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Optimization of marine environmental monitoring sites in the Yangtze River estuary and its adjacent sea, China
Ocean & Coastal Management 73 (2013) 92e100
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Optimization of marine environmental monitoring sites in the Yangtze River estuary and its adjacent sea, China Yaqi Shen, Yanqing Wu* School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, People’s Republic of China
a r t i c l e i n f o
a b s t r a c t
Article history: Available online 3 January 2013
Marine environment in the Yangtze River estuary and its adjacent sea is now facing more and more serious pollution from various sources. Based on the Seawater quality standard of China and the mean values of environmental elements of seawater from 1985 to 2006, Seawater Environmental Quality Index (SEQI) is proposed to characterize the marine environmental quality in this study. On this basis, kriging variance analysis method combining with GIS is applied in the optimization of marine monitoring network in order to get more accurate and abundant information of the marine environment. SEQI of 42 existing monitoring sites is extracted to describe the marine environmental quality of this area. Combining the average standard deviation of the monitoring network and the marine environmental quality, the number of monitoring sites is increased from 42 to 59 by kriging variance analysis method, which can improve the quality of marine environmental monitoring network. This study proposes an alternative optimization method for marine environmental monitoring and management. Ó 2012 Elsevier Ltd. All rights reserved.
1. Introduction Marine environment provides energy and resources for people lives, but are also facing serious pollution from various sources, such as industrial sewage discharges, smelting, irrigation, mining, oil spills and so on (Zutshi and Prasad, 2008; An et al., 2010; Doney, 2010; Mearns et al., 2010). Marine pollution will threaten ocean biodiversity, coastal development, and human food supply (Salomon, 2009; Hardiman and Burgin, 2010; Lloret, 2010; Manzetti and Stenersen, 2010). At present, various kinds of methods are proposed to monitor the marine environment, such as trophic index (TRIX), assessment of estuarine trophic status (ASSETS), Oslo Paris convention for the protection of the Northeast Atlantic (OSPAR) and so on. TRIX is defined by a linear combination of the logarithms of four state variables: Chl-a, DO, DIN and total phosphorus, which is used for assessment of trophic status of coastal waters (Giovanardi and Vollenweider, 2004; Primpas and Karydis, 2011). ASSETS is a screening model that uses a Pressure-State-Response framework to assess eutrophication (Cotovicz Junior et al., 2012; Assessment of estuarine trophic status, 2012). OSPAR is designed to reduce nutrient inputs by 50% compared to 1985 in areas where nutrient
* Corresponding author. E-mail address: [email protected] (Y. Wu). 0964-5691/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ocecoaman.2012.11.012
inputs may cause nutrient pollution (Claussen et al., 2009; Foden et al., 2011). However, these methods lay more emphasis on the eutrophication in the marine environment, and the seawater quality monitoring elements such as heavy metals and oil have not been considered. Further, optimal design of the monitoring network is necessary in the process of marine environmental monitoring, and it can determine the precision of the marine environmental monitoring. In the US, the monitoring sites are selected by a generalized random tessellation stratified (GRTS) survey design, which is a stratified design with unequal probability of selection based on area within each stratum (National coastal condition assessment site evaluation guidelines, 2010). If there is a full understanding of the marine environment in the research area, the stratified random sampling method would make a satisfied classification (Danz et al., 2005). However, it is hard to achieve a full understanding in the actual investigation. In fact, there are related researches in optimal design of groundwater and river pollution monitoring, including Kalman filtering, principal component analysis, Kriging, and so on (Das Gupta et al., 1998; Reed et al., 2000; Dhar and Datta, 2009; Karamouz et al., 2009; Baalousha, 2010; Wu et al., 1993). Considering spatial and time domains, Kalman filtering must couple with numerical model of flow and transport in aquifer. As a result of the complicated hydrodynamic state, it is difficult to apply Kalman filtering method on optimal design of marine environmental
Y. Shen, Y. Wu / Ocean & Coastal Management 73 (2013) 92e100
monitoring network (Wu, 2004; Herrera and Pinder, 2005; Kollat et al., 2011). Principal component analysis is applied for determining relative contributions of individual wells in capturing the spatiotemporal variation of ground water levels. It is usually used to reduce the monitoring sites, which may result in the information loss of the marine monitoring (Danz et al., 2005; Khan et al., 2008). Kriging method is a spatial interpolation method to interpolate the value of a random field at an unobserved location from observations of its value at nearby locations (Cameron and Hunter, 2002; Dhar and Datta, 2009). In kriging variance analysis semi-variogram is a function of distance. This method lays much emphasis on the distance of monitoring sites rather than the values of the monitoring site. The variance is smaller, the quality of monitoring network is better, and the amount of information of marine environment is more abundant. Applying kriging variance analysis method, the optimal monitoring sites and the high amount of information can be obtained in the marine monitoring sites optimization. Due to rapid economic development over the past decades, the Yangtze River estuary and its adjacent sea are polluted from industrial waste, domestic sewage, and nonpoint source. The existing monitoring network in the study area cannot meet the monitoring requirement. Based on Seawater Quality Standard of GB3097-1997 in China and monitoring elements, Seawater Environmental Quality Index (SEQI) is proposed to characterize the quality of marine environment from the most polluted monitoring elements of each monitoring site in this study. On this basis, kriging variance analysis method combining with GIS is adopted in the optimization of marine monitoring network, in order to assess the quality of existing environmental monitoring network and optimize the design of environmental monitoring network. This paper proposed
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optimal design of the monitoring sites in the Yangtze River estuary and its adjacent sea. 2. Materials and methods 2.1. Study area The Yangtze River is the largest river in China and the third largest river in the world, which allows for high levels of sediment loadings into the estuary area (An et al., 2010). The geographic coordination of the Yangtze River estuary and its adjacent sea is from 30 300 to 31450 N in latitude, from the shoreline to 123 E in longitude. There are 42 monitoring sites in the study area. The monitoring frequency is 4 times per year (Feb., May, Aug. and Oct.). The study area and the existing monitoring sites are shown in Fig. 1. The mean values of seawater environmental elements such as inorganic nitrogen (DIN), phosphate (PO4eP), oil, chemical oxygen demand (COD), dissolved oxygen (DO), pH, copper (Cu), mercury (Hg), lead (Pb) and cadmium (Cd) from 1985 to 2006 are presented in Table 1. These monitoring elements were collected at 0e1 m below sea surface level and analyzed based on the procedures in the standards of specification in China (GB 3097-1997, 1998). The value of DIN is the sum of values of nitrate, nitrite and ammonia nitrogen. The concentrations of nitrate, nitrite and ammonia nitrogen in seawater were carried out by zinc cadmium reduction method, hydrochloride naphthalene ethylenediamine spectrophotometry and hypobromite oxidation method. The concentration of PO4eP was determined by ammonium molybdate spectrophotometry. The determination of oil was obtained by fluorescence spectrophotometry. COD and DO concentrations were determined
Fig. 1. The study area and existing monitoring sites *Background image is from Google maps.
