Analysis of the monitoring data intercomparability for the Bulgarian – Romanian common stretch of the Danube River PP2 – National Administration Romanian.

Slides:



Advertisements
Similar presentations
Richard M. Jacobs, OSA, Ph.D.
Advertisements

Lesson Describing Distributions with Numbers parts from Mr. Molesky’s Statmonkey website.
SAMPLE DESIGN: HOW MANY WILL BE IN THE SAMPLE—DESCRIPTIVE STUDIES ?
Data Analysis: Part 2 Lesson 7.1 & 7.2 1/11-12/12.
The Independent- Samples t Test Chapter 11. Independent Samples t-Test >Used to compare two means in a between-groups design (i.e., each participant is.
Statistics.
Business 205. Review Sampling Continuous Random Variables Central Limit Theorem Z-test.
DATA ANALYSIS I MKT525. Plan of analysis What decision must be made? What are research objectives? What do you have to know to reach those objectives?
Chapter 13 Analyzing Quantitative data. LEVELS OF MEASUREMENT Nominal Measurement Ordinal Measurement Interval Measurement Ratio Measurement.
Mean for sample of n=10 n = 10: t = 1.361df = 9Critical value = Conclusion: accept the null hypothesis; no difference between this sample.
Chapter 14 Analyzing Quantitative Data. LEVELS OF MEASUREMENT Nominal Measurement Nominal Measurement Ordinal Measurement Ordinal Measurement Interval.
Review Chapter 1-3. Exam 1 25 questions 50 points 90 minutes 1 attempt Results will be known once the exam closes for everybody.
Chapter 2 Simple Comparative Experiments
VARIABILITY. PREVIEW PREVIEW Figure 4.1 the statistical mode for defining abnormal behavior. The distribution of behavior scores for the entire population.
Business Statistics BU305 Chapter 3 Descriptive Stats: Numerical Methods.
Statistics Or Do our Data mean Diddly?. Why are stat important Sometimes two data sets look different, but aren’t Other times, two data sets don’t look.
Guidance for water quality intercalibration
Mean Tests & X 2 Parametric vs Nonparametric Errors Selection of a Statistical Test SW242.
Activity 5: Intercalibration of the regional water quality laboratories, in conformity with WFD implementation Carmen Hamchevici Dr. Gabriel Chiriac Gabriela.
Analysis & Interpretation: Individual Variables Independently Chapter 12.
Vukovar, December , 2012 EAEMDR Status Report Bulgaria.
© Copyright McGraw-Hill CHAPTER 3 Data Description.
Chapter 9 Hypothesis Testing and Estimation for Two Population Parameters.
Implementation Section Romania-Bulgaria Cross-Border Cooperation Operational Programme Joint Approval Committee, Bucharest, 3 April 2007.
Section 3.1 Measures of Center. Mean (Average) Sample Mean.
Module 4: RBM Planning regarding Blueprint document, training skills, transboundary issues, public consultations, negotiation skills Transboundary cooperation.
Section 1 Topic 31 Summarising metric data: Median, IQR, and boxplots.
Project “Danube WATER integrated management” – WATER MIS ETC 166 Project Manager: Mary-Jeanne Adler, PhD
Determination of Sample Size: A Review of Statistical Theory
Analyses using SPSS version 19
BOX PLOTS (BOX AND WHISKERS). Boxplot A graph of a set of data obtained by drawing a horizontal line from the minimum to maximum values with quartiles.
Application number: 2(4.i) MIS-ETC Code: 774 EMERSYS Toward an integrated, joint cross-border detection system and harmonized rapid responses procedures.
“Common Strategy for Sustainable Territorial Development of the cross-border area Romania-Bulgaria” 26 th of September 2013 Craiova, Romania.
Inferential Statistics 4 Maarten Buis 18/01/2006.
Mr. Magdi Morsi Statistician Department of Research and Studies, MOH
STATISTICS FOR SCIENCE RESEARCH (The Basics). Why Stats? Scientists analyze data collected in an experiment to look for patterns or relationships among.
Principles of statistical testing
Slovak – Hungarian Water Quality Working Group Joint Transboundary Water Commission, JTWC Milan Matuska, Jarmila Makovinska Slovak Republic György Simonfai,
New Information Technologies in Learning Statistics M. Mihova, Ž. Popeska Institute of Informatics Faculty of Natural Sciences and Mathematics, Macedonia.
BUG SURVEYS : An Initiation of Transboundary Co- operation Małgorzata Landsberg-Uczciwek Voivodeship Inspectorate of Environmental Protection in Szczecin.
Medical Statistics (full English class) Ji-Qian Fang School of Public Health Sun Yat-Sen University.
DATA ANALYSIS AND STATISTICS Methodology for Describing and Understanding VARIABILITY.
Review Chapter 1-3. Exam 1 25 questions 50 points 90 minutes 1 attempt Results will be known once the exam closes for everybody.
Models Platform for Danube Forecasting PROJECT DANUBE WATER INTEGRATED MANAGEMENT.
PXGZ6102 BASIC STATISTICS FOR RESEARCH IN EDUCATION
Meeting of the Working Group 2A on Ecological Status (ECOSTAT) – 3+4 July 2006, Stresa (IT) Eastern Continental GIG Draft final report on the results of.
| Slide 1 Reporting Requirements Michael Nagy Umweltbundesamt Wien Workshop on Environment Statistics Budapest, 23 April 2004.
PROJECT HYDROCARE SLOVAK HYDROMETEOROLOGICAL INSTITUTE (Partner No.9) February 2006, Potsdam, Germany.
7 th Grade Math Vocabulary Word, Definition, Model Emery Unit 4.
Chapter 16: Exploratory data analysis: numerical summaries
STAT 4030 – Programming in R STATISTICS MODULE: Basic Data Analysis
Emily Saad EAS 4480 Oral Presentation 27 April 2010
Chapter 2 Simple Comparative Experiments
Carolin Vorstius PhD Showcase Day, 29/03/2017
Bar graphs are used to compare things between different groups
Danube Integrated Water Management
EU Water Framework Directive
Inference on Mean, Var Unknown
Central Tendency.
HMI 7530– Programming in R STATISTICS MODULE: Basic Data Analysis
Danube Integrated Water Management
Basic analysis Process the data validation editing coding data entry
EU Water Framework Directive
Introduction- Link with WG E activity CMEP PLENARY MEETING-PRAGUE
Data Transformation, T-Tools and Alternatives
DESIGN OF EXPERIMENT (DOE)
by B. M. Gawlik, L. Galbiati, J. Zaldivar, G. Bidoglio
Ticket in the Door GA Milestone Practice Test
Descriptive statistics for groups:
Pilot River Basin Project for the Szamos/Somes River Basin
Presentation transcript:

