Spatiotemporal patterns and threshold of chlorophyll-a in Lake Taihu based on microcystins

Chlorophyll-a (Chl-a) is considered as an indicator of phytoplankton biomass dynamically reflecting the growth of algae. Therefore, determination of Chl-a threshold is of vital importance to the health of aquatic ecosystems and drinking water security. This research is aimed to investigate the spatial and temporal distributions of Chl-a and microcystin (MC) concentrations using Geographic Information System (GIS) and identify the Chl-a threshold in Lake Taihu based on available guideline values of MCs. Nearly, the same characteristics of spatiotemporal variation of Chl-a and MCs were observed in Lake Taihu. Overall, the lakewide distributions of Chl-a and MCs were highly variable over time and space. The Chl-a concentration in the winter and spring was relatively low, and gradually increasing in summer and autumn, with the maximum concentration observed in August. But the maximum MCs concentration appeared in October, 2 months lagging behind the Chl-a. The highest annual average Chl-a and MCs concentrations were observed in Zhushan Bay, Meiliang Bay, and Gonghu Bay in northwest of Lake Taihu, following by West Zone and Center Zone. Dongtaihu Bay, East Zone, and South Zone always present good water quality. Referring to the guideline value of MCs, the Chl-a threshold was determined as 10–15 mg·m−3 based on the linear regression correlation between Chl-a and MCs. The establishment of Chl-a threshold is useful for eutrophication control, water quality management, and drinking water utilities in developing water safety plans.


