# High-Resolution Mapping of Japanese Microplastic and Macroplastic Emissions from the Land into the Sea

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

^{6}to 12.7 × 10

^{6}t/yr. They assumed that plastic waste flowing into the ocean was proportional to the amount of mismanaged plastic waste (MMPW). However, sources of plastic waste are not limited to areas near the coast; the waste from inland areas should also be considered. Other problems in their studies are that plastic waste is not generally evaluated by its size class, and verifying the input of plastics into the ocean based on measured data is insufficient.

^{6}to 2.41 × 10

^{6}t/yr. This calculation also involved dividing plastics by size into MicP and MacP fractions. Schmidt et al. [42] also used this concept, but increased the amount of observational plastic inflow data used. They calculated a global plastic input of 0.47 × 10

^{6}to 2.75 × 10

^{6}t/yr. As all of these results included MMPW, their accuracies depend on how well the calculated MMPW matches the actual amount. In general, MMPW is evaluated at a country level, but since it differs depending upon the waste management in each country, it has been challenging to evaluate precisely the amount of MMPW for each country. Additionally, both Lebreton et al. [41] and Schmidt et al. [42] evaluated plastic emissions from large river basins only, without including medium-sized or smaller basins. An evaluation using gridding of the entire land area is desirable.

^{3}) and mass concentrations (mg/m

^{3}) of the MicP fraction were analyzed as the target MicP concentrations in this study. We then prepared a countrywide MicP concentration map using a 1 km mesh-size based on the land area data. In accordance with a simple water balance analysis model, we calculated the annual flow rate across each 1 km mesh to obtain the final MicP emissions from the product of the MicP concentrations and flow rate, and calculated the annual MicP number and mass inputs from the land to the sea. We also estimated the MacP mass concentrations from the MicP mass concentrations and the ratio of MacP/MicP determined by previous studies [41,42] that collected the observed MacP and MicP concentrations. We then calculated the MacP mass emissions from the product of the MacP concentrations and the flow rate and then calculated plastic input, which was taken as the sum of MicP and MacP. From these results, we were able to estimate not only the total mass of plastic inputs, but also their regional properties (by river basin or administrative district). Our goals in this research were to generate new insights that may be used to draft countermeasures against plastic emissions, thereby reducing marine pollution outflow from Japan, and to introduce methods that may also be applied to evaluate plastic inputs in other regions of the world.

## 2. Materials and Methods

#### 2.1. Conceptual Foundation for Evaluating Plastic input

#### 2.2. Evaluating Riverine MicP and MacP Concentrations

#### 2.2.1. Field Sites

^{2}, maximum: 1.3 × 10

^{4}km

^{2}, mean: 1.5 × 10

^{3}km

^{2}) including the Tone River, which has the largest basin area in Japan. The population density at our observation sites ranged from 0 to 7.1 × 10

^{3}persons/km

^{2}(mean: 9.7 × 10

^{2}persons/km

^{2}), and the urbanization rate ranged from 0 to 100% (mean: 17.7%). The overall composition varied widely, from urban areas to regions where people do not live. The field surveys in the rivers were conducted for both the unidirectional flow area, where the flow direction is only downstream, and for the tidal area, where both downstream and upstream flow occur. However, we have excluded data from the tidal area from our analyses. Because MicPs in the tidal reach are transported by upstream flow from the sea to upstream areas due to tides, these observational results were therefore affected by the seas. This work aims to show the MicP transport from land to the sea; thus, the data in tidal reach were beyond the scope of this study.

#### 2.2.2. Measuring MicP in Rivers

- From the top of a bridge, the plankton net was deployed onto the surface of the river using a rope. The net position was located at the center of each stream in cross-section;
- The length of the rope was adjusted so that the net was generally fixed near the water surface and set for 5–10 min;
- After a predetermined installation time, the plankton net was raised to the bridge.

#### 2.2.3. Laboratory Analyses of MicP Concentrations

- The sample was filtered using a 0.1 mm net and the sample remaining on the filter was dried;
- The dried sample was immersed in a 30% hydrogen peroxide solution for approximately one week to decompose any organic matter, such as plant debris;
- The sample was filtered again through a 0.1 mm net and the residue was dried for 24 h in a 60 °C incubator;
- The dried sample was spread in a petri dish containing the tap water, and MicP candidate particles were extracted manually one-by-one;
- The masses of the MicP candidate particles were measured using an ultra-micro balance (XPR2UV, Mettler Toledo, Columbus, OH, USA);
- The sizes of the candidate particles > ~0.1 mm were measured. Here, MicP was photographed using an electron microscope (SZX7, Olympus Corp., Tokyo, Japan) with a charge-coupled device (CCD) camera (HDCE-20C, AS ONE Corp., Osaka, Japan). The ImageJ v.1.52t software package (https://imagej.nih.gov/ij/notes.html) was then used to calculate the MicP sizes (maximum length, etc.) from the captured images;
- A Fourier transform infrared spectrophotometer (FTIR, IRAffinity-1S, Shimadzu Corp., Kyoto, Japan) was used to identify the material compositions of the MicP candidate particles to determine whether or not they were indeed plastic.

#### 2.2.4. Evaluating Basin Characteristics

#### 2.3. Water Balance Analysis at a 1 km Mesh Resolution

#### 2.3.1. Outline of Water Balance Analysis

#### 2.3.2. Precipitation

#### 2.3.3. Evapotranspiration

_{n}is the net radiation (=0.8S, MJ/m

^{2}), G is the ground heat flow (MJ/m

^{2}), λ is the latent heat of vaporization (J/g), β is the canopy interception rate, P is precipitation (mm/month), S is the total solar radiation (MJ/m

^{2}), S

_{*}= 10R

_{n}/λ, M

_{d}is days of month, and l is the latitude of the center of gravity of the mesh.

