Investigating toxicity of urban road deposited sediments using Chinese hamster ovary cells and Chlorella Pyrenoidosa

Journal Pre-proof Investigating toxicity of urban road deposited sediments using Chinese hamster ovary cells and Chlorella Pyrenoidosa Qicong Guo, Yuting Zhan, Yuqing Li, Nian Hong, Yingjie Guan, Zhenxuan Zhang, Bo Yang, Fanhua Meng, Mengting Yang, An Liu PII:

S0045-6535(19)32874-7

DOI:

https://doi.org/10.1016/j.chemosphere.2019.125634

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CHEM 125634

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Received Date: 16 November 2019 Revised Date:

10 December 2019

Accepted Date: 10 December 2019

Please cite this article as: Guo, Q., Zhan, Y., Li, Y., Hong, N., Guan, Y., Zhang, Z., Yang, B., Meng, F., Yang, M., Liu, A., Investigating toxicity of urban road deposited sediments using Chinese hamster ovary cells and Chlorella Pyrenoidosa, Chemosphere (2020), doi: https://doi.org/10.1016/ j.chemosphere.2019.125634. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.

Investigating toxicity of urban road deposited sediments using Chinese hamster ovary cells and Chlorella Pyrenoidosa

Qicong Guo1, Yuting Zhan1, Yuqing Li1, Nian Hong1, Yingjie Guan1, Zhenxuan Zhang1, Bo Yang1, Fanhua Meng2, Mengting Yang*1, An Liu*1

1

College of Chemistry and Environmental Engineering, Shenzhen University, 518060

Shenzhen, China 2

Shenzhen hydrology and water quality center, 518055, Shenzhen, China

*Corresponding author An Liu: e-mail: [email protected]; Tel: 86-755-26557315; Fax: 86-755-26536141 Mengting Yang: [email protected]; Tel: 86-755-13928432825; Fax: 86-755-26536141

＜ Toxicity  Pollutants

Road deposited  sediments (RDS) 

CHO cells are more sensitive than algae in terms of RDS toxicity 

Industrial Areas

Commercial Areas

Assessment Toxicity test

Chinese hamster ovary (CHO) cells Chlorella Pyrenoidosa ‐mammalian cells to  (C. pyrenoidosa ) indicate human health ‐algae to indicate ecological health

High ecological health risk

Higher human health risk

Provide a useful insight to enhancing effectiveness of RDS polluted  urban road stormwater management and ensuring their reuse safety

1

Abstract: Road deposited sediments (RDS) is the key carrier of pollutants in the urban

2

road stormwater processes and hence has been seen as an important pollutant source of

3

urban road stormwater. Although many research studies have focused on RDS and

4

pollutants attached to RDS, the investigation on RDS toxicity is very limited. Toxicity

5

test can permit an overall assessment on whether the RDS polluted stormwater can be

6

safely reused. This paper used two living organisms, namely Chinese hamster ovary

7

(CHO) cells, (mammalian cells to indicate human health related toxicity) and Chlorella

8

Pyrenoidosa (algae to indicate ecological health related toxicity) to test RDS toxicity by

9

using an innovative “equivalent toxicity area (ETA)” approach. The outcomes showed

10

that mammalian cells are more sensitive than algae in terms of RDS toxicity. Pb, Cd and

11

Cr primarily contributed to mammalian cell-based toxicity while Zn, Ni, Cu and TOC are

12

primarily toxic to algae. It is also found that road site characteristics such as land uses

13

exerted an important influence on RDS toxicity. Commercial areas tended to generate

14

RDS with higher human health risk related toxicity while industrial areas had a potential

15

to produce RDS with high ecological health risk related toxicity. The research outcomes

16

also showed that solely focusing on pollutant themselves on RDS can not accurately

17

indicate RDS pollution. An approach to considering both pollutant loads and toxicity is

18

preferred. These results were expected to provide a useful insight to enhancing

19

effectiveness of RDS polluted urban road stormwater management and ensuring their

20

reuse safety.

21

Keywords: Road deposited sediments (RDS); Stormwater reuse safety; Toxicity test;

22

Chinese hamster ovary (CHO) cells; Chlorella Pyrenoidosa

23 1

24

1 Introduction

25

Urban road stormwater has received an increasing attention since it has been seen as an

26

alternative resource to mitigate water shortage (Goonetilleke et al., 2017; Liu et al., 2016).