Introduction
Due to the rapid development of society and economy, water pollution and eutrophication have become most serious problems in China. Frequent outbreak of cyanobacterial blooms not only cause the potential deterioration of water quality but also pose serious direct and indirect threats to aquatic ecosystems and public health (Park et al. 2015;Wang et al. 2018). Nutrient conditions such as nitrogen and phosphorus are the induction factors of harmful cyanobacterial bloom in aquatic ecosystems. A lot of attentions have been paid to the relationship between the amounts, proportions, and chemical composition of N and P sources with the composition, magnitude, and duration of algae blooms (Paerl 2008). International and national nutrient criteria for lakes and reservoirs have widely established to control the water pollution (Gibson et al. 2000).
Chlorophyll-a (Chl-a) is present in all phytoplankton species and it is a measure of the portion of the pigment of photosynthesizing plants that is still alive, so the Chl-a concentration is used for primary productivity studies and the determination of the abundance and variety of phytoplankton biomass Park et al. 2015). Due to a high correlation between the Chl-a and nutrient concentrations, this pigment was used as a proxy of aquatic system's trophic status and used to predict algal bloom (Carlson 1977;Gurlin et al. 2011;Watanabe et al. 2015). However, various surface water quality standards have not involved related regulations on Chl-a concentration.
Previous studies on Chl-a mostly focused on the relationship between nutritive salt and the biomass of algae (Fu et al. 2016). Although there are some researches focused on the reference condition of Chl-a (Huo et al. 2013), but few was on the Chla threshold of the waterbody of lakes, reservoirs, or streams. According to the international trophic state classification, Chl-a concentration is divided into 6 levels: oligotrophic (< 1.6 mg·m −3 , Level I), mesotrophic (1.6-10 mg·m −3 , Level II), eutrophic (10-26 mg·m −3 , Level III), supertrophic (26-64 mg·m −3 , Level IV), hypertrophic (64-160 mg·m −3 , Level V), and ultratrophic (> 160 mg·m −3 , Level VI) (Vollenwieder 1968). However, this classification is only an evaluation standard of trophic state, not the guideline value of Chl-a. Therefore, determining the threshold of Chl-a is of vital importance to provide information of protection of waterbody health and drinking water security.
Chl-a has direct response to the concentration of TN and TP in waterbodies, and large quantities of studies were carried out to clarify the exact correlation between them, but the results varied greatly. There was complicated correlation between Chl-a and its impact factors. For example, phosphorus concentration and temperature could change Chl-a contents and size in speciesspecific algal cells (Chen et al. 2011). In general, the commonly used linear regression analysis is not adequate for interpretation their non-linear relation; thus, non-linear analysis method should be introduced (Wang et al. 2005). It is also revealed that the nutritive salts in Lake Taihu showed different correlation with Chl-a in different seasons (Wang et al. 2007). Because of the complicated relationship between Chl-a and nutrients, another parameter should be induced to identify the Chl-a threshold.
The WHO has reported that 59% of cyanobacteria causing water blooms in the world are harmful cyanobacteria (Xia et al. 2018). Microcystis is the dominant species of cyanobacteria blooms in Taihu Lake, and its biomass can account for 40-98% of the total algae biomass . Microcystins (MCs) is the most widely distributed in water, mainly produced by Microcystis and Anabaena (Xia et al. 2018). The risk caused by MCs in a harmful algal bloom water system has received increased attention (Adamski et al. 2020;Glibert et al. 2021;Park et al. 1993;Rapala et al. 1997;Michelle 2004;Zheng et al. 2004;Zhang et al. 2021). Most common isomers of MCs include microcystin-LR(MC-LR), microcystin-RR (MC-RR), and microcystin-YR (MC-YR) (L, R, and Y represent leucine, arginine, and tyrosine, respectively) Svrcek and Smith 2004), in which the most toxic is MC-LR, with a median lethal dose (LD 50 ) of about 50 μg·kg −1 for mice (Du et al. 2019;Sakai et al. 2013). Due to the severe toxicity of MC, the WHO has published protocols concerning their detection and recommendations for maximum permitted concentrations (1 μg·L −1 ) in water destined for human consumption (WHO 1998) and 20 μg·L −1 for recreational use (WHO 2003). Furthermore, a tolerable daily intake (TDI) of 0.04 mg total MCs per kg body weight per day has been proposed as a provisional guideline (Song et al. 2007). The Ministry of Health of China has also recommended the maximum permitted concentrations of MC-LR as 1 μg·L −1 (The Ministry of Health of P.R. China and Standardization Administration of P.R. China 2006). Meanwhile, the significant correlation between Chl-a and MCs was found by Graham et al. (2004). A regression equation was suggested to estimate the MCs concentration of a given sample based on Chl-a concentration and it was empirically derived as: MCs = 0.01021 + 0.07965(Chl-a) (Otten et al. 2012). As vital links between nutritive salt and MCs, Chl-a threshold is expected to be determined by analyzing the correlation between MCs and Chl-a based on guideline values of MCs.
Lake Taihu, the third largest freshwater lake in China, is an important source of drinking water for Wuxi, Suzhou, and Shanghai, etc. (Wang et al. 2017). However, Lake Taihu suffered from severe nutrient over-enrichment over the past several decades (Qin et al. 2010). Despite many efforts have been undertaken by both government entities and researchers since 1990 for restoring the lake, the lake water quality has not shown significant improvement and harmful algal blooms still occurred frequently (Tang et al. 2016). In the previous studies, the average data of the entire lake or sub-regions were usually used to represent the Chl-a or MC concentrations (Sakai et al. 2013;Tang et al. 2016). However, the distributions of Chl-a and MC concentration in Lake Taihu are extremely uneven and the arithmetic mean is easily subject to the influence of extreme high or low values in a set of data. Therefore, the average values cannot truly reflect the real distribution of Chl-a or MCs in different regions of Lake Taihu.
Based on systematically monitoring of Chl-a and MC concentrations of the entire Lake Taihu, the spatiotemporal distribution patterns of Chl-a and MCs in different seasons were illustrated using Geographic Information System (GIS) interpolation using inverse distance weighted (IDW) method. Furthermore, the correlation between Chl-a and MCs was analyzed and the Chl-a threshold was identified. This study aimed to provide useful information for water quality managers and drinking water utilities in developing water safety plans.

