Journal of Hazardous Materials 287(2015)356–363
法海结局
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Journal of Hazardous
Materials
j o u r n a l h o m e p a g e :w w w.e l s e v i e r.c o m /l o c a t e /j h a z m a
t
一个人一座城Cyanobacterial bloom management through integrated monitoring and forecasting in large shallow eutrophic Lake Taihu (China)
Boqiang Qin ∗,Wei Li,Guangwei Zhu,Yunlin Zhang,Tingfeng Wu,Guang Gao
State Key Laboratory of Lake Science and Environment,Nanjing Institute of Geography and Limnology,Chine Academic of Sciences,73East Beijing Road,Nanjing 210008,China
h i g h l i g h t s
落魂阵•A large scale integrated cyanobacterial bloom monitoring is the first in practice.•The cyanobacterial bloom formation following a storm was obrved in-situ clearly.•Integrated monitoring and forecasting increa removal efficiency of algal bloom.
a r t i c l e
i n f o
Article history:
Received 31July 2014Received in revid form 26December 2014
Accepted 20January 2015
Available online 21January 2015
Keywords:
Eutrophication
Toxic cyanobacteria
Cyanobacterial bloom forecast Risk management
a b s t r a c t
The large shallow eutrophic Lake Taihu in China has long suffered from eutrophication and toxic cyanobacterial blooms.Despite considerable efforts to divert effluents from the watershed,the cyanobac-terial blooms still reoccur and persist throughout summer.To mitigate cyanobacterial bloom pollution risk,a large scale integrated monitoring and forecasting system was developed,and a ries of emer-gency respon measures were instigated bad on early warning.This system has been in place for 2009–2012.With this integrated monitoring system,it was found that the detectable maximum and average cyanobacterial bloom area were similar to that before drinking water crisis,indicating that poor eutrophic status and cyanobacterial bloom had persisted without significant alleviation.It also revealed that cyanobacterial bloom would occur after the inten storm,which may be associated with the increa in buoyance of cyanobacterial colonies.Although the cyanobacterial blooms had persisted during the monitoring period,there had been a reduction in frequency and intensity of the cyanobacterial bloom induced black water agglomerates (a phenomenon of algal bloom death decay to relea a large amount black dissolved organic matter),and there have been no further drinking water cris.This monitoring and respon strategy can reduce the cyanobacteria
l bloom pollution risk,but cannot reduce eutrophication and cyanobacterial blooms,problems which will take decades to resolve.
©2015Elvier B.V.All rights rerved.
1.Introduction
Nutrient enrichment of freshwater systems has led to a global proliferation of harmful cyanobacterial blooms [1,2],which foul water intakes [3],disrupt food webs [4],drive hypoxia [5],reduce biodiversity [6]and produce condary metabolites that are toxic to consumers,ranging from zooplankton,shellfish,fish,cattle,and domestic pets to humans [7,8].The impacts cau great economic loss [9],which may be exacerbated by global warming [10–12].Many planktonic cyanobacterial species can produce cyanotox-ins such as neurotoxins,hepatotoxins,cytotoxins,and irritant and
∗Corresponding author.Tel.:+862586882192;fax:+862557714759.E-mail address:qinbq@niglas.ac (B.Qin).
gastrointestinal toxins [7,8].The proliferations and expansions of toxic cyanobacterial blooms resultin
g from eutrophication threaten human health [13].Many countries have documented the pres-ence of toxic cyanobacterial blooms in their drinking water sources or recreational sites [14],and cyanobacterial blooms are expected to increa in the 21st century due to global warming [10,15].Therefore,mitigating the risk of toxic cyanobacterial blooms,and reducing their negative impacts,is urgent management needs for eutrophic water bodies,especially for lakes with key roles as drink-ing water supply rervoirs or recreational sites [14,16].
Toxic cyanobacterial bloom issues and the potential threat for human health are more vere in China [17].In Lake Taihu,the third largest freshwater lake in China,eutrophication has promoted cyanobacterial blooms which are mainly comprid of Microcys-tis spp.with biovolume accounting for >90%during summer time
dx.doi/10.1016/j.jhazmat.2015.01.0470304-3894/©2015Elvier B.V.All rights rerved.
