Uncertainties in atmospheric observations

Slides:



Advertisements
Similar presentations
Task Force on Modelling and measurement activity : synergies with FAIRMODE Laurence Rouïl (INERIS) Co-chair of the TFMM.
Advertisements

The evaluation of atmospheric pollution in Europe Wenche Aas EMEP/CCC (NILU) The EMEP Programme.
Chemical Coordinating Centre EMEPs contribution to a multi-purpose monitoring capasity for atmospheric composition in Europe.
Intensive measurements and modelling of size segregated chemical composition of aerosols in June 2006 and Jan 2007 Wenche Aas, Rami Alfarra, Elke Bieber,
Trend analysis for HMs and POPs Applications I. Ilyin, EMEP / MSC-East.
Developments in EMEP monitoring strategy and recommendations from AirMonTech Kjetil Tørseth, NILU/EMEP-CCC.
Cooperation of EMEP/CCC and EEA on near real-time air quality data. Presented by Wenche Aas Though most of the work is done by: Tim Haigh, Bernt Rondell,
/tfmm3 EMEP Chemical Coordinating Centre Measurements of particulate matter in EMEP Current implementation Kjetil Tørseth, Wenche Aas, Michael.
Intro to sampling Adapted from “Fundamentals of Environmental Sampling and Analysis” by Chunlong Zhang.
EMEP INTENSIVE MEASUREMENT PERIODS IN CLOSE PARTNERSSHIP WITH EU PROJECTS Wenche Aas, Andres Alastuey, Francesco Canonaco, Fabrizia Cavalli, Franco Lucarelli,
TFMM & TFEIP Workshop, Dublin, 2007 Uncertainties of heavy metal pollution assessment Oleg Travnikov EMEP/MSC-E.
Title Progress in the development and results of the UNIFIED EMEP model Presented by Leonor Tarrason EMEP/MSC-W 29 th TFIAM meeting, Amiens, France,
Relevant activities in EMEP Wenche Aas EMEP/CCC (NILU) EMEP Monitoring programme Expansion to the EECCA region, HTAP QA/QC.
Uncertainties in atmospheric observations Wenche Aas EMEP/CCC.
Synergies between EMEP and EUSAAR Wenche Aas and Kjetil Tørseth EMEP/CCC (NILU)
EMEP Monitoring programme Wenche Aas EMEP/CCC (NILU)
7 th Joint TFEIP & EIONET meeting, Thessaloniki, 2006 Heavy metal modelling: Use of different emission inventories Oleg Travnikov EMEP/MSC-E.
Trend analysis of HMs and POPs on the basis of measurements and modelling data Victor Shatalov and Oleg Travnikov, MSC-E.
EMEP Monitoring Strategy Status and challenges, with main focus on the EECCA region Wenche Aas and Kjetil Tørseth EMEP/CCC (NILU)
ENEON first workshop Observing Europe: Networking the Earth Observation Networks in Europe September, Paris [EMEP,ACTRIS,HELCOM,CAMP and more/NILU]
EMEP Monitoring Activities – Contributions to and Requirements from GAS Aasmund Fahre Vik, Kjetil Tørseth Norwegian Institute for Air Research
European moss survey 2010/11: heavy metals, nitrogen and POPs ICP VEGETATION 30 th  Progress HM & N  New: Pilot study POPs  Review: mosses as biomonitors.
TFEIP Workshop, Istanbul, May 2013 Emissions data for of heavy metal and POP modelling Oleg Travnikov, Alexey Gusev, Ilia Ilyin, Olga Rozovskaya, Victor.
Air Quality trend analyses under EMEP/TFMM and link to EEA work Augustin COLETTE (INERIS), Chair of the TFMM/CLRTAP TFMM National Experts, CCC, MSC-E,
EMEP WGSR, EMEP Progress on HMs, 2006  Review and evaluation of the MSCE-HM model (TFMM)  Atmospheric pollution in 2004 (emissions, monitoring.
29 th TF meeting of the ICP-Vegetation, March, 2016, Dubna, Russia ANALYSIS OF LONG-TERM TRENDS OF ATMOSPHERIC HEAVY METAL POLLUTION IN THE EMEP COUNTRIES.
ADAGIO (Atmospheric Deposition Analysis Generated by optimal Interpolation from Observations): Project plans and status A.S. Cole1, A. Robichaud2, M.D.
Joint thematic session on B(a)P pollution: main activities and results
Svetlana Tsyro, David Simpson, Leonor Tarrason
Calibration in the Ambient Air Quality Monitoring Network
Contribution from AQUILA to Air Policy Review
EMEP stations as part of National Monitoring System in Poland
EMEP intensive measurements, June 2006 and Jan 2007
EMEP case study on heavy metal pollution assessment:
POPs and HMs Summary , EMEP TFMM.
EMEP intensive measurements
Wenche Aas and Karl Espen Yttri (EMEP/CCC)
Data quality of inorganic compounds in air and precipitation
10th TFMM meeting, June, 2009, France, Paris
EMEP Monitoring Strategy
Monitoring strategy, technical issues
EEMEP ASSESSMENT REPORT
EMEP Case study: Assessment of HM pollution levels with fine spatial resolution in Belarus, Poland and UK Ilia Ilyin, Olga Rozovskaya, Oleg Travnikov.
CMAQ model as a tool for generating input data for HM and POP modeling
17th Task Force on Measurement and Modelling Meeting
9th TFMM, Bordeaux, France, April 2008
MSC-E contribution concerning heavy metals
EMEP intensive measurements, June 2006
Uncertainties of heavy metal pollution assessment
TFMM Work plan for 2010 Build-up the appropriate framework for the implementation of the revised monitoring strategy Technical support to the Parties.
First Approach to the EMEP Assessment Report – Slovak Republic
H. Fagerli, TFMM Bordeux, april 2008
Wenche Aas, Kjetil Tørseth, Cathrine Lund Myhre
Future intensive field periods Recommendations
Trend analysis of contamination in the EMEP region by HMs & POPs
Low-cost methods for gas/particle distribution of nitrogen species
Introduction – workshop on EBAS and Data Quality
Lessons learnt from the EMEP intensive measurements
EMEP new monitoring strategy in France Nathalie Poisson - ADEME
Wenche Aas Status of EMEP measurements today Field intercomparison
Experience from the EMEP VOC measurements
Update on activities of Bulgaria within/related to EMEP
Welcome TFMM workshop on the implementation of the EMEP monitoring strategy Introduction to the monitoring strategy QA/QC activities of EMEP organisation.
EC/OC – monitoring within EMEP
Model assessment of HM and POP pollution of the EECCA region
Comparison of model results with measurements
based on EMEP/MSC-W model and EMEP monitoring data
Atmospheric modelling of HMs Sensitivity study
Svetlana Tsyro, David Simpson, Leonor Tarrason
Quality assurance and data quality in relation to trend assessments in EMEP Kjetil Tørseth, Wenche Aas and Jan Schaug Documenting changes in atmospheric.
Presentation transcript:

