Francois Leconte
email: francois.leconte@univ-lorraine.fr

Julien Bouyer
email: julien.bouyer@cerema.fr

Rémy Claverie
email: remy.claverie@cerema.fr

Nocturnal cooling in Local Climate Zone: statistical approach using mobile measurements

Keywords: Urban Heat Island, Air temperature, Mobile measurements, Nocturnal cooling, Local Climate Zone, Urban indicators, Regression analysis

Up-to-date climate scenarios warn that the frequency and intensity of heat waves are likely to increase in Europe during the twenty first century. In these circumstances, urban climate is progressively taken into account in urban planning processes, for instance using terrain classifications. The developed methodology involves the Local Climate Zone scheme (LCZ), which has been applied to Nancy, France. Mobile measurements campaigns have been performed using an instrumented vehicle in order to record daily air temperature dynamics, with a focus on the daily cooling period. So as to quantify LCZ cooling before and after sunset over several days, a climatic indicator called Normalized Cooling Rate (NCR) has been used. This study presents statistical regressions that have been performed between normalized cooling rate values calculated from field measurements and urban indicators. Results indicate that normalized cooling rates and most of the studied urban features are correlated. Regression model using sky view factor and distance to the city center seems enough accurate to estimate nocturnal cooling at the district scale.

Introduction

Urban heat island (UHI), that is described as the air temperature difference between city centers and their surrounding rural areas, has been recognized since the 1970s [START_REF] Oke | City size and the urban heat island[END_REF] and is now a well studied anthropic climatic phenomenon [START_REF] Arnfield | Two decades of urban climate research: a review of turbulence, exchanges of energy and water, and the urban heat island[END_REF] [START_REF] Gartland | Heat Island : Understanding and Mitigating Heat in Urban Areas[END_REF]. It has been identified as a major concern for public policies [START_REF] Rizwan | A review on the generation, determination and mitigation of urban heat island[END_REF], for now and for the next decades when heat waves will be more frequent. UHI needs to be more documented and understood by planners in order to improve its integration in projects and applications, especially at the district scale [START_REF] Grimmond | Climate and more sustainable cities: climate information for improved planning and management of cities (producers/capabilities perspective)[END_REF] [START_REF] Baklanov | From urban meteorology, climate and environment research to integrated city services[END_REF].

The UHI magnitude is both influenced by geographical parameters and meteorological conditions -that can be qualified as uncontrollable variables -and by city morphology, materials, land use and anthropogenic sourcesthat can be qualified as controllable variables [START_REF] Rizwan | A review on the generation, determination and mitigation of urban heat island[END_REF].

In order to perform accurate city climate diagnosis, modelling approaches are improving continuously [START_REF] Mirzaei | Recent challenges in modeling of urban heat island[END_REF]. Surface Energy Balance in meteorological meso-scale models is the more relevant technique to deal with physical processes both from district to city scale [START_REF] Grimmond | Climate and more sustainable cities: climate information for improved planning and management of cities (producers/capabilities perspective)[END_REF]) [START_REF] Grimmond | Initial results from phase 2 of the international energy balance model comparison[END_REF]. However, these models require at least a high level of expertise held by researchers or technical engineers with a strong background in climatology, or even, depending on the accuracy of model parametrisation, well-supplied urban databases.

To take into account climate processes in a first approach, many empirical and statistical modelling of UHI have been investigated. Oke proposed a method to link UHI maximum intensity with population and incident wind, and improved it by a function of aspect ratio to include the impact of city morphology [START_REF] Oke | Boundary Layer Climate[END_REF]. This approach has been pursued by several studies involving sky view factor [START_REF] Unger | Intra-urban relationship between surface geometry and urban heat island: review and new approach[END_REF] [START_REF] Svensson | Sky view factor analysis -implications for urban air temperature differences[END_REF], a combination of urban indicators [START_REF] Balázs | Simulation of the mean urban heat island using 2D surface parameters: empirical modelling, verification and extension[END_REF] or hybrid method between meteorological data and urban indicators [START_REF] Bernard | Urban heat island temporal and spatial variations: Empirical modeling from geographical and meteorological data[END_REF].

Other researches have descriminated urban features using terrain classification such as the Urban Climate Zone [START_REF] Oke | Toward better scientific communication in urban climate[END_REF] and more recently the Local Climate Zone (LCZ) scheme [START_REF] Stewart | Local Climate Zones for urban temperature studies[END_REF] to characterize inner urban climate. Measurements campaigns have highlighted that LCZ demonstrate homogeneous thermal behavior [START_REF] Leconte | Using Local Climate Zone scheme for UHI assessment: evaluation of the method using mobile measurements[END_REF] [START_REF] Stewart | Evaluation of the 'Local Climate Zone' scheme using temperature observations and model simulations[END_REF].

Decision makers are looking for models involving urban indicators that provide quantitative information about thermal behavior of districts. This work aims to develop correlations between LCZ indicators and UHI descriptors. This paper adresses specifically the methodology of statistical analysis and the limits associated to the consistency of LCZ indicators.

Materials and methods

Mobile measurements within Local Climate Zone 2.1.1. Climate classification in Nancy

The conurbation of Nancy is located North East of France and numbers 286, 000 inhabitants. It has an area of approximately 270 km 2 and its elevation ranges between 190 meters and 420 meters. Regarding its geographic features, Nancy is surrounded by two plateaus located West and North East of the conurbation, and crossed by two water bodies from South East to North. The Great Nancy Area is located in the center of the Lorraine region, which exhibits an temperate climate without dry season (Class Cfb according to the Köppen-Geiger classification [START_REF] Peel | Updated world map of the Köppen-Geiger climate classification[END_REF]).

The Local Climate Zones (LCZ) classification has been applied for the area of interest. The determination of LCZ is organized following a threesteps method. The contours of the LCZ have been determined based on land use and building height information as well as global knowledge of the experimental field. Over the conurbation of Nancy, 82 urbanized LCZ have been identified.

LCZ selection for the field experiment

Thirteen LCZ of similar elevation have been selected, representing five different LCZ types. Within these areas, seven urban indicators have been calculated, namely sky view factor, aspect ratio, mean building height, terrain roughness class, building surface fraction, impervious surface fraction and pervious surface fraction. Contours have been corrected and a LCZ type has been assigned to each of these thirteen LCZ regarding urban indicators values (Table 1). Residential areas of Nancy presents urban features at the edge between two LCZ types, namely "Open Lowrise" and "Sparsely Built". It appears that urban indicators calculations did not allow to choose between these two LCZ types, therefore three residential areas have been classified as dual type "Open Lowrise / Sparsely Built".

Field survey with instrumented vehicle

On the basis of this LCZ map, screen-height air temperature has been recorded within these thirteen LCZ during summer 2013 and 2015 (Figure 1). Mobile measurements have been performed using an instrumented vehicle that records air temperature every three meters. A PT100 probe has been placed within a ventilated cylinder, which has been mounted on the roof of the car. This probe has an elevation of two meters above the ground, and an accuracy of 0.2 . Data corresponding to very low speed measurements may be influenced by heat release from other vehicles, therefore measures carrier out below 15 km.h -1 are dismissed. Since the itinerary is mostly urban, the maximum vehicle speed is around 60 km.h -1 . A sensitivity test has been performed in order to study the impact of vehicle speed on air temperature. Results demonstrate that mean air temperature values are marginally impacted by vehicle speed.

The chosen meteorological conditions are low wind speed (below 9 m.s -1 , wind speed at 10 m high), clear sky (from 0 to 2 octas), anticyclonic conditions, dry road and absence of precipitation during the previous twenty-four hours. Regarding these requirements, seven measurements days have been selected. Two measurements hours have been set, which correspond approximately to the beginning of the cooling phase (5 PM local time) and to the expected urban heat island amplitude maximum (three to five hours after sunset, 0 PM local time) [START_REF] Oke | Boundary Layer Climate[END_REF]. A measurement takes approximately two hours and thirty minutes, and during this time the regional temperature can decrease up to 2 at night. A linear time-correction scheme has been applied in order to balance the temperature evolution between the beginning and the end of the measure session. The scheme consists in correcting the measured air temperature proportionally to the elapsed time between the beginning of the session and the measurement.

Nocturnal cooling indicator at district scale

This study focuses on the air temperature behavior at the individual Local Climate Zones unit, which means that spatially averaged air temperature is considered. The spatial homogeneity inside LCZ has been analyzed in previous study [START_REF] Leconte | Using Local Climate Zone scheme for UHI assessment: evaluation of the method using mobile measurements[END_REF]. The daily air temperature cycle is specially investigated. The air temperature difference between two Local Climate Zones is often used to characterize their thermal pattern. However, temperature difference only provides information at a given time of the day. In order to analyse the evolution throughout the time, heating rates or cooling rates can be implemented. These rates are calculated as the derivative of air temperature by the time.

