Predictive Modeling of Microcystin Concentrations in Drinking Water Treatment Systems Of

Home , Anabaenopsis, Aphanizomenonaceae, Coelosphaerium, Cylindrospermopsis, Fischerella, Gloeotrichia

Predictive Modeling of Microcystin Concentrations in Drinking Water Treatment Systems of

Ohio and their Potential Health Effects

Thesis

Presented in Partial Fulfillment of the Requirements for the Degree Master of Science in the

Graduate School of The Ohio State University

By:

Traven A. Wood, B.S.

Graduate Program in Public Health

The Ohio State University

2019

Thesis Committee:

Mark H. Weir (Adviser)

Jiyoung Lee

Allison MacKay

Traven Aldin Wood

2019

Abstract

Cyanobacteria present significant public health and engineering challenges due to their expansive growth and potential synthesis of microcystins in surface waters that are used as a drinking water source. Eutrophication of surface waters coupled with favorable climatic conditions can create ideal growth environments for these organisms to develop what is known as a cyanobacterial harmful algal bloom (cHAB). Development of methods to predict the presence and impact of microcystins in drinking water treatment systems is a complex process due to system uncertainties. This research developed two predictive models, first to estimate microcystin concentrations at a water treatment intake, second, to estimate the risks of finished water detections after treatment and resultant health effects to consumers. The first model uses qPCR data to adjust phycocyanin measurements to improve predictive linear regression relationships. Cyanobacterial 16S rRNA and mcy genes provide a quantitative means of measuring and detecting potentially toxic genera/speciess of a cHAB. Phycocyanin is a preferred predictive tool because it can be measured in real-time, but the drawback is that it cannot distinguish between toxic genera/speciess of a bloom. Therefore, it was hypothesized that genus specific ratios using qPCR data could be used to adjust phycocyanin measurements, making them more specific to the proportion of the bloom that is producing toxin. Data was obtained from a water treatment plant (WTP) intake at Tappan Lake, Ohio, a drinking water source for the

Village of Cadiz. Using Pearson correlations and linear regressive analysis, it was found that adjusted phycocyanin, based on Planktothrix 16S and Planktothrix mcyE gene abundance ratios, exhibits improved correlation with microcystins. Furthermore, the analysis demonstrated the practicality of the adjustment in turning negative correlations between phycocyanin and microcystins to positive. More data from other water systems are needed to validate the findings

ii of this study. The second model utilizes a stochastic method to model the risk of microcystin finished water detections after water treatment. Data needed for such a model include initial and finished water toxin detections, removal efficiencies of various treatment processes, and exposure data related to a consumer. Three different methods for modelling the health status of a bloom in order to determine the intra- to extracellular (E/I) ratio of initial toxin concentrations were explored. Then, water treatment characteristics specific to the 2014 Toledo Water Crisis

(TWC) were modeled to obtain estimated finished water detections. Finally, health risks were estimated using a hazard quotient based on finished water detections and exposure scenarios.

Risk estimates for children were greater than adults and present throughout the crisis. This model produced accurate predictive outputs that are consistent with conditions observed during the

2014 TWC. Furthermore, this model presents a novel method of assigning E/I ratios to initial microcystin concentrations, which is useful for assessing and predicting WTP resiliency amidst a changing bloom. Together, these models can serve as an innovative way of predicting microcystins from intake to tap.

iii Acknowledgements

Thank you to my fiancé and family for their encouragement and support through my academic journey. Thanks to Dr. Ruth Briland, Ohio Environmental Protection Agency; Donna

Francy, United States Geological Survey (USGS); and members of the Tappan Lake Nutrient

Reduction Initiative for data sharing. Most of all, a special thanks to Dr. Mark Weir for his mentorship, as well as Dr. Jiyoung Lee and Dr. Allison MacKay for their guidance and expertise.

iv Vita

May 2013 …………………………………………………………………….. Miller High School

August 2017 ………………………………………. B.S. Environmental Health, Ohio University

May-August 2017 ………Environmental Health Technician, Licking County Health Department

May-September 2018 ………………………. …………………… Ohio EPA Internship Program

January 2019-Present …………………Graduate Research Associate, The Ohio State University

2018 …………………… Lemeshow Student Excellence Scholarship, The Ohio State University

Fields of Study

Major Field: Public Health

Specialization: Environmental Health Sciences

v Table of Contents

Abstract ...... ii

Acknowledgements ...... iv

Vita ...... v

List of Figures ...... x

List of Tables ...... xi

Chapter 1: Introduction to Microcystins and their Impact ...... 1

1.1 Cyanobacteria and Cyanotoxins ...... 1

1.2 Microcystin-Producing Genera ...... 2

1.3 Dominant vs. Mixed Blooms ...... 7

1.4 Microcystin Synthesis ...... 9

1.4.1 Microcystin Synthetase ...... 9

1.4.2 Factors that affect Microcystin Synthesis ...... 10

1.4.3 Extracellular vs. Intracellular Toxin ...... 14

1.5 Monitoring Methods ...... 15

1.5.1 Microcystin Methods of Detection ...... 15

1.5.2 Quantitative Polymerase Chain Reaction (qPCR) ...... 16

1.5.3 mcy Genes and Toxic Blooms ...... 18

1.5.4 Phycocyanin and Chlorophyll-a ...... 23

1.6 Public Health Implications ...... 28

vi 1.6.1 Exposure Matrices ...... 28

1.6.2 Human Health Effects ...... 29

1.6.3 Toxicity and Exposure Values ...... 31

1.7 Drinking Water Treatment Processes ...... 33

1.7.1 Intracellular Toxin Removal ...... 34

1.7.2 Extracellular Toxin Removal ...... 36

1.7.3 Microcystin Drinking Water Guidelines ...... 39

1.8 Modeling and Statistical Methods ...... 40

1.8.1 Quantitative Microbial Risk Assessment Framework ...... 40

1.8.2 Stochastic Models ...... 42

1.8.3 Monte Carlo Simulation ...... 42

1.8.4 Wilcoxon Ranked Sums Test ...... 44

Chapter 2. Phycocyanin Adjustment Model using qPCR Data ...... 46

2.1 Introduction ...... 46

2.2 Methods ...... 47

2.2.1 2016 Data ...... 48

2.2.2 2017 Data ...... 50

2.2.3 Data Analysis ...... 51

2.3 Results ...... 52

2.3.1 Data Summary ...... 52

vii 2.3.2 Pearson Correlation ...... 56

2.3.3 Linear Regression Analysis ...... 59

2.3.4 Phycocyanin “Adjustment” ...... 63

2.3.5 Combining the Years ...... 64

2.4 Discussion ...... 68

Chapter 3. 2014 Toledo Water Crisis Model ...... 74

3.1 Introduction ...... 74

3.1.1 Background ...... 74

3.1.2 Scope of Research ...... 75

3.2 Methods ...... 77

3.2.1 Modelling Approach ...... 77

3.2.2 Exposure Scenario Modeling...... 77

3.2.3 Modeling Initial Microcystin Concentrations at the Intake ...... 79

3.2.4 Water Treatment Reduction Efficiency Modeling ...... 81

3.2.5 Human Specific Exposure Modeling ...... 86

3.3 Results ...... 90

3.3.1 Selection of cHAB Health Method ...... 90

3.3.2 Health Risk Assessment ...... 96

3.4 Discussion ...... 100

Chapter 4. Conclusions and Future Research ...... 108

viii 4.1 Conclusions ...... 108

4.2 Implications ...... 110

4.3 Future Research ...... 111

References ...... 113

Appendix A. Source Code for Initial Toxin State...... 124

Appendix B. Source Code for Health Risk Assessment ...... 139

ix List of Figures

Figure 1. Chemical structure of microcystins ...... 2

Figure 2. Human exposure routes of microcystins ...... 29

Figure 3. QMRA framework...... 41

Figure 4. Depiction of dice throw probability distribution...... 43

Figure 5. Cadiz WTP intake and pump station location at Tappan Lake ...... 48

Figure 6. Rationale of phycocyanin adjustment ...... 52

Figure 7. 2016 Tappan Lake cHAB summary ...... 55

Figure 8. 2017 Tappan Lake cHAB summary ...... 55

Figure 9. 2016 linear regression analysis ...... 61

Figure 10. 2017 linear regression analysis ...... 62

Figure 11. 2016-2017 linear regression analysis ...... 67

Figure 12. 2014 Toledo Water Crisis Finished Water Detects ...... 76

Figure 13. Microcystins impacting drinking water at Collins Park WTP...... 78

Figure 14. QMRA model algorithm...... 91

Figure 15. Finished water detections using Boolean switch method ...... 92

Figure 16. Finished water detections using E/I ratio method ...... 93

Figure 17. Finished water detections using the combination method ...... 93

Figure 18. Hazard quotient estimates from the simulation ...... 97

Figure 19. Comparison of hazard quotients between scenarios of different toxin state ...... 99

Figure 20. Hazard quotient estimates for post-TWC with 14 ug/L initial toxin concentrations 103

x List of Tables

Table 1. Taxonomic review of microcystin-producing genera ...... 6

Table 2. Associations between mcy genes and microcystins in the open literature ...... 22

Table 3. Associations between phycocyanin and microcystins in the open literature ...... 27

Table 4. Drinking water guidelines for microcystins across the globe...... 40

Table 5. Summary of select parameters at Tappan Lake, OH, 2016-2017 ...... 54

Table 6. Pearson correlation coefficients among parameters in 2016 ...... 57

Table 7. Pearson correlation coefficients among parameters in 2017 ...... 58

Table 8. Phycocyanin adjustment for 2016 ...... 63

Table 9. Phycocyanin adjustment for 2017 ...... 63

Table 10. Results (p-values) of the Wilcoxon ranked sums test for 2016-2017 ...... 65

Table 11. Variance-based sensitivity analysis for Planktothrix-adjusted phycocyanin model .... 66

Table 12. Variance-based sensitivity analysis for mcyE-adjusted phycocyanin model...... 66

Table 13. E/I ratio probability distribution test ...... 80

Table 14. TWC chlorination removal efficiency probability distribution selection ...... 85

Table 15. Post-TWC chlorination removal efficiency probability distribution selection ...... 85

Table 16. Variables used for TWC model ...... 88

Table 17. Variables used for post-TWC model ...... 89

Table 18. Descriptive statistics of microcystin finished water detections ...... 91

Table 19. Results (p-values) of the Wilcoxon ranked sums test for TWC ...... 95

Table 20. Results (p-values) of the Wilcoxon ranked sums test for post-TWC ...... 95

Table 21. Descriptive statistics of QMRA simulations ...... 100

xi Chapter 1: Introduction to Microcystins and their Impact

1.1 Cyanobacteria and Cyanotoxins

Cyanobacteria, also known as blue-green algae, are ubiquitous prokaryotes found across the globe.1,2 These microorganisms can thrive in both marine and freshwater environments such as sea coasts, bays, lakes, reservoirs, rivers, lagoons, etc.3 These photosynthetic, gram-negative bacteria present significant public health and engineering challenges due to their potential for expansive growth and synthesis of harmful cyanotoxins in surface waters used as a drinking water source. Eutrophication of surface waters coupled with favorable climatic conditions exacerbated by climate change can create ideal growth conditions for these organisms to develop what is known as a cyanobacterial harmful algal bloom (cHAB).1,2

The term harmful is used to denote the potential of the bloom to contain cyanotoxins that are harmful to both human and animal health. There are many different types and classes of cyanotoxins with varying toxicities and target organs, most notably the hepatotoxins: microcystins, nodularins, and cylindrospermopsins; and the neurotoxins: saxitoxins, and anatoxin-a.3,4 Some genera/speciess produce only one type of toxin, others are known to produce multiple types, and some are not known to produce any toxin at all.5,6 Although any cyanotoxin can potentially be synthesized within a cHAB, the most prevalent and widely distributed cyanotoxin is microcystin.7 Microcystins are a class of cyclic peptides that are comprised of seven different amino acids. Five of these amino acids are constant, while the other two are variable at positions X and Z (see Figure 1). These toxins also have two demethylated positions

(R1, R2). Because of the immense variation in chemical structure, there are approximately 130 microcystin congeners.8 Of the known congeners, microcystin-LR (MC-LR), Leucine at X position and Arginine at Z position, is the most abundant, studied, and toxic form.9,10 Figure 1

1 displays the chemical structure and positions for possible microcystin congeners. Note X at position 2, Z at position 4, and R1, R2 demethylated positions at 3 and 7.

Figure 1. Chemical structure of microcystins

4 (X and Z are variable amino acids; R = H or CH3). Adapted from Humbert, 2009

1.2 Microcystin-Producing Genera

Microcystins were first discovered in the cyanobacteria species, Microcystis aeruginosa, hence the name “microcyst-ins.” 11 M. aeruginosa is the most prominent and studied species in research, however, much work has been done to characterize other potential toxin-forming cyanobacteria. As reported throughout the literature, three main genera have emerged as dominant microcystin producers typically responsible for toxic blooms: Microcystis, Anabaena, and Planktothrix.12–14 In an effort to comprise a library of all microcystin-producing genera currently known, a targeted literature review including critical reviews, journal articles, technical documents, and guidance documents was conducted. Those sources that summarized and/or listed multiple microcystin-producing genera were considered; sources that were specific to only one genus/species were omitted. A total of 28 references were obtained, ranging from years

2003-2018.

Genera were organized based on their taxonomic position as recommended by Komárek et al. (2014) and Guiry et al. (2019).15,16 Three subclasses diverge into five distinct orders, with

Nostocales as the dominant order. A total of 31 genera were identified from this review. Only those genera capable of producing microcystins are reported here, however, other possible cyanotoxins that are synthesized by these genera are reported as well. It is important not to infer that all species of a genus are microcystin producers, but rather the reported genus houses one or several species known to have produced the toxin. Thus, the genus is typically reported rather than reporting specific species. Table 1 displays the findings of the review.

All 28 references list Microcystis, Anabaena, and Planktothrix as microcystin producers, with the exception of Zanchett et al. (2013) who does not list Microcystis as a main producer.17

Nevertheless, the shear reporting frequency of these three genera confirms their dominance as likely sources of microcystins, as described previously. Other widely reported genera include

Anabaenopsis, Aphanizomenon, Hapalosiphon, Nostoc, Oscillatoria, and Aphanocapsa. The only genus reported to produce all five cyanotoxins queried (see 1.1) is Aphanizomenon. It is interesting that Nodularia, is reported thrice as a microcystin producer as it has been reported in several other publications only to be a producer of nodularin.3,4,17 However, because the structure and toxicity of nodularins are similar to microcystins and the fact that they are treated as the same toxin in some monitoring protocols, nodularins may be easily synonymized with microcsytins.6,18 The review also located genera such as Rivularia, Geitlerinema, Arthrospira,

Phormidium, and Lyngbya that have at least two references, neither of which come from highly regarded regulatory agencies such as the World Health Organization (WHO) or the United States

Environmental Protection Agency (USEPA), who have established health standards for microcystins. Lastly, there were five genera with only one source: Umezakia, Gloeocapsa,

Radiocystis, Plectonema, and Coelosphaerium. Further documentation will need to be gathered in order to confirm these genera as microcystin producers.

There are several reasons for the diversity of reporting. One simple explanation is an author’s decision for brevity, that is, it was not their intent to thoroughly list all possible producers. Another obvious explanation is the date of publication. If a given genus had not been found to produce microcystins prior to the publication of the reference, then the genus is lost in the report. For example, this can be observed in two separate papers co-authored by Carmichael et al., dated 2006 and 2016 respectively.3,19 In this ten year span, five newly discovered genera are included in the 2016 review: Anabaenopsis, Dolichospermum, Snowella, Woronichinia, and

Synechocystis. However, the most likely explanation for differences in reporting is the result of constant taxonomic revision within the Cyanobacteria phylum.

Jiří Komárek’s work on the taxonomy of cyanobacteria and their revisions has been pivotal in our understanding of the differences and relatedness between a genus or speciess.15,20

Komárek et al. (2014) presents a review of the recognized and accepted cyanobacterial genera as of June 2014 based on a polyphasic approach.15 A polyphasic approach involves identification using genotypic and chemotaxonomic methods, which are more quantitative, as opposed to using colonial, cellular, and morphological methods, which are more qualitative, to find the taxonomic position of a species. Komárek’s review categorizes genera into two main categories. Category 1 includes genera supported by molecular phylogeny, including 16S rRNA gene sequence of the type speciess. Category 3 includes genera traditionally classified based on morphology and that require taxonomic revision; meaning that all species within the genus do not belong or that the genus has no clear relationship to other genera within the taxonomic construct. The only microcystin-producing genera qualifying as strictly category 1 are: Dolichospermum,

Microcystis, Planktothrix, Cylindrospermopsis, Snowella, and Woronichinia, demonstrating the vast uncertainty to accurately identify a genus/species capable of producing microcystins.15

In the early 2000’s, major taxonomic revisions involving the formation of completely new genera were undertaken. Several species originally categorized within the genus, Anabaena, were reorganized into a new genus called Dolichospermum.21 Revisions were also made to the genus, Oscillatoria, separating some of the species into a new genus called Planktothrix.22 In fact, according to Komárek (2016), many taxonomic revisions have been made due to the presence of aerotopes, gas vesicles that allow cyanobacteria to be buoyant in water.20 The distinction between planktic (having aerotopes) versus benthic (not having aerotopes) has led to the reclassification of many planktic species into new genera; and according to Komárek (2016), all species of Microcystis, Planktothrix, and Dolichospermum contain aerotopes after proper revisions.20 As shown by Table 1, the distinction between Planktothrix and Oscillatoria has widely been accepted in the literature based on the amount of references that acknowledge both genera as potential microcystin producers. However, this is not the case between

Dolichospermum and Anabaena. Because of the immense diversity within the Anabaena genus, the literature appears to be content on not reporting the distinction between the genera. I would contend however that differences in life strategy, that is planktic versus benthic, between

Dolichospermum and Anabaena warrants proper distinction as it relates to source water protection.

Table 1. Taxonomic review of microcystin-producing genera

Phylum Class Subclass* Order Family Genus (N=31) Toxin (N=5) Reference (N=28)

Anabaenopsis Microcystins 1,3,5–7,13,14,17,23–29

Microcystins, Nodularins, Aphanizomenon Saxitoxins, Cylindrospermopsins, 5,13,23,24,28,30–32 Anatoxin-a

Microcystins, Saxitoxins, Cylindrospermopsis Cylindrospermopsins 1,24,32 Aphanizomenonaceae

Microcystins, Saxitoxins, Dolichospermum 3,6,23,30,31,33 Cylindrospermopsins, Anatoxin-a

Nodularia Microcystins, Nodularins 1,34,35

Nostochophycidae Nostocales Umezakia Microcystins, Cylindorspermopsins 36

Gloeotrichiaceae Gloeotrichia Microcystins 6,23,28,34,35

Fischerella Microcystins 24,34,35 Hapalosiphonaceae Hapalosiphon Microcystins 1,3,6,13,14,19,23–25,37

Microcystins, Saxitoxins, 1,3–7,13,14,17,19,23–32,34–40 Anabaena Cylindrospermopsins, Anatoxin-a Nostocaceae 3,6,7,13,14,17,19,23–29,34– Nostoc Microcystins, Nodularins 38,40

Rivylariaceae Rivularia Microcystins 6,24

Gloeocapsa Microcystins 39

1,3–7,13,14,19,23–40 Chroococcales Microcystaceae Cyanobacteria Cyanophyceae Microcystis Microcystins, Anatoxin-a

Radiocystis Microcystins 40 Coleofascicylaceae Geitlerinema Microcystins, Saxitoxins 6,30

Arthrospira Microcystins, Anatoxin-a 3,6

Microcoleaceae 1,3–7,13,14,17,19,23–40 Microcystins, Saxitoxins, Anatoxin- Planktothrix Oscillatoriophycidae a

Microcystins, Saxitoxins, 24,30 Oscillatoriales Lyngbya Cylindrospermopsins

Microcystins, Saxitoxins, Anatoxin- 1,3– Oscillatoria 7,13,19,23,24,26,28,34,35,37,40 a Oscillatoriaceae

Phormidium Microcystins, Anatoxin-a 5,6,24,40

Plectonema Microcystins 6

Coelosphaerium Microcystins 28

Coelosphaeriaceae Snowella Microcystins

3,23,33

Woronichinia Microcystins, Anatoxin-a 3,23,33

Aphanocapsa Microcystins 3,19,23,24,26,33,37

Synechococcophycidae Synechococcales 5,23 Merismopediaceae Merismopedia Microcystins

Synechocystis Microcystins 3,23,36

Limnothrix Microcystins 23,33

Pseudanabaenaceae Pseudanabaena Microcystins 7,23,31,33

Synechococcaceae Synechococcus Microcystins 5,23,24,33,39,40

* Subclasses as recommended by Komárek et al. (2014)15

1.3 Dominant vs. Mixed Blooms

A cHAB is a relative term, defined by the water quality conditions and related risks

(nuisance bloom versus toxic bloom) that are present.41 Qualitative assessments exist through visual observations of a source water or by satellite imagery via remote sensing technologies, but quantitative assessments are more informative. For example, a severe bloom may be characterized by the following conditions: cyanobacteria cell counts > 100,000 cells/mL; total cyanobacteria 16S rRNA gene results > 100,000 gene copies/mL; biovolume > 10 mm3/L; chlorophyll-a > 50 µg/L; and presence of cyanotoxins and/or mcy genes (see 1.5 for

23 definitions). Cyanobacteria can be categorized into four groups: N2 fixation species (e.g.

Aphanizomenon flosaquae); Stratified species that can fine-tune their buoyancy throughout the entire water-column (e.g. Planktothrix rubescens); Turbulent species that are well mixed in the epilimnion (e.g. Planktothrix agardhii); and colony-forming species that remain in the epilimnion and may form surface scums (e.g. Microcystis spp.).42 Buoyancy regulation, allowing vertical migration of some species, presents unique scenarios of competition in waters.2,6

Eutrophication has been found to decrease the diversity of phytoplankton assemblages while selectively pressuring the dominance of cyanobacteria in surface waters. However, some cyanobacteria dominance appears to be site-specific and can be related to other factors such as climate, physical characteristics of a waterbody, and their interactions.42,43 Even within cyanobacterial communities, some genera/species can dominate over others for short or extended periods of time given the conditions. Steffen et al. (2014) argues that climate change will cause lakes to become more thermally stratified, favoring buoyant over benthic speciess.43 While there is no definitive method for predicting what genera/species will dominate, according to Dokulil and Teubner (2000), filamentous species typically dominate shallow lakes, while colony forming species typically dominate deeper lakes.42

A cHAB can be dominated by one genus/species or be comprised of a mixture of genera/species and this has been well documented in the literature. A classic example of dominance has been exhibited in the eutrophic Western Lake Erie Basin. Millie et al. (2009) reports that for years 2003-2004, M. aeruginosa dominated approximately 80% of the bloom in

Western Lake Erie, but fell to less than 20% in 2005, as P. agardhii began to dominate.44 Similar dominance in the Missisquoi Bay of Lake Champlain is reported by McQuaid et al. (2011) where

86.7% of the total cyanobacterial biovolume was dominated by Microcystis spp. over a two year period.45 Mixed assemblages of multiple genera have also been documented in the literature. One study investigating the diversity of planktonic cyanobacteria in a Polish reservoir found that

Microcystis and Aphanizomenon co-dominated throughout the summer period, however,

Microcystis became 100% dominant by the end of the sample period.12 In another study of a

Turkish lake, Gurbuz et al. (2009) found relative abundances of eight different microcystin- producing genera, reporting four as dominant for the study period.40 Furthermore, in a

8 comprehensive review of 70 Finnish lakes, Rantala et al. (2006) discovered Microcystis,

Planktothrix and Anabaena spp. in 70%, 63%, and 37% of all lake samples respectively.46 In fact, this study found that co-occurrence of these three microcystin-producing genera was related with increased eutrophication of the source water, suggesting eutrophication increases the risk of potentially toxic cHAB formation. The formation of a cHAB proves to be a complex process and it is not yet fully understood how or why certain genera/species dominate a waterbody.

Environmental conditions coupled with specific physiological characteristics may be responsible for which cyanobacteria populate.

1.4 Microcystin Synthesis

1.4.1 Microcystin Synthetase

Microcystins are synthesized by a large enzyme complex encoded by the microcystin synthetase gene cluster (mcyABCDEFGHIJ).14,24 The mcy gene cluster is typically comprised within one to three operons and contains the genes responsible for encoding the multiple synthases and synthetases needed to assemble the cyclic peptides.24,47 It was originally believed that cyanobacteria obtained the ability of microcystin synthesis by acquiring the mcy gene cluster through horizontal gene transfer, thus explaining why toxic and non-toxic species can exist within the same genus. However, a study by Rantala et al. (2004) reports that all cyanobacteria originally possessed the mcy gene cluster, but overtime, some strains lost these genes and therefore the ability to produce microcystins.14 Rantala et al. (2004) did however acknowledge that variations in the mcy gene cluster may be explained by more recent evolutionary changes and propose that the existence of different microcystin congeners may be the result of rapid

9 evolution of certain gene domains.14 Both mcy positive and mcy negative genotypes have been detected in populations of Microcystis, Planktothrix and Anabaena spp.24

1.4.2 Factors that affect Microcystin Synthesis

The role that microcystins serve for cyanobacteria and the factors that control and/or promote their synthesis has not been fully determined.48,49 The fact that a cHAB comprised of known microcystin-producers may not produce toxin indicates that the mcy gene cluster is being upregulated for a specific reason. Several theories have emerged. One that is well regarded is that microcystins serve as a toxic defense mechanism against zooplankton grazers that predate on cHABs.14,50,51 One study reports suppression of meso-zooplankton grazing in the presence of heightened intracellular toxin synthesis of Microcystis spp.52 In a recent study, Park et al. (2018) demonstrated a significant positive correlation between protozoan grazers (ciliates and meta- zooplankton) and the proportion of toxic Microcystis to the rest of the bloom. In fact, protozoan grazers were the only environmental factor during the 5-day period when toxic Microcystis reached its peak to significantly fluctuate, demonstrating that toxin production was in response to heightened grazer populations.49 Protozoan grazers may have a significant role in changing the dynamics of a cHAB and ultimately the concentration of toxin.

Another prominent theory is high light intensity promotes microcystin production.5,11,13,51

As explained by Otten et al. (2012), the rationale is that buoyant cyanobacteria at the surface will undergo photooxidative damage from prolonged exposure to high solar radiance. As a result, microcystins are produced to provide intracellular protection from oxidative damage.53

Laboratory experiments have shown that differing colors of light as well as increasing light intensities have positive associations with microcystin concentrations in M. aeruginosa

10 cultures.5,11,51 It has also been reported that non-toxic strains are more vulnerable to intense UVB radiation as compared to toxic-strains, which is consistent with the oxidative-stress theory.5 A study by Pineda-Mendoza et al. (2016) exhibited a growth decrease in M. aeruginosa while simultaneously exhibiting upregulation of the mcyA gene in the presence of the most intense light.36 These findings again would confirm the theory that microcystin production provides a level of resiliency in high light environments. Interestingly, this theory can become highly complex when considering the co-effects of turbidity, buoyancy, and wind on the solar intensity a cyanobacterial cell will experience. Increased turbidity decreases the penetration of light in the water column and thus can reduce the amount of solar radiance a cell/colony will receive.53 High wind events induce mixing of the water column, causing buoyant cells at the surface to constantly be remixed instead of remaining at the surface where photooxidative stress may occur.53 However, some studies have shown a positive correlation between wind speed/wind direction and microcystin concentrations, suggesting that wind plays an important role in spatially distributing the toxin.54,55 These associations of course could reflect the distribution of toxic species that were allowed to grow in calm waters prior to increased wind events. Therefore, the relativeness of a sample may play a key role in determining and comprehending the impact light intensity has on microcystin production.

Decreasing water temperature, typically toward the end of autumn, has also been proposed to be a factor associated with microcystin production.11,13,36,49,51 It has been well documented that the optimal water temperature for cyanobacterial growth is > 25°C.2,11,49

Laboratory studies using M. aeruginosa cultures have determined that high growth rates are not correlated with toxicity, as the highest microcystin concentrations are found at 20°C.11 Likewise,

Harke et al. (2016) reports that microcystin concentrations are generally the greatest between 20-

25°C.51 Interestingly, several studies have shown that water temperature has a profound effect on the type of congener synthesized, as greater compositions of MC-LR are consistently found at lower water temperatures (<25°C).13,36,51 Being that MC-LR is the most toxic microcystin congener known presents an added risk to source waters late in a bloom season. In a study on

Microcystis in pelagic waters, Park et al. (2018) showed that the toxic proportion of a bloom

(having mcy genes) remarkably increased when water temperatures fell below 20°C, concluding that low water temperatures act as an environmental stressor on the bloom.49 But of course, other studies have concluded different results. For example, Amé and Wunderlin (2005) found that increasing water temperature had no effect on microcystin cell quotas of M. aeruginosa.56

Several other studies have reported significant positive correlations with microcystins and total cell abundance/biovolume, which also would prove contrary to the theory that toxin is produced in unfavorable water temperatures.44,54

Another potential factor affecting microcystin production that has received significant attention is eutrophication of waters, specifically of nitrogen and phosphorus. It has been well documented that both nitrogen and phosphorus in excess allow cyanobacteria to grow and form blooms. That is why eutrophication of source waters has been a hot topic for research in both environmental and agricultural sectors. Nitrogen is of particular interest because some cyanobacteria, such as Microcystis and Planktothrix, are incapable of N2 fixation and therefore rely on external nitrogen sources.51 Because these genera have been responsible for major cHAB events suggests that anthropogenic nutrient sources play a significant role in cHAB formation. It has been hypothesized that nutrient depletions may serve as an environmental stressor and thus promote the production of microcystins, however, this conjecture is highly debatable.5,13,51

Several studies have indicated microcystin production when nitrogen concentrations are low.

Ginn et al. (2010) describes NtcA, a nitrogen-controlled transcription factor that binds to the promoter region of the mcy gene cluster, initiating microcystin biosynthesis. Their findings demonstrated that NtcA binds in the presence of low nitrogen, concluding that nitrogen depletion directly controls microcystin synthesis.57 Laboratory studies as early as 1986 also demonstrated that omission of nitrogen in Microcystis cultures resulted in a ten-fold increase in toxicity.11 But other studies have indicated that microcystins are produced when nitrogen concentrations are plentiful. A study by Gobler et al. (2007) discovered that nitrogen application in a pelagic water caused microcystin concentrations to increase during specific periods of the study, and this is further supported by Ha et al. (2009) who reports a moderate correlation (R2 = 0.55) between microcystin and total nitrogen concentrations.52,58 Another nutrient parameter that has been explored is the total nitrogen to phosphorus ratio (N:P), but again results have varied. Some studies such as Downing et al. (2005) have suggested moderate N:P ratios influence microcystin production, while other studies such as Boutte et al. (2008) report associations when N:P ratios are low.12,59 In a study of Ohio surface waters, Francy et al. (2016) reports a positive correlation

( = 0.45) between microcystins and N:P ratios in one source water, but a significant negative correlation ( = -0.65) in another.54

It is quite clear that trying to establish a relationship between any environmental factor and toxin concentration is highly difficult, reinforcing the complexity of these processes. Some theories suggest toxin production is a result of environmental stressors or suboptimal growing conditions, while others suggest favorable growing conditions and bloom proliferation may promote toxin production. Differences across studies could also be related to differences in the eco-physiologic responses of differing genera and site-specific phenomena cannot be ruled out.