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Table 1 Mean values of seawater environmental elements in the Yangtze river estuary and its adjacent sea from 1985 to 2006. Site
DIN (mg/L)
PO4eP (mg/L)
Oil (mg/L)
COD (mg/L)
DO (mg/L)
pH
Cu (mg/L)
Hg (mg/L)
Pb (mg/L)
Cd (mg/L)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42
1092.655 909.808 877.434 1174.814 1161.921 1144.048 1209.340 1178.811 1121.317 1168.325 1271.317 1175.273 1175.258 979.497 750.166 692.160 652.698 555.186 701.241 635.631 600.812 1161.585 786.288 735.141 551.737 1118.362 811.232 621.008 513.589 467.748 1176.052 1146.943 1060.578 845.686 754.446 597.821 464.552 1309.156 1208.146 1030.843 856.667 729.128
23.696 21.277 21.110 23.717 23.718 22.942 25.358 23.897 23.061 28.908 28.263 23.776 24.097 21.417 19.986 17.923 18.262 15.182 18.545 17.332 15.743 27.010 21.393 19.942 14.663 27.413 22.766 18.513 15.471 13.975 34.720 32.920 28.247 26.661 23.041 20.466 15.764 38.564 35.323 31.499 27.968 26.198
202.393 205.422 200.731 40.130 37.875 40.478 45.316 43.874 42.619 45.438 42.409 50.998 50.733 42.537 42.795 44.542 44.607 35.309 43.138 35.417 41.806 61.846 58.087 33.874 33.835 39.401 34.130 39.538 31.807 29.135 45.338 34.397 43.391 37.141 38.661 35.466 40.690 37.513 29.644 36.494 183.520 185.213
1.437 1.234 1.202 1.564 1.550 1.525 1.614 1.584 1.563 1.783 1.941 1.574 1.579 1.603 1.406 1.328 1.309 1.206 1.340 1.241 1.272 1.861 1.362 1.198 1.165 1.629 1.437 1.283 1.181 1.115 1.421 1.516 1.767 1.488 1.374 1.193 1.181 1.647 1.526 1.532 1.344 1.320
8.479 8.294 8.238 8.491 8.468 8.530 8.506 8.494 8.468 8.253 8.208 8.441 8.419 8.343 8.099 8.231 8.100 7.952 8.294 8.178 8.157 8.271 8.225 8.045 8.189 8.251 8.117 8.379 8.285 8.267 8.465 8.454 8.391 8.384 8.219 8.048 8.149 8.376 8.445 8.347 8.269 8.119
7.936 7.981 7.980 7.947 7.942 7.975 7.975 7.975 7.993 8.056 8.033 7.981 7.983 8.004 8.048 8.086 8.085 8.138 8.083 8.132 8.133 8.046 8.104 8.109 8.176 8.039 8.056 8.129 8.152 8.164 8.002 8.000 8.065 8.063 8.083 8.094 8.140 8.039 8.028 8.067 8.077 8.096
6.196 6.497 6.541 6.136 6.121 6.565 6.142 5.939 6.201 7.444 6.834 6.180 6.116 6.525 6.748 7.180 7.398 7.549 7.129 7.049 7.024 6.868 7.159 8.347 8.398 7.024 7.019 7.029 6.980 7.012 8.519 7.463 8.876 8.219 7.573 7.421 7.264 7.656 5.920 8.310 7.827 6.530
0.031 0.032 0.032 0.032 0.031 0.030 0.032 0.032 0.031 0.040 0.039 0.037 0.035 0.036 0.036 0.036 0.037 0.034 0.036 0.034 0.033 0.041 0.032 0.035 0.035 0.037 0.040 0.039 0.036 0.036 0.059 0.054 0.050 0.047 0.049 0.041 0.039 0.076 0.049 0.067 0.052 0.045
7.495 7.060 7.038 8.312 8.327 7.727 8.999 8.187 7.509 11.291 11.284 7.853 7.006 6.004 5.774 5.308 4.996 4.059 5.983 4.942 4.058 11.393 5.487 8.979 8.926 6.755 5.127 3.951 4.344 4.294 8.742 8.638 4.463 4.341 3.807 3.773 4.182 3.234 3.616 5.749 4.779 3.709
0.172 0.162 0.163 0.177 0.172 0.160 0.172 0.164 0.156 0.168 0.167 0.164 0.158 0.156 0.154 0.153 0.144 0.129 0.159 0.133 0.136 0.175 0.144 0.192 0.190 0.429 0.196 0.160 0.171 0.169 0.210 0.205 0.167 0.166 0.163 0.210 0.163 0.169 0.165 0.165 0.173 0.231
by using alkaline permanganate method and iodine titrimetry. The determinations of Cu, Pb and Cd in water were carried out by atomic absorption spectrometry (AAS; PerkinElmer, Analyst 800). The concentration of Hg was measured by WGY e SIM cold atom fluorescence instrument (China National Nuclear Cooperation). All the values of monitoring elements used in this study are measured values. The physical, chemical and biological processes have been reflected in these measured values.
2.2. Geostatistical method Based on regionalized variables and semi-variogram, geostatistics is used to optimal design of the marine environmental monitoring network. In the marine environment, environmental element variables of existing monitoring sites are used to determine semi-variogram model with kriging method (Krige, 1951; Matheron, 1985). The formula can be expressed as:
Table 2 Pearson correlation coefficient values for seawater environmental elements.
DIN PO4eP Oil COD DO pH Cu Hg Pb Cd *
DIN
PO4eP
Oil
COD
DO
pH
Cu
Hg
Pb
Cd
1 0.798** 0.026 0.851** 0.707** 0.828** 0.302* 0.241 0.563** 0.177
1 0.048 0.668** 0.485** 0.502** 0.094 0.717** 0.210 0.264*
1 0.183 0.006 0.273* 0.205 0.093 0.007 0.023
1 0.488** 0.561** 0.166 0.220 0.527** 0.141
1 0.697** 0.348* 0.098 0.303* 0.016
1 0.540** 0.083 0.484** 0.076
1 0.545** 0.159 0.070
1 0.255 0.105
1 0.108
1
Correlations are significant at the 0.05 level. ** Correlations are significant at the 0.01 level.
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Table 3 Some parts of seawater quality standard of GB3097-1997 in China, Seawater Environmental Quality Index (SEQI) values, pollution level and pollution category. SEQI value
0e1
Seawater environmental elements
DIN (mg/L) PO4eP (mg/L) Oil (mg/L) pH COD (mg/L) DO (mg/L) Cu (mg/L) Hg (mg/L) Pb (mg/L) Cd (mg/L)
Quality level Category
b z ðx0 Þ ¼
n X
1e2
0e0.20 0.20e0.30 0e0.015 0.015e0.030 0e0.05 7.8e8.5 (within 0.2 pH) 0e2 2e3 >6 5e6 0e0.005 0.005e0.01 0e0.00005 0.00005e0.0002 0e0.001 0.001e0.005 0e0.001 0.001e0.005 I II Excellent Good
li zðxi Þ
(1)
i¼1
where b z ðx0 Þ is the estimated value in the point x0; li and z(xi) represent the weight and observed value in the point xi, respectively; z(xi) is represented as Seawater Environmental Quality Index. The weight is endowed with measure values of surrounding sites. The variables should be linear, unbiased, and optimal estimated (Kumar and Devi, 2006).
