Methods for quantifying variability and uncertainty in AP-42 emission factors: case studies for natural gas-fueled engines

Quantitative methods for characterizing variability and uncertainty were applied to case studies of oxides of nitrogen and total organic carbon emission factors for lean-burn natural gas-fueled internal combustion engines. Parametric probability distributions were fit to represent inter-engine variability in specific emission factors. Bootstrap simulation was used to quantify uncertainty in the fitted cumulative distribution function and in the mean emission factor. Some methodological challenges were encountered in analyzing the data. For example, in one instance, five data points were available, with each data point representing a different market share. Therefore, an approach was developed in which parametric distributions were fitted to population-weighted data. The uncertainty in mean emission factors ranges from as little as approximately +/-10% to as much as -90 to +180%. The wide range of uncertainty in some emission factors emphasizes the importance of recognizing and accounting for uncertainty in emissions estimates. The skewness in some uncertainty estimates illustrates the importance of using numerical simulation approaches that do not impose restrictive symmetry assumptions on the confidence interval for the mean. In this paper, the quantitative method, the analysis results, and key findings are presented.     

Quantification of Variability and Uncertainty in Lawn and Garden Equipment NOx and Total Hydrocarbon Emission Factors

Abstract

Variability refers to real differences in emissions among multiple emission sources at any given time or over time for any individual emission source. Variability in emissions can be attributed to variation in fuel or feedstock composition, ambient temperature, design, maintenance, or operation. Uncertainty refers to lack of knowledge regarding the true value of emissions. Sources of uncertainty include small sample sizes, bias or imprecision in measurements, nonrepresentativeness, or lack of data. Quantitative methods for characterizing both variability and uncertainty are demonstrated and applied to case studies of emission factors for lawn and garden (L&G) equipment engines. Variability was quantified using empirical and parametric distributions. Bootstrap simulation was used to characterize confidence intervals for the fitted distributions. The 95% confidence intervals for the mean grams per brake horsepower/hour (g/hp-hr) emission factors for two-stroke engine total hydrocarbon (THC) and NO_x emissions were from -30 to +41% and from -45 to +75%, respectively. The confidence intervals for four-stroke engines were from -33 to +46% for THCs and from -27 to +35% for NO_x. These quantitative measures of uncertainty convey information regarding the quality of the emission factors and serve as a basis for calculation of uncertainty in emission inventories (Els).     

Quantification of variability and uncertainty in lawn and garden equipment NOx and total hydrocarbon emission factors

Variability refers to real differences in emissions among multiple emission sources at any given time or over time for any individual emission source. Variability in emissions can be attributed to variation in fuel or feedstock composition, ambient temperature, design, maintenance, or operation. Uncertainty refers to lack of knowledge regarding the true value of emissions. Sources of uncertainty include small sample sizes, bias or imprecision in measurements, nonrepresentativeness, or lack of data. Quantitative methods for characterizing both variability and uncertainty are demonstrated and applied to case studies of emission factors for lawn and garden (L&G) equipment engines. Variability was quantified using empirical and parametric distributions. Bootstrap simulation was used to characterize confidence intervals for the fitted distributions. The 95% confidence intervals for the mean grams per brake horsepower/hour (g/hp-hr) emission factors for two-stroke engine total hydrocarbon (THC) and NOx emissions were from -30 to +41% and from -45 to +75%, respectively. The confidence intervals for four-stroke engines were from -33 to +46% for THCs and from -27 to +35% for NOx. These quantitative measures of uncertainty convey information regarding the quality of the emission factors and serve as a basis for calculation of uncertainty in emission inventories (EIs).     

