Long-Term Satellite Observations of Microcystin Concentrations in

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Long-Term Satellite Observations of Microcystin Concentrations in Lake Taihu during Cyanobacterial Bloom Periods Kun Shi,† Yunlin Zhang,*,† Hai Xu,† Guangwei Zhu,† Boqiang Qin,† Changchun Huang,§ Xiaohan Liu,†,‡ Yongqiang Zhou,†,‡ and Heng Lv§ †

Taihu Laboratory for Lake Ecosystem Research, State Key Laboratory of Lake Science and Environment, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China ‡ University of Chinese Academy of Sciences, Beijing 100049, China § Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210046, China S Supporting Information *

ABSTRACT: Microcystins (MCs) produced by cyanobacteria pose a serious threat to public health. Intelligence on MCs distributions in freshwater is therefore critical for environmental agencies, water authorities, and public health organizations. We developed and validated an empirical model to quantify MCs in Lake Taihu during cyanobacterial bloom periods using the atmospherically Rayleigh-corrected moderate resolution imaging spectroradiometer (MODIS-Aqua) (Rrc) products and in situ data by means of chlorophyll a concentrations (Chla). First, robust relationships were constructed between MCs and Chla (r = 0.91; p < 0.001; t-test) and between Chla and a spectral index derived from Rrc (r = −0.86; p < 0.05; t-test). Then, a regional algorithm to analyze MCs in Lake Taihu was constructed by combining the two relationships. The model was validated and then applied to an 11-year series of MODIS-Aqua data to investigate the spatial and temporal distributions of MCs. MCs in the lake were markedly variable both spatially and temporally. Cyanobacterial bloom scums, temperature, wind, and light conditions probably affected the temporal and spatial distribution of MCs in Lake Taihu. The findings demonstrate that remote sensing reconnaissance in conjunction with in situ monitoring can greatly aid MCs assessment in freshwater.

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INTRODUCTION Eutrophication is a natural process occurring in all lake aquatic systems.1 However, this process has been accelerated because of increased anthropogenic activities throughout the world over the last several years.2,3 Presently, freshwater eutrophication has become one of the most widespread environmental and social problems.4 One indication of such eutrophication is that eutrophic lakes and reservoirs frequently are dominated by cyanobacteria for considerable periods of time.5 Eutrophication and global climate change have accelerated the occurrence of cyanobacterial blooms in lake waters.6 Because cyanobacteria are widely known worldwide to produce a variety of toxins, the frequent occurrence of cyanobacterial blooms produces significant negative effects on human health and aquatic life7 and poses a serious threat to irrigation and drinking water supplies as well as fishing and recreational use of surface waters worldwide.8 The cyanobacterial blooms that occurred in Lake Erie led to a shutdown of water supplies from this lake to 500 000 residents in 2014, highlighting this problem.9 As one of the most ubiquitous causes of cyanobacterial blooms in freshwater systems, Microcystis can produce highly stable and potent polypeptides (microcystins (MCs)).8,10 Cyanotoxins can be divided into hepatotoxins, neurotoxins, and dermatotoxins;11 © XXXX American Chemical Society

MCs are considered to be hepatotoxins because they can induce allergic and irritation reactions and fatal liver hemorrhage, whereas chronic exposure is implicated in the formation of gastric and liver cancer.8,12,13 The majority of MCs remains intracellular in healthy intact cells and thus can result in a large increase in dissolved MCs once cells are lysed by herbicides during drinking water processing.8 MCs can accumulate in freshwater because they have relatively stable biochemical structures, with previously reported half-lives spanning from several days to several weeks.14,15 In addition, MCs cannot be destroyed by boiling.1,16 Therefore, public health concerns about toxic MCs in freshwater systems have recently increased in a large number of countries.17 Quantifying MCs in freshwater is critical for environmental agencies, water authorities, and public health organizations to provide timely warnings of Microcystis blooms. However, MCs abundance is not a routine monitoring parameter because the traditional measurements are costly and Received: December 4, 2014 Revised: April 23, 2015 Accepted: May 2, 2015