2e3
3e4
4e5
0.30e0.40
0.40e0.50 0.030e0.045 0.30e0.50
>0.50 >0.045 >0.50 Without 0.5 pH >5 0e3 >0.05 0.0005 >0.05 >0.01 V Heavy pollution
0.05e0.30 6.8e8.8 (within 0.5 pH) 3e4 4e5 4e5 3e4 0.01e0.05 0.0002e0.0005 0.005e0.01 0.01e0.05 0.005e0.01 III IV Light pollution Moderate pollution
Kriging variance can be calculated as follows:
s2 ¼
n X
li gðxi ; x0 Þ þ m
(2)
i¼1
where s2 is the kriging variance; g(xi,x0) is the semi-variogram between xi and x0; and m is the lagrange multiplier. When the average standard deviation becomes smaller, the spatial distribution of the points is more reasonable, and much
Table 4 Seawater Environmental Quality Index of main seawater environmental elements in the Yangtze river estuary and its adjacent sea from 1985 to 2006. Site
DIN
PO4eP
Oil
COD
DO
pH
Cu
Hg
Pb
Cd
SPI
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42
4.593 4.410 4.377 4.675 4.662 4.644 4.709 4.679 4.621 4.668 4.771 4.675 4.675 4.479 4.250 4.192 4.153 4.055 4.201 4.136 4.101 4.662 4.286 4.235 4.052 4.618 4.311 4.121 4.014 3.677 4.676 4.647 4.561 4.346 4.254 4.098 3.646 4.809 4.708 4.531 4.357 4.229
2.159 1.837 1.815 2.162 2.162 2.059 2.381 2.186 2.075 2.854 2.768 2.170 2.213 1.856 1.665 1.390 1.435 1.024 1.473 1.311 1.099 2.601 1.852 1.659 0.978 2.655 2.035 1.468 1.063 0.932 3.315 3.195 2.766 2.555 2.072 1.729 1.102 3.571 3.355 3.100 2.729 2.493
2.610 2.622 2.603 1.605 1.515 1.619 1.813 1.755 1.705 1.818 1.696 2.004 2.003 1.701 1.712 1.782 1.784 1.412 1.726 1.417 1.672 2.047 2.032 1.355 1.353 1.576 1.365 1.582 1.272 1.165 1.814 1.376 1.736 1.486 1.546 1.419 1.628 1.501 1.186 1.460 2.534 2.541
0.718 0.617 0.601 0.782 0.775 0.763 0.807 0.792 0.782 0.891 0.971 0.787 0.789 0.801 0.703 0.664 0.655 0.603 0.670 0.621 0.636 0.930 0.681 0.599 0.583 0.814 0.719 0.642 0.590 0.558 0.711 0.758 0.883 0.744 0.687 0.597 0.591 0.823 0.763 0.766 0.672 0.660
0.708 0.723 0.728 0.707 0.709 0.703 0.705 0.706 0.709 0.727 0.731 0.711 0.713 0.719 0.741 0.729 0.741 0.755 0.723 0.734 0.736 0.725 0.729 0.746 0.733 0.727 0.739 0.716 0.724 0.726 0.709 0.710 0.715 0.716 0.730 0.746 0.736 0.716 0.710 0.719 0.726 0.739
2.279 1.512 1.522 2.126 2.199 1.622 1.641 1.624 1.279 0.008 0.468 1.513 1.475 1.059 0.178 0.594 0.574 1.637 0.520 1.503 1.530 0.211 0.955 1.047 2.265 0.355 0.020 1.443 1.912 2.099 1.087 1.122 0.174 0.124 0.519 0.748 1.677 0.343 0.578 0.208 0.413 0.793
1.478 1.599 1.616 1.454 1.449 1.626 1.457 1.376 1.480 1.978 1.733 1.472 1.447 1.610 1.699 1.872 1.959 2.020 1.851 1.820 1.810 1.747 1.864 2.339 2.359 1.809 1.808 1.812 1.792 1.805 2.407 1.985 2.550 2.288 2.029 1.968 1.906 2.062 1.368 2.324 2.131 1.612
0.622 0.648 0.636 0.633 0.628 0.592 0.646 0.641 0.610 0.809 0.790 0.749 0.690 0.720 0.712 0.721 0.731 0.676 0.723 0.687 0.657 0.810 0.650 0.698 0.707 0.740 0.791 0.772 0.714 0.716 1.115 1.057 0.993 0.933 0.976 0.817 0.790 1.347 0.977 1.224 1.026 0.907
2.499 2.412 2.408 2.662 2.665 2.545 2.800 2.637 2.502 3.032 3.032 2.571 2.401 2.201 2.155 2.062 1.999 1.765 2.197 1.986 1.764 3.035 2.097 2.796 2.785 2.351 2.025 1.738 1.836 1.824 2.748 2.728 1.866 1.835 1.702 1.693 1.796 1.558 1.654 2.150 1.945 1.677
0.172 0.162 0.163 0.177 0.172 0.160 0.172 0.164 0.156 0.168 0.167 0.164 0.158 0.156 0.154 0.153 0.144 0.129 0.159 0.133 0.136 0.175 0.144 0.192 0.190 0.429 0.196 0.160 0.171 0.169 0.210 0.205 0.167 0.166 0.163 0.210 0.163 0.169 0.165 0.165 0.173 0.231
4.593 4.410 4.377 4.675 4.662 4.644 4.709 4.679 4.621 4.668 4.771 4.675 4.675 4.479 4.250 4.192 4.153 4.055 4.201 4.136 4.101 4.662 4.286 4.235 4.052 4.618 4.311 4.121 4.014 3.677 4.676 4.647 4.561 4.346 4.254 4.098 3.646 4.809 4.708 4.531 4.357 4.229
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more information of the study area can be obtained. The calculation of the standard deviation can be chosen to optimize sites of marine environmental monitoring network. 2.3. Economic budget analysis The monitoring expense of one monitoring site each time is about 6000e8000 CNY, including infrastructure costs, instrument testing costs, staff costs and so on.
E ¼ abi
(3)
Where E is the monitoring expense; a is the expense of one monitoring site each time; b is the monitoring frequency each year, about 4 times; and i is the number of the monitoring sites. When the number becomes larger, the expense would be higher. The optimal design of marine environmental monitoring network in the study area is carried out from the spatial aspect. Using Seawater Environmental Quality Index, the mean values of environmental monitoring elements such as DIN, PO4eP, oil, COD, DO, pH, Cu, Hg, Pb and Cd from 1985 to 2006 are normalized, and the maximum values are extracted to describe the pollution level. Considering the marine environment pollution level and the average standard deviation of the monitoring network by kriging variance analysis method, the monitoring network is optimized in the Yangtze River estuary and its adjacent sea. This paper can provide the support for marine environmental monitoring and management.
3. Results and discussion 3.1. Correlation coefficients and principal component analysis Most of seawater environmental elements showed highly significant positive correlation with each other. It is clear that DIN, PO4eP, COD, DO and pH were shown higher Pearson correlation coefficient with each other, which might be related to terrestrial pollution sources. The moderate correlation of oil and Hg could indicate that they are sourced from oil wells or channel sewage. A significant positive correlation is verified for Hg with Cu indicates industrial wastewater discharge (Table 2). 3.2. Seawater Environmental Quality Index (SEQI) Based on the mean values of seawater environmental elements and the Seawater quality standard of China (GB 3097-1997, 1998), the values of each seawater environmental element should be normalized and divided into corresponding pollution level. The maximum normalized values of each seawater environmental element are chosen as the representative value of each site. Seawater Environmental Quality Index is calculated as the highest index among the pollution indices of the seawater environmental elements. The formula can be given by:
SEQI ¼ max Ij
Fig. 2. Map of seawater environmental quality in the Yangtze river estuary and its adjacent sea.