Development of Probabilistic Emission Inventories of Air Toxics for Jacksonville,Florida

Abstract

Probabilistic emission inventories were developed for 1,3-butadiene, mercury (Hg), arsenic (As), benzene, formaldehyde, and lead for Jacksonville, FL. To quantify inter-unit variability in empirical emission factor data, the Maximum Likelihood Estimation (MLE) method or the Method of Matching Moments was used to fit parametric distributions. For data sets that contain nondetected measurements, a method based upon MLE was used for parameter estimation. To quantify the uncertainty in urban air toxic emission factors, parametric bootstrap simulation and empirical bootstrap simulation were applied to uncensored and censored data, respectively. The probabilistic emission inventories were developed based on the product of the uncertainties in the emission factors and in the activity factors. The uncertainties in the urban air toxics emission inventories range from as small as –25 to +30% for Hg to as large as –83 to +243% for As. The key sources of uncertainty in the emission inventory for each toxic are identified based upon sensitivity analysis. Typically, uncertainty in the inventory of a given pollutant can be attributed primarily to a small number of source categories. Priorities for improving the inventories and for refining the probabilistic analysis are discussed.     

Sustainable Development by Design: Review of Life Cycle Design and Related Approaches

Abstract

The quality of stationary source emission factors is typically described using data quality ratings, which provide no quantification of the precision of the emission factor for an average source, nor of the variability from one source to another within a category. Variability refers to actual differences caused by differences in feedstock composition, design, maintenance, and operation. Uncertainty refers to lack of knowledge regarding the true emissions. A general methodology for the quantification of variability and uncertainty in emission factors, activity factors, and emission inventories (EIs) is described, featuring the use of bootstrap simulation and related techniques. The methodology is demonstrated via a case study for a selected example of NO_x emissions from coal-fired power plants. A prototype software tool was developed to implement the methodology. The range of interunit variability in selected activity and emission factors was shown to be as much as a factor of 4, and the range of uncertainty in mean emissions is shown to depend on the interunit variability and sample size. The uncertainty in the total inventory of ?16 to +19% was attributed primarily to one technology group, suggesting priorities for collecting data and improving the inventory. The implications for decision-making are discussed.     

Development of probabilistic emission inventories of air toxics for Jacksonville, Florida, USA

Probabilistic emission inventories were developed for 1,3-butadiene, mercury (Hg), arsenic (As), benzene, formaldehyde, and lead for Jacksonville, FL. To quantify inter-unit variability in empirical emission factor data, the Maximum Likelihood Estimation (MLE) method or the Method of Matching Moments was used to fit parametric distributions. For data sets that contain nondetected measurements, a method based upon MLE was used for parameter estimation. To quantify the uncertainty in urban air toxic emission factors, parametric bootstrap simulation and empirical bootstrap simulation were applied to uncensored and censored data, respectively. The probabilistic emission inventories were developed based on the product of the uncertainties in the emission factors and in the activity factors. The uncertainties in the urban air toxics emission inventories range from as small as -25 to +30% for Hg to as large as -83 to +243% for As. The key sources of uncertainty in the emission inventory for each toxic are identified based upon sensitivity analysis. Typically, uncertainty in the inventory of a given pollutant can be attributed primarily to a small number of source categories. Priorities for improving the inventories and for refining the probabilistic analysis are discussed.     

Quantification of variability and uncertainty in air pollutant emission inventories: method and case study for utility NOx emissions

The quality of stationary source emission factors is typically described using data quality ratings, which provide no quantification of the precision of the emission factor for an average source, nor of the variability from one source to another within a category. Variability refers to actual differences caused by differences in feedstock composition, design, maintenance, and operation. Uncertainty refers to lack of knowledge regarding the true emissions. A general methodology for the quantification of variability and uncertainty in emission factors, activity factors, and emission inventories (EIs) is described, featuring the use of bootstrap simulation and related techniques. The methodology is demonstrated via a case study for a selected example of NOx emissions from coal-fired power plants. A prototype software tool was developed to implement the methodology. The range of interunit variability in selected activity and emission factors was shown to be as much as a factor of 4, and the range of uncertainty in mean emissions is shown to depend on the interunit variability and sample size. The uncertainty in the total inventory of -16 to +19% was attributed primarily to one technology group, suggesting priorities for collecting data and improving the inventory. The implications for decision-making are discussed.     