#### 2.3.4. Surface Runoff and Underground Infiltration

_{s}, a rational expression [53] that is a centralized conceptual model was used for each grid, such that:

^{2}) is then multiplied. The coefficient of the surface runoff, f, is given according to the land use, as shown in Table 1. The difference between the precipitation, P, and the sum of the evapotranspiration, E, and the surface runoff, Q

_{s}, was obtained, and the underground infiltration, Q

_{i}, was calculated as:

_{s}, and underground infiltration, Q

_{i}, was used as the outflow, Q, from each grid (Q

_{s}+ Q

_{i}), and the product of Q and the MicP (MacP) concentration were calculated as the MicP (MacP) emissions from each grid.

#### 2.3.5. Validating the Water Balance Model

## 3. Results

#### 3.1. Characteristics of MicP Concentrations in Japanese Rivers

^{3}), with mean and median values of 4.34 and 1.51 (particles/m

^{3}), respectively. The MicP mass concentration changed over a wide range, from 0.00008 to 16.15 (mg/m

^{3}), with mean and median values of 0.79 and 0.12 (mg/m

^{3}), respectively. The coefficient of variation was 1.85 for the MicP numerical concentration and 2.40 for the MicP mass concentration, indicating that variation of the mass concentration was larger than that of the numerical concentration. The mean MicP size obtained in this study was 1–2 mm. PE, PP, and PS were the dominant plastic types in the MicPs collected.

_{n}, and mass concentration, C

_{m}. The mean values of both C

_{n}and C

_{m}are larger than their respective median values. The percentage of sites above the mean value was 30% for C

_{n}and 23% for C

_{m}. Moreover, a number of sites with values less than 1/10 of the mean value were also observed, as much as 21% and 46% for C

_{n}and C

_{m}, respectively. From these values, it is apparent that high MicP concentrations at relatively few sites skewed the means.

_{n}and C

_{m}are shown in Figure 4. The results from all 90 sites are displayed. Although some variation was observed between the behaviors of C

_{n}and C

_{m}, the approximately straight line that fits the data has a positive slope. The Pearson’s correlation coefficient, R

^{2}, of this line was 0.748, with a p-value < 0.05, indicating a significant correlation between these two concentrations.

#### 3.2. Relationship between MicP Concentrations and Basin Characteristics

_{0.05}is the t-value (1.987) corresponding to a probability of 5% on both sides of n = 90 (degrees of freedom: 89), s is the expected value of the regression residual, S

_{xx}and S

_{yy}are the sums of squares of the deviations from the mean values ($\overline{x}$ and $\overline{y}$) for x and y, respectively, and S

_{xy}is the sum of the product of the deviations from $\overline{x}$ and $\overline{y}$.When these values are divided by n − 1, S

_{xx}and S

_{yy}become the variances of x and y, respectively, and S

_{xy}becomes the covariance of x and y.

_{n}and C

_{m}, as well as the population density, W

_{p}, and urban area ratio, W

_{u}, we confirmed that all four values were positively correlated. The following linear approximations were obtained for the respective values:

_{u}than with W

_{p}.

_{n}and C

_{m}revealed an increasing trend with both population density and urban area ratio. However, the trend was not linear, instead forming a convex curve. This relationship was observed more distinctly with population density.

_{pth}, and a linear function was used for the range below W

_{pth}. We selected 181 (persons/km

^{2}) as W

_{pth}so that the intercept of the linear function was non-negative and the difference between the two functions at the threshold value was minimized. The correlation coefficients (R

^{2}) for Equations (13–14) were 0.004, 0.912, 0.652, and 0.849 (from top to bottom). Only one low coefficient was observed, but the other approximate curves had favorable R

^{2}values, thus indicating their goodness of fit.

#### 3.3. Calculated Results for Water Balance Analysis

_{s}, and underground infiltration, Q

_{i}, obtained via the water balance analysis. Here, the quantity in each grid was divided by the area (1 km

^{2}) and converted to the quantity per year. Figure A1 shows that precipitation was high in southern Kyushu, Shikoku, the Kii Peninsula, and the Shizuoka prefecture on the Pacific coast. This is because rainfall due to typhoons or similar weather patterns during spring, summer, and autumn is quite abundant in these areas. Meanwhile, on the coast of the Sea of Japan, precipitation was high from Hokuriku to the southern part of the Tohoku region due to snowfall in winter. Precipitation in Hokkaido was generally low, especially in the northeast, which receives less than 1000 mm/yr. Evapotranspiration also changes in conjunction with the magnitude of precipitation. However, evapotranspiration is lower in the north and higher in the south, indicating that it is also affected by latitudinal temperature gradients. The surface runoff map also shows a pattern that is generally similar to that of the precipitation map, but sometimes shows clear differences (for example, between the Hokuriku and Tohoku regions on the coast of the Sea of Japan and southern Kyushu). This reflects the fact that surface runoff and infiltration differ with land use type, suggesting that underground infiltration increases in the areas where surface runoff is low. The annual means of each quantity were: 2161 mm/yr for precipitation, 753 mm/yr for evapotranspiration, 1031 mm/yr for surface runoff, and 377 mm/yr for underground infiltration.