27

However, many pollutants are deposited on road surfaces during dry periods because of

28

various anthropologic activities such as traffic. A number of these pollutants deposited on

29

road surfaces are toxic such as heavy metals and organic matters (Khpalwak et al., 2019;

30

Lanzerstorfer, 2018; Liu et al., 2018; Liu et al., 2019). When rainfall events occur, these

31

pollutants can be washed-off into stormwater, threatening stormwater reuse safety.

32

Road deposited sediments (RDS) is the key carrier of pollutants in the urban road

33

stormwater processes and hence has been seen as an important pollutant source of urban

34

road stormwater. This is because when rainfall events wash-off RDS, pollutants attached

35

to RDS will also enter stormwater runoff. Therefore, many researchers have focused on

36

RDS and pollutants attached to RDS, including their loads/concentrations and expected to

37

provide useful insight to safeguarding urban road stormwater quality and ensuring reuse

38

safety.

39

hydrocarbons attached to RDS and found that they posed high human health risks. Zhao

40

et al. (2014) analyzed the build-up and wash-off processes of heave metals (Cr, Cu, Ni,

41

Pb, and Zn) attached to RDS and developed an index to model heavy metals attached to

42

RDS during stormwater processes. However, the approach to focusing on particular

43

pollutants or pollutant groups can not permit an overall investigation on whether the RDS

44

polluted stormwater can be safely reused since these results were solely based on

45

individual pollutant species targeted. There would be still other pollutants or even

46

unknown pollutant species present in the RDS and resulting stormwater. In this regard,

For instance, Khpalwak et al. (2019) investigated polycyclic aromatic

2

47

an approach capable of representing the comprehensive risks generated by all pollutants

48

present in RDS is highly required.

49

Toxicity test has been commonly applied in different water resources such as reclaimed

50

water (Du et al., 2017; Lv et al., 2017), wastewater (Yang and Zhang, 2013) and drinking

51

water (Shi et al., 2018) and the results show relatively integrated effects all pollutants

52

exert (the integrated effects could be due to synergistic effects or/and antagonistic effects

53

among all pollutants). However, toxicity test has not been widely undertaken in the area

54

of RDS although toxicity tests have been conducted for stormwater (most of studies were

55

to investigate toxicity for stormwater discharged into receiving waters rather than urban

56

road stormwater runoff) in several previous research studies (such as Gosset et al., 2019;

57

Mclntyre et al., 2016; Tang et al., 2013). However, focusing on RDS has three benefits.

58

Firstly, road stormwater runoff is primarily polluted by RDS and pollutants attached (Li

59

et al., 2015; Zhao & Li, 2013). Therefore, RDS loads and resulting toxicity can be used to

60

indicate how polluted the resulting stormwater will be (Djukić et al., 2016; Sharma et al.,

61

2016; Wang et al., 2017). Secondly, focusing on RDS and pollutants attached during dry

62

days can represent the maximum pollutant loads which will enter stormwater during rainy

63

days. This permits to analyze the worst scenario in the context of stormwater reuse.

64

Thirdly, real road stormwater is highly variable in quantity and quality due to different

65

rainfall characteristics, which constrains to effectively compare toxicity among different

66

road sites. To solve this problem, focusing on RDS could overcome the variability due to

67

different rainfall characteristics.

68

To the best of our knowledge, there are a limited number of studies on RDS toxicity

69

comparison among different road sites. The main reason is the difficulty of comparing 3

70

RDS toxicity due to the sampling methods. Generally, a dry and wet vacuuming approach

71

is commonly used to collect RDS samples (Hong et al., 2018; Liu et al., 2019; Zhao et al.,

72

2018). This leads to the high variability of final RDS sample volumes for each road

73

surface since it closely depends on how much volume of water has been used during the

74

vacuuming process. In this context, the different volumes of RDS samples create the

75

difficulty to parallelly compare RDS toxicity among different road surfaces using

76

conventional pollutant amounts (loads or concentrations) comparison methods. In order

77

to solve this issue, our previous study (Zhan et al., 2019) developed an innovative

78

approach, “equivalent toxicity area (ETA)”, which is capable of comparing RDS toxicity

79

among different road sites. This study was undertaken using this ETA approach.

80

In common toxicity tests, according to different purposes, different testing organisms are

81

selected. They could be mammalian cells (Wang et al., 2018), microorganisms (Zhang et

82

al., 2018), algae (Ding et al., 2017), plants (Chen et al., 2019) and complete animals (Yu

83

et al., 2015). For example, testing toxicity related to human health could select animals or

84

their cells (particularly mammalian animals since they are considered as human-like)

85

while the selection could be algae (such as Chlorella.) if toxicity related to ecological

86

health is targeted. For different testing organisms, the toxicity levels obtained and

87

pollutants which primarily contributed to the toxicity might be different due to the

88

difference of their sensitivity and toxicity generation mechanisms.

