The purpose of this work is to evaluate the spatial and seasonal variability of water quality from the Mopanshan Reservoir, which is a typical reservoir in the northern cold regions of China. The results indicate that preventive or remediation actions are necessary to improve its water quality. Water samples were collected between 2012 and 2013 at five sampling stations at the Mopanshan Reservoir, and analyzed for CODMn and total nitrogen (TN). SPSS software was used to carry out analysis of variance and correlation analysis. Spatially, CODMn and TN exhibited a rather small distinction in the horizontal direction, but there was a significant difference regarding TN in the vertical orientation. The concentration of TN also increased with the increase in sampling depth. Seasonally, the concentration of CODMn and TN showed a pronounced seasonal pattern and was divided into four periods. CODMn reached a maximum in September and was at a minimum in June. And TN reached a maximum in June and was at a minimum in November or December. The use of correlation analysis shows that the regular variations of TN were primarily affected by temperature. The main form of nitrogen in the Mopanshan Reservoir is NO3-N, and the change of TN is consistent with that of NO3-N. By Pearson correlation coefficient, the seasonal variability of CODMn correlated to changes in the reservoir's water level. The results showed that the concentration of TN exceeded the guideline values in most months and CODMn was slightly over the standard limit. Thus, it is urgent that preventive actions and remediation processes are developed to improve the water quality in this reservoir.

INTRODUCTION

Drinking water security is critical to the health and safety of citizens, and protecting water sources is an important part of water resources protection. Reservoirs are a relatively common drinking water source in China, and research on the change rules for water quality in them is the basis for protecting the water resource. Water quality of reservoirs may be different due to differences in location, climate, etc. Compared to other headwater areas, reservoirs can ensure a stable water supply and are easy to protect. They also have a distinct advantage in the cost of disposal and water supply quality (Cui et al. 2011). At present, when researchers study water quality in reservoirs, the reservoirs in south China are the main research subjects, and the reservoirs in the northeast have not received enough attention. In China, the northern and southern areas are separated at the latitude of 50 degrees, so there are pronounced differences between northern and southern reservoirs.

He evaluated the water quality of 41 reservoirs in Zhejiang Province by analyzing eutrophication during the rainy and drought periods (He et al. 2011). Zhang studied the seasonal variability of trophic states in backwater areas of tributaries in the Three Gorges Reservoir (Zhang et al. 2009). Chen analyzed the causes of the high content of total nitrogen, and put forward some suggestions about intensifying the safeguards for water quality in the Danjiangkou Reservoir (Chen et al. 2005). Palma assessed the spatial and temporal variability of water and sediment quality in the Alqueva reservoir, and concluded that the major parameters that explained the water quality variability were the nutrients in the water column and trace elements in the sediments (Palma et al. 2014). Wei Hou used the water quality index (WQI) method to evaluate the water quality of reservoirs in the lower reaches of the Yellow River, and suggested that WQI is a helpful tool in evaluating the water quality of reservoirs that are used for drinking water (Hou et al. 2015). Phuong Thuy Kim Doan researched the eutrophication of turbid tropical reservoirs in Mexico and summarized that if there were cut backs in nutrient inputs, the peak-peak of chlorophyll would also be reduced (Doan et al. 2015). To our best knowledge, water quality characterization in the Mopanshan Reservoir have never been performed before. In this scenario, the main aims of the study were: (1) to assess the seasonal and spatial variability of the water quality in the Mopanshan Reservoir; (2) to discuss the change rule of CODMn and total nitrogen (TN); and (3) to analyze the causes of pollution in the Mopanshan Reservoir. The results of this research, together with the conclusions obtained in previous studies, can provide a scientific basis for controlling and preventing pollution, and to maintain and improve the water conditions of northern cold region reservoirs.