1.4.3 Extracellular vs. Intracellular Toxin

Microcystins are produced intracellularly, but can become extracellular when a cHAB begins to dye or cells are lysed.12,60 Laboratory experiments using M. aeruginosa have shown that if cells are in the growth phase, and therefore healthy and actively growing, 75 to 90% of microcystins are intracellular. Contrary to this, if M. aeruginosa cells are in the decay phase, and therefore not actively growing but dying, only 30 to 40% of microcystins are intracellular.2

Furthermore, Pietsch et al. (2002) demonstrated that intracellular toxin generation reached a peak of 25 µg/L on day 13, four days after the initial growth phase (Day 0 to 9). However, as the M. aeruginosa moved beyond the stationary phase, extracellular toxin concentrations peaked to 35

µg/L around day 20, during the decay phase.61 These data demonstrate the importance of the health of the cHAB as it relates to assessing the ratio of extracellular to intracellular toxin (E:I).

In fact, Sakai et al. (2013) suggests that the E/I ratio is an efficient way to characterize the health status of a bloom, that is, the proportion of the bloom dying versus growing.62 The literature supports that the majority of toxin sampled in waters is intracellular.63 In a study by

Zhang et al. (2015), the mean and max concentration of total intracellular microcystins were 0.66

µg/L and 7.70 µg/L respectively, compared to the total extracellular microcystins mean and max concentration of 0.071 µg/L and 0.32 µg/L respectively.55 In another study analyzing 270 pelagic water samples, intracellular toxin was found in all samples with a median concentration of 2.3

µg/L. Conversely, only about 63% of samples had detectable extracellular toxin, with 75% of these detections below 0.5 µg/L.63 Finally, Sakai et al. (2013) provides 16 E/I ratios in their study. Fifteen out of sixteen ratios are in favor of intracellular toxin (E/I = 1.6-19.0%), with one anomaly of 107%. However, even in the sample where extracellular toxin dominated, the extracellular toxin concentration was only 0.22 µg/L.62

This does not mean that large extracellular toxin concentrations cannot exist. Gurbuz et al. (2009) reports extracellular microcystins as high as 48.5 µg/L in their study and report concentrations as high as 226 µg/L from the open literature.40 Another study by Millie et al.

(2009) found extracellular microcystins to be four times greater than intracellular (E/I ratio >

400%) at select off-shore sampling stations, which suggests a localized lysing event.44 The data support the assumption that most microcystins found in surface waters are intracellular. Only when proportions of the bloom are dying or lysing due to environmental stressors will extracellular toxin concentrations increase. Even if extracellular toxins are greater than intracellular, the actual concentration most likely will not be significantly high when considering the max extracellular toxin concentrations reported in the literature.

1.5 Monitoring Methods

1.5.1 Microcystin Methods of Detection

Several analytical techniques such as high-performance liquid chromatography (HPLC), liquid chromatography-mass spectrometry (LC-MC), and enzyme-linked immunosorbent assay

(ELISA) have been developed and are in use for quantification of microcystins in water samples.64,65 ELISA has emerged as the prominent technique because of its sensitivity in detecting most microcystin congeners with variable chemistries; a limitation of HPLC and LC-

MC.65 The assay are based on the detection of antibodies that bind to specific regions of the toxin. Freeze/thaw extraction, which is the freezing and thawing of a sample to lyse cyanobacterial cells and produce all extracellular toxin, is typically deployed prior to ELISA.

The ability of ELISA to detect microcystins at very low concentrations makes it a valuable option for source water and drinking water protection.65,66 The USEPA has established Method

546 for detecting microcystins, using ELISA to detect the ADDA amino acid side chain

[(4E,6E)-3-amino-9-methoxy-2,6,8-trimethyl-10-phenyldeca-4,6-dienoic acid], a unique component of all microcystins.18

The main limitation of ELISA is that it is an analytic technique that takes time to process.

As described by Weller (2013), toxic cHAB events are highly time dependent and thus the time delay between sampling and availability of the final ELISA result may be too late when considering sampling of a water treatment plant (WTP).65 Because of this limitation, different types of highly sensitive biosensors are in development that would provide a means of quantifying microcystins in real-time.67–69 In fact, the sensitivity of the biosensor described by

Zhang et al. (2018) has the ability of detecting toxin concentrations as low as 2.3 ng/L.69

Biosensors provide an innovative way of monitoring microcystin concentration in real-time.

Although their use in drinking water treatment is still in development, they present a clear temporal advantage over current analytical techniques. One limitation of biosensors is the inability to distinguish between intra- and extracellular toxin, an important concept in water treatment (see 1.7). Biosensors are quantification tools and do not provide any predictive application.

1.5.2 Quantitative Polymerase Chain Reaction (qPCR)

Advances in genomics have paved the way in microbial source tracking and monitoring.

Quantitative real-time polymerase chain reaction (qPCR) is a development that allows for the amplification and quantification of a specific gene, which can be used to identify the presence and abundance of an organism in a sample. To utilize qPCR in quantifying cyanobacteria, amplification of the 16S ribosomal rRNA (16S rRNA) target gene is used. The 16S rRNA gene

16 is a regional component of the 30S small subunit of the prokaryotic ribosome that binds to the

Shine-Dalgarno sequence. It is universal to all prokaryotes and has been critical in phylogeny due to its slow evolutionary change.70 Primers have been designed to capture total cyanobacteria

16S rRNA genes in a sample as well as genus/species specific 16S rRNA genes.71 Recently, duplex and/or multiplex qPCR regimes have been developed, allowing for simultaneous amplification and detection of multiple genera.26,72

It is important to note that results of qPCR using 16S rRNA genes do not exactly equate into total cell equivalents. As Vêtrovský and Baldrian (2013) explain, genomes of different prokaryotes can have more than one copy of the 16S rRNA gene per genome (or cell), reporting that cyanobacteria have 2.3±1.2 16S rRNA per genome.70 This could greatly impact estimated total cyanobacteria cell equivalents if multiple genera are present in a sample. Standardized methods typically involve establishing regression equations between cell counts and qPCR DNA counts obtained from the same sample, assuming that gene counts are constant per cell.33,73 As

Ha et al. (2009) explains, intrinsic error may exist when making this conversion due to inefficient DNA amplification during qPCR or the possibility that gene amounts vary in a cell depending on its growth phase. Although Ha et al. acknowledged that a typical Microcystis cell will have two 16S rRNA genes, they report 250 16S rRNA gene copies per cell based on their regression equation.58

It has been argued that mcy genes serve as a better proxy for cell enumeration as it is assumed that there is only one mcy gene cluster per cell, however, not every cell may contain the mcy gene cluster (see 1.4.1). More recently, other genes have been used in translating qPCR results to cell counts. Churro et al. (2012) used the rpoC1 gene, responsible for encoding the unique γ subunit of RNA polymerase, as a proxy for cell number quantification because there is

17 only one copy of this gene per cyanobacterial genome. The results of their regression analysis demonstrated that this gene provided an efficient quantification of Planktothrix cells (R2 =

0.997).72

1.5.3 mcy Genes and Toxic Blooms

As discussed in section 1.4.1, not every strain or species will possess the mcy gene cluster and thus will not have the ability to produce microcystins. Having the ability to detect and quantify what proportion of a cHAB is potentially toxic due to the presence of the mcy gene cluster has large implications in monitoring and control practices. Therefore, methodologies of qPCR have been expanded to target the mcy gene cluster in order to ascertain the toxic potential of a bloom. Zuo et al. (2018) demonstrated that multiple genes of the mcy gene cluster (mcyA, mcyB, mcyD, mcyE) can be amplified and detected using qPCR and Hisbergues et al. (2003) demonstrated the use of genus-specific primers to distinguish the presence of the mcy gene in differing genera.27,74

Because upregulation of the mcy gene cluster is responsible for the synthesis of microcystins, it is obvious that multiple efforts have been made to observe relationships between its presence and the presence of toxins in surface waters. Table 2 provides a summary of the associations between mcy genes and microcystins reported in the open literature. The mcyA, mcyB, and mcyE genes specific to Microcystis and Planktothrix appear to be the most widely used in studies. Several studies have demonstrated that differing mcy genes, quantified using qPCR, are significantly associated with measured microcystin concentrations.26,53,58

Perhaps the real power of using mcy genes is the ability to characterize the toxic dynamics of a bloom. Francy et al. (2015, 2016) has published several qPCR data of waterbodies

18 across Ohio (refer to Table 2). These studies have analyzed samples for the presence of mcyE genes specific to Microcystis and Planktothrix because these are two likely genera responsible for toxic bloom formation. Francy et al. (2016) found significantly strong associations between

Microcystis mcyE gene copies and microcystins in two different Ohio waterbodies, but no association was found for Planktothrix mcyE gene copies.54 Similar work is reported by

Bukowska et al. (2017) who found significantly strong associations between Planktothrix mcyA genes and microcystins, but insignificant and negatively correlated associations with Microcystis mcyB genes in the same study.33 It is of course possible for multiple genera to be simultaneously associated with microcystins, as was the case for Deer Creek, Ohio (refer to Table 2).75

Observing associations between cyanobacterial genes and measured microcystins provides a rudimentary method for determining which genus/species is responsible for toxin production.

Another useful application of mcy genes is the ability to determine the toxic versus non- toxic ratio of a genus and/or bloom. By detecting the presence of mcyE genes, Rinta-Kanto et al.

(2009) determined that the ratio of potentially toxic Microcystis spp. (cells possessing the mcyE gene) ranged from 0-60% during a three year study period on Lake Erie.73 Similarly, Bukowska et al. (2017) demonstrated that the ratio of potentially toxic Planktothrix spp. in a Polish lake was nearly 100% for the entire study period, while the ratio of potentially toxic Microcystis spp. was consistently low.33 Interestingly, when testing the association of the toxic/non-toxic ratio within a genus to measured microcystins, no significant association was found in either study.

However, when testing the association of a genus/total cyanobacteria ratio to measured microcystins, significant associations were found in both studies.33,73 These results suggest that it is not the fluctuation of toxic strains within a genus that dictates microcystin production, but rather it is the shift of the proportion of a genus within the total cyanobacterial population.

Understanding these shifts may help to explain how one genus/species arises to be responsible for toxin production. In a study by Ngwa et al. (2014), monocultures of Microcystis and Planktothrix as well as mixed cultures of the two genera were prepared and analyzed for the presence of mcyE genes. It was discovered that mcyE gene copies and cell counts were consistently high in both monocultures, however mcyE gene copies and cell counts in the mixed cultures were two-threefold less. Most interesting is that Planktothrix mcyE gene copies were between 2-31-fold higher in mixed cultures compared to monocultures, despite a significant reduction in cell count. The authors also observed a downregulation in mcyE expression in the mixed cultures, suggesting a competitive interaction exists between the two genera. While the authors acknowledge there is no clear explanation for the phenomenon exhibited in Planktothrix, the data appear to suggest that the toxic ratio of Planktothrix spp. increases in mixed blooms, even though the mcyE gene is not expressed.76

Toxic interactions between Microcystis and Planktothrix have been reported elsewhere.

Steffen et al. (2014) conducted a study of the historically hypereutrophic Grand Lake Saint

Marys (GLSM) located in Celina, Ohio, that is annually plagued by toxic cHABs. In 2010,

GLSM exhibited a toxic cHAB dominated by approximately 90% Planktothrix. However, after

PCR analysis of the mcyA gene specific to Microcystis (comprising the other 10% of the bloom), it was found that the highest microcystin concentrations sampled in July coincided with increasing concentrations of the Microcystis mcyA gene. This suggests that Microcystis may have been responsible for the majority of toxin present even though it did not dominate the bloom. Furthermore, in late 2010, PCR analysis demonstrated a shift in toxicity, with the majority of mcyA detected in Planktothrix spp.43 More research is needed in order to fully understand these toxic shifts and the complex interactions that exist between genera/species.

What is known is that interactions between potentially toxic genera/species can affect the toxicity of cyanobacterial communities and that the domination of one potentially toxic genus/species may not translate to toxin production.

However, some data in the literature suggest that mcy genes are not useful proxies for determining microcystin concentrations. Francy et al. 2015 found no correlation between

Microcystis/Planktothrix mcyE gene copies and microcystins in Sandusky Bay, even though microcystins were detected in 100% of samples, at concentrations ranging from 2.1-8.8 µg/L.75

In another study, Beversdorf et al. (2015) refutes the notion that mcy gene abundances are accurate indicators of microcystin concentrations. In their study, mcy gene abundances were anti- correlated with microcystin concentrations in three out of four lakes. Secondly, while mcy gene abundances were correlated with the total cyanobacterial community composition (CCC), total toxin concentrations were differentially correlated to CCC.47

All the results reviewed here demonstrate the complexity of understanding and estimating microcystin concentrations in source waters. Because expression of the mcy gene cluster is directly related to toxin synthesis, it is reasonable that the presence of mcy genes may infer the presence of toxin, which some literature supports. There is however no implicit way of knowing if or when the mcy gene cluster will be expressed and some studies have shown that genes may be present when toxin is not or vice-versa. Other environmental factors (see 1.4.2) may influence the level of toxin production in a bloom and interactions between cyanobacterial genera/species cannot be ignored. Therefore, mcy genes may serve as a useful tool in predicting microcystin concentrations, but a level of uncertainty exists.

Table 2. Associations between mcy genes and microcystins in the open literature

Genea Microcystins Statistical Toxic/non- Statistical Genus/speciess Reference (µg/L) Test and toxic ratio Test and Associationb Associationc mcyA Variable Regression Log-normal N/A Microcystis Ha et al. analysis; distribution 2009 58 R2 = 0.66 (0.075, 0.38)k mcyE Variable Spearman 0-60% Spearman Microcystis Rinta-Kanto Rank; (Monthly Rank;  et al. 200973  =0.40, 0.58 means)k =0.12, 0.03 mcyB Variable Pearson N/A N/A Microcystis Chiu et al. Correlation; 2017 26 R2 = 0.62-0.73 mcyE 0.16±0.03- Pearson N/A N/A Ngwa et al. 9.72±0.39 Correlation; 2014 76 R2 = 0.94d Microcystis 0.06±0.03- R2 =0.71e Planktothrix 6.41±1.11 mcyE Variable Regression N/A N/A Microcystis Otten et al. Analysis; 2015 77 R2 = 0.76 0.1-2.1 Spearman Spearman Bukowska Rank; Rank; et al. 201733 mcyBc  = -0.40d 9% ± 24k = -0.41 Microcystis mcyAd  = 0.88e 91% ± 8.5l  = -0.07 Planktothrix mcyE 1.8 ± 3.7 Spearman N/A N/A Francy et al. 24 ± 51 Rank;  = 2016 54 0.92bd, 0.82df Microcystis 0.10ce, -0.09eg Planktothrix mcyE Variable Spearman N/A N/A Francy et al. Rank; 2015 75  = 0.40dh Microcystis  =0.90eh Planktothrix mcyE Variable Spearman N/A N/A Francy et al. Rank; 2015 75  = 0.85di Microcystis  =0.96ei Planktothrix mcyE Variable Spearman N/A N/A Francy et al. Rank; 2015 75  = -0.60dj Microcystis  = 0.12ej Planktothrix mcyB 1.60±0.50 Spearman N/A N/A Microcystis Zhang et al. Rank; 2017 78  = 0.82 a mcy gene DNA b Associations between mcy genes and Microcystins. Bold indicates statistically significant association c Associations between toxic/nontoxic ratio of a genus, based on presence of mcy gene d Association for Microcystis mcy gene copies e Association for Planktothrix mcy gene copies f Harsha (main) Lake; g Maumee Bay State Park-Lake Erie; h Buck Creek; i Deer Creek; j Sandusky Bay k Toxic Microcystis ratio; l Toxic Planktothrix ratio

1.5.4 Phycocyanin and Chlorophyll-a

Another cyanobacterial monitoring technique involves the use of the photosynthetic pigments phycocyanin and chlorophyll-a. Chlorophyll-a is a green pigment that all phytoplankton possess, while phycocyanin is a blue-green pigment that is more specific to cyanobacteria.6 Over the past decade, great strides have been made through fluorometry to utilize these pigments to screen for potentially toxic cHABs in source waters. Fluorometry involves the use of a fluorometric probe that emits a light over a specific wavelength range, exciting electrons within the pigment molecules. The probe then measures the relative fluorescence of the in vivo pigments that results from the electrons returning to a lower state.79 Chlorophyll-a is excited between 400-530 nm and has an emission maxima within the range of 680–685 nm, whereas the excitation wavelengths of phycocyanin range between 550-650 nm, with an emission peak around 645–660 nm.79–81 Pigments are measured in relative fluorometric units (RFU), but some probes have the ability to convert RFU’s into µg/L based on calibration specifics outlined in the user manual. The probes take real-time samples and can be programmed to sample at different time intervals.54

Use of phycocyanin to estimate cHAB assemblages has been investigated because phycocyanin is more specific to cyanobacteria than chlorophyll-a. Early studies utilizing regression analysis report strong linear relationships between fluorometrically measured phycocyanin and cell density, demonstrating that fluorometric probes serve as a convenient and cost-effective way for monitoring cyanobacteria abundances.81,82 However, more recently, it has been suggested that the relationship between phycocyanin and biovolume may serve as a better proxy.79,80,83 According to Macário et al. (2015), a significant species-specific variation in phycocyanin content per cell exists, making it difficult to accurately estimate total cell counts if a

23 mixed bloom is present. More consistent relationships are observed across different species when comparing phycocyanin levels to biovolume measurements.

Remote sensing using satellite imagery has also been deployed as an innovative method of tracking cHABs in real-time. Algorithms have been developed to calculate phycocyanin and chlorophyll-a concentrations based on the absorption spectra of satellite imagery, but these algorithms require standardization.84,85 Research by Stumpf et al (2016) showed that under satellite imagery, chlorophyll-a has a greater sensitivity of being detected, while phycocyanin has a greater specificity for detecting cyanobacteria. This can be explained by the adsorption properties of the two pigments: at peak excitation wavelengths, phycocyanin has an absorption of about 0.0075 m2/mg, while chlorophyll-a has an absorption of 0.015–0.02 m2/mg, meaning that twice the concentration of phycocyanin is needed for the equivalent detection in the satellite data. The study ultimately concluded that phycocyanin was a better proxy for measuring cyanobacteria only when mixed assemblages of phytoplankton exist.84

Most recently, studies have explored the use of phycocyanin to predict/estimate microcystin concentrations. Table 3 provides a summary of statistically significant associations found in the literature. Francy et al. (2016) conducted a study of three Ohio surface waters to identify parameters that could be used for predictive models of microcystins. Phycocyanin measurements and microcystins were available for Harsha (main) Lake and Maumee Bay State

Park (MBSP)-Lake Erie sites and a Spearman rank test of association was performed. A total of four phycocyanin values were reported for both sites: 24-hour average, 3-day average, 7-day average, and 14-day average. The 24-hour averages were calculated based on 10-15-minute interval samples. The 24-hour averages were then used to compute the 3,7, and 14-day averages antecedent to the time of sampling. Statistically significant associations (: 0.90-0.98) were

24 found between microcystins and all four phycocyanin parameters at Harsha Lake, with the highest association using phycocyanin 7-day average. The same analysis was performed for

MBSP-Lake Erie and all associations were statistically significant (: 0.71-1.00), except for phycocyanin 3-day average. At this site, phycocyanin 14-day average had the best association, 

= 1.00.54

In a similar study, Rinta-Kanto et al. (2009) also reported Spearman correlation coefficients between phycocyanin and microcystin samples measured at differing stations of

Lake Erie.73 Again, significantly strong correlations were reported for the Western Lake Erie

Basin and for Lake Erie in whole (refer to Table 3). In an earlier study, Izydorczyk et al. (2005) monitored phycocyanin and microcystins in intake water of a Polish reservoir. They report significant associations only when microcystins were < 3.0 µg/L, noting that samples with greater toxin concentrations were excluded from the study due to their statistical insignificance.86

A different approach aimed toward recreational concentrations of microcystins was performed by Marion et al. (2012). In this study, a logistic regression was conducted between fluorometric phycocyanin and whether microcystin concentrations exceeded a 4.0 µg/L threshold.

Interestingly, the authors found that phycocyanin levels were correlated and substantially greater

(median = 103.2 µg/L) when microcystins were > 5.0 µg/L as compared to phycocyanin levels

(median = 48.6 µg/L) when microcystins were below the threshold.87 Remote sensing technologies to measure phycocyanin levels may also provide a means of estimating microcystins. Hunter et al. (2010) used an algorithm to estimate phycocyanin levels from remote sensing of two lakes in the United Kingdom. Using this algorithm, the authors report a strong correlation (R2 = 0.90) with sampled microcystins.85

Using phycocyanin as a proxy for estimating microcystins has its limitations. The most obvious limitation is that the presence of phycocyanin only indicates the presence of cyanobacteria, but in no way can indicate the toxicity of the bloom. Some studies have demonstrated no relationship between phycocyanin and microcystins.88 In the United States

Geological Survey (USGS) Scientific Investigations Report 2015-5120, of six Ohio surface waters surveyed, four had insignificant correlations between phycocyanin and microcystins. In fact, one site at Deer Creek, Ohio, exhibited a negative  of 0.29.75 Limitations can also exist in fluorometry. Factors related to the efficacy of phycocyanin probes include availability of light, turbidity, probe placement (boundary levels), and other phytoplankton assemblages.80,82 Brient et al. (2008) demonstrated these concerns in that phycocyanin measurements under artificial light were subject to error and lower fluorescence readings; turbidity of the water directly decreased phycocyanin fluorescence signals as particles in the water competed and absorbed light emissions of the probe; and that probes positioned <5 cm from a boundary (e.g. lake bottom) had decreased measurements.82 Macário et al. (2015) also reports that some overlap may exist between phycocyanin and chlorophyll-a emission wavelengths, and therefore probes measuring phycocyanin may be subject to overestimates (false-positives) when other phytoplankton assemblages are present.80 Limitations also exist when using remote sensing for phycocyanin retrieval. Cloud cover can affect the quality of a satellite image and the total biomass of a bloom is nearly impossible to quantify as satellites can only measure up to one meter surface depths.84

Nevertheless, phycocyanin provides a unique way of specifically quantifying cHAB abundances. While more research is needed to understand the relationship between phycocyanin and microcystins, several studies have demonstrated significant associations. Because

26 fluorometric probes and remote sensing provide real-time measurements that can be taken multiple times per day, phycocyanin can serve as a constant predictor of microcystins.

Table 3. Associations between phycocyanin and microcystins in the open literature

Method Phycocyanin Microcystins Statistical Associationa Reference Test Probe 8.1 ± 7.5 RFU 1.8 ± 3.7 µg/L Spearman 0.98b Francy et al. 2016 54 12.5 ± 14.6 RFU 24 ± 51 µg/L Rank () 0.96b

Probe Variable < 3 µg/L Linear 0.51c Izydorczyk et al. Regression 200586 (R2) Probe Variable > 0.1 µg/L Two-way 0.76 Makarewicz et al. ANOVA 2009 32 with post- hoc comparisons (R2) Probe 2,330-74,166 0.003-0.16 µg/L Linear 0.643 Murby, 2009 39 cells/mL Regression (R2) Probe Range: 0-150 µg/L > 4.0 µg/L Logistic 0.669d Marion et al. 2012 87 Regression (R2) Probe Variable Variable Spearman 0.763e Rinta-Kanto et al. Rank () 0.627f 2009 73 Probe Variable Range: 0.23-1.3 Spearman 0.90g Francy et al. 2015 75 μg/L Rank () Range: <0.10- 0.89h 5.3 μg/L

Probe with 0-200+ µg/L Range: 0.1-100 Linear 0.70i Stumpf et al. 2016 84 Remote µg/L Regression 0.44i Sensing (R2) Application Remote Variable Range: 0-30 Linear 0.896 Hunter et al. 2010 85 Sensing µg/L Regression (R2) a All associations statistically significant b Phycocyanin concentrations represent 7-day averages c Regression only represents Microcystin samples < 3 µg/L. Samples > 3 µg/L were statistically insignificant and therefore not included d Logistic regression analysis for samples exceeding a Microcystin threshold of 4 µg/L at 7 Ohio lakes e Data from Lake Erie; f Data from Western Lake Erie Basin only; g Data from Buck Creek, Ohio h Data from Harsha (Main) Lake, Ohio i Statistical significance not reported. Associations represent 2013 and 2014 monitoring periods respectively.

1.6 Public Health Implications

1.6.1 Exposure Matrices

The most obvious exposure route of microcystins for humans is ingestion of contaminated drinking water.8 Underground water reserves are not always an option for a community’s drinking water supply, therefore surface waters are used instead. Problematically, these surface waters are often prone to cHAB formation. Toxic cHABs can form suddenly, sometimes near WTP intakes. In fact, one study involving a Turkish lake reports that the depth at which microcystins were detected coincided with the draw-off depth of a local city’s drinking water supply.89 Conventional drinking water treatment must be optimized for cHAB events to reliably address the abundance of toxin a bloom might produce. Therefore, WTPs elect to implement advanced treatment technologies as they become available.7,90 Based on the chemistry between extracellular toxin and water, these toxins are much more difficult to remove than intracellular toxin during treatment. Therefore, treatment must be optimized to reduce/eliminate cell lysis, or treatment may be futile.90,91 In order to ensure microcystins never enter finished drinking water supplies, He et al. (2016) recommends a multibarrier approach that consists of

7 cHAB prevention, source water control, treatment optimization, and routine monitoring.

Ingestion of contaminated water can also occur during swimming and boating events in recreational waters. However, some research has shown that inhalation of cyanotoxins via aerosolized water droplets may be of greater concern during recreation.92,93 Less common and indirect routes of exposure include ingestion of marine food where the toxin has bioaccumulated, ingestion of crops/vegetables irrigated with contaminated water or fertilized with contaminated

WTP residuals, or ingestion of blue-green algae dietary supplements (BGAS).24,94–96 A summary of the exposure routes is depicted in Figure 2.

Figure 2. Human exposure routes of microcystins

Adapted from Rastogi et al. (2014)24

1.6.2 Human Health Effects

Microcystins are hepatotoxins that mainly target the liver. Once ingested, microcystins are transported to hepatocytes through organic anion transporter polypeptides (OATP) of the cell membrane.3 Here, the toxins act as inhibitors of serine/threonine protein phosphatases 1 and 2a, liver enzymes that remove phosphates from proteins, an important step in many biochemical pathways. Inhibition leads to hyperphosphorylation of proteins associated with the cytoskeleton of hepatocytes, creating hemorrhaging and progressive liver necrosis.3,4,97 Although the liver is

29 the main target organ, it has been recorded that the toxins can affect other organs such as the kidneys, reproductive organs, colon, and brain, that contain the same OATPs found in the liver.3,98

Due to the large concentration of toxin needed for adult human fatality, acute poisonings are very rare. The worst documented poisoning to date occurred in 1996 in Caruaru, Brazil, involving intravenous exposure from contaminated water supplies at a hemodialysis clinic.6,8 52 of the 116 exposed perished from this event; a 45% lethality. Most often, chronic, low-dose exposure is exhibited. Symptoms of low-dose intoxication include diarrhea, vomiting, piloerection, fatigue, and parlor.4 Susceptible populations, especially children of lower body mass, should be more vigilant to avoid chronic exposure. However, as Fitzgeorge et al. (1994) first described, microcystin toxicity is a result of cumulative exposure. Because dose response curves for microcystins are steep, health consequences can develop without warning.99 There are several animal studies that demonstrate microcystins are tumor-promoters due to the inhibition of protein phosphatases.9,10,97 However, when it comes to the carcinogenicity of microcystins, the research is contradictory. Most research to date has been conducted using MC-LR. It is thought that MC-LR interferes with DNA damage repair processes as well as increases expression of proto-oncogenes.9,10 The International Agency for Research on Cancer classifies MC-LR as a

Group 2B possible human carcinogen, however, there is inadequate evidence to determine the ultimate carcinogenicity of the toxin.35

There are also limited epidemiological studies that link chronic exposure and disease outcomes in humans; mainly because it is nearly impossible to quantify individual exposure or duration. A study in China reports a positive correlation between microcystins in drinking water supplies and the mortality of stomach cancer in males, but a negative correlation with intestine

30 cancer mortality.98 In another study, significant clusters of deaths related to non-alcoholic liver disease were identified in counties of the United States (US) where cHABs were present in local surface waters. A Bayesian regression analysis concluded a significant correlation between cHAB coverage and the risk of nonalcoholic liver disease death.100 Contradictory to these findings, a study in Canada reports no evidence of a geographical association between cyanobacterial contamination of freshwater lakes and incidence of liver cancer.101 More studies and improved methodologies of assessing exposure are needed before true associations can be established through epidemiological disciplines.

1.6.3 Toxicity and Exposure Values

As reported above, there are many different microcystin congeners with varying levels of toxicity. Toxicity is related to hydrophobicity of the variant, thus the variable amino acid regions of the microcystin structure play a vital role in determining potency. This is why MC-LR is regarded as the most toxic form, followed by other congeners with hydrophobic amino acids such as MC-LA and MC-YM.35 Due to its toxicity, MC-LR is the most studied congener, used across various toxicological studies in the literature. Several animal studies have established an

LD50 for MC-LR between 36-122 µg/kg via intraperitoneal injection, however, the LD50 for MC-

LR via oral ingestion is orders of magnitude greater.35,102–104 These findings are consistent with the high fatality rate exhibited in Caruaru, Brazil, while demonstrating that the common route of exposure via oral consumption does not present a reasonable risk of human lethality.

Nevertheless, there are still health implications related to oral ingestion of MC-LR.

USEPA has established a reference dose (RfD) of 0.05 µg/kg-day for human consumption of MC-LR.35 An RfD is an estimate of the daily exposure of a given agent to a

31 human population, including sensitive subgroups, that is likely to be without an appreciable risk of deleterious effects during a lifetime.105 After an extensive review of the literature, USEPA determined that a toxicological study by Heinze (1999) possessed the greatest weight of evidence for RfD development because rats were administered microcystins through ingestion of drinking water.35 In this study, MC-LR was dosed for 28 days at concentrations of 0, 50 and 150 μg/kg body weight. Based on the outcome measures of increased liver weight, developed liver lesions with necrosis and hemorrhaging, and increased enzyme levels, a lowest-observable-adverse- effect-level (LOAEL) of 50 µg/kg-day was determined.106 USEPA considered several uncertainty factors (UF) when adopting the RfD applicable to human health outcomes. A UF of

10 was applied to account for natural variability that may exist in human populations related to toxico-kinetics and toxico-dynamics; a UF of 10 was applied to account for uncertainty in extrapolating results from animal studies to humans; a UF of 3 was applied to account for use of a LOAEL instead of a no-observable-adverse-effect-level (NOAEL); and a UF of 3 was applied to account for the deficiencies or lack of carcinogenic, reproductive, and chronic exposure studies in humans.35 Using the listed UFs and LOAEL described above, USEPA established a

RfD of 0.05 µg/kg-day.