_{cal}, which is the sum of surface runoff and underground infiltration, and the observed flow rate, Q

_{obs}. Here, we focused on the results from flow rate observation sites across all 109 primary water systems in Japan. Figure 6 also shows that Q

_{cal}and Q

_{obs}are positively correlated; the slope of the linear approximation between Q

_{cal}and Q

_{obs}is 0.963, the correlation coefficient, R

^{2}, is 0.925, and the p-value is < 0.05. These results demonstrate that the calculated flow rate, Q

_{cal}, obtained from this model is almost coincident with the observed flow rate, Q

_{obs}. It was thereby confirmed that the total outflow of the sum of surface runoff and underground infiltration in a watershed very closely approximates the annual river discharge. Therefore, this method of performing a simple water balance analysis without considering the advection between grids had a high numerical accuracy and also greatly reduced the computational load, thus proving to be a useful technique that is capable of analyzing high-resolution (1 km) grids.

#### 3.4. Calculating Japanese Plastic Emissions from Land to the Sea

_{n}and C

_{m}. Table 2 also defines the approximate equations used. From our results, the annual number of MicP particles emitted ranged from 0.55 to 2.54 trillion, with a median of 1.40 trillion particles. The minimum, median, and maximum values of the annual MicP emissions by mass were 65, 223, and 503 t/yr, respectively. The maximum and minimum values of both the number and mass MicP emissions corresponded to the maximum and minimum values from the linear approximation at a CI = 95%. Concerning this approximation from the raw data and from the curve of the moving average values, the number and mass MicP emissions were 1.27–1.67 trillion particles or 204–294 t/yr, respectively. The differences between these values are low, suggesting that the differences among the various approximations were also minimal.

^{−2}to 10

^{1}. The median and mean values were 0.53 and 5.50 mg/m

^{3}, respectively, which are generally higher than the data for rivers in Japan shown in this study (Figure 3b). The median and mean values were 4.4 and 7.0 times larger than those in this study, respectively. However, the MacP mass concentration was greater than the MicP mass concentration and was distributed from 10

^{−1}to 10

^{1}and the median and mean MacP mass concentrations were 4.02 and 12.3 mg/m

^{3}, respectively. By considering the mass concentration ratios of MacP and MicP shown in Figure 7b, we also found that the order of magnitude varied widely, from 10

^{−2}to 10

^{2}. In Figure 7b, the 25%, 50% (median), and 75% quartiles were 2.28, 8.50, and 35.2, respectively, with a mean value of 20.7.

## 4. Discussion

#### 4.1. Total Plastic Input from the Land to the Sea

#### 4.2. Map of Plastic Emissions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## Appendix A

**Figure A1.**Annual precipitation (

**a**), evapotranspiration (

**b**), surface runoff (

**c**), and underground infiltration (

**d**) obtained from the water-balance analysis.

**Table A1.**MicP numerical concentration C

_{n}(particles/m

^{3}) and mass concentration C

_{m}(mg/m

^{3}), and basin characteristics for the 70 rivers and 90 sites used in this study. For the basin characteristics, population density W

_{p}(persons/km

^{2}) and urban area ratio W

_{u}(%) in the upstream area of each observation site are shown.

No. | River | Survey Site | C_{n} | C_{m} | W_{p} | W_{u} |
---|---|---|---|---|---|---|