Study area and sampling locations
Lake Taihu is located between 30°56′-31°33N and 119°53′-120°36′E in Jiangsu and Zhejiang Provinces, China (see Fig. 1). The shallow lake has a large surface area of approximately 2338 km 2 and average depth of 1.9 m (Wang et al. 2017). As the third largest freshwater lake in China, it serves as an important resource for drinking water and provides various ecosystem services such as agriculture, aquaculture, tourism, and navigation. It also receives considerable point-source and non-point source pollution from the surrounding watershed making the sharply increased load of pollutants such as nutrients and heavy metals during the 1980s, which caused a rapid water quality deterioration and frequent occurrence of algal blooms since the 1990s (Sakai et al. 2013;Wang et al. 2017). At present, the lake still remains at a eutrophic state.
In general, four distinct seasons: spring (March, April, May), summer (June, July, August), fall (September, October, November), and winter (December, January, February) are divided of a year of Taihu Lake basin (Wang et al. 2017). Samples were collected from May to October (summer and autumn), December (winter) in 2013, and March (Spring) in 2014 from 25 locations across the lake (Fig. 1). The water samples were taken for three times on the early, middle, and later days of each sampling month. And the water samples were collected at a depth of 0.5 m at each site by driving a vessel with the help of an instrument of navigation-GPS.

Analysis of MC concentrations
The concentration of both intracellular and extracellular MCs of MC-LR, MC-RR, and MC-YR were analyzed including the pretreatment of water sample and cyanobacteria cells, solid phase extraction (SPE) and the ultra-high performance liquid chromatography-tandem quadrupole mass spectrometry (LC/ MS) procedures.
For extracellular MC analysis, 2 L of water sample was taken and filtered using a 0.45 μm polytetrafluoroethylene membrane. The filtrates were collected and transferred to the following SPE procedure. For intracellular MC analysis, the filter membranes covered with cyanobacteria cells were crushed and vortexed with 5% acetic acid. Then, the samples were ultrasonic wave broken and the solutions were refrigerated centrifuged. The supernatant was transferred to the following SPE procedure.
The samples were isolated and purified with SPE. The SPE system (SUPELCO VisiprepTM) was equipped with a Waters Oasis HLB 500 mg/6 mL cartridge (Waters Corporation, Milford, MA, USA). The HLB cartridge was activated using 10 mL methanol. The samples were loading at 3 mL·min −1 and 20 mL of 10% and 10 mL of 20% methanol solution was used to wash out the impurities. Then, 0.1% TFA methanol is used to elute the MCs in the extraction column (Waters Oasis® HLB DCHP, 20 μm, 2.1 mm × 30 mm) at 1 mL·min −1 . Finally, the eluate was concentrated by nitrogen blowing concentrator (N-EVAP TM 112, Organomation Associate Jnc., USA) and then 50% methanol was added to obtain a constant volume 1 mL.
The ultra-high performance liquid chromatography-tandem quadrupole mass spectrometry (LC/MS) procedure developed by Ott and Carmichael (2006) was performed to analyze MC samples with modifications. The LC/MS system (ACQUITY TQD, Waters®, MA, USA) was equipped with ZORBAX 300 SB C 18 column (5 μm, 4.6 × 150 mm, Agilent®, CA, USA), ACQUITY UPLC® BEH C 18 column (1.7 μm, 2.1 mm × 50 mm, Waters®, MA, USA), and MasslynxTM 4.1 working station. The linear gradient elution conditions of identifying extracellular MC-LR, MC-YR, and MC-RR are shown in Table 1. For intracellular MCs, the aqueous phase A was 0.1% formic acid and solvent B was acetonitrile. A linear gradient of 80%A + 20%B in 0 min, 40% A + 60%B in 3 min, and 10%A + 90%B in 3.5 min, followed by equilibration to 4 min at 80%A + 20%B, was used. The flow rate was set to 0.25 mL/min. Analysis was conducted in positive electrospray ionization with monitoring for the two analytes occurring in selected ion monitoring (SIM) mode at m/z 519.8 (MC-RR), m/z 995.5 (MC-LR), and m/z 1045.6 (MC-YR). The optimum conditions for MC analysis included a heated capillary temperature of 110℃, a sheath gas temperature of 380℃, a nitrogen sheath gas flow of 900 L·h −1 , an auxiliary gas flow of 50 L·h −1 , and a capillary voltage of 3.0 kV.

