Evaluation of Water Environment Quality of Si River Based On

2017 International Conference on Energy, Environment and Sustainable Development (EESD 2017) ISBN: 978-1-60595-452-3

Evaluation of Water Environment Quality of Si River Based on Multivariate Statistical Method Sheng-nan LIU1, Wen-li ZHOU1,*, Yong DOU1, Jin-wei GAO1, Xu-ying JIA1, Feng-yue SHU2 and Ying-hui CHAI1 1Tianjin Agricultural University, Tianjin 300380, China 2Qufu Normal University, Qufu 273165, China *Corresponding author

Keyword: Si River, Multivariate statistical analysis, System cluster analysis, Nitrogen and phosphorus.

Abstract. Application of multivariate statistical analysis and system clustering analysis method, based on the detection index of suspended solids (SS), water temperature, dissolved oxygen (DO), pH, oxidation-reduction potential (ORP), total nitrogen (TN), dissolved total nitrogen (DTN), + - chemical oxygen demand (CODMn), ammonia nitrogen (NH4 -N), nitrate nitrogen (NO3 -N), chlorophyll (Chla), dissolved total phosphorus (DTP), dissolved inorganic phosphorus (DIP), total phosphorus (TP) and alkalinity were measured, analyzed on 21 monitoring sections of Si River and major tributaries. The results show: Nitrogen and phosphorus are the main pollution components in 2013-2014 of Si River. The results provide basic data and theoretical basis for pollution control of Si River.

Introduction Si River belongs to the Huaihe River Basin Nansi Lake water system, is the largest seasonal torrent river in the City of Jining[1], the largest inflow river into the east Nansi lake-the largest freshwater lake in Shandong Province[2,3], and it is also the first water regulating lake in the eastern route of south-to-north water diversion project into Shandong Province[4-6]. Research shows that the water quality of Si River is directly related to the South-to-North Water Transfer Project[7,8]. The informed research is mainly analysis the inflow river water quality for a lake section of the monitoring into the Nansi Lake[9-12], but lack of a more comprehensive water quality research analysis for the Si River and the main tributaries as a whole. Multivariate statistical analysis is a comprehensive analysis method developed from classical statistics, it mainly includes: multivariate data graph representation, clustering analysis, discriminant analysis, regression analysis, principal component analysis, factor analysis, correspondence analysis, canonical correlation analysis, path analysis and multidimensional scaling method, which can be used in multiple objects and multiple indicators analysis its inherent laws [13-16].In this paper, the principal component analysis method and the cluster analysis method of multivariate statistical analysis are used to analyze the common detection indexes of 21 monitoring sections of the main stream of Si River and the tributaries, analyze the factors of water pollution and find out the key pollution factors , a comprehensive and objective evaluation of the quality of the water environment, as the Si River water quality protection and management to provide basic data.

Materials and Methods Monitoring Section Setting From April 2013 to January 2014, the water quality of main stream and main tributaries of Si River were tested. According to the characteristics of Si River Basin and the direction of water flow, a total of 20 sampling sections (Figure 1), Among them, 11 monitoring sections are set up in the main stream of the Si River, 5 sections are set up in the Yihe River, the largest tributary of the Si River,

82 and 1 section is set in the Xian River, Dahuanggou, Ji River, Shilou River and Pushan River, covering the territory of Qu River main river basin.

Figure 1. Sampling point distribution on Si River.

Monitoring Indicators The main parameters were suspended solids (SS), water temperature , dissolved oxygen (DO), pH, redox point (ORP), Total nitrogen (TN), dissolved total nitrogen (TNN), chemical oxygen demand + - (CODMn), ammonia nitrogen (NH4 -N), nitrate nitrogen (NO3 -N), chlorophyll (Chla) Total phosphorus (TSP), dissolved inorganic phosphorus(DIP), total phosphorus (TP), alkalinity and so on. Sample Collection and Determination Method Each monitoring section to collect water samples 1, the collection point as far as possible in the middle of the river, the water sample collection using 2.5L water harvesting, take surface water (underwater 0.5 m), stored at low temperatures, should pay attention to Yes, samples should be processed immediately after sampling, for a maximum of 1-2 days. Determination of indicators including field measurement and laboratory determination of two indicators, on-site determination of indicators of water temperature, pH, dissolved oxygen and oxidation-reduction potential, Laboratory measurements should be used polyethylene bottles underwater 30cm or so to collect water samples, polyethylene bottles should be used in advance with pH <2 of sulfuric acid treatment, acidification of sulfuric acid to pH <2 cryopreservation, and then back to the laboratory for analysis. The total nitrogen was determined by alkaline potassium persulfate-ultraviolet spectrophotometry (GB 11894-89). The nitrate nitrogen was determined by phenol disulfonic acid spectrophotometry (GB 7480-87). Ammonia nitrogen was measured by Nessler's reagent spectrophotometer (GB 7479- 87). Chemical oxygen demand was measured by the method of permanganate (GB 118922-89). Nitrate nitrogen was determined using disulfonic acid phenol spectrophotometry (GB 7480-87). Total phosphorus, dissolved total phosphorus using ammonium molybdate spectrophotometry (GB 11893-89). Phosphate was measured by phosphomolybdate blue colorimetric method (GB / T 8538- 1995). chlorophyll was determined by spectrophotometry (SL 88-1994).

