Science of the Total Environment 485–486 (2014) 1–11

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Science of the Total Environment journal homepage: www.elsevier.com/locate/scitotenv

Satellite data regarding the eutrophication response to human activities in the plateau lake Dianchi in China from 1974 to 2009 Changchun Huang a, Xiaolei Wang b, Hao Yang a,⁎, Yunmei Li a, Yanhua Wang a, Xia Chen a, Liangjiang Xu a a b

Key Laboratory of Virtual Geographic Environment (Nanjing Normal University), Ministry of Education, Nanjing 210046, China School of Biochemical and Environmental Engineering, Nanjing Xiao Zhuang University, Nanjing 210046, China

H I G H L I G H T S

G R A P H I C A L

• Reconstruct the time serials trophic status in Dianchi Lake using satellite data. • Reconstruct the LUCC in Dianchi basin from 1974 to 2009 by Landsat image. • Explore the response relationship between trophic status and human activities.

Distribution of agricultural planting at different altitudes and the nutrients from human activities discharged into the Dianchi Lake via the rivers around the lake. Panel A, low-intensity agriculture (corn planting); panel B, high-intensity agriculture (plastic greenhouse vegetable cultivation); panel C, a river inflowing into lake; and panel D, Dianchi Lake during an algal bloom.

a r t i c l e

a b s t r a c t

i n f o

Article history: Received 25 November 2013 Received in revised form 28 February 2014 Accepted 9 March 2014 Available online xxxx Editor: Simon Pollard Keywords: Eutrophication Human activity Dianchi Lake Landsat image MODIS image

Human activities contribute highly to water eutrophication. In this study, the relationship between human activities and water eutrophication in Dianchi Lake in China was characterized using a combination of satellite imaging, sedimentary physicochemical and meteorological data analyses. The heavy eutrophic status and algal bloom in Dianchi Lake were first observed by satellite in 1977 and 1989, respectively. The C/N ratio, an indicator of organic sources in sediments, also showed that the planktonic organic source in the sediment significantly increased beginning in 1989. The land use cover in the Dianchi basin showed that both farm lands and forests, but particularly farmlands, were reduced during the period from 1974 to 2009. The urbanized land area increased from 1974 to 2009, particularly after 2000. The effects of human activities on water eutrophication were expressed by land use cover, population, gross domestic product (GDP; separated into primary, secondary and tertiary industries) and wastewater discharge. For land use cover, farm and urbanized lands were the main sources of water nutrients; forest contributed slightly to these nutrients. For GDP, primary (correlation coefficient = 0.94, P b 0.001) and tertiary (correlation coefficient = 0.95, P b 0.001) industries were highly correlated with total nitrogen. Secondary (correlation coefficient = 0.95, P b 0.001) and tertiary (correlation coefficient = 0.96, P b 0.001) industries were highly correlated with total phosphorus. The algal bloom area was significantly correlated with wastewater discharge (correlation coefficient = 0.78, P b 0.005) (except

⁎ Corresponding author. Tel.: +86 25 85891740, +86 13770824727 (mobile). E-mail address: [email protected] (H. Yang).

http://dx.doi.org/10.1016/j.scitotenv.2014.03.031 0048-9697/© 2014 Elsevier B.V. All rights reserved.

A B S T R A C T

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industrial wastewater), which was primarily led by the non-agricultural population, from 2000 to 2009. This study suggests that the protection of water environments requires a comprehensive protection policy in addition to a unilateral protection policy. © 2014 Elsevier B.V. All rights reserved.

