AQ eReporting: technical issues – informal discussion document
EIONET AQ workshop 6-7/9/2015
Based on EEA and ETC/ACM experiences to date, several technical questions have been identified
during the compilation and handling AQ data reported under the IPR. The Eionet Air Quality meeting
provides an informal forum where some of these issues may be discussed with air quality reporting
experts. This document briefly summarises six of the issues where some technical discussion and
proposals within the limits of the developed eReporting system may be beneficial to ensure a clear
understanding of how data reported by countries is presently being handled.
1
Rounding
The IPR Guidance Document indicates that “assessment data have to be compared to the
environmental objectives (i.e. limit value, target value, etc.) in the same numeric accuracy as is used
for the specification of the environmental objective in the Directive” (p. 10).
Issue
The rule leads to rounding which might potentially appear excessive in some cases, in particular for
assessment purposes. This is the case for e.g. BaP for which the target value (1 ng/m3) has to be
exceeded by more than 50% to be considered as an exceedance. In practice it appears that several
Member States consider one or two decimals when verifying compliance.
The IPR Guidance also notes that "AQUILA has recommended that in the case of polycyclic aromatic
hydrocarbons the target value, lower and upper assessment thresholds should be quoted to the
number of significant figures commensurate with the allowable uncertainty in their determination. In
most cases this will mean two significant digits (e.g. target value of 1.0 ng/m3 for benzo [a] pyrene).
AQUILA's recommendation will be taken into account in any future revision of the relevant
requirements."
For compliance reporting the legal requirement is to report to the same numeric accuracy as
specified in the Directive (i.e. in the case of BaP this means one significant digit) – nevertheless,
Member States are encouraged to follow the good practice to provide at least two significant digits.
For AQ assessment purposes (e.g. AQ report and other outputs), the EEA is presently using the
rounding rules presented in Annex which allow to obtain relevant classes of values for graphs and
maps.
Discussion question:
Do you agree with the rounding rules presented in Annex for assessment purposes?
2
Data capture, time coverage and data coverage
(from IPR guidance part 1)
The data capture is the proportion (%) of valid measurements obtained within the measurement
period defined by time coverage. For ozone measurements, the measurement period must be
divided into summer and winter seasons.
1
The data capture DC in a given averaging period (e.g. a year) is calculated as follows
DC = Nvalid / Ntotal * 100 %
Where
Nvalid
Ntotal
number of valid hourly/daily measurements in the measurement
period (defined by the time coverage)
total number of hours/days in that measurement period.
The time coverage (TC) is the proportion (%) of a calendar year (or summer season (April September) in the case of indicative ozone measurements), for which measurements were originally
planned.
The time coverage for a given averaging period (year / season …) is calculated as follows:
TC = Nplanned / Naveraging period* 100 %
Where
Nplanned
Naveraging_period
number of hours/days on which measurements were planned to take
place
total number of hours/days in the given averaging period (e.g.
year/season)
Issue
The eReporting system as described in the IPR guidance document and implemented by the EEA
system does not allow to make the difference between “planned” missing (or invalid or not reported
at all) data and “unplanned” missing (or invalid or not reported at all) data. If data is not provided
the system can only assume that the data is missing. The rules on time coverage and data capture
requirements may therefore lead to ambiguous cases.
This is illustrated below using the example given in the IPR Guidance document part 1 p.48 - 4
BaP daily
Situation a
Minimum time coverage
33%
Planned measurement time
156 days
Planned measurement time > time coverage
100 * 156 / 365 = 42.74% > 33%
N valid = 132 days
Data capture = 100 * 132 / 156 -84.6% rounded to 85%
Valid
Situation b
If we modify the planned measurement time from 156 to, say, 200 days, but have at the end of the
monitoring period the same number of valid measurement (132) this becomes
Minimum time coverage
33% over the year
Planned measurement time
200 days
Planned measurement time > time coverage
100 * 200 / 365 = 54.79% > 33%
N valid = 132 days
Data capture = 100 * 132 / 200 -66% < 85%
Not valid although same number of valid measurements and time coverage criteria fulfilled
In practice, the system calculates DC and TC as follows:
2
𝐷𝑎𝑡𝑎 𝐶𝑎𝑝𝑡𝑢𝑟𝑒 (𝐷𝐶) =
𝑇𝑖𝑚𝑒 𝐶𝑜𝑣𝑒𝑟𝑎𝑔𝑒 (𝑇𝐶) =
[count validdata ]
([count validdata ] + [count notvalid maint ] + [count notvalid other ])
([count validdata ] + [count notvalid maint ] + [count notvalid other ])
[Interval]
In order to avoid ambiguities which might result, the system also calculate the data coverage (DCov)
𝐷𝑎𝑡𝑎 𝐶𝑜𝑣𝑒𝑟𝑎𝑔𝑒 (𝐷𝐶𝑜𝑣) =
[count validdata ]
= 𝑇𝐶 ∗ 𝐷𝐶
[Interval]
In case of continuous measurement (TC = 100%), DC = DCov.