MATERIALS AND METHODS

Study areas and sampling sites characterization

The Mopanshan Reservoir (44 °23′40″N, 127 °41′20″E) is located in the northeast of China, on the upper reaches of the Lalin River. It is the only water resource for Harbin urban area, and is also a typical headwaters reservoir in the northern cold region of China. The concentration of dissolved organic matter in the water column is high. During the icebound season, the water body has the following characteristics: low temperature, low turbidity and high chromaticity conditions. The average annual rainfall is 500–800 mm, and the precipitation mainly happens in June to September, accounting for 60% of the annual precipitation (Bai et al. 2013). The yearly average runoff of the Mopanshan Reservoir is 5.61 × 108 m3/a, which is largely affected by natural precipitation. The change interval of the water temperature is 1 °C to 22 °C, the annual mean temperature is about 3 °C. The icebound period is up to 150 days, and is from December to April.

To better understand the change rule of the water quality and the pollutant characteristics of the Mopanshan Reservoir, five monitoring sites were chosen according to its geographic location, the water quality of the upstream water bodies, hydrological characteristics and etc., five monitoring sites were chosen: the water intake at Mopanshan Reservoir (site 1), the center of reservoir (site 2), the inlet of the Lalin River (site 3), the inlet of the Sasha River (site 4), and the inlet of the Dasha River (site 5) (Figure 1). Site 1 is the water intake for Harbin, we can ensure the security of water supply by analyzing water samples taken from there. Site 2 reflects the change rules of contaminants in the center of the Mopanshan Reservoir. The results from this point represent average levels of contaminants in the reservoir. Site 3, 4, and 5 are near the inlets where rivers pour into the Mopanshan Reservoir. We can clarify the input amount of contaminants by monitoring these three sites. There is no drain set around the reservoir, so the three inlets are the main pollution sources of the Mopanshan Reservoir.
Figure 1

The distribution of Mopanshan Reservoir water quality monitoring sites (red dots). Please refer to the online version of this paper to see this figure in colour: http://dx.doi.org/10.2166/ws.2016.118.

Figure 1

The distribution of Mopanshan Reservoir water quality monitoring sites (red dots). Please refer to the online version of this paper to see this figure in colour: http://dx.doi.org/10.2166/ws.2016.118.

Chemical analysis

The determination method of CODMn is according to the standard of water quality-determination of the permanganate index (China MOEP 1989). The concentrations of TN and NO3-N were measured by Flow Injection Analysis (FIA, QC8500) (China MOEP 2012). The determination method for ammonia nitrogen was Nessler's reagent spectrophotometry (China MOEP 2010, 2012).

We selected the year 2012 (a low flow year, inflow was 4.2 × 108 m3/a) and the year 2013 (a high flow year, inflow was 7.9 × 108 m3/a) (Wang 2008) as the research targets in order to study the effect of rainfall on water quality of the Mopanshan Reservoir. Water samples were collected monthly at the five monitoring sites in Figure 1. At each sampling location, 2 L of water were collected at a depth of 1, 7 and 15 m. Hydrological data came from the Harbin Water Research Center. Water quality data were subjected to descriptive statistics (range, mean, and standard deviation). The potential relationship between the studied parameters was evaluated through the correlation matrices. All data were processed by SPSS and analysis of variance (ANOVA), and the Pearson correlation coefficient was used during analysis.

RESULTS AND DISCUSSION

Selection of water quality indexes

The Mopanshan Reservoir belongs to a system of slow-flow water bodies, where the water depth is about 20 m, and the dissolved oxygen (DO) gradually diminishes as the water depth increases. The DO content varies seasonally, but they still conform within the water quality standards. DO decreases as the water temperature increases. Total phosphorus is uniformly distributed in the vertical direction. According to the comprehensive trophic level index (TLI), the values of the reservoir were 43.4 in 2012 and 44.1 in 2013, which shows that the reservoir is mesotrophic. TLI is designed to evaluate water eutrophication. When the value of TLI is between 30 and 50, it shows that the reservoir is mesotrophic. When the value of TLI is below 30, the reservoir is oligotrophic. When the value of TLI is above 50, the reservoir is eutrophic. The ratio of nitrogen and phosphorus were 30.0:1, 30.3:1, 31:1, 29.7:3, 30.3:1 at five sampling sites in 2012, respectively; and the values were 32:1, 30.1:1, 31.7:1, 30.7:1; 32:1 in 2013, respectively. Phosphorus and nitrogen remained stable at five sampling sites during the two years. According to the criterion GB/T 5750–2006 ‘Sanitary Standard for Drinking Water’ and GB 3838–2002 ‘Environmental Quality Standards for Surface Water’ and monitoring dates, the analysis of 109 indicators showed that CODMn and TN exceed the surface water Class II water standard. Meanwhile, 103 of these meet class I water quality standards and the four remaining indicators conformed to the Class II standard (China MOEP 2002, 2007). Therefore, CODMn and TN were chosen as the objects of our study.