Quantification of emission factor uncertainty

Emissions factors are important for estimating and characterizing emissions from sources of air pollution. There is no quantitative indication of uncertainty for these emission factors, most factors do not have an adequate data set to compute uncertainty, and it is very difficult to locate the data for those that do. The objectives are to compare the current emission factors of Electric Generating Unit NOx sources with currently available continuous emission monitoring data, develop quantitative uncertainty indicators for the Environmental Protection Agency (EPA) data quality rated emission factors, and determine the possible ranges of uncertainty associated with EPA's data quality rating of emission factors. EPA's data letter rating represents a general indication of the robustness of the emission factor and is assigned based on the estimated reliability of the tests used to develop the factor and on the quantity and representativeness of the data. Different sources and pollutants that have the same robustness in the measured emission factor and in the representativeness of the measured values are assumed to have a similar quantifiable uncertainty. For the purposes of comparison, we assume that the emission factor estimates from source categories with the same letter rating have enough robustness and consistency that we can quantify the uncertainty of these common emission factors based on the qualitative indication of data quality which is known for almost all factors. The results showed that EPA's current emission factor values for NOx emissions from combustion sources were found to be reasonably representative for some sources; however AP-42 values should be updated for over half of the sources to reflect current data. The quantified uncertainty ranges were found to be 25-62% for A rated emission factors, 45-75% for B rated emission factors, 60-82% for C rated emission factors, and 69-86% for D rated emission factors, and 82-92% for E rated emission factors.     

Design and Verification of a Programmable Gas Dispensing System for Exposing Plants to Dynamic Concentrations of Air Pollutants

Abstract

Landfills represent a source of distributed emissions source over an irregular and heterogeneous surface. In the method termed “Other Test Method-10” (OTM-10), the U.S. Environmental Protection Agency (EPA) has proposed a method to quantify emissions from such sources by the use of vertical radial plume mapping (VRPM) techniques combined with measurement of wind speed to determine the average emission flux per unit area per time from nonpoint sources. In such application, the VRPM is used as a tool to estimate the mass of the gas of interest crossing a vertical plane. This estimation is done by fitting the field-measured concentration spatial data to a Gaussian or some other distribution to define a plume crossing the vertical plane. When this technique is applied to landfill surfaces, the VRPM plane may be within the emitting source area itself. The objective of this study was to investigate uncertainties associated with using OTM-10 for landfills. The spatial variability of emission in the emitting domain can lead to uncertainties of –34 to 190% in the measured flux value when idealistic scenarios were simulated. The level of uncertainty might be higher when the number and locations of emitting sources are not known (typical field conditions). The level of uncertainty can be reduced by improving the layout of the VRPM plane in the field in accordance with an initial survey of the emission patterns. The change in wind direction during an OTM-10 testing setup can introduce an uncertainty of 20% of the measured flux value. This study also provides estimates of the area contributing to flux (ACF) to be used in conjunction with OTM-10 procedures. The estimate of ACF is a function of the atmospheric stability class and has an uncertainty of 10–30%.     

Emission Factors for High-Emitting Vehicles Based on On-Road Measurements of Individual Vehicle Exhaust with a Mobile Measurement Platform

ABSTRACT

Fuel-based emission factors for 143 light-duty gasoline vehicles (LDGVs) and 93 heavy-duty diesel trucks (HDDTs) were measured in Wilmington, CA using a zero-emission mobile measurement platform (MMP). The frequency distributions of emission factors of carbon monoxide (CO), nitrogen oxides (NO_x), and particle mass with aerodynamic diameter below 2.5 μm (PM_2.5) varied widely, whereas the average of the individual vehicle emission factors were comparable to those reported in previous tunnel and remote sensing studies as well as the predictions by Emission Factors (EMFAC) 2007 mobile source emission model for Los Angeles County. Variation in emissions due to different driving modes (idle, low- and high-speed acceleration, low- and high-speed cruise) was found to be relatively small in comparison to intervehicle variability and did not appear to interfere with the identification of high emitters, defined as the vehicles whose emissions were more than 5 times the fleet-average values. Using this definition, approximately 5% of the LDGVs and HDDTs measured were high emitters. Among the 143 LDGVs, the average emission factors of NO_x, black carbon (BC), PM_2.5, and ultrafine particle (UFP) would be reduced by 34%, 39%, 44%, and 31%, respectively, by removing the highest 5% of emitting vehicles, whereas CO emission factor would be reduced by 50%. The emission distributions of the 93 HDDTs measured were even more skewed: approximately half of the NO_x and CO fleet-average emission factors and more than 60% of PM_2.5, UFP, and BC fleet-average emission factors would be reduced by eliminating the highest-emitting 5% HDDTs. Furthermore, high emissions of BC, PM_2.5, and NO_x tended to cluster among the same vehicles.