Heating and cooling rates can be considered as urban climatic indicators to examine the urban heat island phenomenon. For city located in the same climate than Nancy, the urban heat island amplitude is generally weak during daytime and strong during nighttime. This phenomenon starts to develop before sunset and reach a maximum at night. Therefore, the cooling period is a relevant period to study the urban heat island. For low cloud cover and low wind speed, Holmer has observed that the cooling of neighborhoods is divided into two different phases [START_REF] Holmer | Colling rates, sky view factors and the development of intra-urban air temperature differences[END_REF]. During a first phase, urban areas show different cooling dynamics while during the second phase, the air temperature dynamics tend to be uniform between urban zones. The first phase coincides with a time period between one to two hours before sunset and three to five hours after sunset (P1 on Figure 2), while the second phase covers a period between three to five hours after sunset to sunrise (P2 on Figure 2). This two-phases cooling has been observed for several locations, namely in Singapore [START_REF] Chow | Temporal dynamics of the urban heat island of Singapore[END_REF], Adelaide, Australia [START_REF] Erell | Intra-urban differences in canopy layer air temperature at a mid-latitude city[END_REF], Athens, Greece [START_REF] Giannopoulou | The impact of canyon geometry on intra urban and urban suburban night temperature differences under warm weather conditions[END_REF], Ouagadougou, Burkina Faso [START_REF] Holmer | Evening evapotranspirative cooling in relation to vegetation and urban geometry in the city of Ouagadougou, Burkina Faso[END_REF], Novi Sad, Serbia [START_REF] Milosevic | Quantification of temporal changes of urban heat island intensity and cooling and heating rates in different local climate zones of mid-sized central european city (tromp foundation travel award)[END_REF], and Nancy, France [START_REF] Leconte | Analysis of nocturnal air temperature in districts using mobile measurements and a cooling indicator[END_REF]. These previous results highlight that the amplitude of the urban heat island rises during phase 1 and remains stable during phase 2. Neighborhoods demonstrate different cooling rates during phase 1 and similar cooling rates during phase 2. In order to explicit the thermal behavior of Local Climate Zones during the development of the urban heat island, cooling rates during phase 1 are examined. The cooling rate are calculated between the daytime measure (character D on Figure 2) and the nighttime measures (character N on Figure 2). They are defined as :

χ DN = ∆T air,DN ∆t DN (1)
With ∆t DN the elapsed time between the two measures and ∆T air,DN the air temperature decrease between the two measures for a given LCZ.

The diurnal temperature range (DTR) is expressed as the temperature difference between daily maximum and daily minimum temperature. It appears that cooling rates are not identical from one day to another due to diurnal temperature range variation. Thus, cooling rates during phase 1 have been normalized in order to be able to compare them when several days show dissimilar DTR values. When several Local Climate Zones are studied, the cooling rates are divided by the temperature drop observed in the coolest Local Climate Zone during phase 1 (∆T air,DN,max ). In this way, all cooling rates -named normalized cooling rates (NCR) -are expressed relatively to an identified reference, namely the coolest studied area :

N CR = χ DN ∆T air,DN,max (2)

Statistical approach

In order to question the existence of a linear relationship between normalized cooling rate and urban indicators (UI), a regression analysis (ordinary least squares method) has been carried out. The regression model is evaluated by using both the regression coefficient to estimate the linearity and the Breusch-Pagan test to check if the variance around the regression line is the same for all values of the predictor (homoscedasticity) [START_REF] Breusch | A simple test for heteroscedasticity and random coefficient variation[END_REF].

Nine urban indicators have been selected. Seven of them belong to the LCZ scheme, namely sky view factor (SVF), aspect ratio (AR), mean building height (H), terrain roughness class (R), building surface fraction (Built), impervious surface fraction (Imper), pervious surface fraction (Per). In addition, the distance to the center of the conurbation (Pos) and the elevation (Elev) have also been examined, as they may impact the thermal behavior of the districts. The statistical results have been evaluated according three criteria:

The regression coefficient of the linear regression between NCR and one (or more) urban indicator is used to evaluate the regression quality. A correlation coefficient close to one corresponds to an accurate regression model.

Akaike Information Criteria (AIC) is applied on regression equations in order to compare them. This criterion is defined as equation ( 3). [START_REF] Akaike | Likelihood of a model and information criteria[END_REF] [START_REF] Akaike | Information Theory and an Extension of the Maximum Likelihood Principle[END_REF])

AIC = 2K -2 log(ℓ( θ|y)) ( 3
)
Where K is the number of parameters to be estimated and log(ℓ( θ|y)) is the log likelihood at its maximum likelihood estimator θ based on y observations. The most effective model is the model with the lowest AIC score. This approach has been used in previous studies for statistical model selection, including in the geosciences field [START_REF] Gopalan | An effective storage function model for an urban watershed in terms of hydrograph reproducibility and akaike information criterion[END_REF]) [START_REF] Bernard | Urban heat island temporal and spatial variations: Empirical modeling from geographical and meteorological data[END_REF]).

Student's t-test has been performed for each UI's coefficient of the linear regression in order to evaluate their contribution. The significance threshold chosen for the P-value is 0.05.

Regression calculations have been completed over a sample representing 75% of the normalized cooling rate values which has been chosen randomly. The remaining values (25% of the experimental data set) have been used to examine the quality of the regression equation. Thus, NCR estimations from empirical models are compared with independent NCR observations. The goodness of fit is estimated with the Chi-Square statistic (eq 4). As depicted in this equation, the Chi-Square statistic (χ 2 ) is based on the difference between the NCR observed during field measurements and the NCR estimated using the statistical models.

χ 2 = (N CR Obs. -N CR Est. ) 2 N CR Est. (4)
The lower the Chi-Square value, the better the NCR estimated value.

Results and discussion

Single urban indicator approach

Regressions have been performed between NCR and each of the nine studied urban indicator U I i (i ∈ [[1, 9]]), according to equation (5).

NCR = β 0 + U I i • β 1 (5)
The constants β 0 and β 1 are the intercept and the slope of the linear regression respectively.

The relationship between NCR and urban indicators have been investigated based on 75% of the 90 experimental data set which has been chosen randomly. Initial dataset included measurements performed during seven days within thirteen LCZ (91 NCR values). One NCR value has been removed from the dataset due to experimental dysfunction. In order to assess the accuracy of this approach, the impact of the sample selection on regression results have been analysed. Multiple samples have been chosen randomly and then used to perform regression between NCR and urban indicators. The number of random samples is twice larger than the amount of field observations. Figure 3 highlights that terrain roughness class, sky view factor, mean building height and the distance to the center of the conurbation achieve the lowest AIC values. The box plots for each urban indicator tend to be similar in terms of interquartile range and distance between extremum values.

Overall, it seems that indicators related to the urban morphology perform better than land use indicators. Indeed, urban morphology indicators are ranked in 1 st , 2 nd , 4 th and 7 th positions in terms of AIC values, while land use indicators are ranked in 5 th , 6 th and 8 th positions.

After this step, one of the 180 random samples is selected, a regression is performed and it confirms the results of Figure 3 in terms of ranking. Table 2 summarizes the values of β 0 and β 1 coefficients as well as P-values, AIC and R 2 for one random sample. Thus, this sample will be used for following analysis. Results show that all Breusch-Pagan values are positives, which demonstrate the homoscedasticity of the data. All coefficients pass the Student's t-test, except in the case of the elevation. Based on AIC and R 2 values, the terrain roughness class is the indicator that leads to the best correlation with normalized cooling rate values (AIC = -362.4, R 2 = 0.48), followed by the sky view factor (AIC = -359.2, R 2 = 0.45) and the distance to the center of the conurbation (AIC = -358.1, R 2 = 0.45). The model that achieves the best results is presented in equation ( 6). NCR = 0.17 • R -0.001 (6)

Scatter plots of experimental data including regression lines are presented (Figure 4). Each graph presents NCR values, in relation to urban indicators calculated within the each of the thirteen identified LCZ, for seven days of measurements. First, a significant dispersion of NCR values can be noticed for each of the urban indicators, between 0.04 and 0.06 h -1 . Nonetheless, all the scattered charts show trends of monotonic increase or decrease, except for the elevation indicator (R 2 = 0.09) mostly because the measurement surveys have been carried out at almost constant elevation. This allows us to elaborate basic conclusions confirming previous studies which express local intensity of heat island as a linear function of the SVF [START_REF] Svensson | Sky view factor analysis -implications for urban air temperature differences[END_REF]. The graphs show patterns of vertical alignments of dots corresponding to the seven days of measurements in each of thirteen LCZ. For several indicators such as AR, SVF or Imper, clusters can be perceived because same LCZ typologies lead to very close indicator values (cf. Table 1). This is even more obvious for R that shows only four distinct values of terrain roughness class. Indeed, R is calculated using the Davenport classification [START_REF] Davenport | Estimating the roughness of cities and sheltered country[END_REF], which implies that R values are integers (cf. Table 1). The thirteen studied LCZ in Nancy belong to only four roughness class. On the other hand, for the position indicator, a slightly more homogeneous horizontal distribution can be observed, except for the two LCZ D (Low plants) located at the outskirt of the city.

In order to evaluate the regression equation related to the urban indicators, NCR computed from statistical regressions has been confronted with NCR obtained from the measurement validation data set (Figure 5). χ 2 values are also calculated. The lower the χ 2 value, the closer the points from the first bisector. For the nine models, χ 2 values range from 0.02 to 0.065. The best results are obtained for the Sky View Factor (0.02), the distance to the center (0.02), the mean building height (0.022) and the terrain roughness class (0.022). Results presented in Figure 5 are consistent with AIC and R 2 values. Indeed, urban indicators with the lowest AIC values and the highest R 2 show the lowest χ 2 values. The elevation of all the studied LCZ is quite similar. Since the NCR varies depending on the LCZ, the regression between NCR and Elev leads to poor results. Thus, the parameter Elev will not be taken into account for the following section.

Finally, the single urban indicator approach allows to identify urban indicators that explain best the NCR variations. However, nocturnal cooling of LCZ is led by multiple factors. Therefore, a multiple urban indicators approach have to be investigated in order to propose a more comprehensive model.

Multiple urban indicators approach

Multiple linear regression are investigated between NCR and several urban indicators (Eq. ( 7)).

NCR = β 0 + p=n p=1 U I p • β p (7)
A collinearity analysis of the urban indicators is carried out. A strong level of collinearity between independent variables can lower the performance of the statistical model. Correlation matrix between the eight urban indicators has been calculated (Figure 6). 82% of the correlation coefficients are above 0.7, which highlights that many indicators are strongly correlated.

The high level of correlation observed in Figure 6 is explained by the morphological and layout patterns of the neighborhoods. Indeed, an increase of sky view factor value may be linked to a decrease of built surface fraction and mean building height. The LCZ position in the conurbation (Pos) is also correlated with morphological and land use indicators. This result may be due to the specific spatial distribution of the LCZ types for the studied area. Within the Nancy conurbation, the most dense areas with highest building height and built surface fraction are located in the center, while the less dense areas with moderate building height and low built surface fraction can be found close to the conurbation's boundaries. Therefore, an increase of the radial distance from the center of the conurbation can correspond to an urbanisation gradient.