In comparison, WHO has also adopted a regulatory health threshold for MC-LR ingestion. WHO has established a MC-LR Tolerable Daily Intake (TDI) of 0.04 µg/kg-day based on a toxicological study by Fawell et al. (1999).97 In this study, MC-LR was administered to mice via gavage at doses of 0, 40, 200, and 1000 µg/kg over a 13 week period. Based on histopathological examination of the liver that indicated the development of lesions, chronic inflammation, focal degeneration of hepatocytes, and hemosiderin deposits, a NOAEL of 40

µg/kg-day was determined.103 WHO considered several UFs when adopting the TDI applicable

32 to human health outcomes. A UF of 10 was applied to account for uncertainty in extrapolating results from animal studies to humans; a UF of 10 was applied to account for variability of sensitivity between populations; and a UF of 10 was applied to account for inadequate/lack of data in MC-LR exposure studies.97 Using the listed UFs and NOAEL described above, WHO established a MC-LR TDI of 0.04 µg/kg-day. It is important to note that the RfD and TDI, while specific to MC-LR, is used universally for all microcystin congeners and considers only non- carcinogenic health outcomes.

There are different methods used for characterizing risk, but here, a hazard quotient (HQ) will be reviewed. An HQ is a unitless risk ratio that typically takes on the form shown below in equation 1:

HQ =ADD/RfD [1]

where ADD is the average daily dose of a contaminant a person is subject to based on exposure and RfD is an applicable reference dose as explained previously. If the HQ is less than 1, there is minimal to no expectation of adverse health effects as a result of exposure because the ADD does not exceed the RfD. However, if the HQ is greater than 1, there is an appreciable expectation of adverse health effects as a result of exposure, with increasing confidence as the HQ moves further away from 1.

1.7 Drinking Water Treatment Processes

The removal efficiency of microcystins during water treatment processes is directly dependent on the state of the toxin. As reviewed in section 1.4.3, microcystins can exist both

33 intracellularly and extracellularly and this distinction dictates what type of treatment process is necessary. Balancing this delicate state is what is meant by treatment optimization. Intracellular toxin removal can be efficiently performed because it simply involves cell removal. Extracellular toxin removal however is a much more complicated process, involving a careful balance of the interactions between chemical dose, contact time (CT), and water quality conditions. If a cHAB is to suddenly form, assuming algaecide has not been inappropriately applied to the source water, the majority of microcystins to impact a WTP will most likely be intracellular because the cHAB is actively growing. Therefore, it is prudent for a WTP to optimize its treatment process to avoid unnecessary cell lysing, and thus extracellular toxin.90 The following review will cover the removal efficiency and practicality of several common and advanced forms of drinking water treatment used for microcystin removal. The review is not exhaustive but will cover processes relevant to the proposed research.

1.7.1 Intracellular Toxin Removal

Conventional water treatment is defined as sequential use of coagulation, flocculation, sedimentation, filtration, and disinfection (typically chlorine) in drinking water treatment.107

Standard coagulation involves the addition and rapid mixing of a metal salt coagulant (e.g. aluminum sulfate, ferric chloride) with source water, which facilitates the agglomeration of suspended particles (flocculation) that are settled out during sedimentation. After sedimentation follows filtration, such as rapid-sand, characterized by the passage of water via gravity through a filter made of granular material.7,90 Disinfection, typically dosing free chlorine, is applied as the last treatment barrier to remove any remaining microcystins, both intracellular and extracellular.

The literature supports that conventional treatment has historically been efficient in removing intracellular toxin, that is, intact cyanobacterial cells. One study found extremely high removal rates of various cyanobacterial genera during clarification (>99%) and then filtration

(>99.9%). The only outlier was Aphanizomenon spp. which exhibited 54-73% removal during clarification and then 85-96% removal during filtration, suggesting that cell morphology and taxonomy may impact the removal efficiency of these treatment processes.108 In a review on water treatment processes, Westrick et al. (2010) reports >99.5% removal of intact algal cell via clarification and filtration.109 However, these processes are not perfect; that is why it is prudent for a WTP to carefully monitor their treatment train, especially when large cell counts are present in the raw water, to avoid unnecessary cell lysis. A major concern during clarification is water quality constituents (turbidity, NOM, etc.) that will compete with the coagulant, compromising flocculation of algal cells. Therefore, routine jar testing is recommended to determine the required coagulant dose needed for optimal cell removal.90,107 During filtration, visible algae can rapidly build up on filters, causing cells to potentially lyse. To avoid this scenario, it is recommended to increase the frequency of backwash recycling.7,90

Conventional water treatment is not always effective in complete extracellular toxin removal. Although coagulation, flocculation, sedimentation, and filtration processes are efficient in intracellular toxin removal, they have little to no effect on extracellular toxin removal.7,90,91,110

Disinfection via chlorine is effective in removing extracellular toxin, but it may not be efficient if it is the only process used to target this toxin state. Therefore, additional treatment steps beyond conventional treatment are needed to address extracellular microcystins.

1.7.2 Extracellular Toxin Removal

There are several oxidants that are used by WTPs for deactivation and removal of extracellular microcystins. Potassium permanganate (KMnO4) is commonly used by WTPs as a pre-oxidant to control for taste and odor, water color, biofilms, or zebra mussels at the intake.7,107

111 It is pH independent and exhibits a greater kinetic rate in warmer temperatures. KMnO4 is a powerful oxidant, capable of removing large quantities of microcsytins, however, there are tradeoffs. Several studies have shown that doses >3 mg/L result in excessive cell lysis and release of cyanotoxins. A study by Fan et al. (2013) demonstrated that KMnO4 concentrations of

5 and 10 mg/L resulted in significant intracellular toxin release, as compared to doses of 1 and 3 mg/L that exhibited minimal intracellular toxin release.112 Similarly, Schmidt et al. (2002) reported that KMnO4 exhibited the greatest breakthrough (toxin release) of 3 pre-oxidants when

110 dosed up to 5 mg/L, recommending other pre-oxidants before the use of KMnO4.

Nevertheless, studies have shown KMnO4’s ability to remove extracellular microcystins when dosed properly. One study experimenting with lake water found that doses as low as 1-1.25 mg/L were sufficient in reducing initial extracellular microcystin concentrations as high as 7.1 µg/L to

111 < 1.0 µg/L within a 60 minute period. Therefore, even if KMnO4 is not specifically being used to control for microcystins, it may serve as an effective treatment option when dosed appropriately.

Chlorine (Cl-) is another powerful oxidant capable of removing microcystins at relatively low doses. The effectiveness of chlorine is directly related to its CT. Disinfection improves as pH levels decrease (< 8.0) and water temperatures increase.109,113 Sharma et al. (2012) reports that all microcystin congeners are efficiently degraded when treated with >0.5 mg/L residual chlorine, at pH <8 with >30 minute contact time.113 Some studies experimenting with M.

36 aeruginosa have demonstrated that chlorine is capable of cell lysis and thus intracellular toxin release. Although initial cell abundance (cells/mL) and water characteristics have varied across studies, CT values of 100 mg-min/L have been found to be enough for almost complete cell lysis.114,115 Generally, chlorine is applied as a disinfectant at the end of treatment, serving as the last line of defense. Therefore, weaker chlorine-based oxidants such as chlorine dioxide and chloramines should be avoided.7,109,113

Ozone is one of the most powerful oxidants available to WTPs, more efficient than

111 KMnO4 or chlorine. Removal efficiency of this oxidant is dependent on CT values as well as water quality constituents, specifically dissolved organic content (DOC), that can increase the ozone demand of the water.113 In general, the Ohio Environmental Protection Agency (EPA) recommends dosing 0.2 mg/L of ozone for at least five minutes (CT = 1 mg-min/L) to achieve effective extracellular microcystins removal, which is consistent with other findings, assuming low DOC levels.61,91 Ozonation can be applied throughout the treatment train, however, a common use is as a pre-oxidant. Used in this way, higher doses may be required to achieve complete cell lysis and oxidation of both intra- and extracellular toxin. A study in Germany reports that for complete microcystin destruction in concentrations of 105 cells/mL, a dose of 1.5

116 mg/L for 9 minutes (CT = 13.5 mg-min/L) is needed. Sharma et al. (2012) reports that an ozone dose of 3 mg/L was required to cause cell inhibition and excretion of intracellular microcystins.113 Thus, ozone application in increasing algal suspensions can increase the concentration of DOC due to excessive cell lysis, limiting its removal efficiency.

Reactions between oxidants and toxins fit second-order kinetics. The American Water

Works Association (AWWA) has developed a user-friendly Microsoft excel platform called

CyanoTOX® Version 2.0 that calculates extracellular cyanotoxin removal of several common oxidants using a second order constant removal rate shown in equation 2:

1−푘"∗퐶 {1 − exp [ 푇] ∗ 60} ∗ 100 [2] 푀푊

where k’’ is the second order rate constant (L/mol-s) dependent on pH and water temperature; CT is the contact time (mg-min/L) defined as the product of the oxidant dosage and the time the oxidant is in contact with the water being disinfected; and MW is the molecular weight (g/mol) of the oxidant.117

Although not an oxidant, powdered activated carbon (PAC) is another effective means for removing extracellular microcystins. PAC utilizes a physical process via absorption. Smaller

PAC pore sizes provide better adsorptive kinetics; for example, wood-based PAC is much more effective than coconut-based PAC.118,119 Work by Bajracharya (2017) demonstrated that PAC absorption increases when natural organic material (NOM) concentrations decrease and pH levels increase.119 PAC is typically added prior to coagulation and then removed during sedimentation, or, it can be added to settling tanks and removed during filtration. Detention times and NOM should be considered to ensure adequate removal by adsorption.61 Zamyadi et al.

(2013) recommends a contact time of at least 30 minutes in order to achieve effect microcystin removal, however, longer contact times may be needed if PAC is being applied to clarifiers.108

Removal efficiencies using PAC have been difficult to establish and appear to be site-specific.110

For example, in order to reduce 5 µg/L of microcystins to 1 µg/L within a 60 minute period, Ho et al. (2011) reports dosing 11-23 mg/L of PAC, while Cook & Newcombe (2002) report dosing

29 mg/L.118,120 Differences most likely are attributable to the type of microcystin congener

38 treated, type of PAC used, and the conditions of the water. Other advanced forms of treatment such as photolysis, Fenton reagent, ultrasonic degradation, etc. exist, but are not commonly used and are currently being further researched.7,113

1.7.3 Microcystin Drinking Water Guidelines

Drinking water guidelines for microcystins have been established, however, they vary across borders and agencies due to the specific methods used to derive the threshold.3 Examples of these thresholds and their respective derivation are shown in Table 4. The microcystin drinking water guideline value of 1.0 µg/L recommended by WHO is generally recognized as the golden standard because it is used by multiple countries and agencies across the world.121 As

Falconer et al. (2005) explains, it is derived by multiplying the WHO’s TDI of 0.04µg/kg-day by an assumed body weight (60 kg) and assumed toxin dose in drinking water (0.8 µg/L), and then dividing by an assumed volume of water ingested (2 L/day).97 On the other hand, USEPA and thus Ohio EPA present differing thresholds. The Ohio EPA mandates three finished water thresholds: do not drink (0.3 µg/L) for children < 6 years old, including bottle-fed infants; do not drink (1.6 µg/L) for ages ≥ 6 years old; and, do not use (20 µg/L).23 The 0.3 µg/L threshold is more conservative than the WHO guideline of 1.0 µg/L and most likely reflects the difference in body weight of a child versus an adult. Under Ohio EPA regulation, a WTP will typically qualify for schedule 2 monitoring, although schedule 1 may be warranted if recurrent toxin detections manifest or if the WTP is relatively small and lacks sufficient treatment processes for extracellular toxin removal. Both schedule 1 and 2 require biweekly qPCR screening and differing microcystin sampling requirements for the deemed cHAB season.23

Table 4. Drinking water guidelines for microcystins across the globe. Adapted from Carmichael & Boyer (2016)3

Country/Agency Microcystins Threshold Comments WHO MC-LR 1.0 µg/L Guidance Value United States MC-LR 1.6 µg/L > 6 years old 0.3 µg/L ≤ 6 years old Canada MC-LR 1.5 µg/L Health Alert Level Australia MC-LR 3.0 µg/L School age to Adult 1.3 µg/L Provisional maximum value

1.8 Modeling and Statistical Methods

1.8.1 Quantitative Microbial Risk Assessment Framework

Quantitative Microbial Risk Assessment (QMRA) is a computational framework of identifying, quantifying, and characterizing health risks from microbial exposures.122 QMRA incorporates four phases: Hazard identification, dose response, exposure assessment, and risk characterization, although risk management is sometimes included as well. Figure 3 demonstrates the flow of the QMRA framework. Hazard identification involves identifying the microbial hazard present, why it persists in the environment, and how human health is impacted by it. Dose response involves mechanistic modelling to determine the concentration (dose) of a hazard/pathogen required to cause a targeted health outcome (response). Exposure assessment entails how humans are exposed to the hazard, the environmental matrices the hazard is found in, and how long a person is exposed (chronic, sub-chronic, acute). Risk characterization is the interpretation of risk, based on the inputs of the dose response and exposure assessment. Finally,

40 risk management is the decision-making process that is directed based on the results of the risk characterization. As shown in Figure 3, this framework is scenario driven, meaning that specific exposure scenarios must be predetermined.122,123

A typical QMRA evaluates exposures and risks related to microbial pathogens and there have been several studies in the literature that have demonstrated its use in this way.124–126

However, in the case of cHABs and the model presented in this thesis, the hazard is not the organism itself, but the toxin it produces. Nevertheless, despite cyanobacteria not being pathogenic, the framework of QMRA to model the exposure pathway(s) and characterize risks will be used to demonstrate its application in modeling these processes. In the case of microcystins, the dose-response will not be a mechanistic dose response model like typical

QMRA models, but rather it will be a calculated RfD. Thus, risk will be characterized using an

HQ, as described in section 1.6.3.

Figure 3. QMRA framework. Adapted from Rose et al. (2015)123

1.8.2 Stochastic Models

Quantifying risk outcomes from an exposure requires a modeling approach. Broadly, there are two types of models, deterministic or stochastic. Both model types are developed based on the governing laws of conservation of mass and conservation of energy. However, for deterministic models, outputs are fully determined by their inputs. An example of this is the use of differential equations, which when optimized as needed will reproduce the same output if the inputs are always constant.127 In contrast, stochastic models imply a level of randomness in order to address the uncertainty of a modeled scenario. This is achieved by allowing random variation in one or more of the model inputs. Thus, the same output will not be obtained repeatedly in a stochastic model because of the inherent uncertainty in the system.127,128 When a seed for the randomization is set, repeatability can be achieved to a certain degree, but exact replication of results is impossible. For model replication, the seed must be set to a reported value, and then median and quartiles will be repeated or replicated.

As has been described previously, there is an intrinsic uncertainty regarding cHABs, toxin production, and microcystin concentrations that will impact a WTP intake. Therefore, a stochastic method is used to account for this uncertainty when estimating the risks and health impacts of microcystins related to a drinking water treatment system.

1.8.3 Monte Carlo Simulation

While there are different methods used for operating a stochastic model, a common approach is the use of a Monte Carlo simulation. This method requires the optimization, or choice of probability distributions, for each variable in the model that is considered uncertain.124

Those distributions chosen are often called assumed distributions, and are parameterized using

42 empirical observations, data from the open literature, or expert elicitation.129 The underlying principle of the Monte Carlo method has been around for centuries, but the term was first coined during World War II when the method was developed within the Manhattan Project.130

Today, this computational method can easily be performed using mathematical programs such as R programming language. The simulations rely on pseudo random numbers to sample from the probability distributions in order to report an estimated outcome.128 A Monte Carlo simulation can be explained using the classic example of a fair dice roll. Consider trying to calculate the probability of the sum of two dice, each with values 1-6. All possible outcomes would range between 2-12, but calculating the probability of outcome would be tricky, unless multiple dice throws were made. However, using a Monte Carlo simulation, multiple iterations can be made to test this probability. As depicted below in Figure 4, overtime, the output would fit a normal probability distribution, with a median of 7 since it represents the output with the greatest likelihood of occurrence.131

Figure 4. Depiction of dice throw probability distribution. From GoldSim (2019)131

Multiple iterations is an important concept in Monte Carlo simulation. Consider the fair dice throw example again. If two dice were thrown 100 times, the 100 outcomes obtained would not exactly fit the distribution depicted in Figure 4. For example, after 100 iterations, 5 may be

43 the most frequent outcome. This is due to the uncertainty that exists, that is, the random chance that occurs when rolling a die. However, the underlying principle of a Monte Carlo simulation is the law of large numbers, which states that the average of multiple independent random variables having the same distribution will eventually converge to the mean of that distribution.129 In other words, after multiple iterations, the mean outcome would begin to converge toward 7, because it is the most likely outcome. Therefore, the uncertainty of the outcome is reduced, and a more accurate measure is obtained. This has major implications if a modeled system has multiple uncertain variables. Thus, the most likely outcome of an event can be determined when it is otherwise impossible to estimate from one simulation.

1.8.4 Wilcoxon Ranked Sums Test

A Wilcoxon ranked sums (or Mann-Whitney U) test will be briefly reviewed here due to its use in model selection. When sample means of two datasets want to be compared in order to test if they are statistically similar or different to one another, a two-sample t-test is routinely performed. However, if the data are non-parametric, meaning they are not normally distributed, then it is not practical to use a two-sample t test. Non-parametric data are typically the result of a lack of data (small dataset) that inherently do not represent the normal distribution of an expected parameter. To overcome this problem, a Wilcoxon ranked sums test is used because it ranks the data on an ordinal scale, which helps to correct outliers and make the data more normally distributed. For a Wilcoxon ranked sums test, the null hypothesis states that the two datasets being compared are not significantly different from one another and therefore it can be assumed that they represent the same population. If the test rejects the null hypothesis (p-value <

0.05), then there is evidence of a statistical difference between the two datasets. If the null hypothesis is accepted (p-value > 0.05), then in fact the data are similar.

As will be shown in chapter 3, validation of my model is difficult because there are limited “observed” data to compare it too. Because microcystin grab samples take time to analyze, approximately only 15 observed samples exist. Sample sizes this small are certainly non-parametric as they do not fully capture the median and lower/upper bounds of a time series.

Therefore, the Wilcoxon ranked sums tests provides a useful means of comparing this small dataset to the large outputs of my model.

Chapter 2. Phycocyanin Adjustment Model using qPCR Data

2.1 Introduction

Microcystins are found globally and have the potential to impact surface waters used as a drinking water source. Because extracellular microcystins are difficult to remove during water treatment, several different methods of predicting the toxins at the intake before they impact a

WTP have been explored. Detection and quantification of the toxin has been made possible through the power of ELISA, but this laboratory method takes times before results are obtained.54 Time is a critical factor in these bloom events, rendering ELISA as an unfeasible option for source water protection.

Previously in chapter 1, multiple parameters were reviewed for their relationship and/or association with microcystins. The review demonstrated the complexity of cHAB events and the difficulty of finding a consistently accurate predictor. Several environmental parameters including nutrients, water temperature, light intensity, etc. have been investigated for their relationship, but the findings are mostly contradictory. Recently, tracking cyanobacterial 16S rRNA genes using qPCR has become an increasingly popular monitoring tool. Furthermore, the ability to detect the presence of mcy genes with qPCR to distinguish toxic genera/species within cHAB populations has shown promise as several studies in the literature have demonstrated significant associations between mcy gene abundances and microcystins. The drawbacks of qPCR are that it takes time to analyze and that mcy gene do not always correlate with toxin production.47

Another recent tool used to estimate microcystin concentrations is phycocyanin, measured via a fluorometric probe. Several studies in the literature have reported significantly strong associations between phycocyanin and microcystins, making it an ideal monitoring tool

46 for WTP intakes due to its ability to take real-time measurements.45,54,132 Although phycocyanin is more specific to cyanobacteria than chlorophyll-a, it is incapable of distinguishing toxic versus non-toxic genera/species of a cHAB. This presents a drawback to this method, likely explaining some of the inconsistencies and non-significant outcomes recorded in the literature.75,88

The goal of this research was to identify an accurate predictor(s) of microcystins at the raw intake of a WTP. Furthermore, it was hypothesized that qPCR and phycocyanin measurements could be joined together because they inherently characterize the same events.

Specifically, the author investigated whether qPCR could be used as an adjustment-factor for phycocyanin to make it more specific to the proportion of the bloom that is producing the toxin.

Therefore, an abundance of data was needed from a source water including water quality parameters (phycocyanin, microcystins, nutrients, etc.), qPCR gene data, and weather parameters. After collaboration with various agencies, I was able to obtain the necessary data from Tappan Lake, an inland lake of Ohio, US. Tappan Lake is the drinking water source for the

Village of Cadiz. The Cadiz WTP presents a unique case study because it has no other alternative source water should Tappan Lake become unusable due to contamination. Recent cHAB events at Tappan Lake and related microcystin detections in the raw intake have generated a response to determine a practical means of predicting microcystin events at Tappan Lake before they impact Cadiz WTP.

2.2 Methods

In collaboration with USGS and Ohio EPA, data were obtained from Tappan Lake and

Cadiz WTP for the 2016-2017 years. Data were collected through various methods and site locations. All data is considered secondary data and was shared with me for data analysis.

Raw water from Tappan Lake is drawn through two intake pipes at 8- and 12-feet depths respectively. The intakes connect to a pump station that pumps water approximately 9 miles to

Cadiz WTP. In order to demonstrate site location, Google My Maps was used. As shown below in Figure 5, the blue marker represents the Cadiz WTP pump station and the red marker represent the approximate offshore intake location.

Figure 5. Cadiz WTP intake and pump station location at Tappan Lake

2.2.1 2016 Data

In 2016, a total of 12 sampling dates (18 April – 11 October 2016) were obtained. USGS collected intake composite samples at the Cadiz WTP pump station and analyzed them for cyanobacterial genes, mcy genes, total microcystins, and nutrients. Data analysis was conducted per USGS protocol and can be referenced from Francy et al. (2015).75 The following parameters were collected at this location:

1. Anabaena (Treated as Dolichospermum) 16S rRNA gene copies/100mL. Gene copies

determined using qPCR.

2. Microcystis 16S rRNA gene copies/100mL. Gene copies determined using qPCR.

3. Planktothrix 16S rRNA gene copies/100mL. Gene copies determined using qPCR.

4. Total cyanobacteria 16S rRNA gene copies/100mL. Gene copies determined using

qPCR.

5. Planktothrix specfic mcyE gene copies/100mL. Gene copies determined using qPCR.

6. Total microcystins and nodularins (µg/L) – analyzed using freeze/thaw extraction and

ELISA.

7. Total Nitrogen (sum of nitrate, nitrite, ammonia, organic nitrogen) reported in mg/L.

8. Phosphorous (mg/L)

Weather parameters were obtained from the Tappan Lake Weather Station located onsite at the lake. The following parameters were collected at this location.

1. Solar Radiance (Watts/m2): Average of previous 7-day sums.

2. Evapotranspiration (mm/day): Average of previous 7-day sums

3. Lake Level Change (feet): 24-hour change and average of previous 7-day changes

4. Air Temperature (°C): Daily minimum and maximum temperatures were used to

generate daily averages.

5. Precipitation (inches/day): 24-hour sum and averages of previous 7-day sums.

Water quality parameters were collected by Stark County Health Department at the Tappan Lake intake site using a YSI EXO2 data sonde. The following parameters were collected at this location.

1. Phycocyanin (RFU and µg/L) 24-hour averages measured in-vivo. Calibration allows

conversion from RFU to µg/L.

2. Chlorophyll-a (RFU) 24-hour averages measured in-vivo.

3. Turbidity (Nephelometric turbidity unit (NTU)): Measures intensity of light scattered

at 90° as a beam of light passes through a water sample.

4. pH

5. Dissolved Oxygen (DO) in mg/L

6. Water Temperature (°C) 24-hour average and daily maximum

2.2.2 2017 Data

In 2017, a total of 9 sampling dates (30 May – 14 November 2017) were obtained. USGS collected intake composite samples at the Cadiz WTP pump station and analyzed them for cyanobacterial genes, mcy genes, total microcystins, and nutrients. Analyses were performed identical to 2016. The only difference is that USGS did not sample total cyanobacteria 16S rRNA gene data at this location in 2017. Therefore, Cadiz WTP total cyanobacteria 16S compliance data sampled at the intake of the WTP, nine miles downstream the pump station, was used instead. The data was obtained from Ohio EPA and reported in units of gene copies/µL.

Gene copies were normalized to 100mL. Weather parameters for 2017 were again obtained from the Tappan Lake Weather Station located onsite and all parameters were measured accordingly.

Finally, the same water quality parameters were obtained, however, in 2017, they were measured using an in-situ data sonde deployed 1-1.8 meters below the water surface. The data sonde was attached to a buoy positioned overtop the raw intakes of the lake (see red marker in Figure 5).

All appropriate unit conversions were performed between the two years.

2.2.3 Data Analysis

Because of differences in data collection and site location, the two years were not combined. Pearson correlation coefficients were generated to investigate statistically significant relationships between variables. An association can be positive or negative and is characterized by a correlation coefficient (R2) that ranges between -1 to 1, with 0 representing no correlation.

Generally, strong associations are characterized by an R2 ≥ ±0.60. Dependent variables tested include total microcystins, total cyanobacteria 16S rRNA genes, Planktothrix 16S rRNA genes,

Planktothrix mcyE genes, and phycocyanin (RFU). Associations were observed for their agreement with the literature and use of explaining the occurrence and/or development of a toxic cHAB. Regression analysis provides the ability of using a predictor (independent variable) to estimate an outcome (dependent variable). Linear regression analysis was implemented for statistically significant associations found involving total microcystin concentrations. Similarities and differences were compared between 2016 and 2017.

Additionally, it was hypothesized that qPCR data might provide a means of “adjustment” for phycocyanin, creating a more specific predictor of microcystins. Pearson correlation coefficients communicate which genus is responsible for toxin production. Furthermore, mcyE genes can be used to establish the proportion of a genus that is toxic, or capable of producing toxin. Therefore, ratios of specific gene abundances were calculated by dividing total cyanobacteria 16S rRNA genes for a given sample date. Ratios were then multiplied to phycocyanin measurements to adjust the measure to be specific to the cyanobacteria responsible for toxin production. Linear regression analysis was performed and used to compare changes in predictive power using this methodology. Then, using a Wilcoxon ranked sums test and

51 variance-based sensitivity analysis, it was determined whether it would be appropriate to combine data for both years. The rationale of the adjustment is demonstrated in Figure 6.

Cyanobacteria Community

[Total 16S rRNA & Phycocyanin

Planktothrix 16S Anabaena 16S rRNA rRNA

& Its Phycocyanin & Its Phycocyanin

Microcystis 16S rRNA

& Its Phycocyanin

Figure 6. Rationale of phycocyanin adjustment

2.3 Results

2.3.1 Data Summary

Parameters for both years are summarized by their average and standard deviations, as shown in Table 5. Average total microcystins were approximately 2.5 times greater in 2017 with a mean of 3.07 ug/L. Microcystin concentrations ranged from 0.3-4.0 ug/L, with 6 non-detect samples (< 0.3 ug/L) in 2016. Microcystin concentrations ranged from 1.06-6.08 ug/L in 2017, all detects. Temporal changes in toxin concentrations can be observed for both years in Figures 7 and 8. By comparing mean 16S rRNA gene copies, it is apparent that Planktothrix dominated the

52 bloom in both years. Anabaena had limited contribution to the blooms, while Microcystis was nearly non-existent. Comparing total cyanobacteria 16S rRNA gene copy data, it is apparent that the bloom was significantly larger/worse in 2017 than in 2016. However, the author hesitates to make inferences beyond this observation as total 16S rRNA data was sampled differently and at different locations between years.

Accurate cell abundance ratios of the blooms cannot be ascertained from the qPCR data because there are no cell counts to compare them to. Furthermore, as explained previously in section 1.5.1, error can exist when assuming standard 16S rRNA gene concentrations per cell between differing genera. Nevertheless, the qPCR gene data provide a rudimentary means of tracking changes in the blooms for both years, as shown in Figures 7 and 8. In 2016, the cHAB appears to be a late spring/early summer event. Microcystins are observed early, falling to non- detect by early August and remaining through the end of the year. Interestingly, Planktothrix gene abundances decreased through the year, while Microcystis and especially Anabaena gene abundances starkly increased. By mid-August, Anabaena outnumbered Planktothrix abundance.

On the contrary, the 2017 cHAB event was very different. Planktothrix gene abundances were relatively stable throughout the year, increasing slightly in the autumn months. Microcystis and

Anabaena struggled to maintain any stability, fluctuating at low concentrations throughout the year. Opposite of 2016, microcystins were more of a late-bloom event, peaking to 6.08 ug/L on

14 November. While true comparisons cannot be made between total cyanobacteria 16S, the data suggest a larger bloom for 2017.

Consistent with increased gene abundances, both phycocyanin and chlorophyll-a measurements were greater in 2017. In both years, chlorophyll-a levels were slightly greater, which is to be expected since multiple phytoplankton assemblages can possess this pigment.

Surprisingly, nutrient levels were nearly identical for both years, exhibited by nitrogen outweighing phosphorus by a factor of 12. When considering weather parameters, the data suggest a much warmer and dryer season in 2016. Ambient air temperatures (both average and max) were approximately 4°C warmer in 2016, creating warmer water temperatures. Solar radiance sums were also substantially greater in 2016. Consequently, this led to heighten evapotranspiration and lake level changes throughout the year. Lastly, overall precipitation was less in 2016, with daily precipitation totals approximately 2 times greater in 2017.