Other analysis methods
Water quality parameters, such as water temperature (WT), electronic conductivity (EC), oxidation-reduction potential (ORP), dissolved oxygen (DO), and pH, were measured at each sampling site by a YSI 6600 multiprobe sonde (OH, USA). Samples were brought back to the laboratory and filtered for the nutrients and Chl-a analysis. Nutrients including total phosphorus (TP), dissolved total phosphorus (DTP), orthophosphate (PO 4 3− -P), total nitrogen (TN), dissolved total nitrogen (DTN), ammonia nitrogen (NH 4 + -N), and nitrate-nitrogen (NO 3− -N) and COD Cr were analyzed by standard methods of APHA (1998). Concentration of Chl-a was measured by using a spectrophotometer after extraction in ethanol (APHA 1998). All samples were maintained at − 20℃ until further processing.
(1) TMCs = IMC + EMC Monthly Chl-a and MCs concentrations of each sampling site were obtained by averaging the three times of samples in each sampling month. Annual Chl-a and MCs concentrations of each sampling site were obtained by averaging the samples of each sampling month. The spatiotemporal distributions of Chl-a and MCs of every sampling month were illustrated using ArcGIS10.2 interpolation using IDW method. Pearson's correlation analyses were performed using SPSS (Version 18.0) to identify relationships between environmental variables and the concentration of all types of MCs. Two tailed tests of significance were used. Linear regression analysis was used to identify and model the strongest relationship between Chl-a and MCs. The model discarded several variables which had no effect or lowered the regression coefficient (R 2 ) and only retained those which could explain variance within the MCs data.

Seasonal variability of Chl-a
The variation of Chl-a concentration distribution in the four seasons in Lake Taihu was shown in Fig. 2. The Chl-a concentration fluctuated during the year. The temperature rose slowly in spring leading to slow growth of phytoplankton. According to the international trophic state classification (Vollenwieder 1968), the Chl-a concentration of Lake Taihu in March mainly maintained at Level II and III, i.e., mesotrophic (1.6-10 mg·m −3 ) and eutrophic (10-26 mg·m −3 ) states. Then, the Chl-a concentration increased gradually.
Later in summer, in June, a bloom was observed in West Zone and Center Zone. The Chl-a concentration reached hypertrophic state (Level V, 64-160 mg·m −3 ) in these two Zones. Especially, the maximum Chl-a concentration reached 230 mg/m 3 in Center zone. The Chl-a concentration in July Entering autumn, the overall Chl-a concentration of Lake Taihu in September somewhat decreased compared with August, and the Chl-a concentration was relatively low in the range of 1.6 ~ 15 mg·m −3 (Level II and Level III) in South Zone, Dongtaihu Bay, and East Zone. However, a bloom was still observed in Zhushan Bay, Meiliang Bay, and Gonghu Bay with the Chl-a concentration at Level V hypertrophic state (77-200 mg·m −3 ). The high level of Chl-a concentration lasted until October with Meiliang Bay, Gonghu Bay, and West Zone at supertrophic state (Level IV). In autumn, the Chl-a concentration gradually diminished and continued to decrease in winter. The Chl-a concentration in Gonghu Bay, Meiliang Bay, and Center Zone in winter was at supertrophic state, and the other areas were at mesotrophic or eutrophic state.
As above, in summer and autumn with thriving algal growth, three blooms were observed in West Zone and Center Zone in June, Zhushan Bay in August and Meiliang Bay September. However, the concentration of Chl-a was not in unidirectional increase or decrease during the year, while it was in alternative rise and fall in corresponding months.

Spatial variability of Chl-a
The regional distributions of annual average Chl-a concentration of the year is shown in Fig. 2(h). The annual averages of the Chl-a concentration in eight sub-regions in the lake had followed the decreasing sequence: ZB > MB > GB > WZ > CZ > SZ > EZ > DB, meaning that the algae would accumulate in the northern and west part of the lake. The high Chl-a concentration observed in Zhushan Bay can be explained that relative to lakewide averages, Zhushan Bay had 17% higher DTN and 100% higher DTP concentrations (Otten et al. 2012). From the view of the full year, Zhushan Bay, Meiliang Bay, Gonghu Bay, and West Zone were all in supertrophic state (Level IV). As the source of drinking water, East Zone and Dongtaihu Bay were in mesotrophic (Level II) meaning a good water quality and hardly any algae bloom occurring in these regions, while the Center Zone and South Zone was in eutrophic state (Level III).
The spatial distribution of Chl-a was uneven, which was closely correlated with the nutrition conditions of the waters. The highest concentrations of TN, NH 4 + -N, TP, PO 4 3-P, and COD Mn were all observed in Zhushan Bay, respectively, reaching 3.85 mg·L −1 , 1.23 mg·L −1 , 0.34 mg·L −1 , 0.1 mg·L −1 , and 8.65 mg·L −1 , followed by Meiliang Bay in 2013. Some developed cities such as Wuxi and Changzhou located in the upstream area of north part of Taihu. A large amount of pollutions was discharged into the lake with or without treatment, especially some chemical engineering and textile industry contributed the most pollution, which greatly affected the water quality of Lake Taihu.

