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Classification of nutrient emission sources in the Vistula River system
Environmental Pollution 157 (2009) 1867–1872
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Classification of nutrient emission sources in the Vistula River system Tomasz Kowalkowski* ´ , Poland Department of Environmental Chemistry and Bioanalytics, Faculty of Chemistry, Nicholaus Copernicus University, Gagarina 7, 87-100 Torun
Two classification methods applied to evaluate the results of nutrient emission allow definition of major sources of the emissions and classification of catchments with similar pollution.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 10 May 2008 Received in revised form 8 January 2009 Accepted 19 January 2009
Eutrophication of the Baltic sea still remains one of the biggest problems in the north-eastern area of Europe. Recognizing the sources of nutrient emission, classification of their importance and finding the way towards reduction of pollution are the most important tasks for scientists researching this area. This article presents the chemometric approach to the classification of nutrient emission with respect to the regionalisation of emission sources within the Vistula River basin (Poland). Modelled data for mean yearly emission of nitrogen and phosphorus in 1991–2000 has been used for the classification. Seventeen subcatchements in the Vistula basin have been classified according to cluster and factor analyses. The results of this analysis allowed determination of groups of areas with similar pollution characteristics and indicate the need for spatial differentiation of policies and strategies. Three major factors indicating urban, erosion and agricultural sources have been identified as major discriminants of the groups. Ó 2009 Elsevier Ltd. All rights reserved.
Keywords: Eutrophication Water pollution Vistula Factor analysis Cluster analysis
1. Introduction Marine eutrophication has emerged as one of the major environmental issues, especially for enclosed seas with high river runoff. Such regions are strongly affected by increasing riverine nutrient loads. Eutrophication affects biodiversity and fish stocks as well as human health and the recreational use of coastal zones, and causes massive surface phytoplankton blooms. The Baltic Sea is highly exposed to eutrophication through limited mixing with ocean water and high discharges of inland water from river systems. Results of several research projects investigating the eutrophication in the Baltic region showed that without a change in policies, the eutrophication process will increase in the next decades (Panasiuk et al., 2005). Nowadays, approximately 75% of the riverine nitrogen and up to 84% of phosphorus loads in the Baltic basin is discharged by the region’s three large rivers: the Vistula, the Odra and the Nemunas (HELCOM, 2004). From this point of view, Poland is the major country that influences the nutrients balance in the Baltic Sea. The Polish impact on the Baltic Sea is reflected by three issues: (i) about 99.7% of Poland’s territory belongs to the Baltic Sea drainage basin, (ii) more than half of the entire basin’s population lives in Poland, and (iii) approximately 40% of the entire
* Tel.: þ48 56 611 4330; fax: þ48 56 611 4837. E-mail address: [email protected] 0269-7491/$ – see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.envpol.2009.01.018
basin’s farmland is situated in Poland (Makinia et al., 1996; Gren et al., 1997). The ecological policy of Poland assumes several tools (e.g. National Program for Development of Wastewater Treatment Plants) and actions (e.g. implementation of Nitrate Directive on nitrate sensitive zones) to reduce the impact of river systems entering the sea coast. However, other alternative actions aiming to reduce eutrophication are needed, to be implemented on regional level. One of such actions is the use of non-phosphate detergents, aiming at a substantial decrease of P-emissions in river systems. Introduction of P-free detergents seems to be a fast and effective step to reduce the P-inputs from such regions; however such detergents have lower efficiency of washing and they are relatively expensive. Adaptation of the 91/271/EEC EU directive in Poland to limit the highest concentrations of water pollutants after treatment seems to be sufficient to reduce the impact of wastewater treatment plants for both nutrients (Kowalkowski and Buszewski, 2006). Such regulations should be adopted in the largest municipalities as soon as possible. The estimations of agricultural emissions (Sapek et al., 2000) show hot spots localized in the former Lubelskie and Torunskie voivodships. The biggest emission is related to highest use of commercial fertilizers, high mean livestock density, and small percentage of permanent grassland. Over 95% of livestock farms do not have a manure pit for animal waste storage nor a liquid manure container consistent with the requirements of the EU Directive (CEESA, 2003 report). The actions to reduce the impact of agriculture should therefore include proper storage of animal wastes.
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T. Kowalkowski / Environmental Pollution 157 (2009) 1867–1872 2. Materials and methods 2.1. Study area The area of study, the Vistula River, is the longest river in Poland crossing the entire country from Beskid Slaski to the Gulf of Gdansk. Its length is 1047.5 km. It flows into the Baltic Sea forming an estuary delta in Zu1awy Wislane, with the mean flow of 1080 m3/s at its mouth. The basin of the Vistula River covers an area of 194,424 km2, of which 168,966 km2 is located within the borders of Poland (Buszewski et al., 2005). The rest of the area covers territory of Ukraine, Belarus and Slovakia. The majority of the population is concentrated along the course of the Vistula. Sixty percent of Poland’s population lives in the area of the Vistula basin (around 22.9 million people in 2000). The catchment area is divided into three macro-regions with respect to the economic exploitation of the territories located within the region (Fig. 1). 2.2. Database
Fig. 1. Location of selected catchments within the Vistula River.
Successful reduction of pollution depend on the knowledge of farmers. Investment in education and training is therefore as important as the investment in infrastructure. Erosion is third important pathway of emission, especially for the mountainous regions. The results show that P-emission caused by erosion is important in the southern, mountainous part of the Odra river system. Application of ‘‘Best management practices’’ on arable land can lead to 40% reduction of this source (Behrendt et al., 2002). Recent studies (Kronvang et al., 1999; Buszewski and Kowalkowski, 2003; Ledoux et al., 2005; van der Velde et al., 2006; Witter et al., 2006; Kowalkowski et al., 2007) show a need for regionalization of described actions with respect to different types of major emissions and geographical conditions. Moreover, only an integrated approach allows us to combine the analysis of emissions within a certain time period with the observed loads, and finally enables the calculation of scenarios simulating the influence of individual management measures on the emissions and loads in the future (de Paepe et al., 2002). The present study indicates the new concept of differentiation of catchments based on the chemometric evaluation of data obtained from spatial modelling of nitrogen and phosphorus emissions.