89

In this context, the present study focused on RDS toxicity (not for the resulting urban

90

road stormwater) using two living organisms, namely Chinese hamster ovary (CHO) cells

91

(mammalian cells) and Chlorella Pyrenoidosa (algae). The mammalian cell-based

92

toxicity can be related to human health as the CHO cytotoxicity bioassay has been 4

93

extensively used in previous studies to indicate toxicity level of drinking water (Jeong et

94

al., 2012; Richardson et al., 2008; Wagner and Plewa, 2017) while algae-based toxicity

95

can be applied to indicate ecological health. This can reflect the possible effects when

96

RDS polluted stormwater is reused in different purposes. The main objective of this study

97

was to test RDS toxicity using two different living organisms to understand relevant risks

98

related to human health and ecological health. It is noteworthy that the influence of

99

environmental factors on RDS toxicity such as antecedent dry days and rainfall

100

characteristics were not investigated in this study.

101

2 Methods and materials

102

2.1 Study sites

103

The study sites were selected in Shenzhen, South China. Shenzhen City is one of the

104

most developed cities in China with a population of over 13 million. Twelve road sites

105

were selected to collect RDS samples. These road sites have different land uses

106

(residential, commercial and industrial), different traffic volumes and different road

107

surface roughness conditions. All sampling road sites are paved with asphalt and flat.

108

Parameters related to road sites characteristics were also collected. They were land use

109

fractions (representing the percentage of a particular land use type within a given area,

110

residential (R), commercial (C) and industrial (I) fractions in this study), traffic volume

111

(DTV) and road surface texture depth (STD, representing the roughness of road surface,

112

which primarily influences RDS retention and re-distribution on road surfaces. Generally,

113

a rougher surface has more holes and depressions, which facilitates RDS remained within

114

them and hence leads to higher loads of RDS. External factors such as wind and traffic

115

turbulence are difficult to detach RDS from rough surfaces (Zhao et al., 2018)). Data of

116

road site characteristics including geo-coordinates of selected road sites are shown in 5

117

Table S1 while their collection methods are given in Table S2 in the Supplementary

118

Information. Figure 1 gives the map of road site locations.

119 120

Figure 1 Selected road sites

121

2.2 Sample collection and per-treatment

122

One RDS sample was collected from each road site selected by using a classical dry-wet

123

vacuuming method, which has been widely used in RDS sample collection

124

(Gunawardana et al., 2014; Liu et al., 2019; Zhao et al., 2018; Zhao et al., 2014). In this

125

study, a 4.5 m2 road surface area was marked, where RDS sampling was undertaken. A

126

water filter contained vacuum cleaner (Haier, ZTBJ1200, China, Power: 1200 W) to

127

collect RDS samples and a sprayer to wet the marked area of each road site was used. For

128

collecting RDS samples, each marked area was vacuumed twice under dry and once

129

under wet conditions to collect both particulate and dissolved pollutants. Deionized water

130

was used in the sampling process. Then, the collected samples were transferred into glass 6

131

containers and preserved at 4 oC conditions before analysis. In this study, the antecedent

132

dry period before RDS sampling was seven days since the RDS loads generally become a

133

constant value after seven antecedent dry days (Egodawatta, 2007). The final sample

134

volumes of all road sites were 3-5L. A detailed information about sampling is provided in

135

the Supplementary Information.

136

Each RDS sample were pre-treated by going through 75 µm sieving and the toxicity test

137

(including pollutant parameter testing) was conducted for the pre-treated samples. This

138

was because a 60-80% of toxic pollutant loads were adsorbed to RDS in this particle size

139

fraction (Gunawardana et al., 2014; Zhao et al., 2010). The detailed sampling process and

140

per-treatment can be found in Zhan et al. (2019).

141

2.3 Approach to comparing RDS toxicity among different road sites

142

In our previous study (Zhan et al., 2019), an innovative “equivalent toxicity area (ETA)”

143

approach was developed to compare toxicity of RDS on different road surfaces. The ETA

144

theory is to use surface area which generates RDS to represent the toxicity level. This

145

ETA approach overcomes the difficulty to compare RDS toxicity among road sites due to

146

variability of sampling volumes, which constrains the effective application of

147

conventional

148

(loads/concentrations).