Explanatory variables can be considered as weakly correlated if the correlation coefficient ranges between -0.5 and +0.5. It appears that only one pair of urban indicators reaches this criteria, namely the impervious surface fraction with the aspect ratio. However, regarding the single indicator approach (cf. Section 3.1 and Table 2), these two indicators demonstrate poorer correlation with NCR (higher AIC and lower R 2 ) than other indicators. As a result, this pair (i.e. impervious surface fraction and aspect ratio) seems to be not relevant for a regression involving two indicators. This analysis suggests that in the case of multi indicator regression, only a small number of urban indicators can be selected in order to lower the negative effects of the collinearity. The choice of these urban indicators is based on urban and statistical criteria.

First, it appears that urban indicators can be sorted depending on their nature. Sky view factor, aspect ratio, mean building height and terrain roughness class are related to the urban morphology, while the built, impervious and pervious surface fraction refer to the urban land use. The distance to the center of the conurbation can be considered as independent geographical indicator. If the two urban indicators selected for the regression belong to the same group, the model may be redundant and the other group will not be represented. Therefore, the two selected indicator should be chosen in different groups.

Second, indicators that demonstrate low AIC values for single indicator regression can be selected for multiple variables regression. For the land use group, the pervious surface fraction has been selected. Unlike the built and impervious surface fraction, this indicator provides an indirect information regarding the vegetation. Regarding the urban morphology group, R and SVF show the best AIC values. The later is selected for the multiple indicator regressions because it describes the urban geometry in a more detailled way than the terrain roughness class, which is based on the Davenport classification. The distance to the center has been selected due to its low AIC value.

Three urban indicators -namely SVF, Per and Pos -are then selected to carry out regressions. Four regressions are presented, namely: Three regressions between NCR and two explanatory variables (combinations of SVF, Per and Pos) One regression between NCR and three explanatory variables Models are discussed regarding their χ 2 value (cf. section 2.3). Figure 7 presents NCR calculated from the observations in function of NCR estimated by regressions in the multiple variables regression case. χ 2 ranges from 0.016 to 0.02. Among the two indicators model, the pair SVF and Pos obtains the lowest χ 2 value (0.016). This can be considered as an improvement regarding the single variable regression approach, which only reach a χ 2 value of 0.02. Finally, the two indicators model which leads to the better estimation of the normalized cooling rate is expressed in equation (8).

N CR = 4.7 × 10 -3 • P os + 4.4 × 10 -5 • P er + 8.8 × 10 -2 (8)

In addition, a model involving these three indicators is proposed. Regression model involving three urban indicators -namely SVF, Per and Posdo not achieve better performance (χ 2 = 0.016).

Conclusion

This paper questions the relationship between the cooling pattern of districts and several urban indicators. This study has required the application of the Local Climate Zone scheme in Nancy. During this stage, nine urban indicators have been determined within a selection of thirteen LCZ. Nocturnal cooling information has been gathered throughout measurement campaigns, and normalized cooling rate have been calculated based on these field data.

The statistical connection between NCR and the chosen urban indicators has been investigated using regression analysis. The statistical models are built using 75% of the total experiments. The overall performance of the urban indicators (i.e. AIC values) is independent from the sample. Nine single variable regressions are presented. Terrain roughness class, the sky view factor, the mean building height and the distance to the center of the conurbation achieve best results in terms of AIC, R 2 and χ 2 .

Multi indicator regressions have been investigated in order to take into account multiple drivers of the LCZ nocturnal cooling. Correlations appear to be generally strong between urban indicators, which limits the number of explanatory variables for the regression. Three urban indicators are selected for the multiple indicator regression. The best results are obtained with the pair SVF and Pos. The addition of a third explanatory variable do not improve the performance of the model.

Regression analysis underlines the general trends regarding the relationship between normalized cooling rate and the nine selected urban indicators. Overall, it looks like NCR and most of the studied urban features are correlated. However, morphological indicators seems to perform better than land use indicators.

Figure 1 :

 1 Figure 1: LCZ map of the Great Nancy Area. The itinerary is displayed in blue. The thirteen selected LCZ for the field experiment are circled in black.

Figure 2 :

 2 Figure2: Theoretical nocturnal cooling phases for three LCZ of different types. Phase 1 (P1,blue area) is between 1 to 3 h before sunset and 3 to 5 h after sunset. Phase 2 (P2, green area) is between 3 and 5 h after sunset and sunrise. The letter D refers to the sessions performed in the middle of the afternoon, letter N refers to the sessions carried out around 3 h after sunset.

Figure 3 :

 3 Figure 3: AIC values calculated from 180 different models for each urban indicator. Each model is based on 75% of the experimental data set which has been chosen randomly.

Figure 4 :

 4 Figure 4: Scatter plots of experimental data including regression lines. Each observation correspond to a particular day for a given LCZ. Values with identical abscissa refer to a specific LCZ.

Figure 5 :

 5 Figure 5: NCR from statistical regressions compared with NCR from the validation data set for single indicator regression. If the models would estimate perfectly the NCR from the validation data set, all the points would be on the first bisector (blue dashed line).

Figure 6 :

 6 Figure 6: Correlation matrix of the explanatory variables, namely the urban indicators. The bigger the dot, the larger the correlation coefficient. The color indicates the sign of the correlation (positive/negative).

Figure 7 :

 7 Figure 7: NCR from statistical regressions compared with NCR from the validation data set for multiple indicator regression. If the models would estimate perfectly the NCR from the validation data set, all the points would be on the first bisector (blue dashed line).

Table 1

 1

	: Urban indicators values for the thirteen selected LCZ. SVF: Sky view factor, AR:
	Aspect ratio, H: Mean building height, R: Terrain roughness class, Built: Built surface
	fraction, Imper: Impervious surface fraction, Per: Pervious surface fraction.

Table 2 :

 2 Coefficients (β 0 and β 1 ), Student's t-test (P > |t|), Akaike Information Criteria and R 2 regarding regressions between normalized cooling rate and nine explanatory variables. Student's t-test results need to be under the chosen threshold of 0.05, whereas Breusch-Pagan values must be different from zero.

	Urban Indicator	β 0	P > |t|	β 1	P > |t|	AIC	R 2 P BP
	R	0.17 < 10 -3	-0.01 < 10 -3	-362.43 0.48 0.21
	SVF	0.03 < 10 -3	0.10 < 10 -3	-359.22 0.45 0.31
	Pos	0.09 < 10 -3	0.01 < 10 -3	-358.13 0.45 0.12
	H	0.13 < 10 -3	-0.00 < 10 -3	-356.79 0.43 0.42
	Per	0.09 < 10 -3	0.00 < 10 -3	-350.83 0.38 0.20
	Built	0.13 < 10 -3	-0.00 < 10 -3	-350.24 0.38 0.38
	AR	0.12 < 10 -3	-0.04 < 10 -3	-341.93 0.29 0.85
	Imper	0.13 < 10 -3	-0.00 < 10 -3	-340.39 0.28 0.28
	Elev	0.02	0.59	0.00	0.01	-324.80 0.09 0.07

Acknowledgments

This work has been supported by the French Environment and Energy Management Agency (ADEME).


Julia Baudart

Delphine Guillebault

Erik Mielke

Thomas Meyer

Neeraj Tandon

Sabine Fischer

Wilfried Weigel

Linda K Medlin

Microarray (phylochip) analysis of freshwater pathogens at several sites along the Northern German coast transecting both estuarine and freshwaters

Keywords: Phylochips, Microarray, Freshwater pathogens, Cyanobacteria, Molecular barcodes

come   L'archive ouverte pluridisciplinaire

Introduction

Pathogenic organisms occurring in lakes and rivers used as drinking water reservoirs represent a particularly serious health hazard and cause no less than 170,000 cases of water-related diseases annually worldwide. Moreover, global climate changes and massive migration fluxes from Africa and Asia are expected to perturb the existing ecological balance determining major changes in the type, abundance and distribution of pathogenic microbes and likely causing (re)-emergence of waterrelated pathogens. This has created a great deal of concern at the World Health Organization (WHO) and at the UNECE (United Nations Economic Commission for Europe), who have issued a protocol (http://www.unece.org/index.php?id=35086) asking governments to take appropriate actions to cope with the present alarming situation, which will deteriorate further if no direct and strong measures are taken. Reliable and safe water supplies are lacking for approximately 100 million European inhabitants, despite a large proportion of the European drinking water being obtained from surface waters. Escherichia coli has been chosen as faecal indicator bacteria (FIB) of water safety and is part of drinking water regulations (EU Directive 98/83 /EC for the quality of water intended for human consumption) and is also used as a faecal pollution indicator for recreational bathing waters in Europe (Directive 2006/7/EC for the management of bathing water quality, which replaces Directive 76 /160/EEC). This FIB is mainly used to monitor the degradation of water quality caused by human or animal faecal wastes.

Although, widely used in monitoring contamination levels, E. coli alone can lead to misleading information about pathogen levels. Indeed, diverse waterborne pathogens (including bacteria and protozoans) are natural inhabitants of water and have not an oral-faecal transmission pathway (e.g. Legionella spp., Aeromonas spp., Mycobacterium avium complex, some species of Vibrio spp. and Naegleria spp). Their presence in water cannot be correlated to the faecal pollution assessed by E. coli or other faecal indicators. The emerging of non-faecal waterborne outbreaks lead to the urgent monitoring of a larger diversity of pathogens in water resources having different sources. Our microarray for universal freshwater pathogens, both eukaryotic and prokaryotic, fills this need.