1. Introduction Water eutrophication is a critical issue for the global environment; this phenomenon has been comprehensively studied by many researchers since the 1960s (Hutchinson, 1969, 1973; Whiteside, 1983; Jeppesen et al., 1999; Qin et al., 2006, 2007, 2009; Schindler et al., 2008; Conley et al., 2009; Raicevic et al., 2013). In eutrophication, nutrients (such as nitrates and phosphates) are over enriched, leading to excessive phytoplankton growth, harmful algal blooms and the formation of hypoxic environment formation. These conditions significantly affect social and economic development, local animal life and human health (Paerl et al., 2004; Carstensen et al., 2007; Selman and Greenhalgh, 2009; Franzén et al., 2011; Sianipar et al., 2013). Diverse and complex factors stimulate eutrophication. For example, human activities and economic development have been considered two of the major factors causing nutrient pollution. These human activities include industrial and residential sewerage, fertilizer use on agricultural land (which leads to increased nutrient loss to aquatic ecosystems) and land use change (which diminishes the capacity of ecosystems to degrade nutrients) (Mee, 2006; Diaz and Rosenberg, 2008; Sylvan et al., 2007; Howarth, 2008; Selman and Greenhalgh, 2009). Economic development noted as gross domestic product (GDP), which includes primary, secondary, and tertiary industry, also has a significant effect on nutrient pollution (Duan et al., 2009). Land use and land cover change (LUCC) is an indicator of human activities that modify the earth's landscape and societal development. The characteristics of LUCC manifest in biogeochemical cycles, spatial structure and the types of composition of land cover (Geri et al., 2011). LUCC is one of the important factors causing biodiversity loss, climate change, species invasion and environmental pollution (Foley et al., 2005; Piao et al., 2007; Pielke, 2005). The land transferred from primary forests to farm and urbanized lands provides essential livelihoods for humans. With increasing populations, more critical natural resources and ecosystem services are needed. However, intensive cultivation and overdevelopment destroy the ecological balance, reduce soil fertility, induce widespread soil erosion and release high levels of nutrients and sediments to aquatic ecosystems (Rabalais et al., 2009). Remote sensing is an essential tool used to assess eutrophication and monitor LUCC because it facilitates observations across larger regions and at higher frequencies than ground-based observations (Wezernak et al., 1976; Matthews et al., 2012; Olofsson et al., 2013; Xian et al., 2009; Cardille and Foley, 2003). Chlorophyll-a and Secchi disk depth, as assessment indices of eutrophication, have been successfully estimated by remote imaging and used to evaluate the trophic state (Lillesand et al., 1983; Sass et al., 2007; Giardino et al., 2001). Observation and detection of LUCC from remote sensing image data have advantages in monitoring and analyzing land use changes quickly and efficiently (Chen, 2000; Yu et al., 2011; Ruellanda et al., 2011). Historical satellite imaging data can provide information as early as 1972 (the Landsat satellite). Thus, these data can be used to extract a long time series of eutrophication and LUCC information as well as provide spatial distribution information. Several studies have been conducted regarding water eutrophication and algal bloom in Taihu Lake (Wang and Shi, 2008; Guo, 2008; Duan et al., 2009) because this phenomenon is important for the Yangtze River Delta economic area. Dianchi Lake is located in southwestern China, where the economy is smaller than that of Yangtze River Delta economic area. For this reason, studies have rarely been conducted regarding water eutrophication and human activities over a long time-scale and the whole basin spatial scale for Dianchi Lake. However,

Dianchi Lake is the cradle of Dian (or Yunnan) culture and one of the most important economic areas in southwestern China. The present study aimed to investigate the possible relationship between eutrophication and human activities by reconstructing a eutrophic status and LUCC time series in the Dianchi basin from 1974 to 2009, using Landsat and MODIS image data.

2. Material and methods 2.1. Study area Dianchi Lake is a plateau lake located between 24° 01′ N and 24° 40′ N and 102° 36′ E and 102° 47′ E (water surface elevation = 1886 m) (Fig. 1). The lake covers an area of 330 km2 with a mean depth of 5 m. Dianchi Lake has also been known as the “Pearl of the Highland” because of its picturesque scenery. However, the lake has been seriously contaminated with various pollutants, and approximately 90% of Kunming's wastewater has been poured into the lake. Currently, eutrophication is one of the major problems in the lake. As such, water in the lake has been rated Grade V (the worst grade), although billions of US dollars have been spent to clean this lake.

