- Email: [email protected]
Influence of surface properties and antecedent environmental conditions on particulate-associated metals in surface runoff
Journal Pre-proof Influence of surface properties and antecedent environmental conditions on particulate-associated metals in surface runoff Zhenyu Wang, Pei Hua, Heng Dai, Rui Li, Beidou Xi, Dongwei Gui, Jin Zhang, Peter Krebs PII:
S2666-4984(20)30009-0
DOI:
https://doi.org/10.1016/j.ese.2020.100017
Reference:
ESE 100017
To appear in:
Environmental Science and Ecotechnology
Received Date: 4 December 2019 Revised Date:
23 December 2019
Accepted Date: 18 January 2020
Please cite this article as: Z. Wang, P. Hua, H. Dai, R. Li, B. Xi, D. Gui, J. Zhang, P. Krebs, Influence of surface properties and antecedent environmental conditions on particulate-associated metals in surface runoff, Environmental Science and Ecotechnology, https://doi.org/10.1016/j.ese.2020.100017. 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. © 2020 The Author(s). Published by Elsevier B.V. on behalf of Chinese Society for Environmental Sciences, Harbin Institute of Technology, Chinese Research Academy of Environmental Sciences.
3
Influence of surface properties and antecedent environmental conditions on particulate-associated metals in surface runoff
4
Zhenyu Wanga, 1, Pei Huab, 1, Heng Daic, Rui Lid, Beidou Xid,
5
Dongwei Guie, Jin Zhangc, *, Peter Krebsa
1 2
6 a
7
Institute of Urban Water Management, Technische Universität Dresden, 01062
8
Dresden, Germany b
9 10 11
The Environmental Research Institute, MOE Key Laboratory of Environmental
Theoretical Chemistry, South China Normal University, 510006 Guangzhou, China c
Institute of Groundwater and Earth Sciences, Jinan University, 510632 Guangzhou,
12 13 14 15 16 17 18
China d
State Environmental Protection Key Laboratory of Simulation and Control of Groundwater Pollution, Chinese Research Academy of Environmental Sciences, Beijing 100012, China e
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, Xinjiang, China 1
These authors contributed equally to this work *Corresponding author: [email protected]
19
Page 1 of 28
20
ABSTRACT
21
Particulate-associated trace metals have been regarded as an important pollution
22
source for urban surface runoff. Cd, Pb, Cu, Zn and total solids (TS) washed off two
23
different surfaces (low-elevated facade and road surfaces) under two kinds of
24
antecedent environmental conditions (dry and snow-melting) were determined in this
25
study. Wet-vacuuming sweeping (WVS) and surface washing (SW) methods were
26
used to collect the particulate matters to represent the maximum pollution potential
27
and common rainfall-induced wash-off condition respectively. The result shows that
28
the wash-off concentrations of trace metals were found in the order of Cd (2.28±2.08
29
µg/l) < Pb (435.85±412.61 µg/l) < Cu (0.93±0.61 µg/l) < Zn (2.52±2.30 µg/l). The
30
snow-melting process had a considerable influence on the wash-off concentrations of
31
the trace metals on both road and facade surfaces. It reduced >38% and >79% of metals
32
and TS concentrations in the facade surface and road surface runoff respectively. The
33
wash-off concentrations of Cd, Cu, and Zn on the road surface 45-780% higher than
34
those on the facade surfaces. The sensitivity analysis based on the Bayesian network
35
indicates that the wash-off concentrations of metals were mainly dependent on the
36
antecedent environmental conditions or the surface properties while the sampling
37
methods had a minor influence. Therefore, to accurately model the pollutant
38
migration in the surface runoff requires an improving method considering different
39
surfaces and antecedent environment conditions.
Page 2 of 28
40 41
KEYWORDS Trace metal; Road and facade; Wash-off; Snowmelt; Bayesian network
42 43
Page 3 of 28
44
1. INTRODUCTION
45
Urban stormwater runoff affects water quality, water quantity, habitat and
46
biological resources, public health, and the aesthetic appearance of urban waterways
47
(Hua et al., 2020; Li et al., 2019). It has gained increasing attention and the
48
deteriorated stormwater runoff has been regarded as a leading source of pollutants
49
causing water quality impairment related to human activities in the natural
50
hydrological system (Li et al., 2019; Wang et al., 2019; Zhang et al., 2019). The
51
rainfall-induced surface runoff would wash the anthropogenic pollutants of trace
52
metals (Trujillo-González et al., 2016; Zhang et al., 2015a), polycyclic aromatic
53
hydrocarbons (PAHs) (Liu et al., 2017; Zhang et al., 2015c), and other toxics off the
54
impervious surface and eventually discharge into the nearby water bodies through
55
storm sewers or combined sewer overflows (CSOs) (Kaeseberg et al., 2018; Xu et al.,
56
2018). Among the pollutants found in surface runoff, trace metals are of great concern
57
due to their toxicity, persistence, and abiotic degradation in the environment and
58
bioaccumulation in the food webs (Al-Najjar et al., 2011; Dhanakumar et al., 2013).
59
In this context, the wash-off process of trace metals during the rainfall event
60
would affect the potential deterioration level of runoff water quality (Mahbub et al.,
61
2010; Vaze and Chiew, 2002). In a conceptual rainfall-runoff model, the stormwater
62
pollution in the wash-off process is dependent on the build-up process, runoff rate and
63
other influencing variables (Hua et al., 2020; Rossman, 2010). Antecedent dry
64
weather period (ADP) is commonly recognized as one of the most important
Page 4 of 28
65
influencing variables controlling the pollutants’ build-up process (Wicke et al., 2012;
66
Zhang et al., 2015a, 2017a). Therefore, its impact on the wash-off process was
67
discussed in many studies (Gnecco et al., 2005; Liu et al., 2012; Zhang et al., 2017b).
68
However, less attention has been addressed to the effect of other antecedent
69
environmental conditions, such as snowpack melting between the snow and
70
precipitation events, on the pollutants’ build-up process and thereafter the occurrence
71
in the surface runoff. The antecedent snowmelt period (ASP) would play an important
72
role at the high latitude zones in early spring when the snow-melting and
73
rainfall-induced runoff occurs and forms a complicated wash-off process. In addition
74
to the antecedent environmental conditions, most of the studies focused on the
75
pollutants’ wash-off on the road and roof surfaces (Deletic, 1998; Egodawatta et al.,
76
2009; Sage et al., 2015), but the wash-off process from the building facade was
77
neglected. Therefore, an in-depth study on the wash-off pollutants from the facade
78
could provide a complete understanding of the wash-off process in an urban area and
79
reduce the uncertainty of wash-off results in hydrological models.
80
Accordingly, in order to assist the best management practice of stormwater
81
pollution mitigation, the focuses of this study are to (i) characterize the
82
particulate-associated trace metals (Cd, Pb, Cu, and Zn), (ii) determine the influence
83
of antecedent environmental conditions (ADP and ASP) and surface properties (road
84
surfaces and low-elevated facade) on the metal pollution levels, and (iii) rank the
85
influential parameters through the sensitivity analysis based on Bayesian network.
Page 5 of 28
86
2. MATERIALS AND METHODS
87
2.1 Study area
88
The road deposited samples were obtained from George-Baehr-Str. (51°01’47” N,
89
13°43’24” E). The sampling street is near Berg str. and Nuernberger str. with a high
90
traffic load which is 13600-15000 vehicle per day (Landeshauptstadt Dresden, 2010).
91
It was selected due to the elevated levels of road deposited sediment adsorbed
92
pollutants (Zhang et al., 2015). All the building facades along the given road are made
93
of the same materials of bricks and mortar, and the road is paved also with a uniform
94
material, asphalt.
95
2.2 Sample collection
96
The samples collection was conducted on 25th February and 4th March 2016 with
97
two-day accumulation after the last rainfall or snowfall events for each sampling
98
campaign. More explicitly, there were events with rainfall of 11.9 mm and 1.2 mm on
99
22nd and 23rd February respectively, while 50 mm snow/0.9 mm rainfall and 20 mm
100
snow/0.7 mm rainfall fell on 1st and 2nd March (Wetterkontor). On 4th March,
101
snowpack remained on the non-paved pervious areas was adjacent to the road
102
surfaces.
103
In each sampling site, the wash-off samples from the low-elevated facade surface
104
were collected from the area of 2 m height above ground level and 1 m width. The
105
runoff samples from the road surface were obtained from 2 m2 (2 m × 1 m) roadside
106
curb areas, where most sediment is assembled (Sartor and Boyd, 1972; Zhang et al., Page 6 of 28
107
2016). Since there is no standard sampling method for collecting the surface sediment,
108
the samples from the both surfaces were collected by two sampling techniques, which
109
are wet vacuuming sweeping (WVS) method (Amato et al., 2010; Zhang et al., 2017a)
110
and surface washing (SW) approach (Davis and Birch, 2010; Egodawatta et al., 2009).
111
At each sampling site, two sampling methods were performed on the neighboring 2
112
m2 predefined facade or road surfaces. It was initially uniformly sprayed with 1 liter of
113
synthetic rainwater in order to replicate the real wash-off condition.
114
The synthetic rainwater was made of 1.35 µg/L of NaCl, 1.13 µg/L of HNO3 as
115
well as 1.76 µg/L of H2SO4 with CH3COO- and CH3COONa as buffers (Davis et al.,
116
2001; Zhang et al., 2016). NaOH solution was added to adjust the pH of 4.7-5
117
according to the historical values of the rainwater in Dresden. According to the WVS
118
method, the particles within the analyzed area were collected by a professional
119
vacuum sweeper (Puzzi 100 Super, Kärcher) twice in the different directions, which
120
intend to provide the maximum pollution potential in the surface runoff. However, the
121
SW method collected the runoff from the road at the outlet point, while the facade
122
samples were collected in a plastic basin at the bottom of the surface. It intends to
123
provide the pollution potential in the surface runoff after a normal level of rainfall.
