A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
A temporally and spatially resolved ammonia emission inventory for dairy cows in the United States
AbstractPrevious inventories of ammonia emissions for the United States have not characterized the seasonal and geographic variations that are necessary for accurately predicting ambient concentrations of ammonium nitrate and ammonium sulfate aerosol. This research calculates the seasonal and geographic variation in ammonia emissions from dairy cows in the United States. Monthly, county-level emission factors are calculated with a process-based model of dairy farm emissions, the national distribution of farming practices, seasonal climate conditions, and animal populations. Annual, county-level emission factors are estimated to range between 13.1 and 55.5, with a national average of 23.9kgNH3 cow−1 yr−1. The seasonal variation of the emission factor is estimated to be as high as a factor of seven in some counties. Emissions are predicted to be the highest in the spring and fall, because of high manure application rates during the spring planting and after the fall harvest. Summer emissions are higher than winter, resulting from the temperature dependence of housing and storage emissions. In the summer and winter, the majority of emissions are from animal housing. In the spring and fall, the majority of emissions are from field applied manure. The 5% and 95% confidence interval about the national annual average emission factor is between 18 and 36kgNH3cow−1yr−1. Uncertainties in farming practices contribute most to the total uncertainty, yet uncertainty in the timing of manure application, the quantity of manure and nitrogen excreted by cows, and the physical processes of volatilization affecting applied manure are also significant.
A temporally and spatially resolved ammonia emission inventory for dairy cows in the United States
AbstractPrevious inventories of ammonia emissions for the United States have not characterized the seasonal and geographic variations that are necessary for accurately predicting ambient concentrations of ammonium nitrate and ammonium sulfate aerosol. This research calculates the seasonal and geographic variation in ammonia emissions from dairy cows in the United States. Monthly, county-level emission factors are calculated with a process-based model of dairy farm emissions, the national distribution of farming practices, seasonal climate conditions, and animal populations. Annual, county-level emission factors are estimated to range between 13.1 and 55.5, with a national average of 23.9kgNH3 cow−1 yr−1. The seasonal variation of the emission factor is estimated to be as high as a factor of seven in some counties. Emissions are predicted to be the highest in the spring and fall, because of high manure application rates during the spring planting and after the fall harvest. Summer emissions are higher than winter, resulting from the temperature dependence of housing and storage emissions. In the summer and winter, the majority of emissions are from animal housing. In the spring and fall, the majority of emissions are from field applied manure. The 5% and 95% confidence interval about the national annual average emission factor is between 18 and 36kgNH3cow−1yr−1. Uncertainties in farming practices contribute most to the total uncertainty, yet uncertainty in the timing of manure application, the quantity of manure and nitrogen excreted by cows, and the physical processes of volatilization affecting applied manure are also significant.
A temporally and spatially resolved ammonia emission inventory for dairy cows in the United States
Pinder, Robert W (author) / Strader, Ross (author) / Davidson, Cliff I (author) / Adams, Peter J (author)
Atmospheric Environment ; 38 ; 3747-3756
2004-04-06
10 pages
Article (Journal)
Electronic Resource
English
| Taylor & Francis Verlag | 2017
Making the US national forest inventory spatially contiguous and temporally consistent
| DOAJ | 2022
Spatially and Temporally Varying Dispersivity
| British Library Conference Proceedings | 2000