2.2. Data sources and description The data set for this study included four aspects: sediment data (total phosphorus: TP, total nitrogen: TN, 210Pb and C/N); water data (remote sensing reflectance); satellite image (Landsat and MODIS data); and weather and statistical data from the National Bureau of Statistics. In situ measurement of remote sensing reflectance and water quality was conducted in September and December 2009 in Dianchi Lake. An in situ investigation of land use cover was conducted in August 2011. Water, sediment sampling sites and land use cover points are shown in Fig. 1. A gravity-type columnar sediment sampler was used to collect the four columnar sediments with 40 cm depth. The sediment samples were frozen at −50 °C for 48 h, cut into slices 1 cm thick and dried in a lyophilizer. Dry sediment samples were used to measure the TP, TN, TOC, 210Pb and 137Cs. The concentrations of TN and TP were measured with a UV-3600 spectrophotometer (Shimadzu Corp., Japan), and the concentration of TOC was measured with a TOC analyzer (Shimadzu Corp., Japan). The 210Pb and 137Cs isotopes were measured with a Gamma Spectrometer (ORTEC., USA). The sediment cores were dated using 210Pb isotope radiometry (Mizugakia et al., 2006; O'Reilly et al., 2011). The detailed dating method was based on Mizugakia et al. (2006). The estimated time for the 210Pb isotope radiometric technique was calibrated with 137Cs, which has significant peaks in the years 1963 and 1986 (Wang, 2011). The sampling site near Kunming city and the distribution of 210Pb fit well with an exponential function, so it was chosen for sampling for responses to human activities in the Dianchi Lake basin. This sediment sampling site is noted as SD1 in Fig. 1. The geochronology of this sediment core is from 1892 to 2006. The water depth of the sediment sampling site was approximately 7.4 m. The dynamic ratio [(square root of area)/depth] (Bachmann et al., 2000) of Dianchi Lake is 3.6, and the mean wind speed is 2.1 m/s. According to hydrodynamic analysis, the sediment resuspended in Dianchi Lake was insignificant; thus, the influence of the sediment resuspended to use the 210Pb radiometric technique can be neglected. Additionally, the diagenesis after sedimentation can be neglected for a short-time scale (50 years).

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Fig. 1. Study area and sampling sites.

Remote sensing reflectance was derived using a portable analytical spectroradiometer according to methods described in a previous study (Huang et al., 2011). The sampling sites were noted in Fig. 1. Landsat satellite image data were downloaded from the U.S. Geological Survey (http://earthexplorer.usgs.gov/). The Landsat image data from 1974 to 1988, from 1989 to 1996 and from 2000 to 2009 comprised MSS, TM and ETM data, respectively. All of the data were downloaded when satisfying the selection criteria that the percentage of cloud must be under 10%, and a total of 58 images were obtained. These data were narrowed to one image per year, finally yielding 17 images from which to extract land use cover. These data were then used to derive the eutrophic status of Dianchi Lake by a eutrophic index. The MODIS data were downloaded from the National Aeronautics and Space Administration (http://modis.gsfc.nasa.gov/) and used to extract the algal bloom area according to a previously described method (Hu, 2009). A total of 655 MODIS data from 2000 to 2009 were obtained.

The weather and statistical data for Kunming city were used to represent weather and statistical data in the entire Dianchi Lake basin because the jurisdictions of Kunming city cover almost the whole Dianchi Lake basin. The weather data (precipitation) from 1970 to 2009 were downloaded from China Meteorological Data Sharing Service System (http://cdc.cma.gov.cn/). Statistical data (GDP, environmental data and agricultural data) from 1985 to 2009 were downloaded from the National Bureau of Statistics (http://data.cnki.net/). 2.3. Eutrophication indicators and land use classification 2.3.1. Eutrophication indicators The fluorescence peak at approximately 700 nm induced by chlorophyll-a and the high reflectance caused by phytoplankton's scattering property in short-wave infrared (SWIR) wavelengths were easily detected in the SWIR wavelengths due to the strong absorption of

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highly turbid inland water. Fluorescence line height (FLH) is a valuable index for evaluating chlorophyll-a and can be used to monitor the eutrophication (Hu et al., 2005). The high reflectance caused by phytoplankton in the SWIR corresponds to the floating algae index (FAI) and is calculated using an equation similar to that of FLH (Hu, 2009) for MODIS and Landsat-TM (and ETM); FAI is a very useful index for identifying floating algae. Although, FAI was initially developed for MODIS image, Hu (2009) observed that the FAI of Landsat-TM or Landsat-ETM images can reveal extensive thin floating algae, which were not detected in the MODIS FAI images (Hu, 2009). Landsat-TM and Landsat-ETM with high spatial resolution, in particular, are used to detect algae in inland lakes. However, the bands of Landsat-MSS were found to be beyond the band range of FAI. Consequently, an analogous index based on FAI was developed to indicate the eutrophic status of the Landsat image data [Eq. (1)]. A uniform index for Landsat imaging data (MSS, TM and ETM), FLH-S, was used to mark the eutrophication. Hu (2009, 2005, 2010) indicated that FLH-S for Landsat-TM and Landsat-ETM corresponds to FAI for MODIS.   λ −λw1 FLH‐S ¼ Lw2 − Lw1 þ ðLw3 −Lw1 Þ w2 λw3 −λw1