Spatial variability in CODMn and TN

The data in Figure 2 are the detection results of water samples which were taken from the same depth (7 m). CODMn and TN showed a similar variation trend among the five sampling stations during the study period. To further illustrate whether CODMn and TN have significant variation among different sampling sites or not, data were analyzed by SPSS 21 software. The results of ANOVA are shown in Table 1, which indicate that there are no significant differences among the five sampling sites for CODMn (P = 0.977 > 0.05) and TN (P = 0.984 > 0.05).
Table 1

The correlation between sampling sites and TN, as well as CODMn

 Site 1Site2Site 3Site 4Site 5FP
CODMn 4.38 ± 0.744 4.34 ± 0.729 4.44 ± 0.756 4.32 ± 0.736 4.32 ± 0.723 0.115 0.977 
TN 0.92 ± 0.277 0.91 ± 0.297 0.95 ± 0.286 0.93 ± 0.281 0.91 ± 0.276 0.095 0.984 
 Site 1Site2Site 3Site 4Site 5FP
CODMn 4.38 ± 0.744 4.34 ± 0.729 4.44 ± 0.756 4.32 ± 0.736 4.32 ± 0.723 0.115 0.977 
TN 0.92 ± 0.277 0.91 ± 0.297 0.95 ± 0.286 0.93 ± 0.281 0.91 ± 0.276 0.095 0.984 
Figure 2

Spatial and seasonal variations in (a) CODMn concentrations and (b) TN across five sites of Mopanshan Reservoir (sampling depth: 7 m).

Figure 2

Spatial and seasonal variations in (a) CODMn concentrations and (b) TN across five sites of Mopanshan Reservoir (sampling depth: 7 m).

The water quality showed differences at different water depth (Figures 3 and 4). The concentration of TN increased with an increase in sampling depth. But the concentration of CODMn didn't rise with the increase in sampling depth and didn't have spatial variability in the vertical direction. To measure the correlation between sampling depth and TN, as well as CODMn more accurately, ANOVA was adopted (Table 2). This shows that there is a significant difference in TN at different sampling depths (P = 0.002 < 0.05). This means that there is a correlation between the water depth and the concentration of TN. However, this difference didn't exist when the study subject was CODMn (P = 0.509 > 0.05), which means that there is no correlation between the depth of water and the concentration of CODMn.
Table 2

The correlation between sampling depth and TN, as well as CODMn

 Sum of squaresdfMean squareFP
TN 
 Between groups 0.895 0.448 7.061 0.002 
 Within groups 4.375 69 0.063   
 Total 5.27 71    
CODMn 
 Between groups 0.78 0.39 0.683 0.509 
 Within groups 39.405 69 0.571   
 Total 40.185 71    
 Sum of squaresdfMean squareFP
TN 
 Between groups 0.895 0.448 7.061 0.002 
 Within groups 4.375 69 0.063   
 Total 5.27 71    
CODMn 
 Between groups 0.78 0.39 0.683 0.509 
 Within groups 39.405 69 0.571   
 Total 40.185 71    
Figure 3

Spatial and seasonal variation in (a) CODMn concentrations and (b) TN at different sampling depths at site 1 of the Mopanshan Reservoir.

Figure 3

Spatial and seasonal variation in (a) CODMn concentrations and (b) TN at different sampling depths at site 1 of the Mopanshan Reservoir.

Figure 4

Spatial and seasonal variations in nitrogen concentrations at different sampling depths: (a) 1 m, (b) 7 m and (c) 15 m in site 1 of the Mopanshan Reservoir.

