

The number of sites that are collocated within the network. This value is only computed for SUMMARY rows.

The smallest of the following quantities: 100 OR 100 * Num_Actual_colloc/Num_Colloc_Req

Coefficient of Variation of the Percent Differences (d).

The 90% Upper bound on Coefficient of Variance. This is often used as the precision estimate for one point quality control checks. Only valid observations are used in this calculation.

AQS Recommended Certificaiton Flag based on the completeness of the data and the results of the QA assessments.

This field indicates whether the PQAO-level evaluation criteria were met for collocation and is based on the completeness summary statistic and the precision estimate (CV-UB). See the table in the next section for the evaluation criteria.

The number of methods used at the site during the year.

Number of distinct methods audited via PEP assessments.

The number of Collocated Performance Evaluation Program (PEP) audits required to be collected. This value is only computed for SUMMARY rows, where applicable.

Lead Only: Number of Collocated PEP Audits Collected

The percentage of required Collocated Performance Evaluation Program (PEP) audits performed. This value is only computed for SUMMARY rows, where applicable.

The bias estimate made by evaluating the mean of the absolute values of the percent differences. (40 CFR Part 58, Appendix A defines bias as the systematic or persistent distortion of a measurement process which causes errors in one direction.)

AQS Recommended Certificaiton Flag based on the completeness of the data and the results of the QA assessments.

The AQS Site ID in the format XX-YYY-ZZZZ where XX is the State FIPS code, YYY is the County FIPS code, and ZZZZ is the site number within the county.

This is the "Parameter Occurrence Code" used to distinguish different instruments that measure the same parameter at the same site. There is no meaning to the POC (e.g. POC 1 does not indicate the primary monitor). For example, the first monitor established to measure carbon monoxide (CO) at a site could have a POC of 1. If an additional monitor were established at the same site to measure CO, that monitor could have a POC of 2. However, if a new instrument were installed to replace the original instrument used as the first monitor, that would be the same monitor and it would still have a POC of 1.

A three-digit code representing the measurement method. A method code is only unique within a parameter (that is, method 132 for ozone is not the same as method 123 for benzene).

An administrative or regulatory classification for the monitor.

The measure of central tendency obtained from the sum of the observed pollutant data values or National Ambient Air Quality Standards (NAAQS) averages in the yearly data set divided by the number of values that comprise the sum for the yearly data set. For criteria pollutants, the sum of values only adds the values with the appropriate flagging and concurrence for the exceptional data type.

The lowest sample value recorded during the aggregation period.

The maximum value at the given duration for the aggregation period.

The number of samples during the year that exceeded the primary air quality standard.

Number of sample measurements with Stat/CR finding but without "validated value" qualifier flag

Annual percent completeness. The ratio of the number of reported samples to the number of scheduled samples (or assessments).

The bias estimate made by evaluating the mean of the absolute values of the percent differences. (40 CFR Part 58, Appendix A defines bias as the systematic or persistent distortion of a measurement process which causes errors in one direction.)

Annual percent completeness. The ratio of the number of reported samples to the number of scheduled samples (or assessments).

Coefficient of Variation of the Percent Differences (d).

Annual percent completeness. The ratio of the number of reported samples to the number of scheduled samples (or assessments).

AQS Recommended Certificaiton Flag based on the completeness of the data and the results of the QA assessments.

AQS Recommended Certificaiton Flag based on the completeness of the data and the results of the QA assessments.

Status (Y/N) of QAPP meeting in certification criteria.

The QAPP Approval Field is based on QAPP approval dates supplied from the monitoring organizations to the EPA Regions. (Note, the QAPP approval date appears in the header of the Data Certification Report.)

If the QAPP approval date is less than 5 years from the date the report is run, the entry will be a green Y. If the approval date is more than 5 years in the past, the entry will be a red N.

This criteria was established in a 2017 policy memo "EPA Review of Monitoring Organizations QAPP’s for Critical Criteria Conformance" at https://www.epa.gov/sites/production/files/2017-10/documents/qappmemo.pdf.

AQS Recommended Certificaiton Flag based on the completeness of the data and the results of the QA assessments.

Certification Flag value requested by the certifying agency to override the AQS recommended value (there should be an accompanying explanation in the State Comment field of AQS).

Each row may be followed by comments relating to the certification of a particular monitor.

Comment provided by certifying agency about why the requested flag is different than the AQS recommended value

Comment Provided by EPA Regional office about why the requested difference.

Year for which the data is being certified. If the from of the design value is three years, data for the certification year and the two prior years will be considered.

The name of the agency assigned as "Certifying" for the monitor reporting data.

The name or description assigned in AQS to the parameter measured by the monitor. Parameters may be pollutants or non-pollutants (e.g., wind speed).

The name of the agency assigned as Primary Quality Assurance Organization for the monitor reporting data.

Date the Quality Assurance Project Plan was approved.

The number of sites within the network that were active in the year.

The number of collocated audits required. Calculated for PM as 15% of total sites. Calculated for Lead as If there are 5 or fewer lead sites in the PQAO measuring the parameter in a given year then 4 Lead (PM10 or TSP) collocated audits are required; if there more than 5 lead sites in the PQAO within the year, the number required is 6.

The number of sites that are collocated within the network. This value is only computed for SUMMARY rows.

The smallest of the following quantities: 100 OR 100 * Num_Actual_colloc/Num_Colloc_Req

Coefficient of Variation of the Percent Differences (d).

The 90% Upper bound on Coefficient of Variance. This is often used as the precision estimate for one point quality control checks. Only valid observations are used in this calculation.

AQS Recommended Certificaiton Flag based on the completeness of the data and the results of the QA assessments.

This field indicates whether the PQAO-level evaluation criteria were met for collocation and is based on the completeness summary statistic and the precision estimate (CV-UB). See the table in the next section for the evaluation criteria.

