The load of total nitrogen (TN) in stream water was surveyed in the Nagara River Basin (2,000 km2), central Japan. Multivariate analysis placed the TN data in an environmental and social context, relating TN to land use conditions such as geologic features, population density, and percentage of the population using the sewer system. Multivariate analysis was used to examine relationships among the land use distribution with and without human activity and the amount of pollution effluent from waste water treatment plants (WWTP). The pollution load in stream water is related to characteristics of the land cover in the river basin, so the influence of land use on the pollutant load was investigated. However, key factors affecting the pollutant load are human activities associated with the land use. In this study, a relationship between pollutant load, land use, and human activity is developed. Land use was estimated from Landsat data using ISODATA clustering. The distribution of the land cover factors was related to human activities, i.e. population density, agricultural production, industrial wastewater discharge, percentage of sewered population, and stock breeding in the catchment. Multivariate analysis related the TN data to land use and human activities. However, the types of land use were found to be insufficient to evaluate the TN, which appeared to be largely governed by other human-related factors such as industrial wastewater discharge, agricultural production, population density, and livestock density. Socioeconomic data, were obtained from government agencies. The results indicate that the TN load outflow characteristics of the study catchment were affected not only by outside human activity, but also largely by the various human activities in the small drainage basin. Industrial waste water contributed as much to the pollution load outflow as did human activity. This is shown quantitatively in that land use information collected at the same time as that collected on human activities provides effective baseline data. The model proposed here is suitable for evaluating best management practices.

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