ð1Þ

where w1 = 650 nm, w2 = 750 nm, w3 = 950 nm for MSS; w1 = 660 nm, w2 = 830 nm and w3 = 1650 nm for TM and ETM; L is the output radiance from the ENVI Quick Atmospheric Correction Module. Compared with the MODIS image, Fig. 2 shows the calculated FLH-S and the original composograph of TM in 1994. FLH-S can provide phytoplankton information and remove the cloud effect on the extracted phytoplankton information after a quick atmospheric correction (red box in Fig. 2). Different eutrophic conditions (Fig. 2, points A, B, C, D) showed significant variations in radiance peak, particularly at 830 nm (Fig. 3A). This peak (expressed as FLH-S) exhibited good uniformity with the remote sensing reflectance measured in situ (Fig. 3B). This peak also indicated the advantages of the eutrophic status indicator.

A

However, the FLH-S of Landsat-MSS differed from FAI. The FLH-S of Landsat-MSS should be validated for further use as a eutrophic indicator. In situ remote sensing reflectance was used to simulate the FLH-S of Landsat-MSS and validate the relationship between the FLH-S of Landsat-MSS and the concentration of chlorophyll-a (Cchl-a) (Fig. 4A). The obtained coefficient of the linear relationship between the FLH-S and Cchl-a can reach a maximum of 0.63 (total sampling number = 34). The correlation coefficient between the remote sensing reflectance and Cchl-a (Fig. 4B) showed a high correlation coefficient ranging from 709 nm to 834 nm. This finding indicated that the effect of Cchl-a on remote sensing reflectance at 750 nm was very similar to that at 830 nm. The FLH-S of Landsat-MSS may be considered an appropriate index to quantitatively measure eutrophication, although this index may not be considered a good indicator of Cchl-a.

2.3.2. Land use classification Landsat image data obtained from 1974, 1977, 1989 and 1992 and from 2000 to 2013 were utilized to extract land use information with a support vector machine classification algorithm in the ENVI 5.0 Feature Extraction Module. Before classification, Landsat images were subjected to geometric correction and atmospheric correction. The kappa coefficient was used to assess classification accuracy (Liu et al., 2007). The maximum kappa coefficient was observed in 2006 (kappa coefficient = 0.81), and the minimum was observed in 1977 (kappa coefficient = 0.67). We also conducted an in situ investigation in 2011 to validate the classification result. The total range of investigation points (including the 948 points in Fig. 1) covers the land use types shown in Fig. 5. The proportions of water, forest, urbanized land, farm land and bare land were approximately 3.27%, 62.55%, 2.43%, 30.17% and 1.58%, respectively. The accuracy of the classification for each land use type is listed in Table 1. Low accuracy of classification was found along the borders of forest and farm lands because the spectral reflectance of the forest was very similar to that of the farm land. However, urban

B

Fig. 2. A: Calculated FLH-S, B: original RGB composograph. The white areas in the red boxes in B are clouds, which have almost no effect on the FLH-S calculation after quick atmospheric correction, whether in low phytoplankton or high phytoplankton conditions. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

C. Huang et al. / Science of the Total Environment 485–486 (2014) 1–11

B A B C D

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.08 Chl_a:6.35µg/L, TSM:138.67mg/L, OSM:16.04mg/L Chl_a:16.99µg/L, TSM:6.45mg/L, OSM:3.75mg/L Chl_a:27.04µg/L, TSM:37.05mg/L, OSM:28.65mg/L Chl_a:122.84µg/L, TSM:55.24mg/L, OSM:47.31mg/L Chl_a:222.84µg/L, TSM:96.41mg/L, OSM:63.57mg/L Chl_a:942.6µg/L, TSM:213.8mg/L, OSM:198.8mg/L

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Fig. 3. Comparison of radiance shape from the TM data (1994) in Fig. 2 and remote sensing reflectance shape measured in situ. A: Radiance shape of different eutrophic status for points A, B, C and D in Fig. 2, and B: remote sensing reflectance for different chlorophyll-a concentrations and suspended particles.

landscaping occasionally is misclassified as forest or farm land, such as the two black circles in the classification result in Fig. 5.