124
Therefore, WVS and SW methods were used to represent the range of potential
125
pollution levels between the maximum to normal in surface runoffs.
126
2.3 Chemical analysis
127
A total of 48 samples were tested for Cadmium (Cd), Lead (Pb), Copper (Cu), Page 7 of 28
128
and Zinc (Zn). The collected solutions were initially agitated, acidified with 65%
129
HNO3 and stored at 4°C in a refrigerator. The 40 ml samples were then filtered
130
through a Sartorius 0.45 µm membrane filter by using a vacuum. The filters and sludge
131
from the previous step were left in an oven at 105 °C for a minimum of two hours. They
132
were subsequently weighted for total solid (TS) analysis, while the filtrates were
133
analyzed for the total trace metal concentrations.
134
The concentrations of Cu and Zn were determined using flame furnace atomic
135
absorption
136
spectroscopy (GF-AAS) was used to analyze Cd and Pb, which had relatively lower
137
concentrations. The analytical methods and procedures of Cd, Pb, Cu, and Zn were
138
following the German standard Norm DIN EN ISO 5961, DIN38406-6, DIN 38406-7,
139
and DIN 38406-8 respectively. The limits of detection were 0.05 µg/L for Cd, 1.00
140
µg/L for Pb, 0.01 mg/L for Cu, and 0.01 mg/L for Zn. The limits of quantitation were
141
0.10 µg/L for Cd, 2.00 µg/L for Pb, 0.02 mg/L for Cu, and 0.02 mg/L for Zn.
142
2.4 Bayesian network
spectrometry
(FF-AAS).
Graphite
furnace
atomic
absorption
143
Bayesian network is a probabilistic graphical model, which visually interprets a
144
causal relationship between variables (Bonotto et al., 2018; Carvajal et al., 2017).
145
Comparing with other methods for ranking parameters (e.g. hierarchy cluster analysis),
146
Bayesian network has a stronger capacity on dealing with both discrete and
147
continuous data (Jing and Jingqi, 2012). It consists of a directed acyclic graph (DAG)
148
and a conditional probability table (CPT), corresponding to qualificative description Page 8 of 28
149
and quantitative description respectively (Aguilera et al., 2011; Pearl, 1986). DAG,
150
composed of the node variable set X={X1, X2, ..., Xn} and directed edges set,
151
describes the causal relationship between each child node and its parent nodes; CPT
152
quantified the all possible conditional probabilities among these nodes (Lim et al.,
153
2018). Bayesian network factorizes the joint probability distribution to several local
154
probability distributions among the nodes: ,
,…,
=
|
(1)
155
where P is the probability distribution, π(Xi)is the parent node sets of Xi.
156
The sensitivity of parameters was analyzed through the entropy reduction due to
157
the discrete state of those variables in the simple network. It is a robust local
158
sensitivity analysis used in many researches (Miltner and McLaughlin, 2019;
159
Rositano et al., 2017; Shi et al., 2020). The entropy reduction is defined as the
160
expected reduction in uncertainty of the expected real value of the target variable Q
161
due to a finding at an input variable F, and it was calculated as (Marcot et al., 2006;
162
Pearl, 1988): =
−
|
=
,
log [
,
] (2)
163
where H(Q) is the entropy of Q before any new findings and H(Q|F) is the
164
entropy of Q after new findings from F. The larger entropy reduction value represents
165
the higher dependency of the parent variable and the target child variable.
166
The Bayesian network construction and parameter sensitivity analysis were Page 9 of 28
167
carried out by Netica. The network structure was shown in Figure 1. Three influential
168
parameters, namely, surface properties, antecedent environmental conditions, and
169
sampling methods were identified to exert influence on the trace metal concentrations.
170
The continuous variables, such as concentrations, were converted into the discrete
171
quantities before the probabilistic inference.
172
3. RESULTS AND DISCUSSIONS
173
3.1 Wash-off contents of metals and TS
174
The concentrations of four trace metals (Cd, Pb, Cu, and Zn) collected by the two
175
given methods are displayed in Figure 2. The results were categorized into different
176
antecedent environmental conditions of 2-day ADP before 25th February and 2-day
177
ASP before 4th March, surface properties of roads and facades and the sampling
178
methods of WVS and SW.
179
The wash-off concentrations of trace metals were identified in the following
180
orders: Cd (2.28±2.08 µg/l) < Pb (435.85±412.61 µg/l) < Cu (0.93±0.61 µg/l) < Zn
181
(2.52±2.30 µg/l). Accordingly, the wash-off concentrations revealed a considerable
182
variation depending on the metal element, surface properties, antecedent
183
environmental
184
concentrations of Cd, Pb, Cu and Zn determined by SW (indicating a normal potential
185
pollution) and WVS (indicating a maximum potential pollution) methods were
186
5.57±2.36 µg/l, 6.08±0.21 µg/l; 460.00±112.80 µg/l, 965.00±159.03 µg/l; 0.96±0.20
187
mg/l, 2.00±0.56 mg/l; and 3.45±0.48 mg/l, 7.59±1.46 mg/l respectively. The highest
conditions,
and
sampling
methods.
The
highest
wash-off
Page 10 of 28
188
concentrations determined by both methods of most of metals were measured on the
189
road surface after ADP, except that the highest concentration determined by WVS for
190
Pb was found on the facade surface after ADP.
191
The lowest wash-off concentrations of Cd, Pb, Cu, and Zn determined by SW
192
method were 0.22±0.19 µg/l, 64.30±35.55 µg/l, 0.11±0.07 mg/l, and 0.23±0.13 mg/l
193
respectively. Those determined by WVS method were 1.46±1.25 µg/l, 141.93±81.77
194
µg/l, 0.35±0.19 mg/l, and 0.88±0.41 mg/l respectively. The lowest concentration of Pb
195
determined by the WVS method occurred on the road surface after ASP, while the
196
lowest concentrations determined by both methods for the other metal were found on
197
the facade after ASP. Cu concentration (1.22±1.51) collected under the condition of
198
facade/ADP/SW shows a large deviation, which would cause high uncertainty of the
199
result.
200
Besides, Zn and Cu are typical anthropogenic pollutants (Loganathan et al., 2013;
201
Zhang et al., 2016). The relatively higher content of both metals is primarily attributed
202
to the corrosion of metal surface and vehicle abrasion in road traffic (Khan et al., 2016;
203
Zhang et al., 2017a). Pb was mainly contributed by gasoline-powered vehicles, but it
204
was banned in Germany in 1996 (Lovei, 1998). Thus, the presence of Pb on the
205
surface would come mostly from the past usage of leaded fuel (Egodawatta et al.,
206
2013; Mosley and Peake, 2001) and partly from vehicle tires and brakes (Sansalone et
207
al., 1996). Finally, the low content of Cd accumulated on the surface might be
208
contributed by atmospheric deposition (Zhang et al., 2015b).
Page 11 of 28
209
In addition, the wash-off concentrations of TS showed the ranges from 5.23 to
210
10.49 g/L on road surface after ADP, 0.35 to 1.51 g/L on the facade surface after ADP,
211
2.40 to 5.69 g/L on the road surface after ASP, and 0.05 to 1.08 g/L on the facade
212
surface after ASP.
213
3.2 Influence of antecedent environmental conditions on the contents of metal and TS
214
Figure 2 illustrates the wash-off concentrations of Cd, Pb, Cu, and Zn determined
215
by SW and WVS methods. It is obvious that the wash-off concentrations of all trace
216
metals and TS on either road or low-elevated facade surface were significantly higher
217
after ADP than those after ASP. The results suggest that the snowmelt in the
218
antecedent period after a snowfall could considerably influence the accumulation of
219
metals on the surface in the following rainfall event. The concentrations of Cd, Pb, Cu,
220
Zn, and TS determined by the SW method in road surface runoff after ASP were 51%,
221
74%, 38%, 42%, and 54% lower than those after ADP respectively. Besides, ASP
222
showed a clearer impact on the metal content level in the runoff from the low-elevated
223
facade surface than ADP. Their concentrations determined also by the SW method in
224
the facade surface runoff after ASP were 80%, 83%, 91%, 79%, and 84% lower than
225
those after ADP respectively. The wash-off concentrations collected by the WVS
226
method showed the same results.
227
In terms of the individual metal, Pb was the anthropogenic metal that received a
228
strong impact from snow events. It is because the primary source of Pb, past usage of
229
leaded fuel, was considerably more inactive than the pollution sources of other metals, Page 12 of 28
230
e.g. atmospheric deposition for Cd and vehicle-related sources for Cu and Zn. It
231
would be significantly influenced by the continued wash-off process from snowmelt.
232
Compared with the content of metals, the snowmelt showed a relatively stronger
233
influence on the wash-off concentration of TS as determined by the SW method.
234
Generally, snowpack can absorb the atmospheric deposited particles and other
235
pollutants (Kuoppamäki et al., 2014). During the snowmelt, the particulate matters
236
will be coagulated with other particles and move slowly in the snowpack (Oberts,
237
1994). Therefore, the build-up rate of deposited solids on the surface after ASP will be
238
slower than it after ADP. However, the snowmelt had a limited impact on the TS
239
wash-off content determined by the WVS method. It suggests that the snowmelt had a
240
stronger influence on the easily detached particles.
