<?xml version="1.0" encoding="UTF-8"?><!DOCTYPE article  PUBLIC "-//NLM//DTD Journal Publishing DTD v3.0 20080202//EN" "http://dtd.nlm.nih.gov/publishing/3.0/journalpublishing3.dtd"><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" dtd-version="3.0" xml:lang="en" article-type="research article"><front><journal-meta><journal-id journal-id-type="publisher-id">JEP</journal-id><journal-title-group><journal-title>Journal of Environmental Protection</journal-title></journal-title-group><issn pub-type="epub">2152-2197</issn><publisher><publisher-name>Scientific Research Publishing</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.4236/jep.2013.41013</article-id><article-id pub-id-type="publisher-id">JEP-27332</article-id><article-categories><subj-group subj-group-type="heading"><subject>Articles</subject></subj-group><subj-group subj-group-type="Discipline-v2"><subject>Earth&amp;Environmental Sciences</subject></subj-group></article-categories><title-group><article-title>
 
 
  Assessment of the Spatial and Temporal Water Eutrophication for Lake Baiyangdian Based on Integrated Fuzzy Method
 
</article-title></title-group><contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>huxuan</surname><given-names>Liang</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref><xref ref-type="corresp" rid="cor1"><sup>*</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Hong</surname><given-names>Wu</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Yihong</surname><given-names>Wu</given-names></name><xref ref-type="aff" rid="aff2"><sup>2</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Hongbo</surname><given-names>Li</given-names></name><xref ref-type="aff" rid="aff2"><sup>2</sup></xref></contrib></contrib-group><aff id="aff2"><addr-line>Hebei Provincial Academy of Environmental Science, Shijiazhuan, China</addr-line></aff><aff id="aff1"><addr-line>College of Chemistry and Environmental Science, Hebei University, Baoding, China</addr-line></aff><author-notes><corresp id="cor1">* E-mail:<email>liangsx168@yahoo.com.cn(HL)</email>;</corresp></author-notes><pub-date pub-type="epub"><day>25</day><month>01</month><year>2013</year></pub-date><volume>04</volume><issue>01</issue><fpage>120</fpage><lpage>125</lpage><history><date date-type="received"><day>October</day>	<month>29th,</month>	<year>2012</year></date><date date-type="rev-recd"><day>November</day>	<month>29th,</month>	<year>2012</year>	</date><date date-type="accepted"><day>December</day>	<month>28th,</month>	<year>2012</year></date></history><permissions><copyright-statement>&#169; Copyright  2014 by authors and Scientific Research Publishing Inc. </copyright-statement><copyright-year>2014</copyright-year><license><license-p>This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/</license-p></license></permissions><abstract><p>
 
 
   Water quality evaluation entails both randomness and fuzziness. Considering that water eutrophication evaluation involves many indices, different classifications and interval values, fuzzy variable sets theory was developed to Lake Baiyangdian as a study case. Taking reference to eutrophication standard of Chinese lakes and local characteristic of Lake Baiyangdian, eutrophication degree of lake was divided into 8 levels. Total phosphorus, total nitrogen, and COD<sub>Mn</sub> were selected as evaluation indices in this research. Based on the measured data, index feature value matrix of sample was built. Index weights were determined by means of pure threshold value method. Relative membership degree of each index to each classification was calculated with relative difference function model. Then the stability of feature value of classification corresponding was received by the comprehensive calculation with the relative membership degree and index weights. The results show that the proposed models are effective tools for generating a set of realistic and flexible optimal solutions for complicated water quality evaluation issues. It concluded that the model was reasonable and practical. 