A

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led to a drinking water crisis in Wuxi City that interrupted the drinking water supply for approximately two million inhabitants for at least a week.32 These blooms contained multiple algal species and strains of Microcystis with varying degrees of toxicity.33 As a type of hazard in drinking water, MCs can bioaccumulate in the organs and tissue of fish;33 previous studies have found that MCs have been identified in a chronically exposed population of fishermen.34,35 Given that fish from Lake Taihu are a staple of the regional diet and that the authors have personally observed fishermen routinely harvesting fish from areas coved with dense Microcystis blooms, the potential human exposure risk to MCs through biomagnification up the food chain is a cause for concern.36 Therefore, long-term and synoptic information about variations in MCs in Lake Taihu is very important because it can help us understand the response of Microcystis to water environmental changes and help environmental agencies, water authorities, and public health organizations provide timely warnings of Microcystis blooms. To-date, however, long-term and synoptic information is lacking for Lake Taihu. As mentioned previously, satellite remote sensing can provide synoptic and frequent measurements of the water and is therefore particularly suitable to provide long-term, synoptic information. The objectives of this study are to (1) develop a method for quantifying MCs based on moderate resolution imaging spectroradiometer (MODIS-Aqua) data, (2) generate maps of MCs with high spatial and temporal resolution, and (3) characterize the longterm MCs distribution patterns in Lake Taihu during cyanobacterial bloom periods between 2003 and 2013.

require specialist laboratory facilities and technical expertise. Analyses of the relative abundance of Microcystis communities in total phytoplankton biomass require the collection of water samples in the field that are returned to the laboratory to identify and enumerate the cyanobacteria cells; indeed, this is also a time-consuming, labor-intensive, and high-skill process.17 The use of genomics-based techniques (quantitative real-time polymerase chain reaction (PCR)) may improve our ability to determine the relative abundance of Microcystis communities in the field in the future.7 However, the method cannot provide information about Microcystis communities at high spatial and temporal resolution. Sampling at only one station or a limited number of stations cannot provide a representative estimate of Microcystis community abundance, especially in a large water body where blooms are patchily distributed. Additionally, the sampling frequency cannot fulfill the time requirement for tracing the dynamics of Microcystis communities in a large lake; cyanobacteria population can multiply very quickly under desirable conditions, which means that cyanobacteria blooms can rapidly increase or decrease over relatively short time intervals and that the information required to monitor cyanobacteria blooms should be at a high temporal resolution. Hence, there is a clear need to develop techniques that can complement and extend the conventional approach for detecting and quantifying the dominance of Microcystis communities in the total phytoplankton population. Recent advances in satellite remote sensing technology have broadened the perspectives of monitoring toward the quantification of phytoplankton biomass.18 Satellite remote sensing has been successfully used to estimate phytoplankton biomass in a variety of freshwater lakes.19−21 A widely used proxy for phytoplankton bloom monitoring is the concentration of chlorophyll-a (Chla).22 However, to date, there are few reports about the use of remote sensing techniques to quantify MCs, possibly because there are no spectrally detectable characteristics of MCs. Many previous studies have suggested that MCs have significant positive correlations with Chla during cyanobacterial bloom periods.8,23−29 It is should be noted that these studies were based on cyanobacteria dominant aquatic systems and laboratory results; however, for any noncyanobacteria dominant aquatic systems, Chla could not be as an indicator of MCs. These studies suggest the possibility that we can estimate MCs from remote sensing data by means of Chla in a cyanobacteria dominant aquatic system. Large eutrophic shallow lakes generally have spatially and temporally complex water environments resulting from the dynamic interactions of physical, chemical, and biological effects.30 Lake Taihu, the third largest freshwater lake in China, is a typical large eutrophic shallow lake, with a maximum depth of less than 3 m, an average depth of 1.9 m, and a water surface area of 2230 km2.3 This lake plays a critical role in the social and economic development in the surrounding regions due to recreational opportunities and numerous natural resources provided by this lake such as fish, shrimp, crabs, and drinking water. Unfortunately, in recent years, Lake Taihu has experienced significant pollution due to rapid economic growth in the surrounding regions.6 Frequent nuisance cyanobacterial blooms (dominated by Microcystis spp.) occur in this lake, especially in Zhushan Bay and Meiliang Bay (northwest and northern parts of Lake Taihu) throughout the late spring and autumn seasons of every year and pose a major threat to the health of local citizens and livestock.31,32 For example, a severe algae bloom occurred over most of Lake Taihu in 2007, which

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MATERIALS AND METHODS In Situ Data Set. The in situ data set used in this study contains 2528 water samples collected from two sources (Table 1): data set I from January 2003−December 2013, with a total Table 1. Information from the Sampling Cruises sampling period

number of samples

measurements

2003−2013 June−July 2009 and June 2010

2432 96

Chla Chla, MCs

2432 water samples (Chla measurements), was collected from long-term monthly observations at the Lake Taihu Laboratory Ecosystem Research (TLLER), and data set II, with a total of 96 water samples (Chla and MCs measurements), was collected from 12 sampling transects conducted in Lake Taihu between June 22 and July 27, 2009 and from June 3− 21, 2010. The spatial distribution of the sampling sites in Lake Taihu can be found in Figure 1. We divided the lake into six subregions: Meiliang Bay, Zhushan Bay, Gonghu Bay, open area, Xukou Bay, and East Lake Taihu (Figure S1, Supporting Information). In data set I, Chla data were selected to match the MODIS satellite data. We divided data set II into two parts: (1) 75 data pairs of MCs−Chla used to construct relationships between MCs and Chla and (2) 21 MCs measurements (collected in different date) used for validating the proposed algorithm. Chla and MCs Measurements. To obtain algal particles, we used Whatman GF/F fiberglass filters (with an average pore size of 0.7 μm) to filter the water samples. The pigments of Chla were extracted using 90% ethanol at 80 °C from the filtered fiberglass and spectrophotometrically analyzed to measure the absorption coefficients at 750 and 665 nm; Chla B