In the example presented above for BaP, both situation would obtain the same data coverage:
132 valid data over one year 36.17%) so both considered as valid (> 33%).
Discussion question:
Do you agree with the proposed calculation and Data Coverage approach?
3
Maintenance and calibration
1. Should the 5% for maintenance and calibration (and therefore the 85% data capture criteria)
still apply when raw data are aggregated to another averaging time? For example hourly
data aggregated into daily data used to check compliance like for PM10. Applying the 75%
rule for calculating the daily values and the 85 % of daily values for the number of
exceedance means that 64 % of hourly values are sufficient to calculate valid statistics.
It seems reasonable for simplicity reasons to keep the rules are they are applied at the moment.
2. Should the 5% for maintenance and calibration also be considered when time coverage are <
100% (discontinuous measurement e.g. BaP)? Or should it be proportional to the time
coverage?
It seems reasonable to consider that this 5% should only be applied to continuous measurement. In
case of discontinuous measurement, maintenance and calibration should be done outside the
measurement periods. However, iIt must be noted that in the IPR guidance document, this
distinction between continuous and discontinuous method is not considered and the 5% seems to
apply to both cases (see example on BaP above).
Discussion question:
Do you agree with the above answers?
3
4
Validity flag and verification status
Validity flags associated to the data are
1
valid
2
valid, but below detection limit measurement value given
3
valid, but below detection limit and number replaced by 0.5*detection limit
-99
not valid due to station maintenance or calibration
-1
not valid due to other circumstances or data is simply missing.
Verification flags associated to the data are:
1
verified
2
preliminary verified
3
not verified
The Directive foresees minimum percentages of valid values for calculating statistics relevant for
compliance checking.
For the E1a submission (annual submission of data related to the reporting year Y-1 to be done by
end September of the following year) all data are supposed to be fully verified.
This is not the case for the data transferred under E2a (UTD) which can be associated to different
verification levels (usually they are not or partly verified).
Issue
Both E1a data and E2a data are merged in a single database within the system. Verification flags and
the origin of the data remain associated to the values. Because only the last transmitted version of
the data sets is kept within the database, this mixed origin should not raise any issue: the UTD data
(E2a with verification flag set to not or partly verified) should normally be overwritten by the official
delivery submitted later (E1a with verification flag set to fully verified). Some special cases may
however happen:
- the country does not submit officially through E1a data which were previously transmitted
as UTD (E2a).
- the country, for whatever reason, has not proceeded to the verification of all data submitted
in E1a.
As statistics are calculated from all data, it might happen in these cases than unverified or partly
verified data are used for calculating compliance statistics.
It is suggested to flag the statistics in case they are calculated with data other than fully verified. One
single “partly” or “unverified value” would trigger this flagging.
Discussion question:
Do you agree with the suggested approach?
5
AEI calculation method
The table below presents the official method to calculate the AEI.
Official method (AQ Directive)
Select PM2.5 SamplingPoints set up for AEI
Calculate the annual mean for each SamplingPoint and corresponding
data capture for year X, X-1, X-2
Reject those SamplingPoints with data capture <85 %
4
Repeat for each selected PM2.5 SamplingPoint
Calculate the annual average AEI contribution for the years X, X-1 and X-2. The average
concentration for each year is calculated as the simple arithmetic average of the values for retained
locations.
Calculate the AEI as the simple arithmetic average of the values for the three years involved.
Rounding to integer if above 10µg/m3, to 1 decimal if below 10 µg/m3 to done at this level
Issue
The official methodology leaves room for technical interpretation (different set of time series over
the 3 year period, time series rejected …). From the experience in the past, it appears that
(ETC/ACM):
- “not all MS have delivered an "official" AEI-value;
- when AEI has been reported MS may not report over the same 3-year period;
- a list of "official" AEI-monitoring stations as selected by the MS is not available for all
countries …”
The methodology should be further detailed in order to ensure harmonised and consistent
implementation in all Member States.
The methodology used by ETC/ACM until now is as follows:
“
- select for a three-year period all operational (sub)urban background stations (operational is
defined as having a data coverage of 75% or more)
calculate for each year a national average from the station annual means
next average this for the three years
in none of these step numbers have been rounded. Only when presenting the AEI-values,
numbers should be rounded to one decimal.
This calculation procedure is similar as the one described in the AQD, only the selection of stations
differs” but also the percentage of valid data which is the one used by the EEA for assessment
purposes. For compliance evaluation, the threshold of 85% would be applied.
-
Discussion question:
Do you agree with the approach developed by ETC/ACM for assessment purpose? In particular for
selecting stations? What to do in case the set of stations differs from year to year (data rejected
because insufficient percentage of valid data, new stations or stations disappearing)?
This approach would not alter in any way the official method and legal requirement established by
the Ambient Air Quality Directive, which is to apply a threshold of 85% to evaluation and
assessments done to check compliance.