 
</p></abstract><kwd-group><kwd>Eutrophication Evaluation; Fuzzy Method; Spatial Variation; Temporal Variation; Lake Baiyangdian</kwd></kwd-group></article-meta></front><body><sec id="s1"><title>1. Introduction</title><p>Lake Baiyangdian is the largest fresh water lake and natural wetland in north China. The lake plays an important role in balancing the ecosystem there. It is described as the “Kidney of North China” and serves many environmental and economic services. The lake area is divided into various sizes and shapes by 39 villages, over 3700 ditches and 80 km<sup>2</sup> of reeds. The ditches in the lake area are in crisscross patterns. The geographical feature of reed marsh, lotus ponds and fishing villages is unique throughout China. Lotus ponds and boundless reed marshes are the special sights of the lake, thus it enjoys the good reputation of “North China Pearl”.</p><p>While appearing pristine beauty in parts, Lake Baiyangdian is under assault from a variety of sources, most notably industrial and domestic wastewater emptied upstream, the holding back of replenishing waters into reservoirs by upstream communities, and local fish farming [1,2]. In particular, Lake Baiyangdian has 39 in-lake villages, which are surrounded by water in the lake, and 44 semi-in-lake villages, which are partly surrounded by water in the lake. An estimated 100,000 persons live in the in-lake villages and another 100,000 persons in the surrounding areas of the lake. The low water level, rotten organic substance underwater, and other factors also led to deterioration of water quality and shrinkage of the lake area in recent years.</p><p>All the functions depend on the water quality which should be kept at a certain level to maintain a well-balanced environment in terms of its physical, chemical and biological variables. Water-quality deterioration and eutrophication are general problems in the world lake and have acquired more and more attention from the public and government. The introduction of large quantities of nutrients, mainly nitrogen and phosphorus to lake waters can cause eutrophication problems [<xref ref-type="bibr" rid="scirp.27332-ref3">3</xref>]. Eutrophication is the most widespread water quality problem in China and many other countries in the last few decades [<xref ref-type="bibr" rid="scirp.27332-ref4">4</xref>]. Eutrophication is also becoming a sharp problem to Lake Baiyangdian due to the increase of nutrients from upstream and in-lake villages. As biological response to excess nutritive salt importing into the lake, the increase of biomass, in particular aquaplant, in Baiyangdian have weaken the water functions and threatened the biologic health. the cleanup and repristination of Lake Baiyangdian is currently a topic of increasing concern by Chinese government and local environmental protection bureau for its special location and ecological significance. Actually the lake eutrophication, influenced by many factors, is a typical multi-criteria decision-making.</p><p>As an important basic research subject, building appropriate model and accurately evaluating the eutrophication degree is the basis and premise for the control of lake eutrophication [<xref ref-type="bibr" rid="scirp.27332-ref5">5</xref>]. Eutrophication evaluation is such a problem that involves different indices, many classifications and interval values. During eutrophication evaluation, the evaluative result of each index is always incompatible and independent. And directly basing on the evaluation of one index will cause information omission and even wrong result [<xref ref-type="bibr" rid="scirp.27332-ref6">6</xref>].</p><p>In the frame of the present work, a study was performed for select certain evaluation indices and mathematic method. Different methods had been evaluated the water quality [7-9]. Fuzzy synthetic evaluation is a modified and corrected version of conventional synthetic evaluation [10,11]. The fuzzy evaluation is given in accordance with the evaluation criteria and themeasured values to evaluate the fuzzy transformation of things in a way. Fuzzy variable evaluation method can effctively deal with the influence of evaluation standard interval values and set up the comprehensive evaluation model to fulfill the comprehensive evaluation to water environment [12, 13].