149

In the ETA approach, for having same toxicity related value, the required surface area

150

being smaller means that the site has higher toxicity. In order to indicate the toxicity level,

151

an ETA50 value can be obtained for each road. The ETA50 value represents the area

152

generating RDS which leads to 50% of viability rate (or mortality rate). Therefore, a

153

smaller ETA50 value indicates higher toxicity of RDS on one road site. In this study,

toxicity

comparison

method

7

of

using

pollutant

amounts

154

each road site had two types of ETA50 values, namely CHO-based toxicity and algae-

155

based toxicity. The detailed information of the ETA approach is provided in the

156

Supplementary information and can be also found in Zhan et al. (2019).

157

2.4 RDS toxicity test

158

This study used two types of living organisms to undertake toxicity test, namely CHO

159

cells and C. pyrenoidosa (algae). CHO-based toxicity values (ETA50-C) were obtained

160

from our previous study (Zhan et al., 2019), where the study sites were exactly same as

161

this study. The algae-based toxicity was tested in the study. The following discussion is

162

primarily for algae-based toxicity test method while CHO-based toxicity test method can

163

be found in Zhan et al. (2019).

164

Algae cultures

165

C. pyrenoidosa was obtained from the Center of Freshwater Algae Culture Collection at

166

the Institute of Hydrobiology (FACHB-Collection, Wuhan, China). The algae were

167

inoculated into 250 ml sterile BG-11 medium. The cultures were incubated at 25 oC in an

168

incubator under a controlled lighting regime. Fluorescent lamps were used as the light

169

source with an automated light/dark cycle of 12/12 h. The illuminance was maintained at

170

5000 Lux.

171

Algae toxicity test

172

Algal cultures at the exponential growth phase were inoculated for experiments. The

173

tested samples were the solutions which were taken from per-treated samples (<75 µm) at

174

three different volumes (0, 50 and 240 ml). Then, the three samples taken were freeze-

175

dried and diluted using BG-11 medium to 8 ml. The exposure time was 72 h. Number of 8

176

algae cells and chlorophyII fluorescence intensity were tested to indicate the toxicity

177

samples exerted on algae. Number of algae cells were counted using an automatic cell

178

counting instrument (Countstar) while chlorophyII fluorescence intensity was tested

179

using a fluorescence spectrometer (Thermo Fisher Scientific). The algae inhibition rate

180

indicated by number of algae cells and chlorophyII fluorescence intensity were calculated

181

using Eq. 1 and Eq. 2. Regression analysis was applied to calculate ETA50 values for each

182

road site (ETA50-AN for toxicity represented by number and ETA50-AF for toxicity

183

represented by chlorophyII fluorescence). The analysis of data related to toxicity was

184

conducted using SigmaPlot 12. It is noteworthy that for regression analysis, a 100%

185

inhibition percentage at 4.5 m2 (the sampling area for each road) was assigned along with

186

other three areas (indicated by per-treated samples at 0, 50 and 240 ml) to generate the

187

“response- equivalent toxicity area” curves.
ℎ  = 

188

    

−

    



    

 × 100%

Eq. 1

189

where

190


ℎ  −algea inhibition rate indicated by number of algae cells

191

Nt, N0, Nt0- algal cell number at t moment, initial moment in treated group and at t moment in

192

untreated control group, respectively.


ℎ  =

193

  

× 100%

Eq. 2

194

where

195


ℎ  −algea inhibition rate indicated by algae chlorophyII fluorescence

196

intensity 9

197

C0, Ct- chlorophyII fluorescence intensity in untreated control group and treated group,

198

respectively

199

2.5 Pollutant parameters testing

200

It is well known that RDS is the key carrier of other pollutants on urban roads (Bian et al.,

201

2015; Gunawardana et al., 2014; Liu et al., 2019). The RDS toxicity is primarily

202

contributed by these pollutants attached. In this context, pollutants common to urban

203

roads were also tested in order to understand their relationships with the toxicity. These

204

pollutants tested included six heavy metals (closely related to urban traffic, namely Cu,

205

Zn, Ni, Pb, Cd and Cr, mg/m2) and the value of total organic carbon (TOC, representing

206

organic matter attached to RDS, mg/m2) as surrogates was measured. Additionally, the

207

value of total solids (TS, g/m2) was also tested for each sample since it indicates the loads

208

of RDS. These parameters were determined by methods specific in Standard Methods for

209

the Examination of Water and Waste Water (APHA, 2005). All the parameters for

210

pollutants were tested for <75 µm particles, and the original data are given in Table S3 in

211

Supplementary Information.