(4)
Y. Shen, Y. Wu / Ocean & Coastal Management 73 (2013) 92e100
Iij ¼ ðIU IL ÞðC CL Þ=ðCU CL Þ þ IL
(5)
where Iij is the environmental quality index of the seawater environmental elements; i is the seawater environmental element; j is the monitoring site number; IU and IL are the upper and lower standard index in Table 3, respectively; C is the mean value shown in Table 1; CUand CL are the upper and lower values of seawater environmental elements shown in Table 3, respectively (Gao et al., 2011). In the calculation of IDO, CU and CL are chosen reciprocal, while in the calculation of IpH, the value of CU CL is 0.2 or 0.5 as listed in Table 3, and CpH is two times of difference between the initial value and mean value. On the basis of the mean values of seawater environmental elements in Table 1 and 3 and Formula 5, the normalized values of main seawater environmental elements in the study area are shown in Table 4. Based on Formula 4, SEQI is extracted as the maximum normalized values of the seawater environmental elements. SEQI value is not the analysis result of a single element, but the analysis result of comprehensive elements. Based on the SEQI values, the level of seawater environmental quality is shown in Fig. 2.
3.3. Preliminary optimization of the monitoring network The values of seawater environmental quality index are also used to predict standard deviation through kriging variance analysis method. Standard deviation map of existing monitoring network in the Yangtze River estuary and its adjacent sea is shown in
97
Fig. 3. The average standard deviation of original monitoring network in this study area is 0.124. Optimization of marine environmental monitoring sites is to monitor the marine environment with smaller average standard deviation of the monitoring network. Based on the original 42 monitoring sites in the study area, Fig. 4 shows the correlation between the number of designed monitoring sites and average standard deviation of estimated error. When average standard deviation of estimated error is decreased from 0.20 to 0.15, 15 sites should be added. However, when average standard deviation of estimated error keeps decreasing to 0.10, 50 new sites are required to be added. It can be concluded that smaller average standard deviation of estimated error needs more monitoring sites. There are 42 monitoring sites at present, and average standard deviation of estimated error is 0.124. The reliable of the number is correlated to the average standard deviation of estimated error of designed number. In Fig. 4, the average standard deviation of estimated error is smaller, the number of monitoring sites is higher, and the information of marine environment is more abundant. When the number of monitoring sites increases to 59, the decrease of average standard deviation of estimated error needs much more increasing number. 59 monitoring sites are chosen as the optimal number of monitoring sites. The improvement of the quality of marine environmental monitoring network can be satisfied with decreasing the average standard deviation of estimated error. As shown in Fig. 4, when the number of designed monitoring sites is 59, the average standard deviation is 0.111, and the monitoring precision is improved by 10.4%. Fig. 5 shows the standard error map of new monitoring network in the Yangtze River estuary and its adjacent sea.
Fig. 3. Standard deviation map of existing monitoring network in the Yangtze river estuary and its adjacent sea.
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Fig. 4. The relationship curve between the numbers of designed monitoring sites and average standard deviation of estimate error in the study area.
3.4. Improved optimization of the monitoring network Though the monitoring network shown in Fig. 5 improves the quality of marine environmental monitoring network, the uniform distribution of 59 monitoring sites does not consider the marine
environment pollution level. Therefore, an improved monitoring network is designed. As shown in Table 1, sites 2 and 3 can be combined (renamed as N1) because of very similar measurement data, and the same applies to sites 12 and 13 (renamed as N2). Due to the edge error, more
Fig. 5. Standard deviation map of new monitoring network in the Yangtze river estuary and its adjacent sea.
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Fig. 6. Standard deviation map of improved monitoring network in the Yangtze river estuary and its adjacent sea.
sites are added in the deep sea. In fact, the variation of the deep-sea environment pollution level is not obvious according to experiences. Therefore, sites 43, 44, 51, 55, 56, 57 and 58 are deleted. When the SEQI is higher, that is higher pollution level, the monitoring network should be denser. The number of sites in the near shore area should be increased, and sites N3, N4. N11 are added. The standard deviation map of improved monitoring network is shown in Fig. 6. The average standard deviation of improved monitoring network is 0.104, which is more precise than the network as shown in Fig. 5. Meanwhile, the monitoring network quality will be improved by 16.1%. Compared with the original 42 monitoring sites, 4 sites are removed and 21 new ones are added. Therefore, the designed number of the monitoring sites becomes 59 in the Yangtze River estuary and its adjacent sea. The monitoring expense of the original network is about 1 008 000e1 344 000 CNY per year. The expense of the designed monitoring network is increased to 1 416 000e1 888 000 CNY per year, an increase of about 40%. 4. Conclusions This paper proposes the concept and computing method of Seawater Environmental Quality Index (SEQI). Geographical Information System (GIS) and kriging variance analysis method are used to assess the quality of existing environmental monitoring network and optimize the design of environmental monitoring network in the Yangtze River estuary and its adjacent sea. This method is simple and effective for practice application. By calculating the
maximum normalized values of the seawater environmental elements, The SEQI can be obtained and described the seawater environmental quality and pollution level of the Yangtze River estuary and its adjacent sea. Through kriging variance analysis method, the monitoring network is initially optimized in the Yangtze River estuary and its adjacent sea. Considering the marine environment pollution level and the average standard deviation, the number of monitoring sites is increased from 42 to 59 with 21 new ones designed and 4 old ones removed, improving the marine environmental monitoring quality. The monitoring expenses would be increased from 1 008 000e1 344 000 CNY to 1 416 000e1 888 000 CNY per year, an increase of about 40%. This paper proposed optimal design of the monitoring sites in the Yangtze River estuary and its adjacent sea. Acknowledgments The authors are grateful to the State Oceanic Administration People’s Republic of China (serial number: DOMEP (MEA-01-01)) and 908 Special Project Foundation of Shanghai, China (908SPFSC) (serial number: PJ2) for their financial support. Data sources are from East China Sea Environmental Monitoring Center of State Oceanic Administration. References An, Q., Wu, Y., Wang, J., Li, Z., 2010. Assessment of dissolved heavy metal in the Yangtze River estuary and its adjacent sea, China. Environmental Monitoring and Assessment 164, 173e187. Assessment of estuarine trophic status. 2012. http://eutro.org/methods.aspx.