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Rayleigh reflectance.19,37 There were more than 2000 data granules covering this study region from 2003−2013 (from May−October for each year). Among these granules, 579 scenes (cyanobacterial bloom periods, from May−October for each year) with high quality were selected after visual examination to exclude those significantly affected by clouds, sun glint, and thick aerosols. The corresponding MODIS cloud mask products (MOD35) were also downloaded from the U.S. NASA GSFC to perform cloud masking for each selected MODIS-Aqua level 0 data. In this study, Kd(PAR) was used as an indicator of light availability in Lake Taihu; the data were provided by our previous study.38 Areas of cyanobacterial bloom scums can be derived from MODIS Rrc data using the method of Hu et al.19 Meteorological Data. Monthly mean wind speed and temperature data from 2003−2013 were acquired from the nearest meteorological station (Dongshan meteorological station, Figure S1), which can be downloaded from the China Meteorological Data Sharing Service System (http://cdc. nmic.cn/home.do). Detailed introduction regarding these data and the Dongshan meteorological station can be found in our previous studies.30,38 Comparison of Satellite and in Situ Data. Ideally, the satellite data and in situ ground data should be concurrent within a period determined by the natural variation of the process being measured. We set the criterion for matching satellite and in situ observations to ≤ 3 h (the time interval between in situ and corresponding MODIS-Aqua measurements) to minimize the effects of the temporal difference between the in situ and MODIS-Aqua measurements. Among all of the collected samples, our criterion resulted in 250 in situ Chla Rayleigh-corrected Rrc matches of 2432 Chla samples and 21 in situ MCs Rayleigh-corrected Rrc “matches” of data of 96 MCs samples, that is, data pairs of the MODIS images and in situ data collocated in space (same pixel). The 250 in situ Chla Rayleigh-corrected Rrc matches of data were used to determine the relationship between in situ Chla and MODIS image data; the 21 in situ MCs Rayleigh-corrected Rrc matches of data were used to validate the developed algorithm for estimation MCs. Statistical Analysis and Accuracy Assessment. Statistical analyses including calculations of the average, maximum, and minimum values and linear regressions were performed using SPSS 17.0 software (Statistical Program for Social Sciences). The method of ordinary least-squares was used to perform linear regressions in this study. This method is conceptually simple and computationally straightforward and thus commonly used to analyze both experimental and observational data. Pearson correlation analysis was used to investigate the relationships between variables using the SPSS software. Pearson’s correlation coefficient between two variables is defined by the covariance of the two variables divided by their standard deviations. It is widely used in the sciences for determining the linear dependence degree between two variables. Significance levels are reported to be significant (p < 0.05) or not significant (p > 0.05). The accuracy of the algorithms was assessed by calculating the relative error (RE), mean absolute percent error (MAPE), and root−mean−square error (RMSE) between the measured and predicted values using the following equations:

Figure 1. Relationships between MCs and Chla (A) and between Chla and MODIS normalized spectral index (B) and the validation results of MCs using the proposed algorithm (eq 6) (C); MODIS normalized spectral index = Exp(Rrc(645)) − Exp(Rrc(859))/(Exp(Rrc(645)) + Exp(Rrc(859)).

can be calculated from the absorption coefficients at those two wavelengths.5,29 Total MCs (i.e., all isoforms) were analyzed spectrophotometrically using an Abraxis (PA, USA) enzymelinked immunosorbent assay (ELISA) on a Thermo MultiSkan Spectrum plate reader (Vantaa, Finland). The microsystinsADDA ELISA microtiter plates utilizing polyclonal antibodies can allow for congener-independent detection of all total MCs. Detailed information about measuring MCs can be found in a previous study.8 Satellite Data, Diffuse Attenuation Coefficient of Photosynthetically Active Radiation (Kd(PAR)) Data, and Area of Cyanobacterial Bloom Scums. Remote sensing data collected by MODIS-Aqua were used to achieve sufficient spatial and temporal coverage of Lake Taihu. The MODIS-Aqua data have been freely available since 2002 with a maximum spatial resolution of 250 m (the first two bands) at a high frequency (one image per day); these data allow the monitoring of Lake Taihu over multiannual time periods at high spatial resolution. MODIS-Aqua level 0 data (raw digital counts) acquired from 2003−2013 were obtained from the U.S. NASA Goddard Space Flight Center (GSFC, http:// oceancolor.gsfc.nasa.gov/). We used SeaDas 6.0 to produce atmospherically Rayleigh-corrected MODIS-Aqua reflectance (Rrc) data: Rrc = πLt*/(F0 cos θ0) − Rr, where Lt* is the calibrated sensor radiance after adjustment for ozone and other gaseous absorption, F0 is the extraterrestrial solar irradiance at data acquisition time, θ0 is the solar zenith angle, and Rr is