B.Qin et al./Journal of Hazardous Materials287(2015)356–363357
[18,19],which have impaired drinking water supplies becau of the strong odor and taste[20]and the prence of microcystin(MC) from Microcystis spp.[21–26].The documented MC concentration in untreated water in Lake Taihu was4.8–44.00g/L[26]which was much higher than the up limit of safe
value for human being exposure(1g/L)recommend by WHO[13],and MCs were also prent in tap water after treatment[27];the latter may be one of the primary reason for the prevalence of liver cancer in eastern China[17,28,29].With significant public health concerns about the water quality in this lake,it was deemed necessary to implement algal bloom monitoring,forecasting,and emergency respon mea-sures,along with best practices for cyanobacterial bloom pollution risk reduction and maintenance of safe drinking water supply.
Traditional monitoring methods have relied on ship-bad sam-pling,followed by laboratory analys,approaches which are often time-consuming.In addition,traditional ship-borne monitoring is often limited by poor weather,thus precluding adequate spa-tial and temporal coverage.Cyanobacterial blooms are usually highly heterogeneous in space and time in large water bod-ies[30].Subquently,high-frequency automatic measurements have been developed[31,32],with compact nsors for detection of water quality and cyanobacterial species,using specificfluo-rescencefinger-printers andfluorometers[33–36].Recently,this unattended,wireless,high-frequency detection technology,com-bined with spatial remote nsing monitoring of cyanobacterial bloom provided a solution for monitoring highly heterogeneous cyanobacterial blooms in eutrophic water bodies[37–39].But there are lack of large scale application and the practicability examina-tion.
In this paper,we prent a cyanobacterial bloom threat reduction strategy that combined surface cyanobacterial bloom monitoring,forecasting,alert warning and risk management mea-sures,in eutrophic Lake Taihu.This strategy focud on delivering information for risk asssment and management respon,and was a joint venture between government oriented management agencies and rearch institutions.The purpo of this paper is (1)to examine the capability of this integrated monitoring schema to monitor the spatio-temporal changes of surface cyanobacterial bloom,and(2)to evaluate the effectiveness of this integrated mon-itoring bad surface cyanobacterial bloom forecasting and risk management in Lake Taihu.
2.Data and methods
哑铃怎么练背
2.1.Study site
Lake Taihu is a large(with area2338km2),shallow(with mean depth 1.9m)and eutrophic lake located in the delta of River Changjiang(Yangtze River)(Fig.1)where is the most industrial-ized,urbanized and denly populated area in China.It has multiple functions,such as drinking water supply,flood control[40],sup-porting tourism and recreation[41],shipping and aquaculture[42]. The primary function is drinking water supply,with more than10 million people relying on the lake for this resource,particularly in the cities of Shanghai,Suzhou,Wuxi,and Huzhou.
Since the reforming and opening of China in the1980s,this lake has experienced vere eutrophication[43,44].According to monthly monitoring conducted by Taihu laboratory for lake ecosys-tem rearch(TLLER),the total nitrogen(TN)and total phosphorus (TP)concentration incread since earlier1990s and peaked at 2007(Supporting information Fig.S1),which resulted in the phy-toplankton proliferation and algal bloom occurrence.Long-term investigations suggested that the algal bloom mainly comprid Microcystis spp.which could account for90%of total phytoplank-ton biovolume[18],and mostly distributed in the north stretched
to
Fig.1.Location of Lake Taihu and main cities around lake.
the northwest along shoreline(Supporting information Fig.S2).It finally resulted in a rious drinking water supply accident in late May2007and caud millions residents no drinking water sup-ply for nearly a week[3,15].This drinking water crisis became a turning-point.Since that time,the control of lake eutrophication and cyanobacterial blooms have become a priority for local govern-ments.Many efforts have been made toward diversion of effluent in the Taihu catchment and curity of drinking water supply[45]. As a part of the efforts,cyanobacterial bloom monitoring,fore-casting,and drinking water supply curing have been conducted since drinking water crisis.