Figure 4

Spatial and seasonal variations in nitrogen concentrations at different sampling depths: (a) 1 m, (b) 7 m and (c) 15 m in site 1 of the Mopanshan Reservoir.

Sites 3, 4, and 5 are near the inlets where rivers pour into the Mopanshan Reservoir. In fact, they are in the reservoir, and the monitoring results reflect the water quality of the reservoir. And by monitoring the three sites, we can also clarify the input amount of contaminants from the three input rivers to a certain extent. The concentrations of CODMn were highest at site 3 (the inlet of the Lalin River), which revealed that the Lalin River contributed more pollutants than site 4 and site 5 (Figure 2(a)). This may because the water quality of the Lalin River was poorer than the two other rivers. And the flow rate of the Lalin River (1.90 m·s−1, annual average flow) is larger than that of the Sasha River (1.23 m·s−1, annual average flow) and Dasha River (0.37 m·s−1), where a combined water depth and width had a pollution loading of about 4,516 t/a, 1,674 t/a and 694 t/a, respectively. Therefore, we maintain that the Lalin River contributes more pollutants than the other two rivers.

The main form of nitrogen in the Mopanshan Reservoir is NO3-N, and the concentration of NH4-N is low and stays stable (Figure 4). This indicates that the dynamic change in the TN is mainly caused by NO3-N. The change rule for NO3-N in the vertical direction is consistent with that of TN: an increase with increasing water depth.

Seasonal variability in CODMn and TN

As mentioned earlier, the change rule for CODMn and TN showed no significant difference among the five sampling sites, and considering that the intake is important for water supply security, we selected site 1 as the research object for this part. In general, both CODMn and TN exhibited a pronounced seasonal pattern. The change in them can be broken down into four major typical water quality periods, including the icebound period (January to March), spring period (April to June), rainy period (July to September) and the stable winter period (October to December) (Figure 2).

CODMn exhibited a pronounced seasonal pattern, with the lowest values occurring in June and the highest in September (3.23–5.46 mg·L−1 in 2012 and 3.18–6.52 mg·L−1 in 2013, respectively). CODMn presented a changing trend as follows: first, it kept stable during the icebound period. Second, it declined slightly during the spring period. Third, it increased during the rainy period. And fourth, it decreased until October and then finally maintained a stable state (Figure 2(a)). The concentrations of TN reached a maximum in June and a minimum in November or December (0.62–1.28 mg·L−1 in 2012 and 0.64–1.21 mg·L−1 in 2013, respectively). The content of TN fluctuated slightly but remained stable during the icebound period, then gradually increased and peaked in June. It then slowly decreased until September and then stayed stable from October to December (Figure 2(b)).

By the Pearson correlation coefficient, the relationship between water temperature/water level and TN, as well as CODMn, is analyzed in the presented paper. TN concentration had a significant correlation with water temperature (P < 0.05) regardless of sampling depth, but did not correlate with water level (Tables 3 and 4). And there was a pronounced correlation between the CODMn concentrations and water level, no matter how deep the sampling spots were (P < 0.05), but no correlation between the CODMn concentrations and water temperature (Tables 3 and 4). Graphical and correlational analysis come to one conclusion, that the seasonal variability of CODMn correlated to changes in the reservoir's water level and that of TN is consistent with the water temperature of the reservoir.

Table 3

The correlation between water temperature and CODMn, as well as TN

Sampling depth1 m7 m15 m
CODMn 
 Pearson correlation −0.045 −0.175 −0.008 
 Sig. (2-tailed) 0.834 0.412 0.969 
 N 24 24 24 
TN 
 Pearson correlation 0.825** 0.763** 0.890** 
 Sig. (2-tailed) 0.000 0.000 0.000 
 N 24 24 24 
Sampling depth1 m7 m15 m
CODMn 
 Pearson correlation −0.045 −0.175 −0.008 
 Sig. (2-tailed) 0.834 0.412 0.969 
 N 24 24 24 
TN 
 Pearson correlation 0.825** 0.763** 0.890** 
 Sig. (2-tailed) 0.000 0.000 0.000 
 N 24 24 24 