RE = C

Ymeasured − Yestimated Ymeasured

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Figure 2. MODIS-Aqua-derived time (daily average (A) and annual average (B)) series of MCs for the three subregions and the entire Lake Taihu and daily temperature (A) measured at the Dongshan meteorological station from 2003−2013.

1 MAPE = N

RMSE =

N

∑ i=1

1 N

specific MCs-producing content in total phytoplankton. The relationship could reflect the variations in MCs production per unit of phytoplankton biomass and thus the MCs-producing ability of phytoplankton. Apparently, the relationship is crucial for deriving MCs information from Chla; however, until now, the mechanism of the correlation between the two parameters has been unclear because of the complexity of MCs-producing biological processes. To develop an empirical relationship between Chla and MODIS image data, a number of spectral indexes such as simple band, band ratio, and normalized difference index were constructed from atmospherically Rayleigh-corrected MODIS data. To determine the best spectral index, a correlation analysis was carried out between in situ Chla and the corresponding spectral indexes (Table S1). The spectral index [(Exp(Rrc(645)) − Exp(Rrc(859)))/ (Exp(Rrc(645)) + Exp(Rrc(859)))] gave the best correlation with in situ Chla measurements (r = −0.86). Atmospherically Rayleigh-corrected reflectance MODIS data at the two bands were used to derive cyanobacterial bloom information in the previous studies.6,19 Linear, logarithmic, exponential, and quadratic functions were used to develop the relationships between Chla and [(Exp(Rrc(645)) − Exp(Rrc(859)))/(Exp(Rrc(645)) + Exp(Rrc(859)))]. By using the normalized spectral index and in situ Chla, the linear relationship had the best precision, highest correlation coefficient (r = −0.86), and the lowest MAPE (25.3%) and RMSE (10.6 μg/L) (Figure 1B and eq 5):

Ymeasured, i − Yestimated, i Ymeasured, i

(2)

N

∑ (Y measured, i − Yestimated,i)2 i=1

(3)

where N is the number of samples, and Ymeasured and Yestimated are the measured and estimated values, respectively.

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RESULTS Development and Validation of MCs Estimation Algorithm. MCs cannot be quantified directly from the remote sensing data because MCs have no optically detectible characteristics. Thus, we indirectly derived information about MCs using the remote sensing method via the association with Chla. Two steps are needed to develop the indirect approach for estimating MCs from remote sensing data: (1) determining the relationship between MCs and Chla and (2) constructing a correlation between Chla and remote sensing data. From these data (75 samples), a simple regression relationship (Figure 1A and eq 4) was empirically derived between MCs and Chla, which allows us to estimate the abundance of MCs in a given water sample from Chla: (4) MCs = 0.0796 × Chla where MCs = microcystins concentration (μg/L), and Chla = chlorophyll a concentration (μg/L). The highly significant correlation between MCs and Chla [r = 0.80; p < 0.001; t-test (2009 data set). r = 0.96; p < 0.001; t-test (2010 data set). r = 0.91; p < 0.001; t-test (all data sets)] strongly confirms that Chla can serve as a tool to determine MCs during cyanobacterial bloom periods. It should be noted that the relationship derived from the part of the data used by Otten et al. was almost the same as the finding in their study [MCs = 0.01021 + 0.0797 × Chla].8 The result suggests that the empirical relationship between MCs and Chla in Lake Taihu during cyanobacterial bloom periods is relatively stable. Since Chla is actually an indicator of total phytoplankton biomass, the relationship between MCs and Chla could refer to weight-

Chla = − 1588.3 × IndexMODIS + 72.6

(5)

where IndexMODIS = (Exp(Rrc(645)) − Exp(Rrc(859)))/(Exp(Rrc(645)) + Exp(Rrc(859))), and Rrc(645) and Rrc(859) are the atmospherically Rayleigh-corrected MODIS data at 645 and 859 nm, respectively. It should be noted that the MODIS bands at 645 and 859 nm were originally designed for land applications. This relationship allows us to extract Chla information in Lake Taihu from MODIS data. Therefore, a simple empirical model can be derived by combining eqs 4 and 5 to estimate MCs from MODIS image data (eq 6): D