1 | Koetoi R. | Komatsu | 0.19 | 0.00 | 4 | 1 |

2 | Shimoebekorobetsu R. | Toyotomi | 1.81 | 0.19 | 6 | 1 |

3 | Ishikari R. | Tachihu-oohashi | 4.11 | 0.69 | 38 | 2 |

4 | Toyohira R. | Nijunijo-oohashi | 1.24 | 0.06 | 126 | 3 |

5 | Kitakami R. | Meiji | 0.14 | 0.00 | 141 | 5 |

6a | Mogami R. | Shonai-oohashi | 0.36 | 0.08 | 130 | 6 |

6b | Mogami R. | Kurotaki | 0.49 | 0.12 | 182 | 8 |

6d | Mogami R. | Konoki | 1.48 | 0.02 | 94 | 6 |

7 | Su R. | Ochiai | 8.12 | 1.52 | 362 | 15 |

8 | Abukuma R. | Tenjin | 0.39 | 0.01 | 216 | 10 |

9 | Kuji R. | Tomioka | 0.03 | 0.00 | 59 | 3 |

10 | Naka R. | Nakagawa | 0.70 | 0.03 | 145 | 8 |

11 | Sakura R. | Sakaeri | 2.46 | 0.74 | 265 | 17 |

12 | Kinu R. | Toyomizu | 0.40 | 0.01 | 54 | 11 |

13 | Watarase R. | Nowatari | 1.53 | 0.07 | 429 | 15 |

14a | Tone R. | Sakae | 0.37 | 0.07 | 475 | 17 |

14b | Tone R. | Tonegawa | 8.68 | 2.36 | 329 | 14 |

14c | Tone R. | Bando | 0.17 | 0.03 | 414 | 14 |

15a | Ohori R. | Kisaki | 4.40 | 3.31 | 7161 | 85 |

15b | Ohori R. | Kachi | 12.88 | 1.08 | 6066 | 82 |

16 | Edo R. | Noda | 3.32 | 0.58 | 2366 | 57 |

17a | Naka R. | Yoshikoshi | 2.31 | 1.78 | 1784 | 45 |

17b | Naka R. | Shinkai | 5.98 | 1.74 | 1000 | 37 |

18a | Ara R. | Hanekura | 4.57 | 0.97 | 636 | 17 |

18b | Ara R. | Kaihei | 7.40 | 1.37 | 403 | 12 |

18c | Ara R. | Onari | 8.35 | 0.32 | 219 | 8 |

18d | Ara R. | Kumagaya | 4.59 | 0.05 | 157 | 7 |

18e | Ara R. | Tamayodo | 0.44 | 0.02 | 128 | 5 |

18f | Ara R. | Kyu-titibu | 1.15 | 0.16 | 78 | 3 |

19 | Ichino R. | Matsunaga | 2.09 | 0.43 | 1002 | 42 |

20 | Musashi Channel | Gese | 1.31 | 0.04 | 330 | 12 |

21 | Yoshino R. | Mannen | 17.27 | 0.59 | 445 | 26 |

22 | Yoro R. | Kasumi | 0.71 | 0.00 | 208 | 10 |

23 | Obitsu R. | Nakagawa | 3.29 | 0.15 | 110 | 6 |

24 | Koito R. | Rokusan | 1.43 | 0.12 | 116 | 5 |

25 | Tama R. | Maruko | 1.11 | 0.24 | 2931 | 31 |

26a | Tsurumi R. | Shinyokohama | 14.24 | 3.33 | 6619 | 72 |

26b | Tsurumi R. | Kamoike | 13.81 | 3.62 | 6877 | 73 |

26c | Tsurumi R. | Kawawakitahassaku | 30.67 | 1.52 | 5759 | 67 |

26d | Tsurumi R. | Ochiai | 6.15 | 1.16 | 6752 | 72 |

26e | Tsurumi R. | Onmawari | 10.52 | 2.11 | 5230 | 66 |

26f | Tsurumi R. | Sumiyoshi | 2.59 | 0.32 | 5768 | 23 |

27 | Sagami R. | Sagami-oohashi | 0.30 | 0.04 | 446 | 12 |

28 | Toneunga R. | Fureai | 12.66 | 2.81 | 1333 | 50 |

29 | Hayakido R. | Shibasawa | 3.51 | 0.13 | 35 | 2 |

30 | Saka R. | Midori | 0.60 | 0.46 | 600 | 26 |

31 | Shonai R. | Shin-meisei | 63.89 | 16.15 | 2045 | 44 |

32 | Kiso R. | Kawashima-oohashi | 0.55 | 0.04 | 79 | 2 |

33 | Nagara R. | Nagara-oohashi | 1.79 | 0.04 | 110 | 6 |

34 | Ibi R. | Ibi-oohashi | 1.01 | 0.01 | 72 | 3 |

35 | Kuzuryu R. | Nakakado | 2.01 | 0.06 | 72 | 4 |

36 | Asuwa R. | Kujuku | 7.35 | 1.70 | 144 | 5 |

37 | Kamo R. | Kyoukawa | 4.93 | 0.77 | 2378 | 30 |

38 | Katsura R. | Miyamae | 9.57 | 3.61 | 924 | 14 |

39 | Uji R. | Gokou | 1.83 | 1.20 | 333 | 11 |

40 | Yodo R. | Hijikata | 2.01 | 0.11 | 491 | 12 |

41 | Ina R. | Minamizono | 6.39 | 0.68 | 1261 | 22 |

42a | Yamato R. | Taisho | 6.94 | 0.37 | 1266 | 30 |

42b | Yamato R. | Gokou-oohashi | 11.09 | 2.52 | 1192 | 31 |

43 | Toga R. | Shimokawara | 1.38 | 0.03 | 4276 | 28 |

44 | Ikuta R. | Nunohiki | 0.22 | 0.01 | 303 | 3 |

45 | Sendai R. | Sendai-oohashi | 0.99 | 0.01 | 83 | 4 |

46 | Tenjin R. | Tenjin | 1.95 | 0.04 | 86 | 4 |

47 | Hino R. | Shin-hino | 0.45 | 0.04 | 25 | 2 |

48 | Hii R. | Mizuho-oohashi | 0.28 | 0.01 | 53 | 4 |

49 | Goemon R. | Hinode | 3.98 | 0.45 | 643 | 29 |

50 | Asahi R. | Okakita-oohashi | 0.90 | 0.04 | 70 | 4 |

51 | Nishiki R. | Gosho-oohashi | 0.11 | 0.00 | 21 | 2 |

52 | Saba R. | Okinohara | 0.12 | 0.00 | 19 | 1 |

53 | Fushino R. | Takada | 0.65 | 0.02 | 339 | 13 |

54a | Mononobe R. | Mononobe | 1.07 | 0.12 | 26 | 1 |

54b | Mononobe R. | Matchida | 1.48 | 0.18 | 21 | 1 |

55 | Niyodo R. | Niyodo-oohashi | 3.76 | 0.03 | 43 | 2 |

56a | Shimanto R. | Rivermouth | 1.35 | 0.04 | 37 | 2 |

56b | Shimanto R. | Downstream | 0.39 | 0.00 | 37 | 2 |

57 | Shigenobu R. | Deai | 0.64 | 0.06 | 658 | 12 |

58 | Yaoshi.R | Seisei | 0.26 | 0.00 | 218 | 8 |

59 | Hiji R. | Hatanomae | 0.42 | 0.03 | 93 | 8 |

60 | Onga R. | Kanroku | 1.27 | 0.07 | 508 | 19 |

61 | Hikosan R. | Okamori | 5.24 | 3.04 | 406 | 17 |

62 | Kagetsu R. | Kagetsugawa | 1.37 | 0.05 | 100 | 4 |

63 | Kikuchi R. | Yamagaseibu-oohashi | 2.28 | 3.11 | 181 | 11 |

64 | Kuro R. | Kurumagaeri | 0.21 | 0.01 | 125 | 9 |

65 | Shira R. | Yotsugi | 5.51 | 0.01 | 334 | 12 |

66 | Midori R. | Medomachi | 8.25 | 0.43 | 67 | 5 |

67 | Kuma R. | Seibu-oohashi | 0.84 | 0.11 | 50 | 3 |

68 | Sendai R. | Miyanojo | 1.21 | 0.68 | 68 | 6 |

69 | Fukido R. | South side | 0.23 | 0.02 | 0 | 0 |

70a | Miyara R. | Kainan | 12.77 | 0.62 | 12 | 2 |

70b | Miyara R. | Kawara | 0.97 | 0.31 | 11 | 2 |

Prefecture | Low | Middle | High | Prefecture | Low | Middle | High |
---|---|---|---|---|---|---|---|