3. Results

from the late 1980s to the early 1990s: phase I, in which Dianchi Lake suffered relatively low eutrophication from 1974 to 1988; phase II, in which Dianchi Lake experienced high eutrophication from 1989 to 1996; and phase III, in which Dianchi lake exhibited extreme eutrophication after 1997.

3.1. Spatial and temporal distributions of the eutrophic status 3.2. Characteristics of LUCC in the Dianchi basin The blue arrow shows the land reclaimed from the lake in the Great Leap Forward Period (compared with the land use cover in 1974 shown in Fig. 6). This area was recovered from the lake until 1989. The eutrophic status in the early 1970s was very low except in the area indicated by the blue arrow (Fig. 6). However, the eutrophic status significantly improved in the 1980s. Algal blooms were first observed in Caohai and Waihai in 1989 (the red arrow in 1989 in Fig. 6). In situ investigation has further revealed that an algal bloom covered an area of 2 km2 and a depth of 2 mm from 1988 to 1990 (Wang and Dou, 1998). Lake eutrophication was very serious in the 1990s, and a large area of algal bloom was observed in 1994. With exacerbation of the eutrophic conditions, a worse result is expected. The occurrence of an algal bloom covering a large area was a frequent phenomenon from 2000 to 2004. The Chinese government then focused on environmental protection, despite the higher cost; thus, algal blooms were effectively controlled from 2005 to 2009. Eutrophication initially occurred in some parts of Caohai in 1977; however, the whole area of Caohai and some parts of Waihai experienced intense eutrophication in the 1980s. The earliest algal blooms were also observed in Caohai and Waihai in the 1980s. Algal blooms spread to the middle of Dianchi Lake in the 1990s, indicating that eutrophication in Dianchi was not limited to the north of Dianchi Lake after the 1990s. Thus, eutrophication in Dianchi spatially occurred from north to south and can be temporally separated into two phases

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The land-use cover data extracted from Landsat with ENVI 5.0 are shown in Fig. 7. LUCC is primarily caused by human activity. Farm lands are mainly distributed around Dianchi Lake in the 1970s and 1980s. With urbanization and economic development, these farm lands have been gradually transformed into urbanized lands. The low gradient of sparse woodland has been cultivated. Spatially, the farm lands were moved from the lake plain to the intermontane peaks and piedmont zones. The urbanized lands expanded from the center of the lake plain to the southern lakeside and northern flat grounds. The forest land was mainly distributed in the mountainous area. Fig. 8 shows that the increase in covered area of the urbanized land was unremarkable before 2000 (from 2.47% to 5.80) with the growth rate of 3.48 km2. However, with rapid economic development and urbanization, a significant increase in construction land was observed from 2000 to 2013 (from 8.32% to 32.08%), with a growth rate of 55.15 km2. Farm land declined spatially from 1974 to 2013 (from 44.52% to 32.08%), with a loss rate of 16.98 km2, when people recognized the importance of urbanization and economic development. Forest land area was significantly increased compared with the area before 2001 (from 40.38% to 50.64%), which may be attributed to the “Return the Grain Plots to Forestry” project. The water area changed slightly relative to farm, construction and forest lands.

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Dianchi Lake

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Fig. 4. A: Relationship between simulated FLH-S for Landsat-MSS and Cchl-a, B: Correlation coefficient between FLH-S of Landsat-MSS and Cchl-a.

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F B C D Fig. 5. Classification results of the Landsat-ETM in March 2011. Points (A, B, C, D, E and F) are some examples of the confirmation points of the in situ investigation.