Molecular tools are an effective means of monitoring for very diverse biological targets [START_REF] Ramírez-Castillo | Waterborne pathogens: detection methods and challenges[END_REF]. The EU μAQUA project was conceived to implement a reliable system for the early and sensitive detection of waterborne pathogens and toxins based on the phylochip microarray detection method for monitoring. A microarray is an efficient, sensitive, robust, rapid and inexpensive test for the presence of any organisms, and they have been applied to detect and enumerate toxic algae, pathogenic bacteria, parasites and viruses, and to detect the presence of toxins and of potential toxin producers in European freshwaters (see review in [START_REF] Kegel | Application of microarrays (phylochips) for analysis of community diversity by species identification[END_REF]. μAQUA developed an easy to use advanced molecular tool consisting of a universal microarray chip for the (1) high-throughput detection of known and emerging water-borne pathogens (bacteria, viruses, protozoa and cyanobacteria), (2) monitoring the presence of select species of diatoms, whose presence is reliable bioindicators to assess overall water quality and (3) detection the cyanobacterial toxins from gene expression. This universal microarray is designed to replace traditional methods for monitoring, which are laborious, technically demanding and time-consuming and detects RNA rather than amplified gene products and as such is more quantitative and other microarrays that are PCR based and detect only one group of pathogens (see Discussion). The method now has an ISO number (ISO 16578:2013(en), molecular biomarker analysis-general definitions and requirements of specific nucleic acid sequences, (http://www.iso.org/iso/catalogue_detail.htm? csnumber = 57,185) and, thus, is a recognized and validated method to detect nucleic acids in environmental samples. PCR-independent methods using high density microarrays (phylochips) provide the best evidence for analyzing changes in relative abundance in ribosomal ribonucleic acid (rRNA) transcript and gene concentration levels because of its high specificity, sensitivity and repeatability [START_REF] Deangelis | PCR amplification-independent methods for detection of microbial communities by the high-density microarray phylochip[END_REF]) and reveals higher diversity than clone library studies [START_REF] Desantis | High-density universal 16S rRNA microarray analysis reveals broader diversity than typical clone library when sampling the environment[END_REF].

The μAQUA microarray was field tested in six countries. Microarray results for one of the monitoring sites, the Tiber River in Italy, have been published [START_REF] Marcheggiani | Detection of emerging and re-emerging pathogens in surface waters close to an urban area[END_REF] as has the PCR and rtPCR results from a Bulgarian site [START_REF] Panaiotov | Two-year monitoring of water samples from Dam of Iskar and the Black Sea, Bulgaria, by molecular analysis: focus on Mycobacterium spp[END_REF], and in this paper, we present microarray results for bacteria, cyanobacterial and protozoa for selected sites in Germany monitored regularly by Marilim for 3 years from 2012 until 2014.

Material and methods

Sampling site and water sampling

Fifty litres of water were collected at nine freshwater and six brackish water sites (Fig. 1, Tables 1 and2). The estuarine stations Schlei 1/Kappeln) (BW2) and Kieler Förde 1/Neumühlen-Dietrichsdorf (BW1) are located inner coastal waters, which are influenced by steady freshwater influx. Several rivers (Füsinger Au, Koseler Au (near Bohnert), the Hüttener Au and the Osterbyer Au (near Fleckeby) as well as some unnamed brooks flow into the Schlei. The station Kieler Förde 1/Neumühlen-Dietrichsdorf (BW1) is located close to the river Schwentine. Only small salinity fluctuations exist at station Fehmarnsund (BW6). Three stations (Schlei 1, BW2; Kieler Förde 1, BW1 and Eckernförder Bucht, BW5) are close to harbour facilities, which could receive higher charges of abiotic pollution. BW3 (Kieler Förde 2) and BW5 (Eckernförder Bucht) have the highest salinities nearly 15 psu = practical salinity units.

The freshwater stations are Wittensee/Sande (FW8), Schwentine/Wellingdorf (FW3), Schwentine/Rosen See (FW1 and FW2), Selenter See/Fargau (FW4 and FW5), Neukirchener See (FW6 and FW7) and Keller See/Malente (FW9). The latter two are interconnected by creeks and channels.

At every sampling site, 50 l of water were collected approximately 20 cm below the water surface using two 20 and one 10 l sterilized carboys and transported to Marilim in a thermo box (between 0.5 to 1.5 h transport time). All abiotic measurements were done in situ. Oxygen and temperature were measured with a WTW Multi 3420 Set G with FDO 925-3 Probe and salinity/conductivity and temperature were measured with a WTW LF 325-A Set. (WTW GmbH, Weilheim, Germany). Five turbidity measurements were made from every sample using infrared spectrometry (AquaLytic Turbidimeter AL250T-IR, Dortmund, Germany). Wind speed and direction were taken from local weather stations. To maintain light levels approximately constant, all samples were collected between 7:30 am and 10:15 am. From the six freshwater sites, two of them (Keller See and Neukirchener See) were repeatedly sampled from 2012 to 2014.

Sample preparation

In the laboratory, the water sample (50 l) was concentrated to 1 l using the Hemoflow kidney dialysis hollow fibre filter (Hemoflow HF80S, Fresenius, Bad Homberg, Germany) and back-flashed with 1 l of elution buffer (0.01 M phosphate buffered saline, 0.5 M Tween 80, 0.01 % sodium hexametaphosphate, 10 % antifoam B 1 and2 for more details Two hunderd millilitre of the concentrated eluate was sequentially filtered through the eight different filters on the same day as its collection of decreasing pore size (20, 10, 5, 2, 0.8, 0.45, 0.1 and 0.025 μm filters, Millipore, Billerica, Mass, USA). Each filter was placed into 1 ml of Tri-reagent (Sigma Aldrich, St Louis, MO, USA) [START_REF] Chomczynski | Single-step method of RNA isolation by acid guanidinium thiocyanate-phenol-chloroform extraction[END_REF], then stored frozen at -80 °C.

RNA extractions

Before RNA extraction, all filters corresponded to a same water sample were pooled, and total RNA was extracted with Tri-Reagent following the protocol in [START_REF] Lewis | MIDTAL (microarrays for the detection of toxic algae): a protocol for a successful microarray hybridisation and analysis[END_REF] as modified in [START_REF] Kegel | Introduction to project MIDTAL: its methods and samples from Arcachon Bay, France[END_REF]. From the total RNA extractions, one freshwater and two brackish water samples were not analyzed further because their RNA extractions were of poor quality (Tables 1 and2).

Probe design

The protocols used in the EU project μAQUA were adopted from the EU MIDTAL project for toxic algae [START_REF] Lewis | MIDTAL (microarrays for the detection of toxic algae): a protocol for a successful microarray hybridisation and analysis[END_REF]. Nevertheless, the challenge of μAQUA was to provide a single microarray able to analyze in the same water sample, many members of four phylogenetically different taxa of pathogenic bacteria, toxic cyanobacteria, pathogenic protozoa and diatoms as indicator species of water quality. All probes were designed to improve the specificity of the detection and to allow the hybridization with the targets under unique condition (buffer and temperature of hybridization).

Briefly probes representing species, genera, classes or phyla of the four target groups were either collected from the literature and extended to 25 nts or newly designed with the ARB program (http://www.arb-silva.de) from the ribosomal rRNA genes following a hierarchical fashion. Thus, for a species or genus to be present, all of the taxonomic hierarchy leading to that taxon also had to be present (Table 3). This hierarchical approach to positive target signals prevents the detection of false positives. All probes were checked in silico for the specific recognition of their targets using the nucleotide Basic Local Alignment Search Tool (BLAST, http://blast.ncbi.nlm. nih.gov/ Blast.cgi) against GenBank (http://www.ncbi.nlm. nih.gov/genbank/) and Silva (http://www.arb-silva.de) databases. We only accepted probes that had a 100 % match to their target species, with no variations among strains of the intended target as some phylochips allow (Phylochip, DeSantis et al. 2007). In a couple of instances, our probes had a single base mismatch, and for those we designed, a competitor probe to bind to the single base mismatches, allow true targets to bind to the probe barcode. Their biophysical properties were analyzed using the Oligonucleotide Properties Calculator software (http://www.basic.northwestern.edu/ biotools/oligocalc.html). Positive control probes and higher taxonomic probes targeting kingdom and phylum level came from the MIDTAL microarray [START_REF] Lewis | MIDTAL (microarrays for the detection of toxic algae): a protocol for a successful microarray hybridisation and analysis[END_REF], which is commercially available from Microbia Environnement (Banyuls-sur-Mer, France).

Probes, whose results are presented here, were designed for these bacterial families, genera and species: Salmonella, Shigella, Campylobacter, Campylobacter coli and Campylobacter jejuni, Enteriobacteriaceae, E. coli, Legionella, Legionella pneumophila, Clostridium perfringens, Listeria, Listeria monocytogenes, Staphylococcus aureus, Yersinia, Yersinia enterocolitica, Vibrionaceae, Aeromonas, Bacillus cereus, Pseudomonas and Mycobacterium; for these cyanobacterial genera and species: Microcystis aeruginosa, Planktothrix, Nodularia spumigena, Anabaena, Aphanizomenon, Aphanizomenon flosaquae and Cylindrospermopsis; and for these protozoans: Cryptosporidium parvum and Cryptosporidium hominis, Giardia, Entamoeba, Naegleria fowleri and Naegleria lovensis, and various higher group level probes were designed ranging from family, order, class, phylum to domain depending on target availability. This hierarchy is presented in Table 3. Poly-dA is added to block the poly-T spacer on the probe, and TBP is the TATA box gene fragment added as the positive hybridization control [START_REF] Lewis | MIDTAL (microarrays for the detection of toxic algae): a protocol for a successful microarray hybridisation and analysis[END_REF]). The labelled RNA was then denatured for 5 min at 95 °C. After denaturation, the samples were placed on ice and 15 μL of 4X KREA block (background blocker from Leica Biosystems, Nussloch GmbH, Germany) were added. The hybridization mixture was equally distributed to each array, which were precovered with coverslips cleaned with ethanol (LifterSlips, Erie Scientific, VWR International, Radnor, PA, USA). Slides were placed into SCIENION's sciHYBCHAMBER (Scienion, Berlin, Germany) that maintained a moist environment to avoid evaporation, and hybridizations were carried out for 1 h at 65 °C using a water bath. In order to remove unhybridised RNA, the slides were successively washed with three washing steps with increasing buffer stringency under agitation and in the dark to protect the fluorophore. The first buffer (2X SSC/10 mM EDTA/0.05 % SDS) and the second buffer (0.5X SSC/10 mM EDTA) washings were done at room temperature for 10 min. Finally, a third most stringent wash (0.2X SSC/10 mM EDTA) was performed at 50 °C.