83 Research Methods Principal component analysis and cluster analysis were used. Multivariate statistical analysis requires that the indicator be normal or close to normal distribution[17], Before the system cluster analysis and principal component analysis should be standardized before the original data, standardized data as shown in Table 1,use SPSS16.0 software to analyze the data to generate clustering trees with structured hierarchies after the Dimensional effects be eliminated. And then use the principal component analysis method to analyze the standardized data to find out the main factors affecting water quality [18]. Table 1. Standard data of main water chemistry indicators of Si River.

- - ZNO2 - ZTN ZNO3 -N + ZCODMn ZTP ZTSP ZDIP Zchla ZTSN ZNH4 - ZDO Temp ZPH (ug/ml) （ug/ml （ug/ml）（ug/ml） N(ug/ml) (ug/ml）（ug/ml）（ug/ml）（ug/ml） (mg/l) （ug/l）

S1 -1.400 -1.921 -1.278 -1.591 -2.166 -0.538 -1.166 -0.572 -0.481 -0.571 0.375 -0.662 -1.194 S2 -1.385 -1.859 -1.250 -1.545 -2.166 -0.705 -1.099 -0.529 -0.434 -0.665 0.274 -0.632 -1.209 S3 -1.236 -1.156 -1.280 -1.487 -0.039 -0.658 0.291 -0.344 -0.425 -0.705 0.267 -0.640 -0.164 S5 -0.050 -0.322 1.477 1.527 0.268 -0.600 -0.462 -0.581 -0.437 -0.665 0.005 -0.206 -0.250 S6 0.196 0.200 0.108 0.446 -1.279 -0.342 -0.916 -0.294 -0.245 -0.228 -2.788 -0.921 1.201 S7 0.321 1.317 -0.421 0.408 -0.429 -0.353 -1.367 0.023 0.081 0.831 -0.013 -1.042 -1.093 S8 -0.513 -0.481 -0.394 -0.759 0.006 -0.456 -1.117 -0.563 -0.398 -0.545 0.464 -0.382 -0.940 S9 -0.243 0.862 0.263 -0.149 0.196 -0.596 -0.529 -0.331 -0.304 -0.344 0.296 -0.047 1.181 S10 0.048 0.001 0.437 0.096 0.558 -0.652 -0.615 -0.334 -0.396 -0.607 0.468 0.382 0.528 S11 0.884 -0.034 1.220 1.619 2.187 -0.582 -0.658 -0.622 -0.442 -0.603 0.535 -0.098 -1.062 S12 2.699 1.949 2.403 1.955 0.476 2.997 0.739 -0.100 -0.259 -0.226 0.032 -0.331 -0.218 S13 2.015 -0.061 1.533 1.026 0.332 1.496 0.581 -0.330 -0.301 -0.403 0.337 0.167 0.599 S14 0.011 0.235 -1.063 -0.367 -0.429 -0.585 -1.164 -0.544 -0.405 -0.493 -2.949 3.578 -0.950 S15 -0.147 -0.609 -0.173 -0.062 0.015 0.934 0.699 -0.155 -0.260 -0.185 0.065 1.495 -0.771 S16 -0.504 -0.330 -0.121 0.395 0.178 -0.165 1.388 -0.430 -0.458 -0.679 0.308 -0.091 -0.324 S17 0.193 1.061 -0.039 -0.720 0.006 1.766 1.463 0.053 -0.215 -0.601 0.315 -0.125 2.460 S18 0.342 1.052 -0.543 -0.074 0.449 -0.345 0.978 3.865 3.974 1.613 0.502 -0.092 0.636 S19 -0.338 -0.609 -0.416 -0.458 0.241 -0.111 0.966 1.016 0.963 2.959 0.263 -0.449 0.224 S20 -0.275 -0.136 -0.324 -0.099 1.300 -0.332 0.530 0.504 0.415 1.555 0.513 -0.128 0.296 S21 -0.617 0.840 -0.139 -0.162 0.295 -0.172 1.457 0.266 0.028 0.562 0.733 0.222 1.050

Results Analysis The main characteristics of water quality monitoring section of the tributaries of the Si River are shown in Table 2. According to the GB3838-2002 water quality evaluation standard, during the study period, the water quality of the river tributary of the Si River was mainly classified as Class II and Class III, and was in accordance with the national centralized drinking water quality standard for surface water. Among the water quality is mainly NH4-N and TP. The seasonal variation of ORP, DO, and Temp was larger than that of seasonal variation of seasonal variation of the seasonal variation of ORP and DO in the river, suggesting that Temp was influenced by seasonal temperature.