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variability, with an 11-year CV (coefficient of variation = SD/ average) value of 0.34. This was followed by open area (CV = 0.33) and Meiliang Bay (CV = 0.32). Although some regional differences are observed (Figure 2), the interannual repeatability is quite good for MCs temporal variations in various regions in Lake Taihu, showing peaks of MCs values in September and troughs in May for all 11 years. Overall, MCs in Lake Taihu between 2003 and 2013 experienced four markedly different interannual variations: MCs slightly decreased from 2003−2005, increased sharply from 2006−2008, decreased from 2009−2011, and finally increased again in 2012. The extremely high MCs found from 2006−2008 should be noted because the notorious 2007 Wuxi water crisis was related to this issue. Lower than normal wind speeds over Lake Taihu from 2006−2008 can likely explain the high MCs levels during this period.39 Lower wind speeds during this period would have resulted insignificantly lower total suspended matter concentrations between 2006 and 2008. Eventfully, lower total suspended matter concentrations allowed more light to penetrate into the water column and therefore were conducive to cyanobacteria growth in Lake Taihu. This subsequently led to high MCs in Lake Taihu between 2006 and 2008. On the basis of in situ and remote sensing data as well as meteorological data, several previous studies have also demonstrated that the extensive and longlasting cyanobacterial bloom occurred in 2007 from May− October.6,19,40 The MODIS-Aqua-derived MCs data from 2003−2013 obtained during cyanobacterial bloom periods were averaged to calculate the regional MCs distribution for Lake Taihu (Figure 3). The data show that MCs in Lake Taihu experienced

(6)

To rigorously validate the performance of this simple model, we used independent validation data that were previously prepared from a total of 21 MCs samples collected from Lake Taihu during cyanobacterial bloom periods. Without adjusting the optimal locations of the band and reparameterization, the simple model performed well for MCs quantification (Figure 1C). The RE of this model for the data set ranged from 0.2− 129% with a MAPE of 34.4% (RMSE = 1.9). The relative errors of 40% and 60% samples were below 20% and 40%, respectively; comparison between the measured and predicted MCs using the simple model from the normalized spectral index, (Exp(Rrc(645)) − Exp(Rrc(859)))/(Exp(Rrc(645)) + Exp(Rrc(859))), showed that these values are in good agreement, with a noticeably significant linear relationship (r = 0.92; p < 0.001; t-test). In addition, the measured and predicted values of MCs are evenly distributed along the 1:1 line (Figure 1C). These results indicate that the simple model developed from the normalized spectral index (Exp(Rrc(645)) − Exp(Rrc(859)))/(Exp(Rrc(645)) + Exp(Rrc(859))) can be used to estimate MCs abundance for the validation data with satisfactory performance and therefore quantify the spatial distribution of MCs in Lake Taihu from MODIS image data. Long-Term MCs Trend and Variability. MODIS-Aqua measurements from 2003−2013 (from May−October for each year) for Lake Taihu were acquired to generate atmospherically Rayleigh-corrected reflectance products; these products were then used to extract long-term temporal and spatial MCs distribution in Lake Taihu using the developed algorithm (eq 6). Several lake segments, for example, Gonghu Bay, Xukou Bay, and East Lake Taihu, are known to be covered by water plants (weeds, reeds, and other macrophytes).19 The water plants can significantly change the characteristics of the water-leaving reflectance; therefore, MCs in the water covered by water plants cannot be estimated from remote sensing data. Thus, in the following, “entire lake” stands for all of Lake Taihu excluding three segments (Gonghu Bay, Xukou Bay, and East Lake Taihu). Time series of the MODIS-Aqua derived MCs from 2003−2013 were constructed for the three regions (namely, Zhushan Bay, Meiliang Bay, and open area) and the entire lake by averaging (spatially and temporally) over all of the valid pixels over water for each region (Figure 2). Generally, the time-series show both significant spatial and interannual variability from 2003−2013 (p < 0.005; t-test). Higher MCs values were found in Zhushan Bay, while lower values generally were found in Meiliang Bay, followed by open area for all 11 years during cyanobacterial bloom periods. MCs in the entire lake ranged widely, from 1.01−7.86 μg/L, with a long-term mean of 2.97 μg/L (SD (Standard Deviation) = 0.94 μg/L); the lowest and the highest MCs for the entire lake were found in October 2012 and in September 2006, respectively. MCs in Zhushan Bay was consistently higher than in other two regions and ranged from 2.20 μg/L in October 2013 to 16.23 μg/L in July 2006, with a long-term mean of 4.90 (SD = 1.68 μg/L). For open area, MCs were generally lower than in other regions and varied from 0.79 μg/L in October 2012 to 8.06 μg/L in August 2006, with an average value of 2.80 μg/L (SD = 0.93 μg/L). Comparably, MCs in Meiliang Bay have a moderate level, spanned from 1.94 μg/L in October 2004 to 9.80 μg/L in July 2007, with an average of 4.11 μg/L (SD = 1.17 μg/L). Of the three regions, Zhushan Bay showed the highest interannual