**Represents significant correlation at the 0.01 level.

Table 4

The correlation between water level and CODMn, as well as TN

Sampling siteSite 1Site 2Site 3Site 4Site 5
CODMn 
 Pearson correlation 0.407* 0.414* 0.432* 0.419* 0.403 
 Sig. (2-tailed) 0.048 0.044 0.035 0.042 0.051 
 N 24 24 24 24 24 
TN 
 Pearson correlation 0.230 0.139 0.152 0.247 0.116 
 Sig. (2-tailed) 0.279 0.518 0.479 0.245 0.589 
 N 24 24 24 24 24 
Sampling siteSite 1Site 2Site 3Site 4Site 5
CODMn 
 Pearson correlation 0.407* 0.414* 0.432* 0.419* 0.403 
 Sig. (2-tailed) 0.048 0.044 0.035 0.042 0.051 
 N 24 24 24 24 24 
TN 
 Pearson correlation 0.230 0.139 0.152 0.247 0.116 
 Sig. (2-tailed) 0.279 0.518 0.479 0.245 0.589 
 N 24 24 24 24 24 

*Represents significant correlation at the 0.05 level.

Change rule of CODMn and TN during the icebound period

The water temperature was between 0.8 °C to 1.2 °C during this stage (Table 5), so the chemical reactions and biochemical reactions were very slow at such a low temperature (Dai 1987). Meanwhile, ice covers the surface of the reservoir, so the inflow which comes from the upstream catchment areas was zero. This area lacked rainwater during this period. The above two points resulted in surface water runoff decreasing and then leading to outflow exceeding inflow, so that the water level of the reservoir decreased (Figure 5). Thus, pollutants continued to be expelled during this stage, but because of the decrease in water level, the concentration of the contaminants changed little and water quality remained relatively stable. Therefore, the content of CODMn and TN had no significant changes over this period.
Table 5

Summarization of water temperature in 2012 and 2013

 Water temperature (2012)/°CWater temperature (2013)/°C
January 0.8 0.9 
February 0.9 0.95 
March 1.2 1.0 
April 6.8 7.2 
May 12.1 13.4 
June 22.0 21.5 
July 21.8 21.0 
August 21.0 19.6 
September 17.0 15.3 
October 11.3 10.1 
November 1.0 0.8 
December 0.9 0.7 
 Water temperature (2012)/°CWater temperature (2013)/°C
January 0.8 0.9 
February 0.9 0.95 
March 1.2 1.0 
April 6.8 7.2 
May 12.1 13.4 
June 22.0 21.5 
July 21.8 21.0 
August 21.0 19.6 
September 17.0 15.3 
October 11.3 10.1 
November 1.0 0.8 
December 0.9 0.7 
Figure 5

Flow and level relationship in 2012 (dry year) and 2013 (wet year).

Figure 5

Flow and level relationship in 2012 (dry year) and 2013 (wet year).

Change rule of CODMn and TN during the spring period

Ice on the surface of the reservoir began to thaw in April and completely melted by mid-April. Melting ice added to the amount of water, so the reservoir's inflow was more than the outflow in April (Figure 5). Besides, runoff increased when the ice melted. But the rainfall was still relatively low during this period, so a few pollutants inflowed into the reservoir through surface runoff. Meanwhile, water temperature gradually rose to 10 °C and would reach a maximum of 22 °C (Table 5), which caused the reaction rates of chemical reactions and biochemical reactions to increase (Dai 1987). So while total pollutant amounts increased, the value of CODMn decreased slightly during this period. However, TN increased as temperatures went up, and ultimately peaked in June. Excess nutrients in water bodies are primarily caused by animal manure, fertilizer runoff, storm water runoff, sewage treatment plant discharges, and power plant emissions (Glibert et al. 2007). There are few residents and no drain outlet around the reservoir, and the rainfall is relatively low during this period, so runoff and human activities contributed few pollutants. So, the three inlets are the main pollution source. Theoretically, the increasing temperature is supposed to accelerate chemical and biochemical reactions and reduce the amount of TN. According to the preceding text, TN concentrations increased with the increase in sampling depth, and the change in TN was attributed to NO3-N. Nitrification can transform ammonia and organic nitrogen into NO3-N. Figure 4 shows that the concentration of ammonia nitrogen (NH3-N) and organic nitrogen were low, so the nitration of NH3-N and organic nitrogen would not have a significant effect on the amount of NO3-N. When the temperature of the environment increased, the surface water became warmer, but the temperature of the intermediate water stayed stable, which caused thermal stratification. This resulted in NO3-N moving upwards. Therefore, we conclude that the increase in TN during this period was caused by the spatial variation of the original nutrients in the reservoirs, and this spatial variation resulted from the rise in temperature.

