RECETOX MONET Networks and fate studies

Transcription

1 Centre of Excellence RECETOX MONET Networks and fate studies and related modelling activities Pavel Čupr, Tomáš Bartoš, Klára Kubošová, Jiří Komprda, Alice Dvorská, Gerhard Lammel, Jana Klánová, Martina Kobližková, Petra Růžičková, Ivan Holoubek RECETOX, Masaryk University, Brno, CR Joint International Workshop of the UNEP Global Partnership on Atmospheric Mercury Transport Research and TF Centre on Hemispheric for Environmental Chemistry Transport and of Ecotoxicology Air Pollution of the UNECE-CRLTAP, 07-11/04/2008, Rome, Italy 1

2 RECETOX conception of POPs monitoring Study of occurrence of persistent organic pollutants in the environment as a basic source of information for evaluation of potential human and wildlife exposure - global, regional and national inventories Integrated monitoring of POPs the comparison of the contamination on the regional and local levels Why monitoring? Is a monitoring only rutine procedure or can we use this as a tool for study of environmental processes? 2

3 Persistent organic pollutants in soils from the Czech Republic levels, spatial and temporal trends 3

4 Working hypothesis The purpose of this study was to determine soil contamination by POPs (PAHs, OCPs, PCBs, PCDDs/Fs) in the Czech Republic and to identify contribution of non industrial sources. Discrepances between modelling and measurements Contaminated environment as a source of contamination Confirmation of these theories with the using of data from long-term monitoring and field and laboratory experiments 4

5 Working hypothesis Summarised the levels of POPs (PAHs, OCPs, PCBs, PCDDs/Fs) in soils of the Czech Republic for the reference year 2001 and evaluated them; Based on these experimental levels to calculate POPs soil burden in the CR; To measure laboratory volatilisation of basic POPs from various types of soils as a base from the estimation of POPs emission fluxes; Based on the knowledge of real soil POPs burden and using of experimental soil emission factors to calculate the contribution of POPs volatilisation from soils in the CR to the actual levels of these substances in ambient air; Estimation of risk potentials for human and wildlife of contaminated soils in the CR. 5

6 Exchange procesess air - soil - trends Jones and de Voogt,

7 Observatory Košetice comparison of measurements and calculation - ambient air PCB-153 Measured and calculated temporal trends of PCB-153 air concentrations at CZ3 for the period [pg.m -3 ] PCB-153 air conc., pg/m CZ3 Measured Calculated Measured and calculated seasonal variations PCB 53 [pg.m -3 ] air concentrations at CZ3 in (outliers are marked green) PCB-153 air concentrations, pg/m CZ3 Measured Calculated

8 8 Soil POPs Study 2001

9 [ng.m(-3)] S 3 DDTs in air, observatory Košetice, time trends, medians, sampling every week, [ng.m -3 ] 0,06 0,05 Air (Gas phase + Aerosol), S DDTs medians, Košetice, CR 0,04 0,03 0,02 0, Years Effects of floods in Moravian region at 1997 and in South and Central Bohemia at

10 [ng.m-3] HCB and PeCBz in ambient air, observatory Košetice, temporal trends, medians, weekley sampled, [ng.m -3 ] 0,9 0,8 0,7 0,6 0,5 HCB (Gas Phase) PeCB (Gas Phase) 0,4 0,3 0,2 0,1 0 Sampling Date Effects of floods, Moravia, 1997 and in South and Central Bohemia,

11 Soil POPs Study 2001 More than 100 soil sampling sites Characterisation of soil use, organic carbon contents.. POPs soil burden Evaporation of POPs from soils PCBs soil burden in the CR: t 11

12 Measuring volatilization fluxes of SVOC from soils Partition coefficient octanol-air (K OA ) is directly connected with the soil-air partition coefficient of pollutant (K SA ) which is determined as the ratio of solid phase to air concentrations: K SA = C S /C A where C S is the concentration in soil ( g.g -1 ), and C A is the concentration in air ( g.cm -3 ). Recently, direct measurements of K SA have been made for a range of POPs. K SA values vary from 104 (HCB) to 1010 (OCDF) Gas meter Glass volatilization chamber High-volume air sampling pump PUF pre-cleaning air PUF adsorbing the PCBs in the air stream Soil sample 12

13 Preliminary estimation of soil burden in the CR PCB 153: 48 tonnes DDE: 344 tonnes Evaporation flux from the soils in the CR 22 kg/y for PCB 153 / 0 C 65 kg/y for PCB 153 / 20 C 13

14 Working hypothesis Estimation of risk potentials for human and wildlife of contaminated soils in the CR. 14

15 SoilRISK POPs RISK - Lesní plochy výjmuty z analýzy Research (významné Centre for nejistoty) Environmental Chemistry and Ecotoxicology 15

16 SoilRISK POPs Concentration PAHs PCBs HCHs DDTs 16

17 RECETOX - application of environmental distribution models Level 4 box model which includes 7 layers of soil, aging process and vegetation was developed to predicting of concentrations of POPs in time Application of Level 4 box model. Prediction of temporal trends of concentrations of selected PAHs in soil Kosetice during 10 years. Comparison of prediction (lines) with measurements (bars). 17