6
CSI4 – Exceedance of air quality limit values in urban areas
The methodology used at present to calculate the EEA core set indicator on exceedance of air quality
limit values in urban areas is as follows:
“For PM10, PM2.5, O3, NO2 and SO2 only stations with data time coverage of at least 75 % per
calendar year are used. That is, in the case of daily values, having more than 274 valid daily values
per calendar year (or 275 days if leap year). And in the case of hourly values, having more than 6570
valid hourly values per calendar year (or 6588 hours if leap year). For B(a)P the minimum data time
coverage accepted is 14 % (51 days), according to the data quality objectives related to indicative
measurements in the Directive 2004/107/EU (EU, 2004).
5
For every year, each city i in country j, and every pollutant, the total number of urban or suburban
traffic stations (nit) and the total number of urban or suburban background stations (nib) are
obtained. Ptj % of the total population of the city (Popi) is proportionally assigned to each of the
traffic stations and Pbj % of Popi is proportionally assigned to each of the background stations. So,
every traffic station has an allocated population equal to ((Ptj / 100) * Popi / nit) and every
background station has an allocated population equal to ((Pbj /100) *Popi / nib).”
Issue
It happens mostly for PM that two or more methods can be used at the same station. At the
moment, the values associated to both of them are taken into account in the calculation of the
index, provided they both fulfil the time coverage criteria. This introduces a bias into the results i.e. a
‘double’ counting of measurements at a single station. .
Discussion question:
If just one measurement method is taken for incorporation in the CSI004 statistics, which should it
be?
6
7
Annex Rounding EEA assessment
/*NO2*/
statistics_average_group='day' and statistics_percentage_valid>=75 and
component_code='8' and statistics_year=2012 and statistic_shortname='mean'
- <=20
20< - <=30
30< - <=40
40< - <=50
50< (Rounding to integer for all classes)
/*SO2*/
statistics_average_group='day' and statistics_percentage_valid>=75 and
component_code='1' and statistics_year=2012 and statistic_shortname='mean'
- <=5
5< - <=10
10< - <=20
20< - <=25
25< (Rounding to integer for all classes)
/*PM10 mean*/
statistics_average_group='day' and statistics_percentage_valid>=75 and
component_code='5' and statistics_year=2012 and statistic_shortname='mean'
- <=20
20< - <=31
31< - <=40
40< - <=50
50< (Rounding to integer for all classes)
/*PM10 P[90.4]*/ --New map
statistics_average_group='day' and statistics_percentage_valid>=75 and
component_code='5' and statistics_year=2012 and
statistic_shortname='P90.4'
- <=20
20< - <=40
40< - <=50
50< - <=75
75< (Rounding to integer for all classes)
/*PM2.5*/
statistics_average_group='day' and statistics_percentage_valid>=75 and
component_code='6001' and statistics_year=2012 and
statistic_shortname='mean'
- <=10
10< - <=20
20< - <=25
25< - <=30
30< (Rounding to integer for all classes)
/*CO*/
statistics_average_group='dymax' and statistics_percentage_valid>=75 and
component_code='10' and statistics_year=2012 and
statistic_shortname='mean'
- <=0.5
0.5< - <=1.0
1.0< - <=2.0
2.0< - <=2.5
2.5< (Rounding to one decimal for all classes)
/*C6H6*/
7
statistics_percentage_valid>=50 and component_code='20' and
statistics_year=2012 and statistic_shortname='mean'
<=1.7
1.7< - <=2.0
2.0< - <=3.5
3.5< - <=5.0
5.0< (Rounding to one decimal for all classes)
/*O3*/
statistics_average_group='dymax' and statistics_percentage_valid>=75 and
component_code='7' and statistics_year=2012 and statistic_shortname='mean'
- <=60
60< - <=80
80< - <=100
100< (Rounding to integer for all classes)
/*O3 P[93.2]*/
statistics_average_group='dymax' and statistics_percentage_valid>=75 and
component_code='7' and statistics_year=2012 and
statistic_shortname='P93.2'
- <=80
80< - <=100
100< - <=120
120< - <=140
140< (Rounding to integer for all classes)
/*Bap*/
statistics_percentage_valid>=14 and component_code in
('6015','5029','5129','1029') and statistics_year=2012 and
statistic_shortname='mean'
- <=0.12
0.12< - <=0.40
0.40< - <=0.60
0.60< - <=1.00
1.00< (Rounding to two decimals for all classes)
/*Pb*/
statistics_percentage_valid>=14 and component_code in ('12','5012') and
statistics_year=2012 and statistic_shortname='mean'
<=0.02
0.02< - <=0.10
0.10< - <=0.50
0.50< - <=1.00
1.00< (Rounding to two decimals for all classes)
/*Cd*/
statistics_percentage_valid>=14 and component_code in ('14','5014') and
statistics_year=2012 and statistic_shortname='mean'
<=1.0
1< - <=2
2< - <=5
5< - <=8
8< (Rounding to integer for all classes)
/*As*/
statistics_percentage_valid>=14 and component_code in ('18','5018') and
statistics_year=2012 and statistic_shortname='mean'
<=1
1< - <=3
3< - <=6
6< - <=9
9< 8
(Rounding to integer for all classes)
/*Ni*/
<=5
5< - <=10
10< - <=20
20< - <=30
30< (Rounding to integer for all classes)
9