Hokkaido | 5.2 | 91.6 | 594.1 | Shiga | 2.1 | 14.1 | 47.2 |

Aomori | 1.5 | 26.3 | 101.5 | Kyoto | 2.6 | 16.3 | 59.3 |

Iwate | 0.5 | 33.6 | 158.1 | Osaka | 7.8 | 24.0 | 60.5 |

Miyagi | 3.8 | 21.8 | 74.0 | Hyogo | 6.3 | 28.8 | 100.7 |

Akita | 0.8 | 38.1 | 175.3 | Nara | 2.2 | 12.8 | 49.2 |

Yamagata | 1.2 | 32.7 | 137.9 | Wakayama | 1.4 | 18.7 | 73.9 |

Fukushima | 2.2 | 35.3 | 136.4 | Tottori | 1.1 | 13.3 | 49.7 |

Ibaraki | 3.7 | 23.5 | 76.9 | Shimane | 0.6 | 18.9 | 80.2 |

Tochigi | 3.9 | 21.6 | 73.2 | Okayama | 2.7 | 17.2 | 61.3 |

Gunma | 3.5 | 18.4 | 62.7 | Hiroshima | 3.2 | 24.0 | 87.9 |

Saitama | 7.0 | 28.5 | 81.9 | Yamaguchi | 2.0 | 20.8 | 74.6 |

Chiba | 6.7 | 31.2 | 94.9 | Tokushima | 1.3 | 14.0 | 56.6 |

Tokyo | 12.7 | 36.0 | 103.4 | Kagawa | 1.1 | 5.9 | 20.0 |

Kanagawa | 10.0 | 31.6 | 83.0 | Ehime | 1.6 | 18.0 | 67.5 |

Yamanashi | 1.5 | 12.9 | 47.6 | Kochi | 1.2 | 23.1 | 121.6 |

Nagano | 2.0 | 36.2 | 138.7 | Fukuoka | 7.6 | 35.1 | 107.2 |

Niigata | 7.2 | 70.6 | 257.8 | Saga | 2.1 | 12.1 | 38.5 |

Toyama | 3.9 | 24.1 | 82.7 | Nagasaki | 2.2 | 16.0 | 55.3 |

Ishikawa | 3.3 | 22.2 | 77.5 | Kumamoto | 4.0 | 33.9 | 122.8 |

Fukui | 2.4 | 19.8 | 72.0 | Oita | 1.7 | 21.2 | 78.9 |

Gifu | 5.5 | 43.2 | 162.9 | Miyazaki | 2.5 | 35.5 | 136.0 |

Shizuoka | 9.0 | 45.0 | 146.1 | Kagoshima | 3.2 | 40.4 | 149.3 |

Aichi | 8.7 | 40.4 | 117.3 | Okinawa | 2.6 | 11.2 | 36.7 |

Mie | 5.0 | 26.5 | 91.2 |

## References

- Plastics Europe. Association of Plastic Manufacturers; Plastics Europe: Brussles, Belgium, 2016. [Google Scholar]
- Laist, D.W. Overview of the biological effects of lost and discarded plastic debris in the marine environment. Mar. Pollut. Bull.
**1987**, 18, 319–326. [Google Scholar] [CrossRef] - Hansen, J. Draft position statement on plastic debris in marine environments. Fisheries
**1990**, 15, 16–17. [Google Scholar] - Pruter, A.T. Sources, quantities and distribution of persistent plastics in the marine environment. Mar. Pollut. Bull.
**1987**, 18, 305–310. [Google Scholar] [CrossRef] - Gregory, M.R. The hazards of persistent marine pollution: Drift plastics and conservation islands. J. R. Soc. N. Z.
**1991**, 21, 83–100. [Google Scholar] [CrossRef] - Gregory, M.R.; Ryan, P.G. Pelagic plastics and other seaborne persistent synthetic debris: A review of Southern Hemisphere perspectives. In Marine Debris—Sources, Impacts and Solutions; Coe, J.M., Rogers, D.B., Eds.; Springer: New York, NY, USA, 1997; pp. 49–66. [Google Scholar]
- Derraik, J.G. The pollution of the marine environment by plastic debris: A review. Mar. Pollut. Bull.
**2002**, 44, 842–852. [Google Scholar] [CrossRef] - Law, K.L.; Thompson, R.C. Microplastics in the seas. Science
**2014**, 345, 144–145. [Google Scholar] [CrossRef] - Cózar, A.; Echevarría, F.; González-Gordillo, J.I.; Irigoien, X.; Úbeda, B.; Hernández-León, S.; Palma, A.T.; Navarro, S.; García-de-Lomas, J.; Ruiz, A.; et al. Plastic debris in the open ocean. Proc. Natl. Acad. Sci. USA
**2014**, 111, 10239–10244. [Google Scholar] [CrossRef] [PubMed][Green Version] - Shaw, D.G. Pelagic tar and plastic in the Gulf of Alaska and BeringSea: 1975. Sci. Total Environ.
**1977**, 8, 13–20. [Google Scholar] [CrossRef] - Williams, A.T.; Simmons, S.L. Estuarine litter at the river/beach interface in the Bristol Channel, United Kingdom. J. Coast. Res.
**1997**, 13, 1159–1165. [Google Scholar] - Jambeck, J.R.; Geyer, R.; Wilcox, C.; Siegler, T.R.; Perryman, M.; Andrady, A.; Narayan, R.; Law, K.L. Plastic waste inputs from land into the ocean. Science
**2015**, 347, 768–771. [Google Scholar] [CrossRef] - Thompson, R.C.; Olsen, Y.; Mitchell, R.P.; Davis, A.; Rowland, S.J.; John, A.W.G.; McGonigle, D.; Russell, A.E. Lost at sea: Where is all the plastic? Science
**2004**, 34, 838. [Google Scholar] [CrossRef] [PubMed] - Carpenter, E.J.; Smith, K.L. Plastics on the Sargasso Sea surface. Science
**1972**, 175, 1240–1241. [Google Scholar] [CrossRef] [PubMed] - Rothstein, S.I. Plastic particle pollution of the surface of the Atlantic Ocean: Evidence from a seabird. Condor
**1973**, 75, 344–345. [Google Scholar] [CrossRef] - Eriksen, M.; Lebreton, L.C.M.; Carson, H.S.; Thiel, M.; Moore, C.J.; Borerro, J.C.; Galgani, F.; Ryan, P.G.; Reisser, J. Plastic pollution in the world’s oceans: More than 5 trillion plastic pieces weighing over 250,000 tons afloat at sea. PLoS ONE
**2014**, 9, e111913. [Google Scholar] [CrossRef][Green Version] - Mato, Y.; Isobe, T.; Takada, H.; Kanehiro, H.; Ohtake, C.; Kaminuma, T. Plastic resin pellets as a transport medium for toxic chemicals in the marine environment. Environ. Sci. Technol.