Uncertainties in atmospheric observations Wenche Aas EMEP/CCC

Sources of uncertainties Sampling and analytical method Detection limit Interference Instrument drift, calibration Positive or negative artefact Sampling procedure Contamination Temperature and period for storage Transport Representativity. Local farming (NH3) Nearby roads (NOx; O3) Dust (PM, Ca..) Lab- and field intercomparison Field inter-comparison; model comparison Repr. studies, i.e passive sampling. Model comparison

Monitoring programme: Level 1 Main ions in precipitation and in air heavy metals in precipitations ozone PM10 and PM2.5 mass meteorology at ca 125 sites Level 2, supersite (joint EMEP/GAW) POPs Heavy metals in air and aerosols VOC EC/OC, OC speciation Mineral Dust PM speciation incl. gas particle ratio + all level 1 activities 15-20 sites Both levels are mandatory by all Parties

Data quality objectives Acidifying and eutrophying compounds 15-25% uncertainty in annual average (10-15% for indiv meas.) Heavy metals 30% uncertainty in annual average (15-25% for indiv meas.) 40 % uncertainty for As, Cd, Ni in the EU 4th DD (70% in dep) 50% uncertainty for Hg (total gas) in the EU 4th DD POPs (not defined in EMEP) 50% uncertainty for PAH in the EU 4th DD (70% in dep) PM (not defined in EMEP) 25% accuracy in continuous measurements EU 1st DD Photooxidants (not defined in EMEP) : 15% accuracy in continuous O3, NOx measurements, EU 3rd DD

Lab intercomparisons annually, 2005 Bias: RB % Spread: 2RSD %

WMO ICP

SO2 field intercomparison Preila (LT) using filterpack Zarra (ES) , abs (H202) and monitor TCM ain Germany (historic data) at DE09 (left and DE03 (right)

SO2. UV fluoresence monitor, interference

NO2. Chemiluminisence (Mo converter) Not selective for NO2

QA flag Lab flag Green: Bias < 10% Spread <20% (S,N) Blue: Field flag Green: Bias < 10% Spread <25% Blue: Bias < 50% Spread <50%

QA Flag for main ions in 2005 In precip In air Analysis in lab are in general better than 20% (both in air and precip) Total uncertainty (field intercomp): about half the measurements is better than 25% rest better than 50%

Lab intercomparison of HM annually Average per cent error, 2005

Hg intercomparison at DE02 in 2006 Tot Hg(g) in air Tot Hg in precip

Heavy metal deposition, CEN WG20 Birkenes, comparing wet only (analysed at UBA) and bulk (NILU)

Parallell wet only, CEN WG 20 Relative SD in deposition measurements: From To Cd 9% 18% Pb 7% 15% As 6% 19% Ni 11% 20%

POP lab intercomparison,2002

Artefact in gas/particle for N IT01, Jan 2007 Underestimation of N Artefact free measurements using denuders only done at Montelibretti IT01, June 2007

Estimates of the positive artefact of OC in PM10 and PM2 Estimates of the positive artefact of OC in PM10 and PM2.5/PM1 -June 2006 QBQ-approach

Measurement and model intercomparison ?? ES NO

Uncertainties in trends SO2 SO4 in air

Representativity, NO2 ES07 Comparing EMEP model and obs. in light of population density IT01 NL91 BE32 AT02

Conclusions Many factors influence uncertainty in measurements Methodology, sampling procedure, Representativity Need to distinguish between uncertainty in one data point, in averages and trends; and distinguish between bias and spread In general, the measurements are within DQO if reference methods are used and the site is representative, but there are exceptions Need to have better control of artefacts, especially nitrogen gas/particle and EC/OC More intercomparison of other species than main comp and HM are needed

Data quality objectives Acidifying and eutrophying compounds 15-25% uncertainty in annual average (10-15% for indiv meas.) Heavy metals 30% uncertainty in annual average (15-25% for indiv meas.) 40 % uncertainty for As, Cd, Ni in the EU 4th DD (70% in dep) 50% uncertainty for Hg (total gas) in the EU 4th DD POPs (not defined in EMEP) 50% uncertainty for PAH in the EU 4th DD (70% in dep) PM (not defined in EMEP) 25% accuracy in continuous measurements EU 1st DD Photooxidants (not defined in EMEP) : 15% accuracy in continuous O3, NOx measurements, EU 3rd DD