Figure 3. MCs spatial distribution in Lake Taihu averaged from all MCs estimates from the MODIS-Aqua data from 2003−2013.

significant spatial variation. MCs levels varied from 1.61−15 μg/L, with an average of 2.41 μg/L (SD = 0.16 μg/L). The highest MCs levels were more frequently found along the shoreline in Zhushan Bay where cyanobacterial scums often occur, followed by Meiliang Bay. The lowest MCs values were found in open area where total suspended matter concentrations were frequently higher than in the rest of Lake Taihu, which was a result of wind-induced sediment resuspension.30 The results are in agreement with several previous studies on Lake Taihu.8,29,41,42 Because information on MCs distributions E

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collected only in cyanobacterial bloom periods, Chla might not be a consistently good predictor of MCs in all systems. Clearly, in the areas where cyanobacteria is not dominant or in noncyanobacterial bloom periods, MCs are not likely to show a significant relationship with Chla,40 which implies that the proposed MCs estimation algorithm in this study might not be suitable for these areas and in Lake Taihu in noncyanobacterial bloom periods such as winter and early spring. In addition, this indirect method could have another application problem because not every cyanobacterial species generates toxins, and there are some environmental conditions (temperature and light conditions, seeing discussion below) that could promote producing of toxic cyanobacteria species. However, timing for having such environmental conditions may not always there, but Chla could be consistently present. A blind application without considering the timing of such environmental conditions could lead to some problems with applying this simple approach. Despite the limitations imposed by complex lake environmental conditions, the general concept holds that satellite image data via association with Chla to derive spatial and temporal MCs distributions could be applied to other lake waters. Linkage with Environmental Factors and Climate. Previous studies have suggested that MCs are likely affected by several environmental factors such as area of cyanobacterial scums, water optical condition, wind speed, and temperature.28,40,45−47 Long-term MODIS-Aqua-derived MCs data show that MCs levels in Lake Taihu increased with higher light availability, temperature, and area of cyanobacterial scums (Figure 2 and Figure S2). As an index of light availability in the water column, we used Kd(PAR), which can represent the depth that light can penetrate into water column. A relatively strong negative correlation was found between 11-year spatially averaged MCs data and Kd(PAR) in Lake Taihu (Figure S2A), which demonstrated that waters with more light availability for phytoplankton have higher MCs. The mechanism is that buoyant Microcystis cells will experience photooxidative damage from prolonged exposure to high light intensity. This is because microcystin transcription will be up-regulated since they are theoretically expected to offer intracellular protection of phycobilisome proteins against reactive oxygen species induced by photodegradative processes.8 This provides strong support that underwater light conditions regulate MCs in cyanobacterial communities in this shallow lake system. This result has been previously documented.8,36,45,48 Since sediment resuspension induced by wind force determines the light conditions in Lake Taihu,30,38 we can infer that wind speeds likely indirectly affect MCs variations via controlling light conditions. Wind appears to affect MCs, which is evident from the negative correlations (r = −0.24; p < 0.005; t-test) between wind speeds and MCs. High wind speeds mean more sediment resuspension, subsequently causing high turbidity; high turbidity reduces light availability and therefore decreases MCs levels. Figure S2 showed the relationships between MCs in different regions of Lake Taihu and temperature. Correlation analyses indicate that MCs were positively correlated to temperature in the three regions and the entire lake (p < 0.005; t-test). However, sensitivity of MCs to temperature varied in different regions of Lake Taihu. More sensitivity to temperature was found in Zhushan Bay (r = 0.43; p < 0.005; t-test) than in Meiliang Bay (r = 0.22; p < 0.005; t-test) and open area (r = 0.13; p < 0.005; t-test). The sensitivity to temperature may be

was indirectly derived from Chla, the long-term trend and variability of Chla were similar to that of MCs in Lake Taihu during cyanobacterial bloom periods.