The database consists in measurements of run-off, water quality as well as the meteorological and statistical data, and covers the ten years between 1991 and 2000. The database was created during the EuroCat–VisCat project (Kowalkowski and Buszewski, 2003). On the basis of gathered data, the mean yearly emission values of nitrogen and phosphorus for 17 catchments have been calculated and used in this study. Seven major emission pathways for both nutrients have been evaluated using the MONERIS model (Behrendt et al., 2000). The general input of the entire Vistula basin on the Baltic costal zone has been described during the EuroCat project. However, other interesting results can be derived from the emission data via different pathways. Other parameters from the database were used to estimate the nutrient emission and their impact on catchments have been discussed elsewhere (Kowalkowski and Buszewski, 2006). Mean yearly emission values as well as basic characteristics of catchments are presented in Table 1. The sources of emissions of both nitrogen and phosphorus were as follows: (i) point sources: wastewater treatment plants, urban areas; (ii) diffuse sources: deposition from atmosphere, drained areas, overland flow, erosion and emission into groundwater. The division of the catchments is shown in Fig. 1. 2.3. Statistical methods All calculations were performed in Statistica DataMiner 7.1 software running on the Windows platform. Prior to multidimensional chemometric analysis the normality of all variables (nitrogen and phosphorus emission via different pathways) was checked by analysing the histograms and skewness index and by applying the Shapiro–Wilk statistical test (Kowalkowski et al., 2006). In the majority of cases (catchments), the variable distribution was far from normality (skewness index >2, p < 0.05). Hence the transformation function was applied to all pollution parameters (variables) in the form xnorm ¼
xx : SD
The standardization procedure of emission data gives more reasonable results for cluster analysis (CA). Clusters of raw data were created according to total emission values. After the standardization, the ranges of all parameters were similar and catchments in the same clusters shared a similar emission pattern, neglecting the
Table 1 Characteristics of catchments investigated in the study and emission figures via different pathways. Catchment ID Area (km2) Inh (in 2000) Average slope (%) MYP (mm) N_A
N_O
a1 B C a2 D E a3 F G a4 H I J a5 K L a6
1723.4 3480.6 1251.4 1012.0 550.5 421.6 1415.9 1734.8 1617.8 1400.7 123.3 275.6 1179.0 2704.6 384.4 1623.9 638.7 3592.7 367.2 1460.9 2967.9 6924.1 1832.1 10865.5 206.8 4082.6 529.4 3227.7 375.9 3863.2 321.6 2963.9 754.7 7160.6
13125.9 6677.9 4069.0 9251.2 16558.6 1987.2 9383.3 10302.6 9111.0 3887.3 36430.2 35578.9 7695.3 6042.4 5376.7 4639.2 10198.6
4563770 968495 574140 1064917 1639402 335729 1055910 1183359 795300 1652789 2996142 2630243 1380313 609004 430222 481758 1173325
1.61 3.45 1.24 0.43 0.56 0.65 0.30 0.35 0.23 0.08 0.13 0.08 0.10 0.14 0.24 0.07 0.15
810 814 740 688 697 643 626 614 631 583 603 592 587 579 607 627 612
586.7 137.1 82.3 353.4 501.0 43.2 393.1 361.2 362.2 211.7 1449.6 2008.2 311.0 451.6 385.7 358.2 638.6
N_D
N_E
N_G
N_W
N_U
P_A P_O
516.6 326.5 303.7 350.7 715.6 82.0 191.4 164.2 0.0 0.0 338.6 0.0 0.0 0.0 0.0 0.0 0.0
7828.4 4166.9 1711.1 4594.9 6483.0 964.7 3745.4 2889.0 5113.1 937.2 6644.0 8759.3 842.7 3212.9 1207.0 1061.7 5591.6
6197.8 519.2 185.9 931.0 881.2 321.7 684.7 1433.3 313.7 1187.1 2508.4 1225.7 2016.2 675.5 315.4 227.8 439.3
2430.7 668.4 453.8 767.6 1102.1 198.9 926.9 656.4 581.7 1162.1 2198.3 1740.0 823.5 472.5 241.6 343.4 805.6
10.5 2.9 1.7 7.2 12.2 1.0 8.8 9.0 7.4 4.7 38.0 53.9 6.8 10.6 10.2 9.0 16.2
P_D
369.0 33.3 244.1 9.4 113.6 10.0 292.2 18.1 324.1 35.3 22.0 2.6 248.1 23.8 92.2 20.1 109.0 36.8 67.7 11.8 565.0 102.9 339.5 114.3 44.4 32.7 115.6 21.1 88.5 28.2 56.4 18.6 159.2 55.8
P_E
P_G
P_W P_U
412.2 90.7 946.2 555.3 246.1 46.7 98.6 156.4 220.7 32.6 14.3 106.5 293.5 45.0 87.5 176.9 525.0 122.6 143.9 256.9 61.6 8.0 27.1 46.1 134.5 41.7 62.7 215.6 108.4 41.7 302.1 153.4 0.0 58.5 42.6 132.8 0.0 18.3 144.9 269.8 227.7 160.4 541.7 521.3 0.0 196.9 214.6 411.8 0.0 17.5 242.6 189.3 0.0 46.8 101.5 112.0 0.0 23.6 63.1 59.3 0.0 18.4 32.5 82.5 0.0 89.5 54.6 191.3
Ihn, inhabitants; MYP, mean yearly precipitation; N, mean yearly emission of nitrogen (tN/year); P, mean yearly emission of phosphorus (tP/year). Emission pathways: _A, atmospheric deposition; _O, overland flow; _D, drained areas; _E, erosion; _G, groundwater; _W, wastewater treatment plants; _U, urban areas.
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Table 2 Characteristics of Brda and Drweca ˛ catchments and the emission pattern for 2002. Brda
Drweca ˛
Length (km) Population (thousands) Catchment area (km2) Agricultural area (%) Forest (%)
238 472.1 4693 50.1 44.7
207.2 418.3 5477 70.9 24
Emission pathways in 2002 Atmospheric deposition Tile drainage Groundwater Overland flow Erosion WWTP Urban systems
Nitrogen (t/a) 360 1085 2137 314 0 549 177
Phosphorus (t/a) 9 8 29 46 0 42 97
Nitrogen (t/a) 374 3268 1262 388 167 266 227
Phosphorus (t/a) 10 25 25 85 93 28 56
Total emission
4622
232
5952
321
influence of area or population on emission value. Factor analysis (FA) gave similar results for standardized and raw data; only a small variation in loading values was observed. This is due to fact that the FA algorithm uses standardization of data for data treatment, but the algorithm differs from that used in the present study. Ward’s method as an agglomeration rule in cluster analysis has been chosen. Similarity was measured by Euclidean distance. Clusters were derived according to Smith’s rule. Thirty-three percent of maximum distance has been chosen to discriminate the clusters. Application of principal component analysis (PCA) was not sufficient because a strong correlation between emission parameters with more than one component was observed. This problem was solved by the application of factor analysis with rotation. Calculation of factors in FA were based on principal component analysis. The number of principal components has been limited to three with minimal eigenvalue criterion of 1. Computed factors were finally rotated using the normalized Varimax method.