2.2.Water quality and cyanobacterial bloom monitoring
The monitoring comprid three different elements:(i)remote nsing image retrieval,(ii)unattended nsor detection,with wireless data transmission,and(iii)ship-borne sampling and analysis.The lection of monitoring indices depended on the feasibility and availability.In terms of monitoring data of chlorophyll and phytoplankton during2000–2012conducted by Taihu laboratory for lake ecosystem(TLLER),visible surface cyanobacterial bloom in Lake Taihu was defined as the Chl a con
centration20g/L which was equivalent to phytoplankton bio-volume5.0×107cells/L(Supporting information Fig.S3).Surface cyanobacterial bloom occurrence is associated with physiological environmental parameters such as nutrients(nitrogen and phos-phorus)[1,46],temperature[47,48],and light[49–51],and physical environmental conditions such as wind induced wave and current [52–54].The physiological conditions determined cyanobacterial biomass which was a basis for bloom formation.The physical con-ditions determined the formation of visible surface bloom.Becau Microsystis spp.was the overwhelming dominant species during blooming periods[18,19]and Chl a concentration was an easy and economic monitoring parameter,Chl a was lected as an indicator of the cyanobacterial bloom intensity.All above physiological and physical parameters were included in this monitoring strategy.
Becau the occurrence of cyanobacterial blooms in Lake Taihu was often highly heterogeneous in space.Remote nsing technol-ogy was ud to solve this issue.The algal bloom signals in remote nsing images from Lake Taihu were retrieved through using a reflectance wavelength around859nm(band2of moderate-resolution imaging spectroradiometer(MODIS),which is a payload scientific instrument launched into Earth orbit by NASA.The instruments capture data in36spectral bands at varying spatial res-olutions(250–1000m),and image the entire Earth every1–2days)
358 B.Qin et al./Journal of Hazardous Materials287(2015)
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Fig.2.Spatial distribution of18automatic obrvation and manually sampling sites and defined three regions.
[55],combined with band-ratio(859versus555nm)[56],and the algal bloom was delineated with a threshold value of reflectance wavelength(859nm or band-ratio>1).This threshold value was estimated in corresponding to Chl a concentration∼101mg/L.The MODIS images with250m resolution data were downloaded from the NASA EOS data gateway(EDG).
In order to increa the temporal resolution so as to monitor the quick changes of cyanobacterial bloom,eighteen automatic wireless high-frequency monitoring station were deployed in the north and north–west of the lake,where the water was the most eutrophic,with high concentrations of Chl a and frequent recur-rence of cyanobacterial blooms(Supporting information Fig.S2) (Fig.2).Each station was equipped with a buoy,and nsors for water temperature(three layers from the water surface to bot-tom),turbidity,pH,Chl a,phycocyanin,conductivity,dissolved oxygen,and meteorological parameter nsors(air temperature, air pressure,wind speed,and direction,humidity,precipitation, solar radiation).In addition,three monitoring stations were also equipped with hydrodynamic nsors to rec吻腹实践
ord water current and waves(Supporting information Table S1).Every10–30min,the parameters were recorded and then converted to digital signals and transmitted wirelessly to the Taihu laboratory for lake ecosystem rearch(TLLER)bad on the distributed time division multiple access(TDMA)protocol[57](Supporting information Figs.S4–S5, Table S1).
Additional data on nitrogen,phosphorus and Chl a concentra-tions were available from manual monitoring,bad on samples collected by ship.Sample collection and analysis were conducted twice a week(Monday and Thursday),from April until October for four years(2009–2012),for18sites adjacent to the automatic wireless obrvation sites(Fig.2).The transparency was mea-sured in-situ with cchi disk.Water samples from the surface and bottom were mixed and promptly transported to TLLER for anal-ysis.Concentration of TP,TDP,TN,TDN,and Chl a were analyzed according to Chine standard methods[58].Specifically,TN and TP concentrations were determined by spectrophotometry after digestion with alkaline potassium persulfate[59].Absorption coef-ficients at wavelengths of210nm were ud for TN concentration calculation and absorption coefficients at wavelengths of700nm for TP concentration calculation were obtained by molybdenum blue method.Water samples for TDP and TDN werefirstfiltered through GF/Ffiberglassfilter(Whatman,UK),and then followed the same procedures as TN and TP.Samples fo
r Chl a werefil-tered by GF/Cfiberglassfilter(Whatman,UK),and thefilters were frozen in dark under−4◦C.Chl a was calculated from spectrophoto-metric measurements at wavelengths of665nm and750nm after extraction in90%hot ethanol.