212

2.6 Study approach

213

This study included three primary steps.

214

The first step was to compare mammalian cell-based and algae-based toxicity among

215

different road sites. Pollutant loads were also compared among road sites in this step.

216

Mean values and coefficients of variation (CV) were used to conduct the data analysis in

217

this step. Additionally, a ranking analysis of road sites based on pollutant loads and

218

toxicity (both mammalian cell-based and algae-based) was undertaken individually by 10

219

using a PROMETHEE method (Preference Ranking Organization Method for

220

Enrichment Evaluation). PROMETHEE is an unsupervised method for rank-ordering

221

objects (the twelve road sites in this study). Each variable (pollutants and toxicity in the

222

study) has to be modelled by; (i) supplying a preference function and thresholds to

223

indicate how objects are to be compared; (ii) indicating how the objects are to be ordered:

224

top-down (maximized) or bottom-up (minimized) and, (iii) supplying a weighting to

225

reflect the importance of one variable over another (default value=1). A set of net ranking

226

out flow values, Ф, are computed for each object on the basis of the partial ranking out

227

flow indices, +Ф and -Ф. The objects are rank-ordered from the most preferred one (the

228

most positive (+) Ф value) to the least well performing one (the most negative (–) Ф

229

value).

230

literature (Keller et al., 1991). This ranking analysis was to investigate whether road sites

231

have a similar ranking for considering pollutant loads and toxicity respectively since in

232

current RDS research studies, the pollutant based approach (pollutant species and their

233

concentrations/loads) has been widely used to investigate how RDS is polluted while

234

toxicity based approach (all pollutants exert in combination) is quite limited in the area of

235

RDS.

Detailed information regarding the PROMETHEE method can be found in the

236 237

The second step was to analyze relationships between toxicity and influential factors.

238

This was conducted using principal component analysis method (PCA) for mammalian

239

cell-based toxicity and algae-based toxicity separately. For PCA, two matrices were

240

created. The matrix (12 ×14) of mammalian cell-based toxicity included 12 road sites and

241

14 variables (1/ETA50-C, TOC, TS, six heavy metals, DTV, STD, R, C and I) while the

11

242

matrix (12 ×15) of algae-based toxicity had 12 road sites and 15 variables (1/ETA50-AN,

243

1/ETA50-AF, TOC, TS, six heavy metals, DTV, STD, R, C and I). It is noteworthy that

244

the reciprocal form of ETA50 was used in the PCA analysis and hence a higher 1/ETA50

245

(1/ETA50-C, 1/ETA50-AN and 1/ETA50-AF) value means higher toxicity.

246

The third step was to discuss the implications of the research outcomes for safely reusing

247

RDS polluted stormwater and hence provide a useful insight to enhancing effectiveness

248

of urban road stormwater management and ensuring the reuse safety.

249

3 Results and discussions

250

3.1 Comparison of toxicity and pollutant loads

251

Table 1 shows the mean and coefficient of variation (CV) values of pollutant loads and

252

ETA50 (mammalian cell and algae-based respectively) from the twelve road sites. It is

253

noted that the mean values of TOC (86.1 mg/m2) and TS (14.8 g/m2) loads are much

254

higher than heavy metals (9.23 µg/m2, 453 µg/m2, 5.60 µg/m2, 0.625 µg/m2, 1.04 µg/m2

255

and 0.0733 µg/m2 for Cu, Zn, Ni, Pb, Cr and Cd individually). It is also noteworthy that

256

although these values shown in Table 1 correspond to <75 µm particles, they are even

257

higher than corresponding values of the total loads (all particle size fractions) in other

258

places reported by previous literature. For example, Gunawardana et al. (2012) found that

259

the TS loads ranged from 0.78 to 7.03 g/m2 on road sites in Gold Coast, Australia. This

260

could be attributed to much larger population and hence more frequent and diverse

261

anthropologic activities in Shenzhen, China (population: 13 million) than Gold Coast,

262

Australia (0.57 million) (Shenzhen Government, 2019; ABS, 2019).

12

263

In terms of toxicity related values, mammalian cell-based ETA50 (ETA50-C) values are

264

much smaller than algae-based ETA50 (both ETA50-AN and ETA50-AF). The mean value

265

of ETA50-C was 74.6 cm2 while the corresponding values were 0.469 m2 and 0.412 m2

266

for ETA50-AN and ETA50-AF respectively. This means that the leading to 50% viability

267

(or inhibition) of testing organisms requires much less road surface areas for CHO cells

268

than for algae. This implies that mammalian cells (such as CHO cells) are more sensitive

269

to toxicity present in RDS than algae. In other words, if the RDS polluted stormwater is

270

from same road surface area, it could be more toxic to mammalian cells than algae.