3. Results Comparison of the emissions data on a local scale shows that the emission pattern (how much of nutrient is emitted via different processes) is key to understanding the spread of nutrients into the desired area. This fact is obviously related to the catchment’s properties such as population density, urbanization, average slope of catchment etc. Table 2 presents patterns of nitrogen and phosphorus emissions within Brda and Drweca ˛ as well as a summary of characteristics of both catchments (see catchments K and L in Fig. 1). These catchments are localized in the northern part of the Vistula basin and have a similar area. They differ, however, in land
Fig. 3. Regionalization of Vistula catchments by means of cluster analysis.
utilization. The estimated total emissions are in the same range, but pathway patterns are unique and can be used as an indicator to improve local policies aiming to reduce the emission of nitrogen and phosphorus into the environment. Multivariate techniques were used to analyse all Vistula regions simultaneously (Astel et al., 2008). The dendrogram of the location pattern resulting from the CA is presented in Fig. 2. It shows that all the locations may be generally grouped into three main groups. Fig. 3 depicts catchment localization as a member of the clusters. The first cluster is formed by catchments I, H and A1 and represents the regions with the highest influence on water quality (grey areas in Fig 3). The second one consists of two groups. The first group is formed by B, D, A2 and A3 and reflects upper Vistula catchments with higher impact from industry and erosion (dotted areas on the map). The second one consists of the remainder of the Vistula basin localized in the middle and lower Vistula basin (white areas in Fig. 3). The character of the pollution sources in these groups is very different. The Upper Vistula catchments are generally exposed to the point and erosion sources of nitrogen and phosphorus, while Table 3 Factor loadings structure.
Fig. 2. Differentiation of the catchments in Vistula River according to cluster analysis.
Emission source
F1
F2
Ndatmospheric deposition Ndoverland flow Nddrained areas Nderosion Ndgroundwater Ndwastewater treatment plants Ndurban areas Pdatmospheric deposition Pdoverland flow Pddrained areas Pderosion Pdgroundwater Pdwastewater treatment plants Pdurban areas
0.97 0.66 0.91 0.08 0.71 0.07 0.54 0.97 0.62 0.97 0.10 0.90 0.17 0.56
0.00 0.62 0.29 0.96 0.48 0.20 0.32 0.02 0.65 0.01 0.95 0.35 0.23 0.32
F3 0.20 0.31 0.16 0.23 0.30 0.97 0.75 0.13 0.35 0.18 0.25 0.18 0.94 0.74
Eigenvalue Explained variance (%)
6.45 46.00
3.37 24.00
3.51 25.00
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Fig. 5. Differentiation of the catchments in Vistula River according to factor analysis.
Fig. 4. Results of factor analysis: principal component scores for investigated catchments: (A) factor 1 associated with agriculture; (B) factor 2 associated with erosion; (C) factor 3 associated with point sources.
the rest of the Vistula region is recognized as an area of high agricultural activity. Further investigation requires a more complete method to answer the key question: what original parameters are responsible for the differentiation of catchments? The same data were analysed therefore by means of factor analysis (FA). The new factors explained more than 90% of cumulative variance. The factor loadings are presented in Table 3, highlighted loading values being greater than 0.7.
According to these data, Factor 1 is related mainly to parameters describing diffuse emission sources of both nutrients, namely atmospheric deposition, emission to groundwater and emission from drained areas. This can be named as the impact of agriculture due to the fact that deposition from atmosphere is rather small compared with the other two sources. Factor 2 is loaded mainly with those parameters that explain the influence of erosion and finally, Factor 3 correlates with parameters related to point sources. All of the highlighted vales indicate a positive correlation of emission by source with a particular factor. The FA scores for evaluated catchments show the groups of the catchments with similar pollution character. Due to rotation strategy, the results of the classification are different to those obtained by cluster analysis. Factor 1 (Fig. 4A) has the biggest values for the catchments localized at the eastern part of the Vistula basin (H, I) and for A6 (corresponding to Zulawy Wislane) and clearly indicates regions with a high influence of agriculture on nutrient emission. The influence of Factor 2 (Fig. 4B) decreases with the distance from the mountainous regions, having the highest values for catchments D and A2 and corresponding to erosion figures calculated by the model. The influence of erosion sources also remains important for the catchments A1, B and C and also for southern part of the Bug River basin (H). The strongest influence of point sources, represented by means of Factor 3 (Fig. 4C), has been found in the central part of the basin where the biggest cities are localized, and also in the Cracow vicinity (catchments A1, A4, H, J). Fig. 5 shows the geographical location of different catchments with respect to their factorization. Agricultural pressure is the driving force regulating the flow of nutrients on the eastern side of the basin (H, I) and in Zulawy at the Vistula mouth (A6). Emission from point sources is largest in rural regions of Silesia (A1), Warsaw (A4) and surrounding industrial catchments (H, J). The third way of emission is erosion, which occurs and plays an important role in the Southern part of the basin.
