A 1998 estimate predicts that U.S. wastewater treatment systems generated nearly 8 million dry tons of sludge in 2005. This sludge requires significant energy to treat, ranging from 30-80% of total electrical input to a wastewater treatment system.
Charting the Course for Sustainability at Aurora Organic Dairy – Phase 2: Energy, Greenhouse Gas, Nutrient Use, Water Use and Solid Waste Generation Life Cycle Assessment.
This study is the second phase of a three-phase sustainability assessment of milk production by Aurora Organic Dairy (AOD). AOD is a leading provider of private-label organic milk to retailers throughout the U.S., and operates five farms in Colorado and Texas as well as a processing plant in Colorado. This study extended Phase I results to include a second year of data on energy use and greenhouse gas (GHG) emissions throughout the milk production life cycle. It also added three new categories of environmental impact—nutrient use, water use, and solid waste generation—based on their relevance to agricultural production systems. Primary data from AOD were collected over the period of April 2008 to March 2009, supplemented by existing literature, and used to benchmark impacts in each of the five categories across the full milk production life cycle, from feed and bedding production to final disposal. The functional unit of analysis was one gallon of packaged fluid milk. In addition to these life-cycle results, simplified environmental performance indicators (EPIs) were developed to aid management in understanding the environmental effects of operational decisions. Life-cycle results per functional unit were: 68 MJ (energy consumption), 7.8 kg CO2 eq. (greenhouse gas emissions), 4.6 moles H+ eq. (acidification potential), 2.5 g N eq. (eutrophication potential), 810 gallons (water consumption), 12 gallons (water utilization), 160 g (direct municipal solid waste), and 160 g (indirect solid waste). The feed and bedding production life cycle stage was both a major contributor to most impacts, and the stage with the highest data uncertainty. A set of strategies for improvement were identified for each impact area.