Search

We estimate the emissions of the two most important greenhouse gasses (GHG), carbon dioxide (CO2) and methane (CH4), from the use of modern high-efficiency heat pump water heaters compared to the most commonly used domestic hot water systems: natural gas storage tanks, tankless natural gas demand heaters, electric resistance storage tanks, and tankless electric resistance heaters. We considered both natural gas-powered electric plants and coal-powered plants as the source of the electricity for the heat pumps, the thermal electric storage tanks, and the tankless electric demand heaters. The time-integrated radiative forcing associated with using a heat pump water heater was always smaller than any other means of heating water considered in this study across all time frames including at 20 and 100 years. The estimated amount of CH4 lost during its lifecycle was the most critical factor determining the relative magnitude of the climatic impact. The greatest net climatic benefit within the 20-year time frame was predicted to be achieved when a storage natural gas water heater (the most common system for domestic hot water in the United States) fueled by shale gas was replaced with a high efficiency heat pump water heater powered by coal-generated electricity, the heat pump system powered by renewable electricity would have had an even greater climatic benefit, but was not explicitly modeled in this study. Our analysis provides the first assessment of the GHG footprint associated with using a heat pump water heater, which we demonstrate to be an effective and economically viable way of reducing emissions of GHGs.

The main objective of this work was to develop regional settings of the NANI budgeting tool that will address the significant variation in agricultural practices and resulting nutrient accountings among European countries. NANI (Net Anthropogenic Nitrogen Inputs), first introduced by Howarth et al. (1996), estimate the human‐induced nitrogen inputs to a watershed and have been shown to be a good predictor of riverine nitrogen export at a large scale, multi‐year average basis. NANI have been calculated as the sum of four major components: atmospheric N deposition, fertilizer N application, agricultural N fixation, and net food and feed imports, which in turn are composed of crop and animal N production (negative fluxes removing N from watersheds) and animal and human N consumption (positive fluxes adding N to watersheds). Assuming approximate steady-state behavior, riverine N export is a fixed proportion of net nitrogen inputs. Similar calculations can be made for phosphorus (P) inputs, though because atmospheric deposition of P is usually considered negligible and there is no analog in P for atmospheric fixation, the calculation of Net Anthropogenic Phosphorus Inputs (NAPI) reduces to accounting for P fertilizer and P in net food/feed terms. While this document is primarily concerned with calculating NANI, we also describe the data sources and assumptions used to make the parallel calculations of NAPI. Version 2.0 of the Toolbox described in this document is an improvement of version 1.0 developed for US watersheds (http://www.eeb.cornell.edu/biogeo/nanc/nani/nani.htm; Hong et al. 2011). Version 1.0 allows the user to calculate NANI in any area within the contiguous United States (e.g., watershed, county, etc.) from nationally available databases downloadable from the Internet. The toolbox consists of a set of tools that: (1) calculate the proportions of various regions (political or gridded) in which data are collected that fall into areas of interest such as watersheds (“NANI‐GIS tools”), (2) extract and organize relevant data downloaded from web‐based datasets to be used by the accounting tools (“NANI‐extraction tools”), and (3) calculate NANI, their components, and other relevant items such as animal excretion (“NANI-.‐accounting tools”). While attempting to apply version 1.0 of the toolbox to Baltic Sea catchments, we found that the calculation of NANI in Baltic Sea catchments is more challenging than in US watersheds, mainly for two reasons: • Watersheds span international boundaries. Significant variation in agricultural practices and resulting nutrient accountings among European countries exist. For example, a substantial gradient in agricultural practices is expected among the former EU countries, new EU member states with transitional economies, and Belarus and Russia. • Gaps and uncertainties in the available data are much greater than those in the US. In general, the problem of missing information is more severe for the transitional countries, Belarus, and Russia, requiring numerous assumptions and guesswork to be made to deal with the insufficient data issue. Version 2.0 of the Toolbox describe in this document has several modules and improvements added to version 1.0 (which assumes spatially uniform agricultural practices, i.e., fixed values for all the NANI parameters, supported by the availability of well‐established and standardized datasets) to address the above difficulties. These improvements include: • Allowing spatial variation of NANI parameters (in this example, country‐specific NANI parameters) (Sections 4, 5.1, and 5.2) • Distribution of regional data (e.g., country-level crop production) into smaller spatial units (e.g., grid cells containing crop area information) (Section 5.3) • Making post‐calculation adjustments and refinements by accepting auxiliary datasets and manual calculations from the user (Section 3) In the following sections we describe the calculation of NANI and their components in the Baltic Sea catchments, with details of data availability, input preparation, and step-by‐step procedure of the use of various tools, and provide some preliminary results. In addition, Appendix 1 described parameter values used to create NAPI estimates following an accounting methodology in parallel to that for NANI.