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4. Discussion
5. Conclusions
The results clearly indicate the most polluted regions and show similarities between them. The Silesia region (A1) is particularly exposed to the influence of point sources including both municipal and industrial activities. The Silesia region is exposed also to the emission of nutrients via erosion. In this case, 30% reduction of phosphorus emission via point sources causes 20% of total P emission from this area (Kowalkowski and Buszewski, 2006). Such a decrease is significant even on the entire Vistula basin scale and can be achieved efficiently by improving wastewater treatment plant technology and applying the policy of the use of non-phosphate detergents. Moreover, the nutrient load from point sources can be reduced during a short period and, usually, measures to reduce the input from point sources are much cheaper compared to measures dealing with diffuse sources (Neumann and Schernewski, 2005). The second ‘‘hot spot’’ region is the Bug River catchment (H) followed by Narew catchment (I) where agriculture has the biggest influence on water quality. The crucial parameter in this case is the surplus of phosphorus and nitrogen in agricultural soils. Research done by Behrendt (2002) shows that reduction of N, P surplus to 70% by 2020 can lead to a significant decrease of nitrogen emitted into the groundwater and tile drainage (both 8% in 2020). Such reduction of nutrient surplus can be realized by increasing the effectiveness of fertilizer usage and its proper dosage in both catchments. On the other hand, investigations done in Latvian rivers (Stalnacke et al., 2003) shows that even a dramatic decrease of agricultural activity does not reflect much on water quality in the short term. The efficiency of reduction inagriculture can be limited by other factors, e.g. pathways of nutrient compounds between soil profile–root zone and surface water recipient. Site-oriented case studies are therefore necessary to estimate such efficiency values. The management of river basins (Simeonov, 2003) involves the making of informed choices about the desired levels of economic activities and ecosystem functioning in the catchment. A need exists to develop new strategies of nutrient reduction within the Baltic Sea basin. Studies on the Baltic Sea indicate that the limiting factor of eutrophication is the load of nitrogen (Wulff and Niemi, 1992; Ericsson and Hallmans, 1994). Hence, immediate improvements can be achieved only by means of nitrogen reduction. Nowadays, this statement seems still be truthful due to the costs of effective nitrogen reduction. The main source of nitrogen is the input in groundwater (drainage water). Reduction of nitrogen concentrations in this pathway is expensive and takes decades _ (Markowska and Zylicz, 1999). This is the reason for developing effective reduction scenarios and why such scenarios have to be in time and spatially differentiated. The cost-efficient scenario proposed by Gren (2000) for the reduction of nutrient loads within the Baltic Sea is based on significant regional differences in load reduction. It implies that nutrient load reduction takes place in countries and drainage basins where it is most cost-efficient. The optimal reduction of nitrogen and phosphorus costs only 23% of the other so-called proportional reduction scenario (required by HELCOM) and therefore has huge economic benefits. The mid-term simulation done by Neumann and Schernewski (2005) indicates a similar effect for both proportional and cost-efficient scenarios. However, spatial differentiation of the reduction was observed. Both scenarios gave insufficient reduction in several locations. The present results of statistical analysis show the key areas where emission of nutrients is related to particular pathways. The idea of such differentiation can be applied to evaluate new ‘‘pathway-oriented’’ scenarios for nutrient reduction. The combination of both regional and pathway oriented reduction policies offers a good alternative in the future development of the Baltic Sea.
Multivariate analysis of the dataset representing distribution of nutrient emission sources has been evaluated. The results indicate regions sharing similar pollution problems. Two methods have been adopted. Cluster analysis divided the Vistula basin into the Upper and Lower regions. Major information has been obtained by means of factor analysis. Three types of sources of nutrients spatially divide the Vistula River basin. The results indicate two regions having the biggest impact on the Vistula, namely Silesia and the Bug–Narew catchment. The emission pathways in those catchments are different and require application of various reduction strategies. The performed analysis can be an easy-to-use tool to support the evaluation of different scenarios aiming at the reduction of nutrients. Statistics, in this case, simplify the structure of data and indicate areas with a high impact in a particular pathway described by one factor. Further investigation of cost-effectiveness is, however, necessary to simulate real reduction values. Acknowledgements This work was supported by Ministry of Science and Higher Education grant no. N N523 1054 33. The author thanks also Dr Luke Himuka (WITS University, South Africa) for discussion and comments. References Astel, A., Tsakovski, S., Simeonov, V., Reisenhofer, E., Piselli, S., Barbieri, P., 2008. Multivariate classification and modeling in surface water pollution estimation. Analytical and Bioanalytical Chemistry 390 (5), 1283–1292. Behrendt, H., Huber, P., Kormilch, M., Opitz, D., Schmoll, O., Scholz, G., Uebe, R., 2000. Nutrient Emissions into River Basins of Germany. UBATexte, Berlin. 23/00. Behrendt, H., Dannowski, R., Deumlich, D., Dolezal, F., Kajewski, I., Kornmilch, M., Korol, R., Mioduszewski, W., Opitz, D., Steidl, J., Stronska, M., 2002. Investigation on the quantity of diffuse entries in the rivers of the catchment area of the Odra and the Pomeranian Bay to develop decision facilities for an integrated approach on waters protection. Research Report on the joint international project (FKZ: 298 28 299). Buszewski, B., Kowalkowski, T., 2003. Poland’s environmentdpast, present and future state of the environment in the Vistula and Odra River Basins. Environmental Science and Pollution Research 10 (6), 343–349. Buszewski, B., Buszewska, T., Chmarzyn´ski, A., Kowalkowski, T., Kowalska, J., _ Kosobucki, P., Zbytniewski, R., Namiesnik, J., Kot-Wasik, A., Zukowska, B., Pacyna, J., Panasiuk, D., 2005. The present condition of the Vistula River catchment area and its impact on the Baltic Sea coastal zone. Regional Environmental Change 5, 97–110. CEESA, 2003. CEESA report: The Challenge of the Nitrate Directive to Acceding Countries: A Comparative Analysis of Poland, Lithuania and Slovakia. www.fao. org/regional/SEUR/CEESA_Vol2_en.pdf. de Paepe, V., Boschet, A.-F., Nixon, S.C., 2002. Comparability of assessments of nutrient emissions to inland waters undertaken within eight European Union Member States. Journal of Water Supply: Research and Technology 51, 87–108. Ericsson, B., Hallmans, B., 1994. Control of the nutrient pollution discharge from the Vistula River Basin in Poland. Desalination 98, 185–197. Gren, I.-M., 2000. Managing a sea. Cost-Effective Nutrient Reduction to the Baltic Sea. Earthscan Publ., London, pp. 43-56. Gren, I.-M., Soderqvist, T., Wulff, F., 1997. Nutrient reductions to the Baltic Sea: Ecology, costs and benefits. Journal of Environmental Management 51, 123–143. HELCOM, 2004. The fourth Baltic sea pollution load compilation (PLC-4), Baltic Sea Environment Proceedings No. 87. Kowalkowski, T., Buszewski, B., 2003. Modeling of past, present and future state of nutrient emission into the Vistula river system. Implementation of MONERIS model to the Vistula river catchment. VisCat WP4 report Torun´. Kowalkowski, T., Buszewski, B., 2006. Emission of nitrogen and phosphorus in Polish rivers. Past, present and future trends in Vistula river catchment. Environmental Engineering Science 23 (4), 615–622. Kowalkowski, T., Zbytniewski, R., Szpejna, J., Buszewski, B., 2006. Application of chemometrics in river water classification. Water Research 40 (4), 744–752. Kowalkowski, T., Gadza1a-Kopciuch, M., Kosobucki, P., Krupczyn´ska, K., Ligor, T., 2007. Organic and inorganic pollution of Vistula River basin. Journal of Environmental Science and Heath Part A. Buszewski, B. 42 (4), 1–6. Kronvang, B., Svendsen, L.M., Jensen, J.P., Dørge, J., 1999. Scenario analysis of nutrient management at the river basin scale. Hydrobiologia 410, 207–212.