**2001**, 35, 318–324. [Google Scholar] [CrossRef] - Tanaka, K.; Takada, H.; Yamashita, R.; Mizukawa, K.; Fukuwaka, M.A.; Watanuki, Y. Accumulation of plastic-derived chemicals in tissues of seabirds ingesting marine plastics. Mar. Pollut. Bull.
**2013**, 69, 219–222. [Google Scholar] [CrossRef] - Koelmans, A.A.; Besseling, E.; Wegner, A.; Foekema, E.M. Plastic as a carrier of POPs to aquatic organisms: A model analysis. Environ. Sci. Technol.
**2013**, 47, 7812–7820. [Google Scholar] [CrossRef] - Browne, M.A.; Dissanayake, A.; Galloway, T.S.; Lowe, D.M.; Thompson, R.C. Ingested microscopic plastic translocates to the circulatory system of the mussel, Mytilus edulis (L). Environ. Sci. Technol.
**2008**, 42, 5026–5031. [Google Scholar] [CrossRef] - Boerger, C.M.; Lattin, G.L.; Moore, S.L.; Moore, C.J. Plastic ingestion by planktivorous fishes in the North Pacific Central Gyre. Mar. Pollut. Bull.
**2010**, 60, 2275–2278. [Google Scholar] [CrossRef] - Cole, M.; Lindeque, P.; Halsband, C.; Galloway, T.S. Microplastics as contaminants in the marine environment: A review. Mar. Pollut. Bull.
**2011**, 62, 2588–2597. [Google Scholar] [CrossRef] - Tanaka, K.; Takada, H. Microplastic fragments and microbeads in digestive tracts of planktivorous fish from urban coastal waters. Sci. Rep.
**2016**, 6, 34351. [Google Scholar] [CrossRef] [PubMed] - Andrady, A.L. Microplastics in the marine environment. Mar. Pollut. Bull.
**2011**, 62, 1596–1605. [Google Scholar] [CrossRef] - Hidalgo-Ruz, V.; Gutow, L.; Thompson, R.C.; Thiel, M. Microplastics in the marine environment: A review of the methods used for identification and quantification. Environ. Sci. Technol.
**2012**, 46, 3060–3075. [Google Scholar] [CrossRef] - Law, K.L.; Moret-Ferguson, S.; Goodwin, D.S.; Zettler, E.R.; DeForce, E.; Kukulka, T.; Proskurowski, G. Distribution of surface plastic debris in the eastern Pacific Ocean from an 11-year. Environ. Sci. Technol.
**2014**, 48, 4732–4738. [Google Scholar] [CrossRef] [PubMed] - Law, K.L.; Moret-Ferguson, S.; Maximenko, N.A.; Proskurowski, G.; Peacock, E.E.; Hafner, J.; Reddy, C.M. Plastic accumulation in the North Atlantic subtropical gyre. Science
**2015**, 329, 1185–1188. [Google Scholar] [CrossRef] [PubMed][Green Version] - Isobe, A.; Uchida, K.; Tokai, T.; Iwasaki, S. East Asian seas: A hot spot of pelagic microplastics. Mar. Pollut. Bull.
**2015**, 101, 618–623. [Google Scholar] [CrossRef] - Kershaw, P.J.; Rochman, C.M. Sources, fate and effects of microplastics in the marine environment: Part 2 of a global assessment. Rep. Stud.
**2016**, 93, 216. [Google Scholar] - Wagner, M.; Scherer, C.; Alvarez-Muñoz, D.; Brennholt, N.; Bourrain, X.; Buchinger, S.; Fries, E.; Grosbois, C.; Klasmeier, J.; Marti, T.; et al. Microplastics in freshwater ecosystems: What we know and what we need to know. Environ. Sci. Eur.
**2014**, 26, 12. [Google Scholar] [CrossRef][Green Version] - Moore, C.J.; Lattin, G.L.; Zellers, A.F. Quantity and type of plastic debris flowing from two urban rivers to coastal waters and beaches of Southern California. Rev. Gest. Costeira Integr. J. Integr. Coast. Zone Manag.
**2011**, 11, 65–73. [Google Scholar] [CrossRef] - Eriksen, M.; Mason, S.; Wilson, S.; Box, C.; Zellers, A.; Edwards, W.; Farley, H.; Amato, S. Microplastic pollution in the surface waters of the Laurentian Great Lakes. Mar. Pollut. Bull.
**2013**, 77, 177–182. [Google Scholar] [CrossRef] - Mani, T.; Hauk, A.; Walter, U.; Burkhardt-Holm, P. Microplastics profile along the Rhine river. Sci. Rep.
**2014**, 5, 17988. [Google Scholar] [CrossRef] [PubMed] - Yonkos, L.T.; Friedel, E.A.; Perez-Reyes, A.C.; Ghosal, S.; Arthur, C.D. Microplastics in four estuarine rivers in the Chesapeake Bay, U.S.A. Environ. Sci. Technol.