3.3. Distribution of TP, TN and C/N in the sediment Sediment deposits generally indicate environmental and climatic changes. Fig. 9 shows the changes in TP, TN and C/N from 1970 to 2006. TN declined with time from 1974 to 1988 but increased steadily from 1988 to 2006. TP showed an increasing trend before 2001 but significantly decreased after 2002. C/N showed an increasing trend before 1988 and a decreasing trend after 1989, including three peak values in 1989, 1992 and 2001, respectively (marked as a, b and c in Fig. 9). Another minor peak value was obtained in 1987 (marked as d in Fig. 9). 4. Discussion 4.1. C/N ratio indicates eutrophic status The C/N ratio is a useful indicator for identifying the organic sources of sediments and has been widely used (Krishnamurthy et al., 1980; Thornton and McManus, 1994; Lund-Hansen et al., 2004; Yu et al., 2010; Meisel and Struck, 2011). Organic nitrogen is present in proteins and nucleic acids of plants. Thus, the organic nitrogen content in higher plants is less than that in lower plants (such as algae). This result is attributed to lignin and cellulose, which contain low organic nitrogen, being the major components of higher plants (Giresse et al., 1994). A previous study indicated that the C/N ratio of higher plants can reach ≥ 30 (Müller and Mathesius, 1999). However, the C/N ratio of lower Table 1 Validation of the classification result of Landsat-ETM in March 2011 by in situ investigation. The overall accuracy is the number of the correct classification points divided by the total number of investigation points. The “misclassified as” column indicates that the type of land use was misclassified to other land use types. Type

Overall accuracy

Misclassified as

Water Forest Urbanized land Farm land Bare land

100% 86% 95% 82% 100%

– Farm land (13%), urbanized land (1%) Forest (5%) Forest (18%) –

plants can only reach ≤ 10 (Tyson, 1995; Kendall et al., 2001). Fig. 7 shows that the C/N ratio in this study ranged between 9 and 15. This result indicated that the organic sediments in Dianchi Lake are produced by planktonic and terrestrial organic matter. These conditions can be divided into two parts. First, the effect of terrestrial organic matter on organic sediments was significantly increased from 1970 to 1988 under agricultural cultivation around the Dianchi Lake. Second, a large area of algae breeding caused by water eutrophication increased the effect of planktonic organic matter on organic sediments from 1989 to 2006. Based on the Landsat image recorded when the first algal bloom occurred in 1989, the C/N ratios can be used as indicators to identify the sources of organic sediments and monitor the eutrophic status of lakes. However, a low C/N ratio may be attributable not to a planktonic organic source but to a relatively low input of terrestrial organic matter. Thus, the C/N ratio may be affected by climatic factors, such as precipitation, which considerably influences the input of terrestrial organic matter. The arrows marked as a, b, c and d in Fig. 9 did not show gradual variation. Point c (2001) may be explained by the large area of the algal bloom, but points a, b and d cannot be explained by an algal bloom from the Landsat images recorded in 1987, 1989 and 1992. The monthly precipitation data showed that low precipitation corresponds to lower C/N ratios. Points a, b and d in Fig. 10 correspond to low precipitation in 1987, 1989 and 1992. These points correspond to points a, b and d in Fig. 9. Point c in Fig. 10 showed that drought occurred in 2001, indicating that the low C/N ratio in 2001 may be affected by planktonic organic matter (algae) and precipitation. 4.2. Effect of human activity on water eutrophication 4.2.1. Land use and land cover changes The most significant land use changes in the Dianchi basin occurred during the past 35 years. Land use changes may be necessary and indispensable for economic development and urbanization. However, such changes usually entail a high cost to societal development. For example, water environmental pollution may occur as a consequence of economic development and urbanization. With rapid urbanization, TP may significantly increase with increasing in the area of urbanized land. The

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Fig. 6. Time series of eutrophic status from 1974 to 2009 in Dianchi Lake; the time data format is yyyymmdd. (For interpretation of the references to color in this figure, the reader is referred to the web version of this article.)

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Fig. 7. Land use and land cover change from 1974 to 2009 in the Dianchi basin. The satellite data are the Landsat data; the classification Kappa coefficient is 0.83.