Change rule of CODMn and TN during the rainy period

The temperature remained stable during the rainy period, and the range of water temperature was from 15 °C to 22 °C (Table 5). Precipitation mainly happened during this period, and the precipitation in 2013 was much greater than that in 2012 during the same period, so the inflows increased because of rainfall (Figure 5). The change rule for CODMn at the Mopanshan Reservoir was related to inflow during the flood season according to Figure 6: the concentration of CODMn gradually increased during the rainy season (from 3.23 to 5.76 mg·L−1 in 2012 and from 3.18 to 6.52 mg·L−1 in 2013) and reached a maximum in September. The growth rate was positively correlated with rainfall capacity. The peak of CODMn reached 6.52 mg·L−1 in 2013 (a high flow year) and 5.76 mg·L−1 in 2012 (a low flow year), so that there was a 0.86 mg·L−1 difference. The above two points illustrate that it was precipitation that caused the content of CODMn to increase. In northeast China, the soil has a high humus content, which enables the rain to flow into the reservoir by surface runoff, causing a higher CODMn content. Hence, the effect of rainfall on CODMn can be viewed from two aspects: (1) organic pollutants floating in the air flow into the reservoir with rainfall and as surface runoff directly; (2) the pollutants in the soil can flow into the reservoir through surface water runoff. Taken together, rapid inputs of organic pollutants resulting from rainfall are the main reason for the increase in CODMn during this period. The change rule for TN exhibited the opposite tendency, and it seems that the content of TN didn't rise with the increase in rainfall, which can be seen in Figure 6. During this period, water temperature remained stable, so there was no thermal stratification, and an upward tendency of NO3-N was not obvious. And in theory, the reaction rates of biochemical reactions are at a relatively high level, which can deplete TN in the reservoir. Therefore, rainfall has a smaller affect on TN, and it was temperature that caused the changes in TN concentration.
Figure 6

CODMn and TN concentration changes in 2012 (dry year) and 2013 (wet year) at site 1 (sampling depth: 7 m).

Figure 6

CODMn and TN concentration changes in 2012 (dry year) and 2013 (wet year) at site 1 (sampling depth: 7 m).

CODMn is higher during this period, and rainfall is the main reason for the increase in CODMn. Rainfall is a natural phenomenon, therefore there are no effective methods to solve the problems of exceeding the CODMn limit. We summarized earlier that NO3-N will move upwards when thermal stratification occurs. TN in the bottom of the reservoir occurs in high concentrations during the rainy period. Thus, to mitigate pollution in the Mopanshan Reservoir, releasing bottom waters during this period is a possibility.

Change rule of CODMn and TN during winter period

The temperature started to decrease in the winter, and the water temperature gradually reduced to 11 °C and ultimately fell to below 1 °C (Table 5). The reservoir starts turning into ice in the middle to end of November each year. Figure 5 shows that the rainfall was relatively low during this stage, so the inflows decreased, which reduced the water table in the reservoir. Contaminants that are caused by rainfall reduced due to the decrease in rainfall. As a result, the concentration of CODMn decreased in October. But the reaction rates of the chemical reactions and biochemical reactions were at a relatively low level during this period, and vegetation decline made the concentration of CODMn and TN stay stable, maintaining a relatively higher content during this period.

