Table 1: Top 20 polluting rivers as predicted by the global river plastic inputs model.

From: River plastic emissions to the world’s oceans

CatchmentCountryLower mass input estimate (tyr1)Midpoint mass input estimate (tyr1)Upper mass input estimate (tyr1)Total catchment surface area (km2)21Yearly average discharge (m3s1)21
YangtzeChina3.10 × 1053.33 × 1054.80 × 1051.91 × 1061.58 × 104
GangesIndia, Bangladesh1.05 × 1051.15 × 1051.72 × 1051.57 × 1062.08 × 104
XiChina6.46 × 1047.39 × 1041.14 × 1053.89 × 1055.53 × 103
HuangpuChina3.35 × 1044.08 × 1046.73 × 1042.62 × 1044.04 × 102
CrossNigeria, Cameroon3.38 × 1044.03 × 1046.5 × 1042.38 × 1032.40 × 102
BrantasIndonesia3.23 × 1043.89 × 1046.37 × 1041.11 × 1048.18 × 102
AmazonBrazil, Peru, Columbia, Ecuador3.22 × 1043.89 × 1046.38 × 1045.91 × 1061.40 × 105
PasigPhilippines3.21 × 1043.88 × 1046.37 × 1044.07 × 1032.07 × 102
IrrawaddyMyanmar2.97 × 1043.53 × 1045.69 × 1043.77 × 1055.49 × 103
SoloIndonesia2.65 × 1043.25 × 1045.41 × 1041.58 × 1047.46 × 102
MekongThailand, Cambodia, Laos, China, Myanmar, Vietnam1.88 × 1042.28 × 1043.76 × 1047.74 × 1056.01 × 103
ImoNigeria1.75 × 1042.15 × 1043.61 × 1047.92 × 1032.79 × 102
DongChina1.57 × 1041.91 × 1043.17 × 1043.33 × 1048.54 × 102
SerayuIndonesia1.33 × 1041.71 × 1042.99 × 1043.71 × 1033.70 × 102
MagdalenaColombia1.29 × 1041.67 × 1042.95 × 1042.61 × 1055.93 × 103
TamsuiTaiwan1.16 × 1041.47 × 1042.54 × 1042.68 × 1031.08 × 102
ZhujiangChina1.09 × 1041.36 × 1042.31 × 1044.01 × 1031.33 × 102
HanjiangChina1.03 × 1041.29 × 1042.19 × 1042.95 × 1047.35 × 102
ProgoIndonesia9.80 × 1041.28 × 1042.29 × 1042.24 × 1032.79 × 102
Kwa IboNigeria9.29 × 1041.19 × 1042.08 × 1043.63 × 1031.92 × 102
  1. Input rate estimates (in t yr−1) are representative of mismanaged plastic waste (MPW) production and catchment runoff. A lower, midpoint and upper estimate is calculated based on three regression analyses accounting for uncertainties in our field observations data set.