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Classification of nutrient emission sources in the Vistula River system Tomasz Kowalkowski* ´ , Poland Department of Environmental Chemistry and Bioanalytics, Faculty of Chemistry, Nicholaus Copernicus University, Gagarina 7, 87-100 Torun
Two classification methods applied to evaluate the results of nutrient emission allow definition of major sources of the emissions and classification of catchments with similar pollution.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 10 May 2008 Received in revised form 8 January 2009 Accepted 19 January 2009
Eutrophication of the Baltic sea still remains one of the biggest problems in the north-eastern area of Europe. Recognizing the sources of nutrient emission, classification of their importance and finding the way towards reduction of pollution are the most important tasks for scientists researching this area. This article presents the chemometric approach to the classification of nutrient emission with respect to the regionalisation of emission sources within the Vistula River basin (Poland). Modelled data for mean yearly emission of nitrogen and phosphorus in 1991–2000 has been used for the classification. Seventeen subcatchements in the Vistula basin have been classified according to cluster and factor analyses. The results of this analysis allowed determination of groups of areas with similar pollution characteristics and indicate the need for spatial differentiation of policies and strategies. Three major factors indicating urban, erosion and agricultural sources have been identified as major discriminants of the groups. Ó 2009 Elsevier Ltd. All rights reserved.
Keywords: Eutrophication Water pollution Vistula Factor analysis Cluster analysis
1. Introduction Marine eutrophication has emerged as one of the major environmental issues, especially for enclosed seas with high river runoff. Such regions are strongly affected by increasing riverine nutrient loads. Eutrophication affects biodiversity and fish stocks as well as human health and the recreational use of coastal zones, and causes massive surface phytoplankton blooms. The Baltic Sea is highly exposed to eutrophication through limited mixing with ocean water and high discharges of inland water from river systems. Results of several research projects investigating the eutrophication in the Baltic region showed that without a change in policies, the eutrophication process will increase in the next decades (Panasiuk et al., 2005). Nowadays, approximately 75% of the riverine nitrogen and up to 84% of phosphorus loads in the Baltic basin is discharged by the region’s three large rivers: the Vistula, the Odra and the Nemunas (HELCOM, 2004). From this point of view, Poland is the major country that influences the nutrients balance in the Baltic Sea. The Polish impact on the Baltic Sea is reflected by three issues: (i) about 99.7% of Poland’s territory belongs to the Baltic Sea drainage basin, (ii) more than half of the entire basin’s population lives in Poland, and (iii) approximately 40% of the entire
* Tel.: þ48 56 611 4330; fax: þ48 56 611 4837. E-mail address: [email protected] 0269-7491/$ – see front matter Ó 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.envpol.2009.01.018
basin’s farmland is situated in Poland (Makinia et al., 1996; Gren et al., 1997). The ecological policy of Poland assumes several tools (e.g. National Program for Development of Wastewater Treatment Plants) and actions (e.g. implementation of Nitrate Directive on nitrate sensitive zones) to reduce the impact of river systems entering the sea coast. However, other alternative actions aiming to reduce eutrophication are needed, to be implemented on regional level. One of such actions is the use of non-phosphate detergents, aiming at a substantial decrease of P-emissions in river systems. Introduction of P-free detergents seems to be a fast and effective step to reduce the P-inputs from such regions; however such detergents have lower efficiency of washing and they are relatively expensive. Adaptation of the 91/271/EEC EU directive in Poland to limit the highest concentrations of water pollutants after treatment seems to be sufficient to reduce the impact of wastewater treatment plants for both nutrients (Kowalkowski and Buszewski, 2006). Such regulations should be adopted in the largest municipalities as soon as possible. The estimations of agricultural emissions (Sapek et al., 2000) show hot spots localized in the former Lubelskie and Torunskie voivodships. The biggest emission is related to highest use of commercial fertilizers, high mean livestock density, and small percentage of permanent grassland. Over 95% of livestock farms do not have a manure pit for animal waste storage nor a liquid manure container consistent with the requirements of the EU Directive (CEESA, 2003 report). The actions to reduce the impact of agriculture should therefore include proper storage of animal wastes.
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T. Kowalkowski / Environmental Pollution 157 (2009) 1867–1872 2. Materials and methods 2.1. Study area The area of study, the Vistula River, is the longest river in Poland crossing the entire country from Beskid Slaski to the Gulf of Gdansk. Its length is 1047.5 km. It flows into the Baltic Sea forming an estuary delta in Zu1awy Wislane, with the mean flow of 1080 m3/s at its mouth. The basin of the Vistula River covers an area of 194,424 km2, of which 168,966 km2 is located within the borders of Poland (Buszewski et al., 2005). The rest of the area covers territory of Ukraine, Belarus and Slovakia. The majority of the population is concentrated along the course of the Vistula. Sixty percent of Poland’s population lives in the area of the Vistula basin (around 22.9 million people in 2000). The catchment area is divided into three macro-regions with respect to the economic exploitation of the territories located within the region (Fig. 1). 2.2. Database
Fig. 1. Location of selected catchments within the Vistula River.
Successful reduction of pollution depend on the knowledge of farmers. Investment in education and training is therefore as important as the investment in infrastructure. Erosion is third important pathway of emission, especially for the mountainous regions. The results show that P-emission caused by erosion is important in the southern, mountainous part of the Odra river system. Application of ‘‘Best management practices’’ on arable land can lead to 40% reduction of this source (Behrendt et al., 2002). Recent studies (Kronvang et al., 1999; Buszewski and Kowalkowski, 2003; Ledoux et al., 2005; van der Velde et al., 2006; Witter et al., 2006; Kowalkowski et al., 2007) show a need for regionalization of described actions with respect to different types of major emissions and geographical conditions. Moreover, only an integrated approach allows us to combine the analysis of emissions within a certain time period with the observed loads, and finally enables the calculation of scenarios simulating the influence of individual management measures on the emissions and loads in the future (de Paepe et al., 2002). The present study indicates the new concept of differentiation of catchments based on the chemometric evaluation of data obtained from spatial modelling of nitrogen and phosphorus emissions.