**2014**, 48, 14195–14202. [Google Scholar] [CrossRef] [PubMed] - Eerkes-Medrano, D.; Thompson, R.C.; Aldridge, D.C. Microplastics in freshwater systems: A review of the emerging threats, identification of knowledge gaps and prioritisation of research needs. Water Res.
**2015**, 75, 63–82. [Google Scholar] [CrossRef] [PubMed] - Dris, R.; Gasperi, J.; Rocher, V.; Saad, M.; Renault, N.; Tassin, B. Microplastic contamination in an urban area: A case study in Greater Paris. Environ. Chem.
**2015**, 12, 592–599. [Google Scholar] [CrossRef] - McCormick, A.R.; Hoellein, T.J.; London, M.G.; Hittie, J.; Scott, J.W.; Kelly, J.J. Microplastic in surface waters of urban rivers: Concentration, sources, and associated bacterial assemblages. Ecosphere
**2016**, 7, e01556. [Google Scholar] [CrossRef] - Murphy, F.; Ewins, C.; Carbonnier, F.; Quinn, B. Wastewater treatment works (WwTW) as a Source of microplastics in the aquatic environment. Environ. Sci. Technol.
**2016**, 50, 5800–5808. [Google Scholar] [CrossRef][Green Version] - Verster, C.; Minnaar, K.; Bouwman, H. Marine and freshwater microplastic research in South Africa. Integr. Environ. Assess. Manag.
**2017**, 13, 533–535. [Google Scholar] [CrossRef] - Kataoka, T.; Nihei, Y.; Kudou, K.; Hinata, H. Assessment of the sources and inflow processes of microplastics in the river environments of Japan. Environ. Pollut.
**2019**, 244, 958–965. [Google Scholar] [CrossRef] - Lebreton, L.C.M.; van der Zwet, J.; Damsteeg, J.-W.; Slat, B.; Andrady, A.; Reisser, J. River plastic emissions to the world’s oceans. Nat. Commun.
**2017**, 8, 15611. [Google Scholar] [CrossRef] - Schmidt, C.; Krauth, T.; Wagner, S. Export of plastic debris by rivers into the sea. Environ. Sci. Technol.
**2017**, 51, 12246–12253. [Google Scholar] [CrossRef] - Siegfred, M.; Koelmans, A.A.; Besseling, E.; Kroeze, C. Export of microplastics from land to sea. A modelling approach. Water Res.
**2017**, 127, 249–257. [Google Scholar] [CrossRef] [PubMed] - Kudo, K.; Kataoka, T.; Nihei, Y.; Kitaura, F. Estimation of temporal variations and annual flux of microplastics in rivers under low- and high-flow conditions. J. Jpn. Soc. Civ. Eng. Ser. B1 (Hydraul. Eng.)
**2018**, 74, 529–534. (In Japanese) [Google Scholar] [CrossRef] - Kataoka, T.; Hinata, H.; Nihei, Y. Numerical estimation of inflow flux of floating natural macro-debris into Tokyo Bay. Estuar. Coast. Shelf Sci.
**2013**, 134, 69–79. [Google Scholar] [CrossRef] - Motovilov, Y.G.; Gottschalk, L.; Engeland, K.; Rodhe, A. Validation of a distributed hydrological model against spatial observations. Agric. For. Meteorol.
**1999**, 98, 257–277. [Google Scholar] [CrossRef] - Sayama, T.; Ozawa, G.; Kawakami, T.; Nabesaka, S.; Fukami, K. Rainfall–runoff–inundation analysis of the 2010 Pakistan flood in the Kabul River basin. Hydrol. Sci. J.
**2012**, 57, 298–312. [Google Scholar] [CrossRef] - Sawano, S.; Hotta, N.; Tanaka, N.; Suboyama, Y.; Suzuki, M. Development of a simple forest evapotranspiration model using a process-oriented model as a reference to parameterize data from a wide range of environmental conditions. Ecol. Model.
**2015**, 309–310, 93–109. [Google Scholar] [CrossRef] - Motoyama, H. Simulation of seasonal snowcover based on air temperature and precipitation. J. Appl. Meteorol.
**1990**, 29, 1104–1110. [Google Scholar] [CrossRef][Green Version] - Suzuki, M.; Fukusima, Y. Estimates of evapotranspiration from land surface in Shiga Prefecture using digitalized square-grid map database. Lake Biwa Study Monogr.
**1995**, 2, 1–55. [Google Scholar] - Research Group of Evapotranspiration. Evapotranspiration from paddy field. J. Agric. Meteorol.
**1967**, 22, 13–21. (In Japanese) [Google Scholar] - Kondo, J Evaporation from lake or sea surface. Weather
**2012**, 59, 75–78. (In Japanese) - Chow, V.T.; Maidment, D.R.; Mays, L.W. Applied Hydrology; McGraw Hill: New York, NY, USA, 1988. [Google Scholar]
- Montgomery, D.C.; Peck, E.A.; Vining, G.G. Introduction to Linear Regression Analysis; John Wiley & Sons: Hoboken, NJ, USA, 2012; p. 821. [Google Scholar]
- Kataoka, T.; Nihei, Y. Quantification of floating riverine macro-debris transport using an image processing approach. Sci. Rep.
**2020**, 10, 1–11. [Google Scholar] [CrossRef] [PubMed][Green Version]