Microarray scanning

Fluorescence scanning of slides was performed using the LS Reloaded confocal fluorescence scanner (Tecan Group Ltd., Männedorf, Switzerland) with a resolution of 6 μm and an excitation wavelength of 635 nm.

Data analysis

The scanned images were analyzed with GenePix 5.1 software (Molecular Devices, Sunnyvale, Ca) to align spots with the corresponding gal files. The signal to noise ratios of the spots were calculated from the median spot intensities and the local background fluorescence. The mean values for the spot intensities for the eight replicates were calculated from the median spot intensities. The hybridization results were analyzed using the hierarchy file and the GPR-Analyzer v1.28 software [START_REF] Dittami | GPR-analyzer:a simple tool for quantitative analysis of hierarchical multispecies microarrays[END_REF], and signals for the lowest taxon possible were eliminated from analysis if the hierarchy was broken. Multiple probes at each hierarchy level also helped to ensure that we did not have any false negatives. During the testing of probe specificity, we eliminated all probes that did not display clear, strong signals with pure cultures of each target. The cut-off to accept a positive hit of a set of probes was a normalized signal >0.2 and a signal to noise ratio >2 according the MIDTAL protocol [START_REF] Lewis | MIDTAL (microarrays for the detection of toxic algae): a protocol for a successful microarray hybridisation and analysis[END_REF]. Positive controls were represented by the TBP and eukaryotic probes (POSITIVE_25_dT, EUK 328, EUK 1209) and the Dunaliella probes (DunGS05_25_dT, DunGS02_25_dT). To compare different hybridization experiments, fluorescent signals were normalized using the internal positive control TBP (Positive_25_DT). This probe was spotted in five different concentrations, and the concentration giving the lowest signal was used for the normalization. All microarray results were uploaded to the μAQUA database at http://www.microaqua.eu. We analysed the microarray signal of each probes and for each biological target under PermutMatrix developed for microarray analyses [START_REF] Caraux | PermutMatrix:a graphical environment to arrange gene expression profiles in optimal linear order[END_REF]. PermutMatrix is a free software that allows a heat map representation of the microarray data (http://www.atgc-montpellier.fr/permutmatrix/).

Calibration curves for cyanobacteria quantification using microarray

To perform the calibration curves, cyanobacteria were quantified by cell counts using the Utermöhl method [START_REF] Utermöhl | Zur Vervolkommnung der quantitativen Phytoplankton-Methodik[END_REF]. RNA was extracted from a known cell number from pure cultures of Microcystis, Planktothrix, Nodularia, Aphanizomenon and Anabaena. Microarray analysis were performed with four different concentrations of RNA equivalent to 10,000, 50,000, 100,000 and 500,000 cells to construct concentration curves and to convert microarray signals to cell numbers. From these concentration curves, we can extrapolate to numbers of cells per litre in the field samples.

Results

Air temperature ranged from 5.8 °C (Schwentine April 2012) to 21.9 °C (Wittensee June 2012). Water temperatures ranged between 6.9 °C (Schwentine April 2012) and 21.3 °C (Orther Bucht in July 2013 and Kieler Förde in August 2013) (Tables 1 and2). Rising temperatures from April to July/August enhanced the growth rate of Aphanizomenon, Anabaena and Nodularia. Each sample was visually inspected, and the dominant species were noted along with their relative abundance but not enumerated. Aphanizomenon, Anabaena and Nodularia started to bloom at the end of June 2013, whereas Cylindrospermopsis reproduced mainly at lower temperatures (highest reproduction at 16.9 °C-Schlei, June 2013). At the end of the Aphanizomenon, Anabaena and Nodularia blooms, the blooming of Cylindrospermopsis decreased, then increased again for short time. Thus, at temperatures higher than 18 °C, Cylindrospermopsis does not compete with Aphanizomenon, Anabaena and Nodularia.

Turbidity values did not much differ between months and locations, except for the Neukirchener See (42.62 NTU) and the Eckernförder Bucht (0.67 NTU) in August 2013. Longterm data are unavailable. We have no evidence that turbidity affected cyanobacterial growth, but cyanobacteria can colonize successfully low light areas by using phycoerythrin in addition to chlorophyll, which increases the efficiency of light utilization for photosynthesis.

The hollow fibre ultrafiltration method provided a concentrate that was used for downstream RNA extraction for the microarray. We did not encounter any problems with the filtration but only with the RNA extraction of two samples, which were not used. Hollow fibre filtration has been shown to be a very effective means of concentrating microorganisms from large volumes with little or no bias for cell retention and high recovery of spiked cells in experimental setups to test filtration efficiency [START_REF] Hill | Development of a rapid method for simultaneous recovery of diverse microbes in drinking water by ultrafiltration with sodium polyphosphate and surfactants[END_REF]. Cells in the concentrate are not lysed because this is a kidney dialysis filter, and all blood cells must remain intact for effective dialysis. Thus, the results presented below likely reflect an accurate representation of the microbial community present at the time of sampling, and relative abundances of the cyanobacteria were inferred from calibration curves made from actively growing cultures.

Cyanobacteria

All eubacterial probes were highlighted validating bacterial presence in all samples (Fig. 2a). Cyanobacterial phylum level probes produced a high fluorescent signal in all samples except for FW3, FW7, FW8, BW1, and BW6 sites where some fluorescent signals were weaker. One phylum level probe (PhCynaoS05) did not work in all samples. At the order level, fluorescent signal distribution among these probes showed that cyanobacterial populations were different between freshwater and brackish water. The freshwater samplings had stronger signals for the orders Chroococcales and Oscillatoriales, whereas the brackish ones had higher signals for the order Oscillatoriales (Fig. 2a). Nostocales signals were not very strong among samples irrespective of their origin and were more or less absent from the estuarine site, BW1 site Kieler Förde 1/Neumühlen Dietrichsdorf and from the freshwater site, FW3 Schwentine. Schwentine has the highest conductivity of these freshwater sites (Table 1), and this may likely influence the presence of the cyanobacteria. Genus and species cyanobacterial probe signals are presented in Fig. 2b with highest values at two close brackish sites: Kieler Förde/ Heikendorf, (BW3) and Eckernförder Bucht/Eckemfôrde (BW5) and at two freshwater samplings (Neukircher See, FW6 and Kellersee, FW9).

Estuarine sites The Anabaena/Aphanizomenon complex, Cylindrospermopsis and Microcystis signals were weak at these sites, even absent at BW1, BW3, BW5 and BW6. Nodularia genus level probes were well highlighted at different brackish water sites (Kieler Förde/Heikendorf, BW3; Schlei2/ Massholmer Breite, BW4 and Eckernförder Bucht/Eckemfôrde (BW5), and the species N. spumigena was detected in both BW3 and BW5 samplings. Planktothrix signals were very weak at all sites, and the positive signal of the species Planktothrix agardhii at BW3 is likely a false positive because the genus level probe is not highlighted. Cylindrospermopsis signals were strongest at BW2 and BW4.

Freshwater sites The Anabaena/Aphanizomenon complex is found in the freshwater sites of Neukircher See, FW6 and FW7, and Kellersee/Malente FW9 sites. Strong variation of the Anabaena/Aphanizomenon probes suggests that different genera and species were present. In this complex, the probes labelled with multiple generic names, the order of the names indicates the strength of the probe signal when presented with similar amounts of RNA. Thus, a stronger Aph/Ana probe indicates a stronger signal for Aphanizomenon, and a stronger Ana/Aph signal indicates a stronger signal for Anabaena. In general, Aphanizomenon presented higher probe signals and were notably weak or absent at freshwater samplings FW1-3 (see Table 1 and Fig. 1 for names, dates and locations). Cylindrospermopsis was detected in only one site (Selenter See/Fargau FW5), and Nodularia showed a high fluorescent signal especially at sites FW9, with no apparent signal for the spumigena species level probe. Planktothrix was absent or presented a low signal at all samplings. Microcystis was detected at all samplings except in Seelenter See (Fargau) and Wittensee/Sande (FW5 and FW8). The fluorescent signals were especially strong at four samplings (FW1, FW2, FW3, FW6 and FW9) (Fig. 2b). These probes achieved the highest signal in the freshwater samplings/sites of all the cyanobacterial probes, especially at FW6 and 9, both of which were taken in August. The outflow from Neukirchener See (FW6) feeds directly into Kellersee (FW9) by canals and creeks. FW samplings 1, 2 and 3 are taken along the Schwentine River at two different sites with Rosensee (Schwentine) being sampled in August and September. The signals are slightly weaker at the downstream site (FW3) suggesting that the population is being concentrated or increasing as it nears the mouth of the river.

Inferred cell numbers from microarray signal Using the calibration curves constructed using RNA concentrations from known numbers of cells from pure cultures (Fig. 3), we inferred the most likely cell number for five genera and one species (Table 4). We had more than one probe for each of these taxa, except the species level probe for Nodularia spumigens. Thus, we averaged the cell numbers to obtain a relative number based on multiple probes. Numbers for Microcystis, Planktothrix and Nodularia ranged greatly among the different sites and across dates (Table 4). In most cases, the signal intensities for the various probes for the same taxon did not vary greatly, meaning that cell numbers inferred from the signal were more or less equal with the following exceptions. GNMicS04, a genus level probe for Microcystis, was usually two to three times higher than the three other genus level probes, and GNMicS02 was generally half the values of GNMicS01 and S03. GNNodS02 was an order of magnitude greater than GNNodS01, thus cell numbers were very different using the two probes.