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Khan, S., Chen, H.F., Rana, T., 2008. Optimizing ground water observation networks in irrigation areas using principal component analysis. Ground Water Monitoring and Remediation 28, 93e100. Kollat, J.B., Reed, P.M., Maxwell, R.M., 2011. Many-objective groundwater monitoring network design using bias-aware ensemble Kalman filtering, evolutionary optimization, and visual analytics. Water Resources Research 47. art. no. W02529. Krige, D.G., 1951. A statistical approach to some basic mine valuation problems on the Witwatersrand. Journal of the Chemical Metallurgical and Mining Society 52, 119e139. Kumar, V., Devi, R., 2006. Kriging of groundwater levels e a case study. Journal of Spatial Hydrology 6, 81e94. Lloret, J., 2010. Human health benefits supplied by Mediterranean marine biodiversity. Marine Pollution Bulletin 60, 1640e1646. Manzetti, S., Stenersen, J.H.V., 2010. A critical view of the environmental condition of the Sognefjord. Marine Pollution Bulletin 60, 2167e2174. Matheron, G., 1985. Change of support for diffusion-type random functions. Journal of the International Association for Mathematical Geology 17, 137e165. Mearns, A.J., Reish, D.J., Oshida, P.S., Ginn, T., 2010. Effects of pollution on marine organisms. Water Environment Research 82, 2001e2046. National coastal condition assessment: site evaluation guidelines, 2010. http:// water.epa.gov/type/oceb/upload/ncca-siteeval.pdf. United States Environmental Protection Agency. Primpas, I., Karydis, M., 2011. Scaling the trophic index (TRIX) in oligotrophic marine environments. Environmental Monitoring and Assessment 178, 257e269. Reed, P., Minsker, B., Valocchi, A.J., 2000. Cost-effective long-term groundwater monitoring design using a genetic algorithm and global mass interpolation. Water Resources Research 36, 3731e3741. Salomon, M., 2009. Recent European initiatives in marine protection policy: towards lasting protection for Europe’s seas. Environmental Science and Policy 12, 359e366. Sea water quality standard GB 3097-1997, 1998. http://bz.mep.gov.cn/bzwb/shjbh/ shjzlbz/199807/W020061027511546974673.pdf. Ministry of Environmental Protection of the People’s Republic of China. Wu, Y., 2004. Optimal design of a groundwater monitoring network in Daqing, China. Environmental Geology 45, 527e535. Wu, Yanqing, Li, Junting, Zhang, Zhuoyuan, 1993. Optimal Design of Groundwater Level Regime Observation Network. Chengdu Science and Technology University Press (in Chinese). Zutshi, B., Prasad, S.G.R., 2008. Impact of pollution on fresh- and marine water resources e a review. Pollution Research 27, 461e466.
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Optimization of marine environmental monitoring sites in the Yangtze River estuary and its adjacent sea, China Yaqi Shen, Yanqing Wu* School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, People’s Republic of China
a r t i c l e i n f o
a b s t r a c t
Article history: Available online 3 January 2013
Marine environment in the Yangtze River estuary and its adjacent sea is now facing more and more serious pollution from various sources. Based on the Seawater quality standard of China and the mean values of environmental elements of seawater from 1985 to 2006, Seawater Environmental Quality Index (SEQI) is proposed to characterize the marine environmental quality in this study. On this basis, kriging variance analysis method combining with GIS is applied in the optimization of marine monitoring network in order to get more accurate and abundant information of the marine environment. SEQI of 42 existing monitoring sites is extracted to describe the marine environmental quality of this area. Combining the average standard deviation of the monitoring network and the marine environmental quality, the number of monitoring sites is increased from 42 to 59 by kriging variance analysis method, which can improve the quality of marine environmental monitoring network. This study proposes an alternative optimization method for marine environmental monitoring and management. Ó 2012 Elsevier Ltd. All rights reserved.
1. Introduction Marine environment provides energy and resources for people lives, but are also facing serious pollution from various sources, such as industrial sewage discharges, smelting, irrigation, mining, oil spills and so on (Zutshi and Prasad, 2008; An et al., 2010; Doney, 2010; Mearns et al., 2010). Marine pollution will threaten ocean biodiversity, coastal development, and human food supply (Salomon, 2009; Hardiman and Burgin, 2010; Lloret, 2010; Manzetti and Stenersen, 2010). At present, various kinds of methods are proposed to monitor the marine environment, such as trophic index (TRIX), assessment of estuarine trophic status (ASSETS), Oslo Paris convention for the protection of the Northeast Atlantic (OSPAR) and so on. TRIX is defined by a linear combination of the logarithms of four state variables: Chl-a, DO, DIN and total phosphorus, which is used for assessment of trophic status of coastal waters (Giovanardi and Vollenweider, 2004; Primpas and Karydis, 2011). ASSETS is a screening model that uses a Pressure-State-Response framework to assess eutrophication (Cotovicz Junior et al., 2012; Assessment of estuarine trophic status, 2012). OSPAR is designed to reduce nutrient inputs by 50% compared to 1985 in areas where nutrient
* Corresponding author. E-mail address: [email protected] (Y. Wu). 0964-5691/$ e see front matter Ó 2012 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ocecoaman.2012.11.012
inputs may cause nutrient pollution (Claussen et al., 2009; Foden et al., 2011). However, these methods lay more emphasis on the eutrophication in the marine environment, and the seawater quality monitoring elements such as heavy metals and oil have not been considered. Further, optimal design of the monitoring network is necessary in the process of marine environmental monitoring, and it can determine the precision of the marine environmental monitoring. In the US, the monitoring sites are selected by a generalized random tessellation stratified (GRTS) survey design, which is a stratified design with unequal probability of selection based on area within each stratum (National coastal condition assessment site evaluation guidelines, 2010). If there is a full understanding of the marine environment in the research area, the stratified random sampling method would make a satisfied classification (Danz et al., 2005). However, it is hard to achieve a full understanding in the actual investigation. In fact, there are related researches in optimal design of groundwater and river pollution monitoring, including Kalman filtering, principal component analysis, Kriging, and so on (Das Gupta et al., 1998; Reed et al., 2000; Dhar and Datta, 2009; Karamouz et al., 2009; Baalousha, 2010; Wu et al., 1993). Considering spatial and time domains, Kalman filtering must couple with numerical model of flow and transport in aquifer. As a result of the complicated hydrodynamic state, it is difficult to apply Kalman filtering method on optimal design of marine environmental
Y. Shen, Y. Wu / Ocean & Coastal Management 73 (2013) 92e100
monitoring network (Wu, 2004; Herrera and Pinder, 2005; Kollat et al., 2011). Principal component analysis is applied for determining relative contributions of individual wells in capturing the spatiotemporal variation of ground water levels. It is usually used to reduce the monitoring sites, which may result in the information loss of the marine monitoring (Danz et al., 2005; Khan et al., 2008). Kriging method is a spatial interpolation method to interpolate the value of a random field at an unobserved location from observations of its value at nearby locations (Cameron and Hunter, 2002; Dhar and Datta, 2009). In kriging variance analysis semi-variogram is a function of distance. This method lays much emphasis on the distance of monitoring sites rather than the values of the monitoring site. The variance is smaller, the quality of monitoring network is better, and the amount of information of marine environment is more abundant. Applying kriging variance analysis method, the optimal monitoring sites and the high amount of information can be obtained in the marine monitoring sites optimization. Due to rapid economic development over the past decades, the Yangtze River estuary and its adjacent sea are polluted from industrial waste, domestic sewage, and nonpoint source. The existing monitoring network in the study area cannot meet the monitoring requirement. Based on Seawater Quality Standard of GB3097-1997 in China and monitoring elements, Seawater Environmental Quality Index (SEQI) is proposed to characterize the quality of marine environment from the most polluted monitoring elements of each monitoring site in this study. On this basis, kriging variance analysis method combining with GIS is adopted in the optimization of marine monitoring network, in order to assess the quality of existing environmental monitoring network and optimize the design of environmental monitoring network. This paper proposed
93
optimal design of the monitoring sites in the Yangtze River estuary and its adjacent sea. 2. Materials and methods 2.1. Study area The Yangtze River is the largest river in China and the third largest river in the world, which allows for high levels of sediment loadings into the estuary area (An et al., 2010). The geographic coordination of the Yangtze River estuary and its adjacent sea is from 30 300 to 31450 N in latitude, from the shoreline to 123 E in longitude. There are 42 monitoring sites in the study area. The monitoring frequency is 4 times per year (Feb., May, Aug. and Oct.). The study area and the existing monitoring sites are shown in Fig. 1. The mean values of seawater environmental elements such as inorganic nitrogen (DIN), phosphate (PO4eP), oil, chemical oxygen demand (COD), dissolved oxygen (DO), pH, copper (Cu), mercury (Hg), lead (Pb) and cadmium (Cd) from 1985 to 2006 are presented in Table 1. These monitoring elements were collected at 0e1 m below sea surface level and analyzed based on the procedures in the standards of specification in China (GB 3097-1997, 1998). The value of DIN is the sum of values of nitrate, nitrite and ammonia nitrogen. The concentrations of nitrate, nitrite and ammonia nitrogen in seawater were carried out by zinc cadmium reduction method, hydrochloride naphthalene ethylenediamine spectrophotometry and hypobromite oxidation method. The concentration of PO4eP was determined by ammonium molybdate spectrophotometry. The determination of oil was obtained by fluorescence spectrophotometry. COD and DO concentrations were determined
Fig. 1. The study area and existing monitoring sites *Background image is from Google maps.
