Back-calculating emission rates for ammonia and particulate matter from area sources using dispersion modeling

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dc.contributor.advisor Lacey , Ronald E . en_US
dc.contributor.committeeMember Shaw , Bryan W . en_US
dc.creator Price , Jacqueline Elaine en_US
dc.date.accessioned 2004 -11 -15T19 :52 :58Z
dc.date.accessioned 2014 -02 -19T18 :34 :45Z
dc.date.available 2004 -11 -15T19 :52 :58Z
dc.date.available 2014 -02 -19T18 :34 :45Z
dc.date.created 2004 -08 en_US
dc.date.issued 2004 -11 -15T19 :52 :58Z
dc.identifier.uri http : / /hdl .handle .net /1969 .1 /1270
dc.description.abstract Engineering directly impacts current and future regulatory policy decisions . The foundation of air pollution control and air pollution dispersion modeling lies in the math , chemistry , and physics of the environment . Therefore , regulatory decision making must rely upon sound science and engineering as the core of appropriate policy making (objective analysis in lieu of subjective opinion ) . This research evaluated particulate matter and ammonia concentration data as well as two modeling methods , a backward Lagrangian stochastic model and a Gaussian plume dispersion model . This analysis assessed the uncertainty surrounding each sampling procedure in order to gain a better understanding of the uncertainty in the final emission rate calculation (a basis for federal regulation ) , and it assessed the differences between emission rates generated using two different dispersion models . First , this research evaluated the uncertainty encompassing the gravimetric sampling of particulate matter and the passive ammonia sampling technique at an animal feeding operation . Future research will be to further determine the wind velocity profile as well as determining the vertical temperature gradient during the modeling time period . This information will help quantify the uncertainty of the meteorological model inputs into the dispersion model , which will aid in understanding the propagated uncertainty in the dispersion modeling outputs . Next , an evaluation of the emission rates generated by both the Industrial Source Complex (Gaussian ) model and the WindTrax (backward -Lagrangian stochastic ) model revealed that the calculated emission concentrations from each model using the average emission rate generated by the model are extremely close in value . However , the average emission rates calculated by the models vary by a factor of 10 . This is extremely troubling . In conclusion , current and future sources are regulated based on emission rate data from previous time periods . Emission factors are published for regulation of various sources , and these emission factors are derived based upon back -calculated model emission rates and site management practices . Thus , this factor of 10 ratio in the emission rates could prove troubling in terms of regulation if the model that the emission rate is back -calculated from is not used as the model to predict a future downwind pollutant concentration . en_US
dc.format.extent 1572848 bytes
dc.format.medium electronic en_US
dc.format.mimetype application /pdf
dc.language.iso en _US en_US
dc.publisher Texas A &M University en_US
dc.subject regulatory policy en_US
dc.title Back -calculating emission rates for ammonia and particulate matter from area sources using dispersion modeling en_US
dc.type Book en
dc.type.genre Electronic Thesis en_US
dc.type.material text en_US
dc.format.digitalOrigin born digital en_US

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Back-calculating emission rates for ammonia and particulate matter from area sources using dispersion modeling. Available electronically from http : / /hdl .handle .net /1969 .1 /1270 .

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