Plastic debris presents a serious hazard to marine ecosystems worldwide. In this study, we developed a method to evaluate high-resolution maps of plastic emissions from the land into the sea offshore of Japan without using mismanaged plastic waste. Plastics were divided into microplastics (MicPs) and macroplastics (MacPs), and correlations between the observed MicP concentrations in rivers and basin characteristics, such as the urban area ratio and population density, were used to evaluate nationwide MicP concentration maps. A simple water balance analysis was used to calculate the annual outflow for each 1 km mesh to obtain the final MicP emissions, and the MacP input was evaluated based on the MicP emissions and the ratio of MacP/MicP obtained according to previous studies. Concentration data revealed that the MicP concentrations and basin characteristics were significantly and positively correlated. Water balance analyses demonstrated that our methods performed well for evaluating the annual flow rate, while reducing the computational load. The total plastic input (MicP + MacP) was widely distributed from 210–4776 t/yr and a map showed that plastic emissions were high for densely populated and highly urbanized areas in the Tokyo metropolitan area, as well as other large urban areas, especially Nagoya and Osaka. These results provide important insights that may be used to develop countermeasures against plastic pollution and the methods employed herein can also be used to evaluate plastic emissions in other regions.

While plastics are classified by size as microplastics (25 mm), research in rivers has centered on microplastics, followed by macroplastics, with limited research on mesoplastics (research gap). This study aims to clarify the concentration, shape, and polymer composition of microplastics and mesoplastics in Japanese river water. We conducted field surveys for microplastics and mesoplastics in 147 rivers and at 185 measurement stations. The novelty of this study is in the use of a large number of field data to minimize the effect of the spatial difference in the microplastics and mesoplastics on the data analysis. Microplastics and mesoplastics were found at 183 (99 %) and 136 (74 %) stations, respectively. The difference between the concentration of microplastics and mesoplastics increased significantly with the increase in the concentration of microplastics, showing that the concentration of both microplastics and mesoplastics should be monitored to prevent an underestimation of plastic pollution in rivers with the appropriate sampling. A 2-stage size classification with microplastics (5 mm) is not suitable because the mesoplastics may be substantially overlooked. The regression slopes between microplastics and mesoplastics concentrations significant decrease in variance with increasing data number, suggesting the necessity of the large number of samples used in this study. The predominant shapes and polymer types of microplastics and mesoplastics were found to be fragment and fiber and polyethylene and polyethylene terephthalate (PET), respectively, which were affected appreciably by many fiber clusters. The fiber and PET ratios were dominant at stations with small population densities and urban ratios and can be attributed to atmospheric deposition and the underdeveloped rate of wastewater treatment plants (WWTPs). Therefore, it is necessary to monitor the dynamics and fate of fiber clusters inside and outside the basin.

Environmental sampling plays an important role in quantitative and qualitative investigation of plastic pollution. Rivers are a major source, carrying plastic litter into the oceans. Microplastic sampling in riverine and coastal environments is often a challenging task due to limited access, time taken, costs, human resources, etc. Our present study evaluated the performance of newly developed sampling devices (Albatross Mark 5 and 6 (AM-5 and AM-6)) that were suitable to collect floating and suspended microplastic samples in challenging freshwater and coastal environments (95 locations). Our observations indicated a similar magnitude of microplastic concentrations with AM-5 and AM-6 sampling compared to conventional plankton nets. The sampling duration, originally 10–60 min (by plankton net), was reduced to 3 min (AM-5 and AM-6) for sampling water volumes of approximately 10 m³. The developed AM-6 device was used to collect samples from riverine and coastal environments in Japan. The microplastic particle polymer composition (using Fourier transform infrared spectrophotometry (FT-IR)), size, and shape (microscopic images) were investigated. The observations showed a statistically significant particle size reduction from the riverine to coastal areas. The dominant polymer types detected were polyethylene (PE) and polypropylene (PP). The observations were complied with the coastal microplastic observations that were reported for previous studies in Japanese water environments.

In this study, a series of numerical analyses of meteorology, runoff, river flow, and inundation were performed to quantitatively evaluate the effects of historical warming on precipitation, discharge, water level, and flood inundation. This study was on the flood inundation of the Chikuma River Basin in Japan caused by Typhoon Hagibis in 2019. The historical warming impact on Typhoon Hagibis was analyzed by comparing nonglobal warming experiments (NonWs) and control experiments (CTLs) to reproduce the current situation. Calculated results showed that the ratios of the index of CTLs to that of NonWs (mean CTL to mean NonW) as indicators of the impact of historical warming were 1.08, 1.22, and 1.08–3.08 for total precipitation, peak river discharge, and peak water level (from pre‐flood level and high‐water level), respectively, and 48.1 and 595 for overflow depth and inundation volume for the Chikuma River, respectively. Results of the hydrologic‐sensitivity analysis indicated that the influence of historical warming during the last 40‐year period was already evident on overflow and flooding, whose sensitivities were higher than those of discharge and water level.

In recent years, large-scale heavy rainfall disasters have occurred frequently in several parts of the world. Therefore, a quantitative approach to understanding how buildings are damaged during floods is necessary to develop appropriate flood-resistant technologies. In flood inundation simulations for the quantitative evaluation of a building’s resistance to flooding, a subgrid model is necessary to appropriately evaluate the resistance of buildings smaller than the grid size at a medium grid resolution. In this study, a new subgrid (SG) 3D inundation model is constructed to evaluate the fluid force acting on buildings and assess the damage to individual buildings during flood inundation. The proposed method does not increase the computational load. The model is incorporated into a 2D and 3D hybrid model with high computational efficiency to construct a 3D river and inundation flow model. Its validity and effectiveness are evaluated through comparisons with field observations and the conventional equivalent roughness model. Considering horizontal and vertical velocity distributions, the proposed model showed statistically significant improvements in performance in terms of building loss indices such as velocity and fluid force. These results suggest that the SG model can effectively evaluate the fluid force acting on buildings, including the vertical distribution of flow velocities.

Microplastics (MPs), plastic particles <5 mm in diameter, are emerging ubiquitous pollutants in natural environments, including freshwater ecosystems. As rivers facilitate efficient transport among landscapes, monitoring is crucial for elucidating the origin, dynamics, and fate of MPs. However, standardized methodologies for in situ sampling in freshwater environments remain undefined to date. Specifically, evaluating the sampling error of MP concentration estimates is crucial for comparing results among studies. This study proposes a novel method for computing confidence intervals (CIs) from a single estimate of numerical concentration (expressed in particles·m^-3). MPs are expected to disperse according to purely random processes, such as turbulent diffusion, and to consequently exhibit a random distribution pattern that follows a Poisson point process. Accordingly, the present study introduced a framework based on the Poisson point process to compute CIs, which were validated using MP samples from two urban rivers in Chiba, Japan, obtained using a mesh with an opening size of 335 μm. Random number simulations revealed that the CIs were applicable when ≥10 MPs were present in a sample. Further, when ≥50 MPs were present in a sample, the sampling error (95% CI) was within ±30% of the concentration estimates. The proposed framework allows for the intercomparison of single river MP samples despite the lack of sample replicates. Further, the present study emphasizes that the volume of sampled river water is the only controllable parameter that can reduce the sampling error.