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Table 1 Mean values of seawater environmental elements in the Yangtze river estuary and its adjacent sea from 1985 to 2006. Site
DIN (mg/L)
PO4eP (mg/L)
Oil (mg/L)
COD (mg/L)
DO (mg/L)
pH
Cu (mg/L)
Hg (mg/L)
Pb (mg/L)
Cd (mg/L)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42
1092.655 909.808 877.434 1174.814 1161.921 1144.048 1209.340 1178.811 1121.317 1168.325 1271.317 1175.273 1175.258 979.497 750.166 692.160 652.698 555.186 701.241 635.631 600.812 1161.585 786.288 735.141 551.737 1118.362 811.232 621.008 513.589 467.748 1176.052 1146.943 1060.578 845.686 754.446 597.821 464.552 1309.156 1208.146 1030.843 856.667 729.128
23.696 21.277 21.110 23.717 23.718 22.942 25.358 23.897 23.061 28.908 28.263 23.776 24.097 21.417 19.986 17.923 18.262 15.182 18.545 17.332 15.743 27.010 21.393 19.942 14.663 27.413 22.766 18.513 15.471 13.975 34.720 32.920 28.247 26.661 23.041 20.466 15.764 38.564 35.323 31.499 27.968 26.198
202.393 205.422 200.731 40.130 37.875 40.478 45.316 43.874 42.619 45.438 42.409 50.998 50.733 42.537 42.795 44.542 44.607 35.309 43.138 35.417 41.806 61.846 58.087 33.874 33.835 39.401 34.130 39.538 31.807 29.135 45.338 34.397 43.391 37.141 38.661 35.466 40.690 37.513 29.644 36.494 183.520 185.213
1.437 1.234 1.202 1.564 1.550 1.525 1.614 1.584 1.563 1.783 1.941 1.574 1.579 1.603 1.406 1.328 1.309 1.206 1.340 1.241 1.272 1.861 1.362 1.198 1.165 1.629 1.437 1.283 1.181 1.115 1.421 1.516 1.767 1.488 1.374 1.193 1.181 1.647 1.526 1.532 1.344 1.320
8.479 8.294 8.238 8.491 8.468 8.530 8.506 8.494 8.468 8.253 8.208 8.441 8.419 8.343 8.099 8.231 8.100 7.952 8.294 8.178 8.157 8.271 8.225 8.045 8.189 8.251 8.117 8.379 8.285 8.267 8.465 8.454 8.391 8.384 8.219 8.048 8.149 8.376 8.445 8.347 8.269 8.119
7.936 7.981 7.980 7.947 7.942 7.975 7.975 7.975 7.993 8.056 8.033 7.981 7.983 8.004 8.048 8.086 8.085 8.138 8.083 8.132 8.133 8.046 8.104 8.109 8.176 8.039 8.056 8.129 8.152 8.164 8.002 8.000 8.065 8.063 8.083 8.094 8.140 8.039 8.028 8.067 8.077 8.096
6.196 6.497 6.541 6.136 6.121 6.565 6.142 5.939 6.201 7.444 6.834 6.180 6.116 6.525 6.748 7.180 7.398 7.549 7.129 7.049 7.024 6.868 7.159 8.347 8.398 7.024 7.019 7.029 6.980 7.012 8.519 7.463 8.876 8.219 7.573 7.421 7.264 7.656 5.920 8.310 7.827 6.530
0.031 0.032 0.032 0.032 0.031 0.030 0.032 0.032 0.031 0.040 0.039 0.037 0.035 0.036 0.036 0.036 0.037 0.034 0.036 0.034 0.033 0.041 0.032 0.035 0.035 0.037 0.040 0.039 0.036 0.036 0.059 0.054 0.050 0.047 0.049 0.041 0.039 0.076 0.049 0.067 0.052 0.045
7.495 7.060 7.038 8.312 8.327 7.727 8.999 8.187 7.509 11.291 11.284 7.853 7.006 6.004 5.774 5.308 4.996 4.059 5.983 4.942 4.058 11.393 5.487 8.979 8.926 6.755 5.127 3.951 4.344 4.294 8.742 8.638 4.463 4.341 3.807 3.773 4.182 3.234 3.616 5.749 4.779 3.709
0.172 0.162 0.163 0.177 0.172 0.160 0.172 0.164 0.156 0.168 0.167 0.164 0.158 0.156 0.154 0.153 0.144 0.129 0.159 0.133 0.136 0.175 0.144 0.192 0.190 0.429 0.196 0.160 0.171 0.169 0.210 0.205 0.167 0.166 0.163 0.210 0.163 0.169 0.165 0.165 0.173 0.231
by using alkaline permanganate method and iodine titrimetry. The determinations of Cu, Pb and Cd in water were carried out by atomic absorption spectrometry (AAS; PerkinElmer, Analyst 800). The concentration of Hg was measured by WGY e SIM cold atom fluorescence instrument (China National Nuclear Cooperation). All the values of monitoring elements used in this study are measured values. The physical, chemical and biological processes have been reflected in these measured values.
2.2. Geostatistical method Based on regionalized variables and semi-variogram, geostatistics is used to optimal design of the marine environmental monitoring network. In the marine environment, environmental element variables of existing monitoring sites are used to determine semi-variogram model with kriging method (Krige, 1951; Matheron, 1985). The formula can be expressed as:
Table 2 Pearson correlation coefficient values for seawater environmental elements.
DIN PO4eP Oil COD DO pH Cu Hg Pb Cd *
DIN
PO4eP
Oil
COD
DO
pH
Cu
Hg
Pb
Cd
1 0.798** 0.026 0.851** 0.707** 0.828** 0.302* 0.241 0.563** 0.177
1 0.048 0.668** 0.485** 0.502** 0.094 0.717** 0.210 0.264*
1 0.183 0.006 0.273* 0.205 0.093 0.007 0.023
1 0.488** 0.561** 0.166 0.220 0.527** 0.141
1 0.697** 0.348* 0.098 0.303* 0.016
1 0.540** 0.083 0.484** 0.076
1 0.545** 0.159 0.070
1 0.255 0.105
1 0.108
1
Correlations are significant at the 0.05 level. ** Correlations are significant at the 0.01 level.