Table 5. Summary of select parameters at Tappan Lake, OH, 2016-2017

a,d Parameter 2016 2017b Microcystins (µg/L) 1.2 ± 1.3 3.1 ± 1.5 Total Cyanobacteria 16S rRNA (gene copies/100mL) 9.6E07 ± 4.0E07 5.5E08 ± 1.7E08c Planktothrix 16S rRNA (gene copies/100mL) 3.7E07 ± 4.7E07 5.16E07 ± 3.96E07 Microcystis 16S rRNA (gene copies/100mL) 1.4E05 ± 1.9E05 3.3E04 ± 3.1E04 Anabaena 16S rRNA (gene copies/100mL) 6.4E06 ± 1.0E07 1.2E06 ± 2.0E06 Planktothrix mcy E (gene copies/100mL) 6.4E06 ± 7.6E06 1.7E07 ± 1.6E07 Total Nitrogen (mg/L) 0.8 ± 0.2 0.8 ± 0.2 Total Phosphorus (mg/L) 0.1 ± 0.02 0.1 ± 0.01 N:P 11.9 ± 2.0 11.8 ± 2.3 Phycocyanin (RFU) 2.4 ± 1.3 3.6 ± 1.8 Phycocyanin (µg/L) 2.5 ± 1.4 3.5 ± 1.8 Chlorophyll-a (RFU) 3.4 ± 1.0 4.7 ± 1.9 Turbidity (NTU) 9.9 ± 2.2 8.3 ± 2.2 pH 8.2 ± 0.2 8.4 ± 0.3 DO (mg/L) 8.0 ± 1.1 9.2 ± 2.9 Water Temperature (°C) 23.0 ± 4.4 21.6 ± 5.6 Avg. Air Temperature (°C) 20.3 ± 4.7 16.8 ± 6.4 Max Air Temperature (°C) 27.6 ± 3.9 23.1 ± 7.7 Solar Irradiance (Watts/m2) 5018.5 ± 783.9 3866.6 ± 1155.9 Evapotranspiration (mm/day) 0.1 ± 0.03 0.1 ± 0.04 24-hr Lake Level Change (ft) -0.02 ± 0.06 -0.02 ± 0.03 7-day Avg. Lake Level Change (ft) 0.001 ± 0.02 -0.005 ± 0.04 24-hr Precipitation (in) 0.1 ± 0.2 0.2 ± 0.3 7-day Sum Precipitation (in) 0.7 ± 0.6 0.9 ± 0.9 a 12 Sample dates (4/18/16-10/11/16); b 9 Sample dates (5/30/16-11/14/16) c Data collected at intake of WTP and reported by Ohio EPA d reporting as mean ± standard deviation

Figure 7. 2016 Tappan Lake cHAB summary

Figure 8. 2017 Tappan Lake cHAB summary

2.3.2 Pearson Correlation

Pearson correlation coefficients were generated between parameters for 2016 and 2017 separately. Associations were investigated for microcystins, 16S rRNA genes, mcyE genes, and phycocyanin. Results of the Pearson correlation tests are displayed in Tables 6 and 7. For both years, microcystins were significantly associated with Planktothrix 16S rRNA genes,

Planktothrix mcyE genes, and phycocyanin (RFU). In 2016, Planktothrix attributable genes had stronger associations (0.86 and 0.89, versus 0.77 and 0.73 respectively).

As for phycocyanin, a negative association is observed in 2016, while a positive association is observed in 2017. Nutrients were also significantly associated with microcystins for both years, but in opposite directions. In 2016, negative correlations were found for both total nitrogen and total phosphorus, however the N:P ratio had no effect. Conversely, in 2017, nitrogen exhibited a positive correlation, with the N:P ratio nearly significant with a p-value of

0.05.

Because Planktothrix was the only genus with a statistically significant and positive association with microcystins, it was investigated further in the Pearson correlation analysis.

Planktothrix appears to be responsible for toxin production as significant Pearson correlation coefficients for Planktothrix 16S rRNA and mcyE genes are in line with that of microcystins.

Interestingly, a significant negative correlation between Planktothrix genes and phycocyanin are observed in 2016, which is consistent with the decline of Planktothrix over the 2016 season. No association between phycocyanin and Planktothrix is observed in 2017. Strong associations are also observed between Planktothrix 16S rRNA and mcyE gene copies (0.86, 0.97), which is to be expected.

Table 6. Pearson correlation coefficients among parameters in 2016

Parameter MCs 16S Plank 16S Plank mcy E PC (RFU) MCsb 1.00a -0.35 0.86 0.89 -0.84 16Sc -0.35 1 -0.17 0.29 0.22 Plank 16Sd 0.86 -0.17 1 0.86 -0.86 Plank mcy Ee 0.89 -0.29 0.86 1 -0.9 PC (RFU)f -0.84 0.22 -0.82 -0.9 1 Chl-ag -0.59 0.32 -0.52 -0.6 0.68 Mic 16Sh -0.49 0.64 -0.47 -0.51 0.53 Ana 16Si -0.26 -0.005 -0.25 -0.25 0.3 TNj -0.84 -0.046 -0.83 -0.85 0.91 TPk -0.75 0.17 -0.63 -0.68 0.61 N:Pl -0.03 -0.32 -0.3 -0.18 0.32 Turbidity -0.57 0.11 -0.37 -0.43 0.51 pH 0.07 0.1 0.16 0.16 0.07 DOm 0.49 -0.25 0.51 0.71 -0.48 WTn -0.48 0.33 -0.45 -0.66 0.46 Avg. ATo -0.19 0.33 -0.11 -0.22 0.06 Max ATp 0.008 0.38 0.028 0.08 -0.12 SRq 0.32 0.19 0.36 0.44 -0.26 ETr 0.14 0.26 0.2 0.19 -0.1 24h- LLCs -0.1 -0.005 -0.24 -0.48 0.27 7day- LLCt 0.32 -0.13 0.49 0.65 -0.56 24h-Precipu 0.18 -0.04 0.38 0.02 -0.28 v 7day-Precip 0.15 -0.04 0.26 0.16 -0.25 a Bold indicates statistically significant association (p-value < 0.5) b Total microcystins; c Cyanobacteria Total 16SrRNA genes; d Planktothrix 16S rRNA genes; e Planktothrix mcyE genes; f Phycocyanin; g Chlorophyll-a; h Microcystis 16S rRNA genes; I Anabaena 16S rRNA genes; j Total Nitrogen; k Total Phosphorus; l Nitrogen/Phosphorus Ratio; m Dissolved Oxygen; n Water Temperature; o Average Air Temperature; p Maximum Air Temperature; q Average of previous 7 day Solar Irradiance Sums; r Average of previous 7 day Evapotranspiration Sums; s24 hour lake level change; t average of previous 7 day lake level changes; u 24 hour precipitation; v Sum of previous 7 day precipitation

Table 7. Pearson correlation coefficients among parameters in 2017

Parameter MCs 16S Plank 16S Plank mcy E PC (RFU) MCsb 1.00a 0.73 0.77 0.73 0.73 16S 0.73 1 0.69 0.64 0.53 Plank 16S 0.77 0.69 1 0.97 0.21 Plank mcy E 0.73 0.64 0.97 1 0.18 PC (RFU) 0.73 0.53 0.21 0.18 1 Chl-a -0.048 0.26 -0.3 0.15 0.34 Mic 16S -0.32 -0.3 -0.55 -0.45 -0.009 Ana 16S -0.22 0.19 -0.013 -0.05 0.21 TN 0.72 0.38 0.24 0.26 0.72 TP 0.21 0.35 -0.22 -0.17 0.33 N:P 0.70c 0.25 0.48 0.48 0.58 Turbidity 0.67 0.5 0.16 0.17 0.91 pH -0.13 -0.5 -0.21 -0.29 -0.13 DO -0.19 -0.42 -0.007 -0.03 -0.42 WT -0.77 -0.5 -0.91 -0.84 -0.35 Avg. AT -0.86 -0.71 -0.78 -0.7 -0.68 Max AT -0.81 -0.72 -0.84 -0.8 -0.55 SR -0.64 -0.35 -0.61 -0.56 -0.56 ET -0.69 -0.37 -0.65 -0.59 -0.58 24h- LLC -0.19 -0.5 -0.08 -0.22 -0.27 7day- LLC -0.72 -0.43 -0.89 -0.83 -0.2 24h-Precip 0.18 0.01 -0.19 -0.23 0.18 7day-Precip 0.15 -0.013 -0.39 -0.17 0.01 a Bold indicates statistically significant association (p-value < 0.5) b Refer to Table 6 for description of all parameter abbreviations c P-value = 0.05

There is not much to infer from correlations tested for total cyanobacteria 16S rRNA gene copies. In 2017, total 16S is correlated with microcystins, Planktothrix 16S, and nearly

Planktothrix mcyE genes, but this could be explained by Planktothrix dominance and stability in the bloom that year. In 2016, the only parameter correlated with total 16S is Microcystis 16S, in which the author cannot find an explanation for. Considering phycocyanin, it is interesting that correlations are observed between nutrients, specifically nitrogen, in both years. The literature

58 supports that a plentiful nitrogen supply supports the growth of cyanobacteria, which would result in increased phycocyanin levels. The problem is that no correlation is observed between total 16S and nutrients or between total 16S and phycocyanin. On the contrary, positive associations (significant in 2017) are found between phycocyanin and turbidity, which is logical because turbidity and phycocyanin levels increase when cyanobacteria densities increase in waters. Why these inconsistencies are observed across parameters cannot be fully understood.

Lastly, weather parameters had mixed effects on the tested dependent variables between the two years. In 2016, the only associations found were between mcyE genes and water temperature (-0.66) and mcyE genes and average 7-day lake level changes (0.65). Conversely, in

2017, water temperature, air temperature (average and maximum), and average 7-day lake level changes all had significantly negative associations with most of the dependent variables (see

Table 7).

2.3.3 Linear Regression Analysis

From the Pearson correlation tests, it was determined that Plankothrix is the genus driving most of microcystin production. Furthermore, it was determined that phycocyanin was also significantly associated with microcystins, although in opposite directions between the years. From these findings, linear regressions were individually fitted between microcystins and

Planktothrix 16S rRNA, microcystins and Planktothrix mcyE, and microcystins and phycocyanin. The regressions are shown below in Figures 9 and 10 A-C. For both years, phycocyanin provided the worst estimate, with coefficient of determinations (0.68, 0.47) respectively. Planktothrix genes provided better estimates, with mcyE genes as the better predictor in 2016, but 16S genes the better predictor in 2017.

Quantification of mcyE genes provides a means of determining the toxic ratio of

Planktothrix, that is, the proportion of Planktothrix cells capable of toxin production. Although cell abundance/density is not reported here, the ratio of Planktothrix mcyE gene to Planktothrix

16S is still applicable. Ha et al. (2009) determined cell abundance by assuming all Microcystis cells contain 250 16S rRNA genes and 14 mcyA genes.58 Likewise, if similar adjustments were made to this study, all data would linearly change, which would not have any impact on the correlative or regressive analysis. Therefore, mcyE gene to Planktothrix 16S gene ratios were generated and regressed to microcystins. To the author’s surprise, these ratios did not yield any predictive results (Figures 9D and 10D).

A B

C D

E F

Figure 9. 2016 linear regression analysis A

A B

C D

E F

Figure 10. 2017 linear regression analysis

2.3.4 Phycocyanin “Adjustment”

It was hypothesized that a phycocyanin measurement could be adjusted and made more specific to the toxic genera/species it is measuring by using related qPCR gene data. Therefore, as shown in Tables 8 and 9, phycocyanin was adjusted using two parameters, 1. Multiplying the ratio of Planktothrix 16S/Total cyanobacteria 16S, and 2. Multiplying the ratio of Planktothrix mcyE/Total cyanobacteria 16S. The ratio of toxic Planktothrix (mcyE/16S) was not explored due to the lack of correlation shown in the linear regression analysis. Using these adjusted phycocyanin measurements, linear regressions were fitted with microcystin concentrations.

Results of the linear regressions for both years are displayed in Figures 9 and 10 E-F.

Table 8. Phycocyanin adjustment for 2016

Planktothrix mcy E:Total gene Planktothrix mcy E Total cyanobacteria Phycocyanin Planktothrix: Plank:Total Phycocyanin copies/100mL gene copies/100mL gene copie/100mL (RFU) Total mcy E:Total Phycocyanin (RFU) (RFU) 94000000 20500000 86500000 0.690 1.087 0.237 0.750 0.164 69500000 18000000 53000000 0.460 1.311 0.340 0.603 0.156 150000000 13500000 99000000 0.883 1.515 0.136 1.338 0.120 76500000 15500000 86000000 0.790 0.890 0.180 0.703 0.142 8650000 1650000 44000000 3.650 0.197 0.038 0.718 0.137 7750000 1540000 121000000 2.390 0.064 0.013 0.153 0.030 9350000 1650000 160000000 3.265 0.058 0.010 0.191 0.034 5850000 820000 93000000 2.120 0.063 0.009 0.133 0.019 4100000 425000 185000000 3.500 0.022 0.002 0.078 0.008 2250000 455000 75000000 4.200 0.030 0.006 0.126 0.025 2815000 535000 66000000 2.950 0.043 0.008 0.126 0.024 7050000 1660000 86000000 3.400 0.082 0.019 0.279 0.066

Table 9. Phycocyanin adjustment for 2017

Plank:Total mcy E:Total Planktothrix gene Planktothrix mcyE Total cyanobacteria Phycocyanin Planktothrix : Phycocyanin Phycocyanin copies/ 100mL gene copies/100mL gene copies/100mL (RFU) Total mcyE:Total (RFU) (RFU) 33000000 8300000 274215000 0.800 0.120 0.030 0.096 0.024 55000000 15000000 602843000 0.650 0.091 0.025 0.059 0.016 35000000 16000000 584044000 4.200 0.060 0.027 0.252 0.115 25000000 11000000 593588000 3.190 0.042 0.019 0.134 0.059 35000000 14000000 439512000 4.480 0.080 0.032 0.357 0.143 25000000 9300000 326675000 2.740 0.077 0.028 0.210 0.078 48000000 10000000 547212000 5.200 0.088 0.018 0.456 0.095 48000000 9800000 731893000 6.290 0.066 0.013 0.413 0.084 160000000 62000000 832879000 4.520 0.192 0.074 0.868 0.336

For 2016, significant moderate relationships are observed, however, the adjustments fail to improve correlations as compared to the other 3 regressions (see Figure 9). When comparing the two adjustments for 2016, the mcyE adjustment provided a better estimate, with coefficients of determination 0.61 versus 0.58 respectively. However, for 2017, significantly strong relationships are observed using the adjustments, surpassing the correlation of the other 3 regressions (see Figure 10). When comparing the two adjustments here, the Planktothrix 16S adjustment provided the best estimate, with coefficients of determination 0.87 versus 0.69 respectively. Another important observation is that the adjustments provided positive correlations with microcystins, even when initial phycocyanin measurements were negatively correlated. This will be examined further in the discussion.

2.3.5 Combining the Years

Because cHABs are highly uncertain events, the two years were analyzed separately in order to investigate differences between years. However, combining data for multiple years provides a means of determining what parameters maintain a significant relationship with microcystins as well as enhances the certainty of the correlation as the sample size increases. The author wanted to explore the predictive power of the phycocyanin adjustments using combined data but was apprehensive due to the variability in sample location and analysis across years.

Specifically, the total cyanobacteria 16S gene data was sampled at different locations of the

WTP intake and analyzed using different methodologies by different agencies. After normalizing the data, it became apparent that the total cyanobacteria 16S was orders of magnitude greater in

2017, as shown by Table 5.

To test the differences in data across years, a Wilcoxon ranked sums test was performed

(see 1.8.3 for details of this test). Variables tested were those that are used for the phycocyanin adjustment: Planktothrix 16S, Planktothrix mcyE, phycocyanin, and total cyanobacteria 16S. As shown in Table 10, results of the test indicate that Planktothrix 16S, Planktothrix mcyE, and phycocyanin are not statistically different (p-value >0.05 between the two years). However, for total cyanobacteria 16S, that data are statistically different between the two years, with a p-value of 0.00014.

Table 10. Results (p-values) of the Wilcoxon ranked sums test for 2016-2017

Planktothrix 16S Planktothrix mcyE Total Cyanobacteria 16S Phycocyanin p-value 0.126 0.07 0.00014 0.11

Additionally, a variance-based sensitivity analysis was performed to determine which variable of the phycocyanin adjustment is causing the greatest variation in the linear regression when data from both years are combined. Phycocyanin adjustments (see 2.3.4) for the two years combined were performed again in order to fit a linear regression with microcystins. To conduct the analysis, multiple bootstraps of the linear regression were made. In a bootstrap, one variable is sampled and replaced each time, while all other variables are held constant. For each bootstrap, the median variance of the linear model is reported and compared to the variance of the linear model not bootstrapped. In this case, the smallest difference in variation indicates that the bootstrapped variable is responsible for the greatest variability that exists in the model.

Tables 11 and 12 display the results of the sensitivity analysis. For either case, it was found that total cyanobacteria 16S is causing the greatest variability in the adjusted phycocyanin linear models when the data for both years are combined.

Table 11. Variance-based sensitivity analysis for Planktothrix-adjusted phycocyanin model

Variable Variance ∆ Variance Model 0.8878 0 Phycocyanin 0.1417 0.7461 Planktothrix 0.1137 0.7741 Total Cyanobacteria 0.7411 0.1467

Table 12. Variance-based sensitivity analysis for mcyE-adjusted phycocyanin model

Variable Variance ∆ Variance Model 1.732 0 Phycocyanin 0.209 1.523 Planktothrix mcyE 0.1132 1.6188 Total Cyanobacteria 0.8338 0.8982

These results do not indicate that the data should not be combined but do suggest that it may not be reasonable to do so. The Wilcoxon ranked sums test demonstrates that the total cyanobacteria 16S data is statistically different between the years. Consequently, the sensitivity analysis found that total cyanobacteria 16S is driving the variability in model output, hindering the predictive accuracy of the models. It is possible that the total cyanobacteria 16S simply increased in 2017 while all other parameters remained constant. While this is possible, it would mean that Planktothrix maintained its relative abundance between the years while losing its dominance in 2017. It is known that the bloom was not dominated by Microcystis or Anabaena, therefore this would mean another dominate genus was present in 2017. However, without the necessary data, these conclusions cannot be made.

B C

Figure 11. 2016-2017 linear regression analysis

Nevertheless, it was explored what would happen to the phycocyanin adjustment if it

were appropriate to combine data from both years. Results of the linear regression analysis using data of combined years is shown in Figure 11. Figure 11A shows that phycocyanin has no relationship with microcystins across the two years, which is to be expected since a negative correlation was observed in 2016 but a positive correlation was observed in 2017. On the other hand, the two adjustments still provide an improved correlation that is statistically significant, as

67 shown by Figure 11B-C. The Planktothrix-adjusted phycocyanin loses correlation when the data is combined, but surprisingly the mcyE-adjusted phycocyanin still maintains a moderate relationship with microcystin concentrations.

2.4 Discussion

The 2016 and 2017 years at Tappan Lake demonstrated very different cHAB events. In

2016, microcystin concentrations were greatest early in the season, but slowly decreased as the year progressed. In contrast, in 2017 microcystin concentrations slowly increased as the year progressed, peaking late in the fall. Of the three genera surveyed, Planktothrix dominated the bloom in both years and appears to responsible for toxin production based on results of the

Pearson correlation. Microcystis and Anabaena were non-significant and negatively correlated with microcystins in both years.

The goal of this study was to identify a good predictor(s) of microcystins at Tappan Lake.

Several parameters were tested for their association to microcystins and other related dependent variables. By observing associations from the Pearson correlation test, weather parameters were investigated for their impact on toxin concentrations. As previously discussed in section 1.4.2, weather parameters such as water temperature and solar radiation are thought to influence toxin production because they behave as environmental stressors. It has been suggested that decreasing water temperature creates unfavorable growing conditions, and thus promotes toxin production.11,13,36,51 It has also been suggested that increased solar radiation causes photooxidative stress in cyanobacteria at the surface, thus promoting toxin production that is used as a protective shield.5,11,13,51

When comparing results of the Pearson correlation, relationships were starkly different between 2016 and 2017. In 2017, negative correlations were found between water/air temperature and microcystins. Negative correlations were also observed between mcyE genes and microcystins in 2017. These findings are consistent with findings by Park et al. (2018) who found that the toxic proportions of a bloom remarkably increased when water temperatures fell below 20°C.49 However, it is has also been well documented that cyanobacterial growth is optimal in water temperatures > 25°C.2,11 The results from 2017 display conflicting results.

Decreasing air and water temperatures late in the autumn months were negatively correlated with increasing toxin concentrations and mcyE gene abundances, suggesting that temperature acted as an environmental stressor, preventing growth of the bloom. However, amidst these toxin peaks, total cyanobacteria and Planktothtrix 16S gene copy abundances were at their greatest, demonstrating that growth and proliferation of the bloom had not waned. This phenomenon could be explained by prevailing winds and the ability of a cHAB to drift toward a WTP intake, as reported in the literature.55 This would mean that cell growth wasn’t necessarily increasing amidst colder temperatures, but rather, increased cell concentrations were drifting toward the

WTP intake, causing sampled concentrations to increase.

For either year, solar radiance was not found to have any association with toxin or toxin- producing genes. What was however found is as the average of the previous 7-day lake level decreased, toxin concentrations increased. It is believed that co-linearity exists between weather parameters because they represent the same moments in time. For example, it is intuitive to surmise that high solar radiance would cause higher levels of evapotranspiration, which in turn would lead to greater decreases in lake level depths. If this were the case, then solar radiance

69 may be having an indirect impact on toxin production. The complexity of these processes however leaves room for speculation.

Another environmental factor believed to impact toxin concentration is nutrients. In

2016, decreased nutrient levels and N:P ratios were negatively correlated with total microcystin concentrations and Planktothrix mcyE genes, suggesting that limited availability of nutrients may act as a growth stressor on the bloom, promoting toxin production. These findings are corroborated in the literature, such as by Ginn et al. (2010), who suggested that nitrogen depravation directly promotes microcystin synthesis due to the binding of a nitrogen-controlled transcription factor to the promoter of the mcy gene cluster when nitrogen availability is low.57

But as has already been shown, 2017 was a very different year where nitrogen and N:P ratios were positively correlated with toxin concentrations. This too is supported throughout the literature where positive associations have been found between nutrient and toxin abundances.52,58 Due to the inconsistencies found in this study and throughout the literature, our understanding of the associations between environmental factors and microcystins is still unclear. Therefore, the author recommends caution when using these parameters in predictive models that strictly rely on empirical trends.

Other parameters that have been shown to be significantly associated with microcystins include mcy genes and phycocyanin. A summary of these findings from the literature can be found in Table 2 and Table 3 of chapter 1 respectively. In this study, Planktothrix mcyE genes and phycocyanin were found to be significantly correlated with microcystins in both years.

Because the mcyE genes in this case are specific to Planktothrix, it is not surprising that

Planktothrix 16S gene abundances were also significantly correlated in the Pearson correlation.

Using these three parameters, linear regressions were generated to test their ability to accurately

70 predict microcystin concentrations. The results, as shown in Figures 9 and 10 A-C, demonstrate moderate to strong correlations, with 2016 possessing greater predictive power.

Additionally, it was explored to see if the toxic proportion of Planktothrix, determined by dividing Planktothrix mcyE genes by Planktothrix 16S genes, would increase the ability to predict microcystins. To the author’s surprise, the toxic ratio had no effect, decreasing the positive association exhibited between the two genes and toxin when tested separately. Studies by Rinta-Kanto et al. (2009) and Bukowska et al. (2017) have tested similar ratios and found no significant associations with microcystins as well.33,73 However, when testing the association of a genus/total cyanobacteria ratio, based on 16S gene abundances, significant associations were found in these studies. These findings are consistent with the findings of this study.

It was also hypothesized that phycocyanin measurements could be adjusted to achieve better predictive relationships with toxin concentrations. Phycocyanin is a pigment with relative specificity for cyanobacteria, but it is unable to identify toxin-producing cyanobacteria. If a bloom is dominated by a toxic genus/species that is actively producing toxin, then it is quite plausible that phycocyanin will serve as good predictor of toxin. However, in the case of mixed blooms, predictive power will be lost when using phycocyanin. Therefore, in order to make phycocyanin more specific to the proportion of the bloom responsible for the toxin, its measure was adjusted using qPCR gene data. As shown in Figures 9 and 10 E-F, adjusted phycocyanin had moderate to strong associations with microcystins for both years. Planktothrix 16S gene adjustments versus mcyE gene adjustments provided mixed results between the years, however the Planktothrix 16S ratio adjustment in 2017 provided the best correlation (R2 = 0.87).

A highly important observation made during this study is that the adjustments caused an association between phycocyanin and microcystins to switch from negative to positive. The

71 negative association observed in 2016 is consistent with the early season toxin event but is inherently illogical. A negative association communicates that when a probe is placed in the water and little to no phycocyanin measurements are made, increased toxin concentrations are expected. Insignificant associations between phycocyanin and microcystins have been reported before, but to the author’s knowledge, significant negative associations have not been reported.54,88 Thus, the ability of the adjustment to correct this misleading correlation demonstrates its practicality.

The results of this study demonstrate the vast uncertainty that exist for a cHAB in a given year and the difficulty in establishing parameters that maintain relationships that can be harnessed for predicting microcystins. Because the adjusted phycocyanin possessed statistically significant associations with microcystins for both years, it was desired to evaluate the adjustment when data from both years were combined. Problematically, because data were collected and analyzed differently between the two years, it is difficult to know whether the data can be accurately joined together. Results from the Wilcoxon ranked sums demonstrated that total cyanobacteria 16S gene copies were different between the years. Consequently, very different gene ratios are obtained between the years, which explains the variability found in the sensitivity analysis. Nevertheless, data were combined for the two years and phycocyanin adjustments were generated again. When combined, phycocyanin possessed no correlation with microcystins. However, for the phycocyanin adjustments, statistically significant associations were obtained, with the mcyE adjustment providing the greatest correlation (R2 = 0.58).

To the author’s knowledge, this is the first study to use qPCR data to adjust phycocyanin measurements and enhance their specificity. Results of the linear regression demonstrate the effectiveness of the adjustment and its ability to characterize the toxin-producing genus of a

72 bloom. Furthermore, the adjustment appears to provide a means of addressing abhorrent events when toxin concentrations occur early in the season while overall phycocyanin measurements are low. There are of course limitations such as factors that decrease the accuracy of a phycocyanin measure and uncertainty related to qPCR data and its conversion to cell abundances.

Results of this study are preliminary. Phycocyanin adjustments need to be generated and tested for other source waters that experience blooms of differing genera/species. Overall toxin and phycocyanin concentrations were low at Tappan Lake as compared to concentrations exhibited elsewhere, such as Lake Erie.54,73 It is unknown whether the relationship of the adjustment will maintain or have any beneficial impact when bloom concentrations are much greater. As for Tappan Lake, more data is needed to test the validity of the association across multiple years. This calls for better data that is collected and analyzed in the same manner and sampled routinely.

Chapter 3. 2014 Toledo Water Crisis Model 3.1 Introduction

3.1.1 Background

Collins Park WTP supplies municipal drinking water for Toledo, Ohio, and surrounding areas. Raw water is drawn three miles offshore from the western basin of Lake Erie. KMnO4 is applied within this three-mile intake for control of zebra mussels, color, and taste and odor. The intake connects to a low service pump station (LSPS) where PAC is then applied. The LSPS then pumps raw water an additional nine miles to the WTP where aluminum sulfate (alum) is applied as a coagulant within rapid mix channels. The clarification process involves conventional flocculation and sedimentation where lime/soda ash are applied separately for softening. After sedimentation, the water is transported through a series of granular media filtration. Finally, chlorine is applied to achieve primary disinfection and residuals in finished supplies.133,134

Because the western Lake Erie basin is a highly eutrophic water source, there were growing concerns that a toxic cHAB might form or drift near the Collins Park WTP intake.135

Those concerns were confirmed on 1 August, 2014 at 13:30, when Collins Park employees detected a microcystin concentration of 2.50 µg/L in their finished drinking water supplies.7,136 This prompted an issue of a Do-Not Drink, Do-Not Boil advisory from 2-4 August, leaving approximately 500,000 people across 100 square miles without drinking water.78,135

After 4 August, microcystin concentrations decreased to non-detect (< 0.3 µg/L) in finished drinking water supplies, as Collins Park made efforts to optimize their treatment system such as increasing oxidant feeding rates, increasing chlorine CT, or increasing monitoring of clarification and filtration processes.90 However, in wake of a recurrent bloom, finished water detections spiked back up to 0.97 µg/L on 15 August 15, 2014, at 21:30.136 By the end of the crisis, Collins

Park WTP observed a total of 15 microcystin finished water detections from 1-19 August (see

Figure 12), and it is argued that under current Ohio EPA drinking water guidelines, a second advisory should have been issued in mid-August.135 The 2014 Lake Erie cHAB event proved to be problematic based on the proximity of the bloom to the Collins Park WTP raw intake.7 On 1

August, microcystin raw water concentrations were approximately 14 µg/L at the intake, peaking to over 50 µg/L on 15 Aµgust.136

The 2014 Toledo Water Crisis (TWC) received national media attention, demonstrating the large-scale public health threat microcystins and other cyanotoxins can pose. This event received immediate federal regulatory attention to manage the risk of future algal toxins in US drinking water supplies. On 8 January, 2015, a bill known as the Drinking Water Protection Act

(H.R. 212) was introduced to US House of Representatives to amend the Safe Drinking Water

Act and require USEPA to develop a strategic plan for assessing and managing risks associated with algal toxins in drinking water provided by public water systems.135 The TWC also spurred

Ohio EPA to adopt microcystin action levels (see 1.7.3) in raw water and establish new compliance monitoring of cyanobacteria and cyanotoxins for all Ohio WTPs, starting in

2016.135,136

3.1.2 Scope of Research

The 2014 TWC demonstrated that for a large cHAB, conventional water treatment systems may not be optimized nor prepared to remove bloom-level toxins. High finished drinking water detections present significant opportunities for humans to be exposed to harmful concentrations of microcystins. Concurrently, due to a low RfD, microcystins can present a significant health risk given chronic exposure, especially in vulnerable populations. Therefore, a risk model needs to be developed to assess downstream health risks should a water treatment

75 system fail to prevent or fully remove microcystins in finished drinking water supplies, as was the case during the TWC. Furthermore, the model needs to be adaptive, capable of assessing the risk of finished water detections amidst changing cHAB events. The risk model developed here uses the QMRA framework with a slight modification for the dose response. Further, the exposure model developed will be tested for verification and initial validation to microcystin concentrations for the entirety of the 2014 TWC.

Figure 12. 2014 Toledo Water Crisis Finished Water Detects

From Raymond (2016)136

3.2 Methods

3.2.1 Modelling Approach

All coding, modeling inferences, and plotting were conducted in the statistical programming R. The seed in R was set at 24 using (set.seed(24)), in order to obtain reproducible results from the model. A Monte Carlo simulation operating within a QMRA framework was used to model microcystin finished water detections and then estimate health risks for a consuming population. For a cHAB, the cyanobacteria are not pathogenic, rather the cyanotoxins are the microbially derived health hazard. Therefore, the dose response utilizes the

USEPA RfD of 0.05 µg/kg-day for MC-LR. Thus, the characterized risk will be a HQ for rapid risk estimates that can be compared over diverse populations. Although the TWC was an acute exposure event where health risks were related to gastrointestinal (GI) illness, no applicable RfD exists for this scenario. Therefore, the HQ of this model will assess risk based on liver function degeneration, which is the basis of the LOAEL used in the USEPA RfD (refer to 1.6.3).

3.2.2 Exposure Scenario Modeling

The primary exposure scenario is via direct ingestion of contaminated drinking water from the tap. However, as shown in Figure 13, in order to model the health risks from microcystins exposure in finished drinking water supplies, their reduction through drinking water treatment must be modeled first. Furthermore, as previously described in section 1.4.3, the split between intracellular and extracellular toxin needs to be considered in order to account for variations in cHAB health. Three modelling approaches are proposed to characterize this split.

