Comparative analysis of Unmix/PMF modeling for PM₂.₅ source apportionment in rural and urban Kansas and a review of life cycle assessment on carbon footprint of beef production - Liu, Yang (Kansas State University, 2018)
The Unmix and Positive Matrix Factorization (PMF) models for source apportionment were applied to evaluate prescribed burning impacts on air quality, identify model advantages, and establish a relationship between visibility and PM₂.₅ sources. Speciated PM₂.₅ data were from the Flint Hills (FH) rural and the Kansas City (KC) urban sites. At the FH site, the Unmix model identified five sources: nitrate/agricultural, sulfate/industrial, crustal/soil, smoke, and secondary organic aerosol (SOA); while the PMF model identified the copper source in addition. The smoke source from PMF result includes both primary and secondary aerosols from prescribed burning when the smoke source in Unmix result only includes primary burning aerosols. The secondary smoke aerosols at the FH site were combined with secondary aerosols from other origins and formed the SOA source in Unmix result. Comparative analysis of the modeling results estimated the SOA to be 2.3 to 2.7 times of the primary aerosols in burning season. At the KC site, both receptor models derived seven-source solutions: nitrate/agricultural, sulfate/industrial, crustal/soil, smoke, traffic/SOA, heavy-duty diesel vehicle (HDDV), and calcium. The smoke source at the KC site carries an exceedingly organic carbon to elemental carbon (OC/EC) ratio, which is more than five times higher than in FH smoke source. The PMF results at KC site tend to classify more SOA from nitrate/agricultural and sulfate/industrial sources into traffic/SOA source. In the burning season, the smoke source from both sites showed a relatively high correlation when KC is under west and southwest wind, suggesting that part of the smoke originated PM₂.₅ at the urban site could be from the upwind burning activities. The Tobit modeling recognized the nitrate/agricultural as the leading visibility degradation impact factor at both sites. The latter chapter conducted a review of life cycle assessment (LCA) on carbon footprint (CF) of beef production. The objectives were to evaluate CF range in raising systems from different countries, identify the leading CF contributor and dominant source of uncertainty, and summarize LCA inventory defined in cattle production systems. Most existing beef LCA studies followed a “cradle to farm gate” approach. The CF in 3-phase systems ranged from 16 to 29.5 kg CO2e kg⁻¹ carcass weight. The 2-phase raising system reported a slightly lower CF than the 3-phase system (18.9 to 26.9 kg CO2e kg⁻¹ carcass weight), but no significant differences were observed. The grass-fed system in the US has the highest CF, but the CF of grass-fed systems in the European Union (EU) is 40% less than them in the US. This is because more than half of cattle farms in EU produce both beef and milk, and the CF burden was partaken by the dairy production. Cow-calf phase contributed the most CF in 3-phase raising system, while enteric fermentation was the major contributor. Feed production contributed the most in the feedlot phase if forages were applied rather than concentrates. The leading uncertainty sources reported was land use change and disparate dressing percentage. To improve the LCA accuracy, more research is needed in collecting reliable LCA inventory data such as raising period and feed intake efficiency.
Keywords: air pollution; receptor model; secondary organic aerosol; life cycle assessment; carbon footprint