Supplementary Information

1 Characterization of study sites

Figure S1 Characterization of study sites (Note: RD-E, RD-GF, RD-SF, RD-P = excavation, gravel filling, soil filling, planting of rain garden construction; PP-E,

PP-GF, PP-P = excavation, gravel filling, paving of porous pavement

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construction)

2 LID construction information of study sites

Table S1 LID construction information of study sites

Residential district name BHY DWXY -LID RD PP RD PP -

LID construction area [m2] 3768.58 668.112288.8

9 1606.25-

LID construction percentage 46.21% 8.19% 8.77% 6.16% -Individual LID area [m2] 314.05 133.62 55.83 94.49 -Residential district area [m2] 8155 26087 -Residential district name JE SMG -LID RD PP RD PP -LID construction area [m2] 3830 3324 112.65 217.90 -LID construction percentage 3.48% 3.02% 2.11% 4.08% -Individual LID area [m2] - - 28.16 108.95 -Residential district area [m2] 110000 5336 -Residential district name TYYP XINLY YLWLID RD PP RD PP RDLID construction area [m2] 3026.12 2726.01 588.13 801.97 83.58LID construction percentage 13.68% 12.32% 3.61% 4.93% 0.38%Individual LID area [m2] 137.55 340.75 65.35 100.25 83.58Residential district area [m2] 22125 16276 22263

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3 Different sized RDS mass per unit area over LID construction stages

Figure S2 Different sized RDS mass per unit area over LID construction stages

(Note: RD-E, RD-GF, RD-SF, RD-P = excavation, gravel filling, soil filling, planting

of rain garden construction; PP-E, PP-GF, PP-P = excavation, gravel filling, paving of

porous pavement construction)

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4 Analysis of variance result for heavy metals (taken Cd as an example)

Figure S3 Cd build-up concentration variability during rain garden excavation

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Figure S4 Cd build-up concentration variability during rain garden gravel filling

Figure S5 Cd build-up concentration variability during rain garden soil filling

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Figure S6 Cd build-up concentration variability during rain garden planting

Figure S7 Cd build-up concentration variability during porous pavement

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excavation

Figure S8 Cd build-up concentration variability during porous pavement gravel filling

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Figure S9 Cd build-up concentration variability during porous pavement paving

5 Simulation procedure of generalized catchment

The generalized catchment (Figure S10) was simulated according to the LID

construction information of the study sites (Table S1). The generalized catchment area

(30034.57 m2), LID construction percentage (11.18% for rain garden and 6.45% for

porous pavement) and individual LID area (110.88 m2 for rain garden and 150.51 m2

for porous pavement) were the average data of all the study residential districts (Table

S1). Accordingly, the amount of LID construction structures in the generalized

catchment were calculated using Equation S1. Thereby, a total of 43 LID structures

including 30 rain gardens and 13 porous pavements were derived.

Amount of LID=Catchment area×LIDconstruction percentageIndividual LIDstructure area

Equation S1

Where: Catchment area – area of the generalized catchment;

LID construction percentage – percentage of LID construction area in the

generalized catchment;

Individual LID structurearea – average area of individual LID structure of

study sites.

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Figure S10 Generalized catchment and the influenced region due to LID

construction

6 Influenced region of the LID construction in the generalized catchment

It was assumed that the generalized catchment was surrounded by three roads in terms

of arterial road, collector road and access road (Figure S10). Each LID structure was

respect to one laneway in the generalized catchment. The width of the four roads was

considered as the road width of the selected four study roads: 16 m for arterial roads,

10 m for collector road, 8 m for access road and 5 m for laneway. The area of the

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influenced region was calculated using Equation S2. Accordingly, the influenced

region was calculated as 24800 m2 on arterial road, 4000 m2 on collector road, 2400

m2 on access road and 300 m2 on laneway.

Influenced area=Road width× Influenced region Equation S2

Where: Influenced area – area of the influenced region due to LID construction [m2];

Road width – 16 m for arterial roads, 10 m for collector road, 8 m for access

road and 5 m for laneway;

Influenced region – double of the influenced distance according to the data

analysis: 775 m×2 along arterial roads, 200 m×2 along collector roads, 150

m×2 along access roads and 30 m×2 along laneways.

7 Increased total solid load in stormwater

The increased load of total solids (TS) in stormwater was initially calculated for unit

area (i.e. 1 m2). The washed-off load of TS was estimated using Equation S3 (adapted

from Zhao et al. 2014). The Mi values was road deposited sediments (RDS) mass per

unit area of a particle size i at different sites on the four study roads. The calculation

results are presented in Table S2. The increased RDSindex and background RDSindex

were the average values of RDSindex in the influenced region and un-influenced sites,

respectively. ΔRDSindex was the difference between increased RDSindex and background

RDSindex and considered as the increased TS load washed-off by stormwater per unit

area. The increased load of TS in stormwater generated from the whole influenced

region was calculated by multiplying ΔRDSindex with the area of the influenced region

and the results are given in Table S2. As there were 43 LID structures simulated in the

generalized catchment and each LID structure has effects on one laneway, the

increased TS load on laneways in Table S2 was estimated for 43 laneways in the

generalized catchment. Thus, the total increased load of washed-off TS from the

generalized catchment was 105.76 kg. According to the increased TS wash-off load

from the generalized catchment, the potential increased TS load in the stormwater

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runoff over the whole country in China by the year 2020 and 2030 was predicted

(Figure S11).

RDS index=M i×Fwi Equation S3

Where: RDS index – potential amount of RDS in stormwater runoff per unit area [g/m2];

M i – RDS mass per unit area of a particle size i [g/m2];

Fwi – percentage of particles of size i washed-off in runoff, 17% for <44 µm,

10% for 44-62 µm, 4.5% for 62-105 µm, 4.3% for 105-149 µm, 2.9% for

149-250 µm, 1.5% for 250-450 µm and 1.0% for 450-1000 µm in the

condition of rainfall intensity: 12.87 mm/h and rainfall duration: 1 h (Zhao et

al. 2011);

i – number of particle sizes.

Table S2 Increased TS load in stormwater

Roads Distance [m]

RDSindex

[g/m2]Increased RDSindex

[g/m2]

Background RDSindex

[g/m2]

ΔRDSindex

[g/m2]Increased TS load [g]

Arterial road

0 0.96 1.36 0.20 1.17 4668.10150 1.07300 1.01400 1.42600 0.55775 0.18950 0.15

Collector road

0 1.34 1.00 0.17 0.83 20700.7230 1.12100 1.57150 1.42200 0.20

Access road

0 2.34 2.18 0.78 1.39 3345.9950 1.83100 2.37150 0.96300 0.61

Laneway 0 13.81 6.61 0.63 5.97 77048.08

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10 3.4820 2.5430 0.7850 0.6560 0.6880 0.62100 0.55130 0.54

Total 105762

Figure S11 Predicted increased total solids load in stormwater during one

rainfall event over the country in China (Note: ΔTS – increased TS load)

References

Zhao, H., Li, X. and Wang, X. (2011). "Heavy metal contents of road-deposited sediment along the urban-rural gradient around Beijing and its potential contribution to runoff pollution." Environmental Science & Technology 45(17): 7120-7127.

Zhao, H., Zhao, J., Yin, C. and Li, X. (2014). "Index models to evaluate the potential metal pollution contribution from washoff of road-deposited sediment." Water Research 59: 71-79.

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