84 Table 2. General characteristics of the water quality of the Si River and main tributaries. average value range standard deviation Temp 15.17 9.78-17.86 1.64 DO 10.92 2.29-30.11 5.48 PH 7.67 2.66-8.58 1.44 ORP -21.41 -78-(-0.40) 16.58 TDS 0.48 0.11-0.94 0.22 Salinity 0.33 0.08-0.73 0.16 TP 0.23 0.047-1.4 0.30 TSP 0.15 0.02-1.30 0.28 DRP 0.08 0.0026-0.3897 0.10 TN 5.43 0.92-11.54 2.60 TSN 2.53 0.48-6.71 1.53 + NH4 -N 0.68 0.12-3.20 0.82 - NO3 -N 2.99 0.26-6.56 1.78 - NO2 -N 0.04 0.0018-0.0893 0.02 chla 35.29 2.22-106.91 28.57

CODMn 5.26 1.41-7.97 1.89 Alkalinity 155.14 41.91-214.13 42.40

Use SPSS16.0 software to cluster and analyze the water quality indexes of Si River and main tributaries. The cluster results are divided into three categories. As shown in Figure 2.

Figure 2. Cluster of water environment factors of Si River and its main tributaries.

85 South Miaokong monitoring section is in the development stage, near the river found a larger sewage outfall. There are plenty of phytoplankton and sludge in the river, pollutants in addition to industrial waste water and a lot of garbage. Surrounded by the monitoring point as Ji River, Sihe Sluice, Tao Luo, Yaocunn Bridge, Dahuanggou and Xian River are very open, no obvious source of pollution, the main pollution from domestic. In Pushan River and Quanlin Town, the plankton in the water grows well and the river water is not strong, the surrounding large number of sediment, leading to serious water pollution. In Fangshan bridge water plants and phytoplankton much more, Nishan reservoir long-term aquaculture aquatic products, fisheries and aquaculture industry developed, in the town of Nansin, living garbage all over, and found dead pigs exist in the river, the main garbage is the river Contaminants. Xu Village, Guanyin bridge, Jinkou Dam and Hekou Village, this type of river is mainly distributed far away from the urban areas, less industrial pollution, many trees surrounding and phytoplanktons growing, did not find pollution sources as the sewage outfall. Huangyinji Bridge, Huang Yinji Sluice and Confucius Avenue Bridge, in this three rivers the water flow faster, trees and aquatic plants grow lush, the main source of pollution from life. Near the South Qufusi Bridge discovery of large power plants, and found the larger sewage outfall in the river side. The correlation coefficient matrix and eigenvalue of each evaluation index are calculated by SPSS software to determine the main factor of the evaluation. According to the cumulative contribution rate of the eigenvalue variance, the number of selected principal components is determined, as shown in Table 3. Table 3. Eigenvalue and principal component contribution rate and cumulative contribution.

Initial eigenvalue Ingredients total variance % accumulation % 1 4.389 33.761 33.761 2 3.017 23.205 56.966 3 1.515 11.652 68.618 4 1.237 9.518 78.136 5 0.964 7.415 85.551 6 0.737 5.665 91.216 7 0.432 3.326 94.543 8 0.338 2.604 97.146 9 0.190 1.464 98.611 10 0.138 1.060 99.671 11 0.031 0.235 99.906 12 0.011 0.085 99.991 13 0.001 0.009 100.000

According to Table 3, the eigenvalues of the first, second, third and fourth components are all greater than 1 and the variance contribution rates are 33.761%, 23.205%, 11.625% and 9.518% respectively. The cumulative variance rate is 78.136% All indicators of information. The first indicator is the most important, contains the most information, the impact of its water quality changes the most. From the main component load size, the first principal component is closely related to the dissolved total nitrogen, nitrite nitrogen, total nitrogen, nitrate nitrogen, water temperature, ammonia nitrogen, their absolute value of the correlation coefficient with the first principal