IMPLICATIONS This study presents the characterization of on-road vehicle emissions in Wilmington, CA, by sampling individual vehicle plumes. Approximately 5% of the vehicles were high emitters, whose emissions were more than 5 times the fleet-average values. These high emitters were responsible for 30% and more than 50% of the average emission factors of LDGVs and HDDVs, respectively. It is likely that as the overall fleet becomes cleaner due to more stringent regulations, a small fraction of the fleet may contribute a growing and disproportionate share of the overall emissions. Therefore, long-term changes in on-road emissions need to be monitored.     

Trends,temporal and spatial characteristics,and uncertainties in biomass burning emissions in the Pearl River Delta,China

Multi-year inventories of biomass burning emissions were established in the Pearl River Delta (PRD) region for the period 2003–2007 based on the collected activity data and emission factors. The results indicated that emissions of sulfur dioxide (SO₂), nitrogen oxide (NO_x), ammonia (NH₃), methane (CH₄), organic carbon (OC), non-methane volatile organic compounds (NMVOC), carbon monoxide (CO), and fine particulate matter (PM_2.5) presented clear declining trends. Domestic biofuel burning was the major contributor, accounting for more than 60% of the total emissions. The preliminary temporal profiles were established with MODIS fire count information, showing that higher emissions were observed in winter (from November to March) than other seasons. The emissions were spatially allocated into grid cells with a resolution of 3 km × 3  km, using GIS-based land use data as spatial surrogates. Large amount of emissions were observed mostly in the less developed areas in the PRD region. The uncertainties in biomass burning emission estimates were quantified using Monte Carlo simulation; the results showed that there were higher uncertainties in organic carbon (OC) and elemental carbon (EC) emission estimates, ranging from ?71% to 133% and ?70% to 128%, and relatively lower uncertainties in SO₂, NO_x and CO emission estimates. The key uncertainty sources of the developed inventory included emission factors and parameters used for estimating biomass burning amounts.     

Modeling cold soak evaporative vapor emissions from gasoline-powered automobiles using a newly developed method

ABSTRACT

Volatile organic compounds (VOCs) evaporate and vent from a vehicle’s fuel tank to its evaporative control system when the vehicle is both driven and parked. VOCs making it past the control system are emissions. Driving and parking activity, fuel volatility, and temperature strongly affect vapor generation and the effectiveness of control technologies, and the wide variability in these factors and the sensitivity of emissions to these factors make it difficult to estimate evaporative emissions at the macro level. Established modeling methods, such as COPERT and MOVES, estimate evaporative emissions by assuming a constant in-use canister condition and consequently contain critical uncertainty when real conditions deviate from that standard condition. In this study, we have developed a new method to model canister capacity as a representative variable, and estimated emissions for all parking events based on semi-empirical functions derived from real-world activity data and laboratory measurements. As compared to chamber measurements collected during this study, the bias of the MOVES diurnal tank venting simulation ranges from ?100% to 129%, while the bias for our method’s simulation is 1.4% to 8.5%. Our modeling method is compared to the COPERT and MOVES models by estimating evaporative emissions from a Euro-3/4/5 and a Tier 2 vehicle in conditions representative for Chicago, IL, and Guangzhou, China. Estimates using the COPERT and MOVES methods differ from our method by ?56% to 120% and ?100% to 25%, respectively. The study highlights the importance for continued modeling improvement of the anthropogenic evaporative emission inventory and for tightened regulatory standards.