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Table 3 Some parts of seawater quality standard of GB3097-1997 in China, Seawater Environmental Quality Index (SEQI) values, pollution level and pollution category. SEQI value
0e1
Seawater environmental elements
DIN (mg/L) PO4eP (mg/L) Oil (mg/L) pH COD (mg/L) DO (mg/L) Cu (mg/L) Hg (mg/L) Pb (mg/L) Cd (mg/L)
Quality level Category
b z ðx0 Þ ¼
n X
1e2
0e0.20 0.20e0.30 0e0.015 0.015e0.030 0e0.05 7.8e8.5 (within 0.2 pH) 0e2 2e3 >6 5e6 0e0.005 0.005e0.01 0e0.00005 0.00005e0.0002 0e0.001 0.001e0.005 0e0.001 0.001e0.005 I II Excellent Good
li zðxi Þ
(1)
i¼1
where b z ðx0 Þ is the estimated value in the point x0; li and z(xi) represent the weight and observed value in the point xi, respectively; z(xi) is represented as Seawater Environmental Quality Index. The weight is endowed with measure values of surrounding sites. The variables should be linear, unbiased, and optimal estimated (Kumar and Devi, 2006).
2e3
3e4
4e5
0.30e0.40
0.40e0.50 0.030e0.045 0.30e0.50
>0.50 >0.045 >0.50 Without 0.5 pH >5 0e3 >0.05 0.0005 >0.05 >0.01 V Heavy pollution
0.05e0.30 6.8e8.8 (within 0.5 pH) 3e4 4e5 4e5 3e4 0.01e0.05 0.0002e0.0005 0.005e0.01 0.01e0.05 0.005e0.01 III IV Light pollution Moderate pollution
Kriging variance can be calculated as follows:
s2 ¼
n X
li gðxi ; x0 Þ þ m
(2)
i¼1
where s2 is the kriging variance; g(xi,x0) is the semi-variogram between xi and x0; and m is the lagrange multiplier. When the average standard deviation becomes smaller, the spatial distribution of the points is more reasonable, and much
Table 4 Seawater Environmental Quality Index of main seawater environmental elements in the Yangtze river estuary and its adjacent sea from 1985 to 2006. Site
DIN
PO4eP
Oil
COD
DO
pH
Cu
Hg
Pb
Cd
SPI
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42
4.593 4.410 4.377 4.675 4.662 4.644 4.709 4.679 4.621 4.668 4.771 4.675 4.675 4.479 4.250 4.192 4.153 4.055 4.201 4.136 4.101 4.662 4.286 4.235 4.052 4.618 4.311 4.121 4.014 3.677 4.676 4.647 4.561 4.346 4.254 4.098 3.646 4.809 4.708 4.531 4.357 4.229
2.159 1.837 1.815 2.162 2.162 2.059 2.381 2.186 2.075 2.854 2.768 2.170 2.213 1.856 1.665 1.390 1.435 1.024 1.473 1.311 1.099 2.601 1.852 1.659 0.978 2.655 2.035 1.468 1.063 0.932 3.315 3.195 2.766 2.555 2.072 1.729 1.102 3.571 3.355 3.100 2.729 2.493
2.610 2.622 2.603 1.605 1.515 1.619 1.813 1.755 1.705 1.818 1.696 2.004 2.003 1.701 1.712 1.782 1.784 1.412 1.726 1.417 1.672 2.047 2.032 1.355 1.353 1.576 1.365 1.582 1.272 1.165 1.814 1.376 1.736 1.486 1.546 1.419 1.628 1.501 1.186 1.460 2.534 2.541
0.718 0.617 0.601 0.782 0.775 0.763 0.807 0.792 0.782 0.891 0.971 0.787 0.789 0.801 0.703 0.664 0.655 0.603 0.670 0.621 0.636 0.930 0.681 0.599 0.583 0.814 0.719 0.642 0.590 0.558 0.711 0.758 0.883 0.744 0.687 0.597 0.591 0.823 0.763 0.766 0.672 0.660
0.708 0.723 0.728 0.707 0.709 0.703 0.705 0.706 0.709 0.727 0.731 0.711 0.713 0.719 0.741 0.729 0.741 0.755 0.723 0.734 0.736 0.725 0.729 0.746 0.733 0.727 0.739 0.716 0.724 0.726 0.709 0.710 0.715 0.716 0.730 0.746 0.736 0.716 0.710 0.719 0.726 0.739
2.279 1.512 1.522 2.126 2.199 1.622 1.641 1.624 1.279 0.008 0.468 1.513 1.475 1.059 0.178 0.594 0.574 1.637 0.520 1.503 1.530 0.211 0.955 1.047 2.265 0.355 0.020 1.443 1.912 2.099 1.087 1.122 0.174 0.124 0.519 0.748 1.677 0.343 0.578 0.208 0.413 0.793
1.478 1.599 1.616 1.454 1.449 1.626 1.457 1.376 1.480 1.978 1.733 1.472 1.447 1.610 1.699 1.872 1.959 2.020 1.851 1.820 1.810 1.747 1.864 2.339 2.359 1.809 1.808 1.812 1.792 1.805 2.407 1.985 2.550 2.288 2.029 1.968 1.906 2.062 1.368 2.324 2.131 1.612
0.622 0.648 0.636 0.633 0.628 0.592 0.646 0.641 0.610 0.809 0.790 0.749 0.690 0.720 0.712 0.721 0.731 0.676 0.723 0.687 0.657 0.810 0.650 0.698 0.707 0.740 0.791 0.772 0.714 0.716 1.115 1.057 0.993 0.933 0.976 0.817 0.790 1.347 0.977 1.224 1.026 0.907
2.499 2.412 2.408 2.662 2.665 2.545 2.800 2.637 2.502 3.032 3.032 2.571 2.401 2.201 2.155 2.062 1.999 1.765 2.197 1.986 1.764 3.035 2.097 2.796 2.785 2.351 2.025 1.738 1.836 1.824 2.748 2.728 1.866 1.835 1.702 1.693 1.796 1.558 1.654 2.150 1.945 1.677
0.172 0.162 0.163 0.177 0.172 0.160 0.172 0.164 0.156 0.168 0.167 0.164 0.158 0.156 0.154 0.153 0.144 0.129 0.159 0.133 0.136 0.175 0.144 0.192 0.190 0.429 0.196 0.160 0.171 0.169 0.210 0.205 0.167 0.166 0.163 0.210 0.163 0.169 0.165 0.165 0.173 0.231
4.593 4.410 4.377 4.675 4.662 4.644 4.709 4.679 4.621 4.668 4.771 4.675 4.675 4.479 4.250 4.192 4.153 4.055 4.201 4.136 4.101 4.662 4.286 4.235 4.052 4.618 4.311 4.121 4.014 3.677 4.676 4.647 4.561 4.346 4.254 4.098 3.646 4.809 4.708 4.531 4.357 4.229
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more information of the study area can be obtained. The calculation of the standard deviation can be chosen to optimize sites of marine environmental monitoring network. 2.3. Economic budget analysis The monitoring expense of one monitoring site each time is about 6000e8000 CNY, including infrastructure costs, instrument testing costs, staff costs and so on.
E ¼ abi
(3)
Where E is the monitoring expense; a is the expense of one monitoring site each time; b is the monitoring frequency each year, about 4 times; and i is the number of the monitoring sites. When the number becomes larger, the expense would be higher. The optimal design of marine environmental monitoring network in the study area is carried out from the spatial aspect. Using Seawater Environmental Quality Index, the mean values of environmental monitoring elements such as DIN, PO4eP, oil, COD, DO, pH, Cu, Hg, Pb and Cd from 1985 to 2006 are normalized, and the maximum values are extracted to describe the pollution level. Considering the marine environment pollution level and the average standard deviation of the monitoring network by kriging variance analysis method, the monitoring network is optimized in the Yangtze River estuary and its adjacent sea. This paper can provide the support for marine environmental monitoring and management.