Spatiotemporal patterns of MCs in Lake Taihu
TMC-LR was the highest portion of TMCs accounted for 50-80% in the waters of Lake Taihu. Also, due to the high toxicity of MC-LR, the spatiotemporal variation of TMC-LR and TMCs in the waters of Lake Taihu were discussed in the following sections. Although intercellular MCs are not an immediate threat to human health, serious impacts would occur if these MCs were to be released into waters. Therefore, the intercellular and extracellular MCs were both considered during the research.

Spatial and seasonal variation of TMC-LR
The variation of TMC-LR concentration distribution of a year in Lake Taihu was shown in Fig. 3. During spring (March) and winter (December), almost entire region of Lake Taihu showed relatively low concentrations of TMC-LR with little fluctuation (in the range of 0.004-0.09 μg·L −1 ). The concentration increased gradually in summer. Zhushan Bay, Meiliang Bay, and West Zone were in a relatively high level with TMC-LR concentration higher than 1 μg·L −1 . Then, in autumn, the MC-LR seemed to spread over to the whole north part of the lake and reached a peak in October, which is different from the Chl-a diminished in autumn. The particularly high concentrations were observed in the Meiliang Bay (152 μg·L −1 ), followed by Gonghu Bay and West Zone, while Zhushan bay was unexpected to show a low TMC-LR concentration (0.04-0.34 μg·L −1 ). After these blooms, the concentration of TMC-LR in each sub-region decreased and met the bottom in winter.
The temperature and solar radiation provide the phytoplankton an optimum environment to grow; besides, zooplankton grazing, self-shading, and weather events such as wind speed and directions may be other factors that further influence the MCs distribution (Ji et al. 2008;Fu et al. 2016;Walls et al. 2018). The annual average TMC-LR concentration distribution of the lake is shown in Fig. 3 (h). The annual averages of the TMC-LR in eight different sub-regions in the lake had followed the decreasing sequence: MB > ZB > GB, WZ > CZ > EZ, SZ, DB. The northern and western part of Lake Taihu suffered more severe MCs pollution than other parts, while the southern and eastern part nearly experienced little MCs pollution during the full year.

Spatial and seasonal variation of TMCs
There were slight differences between the distribution of MC-LR, MC-RR, and MC-YR. As TMC-LR accounted for 50-80% of TMCs, the spatiotemporal distribution of TMCs was basically consistent with that of TMC-LR as shown in Fig. 4(a-g). MCs are the metabolites of numerous strains of Microcystis spp. Therefore, the production of MCs is promoted by nutrient replete, warm, and slow moving or stagnant waters, which allow Microcystis spp. to proliferate to the point that surface waters are covered with thick green scums (Otten et al. 2012). In summer and autumn, the cyanobacteria cells speed up proliferations, and the TMCs increases accordingly, so the overall TMC concentrations had followed the increasing sequence: winter < spring < summer < autumn. The TMCs concentrations of the entire Lake Taihu peaked in October and the annual maximal TMCs concentration (265 μg·L −1 ) appeared in Meiliang Bay. This result was different from that obtained by Song et al. (2007). They considered that the MCs concentration of Lake Taihu usually peaked in summer from June to August. Generally, MCs are retained in cyanobacterial cells during the growth and steady phase of blooms but released into the water body by senescence of the blooms. So that of MCs peaked in October, slightly lagging the Chl-a blooms in August is reasonable. The seasonal pattern of TMC concentrations suggested that MC production speed and capacity was significantly influenced by temperature, and the temperature was the decisive factor affecting cyanobacteria biological variation and toxin seasonal distribution (Davis et al. 2009;Shen et al. 2003).
Annual spatial pattern of TMCs concentration was: MB > ZB > GB > CZ, WZ > EZ, SZ (Fig. 4h). A large amount of effluent discharged into the Meiliang Bay directly, and the downstream from Wulihu Lake also affected the water quality of Meiliang Bay. Also, the prevalent southeast wind of Lake Taihu is beneficial to the algae gathered in Meiliang Bay, Zhushan Bay, and Gonghu Bay. The two mentioned reasons may contribute to the high TMCs concentration of Meiliang Bay, Zhushan Bay, and Gonghu Bay.