86 component is more than 0.3, indicating that the first principal component reflects the nitrogen is the main pollutant in Sihe. Nitrogen salt as a major source of aquatic organisms in the water body rising, making the number of aquatic organisms increased, easily lead to water eutrophication. Therefore, the first principal component mainly reflects the nitrogen pollution in the Si River as the main factor. And the second principal component is closely related to the total phosphorus, dissolved total phosphorus, dissolved inorganic phosphorus, and their second principal component of the correlation coefficient of the absolute value of more than 0.4, indicating that the second main component reflects the pollution of phosphorus in the Si River Level. In the third principal component of ph, dissolved oxygen load is high, indicating that the increase in the number of organisms in the water caused by the dissolved oxygen content of water decreased, PH plays a certain role in the redox reaction in water, so the third principal component reflects the acidity and organic pollution and the degree of pollution of the river. In the fourth main component of the chlorophyll load is high, indicating that the green body of water and more, in the main component of one or two on the basis of the degree of eutrophication of water. From the contribution rate of variance, it can be seen that the contribution rate of the first principal component variance is 33.761%, which is much larger than the contribution rate of the second, third and fourth principal components by 23.205%, 11.652% and 9.518%. In general, the water quality of the whole Si River and the major tributaries is nitrogen pollution as the leading factor, phosphorus pollution degree after nitrogen pollution. Table 4. Principal component load moment. f1 f2 f3 f4 TSN(ug/ml) 0.405 -0.211 0.124 -0.078

- 0.379 -0.005 0.231 0.077 NO2 -N（ug/ml） TN（ug/ml） 0.373 -0.296 -0.138 -0.147 - 0.363 -0.258 0.060 -0.333 NO3 -N（ug/ml） Temp 0.326 0.003 -0.075 -0.316

+ NH4 -N(ug/ml) 0.321 -0.146 -0.097 0.400 0.279 0.229 -0.208 0.394 CODMn(ug/mL） TP（ug/ml） 0.182 0.493 0.169 -0.093 TSP（ug/ml） 0.159 0.481 0.199 -0.172 DIP（ug/ml） 0.131 0.432 0.106 -0.203 PH 0.085 0.176 -0.669 -0.164 DO(mg/l) -0.007 -0.115 0.567 0.133

chla（ug/l） 0.228 0.150 -0.042 0.563

The comprehensive evaluation results of water quality are shown in Table 5. Table 5. Comprehensive evaluation results of water quality of Si River and its main tributaries. f1 f2 f3 f4 f Ranking Fangshan bridge -4.039 0.200 -1.390 0.006 -1.895 20 South Nanxin -4.025 0.207 -1.338 -0.045 -1.886 19 Nishan reservoir -2.325 0.715 -1.602 0.459 -0.988 15 Ji River 0.444 -1.098 -3.064 -1.777 -0.788 13 Huangyinji Bridge -0.503 -0.900 1.478 0.847 -0.145 10 Quanlin Town -0.132 0.036 0.396 -1.468 -0.167 11

87 Dahuanggou -1.662 -0.229 -1.876 -1.032 -1.187 17 Huang Yin Sluice 0.286 2.171 -12.086 -2.449 -1.370 18 Xu Village -0.043 -0.965 1.049 0.011 -0.130 9 Pushan River 1.343 -0.691 -6.319 -3.765 -1.013 16 Sihe Sluice 4.646 -2.013 -1.728 -0.155 1.169 2 Guanyin Bridge 2.450 -2.249 2.784 1.237 0.997 4 Taoluo -1.786 -1.146 2.458 -0.055 -0.732 12 Xian River -0.146 -0.338 -0.105 0.396 -0.125 8 Yaocun Bridge -0.050 0.924 -7.025 -1.109 -0.946 14 South Miaokong 1.722 1.236 -5.194 2.016 0.561 6 South Qufusi Bridge 2.427 6.327 -4.765 -2.278 1.826 1 Jinkou Dam 0.438 3.132 -1.133 -0.621 0.819 5 Confucius Avenue Bridge 0.673 2.510 -4.672 -1.638 0.095 7 Hekou Village 0.802 0.723 1.954 1.527 1.026 3 Note: the greater the score, indicating that the more serious degree of pollution, which can sample the degree of pollution grading analysis.

Conclusion According to the annual data of 2013-2014, the main pollution of the Si River and the tributaries is nitrogen pollution and phosphorus pollution. High nitrogen and phosphorus content easily lead to eutrophication of water, so that further deterioration of water quality, resulting in dissolved oxygen, chlorophyll and alkalinity and PH content is not normal. The main reason for the high nitrogen and phosphorus content is the discharge of domestic waste and factory waste water, so it is necessary to reduce the consumption of garbage to Hanoi by propaganda, education and other means for the surrounding people, and it is necessary to discharge the waste water after the surrounding power plants, chemical plants and so on. The water quality of Si River is better than other inflow rivers in Nansi Lake, in recent years, water quality indicators have little change, in general, more stable. But it is because the water quality is better in the four inflow rivers into the lake, Si River did not cause a lot of scholars attention and research. Baima River, Dongyu River and other water quality is slightly worse, but in recent years due to proper governance, water quality has been greatly improved[1]. And Si River and tributary although the overall water quality has reached more than level, but there are still many local pollution is more serious. Hope that this study can make Si River get more scholars attention, and for the management of the Si River to provide basic data.

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