Implications: The COPERT and MOVES methodologies contain large uncertainties for estimating evaporative emissions, while our modeling method is developed based on chamber measurements to estimate evaporative emissions and can properly address those uncertainties. Modeling results suggested an urgent need to complete evaporative emissions inventories and also indicated that tightening evaporative emission standards is urgently needed, especially for warm areas.     

A Fuel-Based Motor Vehicle Emission Inventory

Abstract

A fuel-based methodology for calculating motor vehicle emission inventories is presented. In the fuel-based method, emission factors are normalized to fuel consumption and expressed as grams of pollutant emitted per gallon of gasoline burned. Fleet-average emission factors are calculated from the measured on-road emissions of a large, random sample of vehicles. Gasoline use is known at the state level from sales tax data, and may be disaggregated to individual air basins. A fuel-based motor vehicle CO inventory was calculated for the South Coast Air Basin in California for summer 1991. Emission factors were calculated from remote sensing measurements of more than 70,000 in-use vehicles. Stabilized exhaust emissions of CO were estimated to be 4400 tons/day for cars and 1500 tons/day for light-duty and medium- duty trucks, with an estimated uncertainty of ±20% for cars and ±30% for trucks. Total motor vehicle CO emissions, including incremental start emissions and emissions from heavy-duty vehicles were estimated to be 7900 tons/day. Fuelbased inventory estimates were greater than those of California's MVEI 7F model by factors of 2.2 for cars and 2.6 for trucks. A draft version of California's MVEI 7G model, which includes increased contributions from high-emitting vehicles and off-cycle emissions, predicted CO emissions which closely matched the fuel-based inventory. An analysis of CO mass emissions as a function of vehicle age revealed that cars and trucks which were ten or more years old were responsible for 58% of stabilized exhaust CO emissions from all cars and trucks.     

An Estimate of Biogenic Emissions of Volatile Organic Compounds during Summertime in China (7 pp)

Background and Aim An accurate estimation of biogenic emissions of VOC (volatile organic compounds) is necessary for better understanding a series of current environmental problems such as summertime smong and global climate change. However, very limited studies have been reported on such emissions in China. The aim of this paper is to present an estimate of biogenic VOC emissions during summertime in China, and discuss its uncertainties and potential areas for further investigations. Materials and Methods This study was mainly based on field data and related research available so far in China and abroad, including distributions of land use and vegetations, biomass densities and emission potentials. VOC were grouped into isoprene, monoterpenes and other VOC (OVOC). Emission potentials of forests were determined for 22 genera or species, and then assigned to 33 forest ecosystems. The NCEP/NCAR reanalysis database was used as standard environmental conditions. A typical summertime of July 1999 was chosen for detailed calculations. Results and Discussion The biogenic VOC emissions in China in July were estimated to be 2.3×10¹²gC, with 42% as isoprene, 19% as monoterpenes and 39% as OVOC. About 77.3% of the emissions are generated-from forests and woodlands. The averaged emission intensity was 4.11 mgC m⁻² hr⁻¹ for forests and 1.12 mgC m⁻² hr⁻¹ for all types of vegetations in China during the summertime. The uncertainty in the results arose from both the data and the assumptions used in the extrapolations. Generally, uncertainty in the field measurements is relatively small. A large part of the uncertainty mainly comes from the taxonomic method to assign emission potentials to unmeasured species, while the ARGR method serves to estimate leaf biomass and the emission algorithms to describe light and temperature dependence. Conclusions This study describes a picture of the biogenic VOC emissions during summertime in China. Due to the uneven spatial and temporal distributions, biogenic VOC emissions may play an important role in the tropospheric chemistry during summertime. Recommendations and Perspectives Further investigations are needed to reduce uncertainties involved in the related factors such as emission potentials, leaf biomass, species distribution as well as the mechanisms of the emission activities. Besides ground measurements, attention should also be placed on other techniques such as remotesensing and dynamic modeling. These new approaches, combined with ground measurements as basic database for calibration and evaluation, can hopefully provide more comprehensive information in the research of this field. Submission Editor: Prof. Dr. Gerhard Lammel (lammel@recetox.muni.cz)     