</p><p>The aim of this paper is to explore the use of fuzzy classification to eutrophication modelling in Lake Baiyangdian. More specifically, we use fuzzy inference to recognize vague patterns in a set of spatial and temporal data. This evaluation aimed to ascertain the eutrophication problems and then to improve the measures of comprehensive treatment for Baiyangdian.</p></sec><sec id="s2"><title>2. Objectives and Method</title><sec id="s2_1"><title>2.1. Study Area</title><p>The study area is located in Hebei Province (38.850˚N, 116.000˚E). This shallow lake is disk-shaped with surface area 366 km<sup>2</sup> and water depth 1 - 2 m. The lake lies in the middle reaches of the Daqinghe River basin and ultimately discharges into the Bohai Gulf, Yellow Sea. Much of the upstream catchment totaling 31,500 km<sup>2</sup> lies within Baoding municipality. While nine rivers and/or channels flow into Lake Baiyangdian, only two have regular but decreasing flows, namely, Fuhe and Juma River [<xref ref-type="bibr" rid="scirp.27332-ref14">14</xref>]. Water systems of Baiyangdian area was shown in <xref ref-type="fig" rid="fig1">Figure 1</xref>.</p></sec><sec id="s2_2"><title>2.2. Sampling Points and Monitoring Data</title><p>The monitoring study was carried out with water samples. Eight water-quality sampling sites were established. Sampling sites in Lake Baiyangdian were shown in <xref ref-type="fig" rid="fig2">Figure 2</xref>.</p><p>The monitoring data were collected including COD<sub>Mn</sub>, TP, TN concentrations every month from 1996 to 2008. The spatial-temporal eutrophication characteristic analysis will be helpful to watershed management.</p></sec><sec id="s2_3"><title>2.3. Evaluation Method</title><sec id="s2_3_1"><title>2.3.1. Fuzzy Evaluation Method</title><p>The fuzzy evaluation includes the following steps [<xref ref-type="bibr" rid="scirp.27332-ref15">15</xref>].</p><p>1) Set the collection of the factors U = {U1，U2，U3, &#215;&#215;&#215;, Un}, where U is the measured values of the evaluation factors;</p><p>2) Set the collection of the evaluation standard V= {V1, V2, V3, &#215;&#215;&#215;, Vn}, where V is corresponded to the pollution of the water quality standard classification;</p><p>3) Determine index weight A;</p><p>4) Determine fuzzy matrix R;</p><p>5) Calculate evaluation matrix B and get a result.</p></sec><sec id="s2_3_2"><title>2.3.2. Evaluation Standard</title><p>Select the major pollution factors total phosphorus (TP), total nitrogen (TN), COD<sub>Mn</sub> as an evaluation to determine the collection of the factors U = {U<sub>TP</sub>, U<sub>TN</sub>, U<sub>COD</sub>}, and in accordance with the water quality data of the year 1996 to 2008.</p><p>Based on the accepted method and actual feature, evaluation standard was classified into eight levels and listed in <xref ref-type="table" rid="table1">Table 1</xref>. Determine the evaluation standard V={V<sub>1</sub>, V<sub>2</sub>, V<sub>3</sub>, &#215;&#215;&#215;, V<sub>8</sub>}.</p></sec><sec id="s2_3_3"><title>2.3.3. Index Weight A Determination</title><p>At present, there are many methods to calculate the weight of pollution factors, such as entropy value method, equal weight method and pure threshold value method. In this work, standard assignment method was chosen. It is calculated by the following method.</p><p>A<sub>i</sub> =<img src="13-6701701\52619171-453f-4399-a837-0e9d68ba2577.jpg" /></p><p>where A<sub>i</sub> is the weight of the pollution factors i; S<sub>i</sub> is the arithmetic mean of the i kinds of all the standard; and X<sub>i</sub> is the measured values of the factors i.</p><p><xref ref-type="table" rid="table1">Table 1</xref>. Evaluation classification standard of water eutrophication in Baiyangdian.</p><p><img src="13-6701701\14e75aab-1b0b-4b2f-a6f8-1ee9640e9f13.jpg" /></p><p>Then A = {A<sub>TP</sub>, A<sub>TN</sub>, A<sub>COD</sub>}.