Concurrent with modeling direct ingestion, secondary exposures from food preparation and brushing teeth are modeled too. Because children are typically more susceptible to

77 environmental hazards, modeling health effects for both adults (≥ 21 years of age) and children

(< 6 years of age) is performed.

Two model scenarios were developed. First, the initial TWC, spanning 1-4 August 2014, is modeled to demonstrate the risk of finished water detections and associated health effects attributable to an unoptimized treatment system not prepared for a toxic cHAB event. Initial microcystin concentrations are assumed to be 14 µg/L, as reported on 1 August. Second, the recurrent cHAB event (post-TWC), spanning 15-17 August 2014, is modeled to demonstrate the improved ability of a treatment system to address a cHAB when optimized and prepared. Initial microcystin concentrations are assumed to be 50 µg/L, as reported on 15 August. Both scenarios will allow for a relative risk comparison to assess water treatment optimization for health protection likelihood and demonstrate how to use the QMRA model for system optimization and design.

Figure 13. Microcystins impacting drinking water at Collins Park WTP

3.2.3 Modeling Initial Microcystin Concentrations at the Intake

Three methods for modeling initial intracellular versus extracellular toxin concentrations are proposed. The first method involves a computational simulation of overall cHAB health

(growth or decay) and is accomplished in R via a Boolean switch depicted in the pseudo-code below.

> a[i] <- sample(c(0,1),1) > if(a[i]==0) {"Conduct simulation using growing bloom intracellular and extracellular percentages"} > if(a[i]==1) {"Conduct simulation using decaying bloom intracellular and extracellular percentages"}

The Boolean switch informs which part of the split function in equation 3 to apply the total

concentration of microcystins (퐶푀푇). Equation 3 then models the concentration of extracellular or

intracellular toxin (퐶푀퐸 and 퐶푀퐼 respectively), dependent on the Boolean switch selection, initial

concentration of the scenario (퐶푀푂 ), and the relative proportion of toxin state associated with a

decaying or healthy cHAB (푟퐷 and 푟퐻 respectively). For example when a = 1, then 퐶푀퐸 = 퐶푀푂 ∗

푟퐷 and 퐶푀퐼 = 퐶푀푂 ∗ (1 − 푟퐷) , thus simulating extracellular and intracellular toxin concentrations exhibited in a decaying bloom. Both 푟퐻 and 푟퐷 are uncertain variables which are modeled using uniform distributions, where 10-25% of extracellular toxin is associated with a healthy cHAB and 60-70% of extracellular toxin is associated with a decaying cHAB, as reported by Chorus and Bartram (1999).2

퐶 = 퐶 ∗ 푟 푎 = 0 푀퐸 푀푂 퐻

퐶푀퐼 = 퐶푀푂 ∗ (1 − 푟퐻) 퐶푀푇 = (3) 퐶푀퐸 = 퐶푀푂 ∗ 푟퐷 푎 = 1 퐶 = 퐶 ∗ (1 − 푟 ) { 푀퐼 푀푂 퐷

The second method assumes that the health status of a bloom can be described by the E/I ratio of a toxin sample.62 After consulting the open literature, a total of five different sources

(Chorus 2001, Boutte et al. 2008, Sakai et al. 2013, Zhang et al. 2015, Park et al. 2018) were found that report distinguished intracellular versus extracellular microcystin concentrations.12,49,62,63,100 A web plot digitizer was used as needed to extract toxin concentration values from plots. A total of 117 samples were obtained and used to fit a probability distribution.

One sample from Chorus (2001) was omitted because it was an outlier and thus highly skewed the data (E/I = 4.80). Using then 116 samples, multiple distribution types were tested to find the best fit for the data. Based on the result of the lowest AIC and highest AIC weight, shown below in Table 13, a truncated normal distribution was obtained (0.15941627,0.26156562,0, 1.75).

Truncation is applied to prevent negative E/I ratios that are inherently illogical. The normal distribution has a mean of 0.159 and a standard deviation of 0.262. An upper bound of 1.75 represents the largest E/I ratio in the dataset. Within a Monte Carlo simulation, the truncated normal distribution is randomly sampled to report an E/I ratio for each iteration. This then

determines the extracellular versus intracellular toxin concentration, based on 퐶푀푂. This E/I ratio distribution is assumed to be representative of the natural ratio found at Lake Erie.

Table 13. E/I ratio probability distribution test

Distribution Residual Sum of Squares of Prediction AIC AIC Weights BIC normal 3.05 22.07 1.00 27.57

lognormal 9999.10 364.75 0.00 367.97 Weibull 9998.78 349.79 0.00 353.01 geometric 9999.34 NA 0.00 NA exponential 9999.34 385.17 0.00 386.78 logistic 9998.89 351.58 0.00 354.80 Poisson 10000.00 NA 0.00 NA Cauchy 9999.95 367.31 0.00 370.54

The third method is a combination of the previous two methods. Based on the mean of the truncated normal distribution for E/I ratios, this method assumes a 16% chance that a decaying bloom will be present. This is accomplished using a uniform distribution (0-1), where ≥

0.16 corresponds to a growing bloom and everything else corresponds to a decaying bloom, as shown below in the code. The same 푟퐷 and 푟퐻 from the first method are applied.

> a <- runif(1,0,1); > if(a>=0.16){"Conduct simulation using growing bloom"} > else{"Conduct simulation using decaying bloom"}

3.2.4 Water Treatment Reduction Efficiency Modeling

When modeling intracellular toxin removal, the toxin concentration needs to first be converted to an equivalent number of cells. Equation 4 is developed to estimate the cell

abundance (Nc), dependent on 퐶푀퐼 , a microcystin quota typically encountered per cell (MCQ), and the molecular weight of microcystins (MW). The molecular weights of MC-LR, MC-RR, MC-

LA, and MC-YR were pooled in the equation.137 The author acknowledges that other microcystins congeners with differing MWs may exist.

퐶 푀퐼 푁퐶 = [4] 푀퐶푄∗ 푀푊

For modeling the water treatment process, the model equations are separated based on their targeted efficacy for intracellular or extracellular toxin. The clarification and filtration

81 processes at Collins Park WTP are optimized for cell removal but are not effective for extracellular toxin removal. The PAC and KMnO4 feeds remove only extracellular toxin while

Cl- removes both. Therefore, model equations are divided into intracellular and extracellular

concentration reductions 퐶푀퐼 − and 퐶푀퐸 − in equations 5 and 6 respectively. Equations 5 and 퐶푙 푐푒푙푙 퐶푙

6 are referenced to Cl- because evidence holds that chlorination is highly effective at lysing

115,133 cyanobacterial cells at the historical CT values reported by Collins Park WTP.

Consequently, chlorination will convert the intracellular toxins into extracellular and then degrade the total toxin concentration in that process. The following sections will review the treatment process of Collins Park WTP and call back to processes previously described in section

1.7.

3.2.4.1 Clarification and Filtration

As mentioned, these processes are most efficient at removing intracellular microcystins.

There is no more granularity of data than 90% removal of intracellular toxin during the TWC.133

This justifies the use of a point estimate for clarification treatment efficiency (ρcla). A uniform distribution of 90-99% is assumed for the post-TWC scenario to simulate greater removal efficiencies, which is supported by the literature. For filtration, a removal efficiency (ρfilt) for both scenarios is modeled as a uniform distribution, derived from filtered results of mixed taxa as reported by Zamyadi et al. (2013).108

3.2.4.2 KMnO4

Due to Lake Erie being the source water, Collins Park uses KMnO4 to abide by Ohio

EPA regulations for control of zebra mussels, taste/odor, and cHAB byproducts.133 Ohio EPA

107 recommends dosing 0.5-2.5 mg/L of KMnO4 to control for adult zebra mussels. No KMnO4 dose was obtained from Collins Park, so a removal efficiency of 40-60% has been assumed,

111 which corresponds to findings when KMnO4.was dosed at 0.6 mg/L in raw lake water. This range of reduction is modeled using a uniform distribution as parameterized in Tables 16 and 17 for microcystin reduction efficiency from KMnO4 (ρKMnO4) and incorporated into equation 6.

Collins Park does not report a change in KMnO4 feeds during the 2014 TWC, therefore, this uncertain variable remains constant for the post-TWC scenario.

3.2.4.3 PAC

For the 2014 cHAB season, Collins Park reports a 19-99% removal efficiency range using PAC, with a median of 61%. Collins Park also reports that on 15 August, increased PAC doses of 15 mg/L provided 59% removal of microcystins.133 Based on this data, a uniform distribution of 19-61% removal efficiency is assumed for the initial TWC, representing the median and lower bound of 2014 when PAC doses were not increased/optimized. Subsequently, a uniform distribution of 61-99% reduction is assumed for the post-TWC scenario, representing the upper removal efficiencies of 2014 when PAC doses were increased to address the imminent cHAB. PAC removal efficiency (ρPAC) parameters are listed in Tables 16 and 17 and incorporated into equation 6.

퐶푀퐼 − = 푁퐶 ∗ 휌퐶푙푎 ∗ 휌퐹푖푙푡*푀퐶푄 [5] 퐶푙 푐푒푙푙

퐶푀 = 퐶푀 ∗ 휌퐾푀푛푂4 ∗ 휌푃퐴퐶 [6] 퐸퐶푙− 퐸

As mentioned before, once intracellular and extracellular toxin reach the chlorination process, all intracellular toxins are converted to extracellular due to the lysis efficiency of Cl- on cells. This is carried out by re-multiplying 푀퐶푄 to the cells remaining, as shown in equation 5.

Therefore, the concentration of toxin that is delivered to the chlorination process is the sum of

the intracellular and extracellular toxin remaining (퐶푀퐶푙− ; equation 7).

퐶푀퐶푙− = 퐶푀퐼 − + 퐶푀퐸 − [7] 퐶푙 푐푒푙푙 퐶푙

3.2.4.4 Chlorination

Similar to PAC, Collins Park reports microcystin removal efficiencies using chlorination for the 2014 cHAB year. It was assumed that removal rates exhibited from July-August are representative of dosing and contact time observed when the plant was unoptimized to respond to a cHAB event. These data are used for the initial TWC. Removal rates from September-

December of that same year were assumed to be representative of dosing and contact time for chlorine once the plant was optimized and unwilling to allow anymore finished water detections to occur for the remainder of the year. These data are used for the post-TWC. Using a web plot digitizer, data were obtained and used to fit two separate probability distributions for each scenario. Based on the results of best fit, a truncated normal distribution was obtained

(0.55040471,0.19951449,0.0464,0.8907) for TWC, shown below in Table 14. Based on the results of best fit, a truncated normal distribution was also obtained

(0.60355208,0.25252061,0.06954,1.00) for post-TWC, shown below in Table 15. Truncation is applied to prevent negative Cl- removal efficiencies that are inherently illogical. Upper and lower bounds of the truncation represent the highest and lowest removal efficiencies of the datasets

84 respectively. Thus, chlorination removal efficiency (휌퐶푙−) values are used in equation 8, along

with the 퐶푀퐶푙− values from equation 7 to calculate the concentration of microcystins that remain

in finished drinking water supplies and enter the distribution system (퐶푀퐹푊 ; equation 8).

Table 14. TWC chlorination removal efficiency probability distribution selection

Distribution Residual Sum of Squares of Prediction AIC AIC Weights BIC normal 7933.14 242.26 0.49 244.85 lognormal 7933.16 261.63 0.00 264.22 Weibull 7933.14 244.34 0.17 246.94 geometric 7933.97 NA 0.00 NA exponential 7933.33 272.44 0.00 273.73 logistic 7933.18 242.97 0.34 245.56 Poisson 7933.97 NA 0.00 NA Cauchy 7933.91 252.22 0.00 254.81

Table 15. Post-TWC chlorination removal efficiency probability distribution selection

Distribution Residual Sum of Squares of Prediction AIC AIC Weights BIC normal 9999.08 310.76 0.50 313.75 lognormal 9999.25 328.23 0.00 331.23 Weibull 9999.08 312.41 0.22 315.40 geometric 9999.37 NA 0.00 NA exponential 9999.37 338.62 0.00 340.11 logistic 9999.19 311.89 0.28 314.88 Poisson 10000.00 NA 0.00 NA Cauchy 9999.96 323.17 0.00 326.16

− 퐶푀퐹푊 = 퐶푀퐶푙− ∗ 휌퐶푙 [8]

3.2.5 Human Specific Exposure Modeling

Ingestion rates of the proposed exposure pathways and mean body weights will be considered to calculate ADD (equation 10) estimates for microcystin exposure in adults (≥21 years) and children (<6 years) separately. Consumer-only daily water ingestion rates that consider both drinking and food preparation are used from the USEPA Exposure Factors

Handbook and are shown in Tables 16 and 17.138 Triangular distributions using the 10th, 50th, and 95th percentiles available from the exposure factors handbook are assumed and reflected by

(VW+F). Separate volumes are used between adults and children.

During tooth-brushing events, no relevant water ingestion data is available in the literature. Therefore, the volume of water swallowed was assumed to be equivalent to the volume of water film remaining on the surface area of the mouth after brushing. According to Collins

2 2 and Dawes (1987), the average surface area of an adult mouth (푆퐴푀) is 214.7 cm ± 12.9 cm and the average thickness of salivary film (SF) in an adult mouth is 0.07-0.1mm.139 A normal distribution is assumed for 푆퐴푀 and a uniform distribution is assumed for SF. Likewise for a child, according to Wantanabe and Dawes (1990), the average surface area of a 5 year old’s mouth is 117.6 cm2 ± 7.6cm2 and the average thickness of salivary film for a child’s mouth is

0.06-0.09mm.140 Similar probability distributions are assumed. Also, frequency of brushing teeth

141 (푇퐵퐹) is assumed to be twice a day, which is in agreement with the literature. After proper unit conversions, these values are used to calculate the volume of water ingested during toothbrushing (푉푇퐵), as shown in equation 9.

푉푇퐵 = 푆퐴푀 ∗ 푆퐹 ∗ 푇퐵퐹 [9]

The USEPA Exposure Factors Handbook provides a mean adult body weight (BW) of 80 kg and a mean child body weight of 18.6 kg.138 These variables were incorporated into equation

10 to model the ADD from the tap, which in turn is used to model the hazard quotient HQ shown in equation 11. The USEPA RfD of 0.05 µg/kg-day for MC-LR is used in the HQ (refer to 1.6.3).

A summary of variables used in the model is shown in Tables 16 and 17.

퐴퐷퐷 = [(푉푊+퐹 + 푉푇퐵) ∗ 퐶푀퐹푊 ]/퐵푊 [10]

퐴퐷퐷 퐻푄 = [11] 푅푓퐷

Table 16. Variables used for TWC model

Variable 1-4 August 2014 TWC

Citationa 14 136 퐶푀푂 (µg/L) 142 MCQ (fmol/cell) Uniform (0.05, 0.129) MW (g/mol) 910.06, 995.17, 1038.20, 1045.19 137 ρKMnO4 (%) Uniform (40, 60) 111 ρPAC (%) Uniform (19, 62) 133 ρcla (%) 90 133 ρfilt (%) Uniform (85.6, 96.0) 108 ρCl- (%) Truncated Normal (0.55040471,0.19951449,0.0464,0.8907) 133 Adults: Triangular (0.208, 1.006, 2.848) 138 푉푊+퐹 (L) Children: Triangular (0.057, 0.336, 1.099) 138 푉푊+퐹 (L) 141 TBF 2

2 139,140 SAM (cm ) Adults: Normal (214.7,12.9); Children: Normal (117.6, 7.6) 139,140 SF (cm) Adults: Uniform (0.007-0.01); Children: Uniform (0.006-0.009) 138 BW (kg) Adult = 80; Child = 18.6 35 RfDoral µg/kg-day 0.05

a parameters used not distributions chosen associated with citation

Table 17. Variables used for post-TWC model

Variable 15-17 August 2014 post-TWC

Citationa

136 퐶푀푂 (µg/L) 50 142 MCQ (fmol/cell) Uniform (0.05, 0.129) MW (g/mol) 910.06, 995.17, 1038.20, 1045.19 137 ρKMnO4 (%) Uniform (40, 60) 111 ρPAC (%) Uniform (62, 99) 133 ρcla (%) Uniform (90, 99) 109,133 ρfilt (%) Uniform (96, 99) 108 ρCl- (%) Truncated Normal (0.60355208,0.25252061,0.06954,1) 133

138 푉푊+퐹 (L) Adults: Triangular (0.208, 1.006, 2.848) 138 푉푊+퐹 (L) Children: Triangular (0.057, 0.336, 1.099) 141 TBF 2

2 139,140 SAM (cm ) Adults: Normal (214.7,12.9); Children: Normal (117.6, 7.6) SF (cm) Adults: Uniform (0.007-0.01); Children: Uniform (0.006-0.009) 139,140 BW (kg) Adult = 80; Child = 18.6 138

35 RfDoral µg/kg·day 0.05

a parameters used not distributions chosen associated with citation

3.3 Results

Although microcystin concentrations vary with time, the data are from point estimate grab samples, therefore, these QMRA models cannot depict temporal changes in risk.

Subsequently, these models assume that 14 µg/L and 50 µg/L of toxins are uniformly flowing into the WTP during the TWC and post-TWC scenarios respectively. As the model algorithm

(Figure 14) shows, within a single iteration, intracellular and extracellular toxin concentrations are determined based on the method used to evaluate cHAB health. Once the toxin state distribution is known, a dose estimate (finished water detection) can be determined based on the appropriate removal efficiencies of the water treatment process. This dose estimate is used to calculate the ADD to determine the concentration of microcystins ingested. Finally, the ADD is used to generate a risk estimate (HQ), which is saved in a central data frame and the model iterates. The model is operated for 10,000 iterations, thus invoking the law of large numbers for a converging simulation.

3.3.1 Selection of cHAB Health Method

Limited data on initial and finished water detections of microcystins during the crisis were obtained from Collins Park. According to the report by Raymond (2016), a total of 9 samples were taken between 1-4 August and a total of 8 samples were taken between 15-17

Aµgust.136 Table 18 displays the descriptive statistics of the two scenarios.

Figure 14. QMRA model algorithm

Table 18. Descriptive statistics of microcystin finished water detections

From Raymond (2016)136

Date 1-4 August 15-17 August ND, ND, 0.32, 0.33, ND, ND, ND, Samples 0.35, 1.50, 1.60, 0.30, 0.37, 0.40, 1.70, 2.50 0.46, 0.97 Minimum ND ND 5th Percentile ND ND Median 0.35 0.34 95th Percentile 2.18 0.79 Maximum 2.5 0.97

ND < 0.3 ug/L

Finished water detection estimates were generated for the 3 methods assessing cHAB health. The goal was to develop a model, based on the literature, that accurately assigns intracellular versus extracellular toxin ratios to an initial microcystin concentration at a WTP raw intake. Accurate estimates of this split are important when calculating water treatment removal efficiencies and resultant finished water detections. Figures 15-17 compare the finished water estimates of each method for the two scenarios. It is apparent that the Boolean switch is the most liberal, with medians and upper 95th percentiles greater than the other two methods for both scenarios. In contrast, the E/I ratio method is much more conservative, with medians and upper percentiles approximately 2 times smaller than the Boolean switch method. The combined method appears to meet the other two methods somewhere in the middle, with medians more in- line with the E/I ratio, but upper 95th percentiles more in-line with the Boolean switch.

Figure 15. Finished water detections using Boolean switch method

Figure 16. Finished water detections using E/I ratio method

Figure 17. Finished water detections using the combination method

In order to determine which method is most accurately simulating finished water detections observed during August 2014, a Wilcoxon ranked sums test was performed. This test was selected because it provides a means of comparing two datasets, when one or both may be non-parametric (which is the case for the observed data). Data are ranked and differences are pooled, allowing for more accurate comparisons to be made when data are not normally distributed. The null hypothesis is that the two datasets are not significantly different from one another and therefore it can be assumed they represent the same population. If the test rejects the null hypothesis (p-value < 0.05), then there is evidence of a statistical difference between the two datasets. In this case, the desired result is a fail to reject the null hypothesis, indicating that the output from the cHAB growth methods is not statistically different from the observed data.

Results (p-values) of the Wilcoxon ranked sum tests are shown below in Tables 19 and 20. The observed data were compared to the 10,000 iteration outputs of each cHAB growth method. As an additional level of comparison, these 10,000 iterations were subset into groups of 1000, and overall medians of the subsets were reported.

As shown by the p-values, all methods for both scenarios failed to reject the null hypothesis, indicating that the outputs are similar to the observed data. Therefore, to make method selection, higher p-values are observed. For the initial TWC, the Boolean switch method is in excellent agreement with the observed data (median p-value = 0.94). The combined method also is demonstrating relative agreement, while the E/I Ratio method is near rejecting the null hypothesis.

Table 19. Results (p-values) of the Wilcoxon ranked sums test for TWC

Boolean Switch E/I Ratio Combined 10,000 Iterations 0.932 0.07 0.238 1-1000 0.796 0.077 0.198 1001-2000 0.877 0.068 0.214 2001-3000 0.84 0.064 0.248 3001-4000 0.966 0.062 0.255 4001-5000 0.999 0.059 0.307 5001-6000 0.948 0.069 0.248 6001-7000 0.966 0.08 0.189 7001-8000 0.935 0.074 0.261 8001-9000 0.949 0.084 0.284 9001-10000 0.856 0.075 0.218

Median 0.9415 0.0715 0.248

Table 20. Results (p-values) of the Wilcoxon ranked sums test for post-TWC

Boolean Switch E/I Ratio Combined 10,000 Iterations 0.365 0.403 0.71 1-1000 0.466 0.284 0.723 1001-2000 0.277 0.422 0.682 2001-3000 0.374 0.372 0.71 3001-4000 0.412 0.435 0.783 4001-5000 0.292 0.386 0.795 5001-6000 0.273 0.534 0.935 6001-7000 0.461 0.327 1 7001-8000 0.319 0.399 0.587 8001-9000 0.428 0.499 0.665 9001-10000 0.419 0.431 0.677

Median 0.393 0.4105 0.7165

For post-TWC, all 3 methods demonstrate relative agreement with the observed data, however, the combined method appears to offer a better estimate (median p-value = 0.72). Based on these results, the Boolean switch method will be used for the health risk assessment of the initial TWC and the combined method will be used for the health risk assessment of the post-

TWC.

3.3.2 Health Risk Assessment

Using the applicable cHAB health method, HQs were generated for adults and children.

Figure 18 shows the results of the risk simulations for both scenarios of the crisis. In this figure, a HQ of 1 is highlighted with a solid red line. A HQ ≥ 1 (right of red line) is indicative of an excessively high risk and high likelihood that health effects will manifest. For the initial TWC, median HQs for adults and children are 0.20 and 0.32 respectively, indicating that the most frequent exposure scenario falls within the safety boundary. However, when considering the 95th percentiles for adults and children, HQs are 0.80 and 1.30 respectively. Here, risk estimates for children exceed 1.0, suggesting deleterious health outcomes could manifest. For the reoccurring cHAB (post-TWC), overall HQ’s are lower. The median HQs for adults and children are 0.11 and 0.16 respectively, indicating again that the most frequent exposure scenario falls within the safety boundary. When considering the 95th percentiles for adults and children, HQs are 0.63 and

1.00 respectively. Again, children are at greater risk than adults, with the 95th percentile exactly at the threshold. When comparing medians, overall risk is two times smaller during the post-

TWC than the TWC. This comparison will be explored further in the discussion.

A B

C D

Figure 18. Hazard quotient estimates from the simulation where A) is the risk to adults during the initial TWC; B) is the risk to children during the initial TWC; C) is the risk to adults post-TWC; and D) is the risk to children post-TWC.

Additionally, risk estimates were generated if all initial toxin was strictly extracellular or strictly intracellular. This was accomplished by “deactivating” the cHAB health methods and

setting CMO to strictly one toxin state. Figure 19 shows the breakdown of HQ results depending on simulation specificity. The log of the HQ estimates was taken to aid in visualization. As was discussed earlier, the status of the toxin, being intracellular or extracellular impacts the ability to remove the toxin from water effectively. This is reflected in the QMRA model simulations, where mixed intracellular and extracellular toxin proportions are represented by the yellow boxes, strictly extracellular by the red boxes, and strictly intracellular by the blue boxes (Figure

19). As shown, the QMRA model results show significantly higher risks when toxins are extracellular, thus demonstrating the difficulty of their removal in the WTP processes as well as the importance of limiting/preventing cellular lysis. Furthermore, the importance of preparing a system for cHAB treatment while a cHAB is healthy to have sufficient optimization time before the cHAB begins to decay is apparent. The consistently higher risk to children is also demonstrated here. Tabulated results from the simulations are displayed in Table 21. In some cases, means tend to be much greater than the medians, indicating that the results are rightly skewed. In these scenarios, the results are biased to higher risk ranges, with the greatest bias occurring when extracellular toxin dominates the bloom.

Figure 19. Comparison of hazard quotients between scenarios of different toxin state

Table 21. Descriptive statistics of QMRA simulations

Scenario Simulated Mean SD Lower 95th Median Upper 95th

a 0.29 0.26 0.04 0.20 0.80 Adult TWC IE Child TWC IE 0.45 0.42 0.06 0.32 1.30

Adult TWC E 0.66 0.42 0.17 0.57 1.49

Child TWC E 1.05b 0.69 0.25 0.89 2.41

Adult TWC I 0.02 0.01 0.01 0.02 0.05

Child TWC I 0.03 0.02 0.01 0.03 0.08 Adult Post TWC IE 0.18 0.25 0.01 0.11 0.63

Child Post TWC IE 0.29 0.40 0.02 0.16 1.01

Adult Post TWC E 0.71 0.68 0.04 0.51 2.12 Child Post TWC E 1.12 1.10 0.07 0.79 3.43

Adult Post TWC I 0.01 0.01 0.00 0.01 0.03

0.02 0.02 0.00 0.01 0.05 Child Post TWC I a I = intracellular, E = extracellular, I&E = Combined toxin states, I and E alone are simulations where only that toxin state exists b Bolded values are those HQs > 1.0

3.4 Discussion

The 2014 TWC was a major public health disaster that left 500,000 people without safe drinking water for three days. The models developed in this research were able to simulate finished drinking water detections of microcystins that are consistent with the observed detections exhibited between 1-4 August, and then again between 15-17 August 2014. These results demonstrate the application of QMRA modeling for microbiologically derived hazards in water treatment systems. Furthermore, the model presents an innovative method of assigning E/I ratios to initial microcystin concentrations, which is useful for assessing WTP resiliency amidst a real bloom.

One of the main achievements of this model was discovering a method that accurately portrays the health status of a cHAB and thus establishes an E/I toxin ratio. Three methods were explored in this research, with the third method being a combination of the two. For the initial

TWC, it was determined through Wilcoxon ranked sums testing that the Boolean switch method

100 produced results with closest agreement to the observed data. However, for the post-TWC scenario that occurred later in August, it was the combination method that provided the greatest accuracy. It is frustrating that one method was unable to serve both scenarios, however, there is a likely explanation for this. The Boolean switch inherently overestimates finished water detections based on its construct. During each iteration, the Boolean switch allows a 50/50 chance that the cHAB will be decaying. If this option is selected, initial extracellular toxin is between 60-70% of the initial toxin concentration. This feature can become problematic when initial concentrations are extremely high, as was the case for 15-17 August. For this scenario, for every iteration of the Boolean switch, there is a 50% chance that extracellular toxin concentration is at minimum 30 ug/L. Extracellular toxin at this concentration is unprecedently high and most likely to blame for the rightly skewed results and lack of accuracy using the

Boolean switch method for the post-TWC scenario.

On the other hand, the E/I ratio surprisingly underestimates finished water detections.

The truncated normal distribution was generated based on reports of the literature across the globe. It is possible that these E/I ratios are not representative of conditions exhibited at Lake

Erie or that more data is needed to improve these distributions. Finally, the combined method allowed the selection of a decaying bloom for only 16% of the time, based on the mean E/I ratio generated from the literature. This method makes the Boolean switch more conservative, improving the output for the post-TWC scenario. Overall, these methods provide an effective means of estimating the E/I toxin ratio of a cHAB before it impacts a WTP. Conveniently, the

Boolean switch and combined methods can be amended to be more conservative or liberal, providing estimates of differing scenarios.

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Health risk estimates from the models demonstrate that the 2014 TWC was a public health threat. All median HQs are < 1.0 for both scenarios. For adults, 95th percentiles for both scenarios are < 1.0, however, extreme outliers > 1.0 do exist. As for children, risk estimates are significantly greater, with 95th percentiles ≥ 1.0 for both scenarios. After the initial TWC, Collins

Park responded by increasing their feeding rates and optimizing their treatment system. At first glance, it would appear the model output suggests that the WTP optimization had little if any effect to overall health risks. However, this in fact is not the case when considering that initial toxin concentrations ballooned to 50 ug/L during the post-TWC scenario, which is 3.5 times greater than the TWC. To further demonstrate this point, the post-TWC model with improved water treatment processes was simulated this time with initial concentrations of 14 ug/L; synonymous to the initial crisis. As shown in Figure 20, health risks are now significantly less in comparison to the initial TWC model (refer to Figure 18 A-B). This demonstrates that all health risks are virtually eliminated, except for rare events (outliers) involving children. Therefore, these models are demonstrating the efficacy of WTP optimization and its impact on resultant health effects.

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A B

Figure 20. Hazard quotient estimates for post-TWC with 14 ug/L initial toxin concentrations

where A) is the risk to adults and B) is the risk to children

By turning the Boolean switch and combined methods off, health risks could be estimated when initial toxin concentrations are strictly extracellular or intracellular. Results from these simulations support the claim that extracellular toxin is more difficult to remove during water treatment. Referring back to Figure 19 and Table 21, risk estimates for strictly extracellular toxin were several-fold greater than risks for strictly intracellular toxin. For all scenarios, median HQs never exceeded 1.0, however 95th percentiles consistently exceeded 1.0, especially when toxin was strictly extracellular. Considering these abhorrent extracellular exposures, the estimates rose as high as 2.12 and 3.43 for adults and children respectively, based on the 95th percentiles. In comparison, strictly intracellular toxin posed no health risk to either group for either scenario.

Health risks for the models assuming a mixed E/I ratio always fell somewhere in between. Based on these results, it is safe to assume that an extracellular toxin event represents a “worst-case

103 scenario,” while an intracellular toxin event represents a “best-case scenario.” The results of these risk simulations prove three key points: 1. Extracellular toxin is more difficult to remove than intracellular toxin during water treatment and therefore should be avoided when possible; 2.

Health risks are consistently greater for children than adults, most likely attributable to their low body mass; and 3. QMRA modelling is useful for any microcystin and/or cHAB event that may impact drinking water supplies and can be amended as needed to simulate unique scenarios.

