1. 2015-The 17 th GEIA Conference : Influence of Urbanization on Emission Worldwide Influence of Urbanization on Emission Worldwide Quantitative analysis of uncertainty in sector- based emission factors Junyu (Allen) ZHENG Zhuangmin Zhong South China University of Technology Beijing, November, 19, 2015

2. Why Uncertainty Analysis in Emission Inventory? Uncertainties are in inherent in compiling air pollutant emission inventories (EIs); Characterizing uncertainty in EIs can help prioritize source categories for future improvement and assess the quality of Eis; Quantitative uncertainty information in EIs are fundamental input data for analyzing the impact of uncertainty in EIs on air quality modeling results and thereby can help improve air quality model performance.

3. Key Challenges in Conducting Uncertainty Analysis In most cases, a comprehensive quantitative uncertainty analysis is difficult to be conducted, or cannot be done ； Currently available emission factor database has no quantitative information on uncertainty in emission factors ； Lack of information on uncertainties in emission factors or other parameters is the key challenge. Emission Factor Activity Factor Statistical Analysis Emission Inventory Models Total Emission Output Uncertainty Monte Carlo Simulation Input 1 Input n

4. In 2007, EPA conducted a emission factor uncertainty assessment Quantitative uncertainty information were given for only 43 A- rated and 1 B-rated AP-42 emission factors AP -42 Emission Factor Uncertainty Assessment

5. Objectives To develop a sector-based emission factor database with quantitative uncertainty information with the use of available emission testing data and various emission factor database sources such as from AP-42, IIASA, EEA for those sectors where possible ； To help quantify uncertainties or judge possible ranges in urban, regional or even national-scale emission inventories ；

6. Methods and Tools For those sectors where emission testing data or measurements or investigation are available, bootstrap simulation was used to quantify uncertainty in emission factors with the aid of AuvToolPro; For those sectors where emission testing data or measurements are not available, or not enough, a comprehensive review was first made on available emission factor database and literatures (including recently developed Chinese domestic emission factors and internationally well-known database such as USEPA AP-42 database, EEA,IIASA, and others), then probabilistic distributions were developed to represent uncertainties in pollutant-based emission factors with the use of statistical analysis and expert judgment approaches;

7. Example 1: Industrial Coating Sector SectorPollutantEmission factor(kg/kg) Uncertainty Distribution 95% confidence intervalUncertainty range Wood furniture coatingVOCs0.45Weibull(0.51,3.09)0.41-0.50-9%~11% Automotive industryVOCs0.42Gamma(6.29,0.06)0.29-0.57-31%~36% ShipbuildingVOCs0.36 Normal （ 1.15,0.36 ） 0.20-0.54-44%~50% Metal surface coatingVOCs0.35 Gamma （ 6.33,0.05 ） 0.27-0.44-23%~26% Plastic surface coatingVOCs0.34 Normal （ 0.45,0.36 ） 0.17-0.50-50%~47% Fabric coatingVOCs0.4 Weibull （ 0.48,0.88 ） 0.11-0.91-73%~128% SectorPollutantEmission factorUnits Uncertainty Distribution 95% confidence interval Uncertainty range Wood furniture coating VOCs1.42kg/piece of furniture Weibull （ 1.12 ， 0.71 ） 0.83-2.1-42%~48% Automotive industryVOCs20.3kg/car Gamma （ 1.79,11.38 ） 8.84-37.32-56%~84% ShipbuildingVOCs13.72t/ship Weibull （ 9.21,0.61 ） 1.44-45.29-90%~230% Metal surface coatingVOCs1.07kg/metal production Weibull （ 1.25,0.72 ） 0.53-2.87-50%~168% Plastic surface coatingVOCs0.59 kg/plastic production Normal （ 0.45,1.62 ） 0.37-0.82-37%~79% Fabric coatingVOCs27.46kg/m 2 cloth Normal （ 26.18,17.80 ） 8.93-49.54-67%~80% Activity Data : raw materials Activity Data: production Uncertainty of EF based on raw materials < Uncertainty of EF based on production Data from emission testing or investigation