Cell numbers of Microcystis were higher in the freshwater samples than the brackish water samples, and the highest cell numbers at FW6 likely contributed to the highest turbidity measured at this sampling time/site (Table 1). The highest cell numbers were inferred for Microcystis at three freshwater sites (FW2 on 29.08.13 sampling at Schwentine/Rosen See, FW5 on 27.08. 13 at Seelenter See/Fargau and FW9 on 13.08.13 at Kellersee 1).

The cell numbers inferred from genus level probe GNPlankS02, for Planktothrix, were an order of magnitude higher than the other genus level probe. Cell numbers for this genus were more or less the same in both the freshwater and the brackish water sites. The genus level probes for Anabaena and Aphanizomenon recognize both genera but to varying degrees of specificity. Those labelled AnaAmp preferentially recognize Anabaena, and those labelled AmpAna preferentially recognize Aphanizomenon, with two exceptions, GNAphAnaS01 = ANA major and GNAphAnaS06 = ANA major. There is one genus level probe that recognizes only Aphanizomenon, and it occasionally produced a higher signal than the others. GNAphAnaS01 was the weakest probe of all the probes or these genera. Aphanizomenon cell numbers were highest at three freshwater samplings (FW6, 7, 9). Anabaena was not particularly abundant at any of the samplings.

Nodularia had maximum abundances at two brackish sites and one freshwater water site, all collected within the same 2 weeks in August 2013 (Tables 1 and2). N. spumigens was more abundant in brackish water reaching its highest numbers at the same two sites as the genus level probes.

Cylindrospermopsis was present in low numbers in both habitats with slightly higher cell numbers at BW2 in June 2013 (Table 2).

Pathogenic bacteria

A high eubacterial signal was obtained at both the freshwater and estuarine stations throughout the sampling period (Fig. 2a). Signal intensities at the bacterial phylum, and class levels followed the general trend seen at the domain level (Fig. 2a for eubacterial kingdom level probes and Fig. 4a for Fig. 3 Calibration curves for Cyanobacteria. Regression of microarray signal to cell numbers. Each panel (a-e) represents a different species bacterial phylum and class probes). Gammaproteobacteria were detected with a high signal in all samples except for Wittensee/Sande (FW8), Keller See/Malente (FW9) and Eckernförder Bucht/Eckemförde (BW5). One probe of the Gammaproteobacteria (CLGamS13) did not seem to work in any sample, and likely this might have been a spotting error for this probe, hence the benefit of having multiple probes for the same taxon. Globally, Firmicutes probes show higher intensity in freshwater samples. Actinobacteria were widely detected in both freshwater and estuarine water samples, and signal intensities were globally higher for freshwater sites. Family level probes designed for Enterobacteriaceae and Vibrionaceae showed a widely distribution in both freshwater and estuarine sites with weaker signals as compared with the Gammaproteobacteria probes. Thus, at no site was a particular phylum or class on our array over represented as a higher percentage of the total bacterial signal, with a possible exception of Actinobacteria at the freshwater samplings and Gammaproteobacteria at all samplings.

Estuarine sites Gammaproteobacteria and Actinobacteria were detected in all estuarine sites. At the family level, Enterobacteriaceae were present at all of the water sites, with a complete hierarchy recorded (Fig. 4a) but globally signals were still weak. All generic probes for Enterobacteriaceae family members (E. coli, Salmonella and Yersinia including Y. enterocolitica) were positive.

All of the other pathogens were detected at the estuarine sites over the sampling period with a complete probe hierarchy, and their presence could not be rejected because of an incomplete or broken hierarchy.

Freshwater sites All pathogens targeted with genera and species level probes in the study were present at all the freshwater sites over the sampling period and showed globally slightly higher signals than those obtained at the estuarine sites (Fig. 4b). Signal intensities for one Yersinia spp. probe were higher than the rest of the probes for two freshwater sites (FW1 and FW4), but very low signals for Y. enterocolotica were detected at these samplings sites.

Protozoa

Estuarine sites C. parvum and hominis were present at all of the brackish water sites, with a complete hierarchy recorded (Figs 5). Lower levels of Giardia, Entamoeba histolytica and N. fowleri and N. lovensis were also present (Fig. 5b).

Table 4 Summary of inferred cell numbers from calibration curves generated from the regression of known amounts of RNA or cell numbers against microarray signal. Where multiple probes for the same taxon exist, cell numbers were averaged and the three highest cell concentrations are in bold and in red. The vertical separates the freshwater sites (left) from the brackish ones (right) Freshwater sites C. parvum and C. hominis were also present at all of the freshwater sites throughout the sampling periods, with slightly higher signals than at the brackish water sites, especially for the class level probes, likely indicating the presence of other parasitic species/genera than we have probes for on our microarray (Fig. 5b). Lower levels of E. histolytica, N. fowleri and Giardia were also present (Fig. 5b).

Discussion

Differences among sites

At the freshwater sites, nine samples were analyzed during the period of the spring bloom (1× in March, April and June and during that of the summer bloom (4× in August, 1× in October) from 2012 to 2014. Three stations were positioned in rivers and five stations were in standing water. The conductivity was between 292 and 466 μS/cm 2 and is in the expected range. Seven samples were taken at stations that were brackish (ranging from 9.5 psu at Schlei and Kappeln to 14.9 psu at Kieler Förde2/Heikendorf and were regularly examined in June (2×), July (1×), August (3×) to September (1×) for 2 years (2013 and 2014), but one of these was not analyzed further because of poor rRNA extraction (Table 2).

The salinity of the sample locations represents the salinity gradient typical of the German Baltic Coast. The low salinity at Schlei/Kappeln is caused by higher freshwater inflow and decreasing influence of salt water of Baltic Sea runoff from agricultural areas close to these stations. Lakes in Northern Germany are generally considered eutrophic, although some improvement in nutrient status has been seen in recent years and are in general influenced by intense agricultural practices [START_REF] Schernewski | Nutrient budgets, dynamics and storm effects in a eutorophic, stratified Baltic Lake[END_REF].

The distance between the samples of the total community as measured by the signal intensity, and the probes that reacted was analyzed using two different distance methods (Euclidian distance and Pearson's correlation) within the PermutMatrix program. The Euclidian distance can be used if the actual difference in values is important, which in the case of the microarray, a higher signal would represent more cells and more of a health concern. The Pearson's correlation would indicate trends in the data. Both distances were displayed using a complete linkage method in which the distance between two clusters is the distance between the furthest points in those clusters, and both methods show that there is a clear difference between some fresh and brackish water sites (Fig. 6) with the following exceptions: Using Euclidian distance (Fig. 6a), FW1 and FW2 (=temporal differences same site) and FW6 and FW9 (spatial and temporal similarity) were in a separate clade, and all remaining sites, remaining fresh and brackish water sites, were in a second clade. The four freshwater sites are grouped together primarily because of the large population of Microcystis that occurs at these sites, although FW7 which is a winter/spring sampling of FW6, which was sampled in the summer and more closely related to FW9, a nearby lake sampled also in August. The strong signal for Nodularia is responsible for the clustering of BW2 and BW4, which are very close geographically and BW3 and BW5, which have the highest salinity values (Table 2). Using the Pearson's correlation (Fig. 6b), there is a stronger separation between the FW and BW sites with only two FW sites (FW5 and FW8) being correlated with the brackish water sites. The close relationship between FW8 and BW1 is likely related to the overall lower signals at these two sites, whereas the association of FW5 with BW2 and BW4 may be linked because they shared similar cyanobacterial populations. Overall, the cyanobacterial populations seemed to have had more effect on the overall community structure analyses performed here than did the bacterial or protozoan signals.

Cyanobacteria

Cyanobacteria were present in all sites and seemed to show a preference for either the freshwater or the brackish water sites, with the exception of Planktothrix, which was common to both habitat types. This observation is in agreement with Lopes and Vasconcelos (2011) who showed the presence of this genus in brackish waters, although the fluorescent signals tend to show that Planktothrix was present or active at a low level. Microcystis was mainly found in freshwater sites, which is consistent with its natural habitat occupation, being not common in brackish water, whereas microcystins were found with the same two toxin detection methods, with the highest amount at Neukirchener See on (22.08.13), and this corresponded with the lowest signal of Microcystis at this location at this time, which would imply that this population of Microcystis was particularly toxic [START_REF] Rodriguez | Monitoring of freshwater toxins in European environmental waters by using novel multidetection methods[END_REF]). Cylindrospermopsis was not common at the freshwater sites but was found at the brackish water sites according to the high Several studies reported the ability of Cylindrospermopsis to proliferate in low salinity ecosystem similar to our brackish sites for which salinity is very low ranging between 9.5 and 14.9 psu, especially if there are elevated nutrient conditions [START_REF] Chapman | Recent appearance of Cylindrospermopsis (Cyanobacteria) in five hypereutrophic Florida lakes[END_REF][START_REF] Padisák | Cylindrospermopsis raciborskii (Woloszynska) Seenayya et Subba Raju, an expanding, highly adaptative cyanobacterium: worldwide distribution and review of its ecology[END_REF][START_REF] Briand | Cylindrospermopsis raciborskii (Cyanobacteria) invasion at mid-latitudes: selection, wide physiological tolerance, or global warming?[END_REF][START_REF] Antunes | Cylindrospermopsis raciborskii: review of the distribution, phylogeography, and ecophysiology of a global invasive species[END_REF]. Anabaena/Aphanizomenon were preferentially found at the freshwater sites. Our set of probes does not allow a good distinction between Aphanizomenon and Anabaena whose generic classification was recently revised [START_REF] Rajaniemi | Phylogenetic and morphological evaluation of the genera Anabaena, Aphanizomenon, Trichormus and Nostoc (Nostocales, Cyanobacteria)[END_REF], redistributing many Aphanizomenon and Anabaena species, emphasizing the difficulty to design specific sequences targeting either one or the other genus. The definition of these two genera needs a better refining to be able to design an efficient genetic barcode to identify these cyanobacteria genera [START_REF] Rajaniemi | Phylogenetic and morphological evaluation of the genera Anabaena, Aphanizomenon, Trichormus and Nostoc (Nostocales, Cyanobacteria)[END_REF]. In a separate study of the toxins as detected by two different methods [START_REF] Rodriguez | Monitoring of freshwater toxins in European environmental waters by using novel multidetection methods[END_REF], anatoxins were found in all but one site (FW8, Wittensee/Sande, 12.06.13) with the microsphere-based assay and all but two sites (FW4, Selenter See/Fargau (21.08.12) and BW4, Schlei/Maasholmer Breite, 15.08.13) with the UPLC-MS/MS method. The toxin measurements would imply that the strains present were toxic. Microcystins were found with the same two toxin detection methods, with the highest amount at FW6, Neukirchener See on (22.08.13), and this corresponded with the highest cell numbers of Microcystis (average 230,332 cells/l) at this location at this time. Such findings indicate the importance of employing both a cell identification and toxin identification method for cyanobacteria to have an effective monitoring program. Nodularia's presence in brackish sites was in accordance with previous monitoring of this cyanobacteria occurrence in Baltic Sea. The cell numbers inferred from the microarray signals were more or less the same for multiple probes. At present, we do not have inferred cell numbers validated by cell counts. Thus, for those probes whose signals are markedly different for multiple probes for the same taxon, we do not know which of the multiple probes can be validly used to infer cell numbers. Such validation will be obtained in other EU projects (MICROCOKIT), in which microarray signals are being validated by FISH cell counts for cyanobacteria. Without such validation, we can only average the signals for the multiple probes to generate an average cell number. If we find that after validation, the probes that produce lower signals infer too low a cell number, then we will eliminate that probe from the microarray for commercialization. More extended and detailed investigations are necessary to find out what are the main effects on growth of different cyanobacterial species at the sampling sites listed here.

