The National Institute for Environmental Studies (NIES:国立環境研究所, Kokuritsu-Kankyō kenkyūsho) was established in 1974 as a focal point for environmental research in Japan. In 2001 it became an Independent Administrative Institution. NIES is organised into eight centers, each of which is subdivided into a further number of sections responsible for different specializations within the broader field to which they belong.The eight centers are responsible for research in eight different fields, with programs dedicated to these research areas.
Abstract Nitrous oxide (N2O) is a strong greenhouse gas that contributes significantly to global warming and causes depletion of ozone in the stratosphere. Recent observational records show an unprecedented acceleration in atmospheric N₂O growth, reaching 1.15 ppb yr− 1 in 2019–2023, a significant increase compared to 0.68 ppb yr− 1 in 2001–2005. This surge in growth rate is particularly pronounced over tropical regions, and has been measured most prominently at the southern-most island of Japan (Hateruma). In this study, we use N2O observations from globally distributed multi-institutional networks and the MIROC4-ACTM inversion framework to quantify N2O emissions and identify key regions that are driving the recent acceleration. Our results suggest that the major Asian countries, Brazil, Central and Northern Africa, and the Contiguous United States have increased emission sources in the recent 2.5 decades (1998–2023). Further, there has been an increase in land N2O emissions, at a rate of 106 GgN yr− 1 per year during 1998–2002 to 2019–2023 (1Gg = 109g). The inversion inferred trends are consistent with increased fertiliser use and manure production to support extensive agriculture, and terrestrial ecosystem model results. The emissions from oceanic regions did not show significant increases in N2O (rate: 7 ± 2 GgN yr− 1 per year) in our inverse model setup. Our results underscore the importance for improved climate mitigation strategies and emissions reduction policies by increasing nitrogen-fertiliser use efficiency in agricultural land.
Heavy rainfall events can severely reduce salinity in enclosed seas and cause physiological stress to marine organisms such as bivalves, and their frequency and intensity have been in an increasing trend in recent years. In the study area, Nanao Bay, located on Noto Peninsula, Japan, several flood disasters associated with heavy rainfall occurred in recent years, and in some cases, extremely low salinity water was observed in the aquaculture ground. There is no river whose flow rate is monitored in the watershed, and the impact of heavy rainfall events on the environment of this bay remains to be fully understood. In this study, a coastal ocean model with a rainfall-runoff model was developed and optimized using the results of the field observations, and the influences of heavy rainfall events and winds on low salinity water dynamics and seawater exchange were numerically investigated. The results of the field observations showed the influence of two prevailing winds in this area on low salinity water dynamics; northeasterly winds caused low salinity water to stagnate at the bay head, while southwesterly winds quickly dissipated low salinity water. The results of the sensitivity experiments showed that estuary circulation was enhanced by southwesterly winds common in July–August while suppressed by northeasterly winds frequent in late summer. The coastal ocean model with the rainfall-runoff model well reproduced the low salinity water dynamics, suggesting that it would be useful for analyzing the impact of increased heavy rainfall on the coastal area.
The Earth Cloud Aerosol and Radiation Explorer (EarthCARE) is a joint Japanese-European satellite observation mission for understanding the interaction between cloud, aerosol, and radiation processes and improving the accuracy of climate change predictions. The EarthCARE satellite was equipped with four sensors, a 355 nm high-spectral-resolution lidar with depolarization measurement capability (ATLID) as well as a cloud profiling radar, a multi-spectral imager, and a broadband radiometer, to observe the global distribution of clouds, aerosols, and radiation. In this study, we have developed algorithms to produce ATLID Level 2 aerosol products using ATLID Level 1 data. The algorithms estimated the following four products: (1) Layer identifiers such as aerosols, clouds, clear-skies, or surfaces were estimated by the combined use of vertically variable criteria and spatial continuity methods developed for the CALIOP (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation) analysis. (2) Aerosol optical properties such as extinction coefficient, backscatter coefficient, depolarization ratio, and lidar ratio at 355 nm were optimized to ATLID L1 data by the method of maximum likelihood. (3) Six aerosol types, namely smoke, pollution, marine, pristine, dusty-mixture, and dust were identified based on a two-dimensional diagram of the lidar ratio and depolarization ratio at 355 nm developed by cluster-analysis using the AERONET (AErosol RObotic NETwork) dataset with ground-based lidar data. (4) The planetary boundary layer height was determined using the improved wavelet covariance transform method for the ATLID analysis. The performance of various algorithms was evaluated using pseudo ATLID Level 1 data generated by Joint-Simulator (Joint Simulator for Satellite Sensors), which incorporates aerosol and cloud distributions simulated by numerical models. Results from applying the algorithms to the pseudo ATLID Level 1 data with realistic signal noise added for aerosol or cloud predominant cases revealed: (1) misidentification of aerosol and cloud layers was relatively low, approximately 10 %; (2) the retrieval errors of aerosol optical properties were 0.08×10-7±1.12×10-7m-1sr-1 (2±34 % in relative error) for backscatter coefficient and 0.01±0.07 (4±27 % in relative error) for depolarization ratio; (3) aerosol type classification was generally performed well. These results indicate that the algorithm's capability to provide valuable insights into the global distribution of aerosols and clouds, facilitating assessments of their climate impact through atmospheric radiation processes.
Abstract Anthropogenic radionuclides introduced into the ocean through atmospheric nuclear weapons testing, nuclear accidents, and controlled releases from nuclear facilities are powerful tracers of ocean circulation. Because their input histories are temporally well constrained, they provide time markers that can be used to quantify water-mass transport, mixing, and ventilation, processes that regulate ocean heat and carbon uptake. The Pacific Ocean, the largest and one of the most intensively sampled basins, provides an exceptional setting for applying radionuclide tracers to climate-relevant ocean dynamics studies. Here, we synthesize how tritium, cesium-137, and plutonium isotopes have been used, together with ocean general circulation and particle-scavenging models, to constrain basin-scale transport, inter-basin exchange among the Pacific, Indian, and Atlantic Oceans, and multidecadal changes in vertical mixing and eddy activity associated with a warming and increasingly stratified upper ocean.
With the intensification of climate change, heat-related health risks have emerged as a critical challenge to sustainable urban development. This study investigates the spatiotemporal dynamics and correlates of heatrelated illness exposure risk across Japan over the period 2003-2020. Municipality-level heat-related illness risk was estimated using heat-related ambulance transport records alongside multi-source environmental and socio-demographic datasets, with spatial mapping subsequently applied to identify temporal trends and the spatial shift of high-risk zones. Machine learning models were then employed to evaluate the nonlinear and interactive effects of urbanization and social vulnerability on long-term risk trajectories. The key findings are as follows: (1) High-risk areas and exposed populations have expanded outward from major metropolitan regions; (2) Approximately one-quarter of municipalities experienced a significant increase in heat-related illness exposure risk; (3) Marked regional and urban inequalities are evident in both risk levels and temporal trends; and (4) Pronounced nonlinear relationships and interactive effects exist between urbanization, social vulnerability, and temporal trends in exposure risk. This study advances our understanding of the dynamic evolutionary characteristics underpinning heat-related health risks and elucidates their coupling mechanisms with urbanization and demographic attributes, thereby providing empirical support for the formulation of targeted, precise, and sustainable urban planning and heat adaptation strategies.