2.3.Surface cyanobacterial bloom forecast
英语专业好就业吗
Cyanobacterial bloom occurrence prediction model included three key factors:(i)algal biomass indicated as chlorophyll a concentration,(ii)wind-induced turbulence,indicated by wind velocity;and(iii)precipitation which may diminish cyanobacte-ria via irradiance[60].Algal blooms would occur with high algal biomass(>20g/L),low wind velocity(<3.1m/s[61]),and low or abnce of precipitation.The probability of cyanobacterial bloom occurrence was a product of the probabilities of chlorophyll a con-centration,the wind strength(Beaufort scale),and precipitation (Supporting information Table S2).The phytoplankton biomass (indicated as Chl a concentration)in predictive model was calcu-lated by summing the algae growth,death and ttling rate during the predictive period[62].The input for the algal biomass pre-diction model included initial algal biomass(Chl a),temperature (measured by automatic and high frequency monitoring nsor), light(measured manually from the transparency),P and N con-centration(measured by manually monitoring).The input data were gridded with interpolation or extrapolation bad on recip-rocal of distance[62].Forec
asting of algal blooms in space was achieved by running phytoplankton biomass predictive model cou-pled a three dimensional hydrodynamic model.This hydrodynamic model employed an unstructured triangular mesh in the horizontal to reprent the complex geometry and-coordinate in the vertical to reprent the irregular bottom topography,and energy conrv-ing using thefinite volume method[62].Becau Lake Taihu is very shallow,the vertical computational domain was divided into five layers and the algal biomass(indicated as Chl a concentra-tion)at surface layer would determine the visible cyanobacterial bloom.Thus,the future algal biomass(Chl a)spatial distribution was predicted using initial conditions from monitored data and driving input from the weather forecast.The algal biomass(Chl a) prediction model was run twice a week(every Monday and Thurs-day).The input of every run was updated from monitored data, thus avoiding an accumulation of simulation deviation.According to the predicted Chl a concentration at each cell,along with the probability of wind intensity and precipitation from weather fore-cast,the probability of cyanobacterial bloom occurrence in future three days could be calculated and the forecast report was produced (Supporting information Fig.S6).
The cyanobacterial bloom prediction results were evaluated using the following three days obrvation data either from remote nsing image or automatic high-frequency measurement,or shi
p-bad sampling data,depending on the availability.Three most concerned regions were defined to evaluate the forecast precision, i.e.,Meiliang Bay Gonghu Bay and Western Taihu(Fig.2).If one grid cell in the defined region had cyanobacterial occurrence probability greater than0.5,and the obrvation,either from remote ns-ing image or automatic monitoring or manual obrvation,showed the cyanobacterial bloom occurred in the defined region,it meant cyanobacterial bloom occurrence forecast success,and vice versa. The forecast precision for specific region was obtained by count-ing the total number of days in which cyanobacterial bloom were predicted correctly and divided by the total forecasted days.
B.Qin et al./Journal of Hazardous Materials287(2015)356–363
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Fig.3.Annual maximum cyanobacterial bloom area in Lake Taihu in2009(top-left),2010(top-right),2011(bottom-left),and2012(bottom-right).
3.Results
3.1.Surface cyanobacterial bloom monitoring
The monitoring of cyanobacterial blooms in Lake Taihu initi-ated in2008.For the monitoring data continuity,the period from April2009to October2012was lected for implementation of this study.