271

Additionally, since toxicity indicated by mammalian cells can indirectly represent the

272

influence of RDS toxicity on human health while toxicity indicated by algae is related to

273

ecological health, the outcomes show that RDS polluted stormwater might have a

274

stronger influence on human health than ecological health when RDS polluted

275

stormwater are reused as alternative water resources.

276

According to CV values, pollutant loads were generally higher than toxicity related

277

values (except for Cu). Using CV values was due to the fact that pollutant loads and

278

ETA50 values have very different orders of magnitudes and hence CV values are more

279

appropriate than the conventional standard deviations. The CV values were 67.3% (TOC),

280

87.0% (TS), 54.7% (Cu), 273% (Zn), 101% (Ni), 261% (Pb), 323% (Cr) and 173% (Cd)

281

while ETA50-C, ETA50-AN and ETA50-AF were 66.2%, 61.6% and 55.7% respectively.

282

Since the samples were collected from different road sites, a higher CV value means

283

higher variability among these road sites. In this regard, pollutant loads deposited on road

284

surfaces are highly variable, compared to the toxicity pollutants exert in combination.

285

The observation implies that pollutant loads can not accurately indicate resulting toxicity.

13

286

Although pollutant loads could be very different from one site to another, toxicity might

287

not significantly differ. This could be due to interactions among pollutants such as

288

antagonism or synergy. Table 1 Pollutant loads and toxicity

289

Pollutant loads

Mean

290 291 292 293 294 295

TS (g/m2)

Cu (µg/m2)

Zn (µg/m2)

Ni (µg/m2)

Pb (µg/m2)

Cr (µg/m2)

Cd (µg/m2)

86.1

14.8

9.23

453

5.60

0.625

1.04

0.0733

67.3

87.0

54.7

273

101

261

323

173

a

CV (%)

Toxicity

TOC (mg/m2)

Mean

ETA50-ANb (m2)

ETA50-AFb (m2)

ETA50-Cc (cm2)

0.469

0.412

74.6

CV 61.6 55.7 66.2 (%) a Coefficient of variation b ETA50-AN and ETA50-AF refer to algae number based toxicity and algae chlorophyII fluorescence based toxicity, respectively c ETA50-C refers to mammalian cell-based toxicity

In order to further investigate the difference of pollutants and the resulting toxicity in the

296

indication of RDS pollution, a PROMETHEE ranking analysis was conducted based on

297

pollutant loads (TOC, TS and six heavy metals) and toxicity (considering both

298

mammalian cell and algae-based toxicity) individually. Table 2 shows the PROMETHEE

299

ranking results. As shown in Table 2, the road sites have very different rankings for

300

pollutant loads and toxicity. For example, the top ranked road sites were S5, S6 and S7 in

301

terms of RDS toxicity while the three sites were ranked the fourth, seventh and eighth

302

positions respectively when considering pollutant loads they generated. Another example

303

is S2 site. In terms of toxicity, S2 is ranked at the eleventh (the second from the bottom)

304

while S2 site has very high ranking (the top second) in the case of pollutant loads. This

305

means that although RDS on S2 road site had high pollutant loads, its toxicity was not

306

high. These observations further confirmed the inadequacy of solely investigating

14

307

pollutants attached to RDS, which is currently undertaken in most of previous studies. An

308

in-depth understanding of pollutants and resulting toxicity in combination should be the

309

preferred approach for RDS research. Table 2 PROMETHEE ranking results based on pollutant loads and toxicity Pollutant loads based Toxicity based Ranking ID Road site Road site Ø valuea Ø value ranking ranking 1 S4 0.320 S5 0.245 2 S2 0.131 S6 0.189 3 S10 0.122 S7 0.133 4 S5 0.088 S12 0.121 5 S12 0.031 S11 0.113 6 S9 0.021 S10 0.087 7 S6 -0.005 S4 0.067 8 S7 -0.047 S8 0.043 9 S8 -0.124 S9 -0.001 10 S3 -0.158 S3 -0.150 11 S11 -0.175 S2 -0.262 12 S1 -0.204 S1 -0.584

310

311 312 313 314

a

315

Figure 2 shows the principal component analysis (PCA) biplots for mammalian cell-

316

based toxicity (Figure 2a) and algae-based toxicity (Figure 2b). Generally, a percentage

317

of more than 60% explained by all the principal components (PCs) selected is considered

318

as reliable in the analysis of the information within the dataset (Adams, 1995). In this

319

regard, the first three principal components (PC1, PC2 and PC3) were selected for both

320

mammalian cell-based toxicity (a total percentage of 62.45%) and algae based toxicity (a

321

total percentage of 73.93%) biplots. Figure 2 shows PC1 vs. PC2 biplots while PC1 vs.