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DISCUSSION Rationality and Limitations of the Algorithm. In this study, an indirect method is proposed to quantify MCs from MODIS satellite data in Lake Taihu during cyanobacterial bloom periods, which depends on the relationships between MCs and Chla and between Chla and MODIS satellite data. Obviously, the relationship between MCs and Chla is critical to accurately estimate MCs from MODIS-Aqua image data using the proposed algorithm. By using a number of in situ measurements collected in Lake Taihu during cyanobacterial bloom periods, a significant correlation was found between MCs and Chla, indicating that Chla can be used as an indicator of MCs levels in this lake. This highly correlated relationship has been reported in numerous previous studies.8,23,24,26−28 Wu et al. investigated approximately 30 lakes in the middle and lower reaches of the Yangtze River area from 2003−2004 and demonstrated that MCs were closely correlated with Chla and biomass of cyanobacteria;23 Rogalus et al. evaluated the screening techniques for tiered monitoring of MCs based on in situ data sets collected from Lake Champlain (USA) and showed a stronger relationship between MCs and Chla than between MCs and other parameters;28 this study suggests that MCs can serve as a reasonable screening tool to determine MC levels in an inland water system where potentially toxic cyanobacteria are dominant.28 To elucidate the relationships between MCs and environmental factors in Lake Erie, a large number of in situ data were collected from 2003−2005 in cyanobacterial blooms in the study of Rinta-Kanto et al.;25 these data showed that Microcystis is a significant component of the cyanobacterial community in Lake Erie and that the abundance of microcystin-producing Microcystis was strongly correlated with the abundance of cyanobacterial biomass.25 The circumstances of that study are similar to those of Lake Taihu, where Microcystis was a dominant component of the cyanobacterial community during cyanobacterial bloom periods.3,29,43 Similarly, MCs were previously reported to be easily estimated from Chla in a cultural incubation experiment.24 Accordingly, given the results from this study and the numerous related previous reports, it is reasonable that we used Chla to estimate MCs levels in Lake Taihu during cyanobacterial bloom periods. Our proposed algorithm was developed empirically and therefore has two principal limitations: less transferability and a lack of a physical theoretical basis compared to algorithms developed using analytical methods. Note that the current simple algorithm was developed and validated based on the limited data set collected from Lake Taihu in China. Whether the algorithm is applicable to other lakes requires further validation, as the ratio of MCs to Chla, the optical properties, and the corresponding algorithm parameters may vary substantially among different inland waters because of the large diversity and dynamism of the compounds in those waters.44 Although many studies have shown a strong correlation between MCs and Chla, the relationship between the two parameters is system dependent and may be different in different lakes,26 which suggests that the relationship needs to be recalibrated according to the lake’s conditions of toxic cyanobacterial abundance. Because the relationship between the two parameters was developed based on in situ data F

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have occurred in those three years.6,19 Nonetheless, the spatial and temporal variations in MCs cannot adequately be explained solely by these factors. Other variables that also control MCs need to be further studied. Implication for Environmental Management. The MODIS-derived MCs products from 2003−2013 show that MCs levels in the three regions of Lake Taihu significantly exceeded the 1998 WHO (Word Health Organization) threshold value of 1 μg/L. 51 In particular, among these three regions, the lowest long-term average of 2.80 μg/L in open area was more than two times this threshold. These results clearly suggest a significant risk to human health from blooms of potentially toxic cyanobacteria in Lake Taihu over a long-term period. Therefore, the urgent need for a risk management strategy to mitigate the problems presented by cyanobacterial toxins (MCs) in Lake Taihu has been recognized several times in recent years. A key element for ensuring the development and implementation of an appropriate risk management strategy is determining a valid and operational method for monitoring and assessment of past and current MCs levels in Lake Taihu. MCs analysis is somewhat complex using traditional methods and requires a significant amount of time. Furthermore, a once-per-month sampling effort may still cause uncertainties in both short- and long-term statistical analyses of MCs trends due to the rapidly changing nature of Lake Taihu.29 Thus, there is current interest in the development of a simpler and more convenient method that can provide information about MCs with high spatial and temporal resolution. The approach developed in this study, while relatively simple, importantly demonstrates the ability to rapidly acquire information about the presence and concentration of MCs from MODIS satellites. Validated MCs products from MODIS satellite measurements can provide complementally information derived from existing field sampling activities and can therefore help management agencies make prudent decisions when implementing a risk management strategy. In addition, the highly significant correlation between MCs and climate conditions shows that climate conditions may have striking impacts on MCs variations in Lake Taihu when the nutrient level is high. This study strongly recommends that climate conditions be taken into consideration when risk management strategies are developed. In a broader perspective, this approach may be extended to many inland lakes dominated by cyanobacterial communities. Once the algorithm parameters are calibrated using related local data and the new satellite-derived MCs products are validated, similar long-term dynamics can be derived with minimal effort and cost. The efficient and cost-effective satellite-derived MCs products can provide immediate value and useful decision support tools for other inland lake waters. Since many inland lakes in the world are suffering from cyanobacterial blooms and water quality degradation problems, the approach proposed here can be used as a template to reduce cost and increase management efficiency. In particular, this is important in a changing climate; it is usually difficult to investigate causal factors leading to environmental changes without continuous and long-term assessment. Therefore, we recommend that future lake monitoring plans include satellite water color remote sensing to aid in the interpretation of spatial and temporal patterns of important water quality parameters.