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water forest farm land construction land bare land

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Year Fig. 8. Changes in land use cover areas from 1974 to 2013 in the Dianchi basin.

correlation coefficient between TP and construction land area can reach 0.74 (Fig. 10A). This increase in TP may be due to domestic sewage discharge. The effect of declining farmland area significantly decreased food production. Thus, large amounts of chemical fertilizers are used in farm lands to maintain the growth of agricultural yield, thereby rapidly increasing TN in water. The correlation coefficient between TN and chemical fertilizer application can reach 0.78 (Fig. 10B). Water eutrophication has been attributed to nutrient accumulation, mainly as TP and TN. TP and TN increased from 0.088 mg/L to 0.33 mg/L and from 0.19 mg/L to 2.55 mg/L, respectively, in 1982 to 2002.

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4.2.2. Economic development and urbanization Previous studies on eutrophication indicated that human pollution and economic development contribute to water eutrophication (Duan et al., 2009). In the present study, GDP (including primary, secondary and tertiary industries) was used to estimate the contribution of human activity to water eutrophication. Fig. 12 shows the high correlation of TP and TN with the primary, secondary and tertiary industrial GDP, indicating that economic development highly contributes to eutrophication. Fig. 12A shows the comparison of TP to GDP. TP correlates more highly with secondary and tertiary industries than with primary industry. The correlation coefficient of TP and the secondary and tertiary industries can reach 0.95 and 0.96, respectively; with the primary industry, the correlation coefficient is 0.90. This observation may indicate that industrial and residential sewage are the primary sources of TP. Fig. 12B shows the comparison of TN with GDP. TN exhibited a higher correlation with primary and tertiary industries than with secondary industry. The correlation coefficient of TN with the primary and tertiary industries can reach 0.94 and 0.95, respectively. For the secondary industry, the correlation coefficient of TN is 0.87. This finding may indicate that agricultural and residential sewage are the primary sources of TN. The contribution of GDP to TP and TN was estimated by multiple stepwise regressions. The contributions of the primary, secondary and tertiary industries to TP were 12.77%, 58.74% and 28.49%, respectively. For TN, these industries contributed 62.06%, 9.40% and 28.54%, respectively. The number of people living in the watershed, which determines economic and resource demands, is an important factor influencing water eutrophication. Fig. 13 shows the relationship between wastewater and population. The total wastewater exhibited a low correlation

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Fig. 11. Comparison of the time series data for TN, TP, urbanized land and chemical fertilizer application. A: Correlation coefficient between TP and construction land is 0.74; and B: Correlation coefficient between TN and chemical fertilizer application is 0.78. TP, TN and chemical fertilizer application data were obtained from “Yunnan Wetlands” edited by Yang Lan and Li Heng (Yang and Li, 2010).

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GDP (billion)

Fig. 12. Correlation between TP, TN and GDP from 1982 to 2002 (A: TP = 0.046 ∗ GDP + 0.33, R2 = 0.87 for primary industry; TP = 0.008 ∗ GDP + 0.073, R2 = 0.92 for secondary industry; TP = 0.009 ∗ GDP + 0.045, R2 = 0.91 for tertiary industry; B: TN = 0.334 ∗ GDP + 0.371, R2 = 0.88 for primary industry; TN = 0.058 ∗ GDP + 0.603, R2 = 0.84 for secondary industry; TN = 0.065 ∗ GDP + 0.430, R2 = 0.89 for tertiary industry).

with population (Fig. 13A). By contrast, wastewater (non-industrial wastewater) showed a high correlation with population, particularly with non-agricultural population (R = 0.95; Fig. 13B). This finding indicated that an increase in non-agricultural population (also called urbanization) causes large discharges of non-industrial wastewater. This nonindustrial wastewater is the main pollution source after 2000 [as discussed in section 4.2.3, Human control], and an increasing trend

4.2.3. Human control Two policies in China, “Inhibited and limited use of phosphate detergent” and “Application of sewage treatment plant”, may be important in environmental protection. The government has forbidden the discharge

B 450 total population agricultural population nonagricultural population

400

Wastewater (million ton)

Total wastewater (million ton)

A 450

has been observed. A rapid increase in urbanized land area after 2000 also supports this view.