A Maximum Entropy Approach to Estimating Emissions

ABSTRACT

A maximum information entropy method of calculating probabilistic estimates of volatile organic compound (VOC) emissions by the wood furniture and fixture coating industry is presented. The maximum entropy approach is used to produce minimally biased probability distributions for number of firms, coating use, and coating emission factors from existing summary statistics. These distributions are combined to estimate VOC emissions. The maximum entropy emissions estimate provides information to support probabilistic modeling of regional air quality, probabilistic assessment of emission reduction strategies, and risk assessments. Accurate estimation of emission distributions produces more informed regulatory decisionmaking, risk comparisons, and regulatory and scientific priority setting.     

Real-World In-Use Activity,Fuel Use,and Emissions for Nonroad Construction Vehicles: A Case Study for Excavators

Abstract

A study design was developed and demonstrated for deployment of a portable emission measurement system (PEMS) for excavators. Excavators are among the most commonly used vehicles in construction activities. The PEMS measured nitric oxide, carbon monoxide, hydrocarbons, carbon dioxide, and opacity-based particulate matter. Data collection, screening, processing, and analysis protocols were developed to assure data quality and to quantify variability in vehicle fuel consumption and emissions rates. The development of data collection procedures was based on securing the PEMS while avoiding disruption to normal vehicle operations. As a result of quality assurance, approximately 90% of the attempted measurements resulted in valid data. On the basis of field data collected for three excavators, an average of 50% of the total nitric oxide emissions was associated with 29% of the time of operation, during which the average engine speed and manifold absolute pressure were significantly higher than corresponding averages for all data. Mass per time emission rates during non-idle modes (i.e., moving and using bucket) were on average 7 times greater than for the idle mode. Differences in normalized average rates were influenced more by intercycle differences than intervehicle differences. This study demonstrates the importance of accounting for intercycle variability in real-world in-use emissions to develop more accurate emission inventories. The data collection and analysis methodology demonstrated here is recommended for application to more vehicles to better characterize real-world vehicle activity, fuel use, and emissions for nonroad construction equipment.     

Why Industry Needs Performance Standard Zoning

The trend toward increased use of central refuse incinerators is inevitably contributing to urban air pollution. Eventually sampling ports, emission sensing and recording equipment will be required; and more detailed data will be available. But, tradilionally, discharges have been estimated by means of emission factors for nominal design loadings. Such estimates may be unreliable, especially under highly variable processing rates.

Preliminary evidence suggests lhat actual emission factors are higher when the incinerator is charged at greater rates, and vice versa. Observations at the Boston municipal incinerator indicate considerable day-to-day variability in refuse loading. No measurements of emissions are made, but daily input loading records for several years are available.

This study focuses upon the variability in daily loadings. Several functions relating emission factors to charging rates are assumed in order to estimate variability in discharges to the atmosphere. Four years of daily records were analyzed for day-of-week and seasonal components, as well as secular trends. Further analysis suggested that some of the remaining variation in observations could be explained in connection with holidays and precipitation.

The implications of conventionally designed holding pits and charging policies on air pollution problems are discussed in view of such variability, and alternatives are suggested.     

Quantification of Hourly Variability in NOx Emissions for Baseload Coal-Fired Power Plants

Abstract

The objectives of this paper are to (1) quantify variability in hourly utility oxides of nitrogen (NO_x) emission factors, activity factors, and total emissions; (2) investigate the autocorrelation structure and evaluate cyclic effects at short and long scales of the time series of total hourly emissions; (3) compare emissions for the ozone (O₃) season versus the entire year to identify seasonal differences, if any; and (4) evaluate interannual variability. Continuous emissions monitoring data were analyzed for 1995 and 1998 for 32 units from nine baseload power plants in the Charlotte, NC, airshed. Unit emissions have a strong 24-hr cycle attributable primarily to the capacity factor. Typical ranges of the coefficient of variation for emissions at a given hour of the day were from 0.2 to 0.45. Little difference was found when comparing weekend emissions with the entire week or when comparing the O₃ season with the entire year. There were substantial differences in the mean and standard deviation of emissions when comparing 1995 and 1998 data, indicative of the effect of retrofits of control technology during the intervening time. The wide range of variability and its autocorrelation should be accounted for when developing probabilistic utility emission inventories for analysis of near-term future episodes.     