Bacteria

For bacteria, we detected each target with at least one or several probes, which were correctly detected with its higher hierarchy level. All of the targeted bacteria species and genera were present in the freshwater and estuarine water samples with a complete hierarchy. The signals measured for bacteria were weaker as compared to those measured with the cyanobacterial set of probes. This can be explained by a lower abundance of pathogenic bacteria in these water samples because most of pathogenic bacteria investigated in this study have a human-associated waste source, such as urban or domestic wastewater inputs that are highly diluted into receiving waters and thus would present a lower signal. Humanassociated sewage is a well-known source of pathogenic bacteria, but these facilities were not in the vicinity of our samplings. Molecular-based methods have shown that the members of the members Enterobacteriaceae family (E. coli, Yersinia spp., Salmonella spp.), C. perfringens but also the members of the Family Vibrionaceae, Pseudomonas, C a m p y l o b a c t e r, L i s t e r i a , S t a p h y l o c o c c u s a n d Mycobacterium are common pathogenic bacteria found in urban-domestic sewage waters [START_REF] Campbell | Dynamics of marine bacterial community diversity of the coastal waters of the reefs, inlets, and wastewater outfalls of southeast Florida[END_REF][START_REF] Wang | Composition and structure of microbial communities associated with different domestic sewage outfalls[END_REF]. Nevertheless, in urban sewage, their relative abundance in the total bacterial community still low (<1 %), and their abundances can decrease depending on the stages of wastewater treatment [START_REF] Shannon | Application of real-time quantitative PCR for the detection of selected bacterial pathogens during municipal wastewater treatment[END_REF]. By consequence, the abundances in receiving sewage areas of the pathogenic bacteria are very low and their concentrations in environment seem to be close to the detection limit of the microarray Fig. 6 Heat map showing the relative abundance of the protozoan hierarchical probes from the species level to kingdom. Vertical lines separate sampling dates method. Similar results were obtained from an investigation performed in Bulgarian sites samples, where large volumes of water samples (50 l) were analyzed by using the same ultrafiltration system and by PCR-based methods [START_REF] Panaiotov | Two-year monitoring of water samples from Dam of Iskar and the Black Sea, Bulgaria, by molecular analysis: focus on Mycobacterium spp[END_REF]. Our results obtained with the microarray confirm the very low concentration of pathogenic bacteria in surface freshwaters and estuarine waters sampled here. In contrast, the high nutrient load likely present in the water from intense agricultural practices in the area could contribute to the high cyanobacterial signals we recovered, some of which were from toxic strains [START_REF] Rodriguez | Monitoring of freshwater toxins in European environmental waters by using novel multidetection methods[END_REF].

The results obtained by the microarray over 2 years showed the presence of a very high diversity of pathogenic bacteria detected but with a weak signal in the freshwater sites but also at the estuarine water sites. The salinity of the estuarine sites were low (ranging from 9.5 to 14.9 psu), which highlights that these brackish sites could possible receive freshwater input from rivers including several wastewater treatment plants and discharges from rivers or agricultural runoff. The continental inputs could explain the presence of bacterial pathogens at the brackish sites. A storm event can contribute to street runoff or overflow from wastewater treatment plant, increase significantly the counts of bacteria associated to human sewage in receiving water (Berndtsson and Paul 2015). Nevertheless, during the investigated period, no rain events or storm events were noted.

Protozoa

All of the targeted protozoan species were present in the samples, with slightly higher signals in the estuarine sites than in the freshwater sites. Cryptosporidium had the highest signals among all of the protozoans present on the array. The extent to which parasites from these groups live in saline conditions is unknown except that it is known that Cryptosporidium and Naegleria will survive exposure to saline conditions [START_REF] Bradford | Release of Cryptosporidium and Giardia from dairy calf manure: impact of solution salinity[END_REF][START_REF] Zbikowska | Distribution of Legionella pneumophila bacteria and Naegleria and Hartmannella amoebae in thermal saline baths used in balneotherapy[END_REF]. It is also unknown if the cells targeted by our probes are parasitic to humans because the targets highlighted in these studies could be parasites of fish, livestock, etc.

Our microarray has successfully detected a wide range of pathogens in fresh and brackish water in Northern Germany. Microarrays have been successfully used to estimate the diversity and abundance from a variety to organisms. Our microarrays used RNA, whereas nearly all others have a PCR step and hybridize the amplicons to the microarray. The problems of using PCR to obtain products for the hybridization is that it tends to favour the most abundant species present so that the results are not truly quantitative and there is always the possibility of having natural inhibitors to the PCR [START_REF] Maynard | Waterborne pathogen detection by use of oligonuceotide-based microarrays[END_REF][START_REF] Deangelis | PCR amplification-independent methods for detection of microbial communities by the high-density microarray phylochip[END_REF]. [START_REF] Gushin | Oligonucleotide microchips as genosensors for determinative and environmental studies in microbiology[END_REF] suggested, from their study of nitrifying bacteria, that combining RNA and PCR-amplified DNA, each labelled with different dyes, could provide an excellent assessment of the active components and total diversity of any community, respectively. Amplified products of freshwater pathogens, many of those the same as on our microarray, have been detected using a phylochip using both 16S rRNA and/or protein coding genes [START_REF] Wilson | Sequence-specific identification of 18 pathogenic microorganisms using microarray technology[END_REF][START_REF] Wang | Detection and genotyping of Entamoeba histolytica, Entamoeba dispar, Giardia lamblia, and Cryptosporidium parvum by oligonucleotide microarray[END_REF][START_REF] Maynard | Waterborne pathogen detection by use of oligonuceotide-based microarrays[END_REF][START_REF] Lee | Detection of bacterial pathogens in municipal wastewater using an oligonucleotide microarray and real-time quantitative PCR[END_REF][START_REF] Lee | Development and application of an oligonucleotide microarray and real-time quantitative PCR for detection of wastewater bacterial pathogens[END_REF][START_REF] Lee | DNA microarray-based detection and identification of winterbourne protozoan pathogens[END_REF], and in the Lee studies, array results from PCR products compared favourably with qPCR results. [START_REF] Brinkmann | Detection of multiple waterborne pathogens using microsequencing arrays[END_REF] designed a similar microarray to ours targeting most of the same protozoa and several bacteria. Their microarray successfully used extracted DNA with no PCR amplification instead of RNA but only it was only tested with lab-based cultures and no field testing. Now that the microarray method recognized as a valid method for detecting nucleic acids in environmental samples, microarrays, especially those based on RNA detection, such as the μAQUA microarray, can provide a more reliable method of detecting actively growing pathogens in the environment prior to becoming a health issue. The uniqueness of our microarray is the diversity of biological targets belonging to four widely different target groups ranging from prokaryotes to eukaryotes, which includes re-emerging and emerging pathogens that are the target of much active water quality research. Another innovative aspect is our capability of detecting actively growing cells because hybridization with RNA is representative only of active and not dead cells and thus gives an indication about the risk potential of the detected and identified cells. This makes our microarray the obvious tool for an early warning system. The results presented in the paper prove that both pathogenic eukaryotes and prokaryotes even at a low level can be detected and identified and that the use of RNA as the target molecule gives significant signals and useful information about the cell's state and the actively growing community.

Their presence as detected by the microarray is not affected by PCR or qPCR inhibitors, which could yield false-negative results, and because known targets are on the array, turnaround time for results is hours instead of months as is the case with next generation sequencing (NGS). NGS yields the total microbial community composition rather than just pathogenic targets and is independent of culture requirements for selected pathogens. Our microarray is the only array available that not only uses RNA detection with no PCR amplification but also combines all of the major pathogenic species into one chip using SSU and LSU genes for pro-and eukaryotic organisms at the same time. Our results also underscore the importance of employing both a cell identification and toxin identification method for cyanobacteria to have an effective monitoring program and the need for more frequent monitoring as afforded by molecular techniques to serve as early warning systems for harmful events.