241
Although the result indicates that the snowmelt reduced the pollution in
242
rainfall-induced surface runoff, the snowpack might contain a large load of pollutants
243
(Oberts, 1994; Schöndorf and Herrmann, 1987). The rain-on-snow can actually flush
244
the pollutants from the snowpack and possibly cause even higher pollution load in the
245
runoff. Therefore, the influence of snowmelt during rainfall events on pollutant in the
246
runoff should be further evaluated.
247
3.3 Understanding the variance on the road and low-elevated facade surfaces
248
In comparison with the two analyzed surfaces under the same sampling conditions,
249
generally, the concentrations of trace metals on the road were much higher than those
250
on the facade (Figure 2). Specifically, the content of Cd, Cu, and Zn washed off the Page 13 of 28
251
road surfaces was commonly 45-780% higher than them off the facade surfaces. The
252
amount of TS on the road surface was even 420-4500% higher than them on the
253
facade surface. The gravity force and roughness property of the facade might be the
254
reason since only the particles with strong adhesion capacity can be attached stably on
255
the facade surface. Pb content in surface runoff determined by the SW method had
256
comparable concentrations on the road and facade surface. However, the higher Pb
257
content measured by the WVS method was washed off from the low-elevated facade
258
surface than the road surface for both ADP and ASP. It might because a relatively
259
higher Pb content was usually found from bricks made from mine tailings (Davis et
260
al., 2001; Li et al., 2017).
261
Besides, the concentration changes in trace metals and TS from the road to facade
262
surfaces after ADP were consistent with the changes after ASP. It indicates that the
263
pollutants on the low-elevated facade were closely related to the solids on the road
264
surface. Therefore, trace metals and TS on the low-elevated facade surface should
265
mainly originate from the re-suspended pollutants and solids from the surrounding
266
area.
267
Figure 3 illustrates the ratios of trace metal and TS concentrations in different
268
conditions. It reveals the trace metal content absorbed on the unit gram of particles
269
and all ratios on the facade surface were always higher than on the road surface under
270
the same antecedent weather condition by both sampling methods. The ratios on the
271
facade surface were 2-13 times higher than them on the road surface. It suggests that
Page 14 of 28
272
each gram of particles on the facade surface contained more content of trace metals. It
273
also showed the relatively higher concentration ratios when using the SW sampling
274
method, which might be attributed to the fact that more particles with less trace metal
275
content were washed off when using the WVS method.
276
3.4 Sensitivity analysis based on Bayesian network
277
The sensitivity analysis was derived from the entropy reduction with the Bayesian
278
network. The entropy reduction was shown in Figure 4. The antecedent environmental
279
conditions of ADP and ASP, surface properties of roads and facades, sampling
280
methods of WVS and SW were the main variables exerting influences on the
281
concentrations of trace metals.
282
With regard to Cd and Zn, they had similar results of classification. The primary
283
influencing variable on their wash-off concentrations was the surface properties. The
284
antecedent environmental conditions and the sampling methods had a minor influence
285
on the wash-off concentrations. It showed the same results displayed in Figure 2 that
286
the concentration reduction rate from the road to the facade surface (averagely 72%
287
for Cd; 74% for Zn) was obviously higher than the antecedent environmental
288
conditions (from ADP to ASP: 53%; 61%) and the sampling methods (from WVW to
289
SW: 39%; 52%) when only one influencing variable was changed at a time.
290
In terms of Pb and Cu, both concentrations predominantly depended on the
291
antecedent environmental conditions. The concentration reduction rate from ADP to
292
ASP was 81% for Pb and 66% for Cu, which was highest among all variables. For Pb, Page 15 of 28
293
the application of different sampling methods had a relatively stronger impact on
294
surface conditions. It was agreed with the finding that the Pb concentrations in WVS
295
samples were particularly higher on facade than road surface. In contrast, the surface
296
conditions for Cu showed the comparable impact to the antecedent environmental
297
conditions. It might be attributed to the high uncertainty under the condition of
298
facade/ADP/SW as shown in Figure 2.
299
4. CONCLUSIONS
300
The main focuses of this study are to determine the influence of antecedent
301
weather conditions on the metals’ build-up process and their further levels in the
302
surface runoff. The result shows that the snow-melting event had a considerable
303
influence on the wash-off concentrations of the trace metals on both road and facade
304
surfaces. The wash-off concentrations of Cd, Cu, and Zn on the facade surface were
305
lower than them on the road surface. More metals contents could be measured using the
306
WVS than SW, thus WVS could indicate a maximum pollution potential in the surface
307
runoff. The results of sensitivity analysis based on the Bayesian network show that the
308
concentrations of Cd and Zn were mainly dependent on the surface properties, while
309
the antecedent environmental conditions strongly influenced the concentrations of Pb
310
and Cu. The sampling methods had a minor influence on the wash-off concentrations.
311
The data provided herein could assist a better understanding of wash-off process
312
and an improving modeling for the pollutant migration in surface runoff by considering
313
the different surfaces. The influences of snowmelt and rainfall events with a longer Page 16 of 28
314
antecedent period should be further evaluated.
Page 17 of 28
315
ACKNOWLEDGMENTS
316
The authors would like to gratefully thank Mrs. Ann Marie Lotz Valverde, Mr.
317
Julian David Reyes Silva and Mr. Viacheslav Safronskii for their contributions to the
318
sample collection, laboratory determination, and other valuable support to this project.
319
Thank Mrs. Dr. Heike Brückner, Mrs. Ulrike Gebauer, and Mrs. Sinaida Heidt for their
320
assistance with laboratory analysis. This work was jointly supported by the project of
321
“Collaborative early warning information systems for urban infrastructures
322
(COLABIS)” funded by German Federal Ministry of Education and Research (BMBF,
323
Grant No.: 03G0852A). Mention of trade names or commercial products does not
324
constitute endorsement or recommendation for use.
325
Page 18 of 28
326
REFERENCE
327 328 329
Aguilera, P.A., Fernández, A., Fernández, R., Rumí, R., Salmerón, A., 2011. Bayesian networks in environmental modelling. Environmental Modelling & Software 26, 1376-1388.
330 331 332
Al-Najjar, T., Rasheed, M., Ababneh, Z., Ababneh, A., Al-Omarey, H., 2011. Heavy metals pollution in sediment cores from the Gulf of Aqaba, Red Sea. Natural science 3, 775.
333 334 335
Amato, F., Querol, X., Johansson, C., Nagl, C., Alastuey, A., 2010. A review on the effectiveness of street sweeping, washing and dust suppressants as urban PM control methods. Science of The Total Environment 408, 3070-3084.
336 337 338
Bonotto, D.M., Wijesiri, B., Vergotti, M., da Silveira, E.G., Goonetilleke, A., 2018. Assessing mercury pollution in Amazon River tributaries using a Bayesian Network approach. Ecotoxicology and environmental safety 166, 354-358.
339 340 341
Carvajal, G., Roser, D.J., Sisson, S.A., Keegan, A., Khan, S.J., 2017. Bayesian belief network modelling of chlorine disinfection for human pathogenic viruses in municipal wastewater. Water Research 109, 144-154.
342 343
Davis, A.P., Shokouhian, M., Ni, S., 2001. Loading estimates of lead, copper, cadmium, and zinc in urban runoff from specific sources. Chemosphere 44, 997-1009.
344 345 346
Davis, B., Birch, G., 2010. Comparison of heavy metal loads in stormwater runoff from major and minor urban roads using pollutant yield rating curves. Environmental Pollution 158, 2541-2545.
347 348
Deletic, A., 1998. The first flush load of urban surface runoff. Water Research 32, 2462-2470.
349 350 351 352
Dhanakumar, S., Murthy, K.R., Solaraj, G., Mohanraj, R., 2013. Heavy-metal fractionation in surface sediments of the Cauvery River Estuarine Region, Southeastern Coast of India. Archives of environmental contamination and toxicology 65, 14-23.
353 354
Dresden, L., 2010. Verkehrsmengenkarte 2009. Dresden, Autobahnamt Dresden. Landeshauptstadt Dresden, Dresden.
355 356 357
Egodawatta, P., Thomas, E., Goonetilleke, A., 2009. Understanding the physical processes of pollutant build-up and wash-off on roof surfaces. Science of The Total Environment 407, 1834-1841.
358 359
Egodawatta, P., Ziyath, A.M., Goonetilleke, A., 2013. Characterising metal build-up on urban road surfaces. Environmental Pollution 176, 87-91.
360 361
Gnecco, I., Berretta, C., Lanza, L., La Barbera, P., 2005. Storm water pollution in the urban environment of Genoa, Italy. Atmospheric research 77, 60-73.
362
Hua, P., Yang, W., Qi, X., Jiang, S., Xie, J., Gu, X., Li, H., Zhang, J., Krebs, P., 2020. Page 19 of 28
363 364
Evaluating the effect of urban flooding reduction strategies in response to design rainfall and low impact development. Journal of Cleaner Production 242, 118515.
365 366
Jing, C., Jingqi, F., 2012. Fire Alarm System Based on Multi-Sensor Bayes Network. Procedia Engineering 29, 2551-2555.
367 368 369 370
Kaeseberg, T., Zhang, J., Schubert, S., Oertel, R., Siedel, H., Krebs, P., 2018. Sewer sediment-bound antibiotics as a potential environmental risk: Adsorption and desorption affinity of 14 antibiotics and one metabolite. Environmental pollution 239, 638-647.
371 372 373 374
Khan, M., Latif, M.T., Saw, W., Amil, N., Nadzir, M.S.M., Sahani, M., Tahir, N., Chung, J., 2016. Fine particulate matter in the tropical environment: monsoonal effects, source apportionment, and health risk assessment. Atmospheric Chemistry and Physics 16, 597-617.