The actual totality of the deleterious health effects attributable to the 2014 TWC is difficult to ascertain, however, a follow-up assessment by McCarty et al. (2016) sheds some light on the matter.143 Customers of Collins Park WTP municipal water were surveyed in September

2014 to assess their water use habits, adherence to issued public health advisories, and incidence of illness. A total of 171 households were interviewed and local census data were used to extrapolate findings to the entire population. Results show that during the advisory, 10.7% of households report using municipal water for drinking, 10.2% of households report using municipal water for food preparation, and 19.6% of households report using municipal water for brushing teeth. Although there is most likely overlap, these results conclude that a maximum of

40.5% of the estimated 103,058 households were potentially exposed to harmful levels of microcystins. This health assessment also surveyed onset of illness, but these illnesses were not tested for their association to specific exposures. Nevertheless, the results show that 16.2% of households (17,431) reported at least one physical illness, with diarrhea being the most common illness (12.1%).143 Although it is impossible to know the total amount of people and/or vulnerable populations exposed, these findings prove the ineffectiveness of public health advisories and support the risk of deleterious health outcomes estimated by the models.

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It is important to note however that reported illness from the survey are mostly related to

GI distress/disease and not related to liver degeneration. The RfD used in these risk models is specific to liver degeneration and was used because it is the only established RfD to date.

Therefore, in order to model risk of GI illness related to acute exposure (which is the case of the

2014 TWC), the data suggest a need for a new RfD specific to GI illness. This RfD would most likely be smaller, causing estimated HQs of the models to increase. It is also important to recognize that small changes in the values used for ADD and HQ derivation can have major consequences to risk estimates. For example, if the WHO TDI of 0.04 µg/kg-day (equivalent to

RfD) was used or if the mean body weight of an adult was assumed to be 60 kg as opposed to 80 kg, the estimated HQs would certainly be greater.97

This is not the first model involving intracellular versus extracellular microcystin removal during water treatment processes. Schmidt et al. (2002) conducted a study to estimate the risk of microcystin breakthrough, that is intracellular to extracellular, during different water treatment processes.110 Several of their findings are consistent with what has already been reported here; flocculation/filtration are an efficient means of removing intracellular toxin, however breakthrough it possible given the conditions of the filter; PAC provides an effective means of extracellular toxin removal but with large removal efficiency ranges; and KMnO4 used as a pre-oxidant can result in significant cell lysis. Interestingly, they report KMnO4 to have no effect on removing extracellular toxin, which is inconsistent with other studies and the data used for my model.110 My model did not account for any breakthrough during the treatment steps. Once the

E/I ratio was determined by the selected cHAB health method, toxin states remained separate until chlorination. However, by considering breakthrough associated with different treatment steps, the accuracy and specificity of my model could improve.

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This is also not the first model to apply Monte Carlo simulation methodologies to a microcystins health risk assessment. Xiao et al. (2018) developed a stochastic model to assess non-carcinogenic health risks of microcystins.144 In their risk model, drinking water was the exposure route and a hazard index (HI), synonymous to HQ, was used to assess health risks.

Results of this study found no health risks present, as no HI exceeded 0.34. It is important to note however that initial microcystin concentrations in this study were very low, nearly at or below the OEPA 0.3 µg/L threshold, therefore the lack of risk is expected. Still, the results of Xiao et al. (2018) indicate that health risks are greater for children than adults, consistent with the results of my model.144

The 2014 TWC demonstrated that even a large WTP with ample funding can quickly become compromised by microcystins if unprepared. In recent years, Collins Park has made several improvements to their treatment process, including the addition of an ozonation facility, scheduled for installation by September 2019. Current pilot studies conducted by Collins Park have suggested near 99% toxin removal rates, both intra- and extracellular, using ozone.134 This model could be updated to include ozone once the ozonation is installed and true removal efficiencies are determined.

No model is without its limitations and needed improvements. More accurate data is needed to improve the output of these models. Several assumptions in the treatment and exposure models were made related to removal efficiencies. Concentrations of NOM, DOC, and other water quality constituents were not considered, but certainly had an impact on the efficiency of the treatment process during the 2014 TWC. Consistent assessments of treatment dosing, CT, etc. need to be routinely conducted in order to establish a model specific to a WTP.

As reported earlier in section 1.7.2, AWWA has developed CyanoTOX® Version 2.0 that can

106 calculate expected extracellular cyanotoxin removal rates from using various oxidants based on

113 several data inputs such as water quality, dosing, and CT. CyanoTOX® Version 2.0 may serve as an excellent background tool to this model should a WTP operator need to estimate expected removal efficiencies when comprising data inputs.

It is also difficult to assess any model’s accuracy (validation) based on a small set of observed samples. Furthermore, these observed samples present a level of uncertainty because they only represent a single point in time and may not capture the true lower/upper bounds of the event. Therefore, comprehensive sampling is needed, both initial and finished water microcystin concentrations, as well as initial E/I toxin ratios, in order to assess the true accuracy of the model output.

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Chapter 4. Conclusions and Future Research

4.1 Conclusions

cHABs continue to impact surface waters that are used as a drinking water source across the globe. Depending on the right conditions, these blooms can become toxic with microcystins, threatening the quality of safe drinking water. Many efforts have been made in the past to develop innovative and effective means of estimating and predicting these highly complex events, however, high level of variability and uncertainty has consistently made it difficult.

In this thesis, two models have been presented for their use and effectiveness in addressing this variability and uncertainty and improving our ability to predict microcystins as they relate to drinking water treatment systems. The first model uses qPCR data to correct phycocyanin measurements to improve predictive linear regression relationships. It has been well documented throughout the literature that cyanobacteria 16S rRNA genes and mcy genes, as well as phycocyanin, have exhibited strong correlations with microcystin concentrations in source waters. Both parameters inherently measure the same events, but there are drawbacks. While qPCR gene data provide high specificity for detecting cyanobacteria genera/species capable of producing toxin, it is a laboratory process that takes time. In contrast, while phycocyanin can be measured in real-time, it lacks the specificity of detecting toxin-producing genera/species. Thus,

I have developed a method that accounts for both parameters in order to improve statistical relationships.

First, the genus/species responsible for microcystin production is determined through correlative analysis of qPCR gene data and toxin concentrations. Secondly, qPCR abundance ratios of that genus/species are calculated using total cyanobacteria 16S rRNA gene copies as the denominator. Finally, these ratios are multiplied to phycocyanin measurements of the same

108 sample date in order to adjust the measure to be specific to only that genus/species producing the toxin. Through linear regression analysis, I have shown that:

• At Tappan Lake, Ohio, in 2017, adjusted phycocyanin measurements are better predictors

of microcystins over phycocyanin or qPCR gene data alone, based on improved linear

regression relationships. (R2 = 0.89, 0.69 vs. R2 =0.54, 0.47, 0.46)

• In 2016, adjusted phycocyanin measurements convert a negative association between

phycocyanin and microcystins that is inherently illogical, into a positive association.

• The “toxic” ratio of a genus based on the proportion mcyE genes to 16S rRNA genes does

not serve as a useful proxy for estimating microcystin concentrations. 16S or mcyE gene

data alone are better predictors.

• When data are combined for both years, both adjusted phycocyanin measures maintain a

statistically significant correlation with microcystins, even though phycocyanin is not

correlated. The mcyE-adjusted phycocyanin provided the best prediction (R2 = 0.58).

The second model utilizes a stochastic method to model the impacts of cHAB health status and water treatment processes on microcystin finished water detections and associated health effects. The 2014 TWC was selected as a case study because it was an event where high initial toxin concentrations resulted in finished water detections. Two scenarios were modeled: 1.

The initial TWC where water treatment processes were unoptimized, leading to a do not drink, do-no-boil advisory that spanned 3 days; and 2. A recurrent toxic cHAB later in the month that impacted a now optimized Collins Park WTP. Multiple methods for simulating initial E/I toxin ratios were explored while simultaneously providing health risk estimates related to the 2014

TWC. Through the development of this stochastic model, I have demonstrated:

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• My stochastic model produces predictive outputs that are validated to the observed data

of the 2014 TWC.

• Use of the QMRA paradigm to estimate health affects related to cHABs and their

synthesized toxins.

• Use of a Boolean switch and probability distribution (E/I ratio and combined methods) to

characterize the growth status of a cHAB in order to accurately determine the E/I toxin

ratio of initial microcystin concentrations.

• The 2014 TWC presented an appreciable health risk for children, with estimated 95th

percentiles ≥ 1.0 for the entirety of the crisis.

• The recurrent bloom later in August (post-TWC) presented health risks that would have

warranted a second do-not drink advisory.

• Overall health risks related to microcystin exposure are greater for children than adults.

• Compared to intracellular toxin, extracellular toxin is more difficult to remove during

water treatment, thus invoking higher health risk estimates.

• The ability to modify the model to simulate different bloom scenarios related to different

E/I ratios, allowing for WTP resiliency and preparedness.

• The impact of water treatment optimization on removal efficiencies of microcystins, even

when initial toxin concentrations at the intake are great.

4.2 Implications

The implication of these two models is that they can be merged together to provide microcystin predictive capabilities from intake to tap. The reason that the models were not merged together in this research is due the data that was available to me. The rationale I present

110 is that the phycocyanin adjustment model may provide a means of more accurately predicting microcystin concentrations at a WTP raw intake. This can be accomplished using an in-situ real- time fluorometric probe (data sonde) positioned on a buoy, at aWTP pump station, etc. Routine qPCR sampling, that may already be required by the governing regulatory agency, will communicate changes to the cHAB community and whether toxic-producing genera are still present. Based on the qPCR data, new ratios can be generated as needed to continuously adjust the phycocyanin measurement.

With this model in place, accurate initial microcystin concentrations at the WTP intake will be known and then used in the stochastic model. The Boolean switch can be used to determine the E/I ratio of the initial microcystin concentrations as they are simulated through the water treatment process. A WTP plant can then make constant changes to the model related to removal efficiencies, based on their oxidant feeding rates, etc. Finally, the output of the model will provide an estimation of the risk of a finished water detection and associated health effects to the consumer.

4.3 Future Research

Stochastic models are data driven and therefore require a large amount of data. The main limitation of my research is a lack of comprehensive data that spans from source water to WTP for the same time period. cHAB and microcystin events are time sensitive, meaning that they can occur and disappear abruptly. Therefore, in order to accurately model these scenarios and their impact to a WTP, accurate data need to be collected concurrently. This includes source water sampling, WTP intake sampling, WTP process sampling, and finished water detection sampling.

As was seen for the 2014 TWC, a lack of initial toxin and finished water toxin concentrations

111 made it difficult to model and characterize the event due to the uncertainty that existed from a lack of data. As data are assembled and trends are established, the predictive capabilities of these models will improve.

Other future research includes evaluating the reproducibility of the phycocyanin adjustment in other source waters that experience blooms of differing genera/species.

Additionally, the adjustment needs to be tested for blooms where toxin, gene data, and phycocyanin concentrations are much greater to ensure the models are not biased to low concentrations. Thirdly, the effectiveness of the adjustment using data that span multiple years needs to be explored due to data limitation at Tappan Lake.

Finally, by establishing genera/species ratios based on routine sampling, adjustments could be made to phycocyanin measurements across a certain time interval. Because qPCR is not in real-time, the same ratio would be applied to the real-time phycocyanin measurements until a new ratio was determined. I am interested as to whether these adjusted phycocyanin measurements, dependent on ratios of a time interval, would provide better predictive capabilities for microcystins compared to regular real-time phycocyanin.

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Appendix A. Source Code for Initial Toxin State

## Testing the 3 Methods for Toxin State ##Wilcoxon Ranked Sums Test

#1 Boolean Switch #TWC source("TriRand.r")

set.seed(24) iter <- 10000 MC_Ext_Per <- vector(); MC_Ext <- vector(); MC_Int <- vector(); PP <- vector(); PAC <- vector(); MC_Ext_Cl <- vector(); MC_cell_Q <- vector(); MW <- vector(); MC_per_cell <- vector(); Total_cells <- vector(); Clar <- vector(); Filt <- vector(); Total_cells_Cl <- vector(); MC_Int_Cl <- vector(); Total_MC_Cl <- vector(); Cl <- vector();Total_TWC_MC_FWD <- vector(); SA_mouth <- vector(); Water_H <- vector(); TB <- vector(); DW <- vector(); ADD <- vector(); HQ_adult_TWC_IE <- vector()

#------IC and EC Microcystins TWC Adult ------

#Intra/Extracelllular MC during Toledo Water Crisis (TWC) #Computational switch: a=0 (Growing Bloom), a=1 (Dying Bloom) #Initial MC Raw = 14 ug/L, August 1-4, 2014 #MC_cell_Q=MC Quota of cell (fmol/cell), MW=Molecular Weight (g/mol), #MC_per_cells (ug/cell), Total_cells (cells/L), Chlorine: Truncated-Normal Distribution #MC_Int_Cl=Intracellular MC at chlorination (assumes total lysis) #MC_Ext_Cl= Extracellular MC at chlorination, Total_MC_Cl=Sum of MC at Cl #TB=Tooth Brush, Tooth-Brush Frequency assumed to be 2/day, DW=Drinking Water #ADD=Average Daily Dose (assuming 80kg person), HQ=Hazard Quotient

for(i in 1:iter) { a[i] <- sample(c(0,1),1) if(a[i]==0){MC_Ext_Per[i] <- runif(1,0.1,0.25); MC_Ext[i] <- MC_Ext_Per[i]*14; MC_Int[i] <- (1-MC_Ext_Per[i])*14;PP[i] <- runif(1,0.40,0.60); PAC[i] <- runif(1,0.39,0.81); MC_Ext_Cl[i] <- MC_Ext[i]*PP[i]*PAC[i];

MC_cell_Q[i] <- runif(1,0.05,0.129); MW[i] <- sample(c(910.06,995.17,1038.20,1045.19),1); MC_per_cell[i] <- ((MC_cell_Q[i]*1e-15)*MW[i])*1e6; Total_cells[i] <- MC_Int[i]*(MC_per_cell[i])^-1; Clar[i] <- 0.10; Filt[i] <- runif(1,0.04,0.144); Total_cells_Cl[i] <- Total_cells[i]*Clar[i]*Filt[i]; MC_Int_Cl[i] <- Total_cells_Cl[i]*MC_per_cell[i];

Total_MC_Cl[i] <- MC_Ext_Cl[i]+MC_Int_Cl[i]; Cl[i] <- 1-(rtnormal(1,0.55040471,0.19951449,0.0464,0.8907)); Total_TWC_MC_FWD[i] <- Total_MC_Cl[i]*Cl[i]; SA_mouth[i] <- rnorm(1,214.7,12.9); Water_H[i] <- runif(1,0.007,0.01) TB[i] <- SA_mouth[i]*Water_H[i]*2; DW[i] <- TriRand(0.208,1.006,2.848); ADD[i] <- (Total_TWC_MC_FWD[i]*(TB[i]+DW[i]))/80; HQ_adult_TWC_IE[i] <- ADD[i]/0.05}

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if(a[i]==1){MC_Ext_Per[i] <- runif(1,0.60,0.70); MC_Ext[i] <- MC_Ext_Per[i]*14; MC_Int[i] <- (1-MC_Ext_Per[i])*14;PP[i] <- runif(1,0.40,0.60); PAC[i] <- runif(1,0.39,0.81); MC_Ext_Cl[i] <- MC_Ext[i]*PP[i]*PAC[i];

#Wilcoxon Ranked Sums Test y <- Total_TWC_MC_FWD y1 <- y[1:1000]; y2 <- y[1001:2000]; y3 <- y[2001:3000]; y4 <- y[3001:4000]; y5 <- y[4001:5000]; y6 <- y[5001:6000]; y7 <- y[6001:7000]; y8 <- y[7001:8000]; y9 <- y[8001:9000]; y10 <- y[9001:10000] x <- c(2.5,1.7,1.6,1.5,0.29,0.32,0.35,0.33,0.28) wilcoxSwitch1 <- wilcox.test(x,y1) #p-value =0.796 x <- c(2.5,1.7,1.6,1.5,0.29,0.32,0.35,0.33,0.28) wilcoxSwitch2 <- wilcox.test(x,y2) #p-value =0.877 x <- c(2.5,1.7,1.6,1.5,0.29,0.32,0.35,0.33,0.28) wilcoxSwitch3 <- wilcox.test(x,y3) #p-value =0.840 x <- c(2.5,1.7,1.6,1.5,0.29,0.32,0.35,0.33,0.28) wilcoxSwitch4 <- wilcox.test(x,y4) #p-value =0.966 x <- c(2.5,1.7,1.6,1.5,0.29,0.32,0.35,0.33,0.28) wilcoxSwitch5 <- wilcox.test(x,y5) #p-value =0.999 x <- c(2.5,1.7,1.6,1.5,0.29,0.32,0.35,0.33,0.28) wilcoxSwitch6 <- wilcox.test(x,y6) #p-value =0.948 x <- c(2.5,1.7,1.6,1.5,0.29,0.32,0.35,0.33,0.28) wilcoxSwitch7 <- wilcox.test(x,y7) #p-value =0.966 x <- c(2.5,1.7,1.6,1.5,0.29,0.32,0.35,0.33,0.28) wilcoxSwitch8 <- wilcox.test(x,y8) #p-value =0.935 x <- c(2.5,1.7,1.6,1.5,0.29,0.32,0.35,0.33,0.28) wilcoxSwitch9 <- wilcox.test(x,y9)

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#p-value =0.949 x <- c(2.5,1.7,1.6,1.5,0.29,0.32,0.35,0.33,0.28) wilcoxSwitch10 <- wilcox.test(x,y10) #p-value =0.856 x <- c(2.5,1.7,1.6,1.5,0.29,0.32,0.35,0.33,0.28) y <- Total_TWC_MC_FWD wilcoxSwitch1 <- wilcox.test(x,y) #p-value =0.923

#Finished Water Detections #August 1-4, 2014 - Only 9 samples taken (2.5,1.7,1.6,1.5,ND,0.32,0.35,0.33,ND) #ND=0.29,0.28 #ND = Non-detect < 0.3 ug/L #Peak: 2.5ug/L #Lower 95th: 0.1 #Median: 0.35 #Upper 95th: 2.18

TotalTWC_FWDhist <- hist(Total_TWC_MC_FWD, plt=FALSE) TotalTWC_PFWD <- TotalTWC_FWDhist$counts/iter

TWC_FWD_perc <- quantile(Total_TWC_MC_FWD, c(0.05,0.5,0.95))

# x11(); plot(TotalTWC_FWDhist[["mids"]], TotalTWC_PFWD, # xlab= "Finished Water Detection (ug/L)", # ylab= "Estimated Probability", # main= "MCs Finished Water Detection, 1-4 August 2014"); # abline(a = NULL, b = NULL, h = NULL, v = 0.1719, lwd=1, col="black"); # abline(a = NULL, b = NULL, h = NULL, v = 0.6325, lwd=3, col="red"); # abline(a = NULL, b = NULL, h = NULL, v= 2.067, lwd=3, col="blue"); # # legend("topright", c("5th % (0.18)", "Median (0.63)", # "95th % (2.07)"), lty=c(1,1,1), # lwd=c(1,3,3), col=c("black", "red", "blue"))

#Post-TWC set.seed(24) iter <- 10000 MC_Ext_Per <- vector(); MC_Ext <- vector(); MC_Int <- vector(); PP <- vector(); PAC <- vector(); MC_Ext_Cl <- vector(); MC_cell_Q <- vector(); MW <- vector(); MC_per_cell <- vector(); Total_cells <- vector(); Clar <- vector(); Filt <- vector(); Total_cells_Cl <- vector(); MC_Int_Cl <- vector(); Total_MC_Cl <- vector(); Total_MC_PTWC_FWD <- vector(); Cl <- vector(); SA_mouth <- vector(); Water_H <- vector(); TB <- vector(); DW <- vector(); ADD <- vector(); HQ_adult_PostTWC_IE <- vector()

#Intra/Extracelllular MCs, August 15-17, 2014 #Computational switch: a=0 (Growing Bloom), a=1 (Dying Bloom) #Initial MC Raw = 50 ug/L, #MC_cell_Q=MC Quota of cell (fmol/cell), MW=Molecular Weight (g/mol), #MC_per_cells (ug/cell), Total_cells (cells/L), #MC_Int_Cl=Intracellular MC at chlorination (assumes total lysis) #MC_Ext_Cl= Extracellular MC at chlorination, Total_MC_Cl=Sum of MC at Cl #TB=Tooth Brush, DW=Drinking Water #ADD=Average Daily Dose (assuming 80 kg person), HQ=Hazard Quotient

126 for(i in 1:iter) { a[i] <- sample(c(0,1),1) if(a[i]==0){MC_Ext_Per[i] <- runif(1,0.1,0.25); MC_Ext[i] <- MC_Ext_Per[i]*50; MC_Int[i] <- (1-MC_Ext_Per[i])*50;PP[i] <- runif(1,0.40,0.60); PAC[i] <- runif(1,0.01,0.39); MC_Ext_Cl[i] <- MC_Ext[i]*PP[i]*PAC[i];

MC_cell_Q[i] <- runif(1,0.05,0.129); MW[i] <- sample(c(910.06,995.17,1038.20,1045.19),1); MC_per_cell[i] <- ((MC_cell_Q[i]*1e-15)*MW[i])*1e6; Total_cells[i] <- MC_Int[i]*(MC_per_cell[i])^-1; Clar[i] <- runif(1,0.01,0.1); Filt[i] <- runif(1,0.01,0.04); Total_cells_Cl[i] <- Total_cells[i]*Clar[i]*Filt[i]; MC_Int_Cl[i] <- Total_cells_Cl[i]*MC_per_cell[i];

Total_MC_Cl[i] <- MC_Ext_Cl[i]+MC_Int_Cl[i]; Cl[i] <- 1-(rtnormal(1,0.60355208,0.25252061,0.06954,1)); Total_MC_PTWC_FWD[i] <- Total_MC_Cl[i]*Cl[i]; SA_mouth[i] <- rnorm(1,214.7,12.9); Water_H[i] <- runif(1,0.007,0.01) TB[i] <- SA_mouth[i]*Water_H[i]*2; DW[i] <- TriRand(0.208,1.006,2.848); ADD[i] <- (Total_MC_PTWC_FWD[i]*(TB[i]+DW[i]))/80; HQ_adult_PostTWC_IE[i] <- ADD[i]/0.05}

if(a[i]==1){MC_Ext_Per[i] <- runif(1,0.60,0.70); MC_Ext[i] <- MC_Ext_Per[i]*50; MC_Int[i] <- (1-MC_Ext_Per[i])*50;PP[i] <- runif(1,0.40,0.60); PAC[i] <- runif(1,0.01,0.39); MC_Ext_Cl[i] <- MC_Ext[i]*PP[i]*PAC[i];

MC_cell_Q[i] <- runif(1,0.05,0.129); MW[i] <- sample(c(910.06,995.17,1038.20,1045.19),1); MC_per_cell[i] <- ((MC_cell_Q[i]*1e-15)*MW[i])*1e6; Total_cells[i] <- MC_Int[i]*(MC_per_cell[i])^-1; Clar[i] <- runif(1,0.01,0.1); Filt[i] <- runif(1,0.01,0.04); Total_cells_Cl[i] <- Total_cells[i]*Clar[i]*Filt[i]; MC_Int_Cl[i] <- Total_cells_Cl[i]*MC_per_cell[i];

#Wilcoxon Ranked Sums Test yP <- Total_MC_PTWC_FWD yP1 <- yP[1:1000]; yP2 <- yP[1001:2000]; yP3 <- yP[2001:3000]; yP4 <- yP[3001:4000]; yP5 <- yP[4001:5000]; yP6 <- yP[5001:6000]; yP7 <- yP[6001:7000]; yP8 <- yP[7001:8000]; yP9 <- yP[8001:9000]; yP10 <- yP[9001:10000]

xP <- c(0.3,0.97,0.46,0.37,0.29,0.28,0.27,0.4) wilcoxSwitch1P <- wilcox.test(xP,yP1) #p-value =0.466 xP <- c(0.3,0.97,0.46,0.37,0.29,0.28,0.27,0.4) wilcoxSwitch2P <- wilcox.test(xP,yP2) #p-value =0.277

127 xP <- c(0.3,0.97,0.46,0.37,0.29,0.28,0.27,0.4) wilcoxSwitch3P <- wilcox.test(xP,yP3) #p-value =0.374 xP <- c(0.3,0.97,0.46,0.37,0.29,0.28,0.27,0.4) wilcoxSwitch4P <- wilcox.test(xP,yP4) #p-value =0.412 xP <- c(0.3,0.97,0.46,0.37,0.29,0.28,0.27,0.4) wilcoxSwitch5P <- wilcox.test(xP,yP5) #p-value =0.292 xP <- c(0.3,0.97,0.46,0.37,0.29,0.28,0.27,0.4) wilcoxSwitch6P <- wilcox.test(xP,yP6) #p-value =0.273 xP <- c(0.3,0.97,0.46,0.37,0.29,0.28,0.27,0.4) wilcoxSwitch7P <- wilcox.test(xP,yP7) #p-value =0.461 xP <- c(0.3,0.97,0.46,0.37,0.29,0.28,0.27,0.4) wilcoxSwitch8P <- wilcox.test(xP,yP8) #p-value =0.319 xP <- c(0.3,0.97,0.46,0.37,0.29,0.28,0.27,0.4) wilcoxSwitch9P <- wilcox.test(xP,yP9) #p-value =0.428 xP <- c(0.3,0.97,0.46,0.37,0.29,0.28,0.27,0.4) wilcoxSwitch10P <- wilcox.test(xP,yP10) #p-value =0.419 xP <- c(0.3,0.97,0.46,0.37,0.29,0.28,0.27,0.4) wilcoxSwitch1P <- wilcox.test(xP,yP) #p-value =0.365

#Finished Water Detections #August 15-17, 2014 8 samples (0.3,0.97,0.46,0.37,ND,ND,ND,0.4) #ND=0.29,0.28,0.27 #ND = Non-detect < 0.3 ug/L #Peak: 0.97 ug/L 081514 21:00 #Lower 95th: 0.1 #Median: 0.335 #Upper 95th: 0.7915

TotalPost_FWDhist <- hist(Total_MC_PTWC_FWD, plot=FALSE) TotalPost_PFWD <- TotalPost_FWDhist$counts/iter

Post_FWD_perc <- quantile(Total_MC_PTWC_FWD, c(0.05,0.5,0.95))

# x11(); plot(TotalPost_FWDhist[["mids"]], TotalPost_PFWD, # xlab= "Finished Water Detection (ug/L)", # ylab= "Estimated Probability", # main= "Risk of MCs Finished Water Detection, 15-17 August 2014"); # abline(a = NULL, b = NULL, h = NULL, v = 0.0507, lwd=1, col="black"); # abline(a = NULL, b = NULL, h = NULL, v = 0.5118, lwd=3, col="red"); # abline(a = NULL, b = NULL, h = NULL, v= 2.858, lwd=3, col="blue"); # # legend(4.5,0.5, c("5th % (0.05)","50th% (0.52)", "95th% (2.86)"),

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# col=c("black","red", "blue"), lwd=c(1,3,3))

#2 E/I Ratio #TWC source("TriRand.r") set.seed(24) iter <- 10000 E_I_ratio <- vector(); MC_Ext <- vector(); MC_Int <- vector(); PP <- vector(); PAC <- vector(); MC_Ext_Cl <- vector(); MC_cell_Q <- vector(); MW <- vector(); MC_per_cell <- vector(); Total_cells <- vector(); Clar <- vector(); Filt <- vector(); Total_cells_Cl <- vector(); MC_Int_Cl <- vector(); Total_MC_Cl <- vector(); Cl <- vector();Total_TWC_MC_FWD <- vector(); SA_mouth <- vector(); Water_H <- vector(); TB <- vector(); DW <- vector(); ADD <- vector(); HQ_adult_TWC_IE <- vector()

#------IC and EC Microcystins TWC Adult ------

#Intra/Extracelllular MC during Toledo Water Crisis (TWC) #E/I Ratio is suggested to be a method for assessing health status of bloom #Probability Distribution of Ratio established based on sample reports from the literature #Replaces the Switch #Initial MC Raw = 14 ug/L, August 1-4, 2014 #MC_cell_Q=MC Quota of cell (fmol/cell), MW=Molecular Weight (g/mol), #MC_per_cells (ug/cell), Total_cells (cells/L), Chlorine: Truncated-Normal Distribution #MC_Int_Cl=Intracellular MC at chlorination (assumes total lysis) #MC_Ext_Cl= Extracellular MC at chlorination, Total_MC_Cl=Sum of MC at Cl #TB=Tooth Brush, Tooth-Brush Frequency assumed to be 2/day, DW=Drinking Water #ADD=Average Daily Dose (assuming 80kg person), HQ=Hazard Quotient

for(i in 1:iter) { E_I_ratio[i] <- rtnormal(1,0.15941627,0.26156562,0,1.75); MC_Int[i] <- 14/(E_I_ratio[i]+1); MC_Ext[i] <- 14-MC_Int[i]; PP[i] <- runif(1,0.40,0.60); PAC[i] <- runif(1,0.39,0.81); MC_Ext_Cl[i] <- MC_Ext[i]*PP[i]*PAC[i];

#Wilcoxon Ranked Sums Test y <- Total_TWC_MC_FWD

129 y11 <- y[1:1000]; y12 <- y[1001:2000]; y13 <- y[2001:3000]; y14 <- y[3001:4000]; y15 <- y[4001:5000]; y16 <- y[5001:6000]; y17 <- y[6001:7000]; y18 <- y[7001:8000]; y19 <- y[8001:9000]; y20 <- y[9001:10000] x <- c(2.5,1.7,1.6,1.5,0.29,0.32,0.35,0.33,0.28) wilcoxSwitch11 <- wilcox.test(x,y11) #p-value =0.077 x <- c(2.5,1.7,1.6,1.5,0.29,0.32,0.35,0.33,0.28) wilcoxSwitch12 <- wilcox.test(x,y12) #p-value =0.068 x <- c(2.5,1.7,1.6,1.5,0.29,0.32,0.35,0.33,0.28) wilcoxSwitch13 <- wilcox.test(x,y13) #p-value =0.064 x <- c(2.5,1.7,1.6,1.5,0.29,0.32,0.35,0.33,0.28) wilcoxSwitch14 <- wilcox.test(x,y14) #p-value =0.062 x <- c(2.5,1.7,1.6,1.5,0.29,0.32,0.35,0.33,0.28) wilcoxSwitch15 <- wilcox.test(x,y15) #p-value =0.059 x <- c(2.5,1.7,1.6,1.5,0.29,0.32,0.35,0.33,0.28) wilcoxSwitch16 <- wilcox.test(x,y16) #p-value =0.069 x <- c(2.5,1.7,1.6,1.5,0.29,0.32,0.35,0.33,0.28) wilcoxSwitch17 <- wilcox.test(x,y17) #p-value =0.080 x <- c(2.5,1.7,1.6,1.5,0.29,0.32,0.35,0.33,0.28) wilcoxSwitch18 <- wilcox.test(x,y18) #p-value =0.074 x <- c(2.5,1.7,1.6,1.5,0.29,0.32,0.35,0.33,0.28) wilcoxSwitch19 <- wilcox.test(x,y19) #p-value =0.084 x <- c(2.5,1.7,1.6,1.5,0.29,0.32,0.35,0.33,0.28) wilcoxSwitch20 <- wilcox.test(x,y20) #p-value =0.075 x <- c(2.5,1.7,1.6,1.5,0.29,0.32,0.35,0.33,0.28) wilcoxSwitchE <- wilcox.test(x,y) #p-value =0.070