Temporal,spatial characteristics and uncertainty of biogenic VOC emissions in the Pearl River Delta region,China

Using the Global Biosphere Emissions and Interactions System model (GloBEIS), 3 × 3 km gridded and hourly biogenic volatile organic compound (BVOC) emissions in the Pearl River Delta (PRD) were estimated for the year 2006. The study used newly available land cover database, observed meteorological data, and recent measurements of emission rates for tree species in China. The results show that the total BVOC emission in the PRD region in 2006 was 296 kt (2.2 × 10¹¹ gC), of which isoprene contributes about 25% (73 kt, 6.4 × 10¹⁰ gC), monoterpenes about 34% (102 kt, 8.9 × 10¹⁰ gC), and other VOCs (OVOC) about 41% (121 kt, 6.8 × 10¹⁰ gC). BVOC emissions in the PRD region exhibit a marked seasonal pattern with the peak emission in July and the lowest emission in January, and are mainly distributed over the outlying areas of the PRD region, where the economy and land use are less developed. The uncertainties in BVOC emission estimates were quantified using Monte Carlo simulation; the results indicate high uncertainties in isoprene emission estimates, with a relative error of ?82 to +177%, ranging from 12.4 to 186.4 kt; ?41 to +58% uncertainty for monoterpenes emissions, ranging from 67.7 to 181.9 kt; and ?26 to +30% uncertainty in OVOC emissions, ranging from 88.8 to 156.2 kt on the 95% confidence intervals. The key uncertainty sources include emission factors and the model empirical coefficients α, C_T1, C_L, and E_opt for estimating isoprene emission, and emission factors and foliar density for estimating monoterpenes and OVOC emissions. This implies that determining these empirical coefficient values properly and conducting more field measurements of emission rates of tree species are key approaches for reducing uncertainties in BVOC emission estimates. Improving future BVOC emission inventory work in the PRD region requires giving priority to research on shrub land, coniferous forests, and irrigated cropland and pasture.     

Predicting emissions from oil and gas operations in the Uinta Basin,Utah

In this study, emissions of ozone precursors from oil and gas operations in Utah’s Uinta Basin are predicted (with uncertainty estimates) from 2015–2019 using a Monte-Carlo model of (a) drilling and production activity, and (b) emission factors. Cross-validation tests against actual drilling and production data from 2010–2014 show that the model can accurately predict both types of activities, returning median results that are within 5% of actual values for drilling, 0.1% for oil production, and 4% for gas production. A variety of one-time (drilling) and ongoing (oil and gas production) emission factors for greenhouse gases, methane, and volatile organic compounds (VOCs) are applied to the predicted oil and gas operations. Based on the range of emission factor values reported in the literature, emissions from well completions are the most significant source of emissions, followed by gas transmission and production. We estimate that the annual average VOC emissions rate for the oil and gas industry over the 2010–2015 time period was 44.2E+06 (mean) ± 12.8E+06 (standard deviation) kg VOCs per year (with all applicable emissions reductions). On the same basis, over the 2015–2019 period annual average VOC emissions from oil and gas operations are expected to drop 45% to 24.2E+06 ± 3.43E+06 kg VOCs per year, due to decreases in drilling activity and tighter emission standards.

Implications: This study improves upon previous methods for estimating emissions of ozone precursors from oil and gas operations in Utah’s Uinta Basin by tracking one-time and ongoing emission events on a well-by-well basis. The proposed method has proven highly accurate at predicting drilling and production activity and includes uncertainty estimates to describe the range of potential emissions inventory outcomes. If similar input data are available in other oil and gas producing regions, then the method developed here could be applied to those regions as well.