</p></sec><sec id="s2_3_4"><title>2.3.4. Comprehensive Relative Membership Degree Determination</title><p>Comprehensive relative membership degree was gotten as follows.</p><p><img src="13-6701701\aa768bb5-3821-4fe4-b26b-bcd98d42467e.jpg" /></p><p><img src="13-6701701\5ef02cb4-8cf8-438d-a75d-b3ed9f6d8ecb.jpg" /></p><p><img src="13-6701701\2f43d267-6e6d-4325-b156-e0797516c38b.jpg" /></p><p>where j is the level of pollution; X<sub>i</sub> is the first i in the environment of pollutants measured values; S<sub>ij</sub> is the kinds of pollutants that the first i-level for the first j standards values.</p></sec><sec id="s2_3_5"><title>2.3.5. Fuzzy Matrix R Determination</title><p>Input the data of every year into above membership degree determined, then their membership and the fuzzy evaluation matrix R were established.</p><p><img src="13-6701701\1e9bd5fc-5f7a-417e-b6fd-6e93370bc5b9.jpg" /><img src="13-6701701\0d642f92-10cd-4a4b-a88b-e61ba7b1eee2.jpg" /></p></sec><sec id="s2_3_6"><title>2.3.6. Evaluation Matrix B Determination and Get the Result</title><p>After establishment of the A and R, the fuzzy comprehensive evaluation can calculate the matrix B, namely B = AR. According to the principle of the largest degree of membership, if in the matrix B = {b<sub>1</sub>, b<sub>2</sub>, &#215;&#215;&#215;, b<sub>n</sub>} in the presence bj = max{ b<sub>1</sub>, b<sub>2</sub>, &#215;&#215;&#215;, b<sub>n</sub>}, then the level should be evaluated for the j-level. The computation use of “∧ and ∨” that is, multiplication getting a lest value then adds getting biggest value.</p></sec></sec></sec><sec id="s3"><title>3. Results and Discussion</title><sec id="s3_1"><title>3.1. Dominant Pollutants in Lake Baiyangdian</title><p>The annual index weights of pollution factors in 1996 to 2008 were determined and listed in <xref ref-type="table" rid="table2">Table 2</xref>. The index weight results in <xref ref-type="table" rid="table2">Table 2</xref> indicated that TN had largest impact in most of year, so TN was the dominant pollutants throughout the year for the eutrophication in Baiyangdian.</p></sec><sec id="s3_2"><title>3.2. Spatial Change of Water Eutrophication in Lake Baiyangdian</title><p>The eutrophic level of all sites in Lake Baiyangdian in 1996 and 2008 was calculated by fuzzy evaluation method separately. Evaluation results were shown in <xref ref-type="table" rid="table3">Table 3</xref>.</p><p>Baiyangdian presented significantly regional patterns.</p><p><xref ref-type="table" rid="table2">Table 2</xref>. Weight of pollution factors.</p><p><img src="13-6701701\1ff04827-18cb-48cd-872a-c43d98c54486.jpg" /></p><p><xref ref-type="table" rid="table3">Table 3</xref> Evaluation results in different monitoring sites.</p><p><img src="13-6701701\68b0683f-3525-43c6-97e0-30b79c351645.jpg" /></p><p>Site 1 was the most hypereutrophic site among Baiyangdian obviously. It was the catchment basin of Fuhe River from Baoding City. Baoding located in the upper reaches of Lake Baiyangdian, discharged 20 t of sewage water by two sewage treatment plants and citizens each day.</p><p>Other sites were in eutrophic or upper-eutrophic levels. Ultimately, these sites were in upper-eutrophic according the results of 2008. Water eutrophication in Baiyangdian trended to intensify. Seemingly, this conclusion was not corresponding to the enormous effort to Baiyangdian by government and researchers. The release mechanism of nutrimental salts in shallow lake was more complex. This essential feature of shallow lake determined the hysteresis of water quality, so sustaining and extending protection steps were essential to Baiyangdian.</p><p><xref ref-type="table" rid="table4">Table 4</xref>. Evaluation results different water periods of Lake Baiyangdian.</p><p><img src="13-6701701\864fade6-4059-41bc-b523-80cdd0184f91.jpg" /></p></sec><sec id="s3_3"><title>3.3. Temporal Change Chatacter of Water Eutrophication in Lake Baiyangdian</title><p>The eutrophic level of Lake Baiyangdian from 1996 to 2008 years was evaluated by above method. The calculation results indicated the eutrophic levels were between level 5 to 7. Baiyangdian was in a state of seriously eutrophic level during the last decade to now. Actually, many protection and restoration measures have been taken to control the eutrophication. The eutrophic level showed the vulnerability and liability of eutrophication in shallow lakes environment.