The database consists in measurements of run-off, water quality as well as the meteorological and statistical data, and covers the ten years between 1991 and 2000. The database was created during the EuroCat–VisCat project (Kowalkowski and Buszewski, 2003). On the basis of gathered data, the mean yearly emission values of nitrogen and phosphorus for 17 catchments have been calculated and used in this study. Seven major emission pathways for both nutrients have been evaluated using the MONERIS model (Behrendt et al., 2000). The general input of the entire Vistula basin on the Baltic costal zone has been described during the EuroCat project. However, other interesting results can be derived from the emission data via different pathways. Other parameters from the database were used to estimate the nutrient emission and their impact on catchments have been discussed elsewhere (Kowalkowski and Buszewski, 2006). Mean yearly emission values as well as basic characteristics of catchments are presented in Table 1. The sources of emissions of both nitrogen and phosphorus were as follows: (i) point sources: wastewater treatment plants, urban areas; (ii) diffuse sources: deposition from atmosphere, drained areas, overland flow, erosion and emission into groundwater. The division of the catchments is shown in Fig. 1. 2.3. Statistical methods All calculations were performed in Statistica DataMiner 7.1 software running on the Windows platform. Prior to multidimensional chemometric analysis the normality of all variables (nitrogen and phosphorus emission via different pathways) was checked by analysing the histograms and skewness index and by applying the Shapiro–Wilk statistical test (Kowalkowski et al., 2006). In the majority of cases (catchments), the variable distribution was far from normality (skewness index >2, p < 0.05). Hence the transformation function was applied to all pollution parameters (variables) in the form xnorm ¼
xx : SD
The standardization procedure of emission data gives more reasonable results for cluster analysis (CA). Clusters of raw data were created according to total emission values. After the standardization, the ranges of all parameters were similar and catchments in the same clusters shared a similar emission pattern, neglecting the
Table 1 Characteristics of catchments investigated in the study and emission figures via different pathways. Catchment ID Area (km2) Inh (in 2000) Average slope (%) MYP (mm) N_A
N_O
a1 B C a2 D E a3 F G a4 H I J a5 K L a6
1723.4 3480.6 1251.4 1012.0 550.5 421.6 1415.9 1734.8 1617.8 1400.7 123.3 275.6 1179.0 2704.6 384.4 1623.9 638.7 3592.7 367.2 1460.9 2967.9 6924.1 1832.1 10865.5 206.8 4082.6 529.4 3227.7 375.9 3863.2 321.6 2963.9 754.7 7160.6
13125.9 6677.9 4069.0 9251.2 16558.6 1987.2 9383.3 10302.6 9111.0 3887.3 36430.2 35578.9 7695.3 6042.4 5376.7 4639.2 10198.6
4563770 968495 574140 1064917 1639402 335729 1055910 1183359 795300 1652789 2996142 2630243 1380313 609004 430222 481758 1173325
1.61 3.45 1.24 0.43 0.56 0.65 0.30 0.35 0.23 0.08 0.13 0.08 0.10 0.14 0.24 0.07 0.15
810 814 740 688 697 643 626 614 631 583 603 592 587 579 607 627 612
586.7 137.1 82.3 353.4 501.0 43.2 393.1 361.2 362.2 211.7 1449.6 2008.2 311.0 451.6 385.7 358.2 638.6
N_D
N_E
N_G
N_W
N_U
P_A P_O
516.6 326.5 303.7 350.7 715.6 82.0 191.4 164.2 0.0 0.0 338.6 0.0 0.0 0.0 0.0 0.0 0.0
7828.4 4166.9 1711.1 4594.9 6483.0 964.7 3745.4 2889.0 5113.1 937.2 6644.0 8759.3 842.7 3212.9 1207.0 1061.7 5591.6
6197.8 519.2 185.9 931.0 881.2 321.7 684.7 1433.3 313.7 1187.1 2508.4 1225.7 2016.2 675.5 315.4 227.8 439.3
2430.7 668.4 453.8 767.6 1102.1 198.9 926.9 656.4 581.7 1162.1 2198.3 1740.0 823.5 472.5 241.6 343.4 805.6
10.5 2.9 1.7 7.2 12.2 1.0 8.8 9.0 7.4 4.7 38.0 53.9 6.8 10.6 10.2 9.0 16.2
P_D
369.0 33.3 244.1 9.4 113.6 10.0 292.2 18.1 324.1 35.3 22.0 2.6 248.1 23.8 92.2 20.1 109.0 36.8 67.7 11.8 565.0 102.9 339.5 114.3 44.4 32.7 115.6 21.1 88.5 28.2 56.4 18.6 159.2 55.8
P_E
P_G
P_W P_U
412.2 90.7 946.2 555.3 246.1 46.7 98.6 156.4 220.7 32.6 14.3 106.5 293.5 45.0 87.5 176.9 525.0 122.6 143.9 256.9 61.6 8.0 27.1 46.1 134.5 41.7 62.7 215.6 108.4 41.7 302.1 153.4 0.0 58.5 42.6 132.8 0.0 18.3 144.9 269.8 227.7 160.4 541.7 521.3 0.0 196.9 214.6 411.8 0.0 17.5 242.6 189.3 0.0 46.8 101.5 112.0 0.0 23.6 63.1 59.3 0.0 18.4 32.5 82.5 0.0 89.5 54.6 191.3
Ihn, inhabitants; MYP, mean yearly precipitation; N, mean yearly emission of nitrogen (tN/year); P, mean yearly emission of phosphorus (tP/year). Emission pathways: _A, atmospheric deposition; _O, overland flow; _D, drained areas; _E, erosion; _G, groundwater; _W, wastewater treatment plants; _U, urban areas.
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Table 2 Characteristics of Brda and Drweca ˛ catchments and the emission pattern for 2002. Brda
Drweca ˛
Length (km) Population (thousands) Catchment area (km2) Agricultural area (%) Forest (%)
238 472.1 4693 50.1 44.7
207.2 418.3 5477 70.9 24
Emission pathways in 2002 Atmospheric deposition Tile drainage Groundwater Overland flow Erosion WWTP Urban systems
Nitrogen (t/a) 360 1085 2137 314 0 549 177
Phosphorus (t/a) 9 8 29 46 0 42 97
Nitrogen (t/a) 374 3268 1262 388 167 266 227
Phosphorus (t/a) 10 25 25 85 93 28 56
Total emission
4622
232
5952
321
influence of area or population on emission value. Factor analysis (FA) gave similar results for standardized and raw data; only a small variation in loading values was observed. This is due to fact that the FA algorithm uses standardization of data for data treatment, but the algorithm differs from that used in the present study. Ward’s method as an agglomeration rule in cluster analysis has been chosen. Similarity was measured by Euclidean distance. Clusters were derived according to Smith’s rule. Thirty-three percent of maximum distance has been chosen to discriminate the clusters. Application of principal component analysis (PCA) was not sufficient because a strong correlation between emission parameters with more than one component was observed. This problem was solved by the application of factor analysis with rotation. Calculation of factors in FA were based on principal component analysis. The number of principal components has been limited to three with minimal eigenvalue criterion of 1. Computed factors were finally rotated using the normalized Varimax method.