#Finished Water Detections #August 1-4, 2014 - Only 9 samples taken (Grab sample/point estimates) #Peak: 2.5ug/L #Lower 95th: 0.1 #Median: 0.35 #Upper 95th: 2.18

TotalTWC_FWDhist <- hist(Total_TWC_MC_FWD, plt=FALSE) TotalTWC_PFWD <- TotalTWC_FWDhist$counts/iter

TWC_FWD_perc <- quantile(Total_TWC_MC_FWD, c(0.05,0.5,0.95))

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# x11(); plot(TotalTWC_FWDhist[["mids"]], TotalTWC_PFWD, # xlab= "Finished Water Detection (ug/L)", # ylab= "Estimated Probability", # main= "Risk of MCs Finished Water Detection, 1-4 August 2014"); # abline(a = NULL, b = NULL, h = NULL, v = 0.0832, lwd=1, col="black"); # abline(a = NULL, b = NULL, h = NULL, v = 0.3669, lwd=3, col="red"); # abline(a = NULL, b = NULL, h = NULL, v= 1.0348, lwd=1, col="blue"); # # legend(1.90,0.32, c("5th% (0.08)", # "Median (0.37)", "95th % (1.03)"), lty=c(1,1,1), # lwd=c(1,3,1), col=c("black", "red","blue"))

#Intra/Extracelllular MCs, August 15-17, 2014 #E/I Ratio is suggested to be a method for assessing health status of bloom #Probabilty Distribtuion of Ratio established based on sample reports from the literature #Replaces the Switch #Initial MC Raw = 50 ug/L, #MC_cell_Q=MC Quota of cell (fmol/cell), MW=Molecular Weight (g/mol), #MC_per_cells (ug/cell), Total_cells (cells/L), #MC_Int_Cl=Intracellular MC at chlorination (assumes total lysis) #MC_Ext_Cl= Extracellular MC at chlorination, Total_MC_Cl=Sum of MC at Cl #TB=Tooth Brush, DW=Drinking Water #ADD=Average Daily Dose (assuming 80 kg person), HQ=Hazard Quotient

for(i in 1:iter) { E_I_ratio[i] <- rtnormal(1,0.15941627,0.26156562,0,1.75); MC_Int[i] <- 50/(E_I_ratio[i]+1); MC_Ext[i] <- 50-MC_Int[i]; PP[i] <- runif(1,0.40,0.60); PAC[i] <- runif(1,0.01,0.39); MC_Ext_Cl[i] <- MC_Ext[i]*PP[i]*PAC[i];

MC_cell_Q[i] <- runif(1,0.05,0.129); MW[i] <- sample(c(910.06,995.17,1038.20,1045.19),1); MC_per_cell[i] <- ((MC_cell_Q[i]*1e-15)*MW[i])*1e6; Total_cells[i] <- MC_Int[i]*(MC_per_cell[i])^-1; Clar[i] <- runif(1,0.01,0.1); Filt[i] <- runif(1,0.01,0.04); Total_cells_Cl[i] <- Total_cells[i]*Clar[i]*Filt[i]; MC_Int_Cl[i] <- Total_cells_Cl[i]*MC_per_cell[i];

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}

#Wilcoxon Ranked Sums Test yP <- Total_MC_PTWC_FWD yP11 <- yP[1:1000]; yP12 <- yP[1001:2000]; yP13 <- yP[2001:3000]; yP14 <- yP[3001:4000]; yP15 <- yP[4001:5000]; yP16 <- yP[5001:6000]; yP17 <- yP[6001:7000]; yP18 <- yP[7001:8000]; yP19 <- yP[8001:9000]; yP20 <- yP[9001:10000] xP <- c(0.3,0.97,0.46,0.37,0.29,0.28,0.27,0.4) wilcoxSwitch11P <- wilcox.test(xP,yP11) #p-value =0.284 xP <- c(0.3,0.97,0.46,0.37,0.29,0.28,0.27,0.4) wilcoxSwitch12P <- wilcox.test(xP,yP12) #p-value =0.422 xP <- c(0.3,0.97,0.46,0.37,0.29,0.28,0.27,0.4) wilcoxSwitch13P <- wilcox.test(xP,yP13) #p-value =0.372 xP <- c(0.3,0.97,0.46,0.37,0.29,0.28,0.27,0.4) wilcoxSwitch14P <- wilcox.test(xP,yP14) #p-value =0.435 xP <- c(0.3,0.97,0.46,0.37,0.29,0.28,0.27,0.4) wilcoxSwitch15P <- wilcox.test(xP,yP15) #p-value =0.386 xP <- c(0.3,0.97,0.46,0.37,0.29,0.28,0.27,0.4) wilcoxSwitch16P <- wilcox.test(xP,yP16) #p-value =0.534 xP <- c(0.3,0.97,0.46,0.37,0.29,0.28,0.27,0.4) wilcoxSwitch17P <- wilcox.test(xP,yP17) #p-value =0.327 xP <- c(0.3,0.97,0.46,0.37,0.29,0.28,0.27,0.4) wilcoxSwitch18 <- wilcox.test(xP,yP18) #p-value =0.399 xP <- c(0.3,0.97,0.46,0.37,0.29,0.28,0.27,0.4) wilcoxSwitch19P <- wilcox.test(xP,yP19) #p-value =0.499 xP <- c(0.3,0.97,0.46,0.37,0.29,0.28,0.27,0.4) wilcoxSwitch20P <- wilcox.test(xP,yP20) #p-value =0.431 xP <- c(0.3,0.97,0.46,0.37,0.29,0.28,0.27,0.4) wilcoxSwitchEP <- wilcox.test(xP,yP) #p-value =0.403

#Finished Water Detections #August 15-17, 2014 8 samples (0.3,0.97,0.46,0.37,ND,ND,ND,0.4) #Peak: 0.97 ug/L #Lower 95th: 0.1 #Median: 0.335 #Upper 95th: 0.7915 TotalPost_FWDhist <- hist(Total_MC_PTWC_FWD, plot=FALSE) TotalPost_PFWD <- TotalPost_FWDhist$counts/iter

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Post_FWD_perc <- quantile(Total_MC_PTWC_FWD, c(0.05,0.5,0.95))

# x11(); plot(TotalPost_FWDhist[["mids"]], TotalPost_PFWD, # xlab= "Finished Water Detection (ug/L)", # ylab= "Estimated Probability", # main= "Risk of MCs Finished Water Detections, 15-17 August 2014"); # abline(a = NULL, b = NULL, h = NULL, v = 0.0259, lwd=1, col="black"); # abline(a = NULL, b = NULL, h = NULL, v = 0.2925, lwd=3, col="red"); # abline(a = NULL, b = NULL, h = NULL, v= 1.401, lwd=1, col="blue"); # legend(2.5,0.38, c("5th % (0.03)", "Median (0.29)", "95th % (1.40)"), # lwd=c(1,3,1), col=c("black", "red", "blue"))

#3 Combined #TWC source("TriRand.r") set.seed(24) iter <- 10000 MC_Ext_Per <- vector(); MC_Ext <- vector(); MC_Int <- vector(); PP <- vector(); PAC <- vector(); MC_Ext_Cl <- vector(); MC_cell_Q <- vector(); MW <- vector(); MC_per_cell <- vector(); Total_cells <- vector(); Clar <- vector(); Filt <- vector(); Total_cells_Cl <- vector(); MC_Int_Cl <- vector(); Total_MC_Cl <- vector(); Cl <- vector();Total_TWC_MC_FWD <- vector(); SA_mouth <- vector(); Water_H <- vector(); TB <- vector(); DW <- vector(); ADD <- vector(); HQ_adult_TWC_IE <- vector()

#------IC and EC Microcystins TWC Adult ------

#Intra/Extracelllular MC during Toledo Water Crisis (TWC) #This model combines both ratio and Switch based on mean ratio of 16% from #the probability distribution (in other words, 16% of the bloom is in the #decay phase, based on observed samples from the literature). #Initial MC Raw = 14 ug/L, August 1-4, 2014 #MC_cell_Q=MC Quota of cell (fmol/cell), MW=Molecular Weight (g/mol), #MC_per_cells (ug/cell), Total_cells (cells/L), Chlorine: Truncated-Normal Distribution #MC_Int_Cl=Intracellular MC at chlorination (assumes total lysis) #MC_Ext_Cl= Extracellular MC at chlorination, Total_MC_Cl=Sum of MC at Cl #TB=Tooth Brush, Tooth-Brush Frequency assumed to be 2/day, DW=Drinking Water #ADD=Average Daily Dose (assuming 80kg person), HQ=Hazard Quotient for(i in 1:iter) { a[i] <- runif(1,0,1);

if(a[i]>=0.16){MC_Ext_Per[i] <- runif(1,0.1,0.25); MC_Ext[i] <- MC_Ext_Per[i]*14; MC_Int[i] <- (1-MC_Ext_Per[i])*14;PP[i] <- runif(1,0.40,0.60); PAC[i] <- runif(1,0.39,0.81); MC_Ext_Cl[i] <- MC_Ext[i]*PP[i]*PAC[i];

Total_MC_Cl[i] <- MC_Ext_Cl[i]+MC_Int_Cl[i]; Cl[i] <- 1-(rtnormal(1,0.55040471,0.19951449,0.0464,0.8907));

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Total_TWC_MC_FWD[i] <- Total_MC_Cl[i]*Cl[i]; SA_mouth[i] <- rnorm(1,214.7,12.9); Water_H[i] <- runif(1,0.007,0.01) TB[i] <- SA_mouth[i]*Water_H[i]*2; DW[i] <- TriRand(0.208,1.006,2.848); ADD[i] <- (Total_TWC_MC_FWD[i]*(TB[i]+DW[i]))/80; HQ_adult_TWC_IE[i] <- ADD[i]/0.05}

else{MC_Ext_Per[i] <- runif(1,0.60,0.70); MC_Ext[i] <- MC_Ext_Per[i]*14; MC_Int[i] <- (1-MC_Ext_Per[i])*14;PP[i] <- runif(1,0.40,0.60); PAC[i] <- runif(1,0.39,0.81); MC_Ext_Cl[i] <- MC_Ext[i]*PP[i]*PAC[i];

#Wilcoxon Ranked Sums Test y <- Total_TWC_MC_FWD y21 <- y[1:1000]; y22 <- y[1001:2000]; y23 <- y[2001:3000]; y24 <- y[3001:4000]; y25 <- y[4001:5000]; y26 <- y[5001:6000]; y27 <- y[6001:7000]; y28 <- y[7001:8000]; y29 <- y[8001:9000]; y30 <- y[9001:10000] x <- c(2.5,1.7,1.6,1.5,0.29,0.32,0.35,0.33,0.28) wilcoxSwitch21 <- wilcox.test(x,y21) #p-value =0.198 x <- c(2.5,1.7,1.6,1.5,0.29,0.32,0.35,0.33,0.28) wilcoxSwitch22 <- wilcox.test(x,y22) #p-value =0.214 x <- c(2.5,1.7,1.6,1.5,0.29,0.32,0.35,0.33,0.28) wilcoxSwitch23 <- wilcox.test(x,y23) #p-value =0.248 x <- c(2.5,1.7,1.6,1.5,0.29,0.32,0.35,0.33,0.28) wilcoxSwitch24 <- wilcox.test(x,y24) #p-value =0.255 x <- c(2.5,1.7,1.6,1.5,0.29,0.32,0.35,0.33,0.28) wilcoxSwitch25 <- wilcox.test(x,y25) #p-value =0.307 x <- c(2.5,1.7,1.6,1.5,0.29,0.32,0.35,0.33,0.28) wilcoxSwitch26 <- wilcox.test(x,y26) #p-value =0.248 x <- c(2.5,1.7,1.6,1.5,0.29,0.32,0.35,0.33,0.28) wilcoxSwitch27 <- wilcox.test(x,y27)

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#p-value =0.189 x <- c(2.5,1.7,1.6,1.5,0.29,0.32,0.35,0.33,0.28) wilcoxSwitch28 <- wilcox.test(x,y28) #p-value =0.261 x <- c(2.5,1.7,1.6,1.5,0.29,0.32,0.35,0.33,0.28) wilcoxSwitch29 <- wilcox.test(x,y29) #p-value =0.284 x <- c(2.5,1.7,1.6,1.5,0.29,0.32,0.35,0.33,0.28) wilcoxSwitch30 <- wilcox.test(x,y30) #p-value =0.218 x <- c(2.5,1.7,1.6,1.5,0.29,0.32,0.35,0.33,0.28) wilcoxSwitchC <- wilcox.test(x,y) #p-value =0.238

#Finished Water Detections #August 1-4, 2014 - Only 9 samples taken (2.5,1.7,1.6,1.5,ND,0.32,0.35,0.33,ND) #ND = Non-detect < 0.3 ug/L #Peak: 2.5ug/L #Lower 95th: 0.1 #Median: 0.35 #Upper 95th: 2.18

TotalTWC_FWDhist <- hist(Total_TWC_MC_FWD, plt=FALSE) TotalTWC_PFWD <- TotalTWC_FWDhist$counts/iter

TWC_FWD_perc <- quantile(Total_TWC_MC_FWD, c(0.05,0.5,0.95))

# x11(); plot(TotalTWC_FWDhist[["mids"]], TotalTWC_PFWD, # xlab= "Finished Water Detection (ug/L)", # ylab= "Estimated Probability", # main= "Risk of MCs Finished Water Detection, 1-4 August 2014"); # abline(a = NULL, b = NULL, h = NULL, v = 0.1421, lwd=1, col="black"); # abline(a = NULL, b = NULL, h = NULL, v = 0.40496, lwd=3, col="red"); # abline(a = NULL, b = NULL, h = NULL, v= 1.5202, lwd=3, col="blue"); # # legend(2.5,0.35, c("5th % (0.14)", # "Median (0.40)", "95th % (1.52)"), # lwd=c(1,3,3), col=c("black", "red","blue"))

#Intra/Extracelllular MCs, August 15-19, 2014 #This model combines both ratio and Switch based on mean ratio of 16% from #the probability distribution (in other words, 16% of the bloom is in the

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#decay phase, based on observed samples from the literature). #Initial MC Raw = 50 ug/L, August 15, 2014 #MC_cell_Q=MC Quota of cell (fmol/cell), MW=Molecular Weight (g/mol), #MC_per_cells (ug/cell), Total_cells (cells/L), #MC_Int_Cl=Intracellular MC at chlorination (assumes total lysis) #MC_Ext_Cl= Extracellular MC at chlorination, Total_MC_Cl=Sum of MC at Cl #TB=Tooth Brush, DW=Drinking Water #ADD=Average Daily Dose (assuming 80 kg person), HQ=Hazard Quotient

for(i in 1:iter) { a[i] <- runif(1,0,1);

if(a[i]>=0.16){MC_Ext_Per[i] <- runif(1,0.1,0.25); MC_Ext[i] <- MC_Ext_Per[i]*50; MC_Int[i] <- (1-MC_Ext_Per[i])*50;PP[i] <- runif(1,0.40,0.60); PAC[i] <- runif(1,0.01,0.39); MC_Ext_Cl[i] <- MC_Ext[i]*PP[i]*PAC[i];

MC_cell_Q[i] <- runif(1,0.05,0.129); MW[i] <- sample(c(910.06,995.17,1038.20,1045.19),1); MC_per_cell[i] <- ((MC_cell_Q[i]*1e-15)*MW[i])*1e6; Total_cells[i] <- MC_Int[i]*(MC_per_cell[i])^-1; Clar[i] <- runif(1,0.01,0.1); Filt[i] <- runif(1,0.01,0.04); Total_cells_Cl[i] <- Total_cells[i]*Clar[i]*Filt[i]; MC_Int_Cl[i] <- Total_cells_Cl[i]*MC_per_cell[i];

else{MC_Ext_Per[i] <- runif(1,0.6,0.7); MC_Ext[i] <- MC_Ext_Per[i]*50; MC_Int[i] <- (1-MC_Ext_Per[i])*50;PP[i] <- runif(1,0.40,0.60); PAC[i] <- runif(1,0.01,0.39); MC_Ext_Cl[i] <- MC_Ext[i]*PP[i]*PAC[i];

MC_cell_Q[i] <- runif(1,0.05,0.129); MW[i] <- sample(c(910.06,995.17,1038.20,1045.19),1); MC_per_cell[i] <- ((MC_cell_Q[i]*1e-15)*MW[i])*1e6; Total_cells[i] <- MC_Int[i]*(MC_per_cell[i])^-1; Clar[i] <- runif(1,0.01,0.1); Filt[i] <- runif(1,0.01,0.04); Total_cells_Cl[i] <- Total_cells[i]*Clar[i]*Filt[i]; MC_Int_Cl[i] <- Total_cells_Cl[i]*MC_per_cell[i];

#Wilcoxon Ranked Sums Test yP <- Total_MC_PTWC_FWD yP21 <- yP[1:1000]; yP22 <- yP[1001:2000]; yP23 <- yP[2001:3000]; yP24 <- yP[3001:4000]; yP25 <- yP[4001:5000]; yP26 <- yP[5001:6000]; yP27 <- yP[6001:7000]; yP28 <- yP[7001:8000];

136 yP29 <- yP[8001:9000]; yP30 <- yP[9001:10000] xP <- c(0.3,0.97,0.46,0.37,0.29,0.28,0.27,0.4) wilcoxSwitch21P <- wilcox.test(xP,yP21) #p-value =0.723 xP <- c(0.3,0.97,0.46,0.37,0.29,0.28,0.27,0.4) wilcoxSwitch22P <- wilcox.test(xP,yP22) #p-value =0.682 xP <- c(0.3,0.97,0.46,0.37,0.29,0.28,0.27,0.4) wilcoxSwitch23P <- wilcox.test(xP,yP23) #p-value =0.710 xP <- c(0.3,0.97,0.46,0.37,0.29,0.28,0.27,0.4) wilcoxSwitch24P <- wilcox.test(xP,yP24) #p-value =0.783 xP <- c(0.3,0.97,0.46,0.37,0.29,0.28,0.27,0.4) wilcoxSwitch25P <- wilcox.test(xP,yP25) #p-value =0.795 xP <- c(0.3,0.97,0.46,0.37,0.29,0.28,0.27,0.4) wilcoxSwitch26P <- wilcox.test(xP,yP26) View(wilcoxSwitch26P) #p-value =0.935 xP <- c(0.3,0.97,0.46,0.37,0.29,0.28,0.27,0.4) wilcoxSwitch27P <- wilcox.test(xP,yP27) #p-value =1.00 xP <- c(0.3,0.97,0.46,0.37,0.29,0.28,0.27,0.4) wilcoxSwitch28P <- wilcox.test(xP,yP28) #p-value =0.587 xP <- c(0.3,0.97,0.46,0.37,0.29,0.28,0.27,0.4) wilcoxSwitch29P <- wilcox.test(xP,yP29) #p-value =0.665 xP <- c(0.3,0.97,0.46,0.37,0.29,0.28,0.27,0.4) wilcoxSwitch30P <- wilcox.test(xP,yP30) #p-value =0.677 xP <- c(0.3,0.97,0.46,0.37,0.29,0.28,0.27,0.4) wilcoxSwitchCP <- wilcox.test(xP,yP) #p-value =0.710

#Finished Water Detections #Finished Water Detections #August 15-17, 2014 8 samples (0.3,0.97,0.46,0.37,ND,ND,ND,0.4) #Peak: 0.97 ug/L #Lower 95th: 0.1 #Median: 0.335 #Upper 95th: 0.7915

TotalPost_FWDhist <- hist(Total_MC_PTWC_FWD, plot=FALSE) TotalPost_PFWD <- TotalPost_FWDhist$counts/iter

Post_FWD_perc <- quantile(Total_MC_PTWC_FWD, c(0.05,0.5,0.95))

# x11(); plot(TotalPost_FWDhist[["mids"]], TotalPost_PFWD, # xlab= "Finished Water Detection (ug/L)",

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# ylab= "Estimated Probability", # main= "MCs Finished Water Detection, 15-17 August 2014"); # abline(a = NULL, b = NULL, h = NULL, v = 0.038, lwd=1, col="black"); # abline(a = NULL, b = NULL, h = NULL, v = 0.342, lwd=3, col="red"); # abline(a = NULL, b = NULL, h = NULL, v= 1.744, lwd=3, col="blue"); # legend("topright", c("5th % (0.04)", "Median (0.34)", "95th % (1.74)"), # col=c("black","red","blue"), # lwd=c(1,3,3))

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Appendix B. Source Code for Health Risk Assessment

#Health Risk Assessment (Hazard Quotient)

#TWC using Boolean Switch #------IC and EC Microcystins TWC Adult ------source("TriRand.r") set.seed(24) iter <- 10000 MC_Ext_Per <- vector(); MC_Ext <- vector(); MC_Int <- vector(); PP <- vector(); PAC <- vector(); MC_Ext_Cl <- vector(); MC_cell_Q <- vector(); MW <- vector(); MC_per_cell <- vector(); Total_cells <- vector(); Clar <- vector(); Filt <- vector(); Total_cells_Cl <- vector(); MC_Int_Cl <- vector(); Total_MC_Cl <- vector(); Cl <- vector();Total_TWC_MC_FWD <- vector(); SA_mouth <- vector(); Water_H <- vector(); TB <- vector(); DW <- vector(); ADD <- vector(); HQ_adult_TWC_IE <- vector()

#------IC and EC Microcystins TWC Adult ------

Total_MC_Cl[i] <- MC_Ext_Cl[i]+MC_Int_Cl[i]; Cl[i] <- 1-(rtnormal(1,0.55040471,0.19951449,0.0464,0.8907)); Total_TWC_MC_FWD[i] <- Total_MC_Cl[i]*Cl[i]; SA_mouth[i] <- rnorm(1,214.7,12.9); Water_H[i] <- runif(1,0.007,0.01) TB[i] <- ((SA_mouth[i]*Water_H[i])/1000)*2; DW[i] <- TriRand(0.208,1.006,2.848); ADD[i] <- (Total_TWC_MC_FWD[i]*(TB[i]+DW[i]))/80; HQ_adult_TWC_IE[i] <- ADD[i]/0.05}

if(a[i]==1){MC_Ext_Per[i] <- runif(1,0.60,0.70); MC_Ext[i] <- MC_Ext_Per[i]*14; MC_Int[i] <- (1-MC_Ext_Per[i])*14;PP[i] <- runif(1,0.40,0.60);

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PAC[i] <- runif(1,0.39,0.81); MC_Ext_Cl[i] <- MC_Ext[i]*PP[i]*PAC[i];

Total_MC_Cl[i] <- MC_Ext_Cl[i]+MC_Int_Cl[i]; Cl[i] <- 1-(rtnormal(1,0.55040471,0.19951449,0.0464,0.8907)); Total_TWC_MC_FWD[i] <- Total_MC_Cl[i]*Cl[i]; SA_mouth[i] <- rnorm(1,214.7,12.9); Water_H[i] <- runif(1,0.007,0.01) TB[i] <- ((SA_mouth[i]*Water_H[i])/1000)*2; DW[i] <- TriRand(0.208,1.006,2.848); ADD[i] <- (Total_TWC_MC_FWD[i]*(TB[i]+DW[i]))/80; HQ_adult_TWC_IE[i] <- ADD[i]/0.05} }

TotalTWC_HQhist <- hist(HQ_adult_TWC_IE, plot=FALSE) TotalTWC_PHQ = TotalTWC_HQhist$counts/iter

TWC_adult_HQ_perc <- quantile(HQ_adult_TWC_IE, c(0.05,0.5,0.95))

# x11(); plot(TotalTWC_HQhist[["mids"]], TotalTWC_PHQ, xlab= "Hazard Quotient (Risk)", # ylab= "Probability Risk of Occurrance", # main= "Adult Risk, 1-4 August 2014"); # abline(a = NULL, b = NULL, h = NULL, v = 1.0, lwd=3, col="red"); # abline(a = NULL, b = NULL, h = NULL, v = 0.043, lwd=1, lty=1); # abline(a = NULL, b = NULL, h = NULL, v = 0.202, lwd=1, lty=2); # abline(a = NULL, b = NULL, h = NULL, v= 0.804, lwd=1, lty=3); # legend("topright", c("5th % (0.04)", "Median (0.20)", # "95th % (0.80)"), lty=c(1,2,3), lwd=c(1,1,1))

#------IC and EC Microcystins TWC Child ------set.seed(24) iter <- 10000 MC_Ext_Per <- vector(); MC_Ext <- vector(); MC_Int <- vector(); PP <- vector(); PAC <- vector(); MC_Ext_Cl <- vector(); MC_cell_Q <- vector(); MW <- vector(); MC_per_cell <- vector(); Total_cells <- vector(); Clar <- vector(); Filt <- vector(); Total_cells_Cl <- vector(); MC_Int_Cl <- vector(); Total_MC_Cl <- vector(); Cl <- vector(); Total_MC_FWD <- vector(); SA_mouth <- vector(); Water_H <- vector(); TB <- vector(); DW <- vector(); ADD <- vector(); HQ_child_TWC_IE <- vector()

for(i in 1:iter) { a[i] <- sample(c(0,1),1) if(a[i]==0){MC_Ext_Per[i] <- runif(1,0.1,0.25); MC_Ext[i] <- MC_Ext_Per[i]*14;

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MC_Int[i] <- (1-MC_Ext_Per[i])*14;PP[i] <- runif(1,0.40,0.60); PAC[i] <- runif(1,0.39,0.81); MC_Ext_Cl[i] <- MC_Ext[i]*PP[i]*PAC[i];

Total_MC_Cl[i] <- MC_Ext_Cl[i]+MC_Int_Cl[i]; Cl[i] <- 1-(rtnormal(1,0.55040471,0.19951449,0.0464,0.8907)); Total_TWC_MC_FWD[i] <- Total_MC_Cl[i]*Cl[i]; SA_mouth[i] <- rnorm(1,117.6,7.6); Water_H[i] <- runif(1,0.006,0.009) TB[i] <- ((SA_mouth[i]*Water_H[i])/1000)*2 DW[i] <- TriRand(0.057,0.336,1.099); ADD[i] <- (Total_TWC_MC_FWD[i]*(TB[i]+DW[i]))/18.6; HQ_child_TWC_IE[i] <- ADD[i]/0.05}

if(a[i]==1){MC_Ext_Per[i] <- runif(1,0.6,0.7); MC_Ext[i] <- MC_Ext_Per[i]*14; MC_Int[i] <- (1-MC_Ext_Per[i])*14;PP[i] <- runif(1,0.40,0.60); PAC[i] <- runif(1,0.39,0.81); MC_Ext_Cl[i] <- MC_Ext[i]*PP[i]*PAC[i];

Total_MC_Cl[i] <- MC_Ext_Cl[i]+MC_Int_Cl[i]; Cl[i] <- 1-(rtnormal(1,0.55040471,0.19951449,0.0464,0.8907)); Total_TWC_MC_FWD[i] <- Total_MC_Cl[i]*Cl[i]; SA_mouth[i] <- rnorm(1,117.6,7.6); Water_H[i] <- runif(1,0.006,0.009) TB[i] <- ((SA_mouth[i]*Water_H[i])/1000)*2 DW[i] <- TriRand(0.057,0.336,1.099); ADD[i] <- (Total_TWC_MC_FWD[i]*(TB[i]+DW[i]))/18.6; HQ_child_TWC_IE[i] <- ADD[i]/0.05} }

TotalTWCchild_HQhist <- hist(HQ_child_TWC_IE, plot=FALSE) TotalTWCchild_PHQ = TotalTWCchild_HQhist$counts/iter

TWC_child_HQ_perc <- quantile(HQ_child_TWC_IE, c(0.05,0.5,0.95))

# x11(); plot(TotalTWCchild_HQhist[["mids"]], TotalTWCchild_PHQ, xlab= "Hazard Quotient (Risk) ", # ylab= "Estimated Probability", # main= "Child Risk, 1-4 August 2014"); # abline(a = NULL, b = NULL, h = NULL, v = 1.0, lwd=3, col="red"); # abline(a = NULL, b = NULL, h = NULL, v = 0.063, lwd=1, lty=1); # abline(a = NULL, b = NULL, h = NULL, v = 0.315, lwd=1, lty=2); # abline(a = NULL, b = NULL, h = NULL, v= 1.301, lwd=1, lty=3); # legend("topright", c("5th % (0.06)", "Median (0.32)", # "95th % (1.30)"), lty=c(1,2,3), lwd=c(1,1,1))

#b. Code for TWC, all Extracellular MCs

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#------All EC Microcystins TWC Adult ------set.seed(24) iter <- 10000 MC_Ext <- vector(); MC_Ext_TWC_FWD <- vector(); PP <- vector(); PAC <- vector(); Cl <- vector(); SA_mouth <- vector(); Water_H <- vector(); TB <- vector(); DW <- vector(); ADD <- vector(); HQ_adult_TWC_Eonly <- vector()

#Extracellular MC on August 1-4, 2014 #Initial MC, all Extracellular = 14 ug/L, #PP=Potassium Permanganate, PAC=Powdered Activated Carbon, #TB=Tooth Brush, DW=Drinking Water #ADD=Average Daily Dose (assuming 80kg person), HQ=Hazard Quotient

for(i in 1:iter) { MC_Ext[i] <- 14; PP[i] <- runif(1,0.40,0.60); PAC[i] <- runif(1,0.39,0.81); Cl[i] <- 1-(rtnormal(1,0.55040471,0.19951449,0.0464,0.8907)); MC_Ext_TWC_FWD[i] <- MC_Ext[i]*PP[i]*PAC[i]*Cl[i]; SA_mouth[i] <- rnorm(1,214.7,12.9); Water_H[i] <- runif(1,0.007,0.01) TB[i] <- ((SA_mouth[i]*Water_H[i])/1000)*2; DW[i] <- TriRand(0.208,1.006,2.848); ADD[i] <- (MC_Ext_TWC_FWD[i]*(TB[i]+DW[i]))/80; HQ_adult_TWC_Eonly[i] <- ADD[i]/0.05 }

TotalTWCEXT_HQhist <- hist(HQ_adult_TWC_Eonly, plot=FALSE) TotalTWCEXT_PHQ = TotalTWCEXT_HQhist$counts/iter

TWCEXT_adult_HQ_perc <- quantile(HQ_adult_TWC_Eonly, c(0.05,0.5,0.95))

#c. Code for TWC, all Intracellular MCs

#------All IC Microcystins TWC Adult ------set.seed(24) iter <- 10000 MC_Int <- vector(); MC_cell_Q <- vector(); MW <- vector(); MC_per_cell <- vector();Total_cells <- vector(); Clar <- vector(); Filt <- vector();Total_cells_Cl <- vector(); MC_Int_Cl <- vector(); MC_Int_TWC_FWD <- vector(); Cl <- vector(); SA_mouth <- vector(); Water_H <- vector(); TB <- vector(); DW <- vector(); ADD <- vector(); HQ_adult_TWC_Ionly <- vector()