Analysis of the monitoring data intercomparability for the Bulgarian – Romanian common stretch of the Danube River PP2 – National Administration Romanian Waters Carmen Hamchevici

Sub-activity 5.2 Water quality analysis for laboratory intercalibration Long-term Short-term Momentary TNMN data QA/QC analysis 2014 (9 months) Individual sampling June 2015 Common sampling (not bulk sample!)

(1) Long term data comparability 2 transboundary sections (ends of the common BG-RO Danubian stretch):  Pristol / Novo Selo (river km 854) and  Chiciu / Silistra (river km 375) 4 selected water quality parameters:  nutrients forms (N-ammonium, N-nitrates, P- orthophosphates, Total Phosphorous) Period of time: 1996 – 2011 Raw data from TNMN database (the TNMN data management center in the Slovak Hydrometeorological Institute, acknowledged)

Prior to data comparability: Analytical Quality Control (AQC) organisation of interlaboratory comparison in the Danube monitoring: agreed in 1992 based on a list containing reference and optional analytical methods Provider: (until 2012) the Institute for Water Pollution Control of VITUKI, Budapest, Hungary Name of the scheme: QUALCO DANUBE (4 distributions / year) Inventory of analytical methods used and performances (Technical Report)

Methods Entire datasets provided by BG and RO to ICPDR database Selected datasets based on the simultaneous sampling days during 1996 – 2011 Statistical approach STATISTICA 10.0 (StatSoft. Inc., 2005):  Basic statistic descriptives number of values, minimum, maximum, mean, median, confidence intervals for mean – 95%, lower and upper quartiles – percentiles of 25 and 75 respectively, percentiles of 10 and 90 respectively, range and standard deviation  t-test between two independent variables (BG and RO): powerful parametric test, but with strong limitations related to distribution and variance  Box-plots (graphical representation)