375 376
Kuoppamäki, K., Setälä, H., Rantalainen, A.-L., Kotze, D.J., 2014. Urban snow indicates pollution originating from road traffic. Environmental Pollution 195, 56-63.
377 378 379
Li, C., Wen, Q., Hong, M., Liang, Z., Zhuang, Z., Yu, Y., 2017. Heavy metals leaching in bricks made from lead and zinc mine tailings with varied chemical components. Construction and Building Materials 134, 443-451.
380 381 382
Li, R., Hua, P., Cai, J., Wang, X., Zhu, Y., Huang, Z., Li, P., Wang, Z., Bai, Y., Hu, B.X., 2019. A sixteen-year reduction in the concentrations of aquatic PAHs corresponding to source shifts in the Elbe River, Germany. Journal of Cleaner Production.
383 384 385
Lim, R.B.H., Liew, J.H., Kwik, J.T.B., Yeo, D.C.J., 2018. Predicting food web responses to biomanipulation using Bayesian Belief Network: Assessment of accuracy and applicability using in-situ exclosure experiments. Ecological Modelling 384, 308-315.
386 387 388
Liu, A., Duodu, G.O., Mummullage, S., Ayoko, G.A., Goonetilleke, A., 2017. Hierarchy of factors which influence polycyclic aromatic hydrocarbons (PAHs) distribution in river sediments. Environmental Pollution 223, 81-89.
389 390
Liu, A., Goonetilleke, A., Egodawatta, P., 2012. Taxonomy for rainfall events based on pollutant wash-off potential in urban areas. Ecological Engineering 47, 110-114.
391 392 393 394
Loganathan, P., Vigneswaran, S., Kandasamy, J., 2013. Road-deposited sediment pollutants: a critical review of their characteristics, source apportionment, and management. Critical reviews in environmental science and technology 43, 1315-1348.
395 396
Lovei, M., 1998. Phasing out lead from gasoline: worldwide experience and policy implications. The World Bank.
397 398 399
Mahbub, P., Ayoko, G.A., Goonetilleke, A., Egodawatta, P., Kokot, S., 2010. Impacts of traffic and rainfall characteristics on heavy metals build-up and wash-off from urban roads. Environmental Science & Technology 44, 8904-8910.
400
Marcot, B.G., Steventon, J.D., Sutherland, G.D., McCann, R.K., 2006. Guidelines for Page 20 of 28
401 402
developing and updating Bayesian belief networks applied to ecological modeling and conservation. Canadian Journal of Forest Research 36, 3063-3074.
403 404 405
Miltner, R., McLaughlin, D., 2019. Management of headwaters based on macroinvertebrate assemblages and environmental attributes. Science of The Total Environment 650, 438-451.
406 407 408
Mosley, L.M., Peake, B.M., 2001. Partitioning of metals (Fe, Pb, Cu, Zn) in urban runoff from the Kaikorai Valley, Dunedin, New Zealand. New Zealand journal of marine and freshwater research 35, 615-624.
409 410
Oberts, G.L., 1994. Influence of snowmelt dynamics on stormwater runoff quality. Watershed protection techniques 1, 16.
411 412 413 414
Pearl, J., 1986. A Constraint – Propagation Approach to Probabilistic Reasoning* *This work was supported in part by the National Science Foundation, Grant #DSR 83– 13875, in: Kanal, L.N., Lemmer, J.F. (Eds.), Machine Intelligence and Pattern Recognition. North-Holland, pp. 357-369.
415 416
Pearl, J., 1988. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, 2nd edition ed. Morgan Kaufmann, San Mateo, CA.
417 418 419
Pollino, C.A., Woodberry, O., Nicholson, A., Korb, K., Hart, B.T., 2007. Parameterisation and evaluation of a Bayesian network for use in an ecological risk assessment. Environmental modelling & software 22, 1140-1152.
420 421 422 423
Rositano, F., Piñeiro, G., Bert, F.E., Ferraro, D.O., 2017. A comparison of two sensitivity analysis techniques based on four bayesian models representing ecosystem services provision in the Argentine Pampas. Ecological Informatics 41, 33-39.
424 425
Rossman, L.A., 2010. Storm water management model user's manual, version 5.0. National Risk Management Research Laboratory, Office of Research and ….
426 427 428
Sage, J., Bonhomme, C., Al Ali, S., Gromaire, M.-C., 2015. Performance assessment of a commonly used “accumulation and wash-off” model from long-term continuous road runoff turbidity measurements. Water Research 78, 47-59.
429 430
Sansalone, J.J., Buchberger, S.G., Al-Abed, S.R., 1996. Fractionation of heavy metals in pavement runoff. Science of The Total Environment 189, 371-378.
431 432
Sartor, J.D., Boyd, G.B., 1972. Water pollution aspects of street surface contaminants. US Government Printing Office.
433 434
Schöndorf, T., Herrmann, R., 1987. Transport and chemodynamics of organic micropollutants and ions during snowmelt. Hydrology Research 18, 259-278.
435 436 437 438
Shi, H., Luo, G., Zheng, H., Chen, C., Bai, J., Liu, T., Ochege, F.U., De Maeyer, P., 2020. Coupling the water-energy-food-ecology nexus into a Bayesian network for water resources analysis and management in the Syr Darya River basin. Journal of Hydrology 581, 124387. Page 21 of 28
439 440 441 442
Trujillo-González, J.M., Torres-Mora, M.A., Keesstra, S., Brevik, E.C., Jiménez-Ballesta, R., 2016. Heavy metal accumulation related to population density in road dust samples taken from urban sites under different land uses. Science of The Total Environment 553, 636-642.
443 444
Vaze, J., Chiew, F.H., 2002. Experimental study of pollutant accumulation on an urban road surface. Urban Water 4, 379-389.
445 446 447 448
Wang, Z., Hua, P., Li, R., Bai, Y., Fan, G., Wang, P., Hu, B.X., Zhang, J., Krebs, P., 2019. Concentration decline in response to source shift of trace metals in Elbe River, Germany: A long-term trend analysis during 1998–2016. Environmental pollution 250, 511-519.
449
Wetterkontor, Weather report in Dresden-Strehlen.
450 451 452
Wicke, D., Cochrane, T.A., O'Sullivan, A., 2012. Build-up dynamics of heavy metals deposited on impermeable urban surfaces. Journal of environmental management 113, 347-354.
453 454 455
Xu, Z., Wu, J., Li, H., Chen, Y., Xu, J., Xiong, L., Zhang, J., 2018. Characterizing heavy metals in combined sewer overflows and its influence on microbial diversity. Science of the total environment 625, 1272-1282.
456 457 458
Zhang, J., Hua, P., Krebs, P., 2015a. The build-up dynamic and chemical fractionation of Cu, Zn and Cd in road-deposited sediment. Science of The Total Environment 532, 723-732.
459 460 461
Zhang, J., Hua, P., Krebs, P., 2015b. The chemical fractionation and potential source identification of Cu, Zn and Cd on urban watershed. Water science and technology, wst2015355.
462 463 464
Zhang, J., Hua, P., Krebs, P., 2016. The influences of dissolved organic matter and surfactant on the desorption of Cu and Zn from road-deposited sediment. Chemosphere 150, 63-70.
465 466 467
Zhang, J., Hua, P., Krebs, P., 2017a. Influences of land use and antecedent dry-weather period on pollution level and ecological risk of heavy metals in road-deposited sediment. Environmental pollution 228, 158-168.
468 469 470
Zhang, J., Li, R., Zhang, X., Bai, Y., Cao, P., Hua, P., 2019. Vehicular contribution of PAHs in size dependent road dust: A source apportionment by PCA-MLR, PMF, and Unmix receptor models. Science of the total environment 649, 1314-1322.
471 472 473
Zhang, J., Wang, J., Hua, P., Krebs, P., 2015c. The qualitative and quantitative source apportionments of polycyclic aromatic hydrocarbons in size dependent road deposited sediment. Science of the total environment 505, 90-101.
474 475 476 477
Zhang, J., Wu, J., Hua, P., Zhao, Z., Wu, L., Fan, G., Bai, Y., Kaeseberg, T., Krebs, P., 2017b. The influence of land use on source apportionment and risk assessment of polycyclic aromatic hydrocarbons in road-deposited sediment. Environmental Pollution 229, 705-714. Page 22 of 28
478
Page 23 of 28
479
Figure Captions and Figures
480
Figure 1 The structure of the Bayesian network in Netica for modelling the
481
influencing variables (surface properties, antecedent environmental conditions, and
482
sampling methods) to Cd, Pb, Cu and Zn concentrations.
483
Figure 2 Cd, Pb, Cu, Zn, and TS concentrations measured on the facade and the road
484
surfaces after ADP (25th February) and ASP (4th March). Black bars represent the
485
normal wash-off concentration collected by SW method; Red bars represent the
486
maximum wash-off concentration collected by WVS method.
487
Figure 3 The ratios of trace metal and TS concentration collected from the facade and
488
the road surfaces after ADP (25th February) and ASP (4th March). Left: the
489
concentration collected by SW method; Right: the concentration collected by WVS
490
method.
