A disaggregated emissions inventory for Taiwan with uses in hybrid input‐output life cycle analysis (IO‐LCA)

This paper reports on a life‐cycle analysis (LCA) of Taiwan's “agriculture and forestry”, “crude petroleum, coal and natural gas extraction” and “electricity generation” sectors, revealing for the first time Taiwan's CO₂ and CH₄ emissions inventories and matching Taiwan's input‐output sectors. Integrated hybrid input‐output life cycle analysis is used to disaggregate the electricity generation sector into nuclear, hydro, gas, oil and coal, and cogeneration. Results show that the fossil‐fuel‐related electricity sub‐sectors have higher CO₂ emissions intensity than the remaining sectors in the economy and that the “paddy rice” sector is Taiwan's most CH₄‐intensive sector, making rice cultivation an important source of CH₄ emissions. This work is vital to sound policy decisions concerning power generation, coal, and agriculture and forestry at the national level.     

The potential of spatial information in human biomonitoring by example of two German environmental epidemiology studies

This study aimed at statistically investigating the association between the internal exposure of children and young adults to uranium (U) and epidemiologically relevant external determinants of exposure. The investigation was performed with data from two studies within the framework of the German health-related environmental monitoring program: The German Environmental Survey for Children (GerES IV) conducted by the Federal Environment Agency (Umweltbundesamt) with data on 1,780 children 3–14 years of age and their home environment and the German Environmental Specimen Bank (ESB, section: human specimens) with data on 2,253 students 20–29 years of age. Both studies provided data on the U levels in human urine for all probands. GerES IV furthermore provided an extensive environmental and demographic database on, e.g., U levels in drinking water. The data from GerES IV and ESB were linked by GIS to spatially relevant exposure information, including background values of U in stream sediments and in upper and lower soils, U levels in mosses and particulate matter in the lower atmosphere, precipitation and elevation as well as forest density. Bivariate correlation analysis and two decision tree models showed moderate but significant associations between U in human urine and U levels in drinking water, stream sediments and upper and lower soils. Future investigations considering additional epidemiologically relevant data sets may differentiate the results. Furthermore, the sample design of future environmental epidemiology studies should take the spatial evaluation of the data into greater account.