CONCLUSIONS

This paper analyzes the spatial distribution and seasonal variability in the Mopanshan Reservoir. We can draw the following conclusions:

  • (1) Spatially, both CODMn and TN showed few differences among the five sampling stations in the horizontal direction. And the CODMn wasn't significantly correlated with sampling depth, but the TN concentrations increased with the increase in water depth in the vertical direction.

  • (2) Seasonally, both CODMn and TN concentration exhibited obvious seasonal variability and can be broken down into four major different typical water quality periods, including the icebound period, spring period, rainy period and the winter stable period. The CODMn concentrations were at a minimum in June and reached a maximum in September. And TN reached a maximum in June and was at a minimum in November or December.

  • (3) The change in TN is mainly caused by NO3-N, which results from the water temperature, and we can release bottom waters during the rainy period to mitigate pollution in Mopanshan Reservoir. And the change in CODMn concentrations is caused by surface runoff. The Pearson correlation coefficient further confirmed this argument.

ACKNOWLEDGEMENTS

The present research was supported by project Study on the Reasons and Controlling Countermeasures of Water Pollution during the Icebound Season around the Songhua River in Harbin (TaskId: 2013ZX07201007-002-02-03). It is part of the National Key S&T Special Projects of China.

REFERENCES

REFERENCES
Bai
X.
Liu
Z.
Li
Y.
2013
Water quality analysis and prevention measures of Mopanshan Reservoir
.
Environmental Science and Management
38
,
62
64
.
Chen
J.
Ding
W.
Jiao
F.
2005
Analyse of high content of total-nitrogen in Danjiangkou Reservoir
.
Environmental Monitoring in China
21
,
54
57
.
China MOEP
1989
Water quality – determination of permanganate index
. In:
CRAES
(ed.),
GB 11892-89,
pp.
184
187
.
China MOEP
2002
Environmental quality standards for surface water
. In:
CRAES
(ed.),
GB3838-2002,
pp.
207
-
218
.
China MOEP
2007
Standard examination methods for drinking water
. In:
CRAES
(ed.),
GB/T 5750-2006,
pp.
80
92
.
China MOEP
2010
Water quality – determination of ammonia nitrogen-Nessler's reagent spectrophotometry
. In:
CRAES
(ed.),
HJ 535-2009, 42 pp.
China MOEP
2012
Water quality – determination of total nitrogen-Alkaline potassium persulfate digestion UV spectrophotometric method
. In:
CRAES
(ed.),
HJ 636-2012.
Cui
C.
Han
X.
Xu
T.
2011
The influence of humic acid on drinking water supply safety of water resource reservoirs in cold regions
.
China Water & Wastewater
37
,
119
123
.
Dai
S.
1987
Environmental Chemistry, Vol. 2
.
Higher Education Press
,
Beijing, China
.
Glibert
P. M.
Wazniak
C. E.
Hall
M. R.
Sturgis
B.
2007
Seasonal and interannual trends in nitrogen and brown tide in Maryland's coastal bays
.
Ecological Applications
5
(
supplement
),
S79
S87
.
He
X.
Liu
D.
Lu
Z.
2011
Water quality evaluation of large-and medium-sized reservoirs in Zhejiang Province
.
Water Resources Development Research
3
,
40
42
.
Hou
W.
Sun
S.
Wang
M.
Li
X.
Zhang
N.
Xin
X.
Sun
L.
Li
W.
Jia
R.
2015
Assessing water quality of five typical reservoirs in lower reaches of Yellow River, China: using a water quality index method
.
Ecological Indicators
3
,
309
316
.
Palma
P.
Ledo
L.
Soares
S.
Barbosa
I. R.
Alvarenga
P.
2014
Spatial and temporal variability of the water and sediments quality in the Alqueva reservoir (Guadiana Basin; southern Portugal)
.
Science of the Total Environment
470–471
,
780
790
.
Wang
X
.
2008
Numerical Simulation Research on Water Temperature Distribution in Mopanshan Reservoir
.
Harbin Institute of Technology
,
Harbin, China
.
Zhang
S.
Li
C.
Zheng
J.
2009
Seasonal variation of trophic states in backwater areas of tributaries in Three Gorges Reservoir
.
Environmental Science
30
,
64
69
.