**Figure 1.**Schematic of the conceptual framework used to evaluate plastic inputs from the land to the sea in this study.

**Figure 3.**Percentiles of MicP numerical concentration (

**a**) and mass concentration (

**b**) for all data. The x-axis is displayed on a logarithmic scale.

**Figure 4.**Relationship between the MicP numerical concentration C

_{n}and mass concentrations C

_{m}for all data. The solid line represents a linear approximation of the data and the regression coefficient (=0.201 (mg/particle)) corresponds to the mean mass (mg) per MicP particle.

**Figure 5.**Correlation between MicP concentrations and population density, W

_{p}, (

**a**) and urban ratio, W

_{u}, (

**b**) for raw and moving average data. The stippled line represents the 95% confidence interval around the linear approximation.

**Figure 6.**Correlation between annually observed discharge, Q

_{obs}, and calculated river discharge, Q

_{cal}, at the mouths of 109 class-A river systems. The dashed line represents a 1:1 correspondence.

**Figure 7.**Box plots for the mass concentrations of MicP and MacP (

**a**) and the value of MacP/MicP mass concentrations at each measurement site (

**b**). These figures were based on the measurement data of Lebreton et al. [41]. The solid line represents the mean values; the tops and bottoms of the boxes denote the 75% and 25% quartiles, respectively, and the top and bottom of the error bars show the maximum and minimum values, excluding outliers. Crosses denote the mean data.

**Figure 8.**Box plots for annual inputs of MicP, MacP, and total plastics from the land to the sea in Japan.

**Figure 9.**Total plastic emissions over 1 km grids in Japan. The MicP mass concentrations were evaluated via linear approximation for population density (

**a**) and urban area ratio (

**b**) with the same ratio of MacP/MicP = 3.13. Warmer colors indicate higher emissions, while cooler colors indicate lower emissions.

**Table 1.**Evaluation for evapotranspiration, E, surface runoff, Q

_{s}, and underground infiltration, Q

_{i}, in the present study.

Land Use | E | Q_{s} | Q_{i} | ||
---|---|---|---|---|---|

Major | Details | Coefficient f | |||

Forest | Forests | Equation (1) | 0.5 (Quaternary volcanic rock) 0.8(Other) | Equation (6) | |

Bushes | Equation (2) | 0.3 | Equation (6) | ||

Mountainous bushes | P − Q_{s} | 0.95 | 0 | ||

Agriculture area | Paddy fields | Irrigation | Equation (3) | 0.8 | Equation (6) |

No irrigation | Equation (2) | 0.3 | Equation (6) | ||

Other | Equation (2) | 0.3 | Equation (6) | ||

Urban area | Building sites | Infiltration area | Equation (2) | 0.3 | Equation (6) |

No-infiltration area | P − Q_{s} | 0.95 | 0 | ||

Road, railways, and others | P − Q_{s} | 0.95 | 0 | ||

Other | Golf courses | Equation (2) | 0.3 | Equation (6) | |

Rivers and lakes | Equation (4) | P − E | 0 |

**Table 2.**Annual inputs of MicP numbers and masses from land to the sea in Japan, obtained using linear approximations of y $\pm \Delta y/2$ (confidence interval: 95%) obtained from raw data, and curves approximated for moving average data.

Variables | Approximation | Number | Mass | ||
---|---|---|---|---|---|

Equation | 10^{12} Particles | Equation | Tons | ||

Population density, W_{p} | Linear y | 9 | 1.67 | 10 | 293.6 |

Linear y + Δy/2 | 7,8,9 | 2.54 | 7,8,10 | 502.8 | |

Linear y − Δy/2 | 7,8,9 | 0.81 | 7,8,10 | 84.5 | |

Curve | 13 | 1.39 | 14 | 204.1 | |

Urban ratio, W_{u} | Linear y | 11 | 1.41 | 12 | 228.1 |

Linear y + Δy/2 | 7,8,11 | 2.26 | 7,8,12 | 435.7 | |

Linear y − Δy/2 | 7,8,11 | 0.55 | 7,8,12 | 65.1 | |

Curve | 15 | 1.27 | 16 | 217.9 | |

Low | 0.55 | 65.1 | |||

Middle | 1.40 | 223.0 | |||

High | 2.54 | 502.8 |

**Table 3.**Summary of the coefficient, a, which is the ratio of the MacP and MicP mass concentrations.

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## Share and Cite

**MDPI and ACS Style**

Nihei, Y.; Yoshida, T.; Kataoka, T.; Ogata, R.
High-Resolution Mapping of Japanese Microplastic and Macroplastic Emissions from the Land into the Sea. *Water* **2020**, *12*, 951.
https://doi.org/10.3390/w12040951

**AMA Style**

Nihei Y, Yoshida T, Kataoka T, Ogata R.
High-Resolution Mapping of Japanese Microplastic and Macroplastic Emissions from the Land into the Sea. *Water*. 2020; 12(4):951.
https://doi.org/10.3390/w12040951

**Chicago/Turabian Style**

Nihei, Yasuo, Takushi Yoshida, Tomoya Kataoka, and Riku Ogata.
2020. "High-Resolution Mapping of Japanese Microplastic and Macroplastic Emissions from the Land into the Sea" *Water* 12, no. 4: 951.
https://doi.org/10.3390/w12040951