3. Results and discussion 3.1. Correlation coefficients and principal component analysis Most of seawater environmental elements showed highly significant positive correlation with each other. It is clear that DIN, PO4eP, COD, DO and pH were shown higher Pearson correlation coefficient with each other, which might be related to terrestrial pollution sources. The moderate correlation of oil and Hg could indicate that they are sourced from oil wells or channel sewage. A significant positive correlation is verified for Hg with Cu indicates industrial wastewater discharge (Table 2). 3.2. Seawater Environmental Quality Index (SEQI) Based on the mean values of seawater environmental elements and the Seawater quality standard of China (GB 3097-1997, 1998), the values of each seawater environmental element should be normalized and divided into corresponding pollution level. The maximum normalized values of each seawater environmental element are chosen as the representative value of each site. Seawater Environmental Quality Index is calculated as the highest index among the pollution indices of the seawater environmental elements. The formula can be given by:
SEQI ¼ max Ij
Fig. 2. Map of seawater environmental quality in the Yangtze river estuary and its adjacent sea.
(4)
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Iij ¼ ðIU IL ÞðC CL Þ=ðCU CL Þ þ IL
(5)
where Iij is the environmental quality index of the seawater environmental elements; i is the seawater environmental element; j is the monitoring site number; IU and IL are the upper and lower standard index in Table 3, respectively; C is the mean value shown in Table 1; CUand CL are the upper and lower values of seawater environmental elements shown in Table 3, respectively (Gao et al., 2011). In the calculation of IDO, CU and CL are chosen reciprocal, while in the calculation of IpH, the value of CU CL is 0.2 or 0.5 as listed in Table 3, and CpH is two times of difference between the initial value and mean value. On the basis of the mean values of seawater environmental elements in Table 1 and 3 and Formula 5, the normalized values of main seawater environmental elements in the study area are shown in Table 4. Based on Formula 4, SEQI is extracted as the maximum normalized values of the seawater environmental elements. SEQI value is not the analysis result of a single element, but the analysis result of comprehensive elements. Based on the SEQI values, the level of seawater environmental quality is shown in Fig. 2.
3.3. Preliminary optimization of the monitoring network The values of seawater environmental quality index are also used to predict standard deviation through kriging variance analysis method. Standard deviation map of existing monitoring network in the Yangtze River estuary and its adjacent sea is shown in
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Fig. 3. The average standard deviation of original monitoring network in this study area is 0.124. Optimization of marine environmental monitoring sites is to monitor the marine environment with smaller average standard deviation of the monitoring network. Based on the original 42 monitoring sites in the study area, Fig. 4 shows the correlation between the number of designed monitoring sites and average standard deviation of estimated error. When average standard deviation of estimated error is decreased from 0.20 to 0.15, 15 sites should be added. However, when average standard deviation of estimated error keeps decreasing to 0.10, 50 new sites are required to be added. It can be concluded that smaller average standard deviation of estimated error needs more monitoring sites. There are 42 monitoring sites at present, and average standard deviation of estimated error is 0.124. The reliable of the number is correlated to the average standard deviation of estimated error of designed number. In Fig. 4, the average standard deviation of estimated error is smaller, the number of monitoring sites is higher, and the information of marine environment is more abundant. When the number of monitoring sites increases to 59, the decrease of average standard deviation of estimated error needs much more increasing number. 59 monitoring sites are chosen as the optimal number of monitoring sites. The improvement of the quality of marine environmental monitoring network can be satisfied with decreasing the average standard deviation of estimated error. As shown in Fig. 4, when the number of designed monitoring sites is 59, the average standard deviation is 0.111, and the monitoring precision is improved by 10.4%. Fig. 5 shows the standard error map of new monitoring network in the Yangtze River estuary and its adjacent sea.
Fig. 3. Standard deviation map of existing monitoring network in the Yangtze river estuary and its adjacent sea.
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Fig. 4. The relationship curve between the numbers of designed monitoring sites and average standard deviation of estimate error in the study area.
3.4. Improved optimization of the monitoring network Though the monitoring network shown in Fig. 5 improves the quality of marine environmental monitoring network, the uniform distribution of 59 monitoring sites does not consider the marine
environment pollution level. Therefore, an improved monitoring network is designed. As shown in Table 1, sites 2 and 3 can be combined (renamed as N1) because of very similar measurement data, and the same applies to sites 12 and 13 (renamed as N2). Due to the edge error, more
Fig. 5. Standard deviation map of new monitoring network in the Yangtze river estuary and its adjacent sea.
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Fig. 6. Standard deviation map of improved monitoring network in the Yangtze river estuary and its adjacent sea.
sites are added in the deep sea. In fact, the variation of the deep-sea environment pollution level is not obvious according to experiences. Therefore, sites 43, 44, 51, 55, 56, 57 and 58 are deleted. When the SEQI is higher, that is higher pollution level, the monitoring network should be denser. The number of sites in the near shore area should be increased, and sites N3, N4. N11 are added. The standard deviation map of improved monitoring network is shown in Fig. 6. The average standard deviation of improved monitoring network is 0.104, which is more precise than the network as shown in Fig. 5. Meanwhile, the monitoring network quality will be improved by 16.1%. Compared with the original 42 monitoring sites, 4 sites are removed and 21 new ones are added. Therefore, the designed number of the monitoring sites becomes 59 in the Yangtze River estuary and its adjacent sea. The monitoring expense of the original network is about 1 008 000e1 344 000 CNY per year. The expense of the designed monitoring network is increased to 1 416 000e1 888 000 CNY per year, an increase of about 40%. 4. Conclusions This paper proposes the concept and computing method of Seawater Environmental Quality Index (SEQI). Geographical Information System (GIS) and kriging variance analysis method are used to assess the quality of existing environmental monitoring network and optimize the design of environmental monitoring network in the Yangtze River estuary and its adjacent sea. This method is simple and effective for practice application. By calculating the
maximum normalized values of the seawater environmental elements, The SEQI can be obtained and described the seawater environmental quality and pollution level of the Yangtze River estuary and its adjacent sea. Through kriging variance analysis method, the monitoring network is initially optimized in the Yangtze River estuary and its adjacent sea. Considering the marine environment pollution level and the average standard deviation, the number of monitoring sites is increased from 42 to 59 with 21 new ones designed and 4 old ones removed, improving the marine environmental monitoring quality. The monitoring expenses would be increased from 1 008 000e1 344 000 CNY to 1 416 000e1 888 000 CNY per year, an increase of about 40%. This paper proposed optimal design of the monitoring sites in the Yangtze River estuary and its adjacent sea. Acknowledgments The authors are grateful to the State Oceanic Administration People’s Republic of China (serial number: DOMEP (MEA-01-01)) and 908 Special Project Foundation of Shanghai, China (908SPFSC) (serial number: PJ2) for their financial support. Data sources are from East China Sea Environmental Monitoring Center of State Oceanic Administration. References An, Q., Wu, Y., Wang, J., Li, Z., 2010. Assessment of dissolved heavy metal in the Yangtze River estuary and its adjacent sea, China. Environmental Monitoring and Assessment 164, 173e187. Assessment of estuarine trophic status. 2012. http://eutro.org/methods.aspx.
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