8. FuelCapacity Polluta nt Emission factor(Mean) Unit Uncertainty Distribution 95% confidence interval Uncertainty range Coal<125MWNOx7.16kg/tGamma(9.13,0.79)5.53-8.93-23%-25% Coal >125MWbut<300 MW NOx6.48kg/tNormal(6.52,2.62)4.07-9.14-37%-41% Coal >300MWbut<600 MW NOx8.14kg/tNormal(8.17,2.36)5.92-10.67-27%-31% Coal>600MWNOx5.11kg/tWeibull(5.80,3.13)3.33-7.08-35%-39% Heavy oilallNOx9.75kg/tNormal(9.77,4.26)7.49-12.50-23%-28% GasallNOx12.89kg/m 3 Gamma(0.57,22.81)4.64-28.24-64%-119% CoalallPM109.28kg/tNormal(9.39,7.75)3.2-16.59-66%-79% Heavy oilallPM100.78kg/tNormal(0.78,0.06))0.72-0.85-58%-59% GasallPM100.09kg/m 3 Gamma(4.54,2.71)0.03-0.18-67%-100% CoalallPM2.54.5kg/tNormal(4.54,2.72)2.49-6.68-45%-48% Heavy oilallPM2.50.58kg/tGamma(24.2,0.01)0.55-0.62-65%-77% GasallPM2.50.07kg/m 3 Gamma(1.34,0.05))0.03-0.14-57%-100% CoalallVOC0.13kg/tWeibull(0.13,1.15)0.05-0.24-62%-85% Heavy oilallVOC0.08kg/tWeibull(0.10.2.27)0.05-0.12-38%-50% GasallVOC0.08kg/m 3 Gamma(2.27,0.04)0.04-0.14-50%-75% CoalallCO1.18kg/tNormal(1.15,1.11)0.05-2.32-36%-37% Heavy oilallCO0.48kg/tWeibull(0.54,3.27)0.31-0.65-35%-35% GasallCO1.12kg/m 3 Weibull(1.22,1.44)0.47-2.02-58%-80% Example 2: Power Plant Sector Data from emission factor database or literatures

9. Example 3: Residential Combustion Sector SubsectorPollutant Emission factor(Mean) Unit Uncertainty Distribution 95% confidence interval Uncertainty range Honeycomb briquet BC0.2kg/t Weibull （ 0.11,0.52 ） 0.06-0.47-70%-135% lump coalBC2.47kg/t Weibull （ 0.83,0.41) 0.50-6.70-80%-171% soft coalBC1.55kg/tWeibull(1.40,0.78)0.55-3.27-65%-111% blind coalBC0.01kg/tGamma(0.98,0.01)0.01-0.02-50%-100% Honeycomb briquet OC1.58kg/tWeibull(0.86,0.53)0.48-3.72-70%-135% lump coalOC1.76kg/tWeibull(1.52,0.72)0.81-3.25-54%-85% soft coalOC3.35kg/tNormal(3.34,1.73)2.30-4.36-31%-30% blind coalOC0.15kg/tGamma(0.65,0.23)0.07-0.30-53%-100% Honeycomb briquet PM101.26kg/tNormal(1.30,1.46)0.03-2.61-98%-107% lump coalPM105.41kg/tNormal(5.58,4.86)1.33-9.36-75%-73% soft coalPM2.57.86kg/tNormal(7.75,3.10)4.70-11.58-40%-47% blind coalPM2.52.09kg/tGamma(1.59,1.25)0.75-4.34-64%-108% Data from emission factor database or literatures

10. Example 4: Dust Source Sector TypePollutant Emission factor(Mean) Unit Uncertainty Distribution 95% confidence interval Uncertainty range Construction dust PM100.14g/(m2·h)Lognormal(2.22,0.9)0.08-0.24-43%-71% Road dustPM102.83g/VKTGamma(2.22,1.29)2.03-3.66-28%-29% Road construction dust PM102.12 g/ （ m2.d ） Weibull(1.91,0.87)0.23-5.60-89%-164% Construction dust PM2.50.04g/(m2·h)Weibull(0.04,1.05)0.02-0.07-50%-75% Road dustPM2.50.45g/VKTWeibull(0.50,1.44)0.33-0.62-27%-38% Road construction dust PM2.50.66 g/ （ m2.d ） Weibull(0.67,0.94)0.17-1.82-74%-176% Data from field emission testing and emission factor database

11. Example 5: Biomass Burning Sector TypesPollutant Emission factor(Mean) Unit Uncertainty Distribution 95% confidence interval Uncertainty range FirewoodBC0.83kg/tNormal(0.85,51)0.47-1.18-43%-42% Straw-householdBC0.69kg/tWeibull(0.75,1.25)0.38-1.07-45%-55% Straw-opening burnign BC0.44kg/tGamma(2.88,0.15)0.32-0.57-27%-30% Forest fireBC0.42kg/tNormal(0.42,0.29)0.11-0.71-74%-69% FirewoodOC1.52kg/tGamma(2.51,0.58)0.92-2.32-39%-53% Straw-householdOC2.77kg/tGamma(1.29,2.13)1.54-4.79-44%-73% Straw-opening burnign OC2.23kg/tNormal(2.26,1.43)1.47-3.08-34%-38% Forest fireOC4.47kg/tNormal(4.51,2.92)1.3-7.35-69%-64% FirewoodPM103.17kg/tGamma(1.42,2.23)1.91-4.98-40%-57% Straw-householdPM108.57kg/tGamma(2.98,2.88)6.36-11.36-26%-33% Straw-opening burnign PM108.21kg/tNormal(8.33,3.10)6.2-10.19-24%-24% Forest firePM1027.71kg/tGamma(0.91,28.61)5.9-69.595-79%-151% Grassland firePM106.25kg/tNormal(6.23,0.86)5.2-7.28-17%-16% FirewoodPM2.53.81kg/tGamma(2.86,1.34)2.69-5.14-29%-35% Straw-householdPM2.58.49kg/tNormal(8.44,4.27)6.95-10.39-18%-22% Straw-opening burnign PM2.57.11kg/tNormal(7.17,3.33)5.64-8.73-21%-23% Forest firePM2.525.09kg/tGamma(0.93,25.89)6.62-55.07-74%-119% Grassland firePM2.55.84kg/tWeibull(6.11,10.81)5.07-6.40-13%-10% Data from emission factor database or literatures