#Intracellular MC, August 1-4, 2014 #Initial MC Raw, all Intracellular = 14 ug/L, #MC_cell_Q=MC Quota of cell (fmol/cell), MW=Molecular Weight (g/mol), #MC_per_cells (ug/cell), Total_cells (cells/L), #TB=Tooth Brush, DW=Drinking Water #ADD=Average Daily Dose (assuming 80 kg person), HQ=Hazard Quotient

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Filt[i] <- runif(1,0.04,0.144); Total_cells_Cl[i] <- Total_cells[i]*Clar[i]*Filt[i]; MC_Int_Cl[i] <- Total_cells_Cl[i]*MC_per_cell[i]; Cl[i] <- 1-(rtnormal(1,0.55040471,0.19951449,0.0464,0.8907)); MC_Int_TWC_FWD[i] <- MC_Int_Cl[i]*Cl[i]; SA_mouth[i] <- rnorm(1,214.7,12.9); Water_H[i] <- runif(1,0.007,0.01) TB[i] <- ((SA_mouth[i]*Water_H[i])/1000)*2; DW[i] <- TriRand(0.208,1.006,2.848); ADD[i] <- (MC_Int_TWC_FWD[i]*(TB[i]+DW[i]))/80; HQ_adult_TWC_Ionly[i] <- ADD[i]/0.05 }

TotalTWCINT_HQhist <- hist(HQ_adult_TWC_Ionly, plot=FALSE) TotalTWCINT_PHQ = TotalTWCINT_HQhist$counts/iter

TWCINT_adult_HQ_perc <- quantile(HQ_adult_TWC_Ionly, c(0.05,0.5,0.95))

#------All EC Microcystins TWC Child ------set.seed(24) iter <- 10000 MC_Ext <- vector(); MC_Ext_TWC_FWD <- vector(); PP <- vector(); PAC <- vector(); Cl <- vector(); SA_mouth <- vector(); Water_H <- vector(); TB <- vector(); DW <- vector(); ADD <- vector(); HQ_child_TWC_Eonly <- vector()

#Extracellular MC on August 1-4, 2014; Worst Case Scenario (WCS) #Initial MC, all Extracellular = 14 ug/L, #PP=Potassium Permanganate, PAC=Powdered Activated Carbon, #TB=Tooth Brush, DW=Drinking Water #ADD=Average Daily Dose (assuming 18.6 kg child), HQ=Hazard Quotient

for(i in 1:iter) { MC_Ext[i] <- 14; PP[i] <- runif(1,0.40,0.60); PAC[i] <- runif(1,0.39,0.81); Cl[i] <- 1-(rtnormal(1,0.55040471,0.19951449,0.0464,0.8907)); MC_Ext_TWC_FWD[i] <- MC_Ext[i]*PP[i]*PAC[i]*Cl[i]; SA_mouth[i] <- rnorm(1,117.6,7.6); Water_H[i] <- runif(1,0.006,0.009) TB[i] <- ((SA_mouth[i]*Water_H[i])/1000)*2; DW[i] <- TriRand(0.057,0.336,1.099); ADD[i] <- (MC_Ext_TWC_FWD[i]*(TB[i]+DW[i]))/18.6; HQ_child_TWC_Eonly[i] <- ADD[i]/0.05 }

TotalTWCEXTchild_HQhist <- hist(HQ_child_TWC_Eonly, plot=FALSE) TotalTWCEXTchild_PHQ = TotalTWCEXTchild_HQhist$counts/iter

TWCEXT_child_HQ_perc <- quantile(HQ_child_TWC_Eonly, c(0.05,0.5,0.95))

#------All INT Microcystins TWC Child ------set.seed(24) iter <- 10000 MC_Int <- vector(); MC_cell_Q <- vector(); MW <- vector(); MC_per_cell <- vector();Total_cells <- vector(); Clar <- vector(); Filt <- vector();Total_cells_Cl <- vector(); MC_Int_Cl <- vector(); Cl <- vector(); MC_Int_TWC_FWD <- vector(); SA_mouth <- vector(); Water_H <- vector(); TB <- vector(); DW <- vector(); ADD <- vector(); HQ_child_TWC_Ionly <- vector()

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#Intracellular MC, August 1-4, 2014; Best Case Scenario (WCS) #Initial MC Raw, all Intracellular = 14 ug/L, #MC_cell_Q=MC Quota of cell (fmol/cell), MW=Molecular Weight (g/mol), #MC_per_cells (ug/cell), Total_cells (cells/L), #TB=Tooth Brush, DW=Drinking Water #ADD=Average Daily Dose (assuming 18.6 kg child), HQ=Hazard Quotient

for(i in 1:iter) { MC_Int[i] <- 14; MC_cell_Q[i] <- runif(1,0.05,0.129); MW[i] <- sample(c(910.06,995.17,1038.20,1045.19),1); MC_per_cell[i] <- ((MC_cell_Q[i]*1e-15)*MW[i])*1e6; Total_cells[i] <- MC_Int[i]*(MC_per_cell[i])^-1; Clar[i] <- 0.10; Filt[i] <- runif(1,0.04,0.144); Total_cells_Cl[i] <- Total_cells[i]*Clar[i]*Filt[i]; MC_Int_Cl[i] <- Total_cells_Cl[i]*MC_per_cell[i]; Cl[i] <- 1-(rtnormal(1,0.55040471,0.19951449,0.0464,0.8907)); MC_Int_TWC_FWD[i] <- MC_Int_Cl[i]*Cl[i]; SA_mouth[i] <- rnorm(1,117.6,7.6); Water_H[i] <- runif(1,0.006,0.009) TB[i] <- ((SA_mouth[i]*Water_H[i])/1000)*2; DW[i] <- TriRand(0.057,0.336,1.099); ADD[i] <- (MC_Int_TWC_FWD[i]*(TB[i]+DW[i]))/18.6; HQ_child_TWC_Ionly[i] <- ADD[i]/0.05 }

TotalTWCINTchild_HQhist <- hist(HQ_child_TWC_Ionly, plot=FALSE) TotalTWCINTchild_PHQ = TotalTWCINTchild_HQhist$counts/iter

TWCINT_child_HQ_perc <- quantile(HQ_child_TWC_Ionly, c(0.05,0.5,0.95))

#Post-TWC Using Combined Method

#------IC and EC Microcystins PTWC Adult ------

#d. Code for Post-TWC #Adult set.seed(24) iter <- 10000 MC_Ext_Per <- vector(); MC_Ext <- vector(); MC_Int <- vector(); PP <- vector(); PAC <- vector(); MC_Ext_Cl <- vector(); MC_cell_Q <- vector(); MW <- vector(); MC_per_cell <- vector(); Total_cells <- vector(); Clar <- vector(); Filt <- vector(); Total_cells_Cl <- vector(); MC_Int_Cl <- vector(); Total_MC_Cl <- vector(); Total_MC_PTWC_FWD <- vector(); Cl <- vector(); SA_mouth <- vector(); Water_H <- vector(); TB <- vector(); DW <- vector(); ADD <- vector(); HQ_adult_PostTWC_IE <- vector()

#Intra/Extracelllular MCs, August 15-17, 2014 #E/I Ratio is suggested to be a method for assessing health status of bloom #This model combines both ratio and Switch based on mean ratio of 16% from #the probability distribution (in other words, 16% of the bloom is in the #decay phase, based on observed samples from the literature). #Computational switch: a>=0.16 (Growing Bloom), else (Dying Bloom) #Initial MC Raw = 50 ug/L, August 15, 2014 #MC_cell_Q=MC Quota of cell (fmol/cell), MW=Molecular Weight (g/mol), #MC_per_cells (ug/cell), Total_cells (cells/L), Chlorine: Truncated-Normal Distribution #MC_Int_Cl=Intracellular MC at chlorination (assumes total lysis) #MC_Ext_Cl= Extracellular MC at chlorination, Total_MC_Cl=Sum of MC at Cl #TB=Tooth Brush, DW=Drinking Water

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#ADD=Average Daily Dose (assuming 80 kg person), HQ=Hazard Quotient

for(i in 1:iter) { a[i] <- runif(1,0,1);

MC_cell_Q[i] <- runif(1,0.05,0.129); MW[i] <- sample(c(910.06,995.17,1038.20,1045.19),1); MC_per_cell[i] <- ((MC_cell_Q[i]*1e-15)*MW[i])*1e6; Total_cells[i] <- MC_Int[i]*(MC_per_cell[i])^-1; Clar[i] <- runif(1,0.01,0.1); Filt[i] <- runif(1,0.01,0.04); Total_cells_Cl[i] <- Total_cells[i]*Clar[i]*Filt[i]; MC_Int_Cl[i] <- Total_cells_Cl[i]*MC_per_cell[i];

Total_MC_Cl[i] <- MC_Ext_Cl[i]+MC_Int_Cl[i]; Cl[i] <- 1-(rtnormal(1,0.60355208,0.25252061,0.06954,1)); Total_MC_PTWC_FWD[i] <- Total_MC_Cl[i]*Cl[i]; SA_mouth[i] <- rnorm(1,214.7,12.9); Water_H[i] <- runif(1,0.007,0.01) TB[i] <- ((SA_mouth[i]*Water_H[i])/1000)*2; DW[i] <- TriRand(0.208,1.006,2.848); ADD[i] <- (Total_MC_PTWC_FWD[i]*(TB[i]+DW[i]))/80; HQ_adult_PostTWC_IE[i] <- ADD[i]/0.05}

MC_cell_Q[i] <- runif(1,0.05,0.129); MW[i] <- sample(c(910.06,995.17,1038.20,1045.19),1); MC_per_cell[i] <- ((MC_cell_Q[i]*1e-15)*MW[i])*1e6; Total_cells[i] <- MC_Int[i]*(MC_per_cell[i])^-1; Clar[i] <- runif(1,0.01,0.1); Filt[i] <- runif(1,0.01,0.04); Total_cells_Cl[i] <- Total_cells[i]*Clar[i]*Filt[i]; MC_Int_Cl[i] <- Total_cells_Cl[i]*MC_per_cell[i];

Total_MC_Cl[i] <- MC_Ext_Cl[i]+MC_Int_Cl[i]; Cl[i] <- 1-(rtnormal(1,0.60355208,0.25252061,0.06954,1)); Total_MC_PTWC_FWD[i] <- Total_MC_Cl[i]*Cl[i]; SA_mouth[i] <- rnorm(1,214.7,12.9); Water_H[i] <- runif(1,0.007,0.01) TB[i] <- ((SA_mouth[i]*Water_H[i])/1000)*2; DW[i] <- TriRand(0.208,1.006,2.848); ADD[i] <- (Total_MC_PTWC_FWD[i]*(TB[i]+DW[i]))/80; HQ_adult_PostTWC_IE[i] <- ADD[i]/0.05} }

TotalPost_HQhist <- hist(HQ_adult_PostTWC_IE, plot=FALSE) TotalPost_PHQ = TotalPost_HQhist$counts/iter

Post_HQ_perc <- quantile(HQ_adult_PostTWC_IE, c(0.05,0.5,0.95))

# x11(); plot(TotalPost_HQhist[["mids"]], TotalPost_PHQ, xlab= "Hazard Quotient (Risk)", # ylab= "Estimated Probability", # main= "Adult Risk, 15-17 August 2014"); # abline(a = NULL, b = NULL, h = NULL, v = 1.0, lwd=3, col="red"); # abline(a = NULL, b = NULL, h = NULL, V = 0.0104, lwd=1, lty=1); # abline(a = NULL, b = NULL, h = NULL, v = 0.1054, lwd=1, lty=2);

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# abline(a = NULL, b = NULL, h = NULL, v= 0.629, lwd=1, lty=3); # legend("topright", c("5th % (0.01)", "Median (0.11)", # "95th % (0.63)"), lty=c(1,2,3))

#------IC and EC Microcystins PTWC Child ------

#Child set.seed(24) iter <- 10000 MC_Ext_Per <- vector(); MC_Ext <- vector(); MC_Int <- vector(); PP <- vector(); PAC <- vector(); MC_Ext_Cl <- vector(); MC_cell_Q <- vector(); MW <- vector(); MC_per_cell <- vector(); Total_cells <- vector(); Clar <- vector(); Filt <- vector(); Total_cells_Cl <- vector(); MC_Int_Cl <- vector(); Total_MC_Cl <- vector(); Cl <- vector(); Total_MC_FWD <- vector(); SA_mouth <- vector(); Water_H <- vector(); TB <- vector(); DW <- vector(); ADD <- vector(); HQ_child_PostTWC_IE <- vector()

#Intra/Extracelllular MC Post-Toledo Water Crisis (TWC), August 15-17 #E/I Ratio is suggested to be a method for assessing health status of bloom #This model combines both ratio and Switch based on mean ratio of 16% from #the probability distribution (in other words, 16% of the bloom is in the #decay phase, based on observed samples from the literature). #Computational switch: a>=0.16 (Growing Bloom), else (Dying Bloom) #Initial MC Raw = 50 ug/L, August 15, 2014 #MC_cell_Q=MC Quota of cell (fmol/cell), MW=Molecular Weight (g/mol), #MC_per_cells (ug/cell), Total_cells (cells/L), Chlorine: Truncated-Normal Distribution #MC_Int_Cl=Intracellular MC at chlorination (assumes total lysis) #MC_Ext_Cl= Extracellular MC at chlorination, Total_MC_Cl=Sum of MC at Cl #TB=Tooth Brush, DW=Drinking Water #ADD=Average Daily Dose (assuming 18.6kg child), HQ=Hazard Quotient

for(i in 1:iter) { a[i] <- runif(1,0,1);

MC_cell_Q[i] <- runif(1,0.05,0.129); MW[i] <- sample(c(910.06,995.17,1038.20,1045.19),1); MC_per_cell[i] <- ((MC_cell_Q[i]*1e-15)*MW[i])*1e6; Total_cells[i] <- MC_Int[i]*(MC_per_cell[i])^-1; Clar[i] <- runif(1,0.01,0.1); Filt[i] <- runif(1,0.01,0.04); Total_cells_Cl[i] <- Total_cells[i]*Clar[i]*Filt[i]; MC_Int_Cl[i] <- Total_cells_Cl[i]*MC_per_cell[i];

Total_MC_Cl[i] <- MC_Ext_Cl[i]+MC_Int_Cl[i]; Cl[i] <- 1-(rtnormal(1,0.60355208,0.25252061,0.06954,1)); Total_MC_PTWC_FWD[i] <- Total_MC_Cl[i]*Cl[i]; SA_mouth[i] <- rnorm(1,117.6,7.6); Water_H[i] <- runif(1,0.006,0.009) TB[i] <- ((SA_mouth[i]*Water_H[i])/1000)*2 DW[i] <- TriRand(0.057,0.336,1.099); ADD[i] <- (Total_MC_PTWC_FWD[i]*(TB[i]+DW[i]))/18.6; HQ_child_PostTWC_IE[i] <- ADD[i]/0.05}

else{MC_Ext_Per[i] <- runif(1,0.60,0.70); MC_Ext[i] <- MC_Ext_Per[i]*50; MC_Int[i] <- (1-MC_Ext_Per[i])*50;PP[i] <- runif(1,0.40,0.60); PAC[i] <- runif(1,0.01,0.39); MC_Ext_Cl[i] <- MC_Ext[i]*PP[i]*PAC[i];

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MC_cell_Q[i] <- runif(1,0.05,0.129); MW[i] <- sample(c(910.06,995.17,1038.20,1045.19),1); MC_per_cell[i] <- ((MC_cell_Q[i]*1e-15)*MW[i])*1e6; Total_cells[i] <- MC_Int[i]*(MC_per_cell[i])^-1; Clar[i] <- runif(1,0.01,0.1); Filt[i] <- runif(1,0.01,0.04); Total_cells_Cl[i] <- Total_cells[i]*Clar[i]*Filt[i]; MC_Int_Cl[i] <- Total_cells_Cl[i]*MC_per_cell[i];

Total_MC_Cl[i] <- MC_Ext_Cl[i]+MC_Int_Cl[i]; Cl[i] <- 1-(rtnormal(1,0.60355208,0.25252061,0.06954,1)); Total_MC_PTWC_FWD[i] <- Total_MC_Cl[i]*Cl[i]; SA_mouth[i] <- rnorm(1,117.6,7.6); Water_H[i] <- runif(1,0.006,0.009) TB[i] <- ((SA_mouth[i]*Water_H[i])/1000)*2 DW[i] <- TriRand(0.057,0.336,1.099); ADD[i] <- (Total_MC_PTWC_FWD[i]*(TB[i]+DW[i]))/18.6; HQ_child_PostTWC_IE[i] <- ADD[i]/0.05} }

TotalPostchild_HQhist <- hist(HQ_child_PostTWC_IE, plot=FALSE) TotalPostchild_PHQ = TotalPostchild_HQhist$counts/iter

Postchild_HQ_perc <- quantile(HQ_child_PostTWC_IE, c(0.05,0.5,0.95))

# x11(); plot(TotalPostchild_HQhist[["mids"]], TotalPostchild_PHQ, xlab= "Hazard Quotient (Ris k)", # ylab= "Estimated Probability", # main= "Child Risk, 15-17 August 2014"); # abline(a = NULL, b = NULL, h = NULL, v = 1.0, lwd=3, col="red"); # abline(a = NULL, b = NULL, h = NULL, V = 0.016, lwd=1, lty=1); # abline(a = NULL, b = NULL, h = NULL, v = 0.163, lwd=1, lty=2); # abline(a = NULL, b = NULL, h = NULL, v= 1.007, lwd=1, lty=3); # legend("topright", c("5th % (0.02)", "Median (0.16)", # "95th % (1.00)"), lty=c(1,2,3))

#------All EC Microcystins PTWC Adult ------set.seed(24) iter <- 10000 MC_Ext_Per <- vector(); MC_Ext <- vector(); MC_Int <- vector(); PP <- vector(); PAC <- vector(); MC_Ext_Cl <- vector(); MC_cell_Q <- vector(); MW <- vector(); MC_per_cell <- vector(); Total_cells <- vector(); Clar <- vector(); Filt <- vector(); Total_cells_Cl <- vector(); MC_Int_Cl <- vector(); Total_MC_Cl <- vector(); Total_MC_PTWC_FWD <- vector(); Cl <- vector(); SA_mouth <- vector(); Water_H <- vector(); TB <- vector(); DW <- vector(); ADD <- vector(); HQ_adult_PostTWC_E_only <- vector()

#All Extracelllular MCs, August 15-17, 2014 #Initial MC Raw = 50 ug/L, August 15, 2014 #MC_cell_Q=MC Quota of cell (fmol/cell), MW=Molecular Weight (g/mol), #MC_per_cells (ug/cell), Total_cells (cells/L), #MC_Int_Cl=Intracellular MC at chlorination (assumes total lysis) #MC_Ext_Cl= Extracellular MC at chlorination, Total_MC_Cl=Sum of MC at Cl #TB=Tooth Brush, DW=Drinking Water #ADD=Average Daily Dose (assuming 80 kg person), HQ=Hazard Quotient

for(i in 1:iter) { MC_Ext[i] <- 50; PP[i] <- runif(1,0.40,0.60);

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PAC[i] <- runif(1,0.01,0.39); Cl[i] <- 1-(rtnormal(1,0.60355208,0.25252061,0.06954,1)); Total_MC_PTWC_FWD[i] <- MC_Ext[i]*PP[i]*PAC[i]*Cl[i] SA_mouth[i] <- rnorm(1,214.7,12.9); Water_H[i] <- runif(1,0.007,0.01) TB[i] <- ((SA_mouth[i]*Water_H[i])/1000)*2; DW[i] <- TriRand(0.208,1.006,2.848); ADD[i] <- (Total_MC_PTWC_FWD[i]*(TB[i]+DW[i]))/80; HQ_adult_PostTWC_E_only[i] <- ADD[i]/0.05 }

TotalPost_EXT_adult_HQhist <- hist(HQ_adult_PostTWC_E_only, plot=FALSE) TotalPost_EXT_adultPHQ = TotalPost_EXT_adult_HQhist$counts/iter

Post_HQ_perc <- quantile(HQ_adult_PostTWC_IE, c(0.05,0.5,0.95))

#------All EC Microcystins PTWC Child ------set.seed(24) iter <- 10000 MC_Ext_Per <- vector(); MC_Ext <- vector(); MC_Int <- vector(); PP <- vector(); PAC <- vector(); MC_Ext_Cl <- vector(); MC_cell_Q <- vector(); MW <- vector(); MC_per_cell <- vector(); Total_cells <- vector(); Clar <- vector(); Filt <- vector(); Total_cells_Cl <- vector(); MC_Int_Cl <- vector(); Total_MC_Cl <- vector(); Cl <- vector(); Total_MC_FWD <- vector(); SA_mouth <- vector(); Water_H <- vector(); TB <- vector(); DW <- vector(); ADD <- vector(); HQ_child_PostTWC_E_only <- vector()

for(i in 1:iter) { MC_Ext[i] <-50; PP[i] <- runif(1,0.40,0.60); PAC[i] <- runif(1,0.01,0.39); Cl[i] <- 1-(rtnormal(1,0.60355208,0.25252061,0.06954,1)); Total_MC_PTWC_FWD[i] <- MC_Ext[i]*PP[i]*PAC[i]*Cl[i] SA_mouth[i] <- rnorm(1,117.6,7.6); Water_H[i] <- runif(1,0.006,0.009) TB[i] <- ((SA_mouth[i]*Water_H[i])/1000)*2 DW[i] <- TriRand(0.057,0.336,1.099); ADD[i] <- (Total_MC_PTWC_FWD[i]*(TB[i]+DW[i]))/18.6; HQ_child_PostTWC_E_only[i] <- ADD[i]/0.05 }

TotalPostchild_HQhist <- hist(HQ_child_PostTWC_IE, plot=FALSE) TotalPostchild_PHQ = TotalPostchild_HQhist$counts/iter

Postchild_HQ_perc <- quantile(HQ_child_PostTWC_IE, c(0.05,0.5,0.95))

#------All IC Microcystins PTWC Adult ------set.seed(24) iter <- 10000

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MC_Ext_Per <- vector(); MC_Ext <- vector(); MC_Int <- vector(); PP <- vector(); PAC <- vector(); MC_Ext_Cl <- vector(); MC_cell_Q <- vector(); MW <- vector(); MC_per_cell <- vector(); Total_cells <- vector(); Clar <- vector(); Filt <- vector(); Total_cells_Cl <- vector(); MC_Int_Cl <- vector(); Total_MC_Cl <- vector(); Total_MC_PTWC_FWD <- vector(); Cl <- vector(); SA_mouth <- vector(); Water_H <- vector(); TB <- vector(); DW <- vector(); ADD <- vector(); HQ_adult_PostTWC_I_only <- vector()

#All Intracelllular MCs, August 15-17, 2014 #Initial MC Raw = 50 ug/L, August 15, 2014 #MC_cell_Q=MC Quota of cell (fmol/cell), MW=Molecular Weight (g/mol), #MC_per_cells (ug/cell), Total_cells (cells/L), #MC_Int_Cl=Intracellular MC at chlorination (assumes total lysis) #MC_Ext_Cl= Extracellular MC at chlorination, Total_MC_Cl=Sum of MC at Cl #TB=Tooth Brush, DW=Drinking Water #ADD=Average Daily Dose (assuming 80 kg person), HQ=Hazard Quotient for(i in 1:iter) {

MC_Int[i] <- 50;PP[i]; MC_cell_Q[i] <- runif(1,0.05,0.129); MW[i] <- sample(c(910.06,995.17,1038.20,1045.19),1); MC_per_cell[i] <- ((MC_cell_Q[i]*1e-15)*MW[i])*1e6; Total_cells[i] <- MC_Int[i]*(MC_per_cell[i])^-1; Clar[i] <- runif(1,0.01,0.1); Filt[i] <- runif(1,0.01,0.04); Total_cells_Cl[i] <- Total_cells[i]*Clar[i]*Filt[i]; MC_Int_Cl[i] <- Total_cells_Cl[i]*MC_per_cell[i];

Cl[i] <- 1-(rtnormal(1,0.60355208,0.25252061,0.06954,1)); Total_MC_PTWC_FWD[i] <- MC_Int_Cl[i]*Cl[i]; SA_mouth[i] <- rnorm(1,214.7,12.9); Water_H[i] <- runif(1,0.007,0.01) TB[i] <- ((SA_mouth[i]*Water_H[i])/1000)*2; DW[i] <- TriRand(0.208,1.006,2.848); ADD[i] <- (Total_MC_PTWC_FWD[i]*(TB[i]+DW[i]))/80; HQ_adult_PostTWC_I_only[i] <- ADD[i]/0.05 }

TotalPost_HQhist <- hist(HQ_adult_PostTWC_IE, plot=FALSE) TotalPost_PHQ = TotalPost_HQhist$counts/iter

Post_HQ_perc <- quantile(HQ_adult_PostTWC_IE, c(0.05,0.5,0.95))

#------All IC Microcystins PTWC Child ------set.seed(24) iter <- 10000 MC_Ext_Per <- vector(); MC_Ext <- vector(); MC_Int <- vector(); PP <- vector(); PAC <- vector(); MC_Ext_Cl <- vector(); MC_cell_Q <- vector(); MW <- vector(); MC_per_cell <- vector(); Total_cells <- vector(); Clar <- vector(); Filt <- vector(); Total_cells_Cl <- vector(); MC_Int_Cl <- vector(); Total_MC_Cl <- vector(); Cl <- vector(); Total_MC_FWD <- vector(); SA_mouth <- vector(); Water_H <- vector(); TB <- vector(); DW <- vector(); ADD <- vector(); HQ_child_PostTWC_I_only <- vector()

#All Intracelllular MCs, August 15-17, 2014 #Initial MC Raw = 50 ug/L, August 15, 2014 #MC_cell_Q=MC Quota of cell (fmol/cell), MW=Molecular Weight (g/mol),

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#MC_per_cells (ug/cell), Total_cells (cells/L), #MC_Int_Cl=Intracellular MC at chlorination (assumes total lysis) #MC_Ext_Cl= Extracellular MC at chlorination, Total_MC_Cl=Sum of MC at Cl #TB=Tooth Brush, DW=Drinking Water #ADD=Average Daily Dose (assuming 18.6 kg child), HQ=Hazard Quotient

for(i in 1:iter) { MC_Int[i] <- 50; MC_cell_Q[i] <- runif(1,0.05,0.129); MW[i] <- sample(c(910.06,995.17,1038.20,1045.19),1); MC_per_cell[i] <- ((MC_cell_Q[i]*1e-15)*MW[i])*1e6; Total_cells[i] <- MC_Int[i]*(MC_per_cell[i])^-1; Clar[i] <- runif(1,0.01,0.1); Filt[i] <- runif(1,0.01,0.04); Total_cells_Cl[i] <- Total_cells[i]*Clar[i]*Filt[i]; MC_Int_Cl[i] <- Total_cells_Cl[i]*MC_per_cell[i];

Cl[i] <- 1-(rtnormal(1,0.60355208,0.25252061,0.06954,1)); Total_MC_PTWC_FWD[i] <- MC_Int_Cl[i]*Cl[i]; SA_mouth[i] <- rnorm(1,117.6,7.6); Water_H[i] <- runif(1,0.006,0.009) TB[i] <- ((SA_mouth[i]*Water_H[i])/1000)*2 DW[i] <- TriRand(0.057,0.336,1.099); ADD[i] <- (Total_MC_PTWC_FWD[i]*(TB[i]+DW[i]))/18.6; HQ_child_PostTWC_I_only[i] <- ADD[i]/0.05 }

TotalPostchild_HQhist <- hist(HQ_child_PostTWC_IE, plot=FALSE) TotalPostchild_PHQ = TotalPostchild_HQhist$counts/iter

Postchild_HQ_perc <- quantile(HQ_child_PostTWC_IE, c(0.05,0.5,0.95))

#------Plotting and Descriptive Statistics -----

all_HQs <- data.frame(HQ_adult_TWC_IE, HQ_child_TWC_IE, HQ_adult_TWC_Eonly, HQ_child_TWC_Eonly , HQ_adult_TWC_Ionly, HQ_child_TWC_Ionly, HQ_adult_PostTWC_IE, HQ_child_PostTWC_IE, HQ_adult_P ostTWC_E_only, HQ_child_PostTWC_E_only, HQ_adult_PostTWC_I_only, HQ_child_PostTWC_I_only) colnames(all_HQs) <- c("Adult TWC IE", "Child TWC IE", "Adult TWC E", "Child TWC E", "Adult TW C I", "Child TWC I", "Adult Post TWC IE", "Child Post TWC IE", "Adult Post TWC E", "Child Post TWC E", "Adult Post TWC I", "Child Post TWC I") interval <- ncol(all_HQs) HQmeans <- vector(); HQSD <- vector(); HQMED <- vector(); HQ05P <- vector(); HQ95P <- vector() for(j in 1:interval) { HQmeans[j] <- mean(all_HQs[,j]) HQSD[j] <- sd(all_HQs[,j]) HQ05P[j] <- quantile(all_HQs[,j], probs = 0.05) HQMED[j] <- median(all_HQs[,j]) HQ95P[j] <- quantile(all_HQs[,j], probs = 0.95) }

HQ_Stats <- data.frame(HQmeans,HQSD,HQ05P,HQMED,HQ95P) rownames(HQ_Stats) <- c("Adult TWC IE", "Child TWC IE", "Adult TWC E", "Child TWC E", "Adult T WC I", "Child TWC I", "Adult Post TWC IE", "Child Post TWC IE", "Adult Post TWC E", "Child Pos t TWC E", "Adult Post TWC I", "Child Post TWC I") plotting <- data.frame(HQ_adult_TWC_IE, HQ_child_TWC_IE, HQ_adult_TWC_Eonly, HQ_child_TWC_Eonl y, HQ_adult_TWC_Ionly, HQ_child_TWC_Ionly, HQ_adult_PostTWC_IE, HQ_child_PostTWC_IE, HQ_adult_

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PostTWC_E_only, HQ_child_PostTWC_E_only, HQ_adult_PostTWC_I_only, HQ_child_PostTWC_I_only) colnames(plotting) <- c("Adult TWC", "Child TWC", "Adult TWC", "Child TWC", "Adult TWC", "Chil d TWC", "Adult PTWC", "Child PTWC", "Adult PTWC", "Child PTWC", "Adult PTWC", "Child PTWC") x11(); par(omi=c(.5,.25,.25,.25)) boxplot(log(plotting), las=2, col=c("yellow", "yellow", "red", "red", "blue", "blue", "yellow" , "yellow", "red", "red", "blue", "blue"), ylab = expression(bold(Log~of~Hazard~Quotient)), ma in= "Simulations of Different Initial Toxin States"); legend("bottomleft", c("Intra and Extracellular","All Extracellular","All Intracellular"), pch =c(15,15,15), col=c("yellow","red","blue"))

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