3. Results Comparison of the emissions data on a local scale shows that the emission pattern (how much of nutrient is emitted via different processes) is key to understanding the spread of nutrients into the desired area. This fact is obviously related to the catchment’s properties such as population density, urbanization, average slope of catchment etc. Table 2 presents patterns of nitrogen and phosphorus emissions within Brda and Drweca ˛ as well as a summary of characteristics of both catchments (see catchments K and L in Fig. 1). These catchments are localized in the northern part of the Vistula basin and have a similar area. They differ, however, in land
Fig. 3. Regionalization of Vistula catchments by means of cluster analysis.
utilization. The estimated total emissions are in the same range, but pathway patterns are unique and can be used as an indicator to improve local policies aiming to reduce the emission of nitrogen and phosphorus into the environment. Multivariate techniques were used to analyse all Vistula regions simultaneously (Astel et al., 2008). The dendrogram of the location pattern resulting from the CA is presented in Fig. 2. It shows that all the locations may be generally grouped into three main groups. Fig. 3 depicts catchment localization as a member of the clusters. The first cluster is formed by catchments I, H and A1 and represents the regions with the highest influence on water quality (grey areas in Fig 3). The second one consists of two groups. The first group is formed by B, D, A2 and A3 and reflects upper Vistula catchments with higher impact from industry and erosion (dotted areas on the map). The second one consists of the remainder of the Vistula basin localized in the middle and lower Vistula basin (white areas in Fig. 3). The character of the pollution sources in these groups is very different. The Upper Vistula catchments are generally exposed to the point and erosion sources of nitrogen and phosphorus, while Table 3 Factor loadings structure.
Fig. 2. Differentiation of the catchments in Vistula River according to cluster analysis.
Emission source
F1
F2
Ndatmospheric deposition Ndoverland flow Nddrained areas Nderosion Ndgroundwater Ndwastewater treatment plants Ndurban areas Pdatmospheric deposition Pdoverland flow Pddrained areas Pderosion Pdgroundwater Pdwastewater treatment plants Pdurban areas
0.97 0.66 0.91 0.08 0.71 0.07 0.54 0.97 0.62 0.97 0.10 0.90 0.17 0.56
0.00 0.62 0.29 0.96 0.48 0.20 0.32 0.02 0.65 0.01 0.95 0.35 0.23 0.32
F3 0.20 0.31 0.16 0.23 0.30 0.97 0.75 0.13 0.35 0.18 0.25 0.18 0.94 0.74
Eigenvalue Explained variance (%)
6.45 46.00
3.37 24.00
3.51 25.00
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Fig. 5. Differentiation of the catchments in Vistula River according to factor analysis.
Fig. 4. Results of factor analysis: principal component scores for investigated catchments: (A) factor 1 associated with agriculture; (B) factor 2 associated with erosion; (C) factor 3 associated with point sources.
the rest of the Vistula region is recognized as an area of high agricultural activity. Further investigation requires a more complete method to answer the key question: what original parameters are responsible for the differentiation of catchments? The same data were analysed therefore by means of factor analysis (FA). The new factors explained more than 90% of cumulative variance. The factor loadings are presented in Table 3, highlighted loading values being greater than 0.7.
According to these data, Factor 1 is related mainly to parameters describing diffuse emission sources of both nutrients, namely atmospheric deposition, emission to groundwater and emission from drained areas. This can be named as the impact of agriculture due to the fact that deposition from atmosphere is rather small compared with the other two sources. Factor 2 is loaded mainly with those parameters that explain the influence of erosion and finally, Factor 3 correlates with parameters related to point sources. All of the highlighted vales indicate a positive correlation of emission by source with a particular factor. The FA scores for evaluated catchments show the groups of the catchments with similar pollution character. Due to rotation strategy, the results of the classification are different to those obtained by cluster analysis. Factor 1 (Fig. 4A) has the biggest values for the catchments localized at the eastern part of the Vistula basin (H, I) and for A6 (corresponding to Zulawy Wislane) and clearly indicates regions with a high influence of agriculture on nutrient emission. The influence of Factor 2 (Fig. 4B) decreases with the distance from the mountainous regions, having the highest values for catchments D and A2 and corresponding to erosion figures calculated by the model. The influence of erosion sources also remains important for the catchments A1, B and C and also for southern part of the Bug River basin (H). The strongest influence of point sources, represented by means of Factor 3 (Fig. 4C), has been found in the central part of the basin where the biggest cities are localized, and also in the Cracow vicinity (catchments A1, A4, H, J). Fig. 5 shows the geographical location of different catchments with respect to their factorization. Agricultural pressure is the driving force regulating the flow of nutrients on the eastern side of the basin (H, I) and in Zulawy at the Vistula mouth (A6). Emission from point sources is largest in rural regions of Silesia (A1), Warsaw (A4) and surrounding industrial catchments (H, J). The third way of emission is erosion, which occurs and plays an important role in the Southern part of the basin.