Correlation between Chl-a, MCs, and nutrients
Various environmental factors such as nutrients, pH, and ORP have complicated effects on cyanobacterial growth, Chl-a concentration and production, release and migration of MCs (Tong et al. 2020;Krausfeldt et al. 2020). Their interaction and the presence of conflict and confrontation varied with the change of time, geographic locations, and meteorological conditions. The correlation between the Chl-a concentrations, MCs, and physiochemical factors was analyzed ( Table 2). The results revealed that the Chl-a in the waters of Lake Taihu was significantly positively correlated to TN, TP, and COD Mn (P < 0.01), and also significantly correlated to NH 4 + -N and PO 4 3 -P (P < 0.05). Significantly positive correlations were also observed between Chl-a and nearly all forms of MCs (P < 0.01) except for extracellular microcystins.
The MCs in the waters of Lake Taihu was significantly positively correlated to TN, TP, and COD Mn (P < 0.01), but except NH 4 + -N and PO 4 3 -P. All forms of MCs were all highly correlated to TP, so the phosphorus was presumed to be the limiting nutrient factor in Lake Taihu. Thus, slight change of phosphorus concentration would affect cyanobacteria growth and MC production. Oh et al. (2000) revealed that the MC-LR concentration was more than MC-RR in the P limiting water environment, which may be the reason why MC-LR is the main MC isomer existing in Lake Taihu.

Determining of Chl-a threshold based on MCs standards
The WHO recommends that MCs not exceed 1 μg·L −1 in drinking water (WHO, 1998;Chorus and Bartram 1999;Wang et al. 2021) and 20 μg·L −1 in recreational water (WHO 2003). According to Duy et al. (2000), the tolerable daily intake (TDI) of TMC-LR and TMCs is 0.0133 and 0.0368 μg·kg −1 ·day −1 , respectively. Assuming daily drinking water consumption of 2 L for a 60 kg adult, l L for a 10 kg child, and 0.75 L for a 5 kg infant, the guideline values of TMC-LR for adults, children, and infant were 0.32, 0.11, and 0.07 μg·L −1 calculated by the method provided by Duy et al. (2000); and accordingly, that of TMCs for adults, children, and infant was 0.88, 0.29, and 0.2 μg·L −1 (Duy et al. 2000).
The correlation coefficient between Chl-a and TMC-LR was 0.886 (P < 0.01). A first-order linear regression was used to simulate the relationship between TMC-LR and Chl-a concentrations. As seen from Fig. 5, a regression equation to estimate the Chl-a concentration of a given sample based on TMC-LR was derived as: The correlation coefficient (R 2 = 0.593) was relatively low, and this is because Chl-a and MCs were both easily subject to wind speed and directions, sunlight and hydraulic conditions, etc., which may intervene the correlation analysis between MCs and Chl-a. In addition, the Chl-a and MCs of the entire Lake Taihu area were investigated, with wide coverage and huge various in different areas, which may also bring deviation to the analysis.
Similarly, the linear regression between lgTMCs and lg Chl-a is shown in Fig. 6. A regression equation to estimate the Chl-a concentration of a given sample based on TMCs was derived as: Likewise, referring to the guideline values of TMCs in drinking water, i.e., 0.88 μg·L −1 for adults, 0.29 μg·L −1 for children, and 0.2 μg·L −1 for babies, the corresponding threshold of Chl-a based on TMCs was 35.8 mg·m −3 , 18.36 mg·m −3 , and 14.68 mg·m −3 .
As above, the Chl-a threshold identified in this study was 10.15-14.68 mg·m −3 , which is attributed to eutrophic state (Level III) according to the international trophic state lgChl − a = (lgTMCs + 2.654)∕1.682(R 2 = 0.595) Table 2 Correlations among water chemistry parameters in Lake Taihu and Chl-a and all forms of MCs * Correlation is significant at the 0.05 level; ** correlation is significant at the 0.01 level; TMC-LR is the sum of IMC-LR and EMC-LR; TMCs is the sum of TMC-LR, TMC-RR, and TMC-YR classification (Vollenwieder 1968). The reference condition value for the lakes in the eastern plain ecoregion of China by the trisection method acquired by Huo et al. (2013) is Chla of 1.78-4.73 mg·m −3 . These reference conditions were relatively lower than the Chl-a threshold obtained in this study. Reference conditions realistically represent the least impacted conditions or what is considered to be the most attainable conditions (US EPA 2000). Reference conditions refer to the "naturalness" of the biota, in the absence of human disturbance or alteration and, as such, represent a target for remediation and restoration (Huo et al. 2013;Pardo et al. 2012). However, most lakes have been impacted by human activity to some degree in China, so the Chl-a threshold of Lake Taihu acquired in this study was of more applicability and feasibility than reference condition to guide the water quality management. As shown in Fig. 2, the average annual Chl-a concentration of nearly all sub-regions are higher than the determined Chl-a threshold except for Dongtaihu Bay and East Zone. Measures are urgent to be taken to reduce the trophic level in Lake Taihu. Moreover, more monitoring sites should be set up in Meiliang Bay, Zhushan Bay, Gonghu Bay, and West Zone to brought forward early warning of algal bloom due to the higher Chl-a concentration in summer and autumn. In particular, since Chl-a can be used as an additional marker, more parameters such as TN, TP still should be monitored continuously.