The number of methods used at the site during the year.

Number of distinct methods audited via PEP assessments.

The number of Collocated Performance Evaluation Program (PEP) audits required to be collected. This value is only computed for SUMMARY rows, where applicable.

Lead Only: Number of Collocated PEP Audits Collected

The percentage of required Collocated Performance Evaluation Program (PEP) audits performed. This value is only computed for SUMMARY rows, where applicable.

The bias estimate made by evaluating the mean of the absolute values of the percent differences. (40 CFR Part 58, Appendix A defines bias as the systematic or persistent distortion of a measurement process which causes errors in one direction.)

AQS Recommended Certificaiton Flag based on the completeness of the data and the results of the QA assessments.

The number of lead analysis audits required.

The number of lead analysis audits submitted.

The percentage of required observations (or scheduled days) made for the given assessment time period.

The bias estimate made by evaluating the mean of the absolute values of the percent differences. (40 CFR Part 58, Appendix A defines bias as the systematic or persistent distortion of a measurement process which causes errors in one direction.)

AQS Recommended Certificaiton Flag based on the completeness of the data and the results of the QA assessments.

The AQS Site ID in the format XX-YYY-ZZZZ where XX is the State FIPS code, YYY is the County FIPS code, and ZZZZ is the site number within the county.

This is the "Parameter Occurrence Code" used to distinguish different instruments that measure the same parameter at the same site. There is no meaning to the POC (e.g. POC 1 does not indicate the primary monitor). For example, the first monitor established to measure carbon monoxide (CO) at a site could have a POC of 1. If an additional monitor were established at the same site to measure CO, that monitor could have a POC of 2. However, if a new instrument were installed to replace the original instrument used as the first monitor, that would be the same monitor and it would still have a POC of 1.

An administrative or regulatory classification for the monitor.

The measure of central tendency obtained from the sum of the observed pollutant data values or National Ambient Air Quality Standards (NAAQS) averages in the yearly data set divided by the number of values that comprise the sum for the yearly data set. For criteria pollutants, the sum of values only adds the values with the appropriate flagging and concurrence for the exceptional data type.

The lowest sample value recorded during the aggregation period.

The maximum value at the given duration for the aggregation period.

The number of samples during the year that exceeded the primary air quality standard.

Number of sample measurements with Stat/CR finding but without "validated value" qualifier flag

Annual percent completeness. The ratio of the number of reported samples to the number of scheduled samples (or assessments).

The bias estimate made by evaluating the mean of the absolute values of the percent differences. (40 CFR Part 58, Appendix A defines bias as the systematic or persistent distortion of a measurement process which causes errors in one direction.)

Annual percent completeness. The ratio of the number of reported samples to the number of scheduled samples (or assessments).

The 90% Upper bound on Coefficient of Variance. This is often used as the precision estimate for one point quality control checks. Only valid observations are used in this calculation.

The percentage of required observations (or scheduled days) made for the given assessment time period.

AQS Recommended Certificaiton Flag based on the completeness of the data and the results of the QA assessments.

AQS Recommended Certificaiton Flag based on the completeness of the data and the results of the QA assessments.

AQS Recommended Certificaiton Flag based on the completeness of the data and the results of the QA assessments.

Status (Y/N) of QAPP meeting in certification criteria.

The QAPP Approval Field is based on QAPP approval dates supplied from the monitoring organizations to the EPA Regions. (Note, the QAPP approval date appears in the header of the Data Certification Report.)

If the QAPP approval date is less than 5 years from the date the report is run, the entry will be a green Y. If the approval date is more than 5 years in the past, the entry will be a red N.

This criteria was established in a 2017 policy memo "EPA Review of Monitoring Organizations QAPP’s for Critical Criteria Conformance" at https://www.epa.gov/sites/production/files/2017-10/documents/qappmemo.pdf.

AQS Recommended Certificaiton Flag based on the completeness of the data and the results of the QA assessments.

Certification Flag value requested by the certifying agency to override the AQS recommended value (there should be an accompanying explanation in the State Comment field of AQS).

Each row may be followed by comments relating to the certification of a particular monitor.

Comment provided by certifying agency about why the requested flag is different than the AQS recommended value

Comment Provided by EPA Regional office about why the requested difference.

The AQS parameter name (followed by the AQS parameter code in parentheses).

The Units of Measure in which data will be compared to the standard (followed by the AQS parameter code in parentheses).

A description of the ambient air quality standard rules used to aggregate statistics. A pollutant standard will include the year of promulgation and the form of the standard.

The annual statistic from AQS that is used to calculate the 3-year form of the design value.

The year to which the degin value applies. This is the last year of a three year stretch for design values with a three year form.

The level (concentration) of the listed standard in the standard units.

The name of the state where the monitoring site is located.

The header contains a reminder of the exceptional data type selected when creating the report: what kinds of qualified data are included or excluded from the calculations.

The AQS Site ID in the format XX-YYY-ZZZZ where XX is the State FIPS code, YYY is the County FIPS code, and ZZZZ is the site number within the county.

The street address giving an approximate location of the site.

For the year listed, the number of complete quarters of SO2 data at the site.

The sample value in the summarized sample set where 99% of the values in that set are less than or equal to it. (For ozone, based on valid daily maxima; for PM2.5, based on seasonal and non-seasonal algorithms.)

A Flag indicating the official certification status of the monitor-year, as assigned by the applicable regional office user.

Meanings of the abbreviations can be found on the code tables page

The AQS calculated design value in the form of the indicated standard. It may be at the site level (for PM2.5 or Lead) or at the monitor level (all other parameters).

Flag indicating whether the Design Value is valid (calculated using data that meets completeness criteria).