322

PC3 biplots are given in Supplementary Information (see Figure S3).

Net ranking out flow values; it was computed for each object based on variables (pollutant loads or toxicity in this study). The objects are rank-ordered from the largest Ф value) to the smallest Ф value.

3.2 Understanding relationships between toxicity and their influential factors

15

323

As shown in Figure 2 and Figure S3, toxicity related to mammalian cells and algae has a

324

very different relationships with pollutants. The 1/ETA50-C vector forms a small angle

325

with vectors of Pb, Cd and Cr (see Figure 2a and Figure S3a) while the 1/ETA50-AN and

326

1/ETA50-AF vectors have a small angle with Zn, Ni, Cu and TOC (see Figure 2b and

327

Figure S3b). These observations indicate that the key pollutant species contributing to

328

toxicity of mammalian cells and algae significantly differed. The toxicity leading to the

329

death of mammalian cells could be primarily from Cr, Pb and Cd, which have lower

330

loads but higher toxicity. Zn, Ni and Cu, having high loads but relatively low toxicity are

331

primarily toxic to algae. For example, toxic response factor (representing toxicity level)

332

of Cd is 30 while the factor value is 1 for Zn (Hakanson, 1980). Additionally, As shown

333

in Table 1, the mean values of Cr, Pb and Cd loads were 1.04 µg/m2, 0.63 µg/m2 and 0.07

334

µg/m2 while Cu, Zn and Ni were 9.23 µg/m2, 452.5 µg/m2 and 5.60 µg/m2. In addition,

335

organic matters (indicated by TOC) showed a strong correlation with algae toxicity while

336

they had no obvious toxic effect on mammalian cells. This implied that organic matters

337

have relatively higher bioavailability for algae than mammalian cells. The close

338

relationship between toxicity potencies of pollutants and their bioavailability can be also

339

supported by previous studies such as Babele et al. (2018) and Gutierrez et al. (2002). In

340

this context, further studies regarding the bioavailability of pollutants in RDS towards

341

different bioassay organisms are need in the future for better understanding of the

342

corresponding toxicity mechanism.

343

Other than pollutant species, external factors such as land use also exert different

344

influences on toxicity of mammalian cells and algae. The 1/ETA50-C vector forms an

345

acute angle with commercial land use vector (C) while the 1/ETA50-AN and 1/ETA50-AF

16

346

vectors have a small angle with industrial land use vector (I). However, both mammalian

347

cells and algae related toxicity vectors did not show close relationships with traffic

348

(DTV), road surface condition (STD) and residential land use (R). These results mean

349

that land use (particularly industrial and commercial land uses) could have a more

350

important influence on RDS toxicity than traffic and road surface condition. RDS

351

polluted stormwater generated from industrial areas could have high ecotoxicity

352

(indicated by algae) while the stormwater from commercial areas would have high

353

toxicity related to human health (indicated by mammal animal cells). This highlights the

354

importance that the road site characteristics such as land use and targeted reuse purposes

355

should be taken into account when reusing RDS polluted stormwater. For example,

356

targeting surface water recharging tends not to use stormwater from industrial land use

357

since they have high potential to threaten ecological health while the stormwater reuse for

358

recreational water bodies which people might touch could not be from commercial land

359

use due to high toxicity related to human health.

17

a

360

b

361 362

Figure 2 PCA biplots for mammalian cell-based (a) and algae-based (b) toxicity

18

363

(DTV=traffic volume; STD=road texture depth; I, C and R=Industrial, Commercial and

364

Residential land use fractions)

365

3.3 Implications for reusing RDS polluted stormwater

366

According to results above, it is found that RDS showed different toxicity characteristics

367

to mammalian cells and algae. In terms of identifying toxicity, mammalian cells are more

368

sensitive to RDS than algae. This means that selecting appropriate toxicity testing

369

organisms is important to characterize RDS toxicity. Additionally, the key pollutant

370

species contributing toxicity differed for mammalian cells and algae. This implies that

371

removal of pollutants from RDS polluted stormwater should be based on reuse purposes,

372

targeting human health or ecological health. For example, organic matters have a more

373

important effect on algae toxicity while they are less influential to mammalian cells,

374

probably due to the difference on their bioavailability as discussed above (see Section

375

3.2). Therefore, removing organic matters could be necessary during ecological health

376

targeted reuse purposes while highly toxic heavy metals removal such as Pb, Cr and Cd

377

should be undertaken for human health targeted reuse purposes.