350 300 250 200 1

2

3

4

5

population (million)

6

7

total population agricultural population nonagricultural population

400 350 300 250 200 150 100 50

1

2

3

4

5

6

7

population (million)

Fig. 13. Correlation between total wastewater (TW), wastewater (excluding industrial wastewater) (W) and population (P) from 1990 to 2010 (A: TW = 38.37 ∗ P + 41.2, R2 = 0.53 for the total population; TW = 60.09 ∗ TP + 53.7, R 2 = 0.46 for the agricultural population; TW = 97.32 ∗ P + 37.7, R2 = 0.60 for the nonagricultural population; B: W = 72.90 ∗ P − 212.9, R2 = 0.4 for the total population; W = 118.6 ∗ P − 198, R2 = 0.76 for the agricultural population; W = 181.6 ∗ P − 207.7, R2 = 0.89 for the nonagricultural population).

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of untreated industrial sewage into water since 1990. Numerous industrial sewage treatment plants have been built, and the discharge of industrial wastewater has been significantly reduced, although this figure increased slightly in 1999 (Fig. 14A). Implementation of the “inhibited and limited use of phosphate detergent” policy in 1999 may have significantly decreased the TP in 2000 (Fig. 11A, the arrow points to the peak value). However, good results can not be easily achieved with a unilateral policy concerning environmental protection. For example, although industrial wastewater was regulated, the total wastewater increased from 1991 to 2010 (Fig. 14A). This may indicate that the agricultural and residential sewage were ineffectively controlled. Fig. 14B shows the relationship between the application of chemical fertilizer and agricultural production from 1995 to 2008. As agricultural production and time increased, chemical fertilizer application also increased. This may release large amounts of nutrients into water via surface and ground water runoff. Fig. 14C shows the time series data for the aerial coverage of algal blooms and wastewater discharge from 2000 to 2009. The total wastewater (irrespective of industrial wastewater) increased with time. The average annual aerial coverage of the algal bloom was highly correlated with wastewater discharge (correlation coefficient = 0.78). This finding indicated that agriculture and residential areas were the main sources of wastewater discharged into Dianchi Lake. This wastewater was also the main contributor to water eutrophication. Water environmental protection should be covered in a comprehensive policy in

5. Conclusions A better understanding of the human driving forces that influence water eutrophication can provide more accurate guidance for controlling lake eutrophication. The temporal and spatial distributions of eutrophication from 1974 to 2009 showed that this phenomenon began in the mid of 1970s and that algal blooms began in the late 1980s, although the missing Landsat data may underestimate these periods. The LUCC time series indicated that the farm land around Dianchi Lake was replaced with urbanized land. The area of farm and forest lands decreased from 1048 km2 to 386 km2 and from 931 km2 to 805 km2, respectively. The increase in urbanized land was very fast, particularly after 2000. The contribution of human activities to eutrophication was drastic. Farm land was the main source of nutrients, particularly TN, during the period from 1974 to 2009 according to the data in this study. Urbanized land was another major source of nutrients during the period from 1990 to 2009 in this study. The contribution of forest land to water eutrophication was relatively small. After 2000, agriculture and residential areas were the main sources of wastewater resulting in algal blooms. Human control policies should be implemented for environmental protection; however, comprehensive policies may be more effective than

B

350

Agricultural production (billion)

A 400 Industrial wastewater Total wastewater

300 250 200 150 100 50 0 1990

1995

2000

2005

10 9 8 7 6 5 4 3

2010

.8

Year

C

.9

1.0

1.1

1.2

1.3

1.4

1.5

Chemical fertilizer (million ton) 400

Percentage of area (%)

80

Percentage of algal bloom area Total wastewater (industrial was removed)

60

350

300

250

40

200 20

Discharge (million tons)

Discharge (million tons)

addition to unilateral policies, such as controlling the discharge of residential sewage and agricultural non-point pollution sources.

150

0 2000

2002

2004

2006

2008

100 2010

Year Fig. 14. A: Sewage discharge in the Dianchi Lake basin from 1991 to 2010, B: Comparison of chemical fertilizer application and agricultural production from 1995 to 2008, C: Time series plots from Jan. 2000 to Dec. 2009. The histogram shows the percentage of the monthly average aerial coverage of bloom, extracted from MODIS time series; the solid line shows the time series discharge of total wastewater (industrial wastewater was excluded).

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