related to nutrient levels in the water. Nutrients in Zhushan Bay were reported to be replete and therefore have not been the limiting factors for cyanobacterial growth in this region; temperature may have more impact on cyanobacterial growth.8 However, in open area where nutrients are not rich, cyanobacterial growth is limited by both factors. There are two ways that cause the increase in MCs with higher temperature. First, an increase in temperature would foster a faster growth rate of toxic Microcystis rather than nontoxic Microcystis.46−49 On the basis of several laboratory experiments, Davis et al. observed that with continually increasing surface water temperature, toxic Microcystis could out-grow nontoxic Microcystis or could synthesize more microcystin synthetase, yielding blooms that are composed of a larger proportion of toxic cells or have higher MCs.47 Second, higher temperature could prompt Microcystis-dominated cyanbacterial growth, consequently leading to MCs increase. Higher temperature can selectively promote Microcystis growth because Microcystis grows and photosynthesizes optimally at, or above, 25 °C, while when temperatures approach and exceed 20 °C, the growth rates of freshwater eukaryotic phytoplankton generally stabilize or decrease.47 This offers a distinct growth advantage to Microcystis under eutrophic conditions, when in competition with eukaryotic primary producers.49 In addition, higher temperature could decrease surface water viscosity. Because many cyanobacteria can regulate buoyancy for offsetting their sedimentation, a decrease in viscosity would increase the sedimentation rate of eukaryotic algae and further strengthen the competitive advantage of Microcystis.50 Moreover, MCs were strongly linked to areas of cyanobacterial bloom scums (r = 87; p < 0.005; t-test), which suggests a significant effect of cyanobacterial blooms on MCs. It also cautions us that water has a potential high human exposure risk to MCs in regions where cyanobacterial bloom scums are forming. The high MCs levels were generally found along the shoreline where cyanobacterial bloom scums are easily accumulated.26 However, the reason why MCs levels increase with the area of cyanobacterial bloom scums is still unknown. In addition, nutrient levels also have some impact on MCs. This relationship requires further investigation in future studies.8 Mechanisms of the Spatial−Temporal Variations in MCs. In addition to the fact that Zhushan Bay has the richest nutrient level among the three regions in Lake Taihu,8 it also has a shorter wind fetch than open area, indicating a weaker intensity of wind waves and a lower total suspended matter concentration by wind-induced resuspension, which would in turn result in more light being available for cyanobacterial growth.30 Because the prevailing wind direction is Southeast in Lake Taihu during cyanobacterial bloom periods, the surface water in Lake Taihu usually moves southeastward; given the location of Zhushan Bay in Lake Taihu, massive cyanobacteria accumulation occurs in Zhushan Bay. The combination of these factors accounts for the high MCs levels in Zhushan Bay. Strong wind force, less available light, and lower nutrient levels led to the lower MCs levels in open area. Temperature may explain the temporal variations in MCs levels. In particular, the relatively higher MCs levels that appeared in Lake Taihu from 2006−2008 were likely the result of unusual climate conditions. The number of days with low wind speed and high temperature from 2006−2008 was more than in other periods, which favored cyanobacterial growth and cyanobacterial bloom events. More severe cyanobacterial bloom events were also reported to G

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ASSOCIATED CONTENT

S Supporting Information *

Distributions of sampling sites. Relationship between MCs data averaged from all 11-year MODIS-Aqua-derived MCs products and Kd(PAR). List of the testing spectral indices for deriving Chla information. The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/ es505901a.

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AUTHOR INFORMATION

Corresponding Author

*Phone: (+86) −25-86882198; fax: (+86) 25-57714759; email: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This study was supported by grants from the National Natural Science Foundation of China (Nos. 41325001, 41301376, 41271355, and 41471282), the Provincial Natural Science Foundation of Jiangsu in China (No. BK20141515), and the International S&T Cooperation Program of China (ISTCP) (No. 2015DFG91980). The authors thank all members of Taihu Laboratory for Lake Ecosystem Research (TLLER) for their participation in the field experiments. The authors thank T. G. Otten (University of North Carolina at Chapel Hill) for providing MCs data. The authors would also like to thank the two anonymous reviewers for their useful comments and constructive suggestions.

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REFERENCES

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