红糖馒头During the warmer months from April to October in the four concutive years of monitoring(2009–2012),there were a total of 258images from which cyanobacterial bloom could be retrieved. Of the,217images showed cyanobacterial bloom reflectance signals,and the maximum area of cyanobacterial blooms were 524.2km2,982.8km2,997.5km2,and991.4km2(Fig.3),and the mean areas were154.3km2,191.6km2,242.8km2,and191.1km2 for2009–2012,respectively,which suggested that Lake Taihu continued to suffer from vere eutrophication,and that surface cyanobacterial blooms recurred without significant alleviation.
In August2009,remote nsing and wireless automatic online obrvation showed that the cyanobacterial bloom dynamics fol-lowing the passage of a typhoon over Lake Taihu.Typhoon Morakot pasd Lake Taihu during August11–13,2009,with wind velocity peaking around8.7m/s at17:00August11,2009(Fig.4a).Water current measurement showed that before early morning of August 12,the verticalflow velocity had bi-direction movement(up and down),and after that time,there was uniform upward movement near surface layer(Fig.4b).Interestingly,around the peak typhoon, thefluorescence-measured Chl a concentrations at the surface and bottom were destratified and fully mixed,while the surface Chl a concentration incread and bottom Chl a concentration decread in the post-typhoon period.Correspondingly,the satellite image showed that there were veral small ctions of bloom with total area92.3km2in the center of lake at10:27am when the wind strength was at beginning of decline(Fig.4c);one day later the bloom area had expanded to467.6km2(Fig.4d).The data from August11–13,2009showed that cyanobacterial bloom formation
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B.Qin et al./Journal of Hazardous Materials 287(2015)
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Fig.4.Formation of the cyanobacterial bloom during typhoon Morakot in August 2009.(a)The wind speed and direction,(b)vertical flow velocity of cyanobacterial particles,and (c and d)satellite images of the cyanobacterial blooms after the peak of the typhoon at 10:27am (top-right)and 12:48am (bottom-right),on August 13.
in the short period after the peak of the typhoon was determined by hydrodynamic conditions.
3.2.Forecasting surface cyanobacterial bloom
During the four concutive summers of monitoring,the model predicted the cyanobacterial bloom outbreaks and accumulation at the surface as 141,150,138,and 102days in 2009–2012,respectively.The prediction precision was evaluated by compar-ing chlorophyll a concentration from model output with data from satellite images,automatic monitoring parameters or boat survey data in the subquent three days.The mean forecast precision was 82.2±11.7%(Table 1).In addition,the satellite images were ud to evaluate forecasted spatial distribution of cyanobacterial bloom.An example of this asssment was the comparison of cyanobacterial bloom model prediction against the retrieved algal bloom imagery from the satellite on July 312009(Supporting information Fig.S7).Visible comparison suggested that the forecasted spatial pattern of cyanobacterial bloom ma
梦见老公杀人tched the images very well.The good agreement between prediction and obrvation of algal blooms provided confidence that the data acquisition and management system in Lake Taihu can be ud as an alert tool for subquent risk management.
3.3.Risk management for cyanobacterial bloom
The aim of the extensive monitoring and forecasting system was to provide early warning of cyanobacterial bloom issues to government and water authorities via generation of forecasts and their distribution to Lake Taihu management agencies.To interpret this forecast data and u it for risk management,three levels of cyanobacterial bloom events were defined bad on the historical obrvations:
Level 1:Cyanobacterial bloom area was greater than 60%of the lake area (i.e.,1403km 2),or a Microcystis spp.biomass higher than 8×108cells/L,or cyanobacterial bloom caud intrusion of black water agglomerate within the 2500m distance of the main intake of drinking water.
Level 2:Cyanobacterial bloom area was greater than 40%of the lake area (i.e.,935km 2),or the Microcystis spp.biomass was higher than 5×108cells/L and less than 8×108cells/L,or the cyanobacte-rial bloom caud intrusion of black water agglomerate within the 5000m distance of the
main intake for drinking water.
Level 3:Cyanobacterial bloom area was greater than 10%of lake area (i.e.,267km 2),or the Microcystis spp.biomass was higher than 3×108cells/L and less than 5×108cells/L.
The different levels of cyanobacterial bloom events were accorded different counter measures to reduce the risk of pollution.The emergency respons included water diversion for flushing,