Entire datasets: number of annually results produced by BG and RO at Pristol / Novo Selo transboundary section for N-NH4 N-NO3, P-PO4 and Total P (1996 – 2012)

Basic descriptive statistics – Pristol / Novo Selo (all data) PARA M. COU NTR Y VALI D N MEA N CONF. - 95% CONF. + 95% MEDIANMIN.MAX.P25P75P10P90 RANG E STD.DE V. N-NH 4 BG1100,160,130,180,140,000,860,080,220,030,300,860,12 RO1730,230,200,260,190,021,320,120,300,050,451,300,17 N-NO 3 BG1111,481,361,601,390,374,611,071,900,772,214,240,64 RO1731,241,161,311,150,232,840,871,560,661,942,610,50 P-PO 4 BG950,0650,0570,0720,0600,0000,2500,0400,0800,0200,1100,2500,035 RO1730,0780,0720,0840,0700,0120,2700,0500,1000,0320,1300,2580,041 TP BG820,1470,1050,1900,1000,0401,6000,0820,1410,0680,2001,5600,194 RO1660,1170,0930,1400,0900,0181,9300,0700,1300,0530,1701,9120,151

T-test: the null hypothesis of H 0 : μ x = μ y (p=0.05) BG VS RO MEAN GROUP 1 MEAN GROUP 2 t- VALUE DFp VALID GROUP 1 VALID GROU P 2 F-RATIO VARIANCE S p VARIANCE S N-NH 4 0,160,23-3,792810, ,960, N-NO 3 1,481,243,572820, ,630, P-PO 4 0,0650,078-2, , ,3130, TP 0,1470,1171, , ,6480,007285

Box-plots (Pristol / Novo Selo – all data)

Basic descriptive statistics – Pristol / Novo Selo (simultaneous data) PARA M. CO UN TRY VALI D N MEA N CONF. - 95% CONF. + 95% MEDIA N MIN.MAX.P25P75P10P90 RAN GE STD.D EV. N-NH 4 BG RO N-NO 3 BG RO P-PO 4 BG RO TP BG RO

T-test: the null hypothesis of H 0 : μ x = μ y (p=0.05) BG VS RO MEAN GROUP 1 MEAN GROUP 2 t- VALUE DFp VALID GROUP 1 VALID GROUP 2 F-RATIO VARIANCE S p VARIANCE S N-NH N-NO P-PO TP

Box-plots (Pristol / Novo Selo – simultaneous)

Pristol / Novo Selo PARAM. COU NTRY VAL ID N MEANMEDIANMIN.MAX.P25P75P10P90RANGE STD.DEV. Water temp. RO BG SS RO BG DO RO BG pH RO BG Cond. RO BG (2) Short – term data comparability January – September

PARAM. COU NTRY VALI D N MEANMEDIANMIN.MAX.P25P75P10P90RANGESTD.DEV N-NH 4 RO BG N-NO 3 RO BG N-NO3 RO BG TOTAL N RO BG P-PO4 RO BG TOTAL P RO BG (2) Short – term data comparability January – September Pristol / Novo Selo

T-test: the null hypothesis of H 0 : μ x = μ y (p=0.05) BG VS RO (MIDDLE) MEAN GROUP 1 MEAN GROUP 2 t-VALUEDFp VALID GROUP 1 VALID GROUP 2 F-RATIO VARIANC ES p VARIAN CES w.temp SS DO pH Cond N-NH N-NO N-NO N Total P-PO P Total

Pristol / Novo Selo 2014 – Water temperature

Pristol / Novo Selo 2014 – Suspended Solids

(3) Momentary data comparability - June 2015 Chiciu / Silistra Pristol / Novoselo

First approach

Performance analysis Compliance checking: QA/QC Directive (2009/90/CE) criteria - EQSs set-out by the 2013/39/EC

Conclusions on data comparability Higher degree of comparability for simultaneous sampling days (same hydrological regime) Good comparability for short-term and momentary data (2014 and 2015) Differences: Need for bilateral discussion (sampling, preservation, storage and analysis) Technical performances to be analysed vs the QA/QC Directive Future steps : bilateral agreement in place and operational common sampling (bulk sample) intercomparison exercises with agreed frequency on-table discussions on obtained results

Contacts Thank you for your attention! Investing in your future! Romania-Bulgaria Cross Border Cooperation Programme is co-financed by the European Union through the European Regional Development Fund