</p></sec><sec id="s3_4"><title>3.4. Comparison of Temporal Variation</title><p>May, August and October were corresponded to drywater period, wet-water period and flat-water period respectively. The eutrophication status of different water periods from 1996 to 1999 were calculated and the results were shown in <xref ref-type="table" rid="table4">Table 4</xref>.</p><p>The results in <xref ref-type="table" rid="table4">Table 4</xref> exhibited the significantly temporal variation in Baiyangdian. Eutrophic degrees were changed between Level 4 to Level 7. From <xref ref-type="table" rid="table4">Table 4</xref>, the lake eutrophication was more serious in the dry period and alleviative in the wet period.</p></sec></sec><sec id="s4"><title>4. Conclusions</title><p>It is feasible to evaluate the degree of water eutrophication by this method. We can get a better understanding of the situation of Baiyangdian by calculating the eutrophication of the lake and later provide the basis for the management of Baiyangdian.</p><p>The degree of eutrophication of Baiyangdian is closely relevant to the factor TN and COD<sub>Mn</sub>, so TN and COD<sub>Mn</sub> should be studied to improve the situation of Baiyangdian.</p></sec><sec id="s5"><title>5. Acknowledgements</title><p>This work was supported by the National 11th Five-Year Major Science and Technology Projects on Control and Prevention of Water Pollution (2008ZX07209-007).</p></sec><sec id="s6"><title>REFERENCES</title></sec></body><back><ref-list><title>References</title><ref id="scirp.27332-ref1"><label>1</label><mixed-citation publication-type="other" xlink:type="simple">W. Wang, X. Q. Tang, S. L. Huang, et al., “Ecological Restoration of Polluted Plain Rivers within the Haihe River Basin in China,” Water Air Soil Pollution, Vol. 211, No. 1-4, 2010, pp. 341-357.  
doi:10.1007/s11270-009-0304-5</mixed-citation></ref><ref id="scirp.27332-ref2"><label>2</label><mixed-citation publication-type="other" xlink:type="simple">C. Q. Yin and Z. W. Lan, “The Nutrient Retention by Ecotone Wetlands and Their Modification for Baiyang dian Lake Restoration,” Water Science and Technology, Vol. 32, No. 3, 1995, pp. 159-167. 
doi:10.1016/0273-1223(95)00616-8</mixed-citation></ref><ref id="scirp.27332-ref3"><label>3</label><mixed-citation publication-type="other" xlink:type="simple">M. Varol and B. Sen, “Assessment of Surface Water Quality Using Multivariate Statistical Techniques: A Case Study of Behrimaz Stream, Turkey,” Environmental Monitoring and Assessment, Vol. 159, No. 1-4, 2009, pp. 543-553. doi:10.1007/s10661-008-0650-6</mixed-citation></ref><ref id="scirp.27332-ref4"><label>4</label><mixed-citation publication-type="other" xlink:type="simple">K. X. Xing, H. C. Guo, Y. Y. Sun, et al., “Assessment of the Spatial-Temporal Eutrophic Character in the Lake Dianchi,” Journal of Geographical Sciences, Vol. 15, No. 1, 2005, pp. 37-43.</mixed-citation></ref><ref id="scirp.27332-ref5"><label>5</label><mixed-citation publication-type="other" xlink:type="simple">J. J. Berzas Nevado, R. C. Rodríguez Martín-Doimeadios, F. J. Guzmán Bernardo, et al., “Integrated Pollution Evaluation of the Tagus River in Central Spain,” Environ mental Monitoring and Assessment, Vol. 156, No. 1-4, 2009, pp. 461-477. doi:10.1007/s10661-008-0498-9</mixed-citation></ref><ref id="scirp.27332-ref6"><label>6</label><mixed-citation publication-type="other" xlink:type="simple">Z. Z. Li, “The Multiple Objective Fuzzy-Grey Method for Regional Water Quality Assessment,” International Journal of Hydroelectric Energy, Vol. 15, No. 4, 1997, pp. 35 40.</mixed-citation></ref><ref id="scirp.27332-ref7"><label>7</label><mixed-citation publication-type="other" xlink:type="simple">M. Karydis, “Quantitative Assessment of Eutrophication: A Scoring System for Characterizing Water Quality in Coastal Marine Ecosystems,” Environmental Monitoring and Assessment, Vol. 41, No. 3, 1996, pp. 233-246. 