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4. Discussion
5. Conclusions
The results clearly indicate the most polluted regions and show similarities between them. The Silesia region (A1) is particularly exposed to the influence of point sources including both municipal and industrial activities. The Silesia region is exposed also to the emission of nutrients via erosion. In this case, 30% reduction of phosphorus emission via point sources causes 20% of total P emission from this area (Kowalkowski and Buszewski, 2006). Such a decrease is significant even on the entire Vistula basin scale and can be achieved efficiently by improving wastewater treatment plant technology and applying the policy of the use of non-phosphate detergents. Moreover, the nutrient load from point sources can be reduced during a short period and, usually, measures to reduce the input from point sources are much cheaper compared to measures dealing with diffuse sources (Neumann and Schernewski, 2005). The second ‘‘hot spot’’ region is the Bug River catchment (H) followed by Narew catchment (I) where agriculture has the biggest influence on water quality. The crucial parameter in this case is the surplus of phosphorus and nitrogen in agricultural soils. Research done by Behrendt (2002) shows that reduction of N, P surplus to 70% by 2020 can lead to a significant decrease of nitrogen emitted into the groundwater and tile drainage (both 8% in 2020). Such reduction of nutrient surplus can be realized by increasing the effectiveness of fertilizer usage and its proper dosage in both catchments. On the other hand, investigations done in Latvian rivers (Stalnacke et al., 2003) shows that even a dramatic decrease of agricultural activity does not reflect much on water quality in the short term. The efficiency of reduction inagriculture can be limited by other factors, e.g. pathways of nutrient compounds between soil profile–root zone and surface water recipient. Site-oriented case studies are therefore necessary to estimate such efficiency values. The management of river basins (Simeonov, 2003) involves the making of informed choices about the desired levels of economic activities and ecosystem functioning in the catchment. A need exists to develop new strategies of nutrient reduction within the Baltic Sea basin. Studies on the Baltic Sea indicate that the limiting factor of eutrophication is the load of nitrogen (Wulff and Niemi, 1992; Ericsson and Hallmans, 1994). Hence, immediate improvements can be achieved only by means of nitrogen reduction. Nowadays, this statement seems still be truthful due to the costs of effective nitrogen reduction. The main source of nitrogen is the input in groundwater (drainage water). Reduction of nitrogen concentrations in this pathway is expensive and takes decades _ (Markowska and Zylicz, 1999). This is the reason for developing effective reduction scenarios and why such scenarios have to be in time and spatially differentiated. The cost-efficient scenario proposed by Gren (2000) for the reduction of nutrient loads within the Baltic Sea is based on significant regional differences in load reduction. It implies that nutrient load reduction takes place in countries and drainage basins where it is most cost-efficient. The optimal reduction of nitrogen and phosphorus costs only 23% of the other so-called proportional reduction scenario (required by HELCOM) and therefore has huge economic benefits. The mid-term simulation done by Neumann and Schernewski (2005) indicates a similar effect for both proportional and cost-efficient scenarios. However, spatial differentiation of the reduction was observed. Both scenarios gave insufficient reduction in several locations. The present results of statistical analysis show the key areas where emission of nutrients is related to particular pathways. The idea of such differentiation can be applied to evaluate new ‘‘pathway-oriented’’ scenarios for nutrient reduction. The combination of both regional and pathway oriented reduction policies offers a good alternative in the future development of the Baltic Sea.
Multivariate analysis of the dataset representing distribution of nutrient emission sources has been evaluated. The results indicate regions sharing similar pollution problems. Two methods have been adopted. Cluster analysis divided the Vistula basin into the Upper and Lower regions. Major information has been obtained by means of factor analysis. Three types of sources of nutrients spatially divide the Vistula River basin. The results indicate two regions having the biggest impact on the Vistula, namely Silesia and the Bug–Narew catchment. The emission pathways in those catchments are different and require application of various reduction strategies. The performed analysis can be an easy-to-use tool to support the evaluation of different scenarios aiming at the reduction of nutrients. Statistics, in this case, simplify the structure of data and indicate areas with a high impact in a particular pathway described by one factor. Further investigation of cost-effectiveness is, however, necessary to simulate real reduction values. Acknowledgements This work was supported by Ministry of Science and Higher Education grant no. N N523 1054 33. The author thanks also Dr Luke Himuka (WITS University, South Africa) for discussion and comments. References Astel, A., Tsakovski, S., Simeonov, V., Reisenhofer, E., Piselli, S., Barbieri, P., 2008. Multivariate classification and modeling in surface water pollution estimation. Analytical and Bioanalytical Chemistry 390 (5), 1283–1292. Behrendt, H., Huber, P., Kormilch, M., Opitz, D., Schmoll, O., Scholz, G., Uebe, R., 2000. Nutrient Emissions into River Basins of Germany. UBATexte, Berlin. 23/00. Behrendt, H., Dannowski, R., Deumlich, D., Dolezal, F., Kajewski, I., Kornmilch, M., Korol, R., Mioduszewski, W., Opitz, D., Steidl, J., Stronska, M., 2002. Investigation on the quantity of diffuse entries in the rivers of the catchment area of the Odra and the Pomeranian Bay to develop decision facilities for an integrated approach on waters protection. Research Report on the joint international project (FKZ: 298 28 299). Buszewski, B., Kowalkowski, T., 2003. Poland’s environmentdpast, present and future state of the environment in the Vistula and Odra River Basins. Environmental Science and Pollution Research 10 (6), 343–349. Buszewski, B., Buszewska, T., Chmarzyn´ski, A., Kowalkowski, T., Kowalska, J., _ Kosobucki, P., Zbytniewski, R., Namiesnik, J., Kot-Wasik, A., Zukowska, B., Pacyna, J., Panasiuk, D., 2005. The present condition of the Vistula River catchment area and its impact on the Baltic Sea coastal zone. Regional Environmental Change 5, 97–110. CEESA, 2003. CEESA report: The Challenge of the Nitrate Directive to Acceding Countries: A Comparative Analysis of Poland, Lithuania and Slovakia. www.fao. org/regional/SEUR/CEESA_Vol2_en.pdf. de Paepe, V., Boschet, A.-F., Nixon, S.C., 2002. Comparability of assessments of nutrient emissions to inland waters undertaken within eight European Union Member States. Journal of Water Supply: Research and Technology 51, 87–108. Ericsson, B., Hallmans, B., 1994. Control of the nutrient pollution discharge from the Vistula River Basin in Poland. Desalination 98, 185–197. Gren, I.-M., 2000. Managing a sea. Cost-Effective Nutrient Reduction to the Baltic Sea. Earthscan Publ., London, pp. 43-56. Gren, I.-M., Soderqvist, T., Wulff, F., 1997. Nutrient reductions to the Baltic Sea: Ecology, costs and benefits. Journal of Environmental Management 51, 123–143. HELCOM, 2004. The fourth Baltic sea pollution load compilation (PLC-4), Baltic Sea Environment Proceedings No. 87. Kowalkowski, T., Buszewski, B., 2003. Modeling of past, present and future state of nutrient emission into the Vistula river system. Implementation of MONERIS model to the Vistula river catchment. VisCat WP4 report Torun´. Kowalkowski, T., Buszewski, B., 2006. Emission of nitrogen and phosphorus in Polish rivers. Past, present and future trends in Vistula river catchment. Environmental Engineering Science 23 (4), 615–622. Kowalkowski, T., Zbytniewski, R., Szpejna, J., Buszewski, B., 2006. Application of chemometrics in river water classification. Water Research 40 (4), 744–752. Kowalkowski, T., Gadza1a-Kopciuch, M., Kosobucki, P., Krupczyn´ska, K., Ligor, T., 2007. Organic and inorganic pollution of Vistula River basin. Journal of Environmental Science and Heath Part A. Buszewski, B. 42 (4), 1–6. Kronvang, B., Svendsen, L.M., Jensen, J.P., Dørge, J., 1999. Scenario analysis of nutrient management at the river basin scale. Hydrobiologia 410, 207–212.
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