Fig. 1

 1 Fig.1Location of freshwater (FW) and estuarine (BW) sampling sites. The sampling sites and their numbers can be cross-referenced to Tables1 and 2for more details

Fig. 2

 2 Fig. 2 Hierarchical probes for taxonomic levels in Cyanobacteria. Normalized fluorescent signal measured for higher hierarchy level (a) and for genus and species of cyanobacteria (b) at both fresh and brackish water sites

  . The stations Schlei (Kappeln and Maasholmer Breite) have a low exposure to wind, Kieler Förde 1/Neumühlen-Dietrichsdorf, and Orther Bucht is moderately exposed, and Kieler Förde/Heikendorf and Fehmarnsund is heavily exposed, which resulted in frequent water column turnover. High nutrient values at the stations Schlei 1/Kappeln (BW2) and Kieler Förde 1/Neumühlen-Dietrichsdorf (BW1) are possibly caused by

Fig. 4

 4 Fig. 4 Heat map of the relative abundance of (a) the bacterial hierarchical probes from family to kingdom and (b) from genus to species. Vertical lines separate sampling dates

Fig. 5

 5 Fig. 5 Heat map of the relative abundance of (a) the bacterial hierarchical probes from family to kingdom and (b) from genus to species. Vertical lines separate sampling dates

Table 1

 1 Freshwater , the eluate was examined to see if any cells were lysed. No lysed cells were noted, and if they had been, the cut-off of the filter is 70 Da; the size of urea and any DNA/RNA from lysed cells would have been retained by the filter.

	Location	Sample	Date	Time	Air	Water	Air moisture	Turbidity	Conductivity
		site code			temperature	temperature	[%]	[NTU]	[μS/cm 2 ]
					[°C]	[°C]
	Schwentine	xxx	04.04.12	07:30	5.8	6.9	71.8	4.0	458
	Seelenter See (Fargau)	FW4	21.08.12	09:15	19.5	21	75.9	1.11	301
	Rosensee (Schwentine)	FW1	12.09.12	10:00	17.1	17.3	63.6	4.31	415
	Wittensee (Sande)	FW8	12.06.13	09:00	21.9	17.8	-	1.13	396
	Kellersee 1	FW9	13.08.13	09:20	19.2	20.0	-	3.25	410
	Neukirchener See 1	FW6	22.08.13	08:40	20.5	19.9	-	42.62	292
	Seelenter See (Fargau)	FW5	27.08.13	08:50	20.2	19.0	-	1.92	327
	Rosensee (Schwentine)	FW2	29.08.13	08:44	18.6	19.1	48.9	3.67	437
	Schwentine	FW3	14.10.13	09:40	15.0	11.2	59.5	1.59	466
	Neukirchener See 2	FW7	27.03.14	08:00	6.2	8.1	76.0	5.43	430

sites, sampling dates and abiotic on-site measurement data. (-) no data. The sample collected in Schwentine in April 2012 was not analyzed further because of poor RNA quality after extraction (xxx)

Table 2

 2 Estuarine sites with mesohaline conditions, sampling dates and abiotic on-site measurement data. (-) = no data. The sample collected in Orther Bucht in July 2013 was not analyzed further because of poor RNA quality after extraction(xxx)

	Location	Sampling	Date	Time	Air temperature	Water temperature	Turbidity	Salinity
		site code			[°C]	[°C]	[NTU]	[psu]
	Schlei 1 (Kappeln)	BW2	05.06.13	09:00	21.7	16.9	7.04	9.5
	Kieler Förde 1 (Neumühlen Dietrichsdorf)	BW1	26.06.13	09:05	13.9	15.0	2.0	12.1
	Orther Bucht (Fehmarn)	xxx	30.07.13	09:53	19.3	21.3	1.16	11.0
	Kieler Förde 2 (Heikendorf)	BW3	06.08.13	10:00	20.9	21.3	1.42	14.9
	Schlei 2 (Maasholmer Breite)	BW4	15.08.13	10:15	19.8	17.8	2.08	13.4
	Eckernförder Bucht	BW5	20.08.13	09:53	19.0	18.2	0.67	14.8
	Fehmarnsund	BW6	09.09.13	09:55	14.5	16.7	1.48	12.7

Table 3

 3 Summary of hierarchical probes for Cyanobacteria, Bacteria and Protozoa. The hierarchy should be read from left to right across each row, beginning with either a target species (if a species probe was designed) or a target genus and ending with the kingdom level probes. Where multiple order levels are noted, then the first order probe comes under the second order and so on A. Hierarchy for Cyanobacteria (has multiple levels of order probes) SpeciesMicroarray construction and hybridizationA total of 246 probes (taxon and controls) were spotted by Scienion AG (Berlin, Germany) on sciCHIP Epoxy using a sciFLEXARRAYER S11 as follows: each microarray slide contained two arrays with eight replicates for each probe. Hybridizations of each sample were performed on different slides, thus producing a pseudo-replicate. Considering two arrays per sample, each probe is therefore represented by 16 spots and the signal for the 16 spots was averaged. One millilitre of the mixture obtained from the pooled filters previously stored in TRlzol® Reagent at -80 °C plus an internal extraction quality control (500,000 cells of Dunaliella tertiolecta) was processed for total RNA extraction using TRlzol® Reagent according to the patented MIDTAL procedure (patent WO2015008011 A1). RNA quality and purity (260/280 ratio 1.8-2.2 and 260/230 ratio 1.8-2.3) were mea-

	Phylum/division Kingdom	Eubacteria Cyanobacteria	Eubacteria	Eubacteria Cyanobacteria	Eubacteria Cyanobacteria	Eubacteria Cyanobacteria	Eubacteria Cyanobacteria	Eubacteria Cyanobacteria	Eubacteria Cyanobacteria	Eubacteria Cyanobacteria	Eubacteria Cyanobacteria	Eubacteria Cyanobacteria	Eubacteria Cyanobacteria																Kingdom	Eukaryote		Eukaryote	Eukaryote	Eukaryote
	3rd level of multiple orders a			Chroococales/Nostocales	Chroococales/Nostocales	Chroococales/Nostocales	Chroococales/Nostocales	Chroococales/Nostocales	Chroococales/Nostocales	Chroococales/Nostocales	Chroococales/Nostocales	Chroococales/Nostocales			Kingdom	Eubacteria	Eubacteria	Eubacteria	Eubacteria	Eubacteria	Eubacteria	Eubacteria	Eubacteria	Eubacteria	Eubacteria	Eubacteria	Eubacteria	Eubacteria	Class	Conoidasida		Diplomonadida
	Genus or multiple genera Order 2nd level of multiple orders a	Microcystis Chlorococcales Chroococales/Nostocales		Oscillatoriales/Nostocales Anabaena/Aphanizomenon/Cylindrospermopsis Nostocales	Nostocales Oscillatoriales/Nostocales Anabaena/Aphanizomenon	Nostocales Oscillatoriales/Nostocales Anabaena/Aphanizomenon/Nodularia	Nostocales Oscillatoriales/Nostocales Aphanizomenon	Nostocales Oscillatoriales/Nostocales Cylindrospermopsis	Nostocales Oscillatoriales/Nostocales Nodularia/Anabaena	Nostocales Oscillatoriales/Nostocales Nodularia/Anabaena/Aphanizomenon	Nostocales Oscillatoriales/Nostocales Nodularia	Nostocales Oscillatoriales/Nostocales Nodularia	Oscillatoriales Nostocales/Oscillatoriales Planktothrix		Family/class Phylum/division Genus	Enterobacteriaceae Gammaproteobacteria E. coli/Shigella	Enterobacteriaceae Gammaproteobacteria E. coli/Shigella/Salmonella	Enterobacteriaceae Gammaproteobacteria Salmonella	Enterobacteriaceae Gammaproteobacteria Yersinia	Gammaproteobacteria Vibrio	Gammaproteobacteria Legionella	Gammaproteobacteria Pseudomonas	Campylobacter	Firmicutes Bacilli Staphylococcus	Firmicutes Bacilli Listeria	Firmicutes Bacilli Bacillus	Firmicutes	Actinobacteria Mycobacterium	Order Family Genus	Eimeriorina Cryptosporidae Cryptosporidium		Giardia	Naegleria	Entamoeba
												Nodularia spumigena	Planktothrix agardii	B. Hierarchy for Bacteria	Species				Yersinia enterocolotica		Legionella pneumophila		Campylobacter coli/jejuni	Staphylococcus aureus			Clostridium perfringens	C. Hierarchy for Protozoa	Species	Cryptosporidium hominis	Cryptosporidium parvum/hominis	Cryptosporidium parvum/hominis	Cryptosporidium parvum	Giardia intestinalis	Naegleria fowleri	Naegleria lovaniensis	Entamoeba histolytica

a

The second name is the stronger probe and the probe in the hierarchy sured by NanoDrop® Spectrophotometer (Thermo Scientific, Wilmington, DE, USA). The integrity and size distribution of total RNA were checked with a 2100 Bioanalyzer (Agilent Technologies Inc. Santa Clara, CA, USA). Clean-up was performed using the RNeasy Plus Mini kit (Qiagen, Düsseldorf, Germany). An extra clean-up round for samples that did not meet the total RNA purity (260/230 and 260/280 ratios) criteria using the PowerClean® Pro RNA Clean-Up Kit (MO BIO Laboratories, Inc., Carlsbad, CA, USA). The PowerClean® Pro RNA Clean-Up Kit will remove this brown colour as well as any PCR-inhibiting substances, such as heme, polysaccharides, polyphenols fulvic acids and dyes. (1 μg RNA) were mixed with 5X (2X) hybridization buffer containing 3 μL Poly-dA (1 μM) and 10 ng TATA box protein (TBP) control made up to a final volume of 60 μL (30 μL).

Appl Microbiol Biotechnol

Acknowledgments This work was supported by the EU μAQUA project (FP7- KBBE-2010-4, 265409).

Compliance with ethical standards LKM declares on behalf of all of the authors that there are no conflicts of interest, no research on humans of animals and no informed consents in this study.