doi:10.1007/BF00419744</mixed-citation></ref><ref id="scirp.27332-ref8"><label>8</label><mixed-citation publication-type="other" xlink:type="simple">A. Morihiro, A. Outoski, T. Kawai, et al., “Application of Modified Carlson’s Trophic State Index to Japanese Lakes and Its Relationship to Other Parameters Related to Trophic State,” Research Report on National Institute of Environmental Studies, Vol. 23, No. 1, 1981, pp. 12-30.</mixed-citation></ref><ref id="scirp.27332-ref9"><label>9</label><mixed-citation publication-type="other" xlink:type="simple">A. Islam, S. Mozafar and M. G. Kelly, “Evaluation of the Trophic Diatom Index for assessing water quality in River Gharasou western Iran,” Hydrobiologia, Vol. 589, No. 1, 2007, pp. 165-173. doi:10.1007/s10750-007-0736-0</mixed-citation></ref><ref id="scirp.27332-ref10"><label>10</label><mixed-citation publication-type="other" xlink:type="simple">R. S. Lu, S. L. Lo and J. Y. Hu, “Analysis of Reservoir Water Quality Using Fuzzy Synthetic Evaluation,” Stochastic Environmental Research and Risk Assessment, Vol. 13, No. 5, 1999, pp. 327-336. 
doi:10.1007/s004770050054</mixed-citation></ref><ref id="scirp.27332-ref11"><label>11</label><mixed-citation publication-type="other" xlink:type="simple">H. Kung, L. Ying and Y. C. Liu, “A Complementary Tool to Water Quality Index: Fuzzy Clustering Analysis,” Water Resources Bulletin, Vol. 28, No. 3, 1992, pp. 525-533. 
doi:10.1111/j.1752-1688.1992.tb03174.x</mixed-citation></ref><ref id="scirp.27332-ref12"><label>12</label><mixed-citation publication-type="other" xlink:type="simple">W. J. Xu, W. G. Chen and X. P. Zhang, “Fuzzy Variable Model for Eutrophication Evaluation and Sustainable Development Countermeasure,” Journal of American Science, Vol. 4, No. 3, 2008, pp. 11-17.</mixed-citation></ref><ref id="scirp.27332-ref13"><label>13</label><mixed-citation publication-type="other" xlink:type="simple">Q. W. Chen, M. C. Yenory, H. Li, et al., “Hydro informatics Techniques in Eco-Environmental Modelling and Management,” Journal of Hydroinformatics, Vol. 8, No. 4, 2006, pp. 297-316. doi:10.2166/hydro.2006.011</mixed-citation></ref><ref id="scirp.27332-ref14"><label>14</label><mixed-citation publication-type="other" xlink:type="simple">C. L. Liu, G. D. Xie and Y. Xiao, “Impact of Climatic Change on Baiyangdian Wetland,” Resource and environment in the Yangtze basin, Vol. 16, No. 2, 2007, pp. 245 250.</mixed-citation></ref><ref id="scirp.27332-ref15"><label>15</label><mixed-citation publication-type="other" xlink:type="simple">X. Zhao, X. Chen and A. Stein, “Integrating Multi-Source Information via Fuzzy Classification Method for Wetland Grass Mapping,” International Society for Photogramme try and Remote Sensing, Beijing, 2008.</mixed-citation></ref></ref-list></back></article>