491
Figure 4 Sensitivity analysis based on the entropy reduction associated with Bayesian
492
network
Page 24 of 28
493
Figure 1
494
Page 25 of 28
495
Figure 2
496 497
Page 26 of 28
498
Figure 3
499
Page 27 of 28
500
Figure 4
501 502
Page 28 of 28
1
Research highlights
2
•
Antecedent conditions affect metal levels in surface runoff
3
•
The wash-off processes off facade/road surfaces were analyzed
4
•
The influencing variables were ranked by entropy reduction with Bayesian network
5
•
The snowmelt process in dry period had a significant impact on build-up process
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:
S2666-4984(20)30009-0
DOI:
https://doi.org/10.1016/j.ese.2020.100017
Reference:
ESE 100017
To appear in:
Environmental Science and Ecotechnology
Received Date: 4 December 2019 Revised Date:
23 December 2019
Accepted Date: 18 January 2020
Please cite this article as: Z. Wang, P. Hua, H. Dai, R. Li, B. Xi, D. Gui, J. Zhang, P. Krebs, Influence of surface properties and antecedent environmental conditions on particulate-associated metals in surface runoff, Environmental Science and Ecotechnology, https://doi.org/10.1016/j.ese.2020.100017. 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. © 2020 The Author(s). Published by Elsevier B.V. on behalf of Chinese Society for Environmental Sciences, Harbin Institute of Technology, Chinese Research Academy of Environmental Sciences.
3
Influence of surface properties and antecedent environmental conditions on particulate-associated metals in surface runoff
4
Zhenyu Wanga, 1, Pei Huab, 1, Heng Daic, Rui Lid, Beidou Xid,
5
Dongwei Guie, Jin Zhangc, *, Peter Krebsa
1 2
6 a
7
Institute of Urban Water Management, Technische Universität Dresden, 01062
8
Dresden, Germany b
9 10 11
The Environmental Research Institute, MOE Key Laboratory of Environmental
Theoretical Chemistry, South China Normal University, 510006 Guangzhou, China c
Institute of Groundwater and Earth Sciences, Jinan University, 510632 Guangzhou,
12 13 14 15 16 17 18
China d
State Environmental Protection Key Laboratory of Simulation and Control of Groundwater Pollution, Chinese Research Academy of Environmental Sciences, Beijing 100012, China e
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, Xinjiang, China 1
These authors contributed equally to this work *Corresponding author: [email protected]
19
Page 1 of 28
20
ABSTRACT
21
Particulate-associated trace metals have been regarded as an important pollution
22
source for urban surface runoff. Cd, Pb, Cu, Zn and total solids (TS) washed off two
23
different surfaces (low-elevated facade and road surfaces) under two kinds of
24
antecedent environmental conditions (dry and snow-melting) were determined in this
25
study. Wet-vacuuming sweeping (WVS) and surface washing (SW) methods were
26
used to collect the particulate matters to represent the maximum pollution potential
27
and common rainfall-induced wash-off condition respectively. The result shows that
28
the wash-off concentrations of trace metals were found in the order of Cd (2.28±2.08
29
µg/l) < Pb (435.85±412.61 µg/l) < Cu (0.93±0.61 µg/l) < Zn (2.52±2.30 µg/l). The
30
snow-melting process had a considerable influence on the wash-off concentrations of
31
the trace metals on both road and facade surfaces. It reduced >38% and >79% of metals
32
and TS concentrations in the facade surface and road surface runoff respectively. The
33
wash-off concentrations of Cd, Cu, and Zn on the road surface 45-780% higher than
34
those on the facade surfaces. The sensitivity analysis based on the Bayesian network
35
indicates that the wash-off concentrations of metals were mainly dependent on the
36
antecedent environmental conditions or the surface properties while the sampling
37
methods had a minor influence. Therefore, to accurately model the pollutant
38
migration in the surface runoff requires an improving method considering different
39
surfaces and antecedent environment conditions.
Page 2 of 28
40 41
KEYWORDS Trace metal; Road and facade; Wash-off; Snowmelt; Bayesian network
42 43
Page 3 of 28
44
1. INTRODUCTION
45
Urban stormwater runoff affects water quality, water quantity, habitat and
46
biological resources, public health, and the aesthetic appearance of urban waterways
47
(Hua et al., 2020; Li et al., 2019). It has gained increasing attention and the
48
deteriorated stormwater runoff has been regarded as a leading source of pollutants
49
causing water quality impairment related to human activities in the natural
50
hydrological system (Li et al., 2019; Wang et al., 2019; Zhang et al., 2019). The
51
rainfall-induced surface runoff would wash the anthropogenic pollutants of trace
52
metals (Trujillo-González et al., 2016; Zhang et al., 2015a), polycyclic aromatic
53
hydrocarbons (PAHs) (Liu et al., 2017; Zhang et al., 2015c), and other toxics off the
54
impervious surface and eventually discharge into the nearby water bodies through
55
storm sewers or combined sewer overflows (CSOs) (Kaeseberg et al., 2018; Xu et al.,
56
2018). Among the pollutants found in surface runoff, trace metals are of great concern
57
due to their toxicity, persistence, and abiotic degradation in the environment and
58
bioaccumulation in the food webs (Al-Najjar et al., 2011; Dhanakumar et al., 2013).
59
In this context, the wash-off process of trace metals during the rainfall event
60
would affect the potential deterioration level of runoff water quality (Mahbub et al.,
61
2010; Vaze and Chiew, 2002). In a conceptual rainfall-runoff model, the stormwater
62
pollution in the wash-off process is dependent on the build-up process, runoff rate and
63
other influencing variables (Hua et al., 2020; Rossman, 2010). Antecedent dry
64
weather period (ADP) is commonly recognized as one of the most important
Page 4 of 28
65
influencing variables controlling the pollutants’ build-up process (Wicke et al., 2012;
66
Zhang et al., 2015a, 2017a). Therefore, its impact on the wash-off process was
67
discussed in many studies (Gnecco et al., 2005; Liu et al., 2012; Zhang et al., 2017b).
68
However, less attention has been addressed to the effect of other antecedent
69
environmental conditions, such as snowpack melting between the snow and
70
precipitation events, on the pollutants’ build-up process and thereafter the occurrence
71
in the surface runoff. The antecedent snowmelt period (ASP) would play an important
72
role at the high latitude zones in early spring when the snow-melting and
73
rainfall-induced runoff occurs and forms a complicated wash-off process. In addition
74
to the antecedent environmental conditions, most of the studies focused on the
75
pollutants’ wash-off on the road and roof surfaces (Deletic, 1998; Egodawatta et al.,
76
2009; Sage et al., 2015), but the wash-off process from the building facade was
77
neglected. Therefore, an in-depth study on the wash-off pollutants from the facade
78
could provide a complete understanding of the wash-off process in an urban area and
79
reduce the uncertainty of wash-off results in hydrological models.
80
Accordingly, in order to assist the best management practice of stormwater
81
pollution mitigation, the focuses of this study are to (i) characterize the
82
particulate-associated trace metals (Cd, Pb, Cu, and Zn), (ii) determine the influence
83
of antecedent environmental conditions (ADP and ASP) and surface properties (road
84
surfaces and low-elevated facade) on the metal pollution levels, and (iii) rank the
85
influential parameters through the sensitivity analysis based on Bayesian network.
Page 5 of 28
86
2. MATERIALS AND METHODS
87
2.1 Study area
88
The road deposited samples were obtained from George-Baehr-Str. (51°01’47” N,
89
13°43’24” E). The sampling street is near Berg str. and Nuernberger str. with a high
90
traffic load which is 13600-15000 vehicle per day (Landeshauptstadt Dresden, 2010).
91
It was selected due to the elevated levels of road deposited sediment adsorbed
92
pollutants (Zhang et al., 2015). All the building facades along the given road are made
93
of the same materials of bricks and mortar, and the road is paved also with a uniform
94
material, asphalt.
95
2.2 Sample collection
96
The samples collection was conducted on 25th February and 4th March 2016 with
97
two-day accumulation after the last rainfall or snowfall events for each sampling
98
campaign. More explicitly, there were events with rainfall of 11.9 mm and 1.2 mm on
99
22nd and 23rd February respectively, while 50 mm snow/0.9 mm rainfall and 20 mm
100
snow/0.7 mm rainfall fell on 1st and 2nd March (Wetterkontor). On 4th March,
101
snowpack remained on the non-paved pervious areas was adjacent to the road
102
surfaces.
103
In each sampling site, the wash-off samples from the low-elevated facade surface
104
were collected from the area of 2 m height above ground level and 1 m width. The
105
runoff samples from the road surface were obtained from 2 m2 (2 m × 1 m) roadside
106
curb areas, where most sediment is assembled (Sartor and Boyd, 1972; Zhang et al., Page 6 of 28
107
2016). Since there is no standard sampling method for collecting the surface sediment,
108
the samples from the both surfaces were collected by two sampling techniques, which
109
are wet vacuuming sweeping (WVS) method (Amato et al., 2010; Zhang et al., 2017a)
110
and surface washing (SW) approach (Davis and Birch, 2010; Egodawatta et al., 2009).
111
At each sampling site, two sampling methods were performed on the neighboring 2
112
m2 predefined facade or road surfaces. It was initially uniformly sprayed with 1 liter of
113
synthetic rainwater in order to replicate the real wash-off condition.
114
The synthetic rainwater was made of 1.35 µg/L of NaCl, 1.13 µg/L of HNO3 as
115
well as 1.76 µg/L of H2SO4 with CH3COO- and CH3COONa as buffers (Davis et al.,
116
2001; Zhang et al., 2016). NaOH solution was added to adjust the pH of 4.7-5
117
according to the historical values of the rainwater in Dresden. According to the WVS
118
method, the particles within the analyzed area were collected by a professional
119
vacuum sweeper (Puzzi 100 Super, Kärcher) twice in the different directions, which
120
intend to provide the maximum pollution potential in the surface runoff. However, the
121
SW method collected the runoff from the road at the outlet point, while the facade
122
samples were collected in a plastic basin at the bottom of the surface. It intends to
123
provide the pollution potential in the surface runoff after a normal level of rainfall.