Compare Chl-a threshold of Lake Taihu with American lakes
China has a vast territory making natural geography and environmental conditions have abundant diversity. America is of the same condition. Some states of America established thresholds on Chl-a taking full account of the differences in geographical and ecological environment of the lakes, in which the latitudes of Arizona State were close to that of the Lake Taihu, so their thresholds were compared.
The Chl-a thresholds of different lakes and reservoirs in the Arizona State were set up according to different application and lake types. For full-body contact and partialbody contact, the Chl-a thresholds of shallow and deep lakes and reservoirs were 10-15 mg·m −3 and that of city lakes are 20-30 mg·m −3 ; and the Chl-a thresholds of all lake types for aquatic and wild cold water and domestic water source are 5-15 mg·m −3 and 10-20 mg·m −3 , respectively (US EPA 2003). The Chl-a threshold of Lake Taihu obtained in this study is 10-15 mg·m −3 , which is within the range of function of body contact and domestic water source in Arizona State.
Lake Taihu is a typical city shallow lake integrated functions of drinking water, landscape, recreation, fisheries, and irrigation, etc. Therefore, compared with the Chl-a threshold of the lakes and reservoirs in the Arizona State of America, the Chl-a threshold determined in this study is scientific and rational. The establishment of Chl-a threshold that once reached would trigger additional environmental management policies and water treatment processes would provide the greatest public health protection while limiting the cost of these measures by employing them only as needed.

Conclusions
Monitoring and analyzing the variation of spatiotemporal pattern of Chl-a and MCs concentration in a whole year was conducted and the Chl-a threshold was determined based on the existing standards of MCs. The results revealed that nearly, the same characteristics of the spatiotemporal distribution pattern of Chl-a and MCs were observed. The distributions of Chl-a and MCs were highly variable over time and space in Lake Taihu. The maximum Chl-a concentration was observed in Zhushan Bay in August, while the maximum MCs concentration was appeared in Meiliang Bay in October, slightly lagging behind the Chl-a. There was significant positive linear correlation between Chl-a and MCs and the Chl-a threshold was determined as 10-15 mg·m −3 . The establishment of Chl-a threshold can be an early warning indicator of algae blooms and that once reached would trigger comprehensive environmental diagnosis, such as other environmental parameters' monitoring. After that, additional environmental management measures and water treatment processes should be taken.
Author contribution Xuemei Fu contributed to data acquisition, data curation, writing, and original draft preparation. Mingxia Zheng contributed to data acquisition, supervision. Jing Su contributed to reviewing and editing the manuscript. Beidou Xi contributed to supervision and review.