18 RECETOX- application of environmental distribution models Stochastic models based on regression trees are used for prediction of background spatial distribution of POPs in soil over the Czech Republic in grid 1x1 km koncentra number of observation 100 Regression tree Histogram: lnhcb category The predictors used for the distribution modelling of the selected POPs were environmental variables such as a type of soil, organic carbon content, distance from industry, distance from populated areas, elevation and land use Total area of the Czech Republic was divided into grid of squares of 1 km 2, using the ArcGIS 9.2 geographical information system. ID=1 N=186 Mu = 1,50 Var = 1,26 = agricultural area, meadow forest ID=2 N=110 ID=3 N=76 Mu = 1,12 Var = 1,17 <= 1470 m > 1470 m ID=4 N=33 ID=5 N=77 Mu = 1,81 Var = 0,72 dist_industrial area ID=6 N=28 Mu = 1,23 Var = 0,42 Mu = 0,82 Var = 1,07 ID=7 N=49 Mu = 0,56 Var = 0,92 <= 2922 m > 2922 m ID=8 N=19 ID=9 N=30 Mu = 0,98 Var = 0,33 Mu = 0,29 Var = 0,28 landuse type of soil = T1, T4, T5, T7 T2, T3, T6... dist_industrial area Mu = 2,05 Var = 0,86 <= 2,45 > 2,45 ID=10 N=66 ID=11 N=10 Mu = 1,86 Var = 0,69 <= 187 m n.m. > 187 m n.m. ID=12 N=4 ID=13 N=62 Mu= 3,2 Var = 0,55 elevation Mu = 1,79 Var = 0,58 <= 0,25 > 0,25 ID=14 N=18 ID=15 N=44 Mu = 1,29 Var = 0,31 organic carbon organic carboc Mu = 1,97 Var = 0,26 Mu = 3,29 Var = 0,12 18

19 RECETOX- application of environmental distribution models - Future plans are to connect both stochastic and deterministic modeling approaches for prediction of distribution of all mass fluxes of POPs between environmental compartments on the area of Czech Republic. Generalized version of model calibrated on data collected by RECETOX during last years will be used for future routine applications e.i. spatial and temporal predictions of environmental concentrations, mass fluxes and trends. Prediction of air concentrations Air conditions, Input to box model Layer of box models Prediction of soil concentration Soil properties, input to box model GIS source predictor layer for stochastic model 19

20 RECETOX- prediction of distribution of POPs on the area of Czech Republic connection of stochastic and deterministic modelling approaches ID=1 N=82 Mu = - 0,20 Var = 0,7 <= 0,71 > 0,71 ID=2 N=34 ID=3 N=48 Mu = - 0,61 Var = 0,49 <= 245 m n.m. > 245 m n.m. ID=4 N=5 ID=5 N=29 Mu = 0,34 Var = 0,31 elevation Mu = - 0,77 Var = 0,33 <= 12508m > 12508m ID=6 N=26 ID=7 N=3 Mu = - 0,65 Var = 0,21 <= 317 m > 317 m ID=8 N=8 ID=9 N=18 Mu = - 0,28 Var = 0,16 dist_urban_area dist_industry Mu = - 0,82 Var = 0,15 corg Mu= - 1,8 Var = 0,16 Mu = 0,1 Var = 0,63 <= 2,92 > 2,92 ID=10 N=42 ID=11 N=6 Mu = - 0,02 Var = 0,59 <= 6473 m > 6473 m ID=12 N=32 ID=13 N=10 Mu = 0,15 Var = 0,37 dist_industry corg Mu = -0,58 Var = 0,49 Mu = 0,94 Var = 0,22 Prediction of air concentrations Air conditions, Input to box model Layer of box models Prediction of soil concentration Soil properties, input to box model GIS source predictor layer for stochastic model 20

21 Evaluation of transport to sampling site Air parcel back-trajectories generated for Czech background monitoring station Košetice, 1997 The transport path of air to a site during sampling is reconstructed from observed wind fields. 21

22 Method of analysis SUGGESTED MEAN LOADINGS, L g, BY SURFACE SOURCES ON THE COMPOSITION OF PASSING AIR MASSES: The number of trajectories passing grid cells reflects the wind field statistics. The number of trajectory hourly points of one trajectory in a grid cell reflects the velocity of the air mass. We use a linear time weight, 1 0 within 96 hourly steps, to account for the influence of dispersion on the accuracy of the location of the trajectory hourly points and of deposition on trace components abundances. I total number of trajectories G total number of grid cells c ij total atmospheric concentration of pollutant at the receptor site upon arrival of the i th trajectory n g total number of trajectory hourly points in grid cell g h ij number of hours counted back from arrival time for the j th hourly point, i th trajectory 1 - (h ij /96) time weights of hourly points L 0 unit loading of surface sources to air mass, set equal to 1 for trajectory hourly points which fall into grid cell g and set equal to 0 for all other trajectory hourly points 22

23 Evaluation of trajectories by counting mean loadings (L g ) - 3D HYSPLIT trajectories - years and trajectories per 24h sampling - trajectories with starting time during inversion situations excluded - trajectory segments above mixing height excluded - data from active air samplers - PCB 52, PCB 153, lindane, α/γhch, Σ DDTs, DDT/breakdown products, chosen PAHs L g value suggested mean loadings by surface sources on the composition of passing air masses, relative mean concentrations in grid cells lindane 23

24 Evaluation of transport to sampling site use of data from passive air sampling? Advantages: - evaluate atmospheric transport to many new sites which lack characterization by concentration data obtained by active air sampling - together with freely available program for trajectory generation a cheap, fast and simple method to characterize sampling sites Problems: - Semi-quantitative character of passive air sampling data - long sampling intervals 24