124
Therefore, WVS and SW methods were used to represent the range of potential
125
pollution levels between the maximum to normal in surface runoffs.
126
2.3 Chemical analysis
127
A total of 48 samples were tested for Cadmium (Cd), Lead (Pb), Copper (Cu), Page 7 of 28
128
and Zinc (Zn). The collected solutions were initially agitated, acidified with 65%
129
HNO3 and stored at 4°C in a refrigerator. The 40 ml samples were then filtered
130
through a Sartorius 0.45 µm membrane filter by using a vacuum. The filters and sludge
131
from the previous step were left in an oven at 105 °C for a minimum of two hours. They
132
were subsequently weighted for total solid (TS) analysis, while the filtrates were
133
analyzed for the total trace metal concentrations.
134
The concentrations of Cu and Zn were determined using flame furnace atomic
135
absorption
136
spectroscopy (GF-AAS) was used to analyze Cd and Pb, which had relatively lower
137
concentrations. The analytical methods and procedures of Cd, Pb, Cu, and Zn were
138
following the German standard Norm DIN EN ISO 5961, DIN38406-6, DIN 38406-7,
139
and DIN 38406-8 respectively. The limits of detection were 0.05 µg/L for Cd, 1.00
140
µg/L for Pb, 0.01 mg/L for Cu, and 0.01 mg/L for Zn. The limits of quantitation were
141
0.10 µg/L for Cd, 2.00 µg/L for Pb, 0.02 mg/L for Cu, and 0.02 mg/L for Zn.
142
2.4 Bayesian network
spectrometry
(FF-AAS).
Graphite
furnace
atomic
absorption
143
Bayesian network is a probabilistic graphical model, which visually interprets a
144
causal relationship between variables (Bonotto et al., 2018; Carvajal et al., 2017).
145
Comparing with other methods for ranking parameters (e.g. hierarchy cluster analysis),
146
Bayesian network has a stronger capacity on dealing with both discrete and
147
continuous data (Jing and Jingqi, 2012). It consists of a directed acyclic graph (DAG)
148
and a conditional probability table (CPT), corresponding to qualificative description Page 8 of 28
149
and quantitative description respectively (Aguilera et al., 2011; Pearl, 1986). DAG,
150
composed of the node variable set X={X1, X2, ..., Xn} and directed edges set,
151
describes the causal relationship between each child node and its parent nodes; CPT
152
quantified the all possible conditional probabilities among these nodes (Lim et al.,
153
2018). Bayesian network factorizes the joint probability distribution to several local
154
probability distributions among the nodes: ,
,…,
=
|
(1)
155
where P is the probability distribution, π(Xi)is the parent node sets of Xi.
156
The sensitivity of parameters was analyzed through the entropy reduction due to
157
the discrete state of those variables in the simple network. It is a robust local
158
sensitivity analysis used in many researches (Miltner and McLaughlin, 2019;
159
Rositano et al., 2017; Shi et al., 2020). The entropy reduction is defined as the
160
expected reduction in uncertainty of the expected real value of the target variable Q
161
due to a finding at an input variable F, and it was calculated as (Marcot et al., 2006;
162
Pearl, 1988): =
−
|
=
,
log [
,
] (2)
163
where H(Q) is the entropy of Q before any new findings and H(Q|F) is the
164
entropy of Q after new findings from F. The larger entropy reduction value represents
165
the higher dependency of the parent variable and the target child variable.
166
The Bayesian network construction and parameter sensitivity analysis were Page 9 of 28
167
carried out by Netica. The network structure was shown in Figure 1. Three influential
168
parameters, namely, surface properties, antecedent environmental conditions, and
169
sampling methods were identified to exert influence on the trace metal concentrations.
170
The continuous variables, such as concentrations, were converted into the discrete
171
quantities before the probabilistic inference.
172
3. RESULTS AND DISCUSSIONS
173
3.1 Wash-off contents of metals and TS
174
The concentrations of four trace metals (Cd, Pb, Cu, and Zn) collected by the two
175
given methods are displayed in Figure 2. The results were categorized into different
176
antecedent environmental conditions of 2-day ADP before 25th February and 2-day
177
ASP before 4th March, surface properties of roads and facades and the sampling
178
methods of WVS and SW.
179
The wash-off concentrations of trace metals were identified in the following
180
orders: Cd (2.28±2.08 µg/l) < Pb (435.85±412.61 µg/l) < Cu (0.93±0.61 µg/l) < Zn
181
(2.52±2.30 µg/l). Accordingly, the wash-off concentrations revealed a considerable
182
variation depending on the metal element, surface properties, antecedent
183
environmental
184
concentrations of Cd, Pb, Cu and Zn determined by SW (indicating a normal potential
185
pollution) and WVS (indicating a maximum potential pollution) methods were
186
5.57±2.36 µg/l, 6.08±0.21 µg/l; 460.00±112.80 µg/l, 965.00±159.03 µg/l; 0.96±0.20
187
mg/l, 2.00±0.56 mg/l; and 3.45±0.48 mg/l, 7.59±1.46 mg/l respectively. The highest
conditions,
and
sampling
methods.
The
highest
wash-off
Page 10 of 28
188
concentrations determined by both methods of most of metals were measured on the
189
road surface after ADP, except that the highest concentration determined by WVS for
190
Pb was found on the facade surface after ADP.
191
The lowest wash-off concentrations of Cd, Pb, Cu, and Zn determined by SW
192
method were 0.22±0.19 µg/l, 64.30±35.55 µg/l, 0.11±0.07 mg/l, and 0.23±0.13 mg/l
193
respectively. Those determined by WVS method were 1.46±1.25 µg/l, 141.93±81.77
194
µg/l, 0.35±0.19 mg/l, and 0.88±0.41 mg/l respectively. The lowest concentration of Pb
195
determined by the WVS method occurred on the road surface after ASP, while the
196
lowest concentrations determined by both methods for the other metal were found on
197
the facade after ASP. Cu concentration (1.22±1.51) collected under the condition of
198
facade/ADP/SW shows a large deviation, which would cause high uncertainty of the
199
result.
200
Besides, Zn and Cu are typical anthropogenic pollutants (Loganathan et al., 2013;
201
Zhang et al., 2016). The relatively higher content of both metals is primarily attributed
202
to the corrosion of metal surface and vehicle abrasion in road traffic (Khan et al., 2016;
203
Zhang et al., 2017a). Pb was mainly contributed by gasoline-powered vehicles, but it
204
was banned in Germany in 1996 (Lovei, 1998). Thus, the presence of Pb on the
205
surface would come mostly from the past usage of leaded fuel (Egodawatta et al.,
206
2013; Mosley and Peake, 2001) and partly from vehicle tires and brakes (Sansalone et
207
al., 1996). Finally, the low content of Cd accumulated on the surface might be
208
contributed by atmospheric deposition (Zhang et al., 2015b).
Page 11 of 28
209
In addition, the wash-off concentrations of TS showed the ranges from 5.23 to
210
10.49 g/L on road surface after ADP, 0.35 to 1.51 g/L on the facade surface after ADP,
211
2.40 to 5.69 g/L on the road surface after ASP, and 0.05 to 1.08 g/L on the facade
212
surface after ASP.
213
3.2 Influence of antecedent environmental conditions on the contents of metal and TS
214
Figure 2 illustrates the wash-off concentrations of Cd, Pb, Cu, and Zn determined
215
by SW and WVS methods. It is obvious that the wash-off concentrations of all trace
216
metals and TS on either road or low-elevated facade surface were significantly higher
217
after ADP than those after ASP. The results suggest that the snowmelt in the
218
antecedent period after a snowfall could considerably influence the accumulation of
219
metals on the surface in the following rainfall event. The concentrations of Cd, Pb, Cu,
220
Zn, and TS determined by the SW method in road surface runoff after ASP were 51%,
221
74%, 38%, 42%, and 54% lower than those after ADP respectively. Besides, ASP
222
showed a clearer impact on the metal content level in the runoff from the low-elevated
223
facade surface than ADP. Their concentrations determined also by the SW method in
224
the facade surface runoff after ASP were 80%, 83%, 91%, 79%, and 84% lower than
225
those after ADP respectively. The wash-off concentrations collected by the WVS
226
method showed the same results.
227
In terms of the individual metal, Pb was the anthropogenic metal that received a
228
strong impact from snow events. It is because the primary source of Pb, past usage of
229
leaded fuel, was considerably more inactive than the pollution sources of other metals, Page 12 of 28
230
e.g. atmospheric deposition for Cd and vehicle-related sources for Cu and Zn. It
231
would be significantly influenced by the continued wash-off process from snowmelt.
232
Compared with the content of metals, the snowmelt showed a relatively stronger
233
influence on the wash-off concentration of TS as determined by the SW method.
234
Generally, snowpack can absorb the atmospheric deposited particles and other
235
pollutants (Kuoppamäki et al., 2014). During the snowmelt, the particulate matters
236
will be coagulated with other particles and move slowly in the snowpack (Oberts,
237
1994). Therefore, the build-up rate of deposited solids on the surface after ASP will be
238
slower than it after ADP. However, the snowmelt had a limited impact on the TS
239
wash-off content determined by the WVS method. It suggests that the snowmelt had a
240
stronger influence on the easily detached particles.
241
Although the result indicates that the snowmelt reduced the pollution in
242
rainfall-induced surface runoff, the snowpack might contain a large load of pollutants
243
(Oberts, 1994; Schöndorf and Herrmann, 1987). The rain-on-snow can actually flush
244
the pollutants from the snowpack and possibly cause even higher pollution load in the
245
runoff. Therefore, the influence of snowmelt during rainfall events on pollutant in the
246
runoff should be further evaluated.