378

Other than pollutant species, road site characteristics such as land uses also exerted an

379

important influence on RDS toxicity. Commercial areas tended to generate RDS with

380

higher human health risk related toxicity while industrial areas had a potential to produce

381

RDS with high ecological health risk related toxicity. These results imply that selecting

382

appropriate road sites to collect stormwater is essential for ensuring reuse safety. This

383

should be based on different reuse purposes.

19

384

The research outcomes also showed that pollutant loads can not accurately indicate how

385

toxic RDS generated from road surfaces were. As shown in Table 1, although pollutant

386

loads were highly variable among road sites, the resulting toxicity (both mammalian cell

387

and algae-based toxicity) was less variable. This means that the conventional approach

388

focusing on pollutants themselves to investigate RDS pollution can not permit a

389

comprehensive understanding of hazard effects of RDS. A combined approach to

390

focusing on both pollutants and resulting toxicity should be a preferred option.

391

It is also noteworthy that this study only investigated six heavy metals and TOC as

392

surrogates to indicate RDS pollution and resulting toxicity. However, as discussed

393

previously, there are a number of pollutants attached to RDS and these pollutants do

394

contribute toxicity. In this context, the future research would investigate on more toxic

395

pollutant types and their relationships with the overall toxicity of RDS. These can provide

396

a more detailed information on which type/s of pollutants contribute toxicity qualitatively

397

and quantitatively.

398

4 Conclusions

399

This research study used an “equivalent toxicity area (ETA)” approach to comparing

400

RDS toxicity among different road sites, where the road surface area was used to

401

represent how toxic the RDS generated from the area was. Based on ETA approach, two

402

types of living organisms namely mammalian CHO cells and C. pyrenoidosa algae were

403

used to undertake toxicity test. Mammalian cell-based toxicity was expected to be related

404

to human health while algae-based toxicity is relevant to ecological health.

20

405

It is noted that mammalian cells are more sensitive to toxicity present in RDS than algae.

406

Additionally, the key pollutant species contributing toxicity to mammalian cells and algae

407

significantly differ. The toxicity leading to the death of mammalian cells could be

408

primarily from Cr, Pb and Cd while Zn, Ni, Cu and TOC are primarily toxic to algae. It is

409

also found that algae-based RDS toxicity is strongly related to industrial areas while

410

mammalian cell-based toxicity was primarily from commercial areas. These results

411

highlight the importance that the road site characteristics such as land use and targeted

412

reuse purposes should be taken into account when reusing the RDS polluted stormwater.

413

Additionally, the research outcomes also showed that solely focusing on pollutant

414

themselves on RDS can not accurately indicate RDS pollution. An approach to

415

considering both pollutant loads and toxicity is preferred.

416

Supplementary Information

417

Data related to road site characteristics and their collection methods, geo-coordinates of

418

road sites, pollutant load data, RDS sampling processes and PC1 vs. PC3 bioplots of PCA

419

results and a detailed information of ETA approach are given in Supplementary

420

Information.

421

Acknowledgement

422

We thank Key-Area Research and Development Program of Guangdong Province

423

(2019B110205003), Guangdong Basic and Applied Basic Research Foundation

424

(2019A1515010843) and National Natural Science Foundation of China (21806110) for

425

supporting this study.

426

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26

• • • • •

Road deposited sediments (RDS) toxicity was tested by two types of living organisms Mammalian cells are more sensitive than algae in terms of RDS toxicity Urban land uses exerted an important influence on RDS toxicity Pb, Cd and Cr attached to RDS primarily contributed to mammalian cell-based toxicity Zn, Ni, Cu and TOC attached to RDS are primarily toxic to algae

An Liu and Mengting Yang: Conceptualization, Methodology; Qicong Guo, Yuting Zhan and Yuqing Li: Investigation; Nian Hong and Yingjie Guan: Formal analysis; Zhenxuan Zhang and Mengting Yang: Visualization; Bo Yang and Fanhua Meng: Writing- reviewing and editing; An Liu and Mengting Yang: Writing-Original Draft

Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: