AiR quality monitoring: an alternative way by L.Spanu and F. Lenartz for data analysis Institut Scientifique de Service Public, Rue du Chéra 200, 4000 Liège (Belgium)
1. About ISSeP Institut Scientifique de Service Public (ISSeP) Generality Organization under the administrative supervision of the Walloon Minister for the Environment, land settlement and mobility. Two sites: Liège and Colfontaine (Mons). The head office is located in Liège. About 300 employees work in this institute. Core business: - Risk evaluation - Research and Technology - Environmental monitoring : Air, Water and Soil
2. Focus on air quality monitoring Air quality department Air quality monitoring -Ambient air Stakeholder: AWAC (financial support) in close collaboration with CQA -Emission (industrial chimney) Research project -PM-Lab -SPECIMEN -ExTraCar
3. Ambient air quality Ambient air quality department About 40 people work in this departement Nine entities called «Technical Unit» Each of those TU is dedicated to a specific network regarding a pollutant (compliance with air quality standards and guidelines). Regulated pollutants are (directives 2008/50/CE and 2004/107/CE): - Particulate Matter (PM 10 and PM 2.5 ) - Classical pollutants (O 3, NO 2, SO 2, CO) - Organic pollutants (VOCs, PAHs) - Inorganic pollutants (heavy metals, fluoride) -
3. Ambient air quality Ambient air quality department - Real time air quality monitoring network for the walloon Region (24 stations) Public information, pollution alert, compliance with air quality standards. - Mobile laboratories network (17 trailers) AWAC s specific demands (temporary air quality problem, environmental permission, model validation, ) private study (mostly industries).
3. Ambient air quality Ambient air quality department Our job in a few words - Network managment - Measurement campaign - Data analysis - Reporting - Quality Managment System -
4. Data analysis (1) Conventional IT tools (most used!) Excel spreadsheet, macros (with SQL db) Data viewer program (C#.NET) Specific software (with Oracle db) Alternative? Sure, with And especially the Openair package Open-source tools for analysing air pollution data (Carslaw, D.C. and K.Ropkins, (2012); Carslaw, D.C. (2013))
4. Data analysis (2) Openair : what is it? The openair project is a Natural Environment Research Council knowledge exchange project that aims to provide a collection of open-source tools for the analysis of air pollution data. [ ] The project is led by the Environmental Research Group at King's College London, supported by the University of Leeds. Encourage the air quality community to use and help further develop these tools. (source: http://www.openair-project.org/default.aspx ) Person to contact: Dr D. Carslaw
4. Data analysis (3)
4. Data analysis (4) Open R and the Openair package Goals - Promote air quality data handling (from Oracle db, MSSQL or.csv files) with R functions, reshape2 library, lattice library, and openair library -Promote data visualization with R graphics, ggplot2 library, lattice library and openair library
4. Data analysis (4) Openair - features Functions for analysing air pollution data - calendarplot; - polarplor; - pollutionrose; - windrose; - percentilerose; - summaryplot; - calcpercentile; - timeaverage; - selectbydate; - Import; - cutdata; - quicktext; -
avr. mai sept. oct. nov. déc. août 5. Graphics (1) 400 300 200 100 0 NO 2 et NO juin juil. valeurs horaires année 2013 mars févr. janv. lv NO NO 2 µg/m³ With «traditional» R functions
5. Graphics (2) Résumé des mesures NO 2 With «traditional» R functions 250 200 lv µg/m³ 150 100 50 0 janv. févr. mars avr. mai juin juil. août sept. oct. nov. déc. valeurs horaires année 2013
23:00 21:00 19:00 01:00 03:00 05:00 07:00 09:00 11:00 13:00 15:00 17:00 PM10(µg/m³) 0 50 100 150 NO2(µg/m³) 0 50 100 150 200 250 300 5. Graphics (3) Pic de pollution NO2 PM 10 vitessse du vent v.v (m/s) 0 1 2 3 4 5
5. Graphics (4) Semaine type du NO 50 hiver été 40 30 µg/m³ 20 10 0 lundi mardi mercredi jeudi vendredi samedi dimanche moyenne horaire année 2013
20:00 21:00 22:00 23:00 18:00 19:00 16:00 17:00 5. Graphics (5) Journée type du NO 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 heure locale année 2013 6:00 7:00 4:00 5:00 2:00 3:00 hiver été 0:00 1:00 µg/m³ 0 5 10 15 20 25
5. Graphics (6) N 20% With openair library 15% Station de Cointe année 2013 windrose( ) 10% 6 to 12.94 5% W E 4 to 6 2 to 4 0 to 2 (m s 1 ) S mean = 3.81 calm = 0.1% Frequency of counts by wind direction (%)
5. Graphics (7) Rose de pollution NO N 18% 20% pollutionrose( ) 16% 14% 12% 10% valeurs horaires (µg/m³) 30 to 460.5 6% 8% 25 to 30 2% 4% 20 to 25 W E 15 to 20 10 to 15 5 to 10 0 to 5 météo Cointe S mean = 11.2 calm = 0% Frequency of counts by wind direction (%)
5. Graphics (8) Polar plot PM 10 N moyenne PM 10 (µg/m³) polarplot( ) 14 12 40 10 35 8 6 30 4 h201w ind spd. W 2 0 E 25 20 15 10 5 0 S Météo Cointe
5. Graphics (9) calendarplot( ) PM 10 moyenne journalière 29 30 31 1 2 3 4 29 30 31 1 2 3 4 29 30 31 1 2 3 4 29 30 31 1 2 3 4 29 30 31 1 2 3 4 29 30 31 1 2 3 4 29 30 31 1 2 3 4 29 30 31 1 2 3 4 29 30 31 1 2 3 4 29 30 31 1 2 3 4 29 30 31 1 2 3 4 29 30 31 1 2 3 4 janvier février mars avril 18 19 20 21 22 23 24 11 12 13 14 15 16 17 18 19 20 21 22 23 24 11 12 13 14 15 16 17 18 19 20 21 22 23 24 11 12 13 14 15 16 17 18 19 20 21 22 23 24 11 12 13 14 15 16 17 18 19 20 21 22 23 24 11 12 13 14 15 16 17 18 19 20 21 22 23 24 11 12 13 14 15 16 17 18 19 20 21 22 23 24 11 12 13 14 15 16 17 18 19 20 21 22 23 24 11 12 13 14 15 16 17 18 19 20 21 22 23 24 11 12 13 14 15 16 17 18 19 20 21 22 23 24 11 12 13 14 15 16 17 18 19 20 21 22 23 24 11 12 13 14 15 16 17 18 19 20 21 22 23 24 11 12 13 14 15 16 17 mai 22 23 24 25 26 27 28 15 16 17 18 19 20 21 8 9 10 11 12 13 14 22 23 24 25 26 27 28 15 16 17 18 19 20 21 8 9 10 11 12 13 14 22 23 24 25 26 27 28 15 16 17 18 19 20 21 8 9 10 11 12 13 14 22 23 24 25 26 27 28 15 16 17 18 19 20 21 8 9 10 11 12 13 14 22 23 24 25 26 27 28 15 16 17 18 19 20 21 8 9 10 11 12 13 14 22 23 24 25 26 27 28 15 16 17 18 19 20 21 8 9 10 11 12 13 14 22 23 24 25 26 27 28 15 16 17 18 19 20 21 8 9 10 11 12 13 14 22 23 24 25 26 27 28 15 16 17 18 19 20 21 8 9 10 11 12 13 14 22 23 24 25 26 27 28 15 16 17 18 19 20 21 8 9 10 11 12 13 14 22 23 24 25 26 27 28 15 16 17 18 19 20 21 8 9 10 11 12 13 14 22 23 24 25 26 27 28 15 16 17 18 19 20 21 8 9 10 11 12 13 14 22 23 24 25 26 27 28 15 16 17 18 19 20 21 8 9 10 11 12 13 14 juin juillet 24 25 26 27 28 29 30 17 18 19 20 21 22 23 10 11 12 13 14 15 16 24 25 26 27 28 29 30 17 18 19 20 21 22 23 10 11 12 13 14 15 16 24 25 26 27 28 29 30 17 18 19 20 21 22 23 10 11 12 13 14 15 16 24 25 26 27 28 29 30 17 18 19 20 21 22 23 10 11 12 13 14 15 16 24 25 26 27 28 29 30 17 18 19 20 21 22 23 10 11 12 13 14 15 16 24 25 26 27 28 29 30 17 18 19 20 21 22 23 10 11 12 13 14 15 16 24 25 26 27 28 29 30 17 18 19 20 21 22 23 10 11 12 13 14 15 16 24 25 26 27 28 29 30 17 18 19 20 21 22 23 10 11 12 13 14 15 16 24 25 26 27 28 29 30 17 18 19 20 21 22 23 10 11 12 13 14 15 16 24 25 26 27 28 29 30 17 18 19 20 21 22 23 10 11 12 13 14 15 16 24 25 26 27 28 29 30 17 18 19 20 21 22 23 10 11 12 13 14 15 16 24 25 26 27 28 29 30 17 18 19 20 21 22 23 10 11 12 13 14 15 16 août septembre octobre novembre 28 29 30 31 1 2 3 28 29 30 31 1 2 3 28 29 30 31 1 2 3 28 29 30 31 1 2 3 28 29 30 31 1 2 3 28 29 30 31 1 2 3 28 29 30 31 1 2 3 28 29 30 31 1 2 3 28 29 30 31 1 2 3 28 29 30 31 1 2 3 28 29 30 31 1 2 3 28 29 30 31 1 2 3 décembre En dépassement si >= à médiocre Indice CELINE excellent très bon bon assez bon moyen médiocre très médiocre mauvais très mauvais exécrable PM 10 moyenne journalière janvier février mars avril mai juin juillet août septembre octobre novembre décembre En dépassement si >= à médiocre Indice CELINE excellent très bon bon assez bon moyen médiocre très médiocre mauvais très mauvais exécrable
5. Graphics (10) Relation entre PM10 et température With ggplot2 75 PM10 (µg/m³) 50 saison chaude froide 25 0 0 10 20 T ( C)
6. Test case: From black smoke to black carbon
6. Test case: From black smoke to black carbon µgm-3 0 150 Liège - West 1970 1980 1990 2000 2010 Time Min.: 1 1st Qu.: 9 Median: 15 Mean: 21.7 3rd Qu.: 27 Max.: 462 µgm-3 0 150 Liège - Center 1970 1980 1990 2000 2010 Time Min.: 1 1st Qu.: 10 Median: 17 Mean: 23.8 3rd Qu.: 29 Max.: 251 µgm-3 0 150 Liège - East 1970 1980 1990 2000 2010 Time Min.: 1 1st Qu.: 7 Median: 13 Mean: 20.4 3rd Qu.: 25 Max.: 345
6. Test case: From black smoke to black carbon Liège - West µgm-3 0 20 40 1970 1980 1990 2000 2010 Time Liège - Center µgm-3 0 20 40 1970 1980 1990 2000 2010 Time Liège - East µgm-3 0 20 40 1970 1980 1990 2000 2010 Time
6. Test case: From black smoke to black carbon From October 3 rd to December 31 st, 2014 Black smoke 0 10 20 30 40 50 0 2 4 6 8 10 Black carbon
6. Test case: From black smoke to black carbon From October 3 rd to December 31 st, 2014 Black smoke 0 10 20 30 40 50 > ls.print(m1) Residual Standard Error=3.1769 R-Square=0.8538 F-statistic (df=1, 87)=508.2576 p-value=0 Estimate Std.Err t-value Pr(> t ) Intercept -0.2400 0.5971-0.4020 0.6887 X 5.1452 0.2282 22.5446 0.0000 > ls.print(m2) Residual Standard Error=3.1617 R-Square=0.9468 F-statistic (df=1, 88)=1566.204 p-value=0 Estimate Std.Err t-value Pr(> t ) X 5.0694 0.1281 39.5753 0 2 4 6 8 10 Black carbon
7. Future (potential) uses of R
7. Future (potential) uses of R
7. Future (potential) uses of R
References Carslaw, D.C. and K. Ropkins, (2012) Openair - An R package for air quality data analysis. Environmental Modelling & Software. Volume 27-28, 52-61. R Development Core Team (2011). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Janicke U and L. Janicke, (2007)
References Carslaw, D.C. and K. Ropkins, (2012) Openair - An R package for air quality data analysis. Environmental Modelling & Software. Volume 27-28, 52-61. R Development Core Team (2011). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Janicke U and L. Janicke, (2007)