12. A Case Study: Uncertainty Analysis of NH 3 Emissions from Agricultural and Husbandry Source in the PRD Region 1. Development of uncertainty analysis model 2. Distribution types 3. Bootstrap simulation 4. Uncertainty analysis SourceEmission(t) 95% Confidence interval Uncertainty range Livestock235357 (159248 ， 323382) （ -30% ， 42% ） dairy-L888 (478 ， 2024 ） （ -51% ， 108% ） dairy-S383 (137 ， 660 ） （ -56% ， 111% ） Beef-L551 (185 ， 1204 ） （ -69% ， 104% ） Beef-S8215 (2872 ， 18046 ） （ -68% ， 103% ） yellow cattle-L1000 (548 ， 2013 ） （ -50% ， 82% ） yellow cattle-S14907 (7966 ， 29379 ） （ -51% ， 80% ） buffalo11762 (5246 ， 19146 ） （ -50% ， 81% ） Sow-L13030 (7769 ， 41648 ） （ -59% ， 118% ） Sow-S13282 (5568 ， 28943 ） （ -60% ， 110% ） Pig-L64471 (14653 ， 123856 ） （ -72% ， 134% ） Pig-S60771 (10904 ， 86327 ） （ -71% ， 126%) Layers4904 (2968 ， 11379 ） （ -52% ， 83% ） Dorking26619 (11873 ， 81683 ） （ -68% ， 122% ） Laying duck3543 (2875 ， 4638 ） （ -23% ， 24% ） Duck5178 (4165 ， 7081 ） （ -26% ， 27% ） Goose4485 (1533 ， 23984 ） （ -86% ， 125% ） Pigeon397 (315 ， 464 ） （ -19% ， 19% ） Rabbit618 (382 ， 785 ） （ -35% ， 33% ） Goat352 (204 ， 850 ） （ -50% ， 110% ） N fertilizer application 238593 (81963 ， 541835 ） （ -68% ， 111% ） Total473950 (287728 ， 781726 ） （ -41% ， 61% ） Source Emission factor UnitUncertainty Distribution 95% confidence interval Uncertainty range dairy-L27.63kg/aGamma(13.43,2.06)23.26-32.83-16%-16% dairy-S28.51kg/aLognormal(3.32,0.23)24.49-33.73-14%-15% Beef-L20.94kg/aNormal(21.01,4.39)17.79-24.03-15%-13% Beef-S20.94kg/aGamma(22.85,0.91)17.99-24.04-14%-13% yellow cattle-L16.13kg/aNormal(15.96,6.46)11.34-20.32-30%-21% yellow cattle-S14.93kg/aGamma(4.79,3.11)11.06-19.75-26%-24% buffalo12.24kg/aGamma(11.33,1.08)8.65-17.02-29%-28% Sow-L6.82kg/aNormal(6.79,4.07)2.36-11.22-65%-39% Sow-S9.2kg/aWeibull(10.38,2.40)5.34-13.38-42%-31% Pig-L3.48kg/aGamma(9.53,0.36)2.32-4.85-33%-28% Pig-S9.21kg/aWeibull(10.37,2.41)5.54-13.12-40%-30% Layers0.34kg/aNormal(0.34,0.11)0.26-0.43-24%-21% Dorking0.17kg/aNormal(0.17,0.09)0.06-0.26-65%-35% Laying duck0.28kg/aLognormal(-1.29,0.17)0.23-0.33-18%-15% Duck0.16kg/aWeibull(0.15,0.83)0.03-0.41-81%-61% Goose0.21kg/aWeibull(0.24,2.12)0.12-0.32-43%-34% Pigeon0.19kg/aWeibull(0.12,0.54)0.01-0.71-95%-73% Rabbit0.36kg/aGamma(2.64,0.14)0.15-0.68-58%-47% Goat2.34kg/aGamma(1.12,2.11)0.41-5.62-82%-58% N fertilizer application 0.19kg/t NWeibull(0.22,3.37)0.16-0.24-16%-21%

13. Future Work Continuing to develop probabilistic distributions in more detailed sector-based and pollutant-based emission factors, hopefully to have a sector-based emission factor uncertainty database for most emission source sectors if possible; Demonstration of this emission factor uncertainty database to quantify uncertainty in the Pearl River Delta regional air pollutant emission inventories;