247
3.3 Understanding the variance on the road and low-elevated facade surfaces
248
In comparison with the two analyzed surfaces under the same sampling conditions,
249
generally, the concentrations of trace metals on the road were much higher than those
250
on the facade (Figure 2). Specifically, the content of Cd, Cu, and Zn washed off the Page 13 of 28
251
road surfaces was commonly 45-780% higher than them off the facade surfaces. The
252
amount of TS on the road surface was even 420-4500% higher than them on the
253
facade surface. The gravity force and roughness property of the facade might be the
254
reason since only the particles with strong adhesion capacity can be attached stably on
255
the facade surface. Pb content in surface runoff determined by the SW method had
256
comparable concentrations on the road and facade surface. However, the higher Pb
257
content measured by the WVS method was washed off from the low-elevated facade
258
surface than the road surface for both ADP and ASP. It might because a relatively
259
higher Pb content was usually found from bricks made from mine tailings (Davis et
260
al., 2001; Li et al., 2017).
261
Besides, the concentration changes in trace metals and TS from the road to facade
262
surfaces after ADP were consistent with the changes after ASP. It indicates that the
263
pollutants on the low-elevated facade were closely related to the solids on the road
264
surface. Therefore, trace metals and TS on the low-elevated facade surface should
265
mainly originate from the re-suspended pollutants and solids from the surrounding
266
area.
267
Figure 3 illustrates the ratios of trace metal and TS concentrations in different
268
conditions. It reveals the trace metal content absorbed on the unit gram of particles
269
and all ratios on the facade surface were always higher than on the road surface under
270
the same antecedent weather condition by both sampling methods. The ratios on the
271
facade surface were 2-13 times higher than them on the road surface. It suggests that
Page 14 of 28
272
each gram of particles on the facade surface contained more content of trace metals. It
273
also showed the relatively higher concentration ratios when using the SW sampling
274
method, which might be attributed to the fact that more particles with less trace metal
275
content were washed off when using the WVS method.
276
3.4 Sensitivity analysis based on Bayesian network
277
The sensitivity analysis was derived from the entropy reduction with the Bayesian
278
network. The entropy reduction was shown in Figure 4. The antecedent environmental
279
conditions of ADP and ASP, surface properties of roads and facades, sampling
280
methods of WVS and SW were the main variables exerting influences on the
281
concentrations of trace metals.
282
With regard to Cd and Zn, they had similar results of classification. The primary
283
influencing variable on their wash-off concentrations was the surface properties. The
284
antecedent environmental conditions and the sampling methods had a minor influence
285
on the wash-off concentrations. It showed the same results displayed in Figure 2 that
286
the concentration reduction rate from the road to the facade surface (averagely 72%
287
for Cd; 74% for Zn) was obviously higher than the antecedent environmental
288
conditions (from ADP to ASP: 53%; 61%) and the sampling methods (from WVW to
289
SW: 39%; 52%) when only one influencing variable was changed at a time.
290
In terms of Pb and Cu, both concentrations predominantly depended on the
291
antecedent environmental conditions. The concentration reduction rate from ADP to
292
ASP was 81% for Pb and 66% for Cu, which was highest among all variables. For Pb, Page 15 of 28
293
the application of different sampling methods had a relatively stronger impact on
294
surface conditions. It was agreed with the finding that the Pb concentrations in WVS
295
samples were particularly higher on facade than road surface. In contrast, the surface
296
conditions for Cu showed the comparable impact to the antecedent environmental
297
conditions. It might be attributed to the high uncertainty under the condition of
298
facade/ADP/SW as shown in Figure 2.
299
4. CONCLUSIONS
300
The main focuses of this study are to determine the influence of antecedent
301
weather conditions on the metals’ build-up process and their further levels in the
302
surface runoff. The result shows that the snow-melting event had a considerable
303
influence on the wash-off concentrations of the trace metals on both road and facade
304
surfaces. The wash-off concentrations of Cd, Cu, and Zn on the facade surface were
305
lower than them on the road surface. More metals contents could be measured using the
306
WVS than SW, thus WVS could indicate a maximum pollution potential in the surface
307
runoff. The results of sensitivity analysis based on the Bayesian network show that the
308
concentrations of Cd and Zn were mainly dependent on the surface properties, while
309
the antecedent environmental conditions strongly influenced the concentrations of Pb
310
and Cu. The sampling methods had a minor influence on the wash-off concentrations.
311
The data provided herein could assist a better understanding of wash-off process
312
and an improving modeling for the pollutant migration in surface runoff by considering
313
the different surfaces. The influences of snowmelt and rainfall events with a longer Page 16 of 28
314
antecedent period should be further evaluated.
Page 17 of 28
315
ACKNOWLEDGMENTS
316
The authors would like to gratefully thank Mrs. Ann Marie Lotz Valverde, Mr.
317
Julian David Reyes Silva and Mr. Viacheslav Safronskii for their contributions to the
318
sample collection, laboratory determination, and other valuable support to this project.
319
Thank Mrs. Dr. Heike Brückner, Mrs. Ulrike Gebauer, and Mrs. Sinaida Heidt for their
320
assistance with laboratory analysis. This work was jointly supported by the project of
321
“Collaborative early warning information systems for urban infrastructures
322
(COLABIS)” funded by German Federal Ministry of Education and Research (BMBF,
323
Grant No.: 03G0852A). Mention of trade names or commercial products does not
324
constitute endorsement or recommendation for use.
325
Page 18 of 28
326
REFERENCE
327 328 329
Aguilera, P.A., Fernández, A., Fernández, R., Rumí, R., Salmerón, A., 2011. Bayesian networks in environmental modelling. Environmental Modelling & Software 26, 1376-1388.
330 331 332
Al-Najjar, T., Rasheed, M., Ababneh, Z., Ababneh, A., Al-Omarey, H., 2011. Heavy metals pollution in sediment cores from the Gulf of Aqaba, Red Sea. Natural science 3, 775.
333 334 335
Amato, F., Querol, X., Johansson, C., Nagl, C., Alastuey, A., 2010. A review on the effectiveness of street sweeping, washing and dust suppressants as urban PM control methods. Science of The Total Environment 408, 3070-3084.
336 337 338
Bonotto, D.M., Wijesiri, B., Vergotti, M., da Silveira, E.G., Goonetilleke, A., 2018. Assessing mercury pollution in Amazon River tributaries using a Bayesian Network approach. Ecotoxicology and environmental safety 166, 354-358.
339 340 341
Carvajal, G., Roser, D.J., Sisson, S.A., Keegan, A., Khan, S.J., 2017. Bayesian belief network modelling of chlorine disinfection for human pathogenic viruses in municipal wastewater. Water Research 109, 144-154.
342 343
Davis, A.P., Shokouhian, M., Ni, S., 2001. Loading estimates of lead, copper, cadmium, and zinc in urban runoff from specific sources. Chemosphere 44, 997-1009.
344 345 346
Davis, B., Birch, G., 2010. Comparison of heavy metal loads in stormwater runoff from major and minor urban roads using pollutant yield rating curves. Environmental Pollution 158, 2541-2545.
347 348
Deletic, A., 1998. The first flush load of urban surface runoff. Water Research 32, 2462-2470.
349 350 351 352
Dhanakumar, S., Murthy, K.R., Solaraj, G., Mohanraj, R., 2013. Heavy-metal fractionation in surface sediments of the Cauvery River Estuarine Region, Southeastern Coast of India. Archives of environmental contamination and toxicology 65, 14-23.
353 354
Dresden, L., 2010. Verkehrsmengenkarte 2009. Dresden, Autobahnamt Dresden. Landeshauptstadt Dresden, Dresden.
355 356 357
Egodawatta, P., Thomas, E., Goonetilleke, A., 2009. Understanding the physical processes of pollutant build-up and wash-off on roof surfaces. Science of The Total Environment 407, 1834-1841.
358 359
Egodawatta, P., Ziyath, A.M., Goonetilleke, A., 2013. Characterising metal build-up on urban road surfaces. Environmental Pollution 176, 87-91.
360 361
Gnecco, I., Berretta, C., Lanza, L., La Barbera, P., 2005. Storm water pollution in the urban environment of Genoa, Italy. Atmospheric research 77, 60-73.
362
Hua, P., Yang, W., Qi, X., Jiang, S., Xie, J., Gu, X., Li, H., Zhang, J., Krebs, P., 2020. Page 19 of 28
363 364
Evaluating the effect of urban flooding reduction strategies in response to design rainfall and low impact development. Journal of Cleaner Production 242, 118515.
365 366
Jing, C., Jingqi, F., 2012. Fire Alarm System Based on Multi-Sensor Bayes Network. Procedia Engineering 29, 2551-2555.
367 368 369 370
Kaeseberg, T., Zhang, J., Schubert, S., Oertel, R., Siedel, H., Krebs, P., 2018. Sewer sediment-bound antibiotics as a potential environmental risk: Adsorption and desorption affinity of 14 antibiotics and one metabolite. Environmental pollution 239, 638-647.
371 372 373 374
Khan, M., Latif, M.T., Saw, W., Amil, N., Nadzir, M.S.M., Sahani, M., Tahir, N., Chung, J., 2016. Fine particulate matter in the tropical environment: monsoonal effects, source apportionment, and health risk assessment. Atmospheric Chemistry and Physics 16, 597-617.
