Comparison of spatial and temporal changes in riverine nitrate concentration from terrestrial basins to the sea between the 1980s and the 2000s in Japan: Impact of recent demographic shifts

Nitrogen (N) is an essential nutrient but may become a pollution source in the environment when the N concentration exceeds a certain threshold for humans and nature. Nitrate is a major N species in river water with notable spatial and temporal variations under the influences of natural factors and anthropogenic N inputs. We analyzed the relationship between riverine N (focusing on nitrate) concentration and various factors (land use, climate, basin topography, atmospheric N deposition, agricultural N sources and human-derived N) in 104 rivers located throughout the Japanese Archipelago except small remote islands. We aimed to better understand processes and mechanisms to explain the spatial and temporal changes in riverine nitrate concentration. A publicly available river water quality database observed in the 1980s (1980–1989) and 2000s (2000–2009) was used. This study is the first to evaluate the long-term scale of 20 years in the latter half of Japan's economic growth period at the national level. A geographic information system (GIS) was employed to determine average values of each variable collected from multiple sources of statistical data. We then performed regression analysis and structural equation modeling (SEM) for each period. The forestland area influenced by the basin topography, climate (i.e., air temperature) and other land uses (i.e., farmland and urban area) played a major role in decreasing nitrate concentrations in both the 1980s and 2000s. Atmospheric N deposition (especially N oxides) and agricultural N sources (fertilizer and manure) were also significant variables regarding the spatial variations in riverine nitrate concentrations. The SEM results suggested that human-derived N (via food consumption) intensified by demographic shifts during the 2000s increased riverine nitrate concentrations over other variables within the context of spatial variation. These findings facilitate better decision making regarding land use, agricultural practices, pollution control and individual behaviors toward a sustainable society.