375 376
Kuoppamäki, K., Setälä, H., Rantalainen, A.-L., Kotze, D.J., 2014. Urban snow indicates pollution originating from road traffic. Environmental Pollution 195, 56-63.
377 378 379
Li, C., Wen, Q., Hong, M., Liang, Z., Zhuang, Z., Yu, Y., 2017. Heavy metals leaching in bricks made from lead and zinc mine tailings with varied chemical components. Construction and Building Materials 134, 443-451.
380 381 382
Li, R., Hua, P., Cai, J., Wang, X., Zhu, Y., Huang, Z., Li, P., Wang, Z., Bai, Y., Hu, B.X., 2019. A sixteen-year reduction in the concentrations of aquatic PAHs corresponding to source shifts in the Elbe River, Germany. Journal of Cleaner Production.
383 384 385
Lim, R.B.H., Liew, J.H., Kwik, J.T.B., Yeo, D.C.J., 2018. Predicting food web responses to biomanipulation using Bayesian Belief Network: Assessment of accuracy and applicability using in-situ exclosure experiments. Ecological Modelling 384, 308-315.
386 387 388
Liu, A., Duodu, G.O., Mummullage, S., Ayoko, G.A., Goonetilleke, A., 2017. Hierarchy of factors which influence polycyclic aromatic hydrocarbons (PAHs) distribution in river sediments. Environmental Pollution 223, 81-89.
389 390
Liu, A., Goonetilleke, A., Egodawatta, P., 2012. Taxonomy for rainfall events based on pollutant wash-off potential in urban areas. Ecological Engineering 47, 110-114.
391 392 393 394
Loganathan, P., Vigneswaran, S., Kandasamy, J., 2013. Road-deposited sediment pollutants: a critical review of their characteristics, source apportionment, and management. Critical reviews in environmental science and technology 43, 1315-1348.
395 396
Lovei, M., 1998. Phasing out lead from gasoline: worldwide experience and policy implications. The World Bank.
397 398 399
Mahbub, P., Ayoko, G.A., Goonetilleke, A., Egodawatta, P., Kokot, S., 2010. Impacts of traffic and rainfall characteristics on heavy metals build-up and wash-off from urban roads. Environmental Science & Technology 44, 8904-8910.
400
Marcot, B.G., Steventon, J.D., Sutherland, G.D., McCann, R.K., 2006. Guidelines for Page 20 of 28
401 402
developing and updating Bayesian belief networks applied to ecological modeling and conservation. Canadian Journal of Forest Research 36, 3063-3074.
403 404 405
Miltner, R., McLaughlin, D., 2019. Management of headwaters based on macroinvertebrate assemblages and environmental attributes. Science of The Total Environment 650, 438-451.
406 407 408
Mosley, L.M., Peake, B.M., 2001. Partitioning of metals (Fe, Pb, Cu, Zn) in urban runoff from the Kaikorai Valley, Dunedin, New Zealand. New Zealand journal of marine and freshwater research 35, 615-624.
409 410
Oberts, G.L., 1994. Influence of snowmelt dynamics on stormwater runoff quality. Watershed protection techniques 1, 16.
411 412 413 414
Pearl, J., 1986. A Constraint – Propagation Approach to Probabilistic Reasoning* *This work was supported in part by the National Science Foundation, Grant #DSR 83– 13875, in: Kanal, L.N., Lemmer, J.F. (Eds.), Machine Intelligence and Pattern Recognition. North-Holland, pp. 357-369.
415 416
Pearl, J., 1988. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, 2nd edition ed. Morgan Kaufmann, San Mateo, CA.
417 418 419
Pollino, C.A., Woodberry, O., Nicholson, A., Korb, K., Hart, B.T., 2007. Parameterisation and evaluation of a Bayesian network for use in an ecological risk assessment. Environmental modelling & software 22, 1140-1152.
420 421 422 423
Rositano, F., Piñeiro, G., Bert, F.E., Ferraro, D.O., 2017. A comparison of two sensitivity analysis techniques based on four bayesian models representing ecosystem services provision in the Argentine Pampas. Ecological Informatics 41, 33-39.
424 425
Rossman, L.A., 2010. Storm water management model user's manual, version 5.0. National Risk Management Research Laboratory, Office of Research and ….
426 427 428
Sage, J., Bonhomme, C., Al Ali, S., Gromaire, M.-C., 2015. Performance assessment of a commonly used “accumulation and wash-off” model from long-term continuous road runoff turbidity measurements. Water Research 78, 47-59.
429 430
Sansalone, J.J., Buchberger, S.G., Al-Abed, S.R., 1996. Fractionation of heavy metals in pavement runoff. Science of The Total Environment 189, 371-378.
431 432
Sartor, J.D., Boyd, G.B., 1972. Water pollution aspects of street surface contaminants. US Government Printing Office.
433 434
Schöndorf, T., Herrmann, R., 1987. Transport and chemodynamics of organic micropollutants and ions during snowmelt. Hydrology Research 18, 259-278.
435 436 437 438
Shi, H., Luo, G., Zheng, H., Chen, C., Bai, J., Liu, T., Ochege, F.U., De Maeyer, P., 2020. Coupling the water-energy-food-ecology nexus into a Bayesian network for water resources analysis and management in the Syr Darya River basin. Journal of Hydrology 581, 124387. Page 21 of 28
439 440 441 442
Trujillo-González, J.M., Torres-Mora, M.A., Keesstra, S., Brevik, E.C., Jiménez-Ballesta, R., 2016. Heavy metal accumulation related to population density in road dust samples taken from urban sites under different land uses. Science of The Total Environment 553, 636-642.
443 444
Vaze, J., Chiew, F.H., 2002. Experimental study of pollutant accumulation on an urban road surface. Urban Water 4, 379-389.
445 446 447 448
Wang, Z., Hua, P., Li, R., Bai, Y., Fan, G., Wang, P., Hu, B.X., Zhang, J., Krebs, P., 2019. Concentration decline in response to source shift of trace metals in Elbe River, Germany: A long-term trend analysis during 1998–2016. Environmental pollution 250, 511-519.
449
Wetterkontor, Weather report in Dresden-Strehlen.
450 451 452
Wicke, D., Cochrane, T.A., O'Sullivan, A., 2012. Build-up dynamics of heavy metals deposited on impermeable urban surfaces. Journal of environmental management 113, 347-354.
453 454 455
Xu, Z., Wu, J., Li, H., Chen, Y., Xu, J., Xiong, L., Zhang, J., 2018. Characterizing heavy metals in combined sewer overflows and its influence on microbial diversity. Science of the total environment 625, 1272-1282.
456 457 458
Zhang, J., Hua, P., Krebs, P., 2015a. The build-up dynamic and chemical fractionation of Cu, Zn and Cd in road-deposited sediment. Science of The Total Environment 532, 723-732.
459 460 461
Zhang, J., Hua, P., Krebs, P., 2015b. The chemical fractionation and potential source identification of Cu, Zn and Cd on urban watershed. Water science and technology, wst2015355.
462 463 464
Zhang, J., Hua, P., Krebs, P., 2016. The influences of dissolved organic matter and surfactant on the desorption of Cu and Zn from road-deposited sediment. Chemosphere 150, 63-70.
465 466 467
Zhang, J., Hua, P., Krebs, P., 2017a. Influences of land use and antecedent dry-weather period on pollution level and ecological risk of heavy metals in road-deposited sediment. Environmental pollution 228, 158-168.
468 469 470
Zhang, J., Li, R., Zhang, X., Bai, Y., Cao, P., Hua, P., 2019. Vehicular contribution of PAHs in size dependent road dust: A source apportionment by PCA-MLR, PMF, and Unmix receptor models. Science of the total environment 649, 1314-1322.
471 472 473
Zhang, J., Wang, J., Hua, P., Krebs, P., 2015c. The qualitative and quantitative source apportionments of polycyclic aromatic hydrocarbons in size dependent road deposited sediment. Science of the total environment 505, 90-101.
474 475 476 477
Zhang, J., Wu, J., Hua, P., Zhao, Z., Wu, L., Fan, G., Bai, Y., Kaeseberg, T., Krebs, P., 2017b. The influence of land use on source apportionment and risk assessment of polycyclic aromatic hydrocarbons in road-deposited sediment. Environmental Pollution 229, 705-714. Page 22 of 28
478
Page 23 of 28
479
Figure Captions and Figures
480
Figure 1 The structure of the Bayesian network in Netica for modelling the
481
influencing variables (surface properties, antecedent environmental conditions, and
482
sampling methods) to Cd, Pb, Cu and Zn concentrations.
483
Figure 2 Cd, Pb, Cu, Zn, and TS concentrations measured on the facade and the road
484
surfaces after ADP (25th February) and ASP (4th March). Black bars represent the
485
normal wash-off concentration collected by SW method; Red bars represent the
486
maximum wash-off concentration collected by WVS method.
487
Figure 3 The ratios of trace metal and TS concentration collected from the facade and
488
the road surfaces after ADP (25th February) and ASP (4th March). Left: the
489
concentration collected by SW method; Right: the concentration collected by WVS
490
method.
491
Figure 4 Sensitivity analysis based on the entropy reduction associated with Bayesian
492
network
Page 24 of 28
493
Figure 1
494
Page 25 of 28
495
Figure 2
496 497
Page 26 of 28
498
Figure 3
499
Page 27 of 28
500
Figure 4
501 502
Page 28 of 28
1
Research highlights
2
•
Antecedent conditions affect metal levels in surface runoff
3
•
The wash-off processes off facade/road surfaces were analyzed
4
•
The influencing variables were ranked by entropy reduction with Bayesian network
5
•
The snowmelt process in dry period had a significant impact on build-up process
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: