Estimated Emission Factors Research Articles

Analysis of dust emission at coal train loading facility

The objective of this research was to conduct an analysis of dust emission for a coal train loading facility. Dust emission for the train loading point was determined by two methods: (i) the EPA AP-42 emission factor estimation equations (AP-42 Dust Emission Estimation) and (ii) the methodology used in development of AP-42 equations (Type-2 Dust Emission Estimation). Field measurements and data collection were carried out at a facility that operates along with a coal mine in West Virginia, U.S. Results of this study revealed that dust emission obtained by the former method exceeded the field-based emission determined by Type-2 Dust Emission Estimation method for this particular loading facility. This research may assist mining and environmental professionals in quantifying dust emission at train loading facilities, and developing strategies for reducing its health and environmental impacts.

Developing particle emission inventories using remote sensing (PEIRS)

ABSTRACTInformation regarding the magnitude and distribution of PM2.5 emissions is crucial in establishing effective PM regulations and assessing the associated risk to human health and the ecosystem. At present, emission data is obtained from measured or estimated emission factors of various source types. Collecting such information for every known source is costly and time-consuming. For this reason, emission inventories are reported periodically and unknown or smaller sources are often omitted or aggregated at large spatial scale. To address these limitations, we have developed and evaluated a novel method that uses remote sensing data to construct spatially resolved emission inventories for PM2.5. This approach enables us to account for all sources within a fixed area, which renders source classification unnecessary. We applied this method to predict emissions in the northeastern United States during the period 2002–2013 using high-resolution 1 km × 1 km aerosol optical depth (AOD). Emission estimates moderately agreed with the EPA National Emission Inventory (R2 = 0.66–0.71, CV = 17.7–20%). Predicted emissions are found to correlate with land use parameters, suggesting that our method can capture emissions from land-use-related sources. In addition, we distinguished small-scale intra-urban variation in emissions reflecting distribution of metropolitan sources. In essence, this study demonstrates the great potential of remote sensing data to predict particle source emissions cost-effectively.Implications: We present a novel method, particle emission inventories using remote sensing (PEIRS), using remote sensing data to construct spatially resolved PM2.5 emission inventories. Both primary emissions and secondary formations are captured and predicted at a high spatial resolution of 1 km × 1 km. Using PEIRS, large and comprehensive data sets can be generated cost-effectively and can inform development of air quality regulations.

A fuel-based approach for emission factor development for highway paving construction equipment in China

ABSTRACTThe objective of this paper is to develop and demonstrate a fuel-based approach for emissions factor estimation for highway paving construction equipment in China for better accuracy. A highway construction site in Chengdu was selected for this study with NO emissions being characterized and demonstrated. Four commonly used paving equipment, i.e., three rollers and one paver were selected in this study. A portable emission measurement system (PEMS) was developed and used for emission measurements of selected equipment during real–world highway construction duties. Three duty modes were defined to characterize the NO emissions, i.e., idling, moving, and working. In order to develop a representative emission factor for these highway construction equipment, composite emission factors were estimated using modal emission rates and the corresponding modal durations in the process of typical construction duties. Depending on duty mode and equipment type, NO emission rate ranged from 2.6–63.7mg/s and 6.0–55.6g/kg–fuel with the fuel consumption ranging from 0.31–4.52 g/s correspondingly. The NO composite emission factor was estimated to be 9–41mg/s with the single-drum roller being the highest and double-drum roller being the lowest and 6–30g/kg-fuel with the pneumatic tire roller being the highest while the double-drum roller being the lowest. For the paver, both time-based and fuel consumption-based NO composite emission rates are higher than all of the rollers with 56mg/s and 30g/kg-fuel, respectively. In terms of time–based quantity, the working mode contributes more than the other modes with idling being the least for both emissions and fuel consumption. In contrast, the fuel-based emission rate appears to have less variability in emissions. Thus, in order to estimate emission factors for emission inventory development, the fuel-based emission factor may be selected for better accuracy.Implications: The fuel-based composite emissions factors will be less variable and more accurate than time-based emission factors. As a consequence, emissions inventory developed using this approach will be more accurate and practical.

Modeling the impact of solid noise barriers on near road air quality

Studies based on field measurements, wind tunnel experiments, and controlled tracer gas releases indicate that solid, roadside noise barriers can lead to reductions in downwind near-road air pollutant concentrations. A tracer gas study showed that a solid barrier reduced pollutant concentrations as much as 80% next to the barrier relative to an open area under unstable meteorological conditions, which corresponds to typical daytime conditions when residents living or children going to school near roadways are most likely to be exposed to traffic emissions. The data from this tracer gas study and a wind tunnel simulation were used to develop a model to describe dispersion of traffic emissions near a highway in the presence of a solid noise barrier. The model is used to interpret real-world data collected during a field study conducted in a complex urban environment next to a large highway in Phoenix, Arizona, USA. We show that the analysis of the data with the model yields useful information on the emission factors and the mitigation impact of the barrier on near-road air quality. The estimated emission factors for the four species, ultrafine particles, CO, NO2, and black carbon, are consistent with data cited in the literature. The results suggest that the model accounted for reductions in pollutant concentrations from a 4.5 m high noise barrier, ranging from 40% next to the barrier to 10% at 300 m from the barrier.

Modeling nitrous oxide emission from rivers: a global assessment.

Estimates of global riverine nitrous oxide (N2 O) emissions contain great uncertainty. We conducted a meta-analysis incorporating 169 observations from published literature to estimate global riverine N2 O emission rates and emission factors. Riverine N2 O flux was significantly correlated with NH4 , NO3 and DIN (NH4 +NO3 ) concentrations, loads and yields. The emission factors EF(a) (i.e., the ratio of N2 O emission rate and DIN load) and EF(b) (i.e., the ratio of N2 O and DIN concentrations) values were comparable and showed negative correlations with nitrogen concentration, load and yield and water discharge, but positive correlations with the dissolved organic carbon:DIN ratio. After individually evaluating 82 potential regression models based on EF(a) or EF(b) for global, temperate zone and subtropical zone datasets, a power function of DIN yield multiplied by watershed area was determined to provide the best fit between modeled and observed riverine N2 O emission rates (EF(a): R2 =0.92 for both global and climatic zone models, n=70; EF(b): R2 =0.91 for global model and R2 =0.90 for climatic zone models, n=70). Using recent estimates of DIN loads for 6400 rivers, models estimated global riverine N2 O emission rates of 29.6-35.3 (mean=32.2) Gg N2 O-Nyr-1 and emission factors of 0.16-0.19% (mean=0.17%). Global riverine N2 O emission rates are forecasted to increase by 35%, 25%, 18% and 3% in 2050 compared to the 2000s under the Millennium Ecosystem Assessment's Global Orchestration, Order from Strength, Technogarden, and Adapting Mosaic scenarios, respectively. Previous studies may overestimate global riverine N2 O emission rates (300-2100 Gg N2 O-Nyr-1 ) because they ignore declining emission factor values with increasing nitrogen levels and channel size, as well as neglect differences in emission factors corresponding to different nitrogen forms. Riverine N2 O emission estimates will be further enhanced through refining emission factor estimates, extending measurements longitudinally along entire river networks and improving estimates of global riverine nitrogen loads.

Emission factors of air pollutants from CNG-gasoline bi-fuel vehicles: Part II. CO, HC and NOx

The estimation of emission factors (EFs) is the basis of accurate emission inventory. However, the EFs of air pollutants for motor vehicles vary under different operating conditions, which will cause uncertainty in developing emission inventory. Natural gas (NG), considered as a “cleaner” fuel than gasoline, is increasingly being used to reduce combustion emissions. However, information is scarce about how much emission reduction can be achieved by motor vehicles burning NG (NGVs) under real road driving conditions, which is necessary for evaluating the environmental benefits for NGVs. Here, online, in situ measurements of the emissions from nine bi-fuel vehicles were conducted under different operating conditions on the real road. A comparative study was performed for the EFs of black carbon (BC), carbon monoxide (CO), hydrocarbons (HCs) and nitrogen oxides (NOx) for each operating condition when the vehicles using gasoline and compressed NG (CNG) as fuel. BC EFs were reported in part I. The part II in this paper series reports the influence of operating conditions and fuel types on the EFs of CO, HC and NOx. Fuel-based EFs of CO showed good correlations with speed when burning CNG and gasoline. The correlation between fuel-based HC EFs and speed was relatively weak whether burning CNG or gasoline. The fuel-based NOx EFs moderately correlated with speed when burning CNG, but weakly correlated with gasoline. As for HC, the mileage-based EFs of gasoline vehicles are 2.39–12.59 times higher than those of CNG vehicles. The mileage-based NOx EFs of CNG vehicles are slightly higher than those of gasoline vehicles. These results would facilitate a detailed analysis of the environmental benefits for replacing gasoline with CNG in light duty vehicles.

Indirect estimation of emission factors for phosphate surface mining using air dispersion modeling

To date, phosphate surface mining suffers from lack of reliable emission factors. Due to complete absence of data to derive emissions factors, we developed a methodology for estimating them indirectly by studying a range of possible emission factors for surface phosphate mining operations and comparing AERMOD calculated concentrations to concentrations measured around the mine. We applied this approach for the Khneifiss phosphate mine, Syria, and the Al-Hassa and Al-Abyad phosphate mines, Jordan. The work accounts for numerous model unknowns and parameter uncertainties by applying prudent assumptions concerning the parameter values. Our results suggest that the net mining operations (bulldozing, grading and dragline) contribute rather little to ambient TSP concentrations in comparison to phosphate processing and transport. Based on our results, the common practice of deriving the emission rates for phosphate mining operations from the US EPA emission factors for surface coal mining or from the default emission factor of the EEA seems to be reasonable. Yet, since multiple factors affect dispersion from surface phosphate mines, a range of emission factors, rather than only a single value, was found to satisfy the model performance.

Estimating the Greenhouse Gas Balance of Individual Gas‐Fired and Oil‐Fired Electricity Plants on a Global Scale

SummaryLife cycle greenhouse gas (LC‐GHG) emissions from electricity generated by a specific resource, such as gas and oil, are commonly reported on a country‐by‐country basis. Estimation of variability in LC‐GHG emissions of individual power plants can, however, be particularly useful to evaluate or identify appropriate environmental policy measures. Here, we developed a regression model to predict LC‐GHG emissions per kilowatt‐hour (kWh) of electricity produced by individual gas‐ and oil‐fired power plants across the world. The regression model uses power plant characteristics as predictors, including capacity, age, fuel type (fuel oil or natural gas), and technology type (single or combined cycle) of the plant. The predictive power of the model was relatively high (R2 = 81% for predictions). Fuel and technology type were identified as the most important predictors. Estimated emission factors ranged from 0.45 to 1.16 kilograms carbon dioxide equivalents per kilowatt‐hour (kg CO2‐eq/kWh) and were clearly different between natural gas combined cycle (0.45 to 0.57 kg CO2‐eq/kWh), natural gas single cycle (0.66 to 0.85 kg CO2‐eq/kWh), oil combined cycle power plants (0.63 to 0.79 kg CO2‐eq/kWh), and oil single cycle (0.94 to 1.16 kg CO2‐eq/kWh). Our results thus indicate that emission data averaged by fuel and technology type can be profitably used to estimate the emissions of individual plants.

Estimation of emission factor for odorants released from swine excretion slurries

In this study, the odorant emission rates from excretory wastes collected in sealed containers from a large swine facility were determined offsite in a laboratory using both raw slurry from ([1] windowless pigpen (WP) and [2] open pigpen (OP)) and treated waste samples ([3] composting facility (CF) and [4] slurry treatment facility (SF)). The emission rates of up to 41 volatile odorants were measured for 100g waste samples (of all four types) in a 0.75L impinger with an air change rate of 8h−1. The initial emission rates (mgkg−1·h−1) for the most dominant species from each waste type can be summarized as: (1) WP: NH3 (16.3) and H2S (0.54); (2) OP: H2S (1.78), NH3 (1.69), and p-cresol (0.36); (3) CF: NH3 (7.04), CH3SH (0.30), and DMS (0.12); and (4) SF: NH3 (11.7), H2S (11.7), and p-cresol (0.25). Accordingly, the emission factors for the key odorant (mE, kg·pig−1)) for fattening pigs in the WP and OP facilities of S. Korea were extrapolated as 3.46 (NH3) and 0.38 (H2S), respectively. The emission factors were estimated assuming exponentially decaying emission rates and slurry production rates obtained from the literature.

Black Carbon Emissions from Associated Natural Gas Flaring.

Approximately 150 billion cubic meters (BCM) of natural gas is flared and vented in the world annually, emitting greenhouse gases and other pollutants with no energy benefit. About 7 BCM per year is flared in the United States, and half is from North Dakota alone. There are few emission measurements from associated gas flares and limited black carbon (BC) emission factors have been previously reported from the field. Emission plumes from 26 individual flares in the Bakken formation in North Dakota were sampled. Methane, carbon dioxide, and BC were measured simultaneously, allowing the calculation of BC mass emission factors using the carbon balance method. Particle optical absorption was measured using a three-wavelength particle soot absorption photometer (PSAP) and BC particle number and mass concentrations were measured with a single particle soot photometer. The BC emission factors varied over 2 orders of magnitude, with an average and uncertainty range of 0.14 ± 0.12 g/kg hydrocarbons in associated gas and a median of 0.07 g/kg which represents a lower bound on these measurements. An estimation of the BC emission factor derived from PSAP absorption provides an upper bound at 3.1 g/kg. These results are lower than previous estimations and laboratory measurements. The BC mass absorption cross section was 16 ± 12 m(2)/g BC at 530 nm. The average absorption Ångström exponent was 1.2 ± 0.8, suggesting that most of the light absorbing aerosol measured was black carbon and the contribution of light absorbing organic carbon was small.

Emission Profiles of PM10-Bound Polycyclic Aromatic Hydrocarbons from Biomass Burning Determined in Chamber for Assessment of Air Pollutants from Open Burning

In order to estimate emission factors (EFs) of air pollutants, three types of biomass (rice straw, maize residue and leaf litter) were collected and burnt in a self-designed stainless steel chamber. The EFs of PM_(10) from biomass burning were leaf litter (1.22 ± 0.29 g kg_(dry)^(-1)) ＞ rice straw (0.89 ± 0.25 g kg_(dry)^(-1)) ＞ maize residue (0.59 ± 0.13 g kg_(dry)^(-1)), while those of PM_(10)-bound polycyclic aromatic hydrocarbons (PAHs) were leaf litter (0.91 ± 0.28 mg kg_(dry)^(-1)) ＞ maize residue (0.47 ± 0.11 mg kg_(dry)^(-1)) ~ rice straw (0.46 ± 0.21 mg kg_(dry)^(-1)). The results revealed that burning of forest leaf litter emitted higher amounts of particulate pollutants than the agricultural residue burning. New values of diagnostic ratios of some PAHs, including FLA/(FLA + PYR, BaA/(BaA + CHR) and IND/(IND + BPER), were proposed to identify biomass burning sources. Emission rates (ERs) of PM_(10) and PAHs from biomass burning in Chiang Mai, Thailand were estimated based on the EFs and burning areas recorded in the dry season of 2010 and 2011. The ERs of pollutants from forest burning were found to be much higher than those from agricultural field burning, mainly due to larger burnt areas in the forest. In 2010, PM_(10) was mainly emitted from the forest fire (2,250 tons), followed by crop burning (133 tons) and paddy field burning (66.9 tons). The same trend was found in 2011 but with much lower emission rates. The ERs of PAHs from biomass burning were 1,815 kg in 2010 and 416 kg in 2011. The ERs of PM_(10) and PAHs in 2011 were 77% decreased from those in 2010 due to unusually high precipitation in the dry season, causing relatively smaller burnt areas and lower pollutant emissions from open burning. It is expected that the results from this study will be significant information for regulatory actions of air quality management in the northern part of Thailand.

국내 봄배추 재배지의 아산화질소 배출계수 개발에 관한 연구

Greenhouse-gas emission factors are widely used to estimate emissions arising from a defined unit of a specific activity. Such estimates are used both for international reporting to the United Nations Framework Convention on Climate Change (UNFCCC) and for a myriad of national and subnational reporting purposes. The Intergovernmental Panel on Climate Change (IPCC) provides a methodology for national and sub-national estimation of known greenhouse gas emissions including N2O for each sector from which the emissions arise. The objective of this study was to develop an emission factor to estimate the direct N2O emission from an agricultural field cultivated with Chinese cabbage during spring season in 2010-2012. An estimated emission factor of N2O calculated over three years from field experiment accounting for cumulative N2O emission, nitrogen fertilization rate, and background N2O emission was 0.0056±0.00254 (95% CI) Kg N2O-N/kg N. More extensive studies are needed to develop N2O emission factors for other upland crops in various regions of Korea because N2O emission is influenced by many factors including climate characteristics, soil properties agricultural practices and crop species.

Improving the modeling of road dust levels for Barcelona at urban scale and street level

Road dust emission is an emerging issue in air quality due to the lack of remediation measures in contrast to vehicle exhaust emissions. The evidence of receptor modeling studies allows for quantifying impact on a few receptors, but the high cost of PM chemical speciation data and the questionable representativeness of single monitoring sites, limit considerably the development of population exposure estimates and epidemiologic studies based on georeferenced data. This study attempts to initiate and promote urban-scale dispersion modeling for road dust emissions, which will allow for a more robust estimate of population exposure and health outcomes. The TNO URBIS (URBan Information System) model was applied in the city of Barcelona, implementing a Gaussian line source and a street canyon dispersion model, together with new experimental estimates of road dust emission factors and algorithm to describe the time variability. Annual, daily and hourly road dust contributions were simulated and validated against observation of PM10, mineral dust and hourly PM2.5-10 concentrations. Results show that road dust contributed 9-15% to PM10 levels at background sites, and 23-44% at traffic sites. Highest contributions were modeled in the commercial/residential district where most of population live and work (Eixample) structured by 120 m wide square blocks, separated by roads with >10,000 vehicles per day. Street level contributions rise up to 20 μg/m3 (96% of roads) and an additional 3% of roads within 20-40 μg/m3. Hourly simulations of road dust contributions revealed to benefit from the implementation of the new emission module (Amato et al., 2012), able to describe the exponential recovery of road dust emission potential after rain events, when compared to common approach such as the use of constant emission factor or an ON/OFF approach. Correlation coefficients with observed data varied from 0.61, 0.58 and 0.43 for annual, daily and hourly means, respectively, revealing a clear improvement in terms of both spatial and temporal variability. However, more efforts need to be done in validating the model in different climatic scenarios and evaluating the seasonal variation of road dust emissions, due to droughts or Saharan dust events. © 2015 Elsevier Ltd.

Through-tunnel estimates of vehicle fleet emission factors

Abstract On-road measurements of traffic-related gas and particle pollutant concentrations in three tunnels in Hong Kong and high resolution pollutant concentration profiles obtained while driving through the tunnels were used to derive the individual pollutant gradients using parametric and non-parametric (Sen–Thiel) slopes and compared with the commonly used entrance-exit two points calculation. The fuel based emission factors of measured pollutants for individual tunnels at different times of day were derived from gradients using a new method based on fuel carbon balance principle. Combined with the tunnel traffic volume and composition, the average tunnel emission factors were analyzed by linear regression to derive the diesel fleet emission factors. Average nitrogen oxides (NOx) and black carbon (BC) emission factor for diesel fleets are 29.3 ± 11.0 gNO2 kg−1 and 1.28 ± 0.76 g kg−1 of fuel, respectively. The results from the study were compared with the emission data from vehicle chasing approaches and the literature, showing reasonable agreement. Practical limitations and future direction for improvement of our method were also discussed. The method presented in this study provides a convenient drive-through approach for fast determination of tunnel and individual vehicle fleet emission factors. It can be used as an effective and fast approach to validate the emission inventory and to evaluate the effectiveness of policy intervention on the traffic emissions.

Error in the estimation of emission factors for forest degradation in central Africa

The implementation of forest-based projects to mitigate greenhouse gas emissions requires the estimation of emission factors (here the difference in biomass stocks between two forest types). The estimation of these quantities using forest inventory data and allometric models implies different sources of errors that need to be prioritized to improve the precision of estimation. Using data from permanent sample plots in a tropical moist forest in central Africa and considering four allometric models with equal likelihood, the largest source of error in the estimate of the difference of biomass between intact and logged-over forest was that due to the model choice (40 % of the sum of squares). The error due to the model choice did not cancel out in the difference due to an interaction between the model's prediction and the diameter structure of the forest. The variability in biomass between plots was the second largest source of error, but was underestimated because of post-stratification. The error due to the model choice could be reduced by weighting the models' predictions.

Biogas: Clean Energy Access with Low-Cost Mitigation of Climate Change

With data from the nearly 6,000 households in the Nepal Living Standards Survey of 2010–11, this paper finds that the mean reduction in household firewood collection associated with use of a biogas plant for cooking is about 1,100 kilograms per year from a mean of about 2,400 kilograms per year. This estimate is derived by comparing only households with and without biogas in the same village, thus effectively removing the influence of many potential confounders. Further controls for important determinants of firewood collection, such as household size, per capita consumption expenditure, cattle ownership, and unemployment are used to identify the effect of biogas adoption on firewood collection. Bounds on omitted variable bias are derived with the proportional selection assumption. The central estimate is much smaller than those in the previous literature, but is still large enough for the cost of adopting biogas to be significantly reduced via carbon offsets at a modest carbon price of $10 per ton of CO2e when using central estimates of emission factors and global warming potentials of pollutants taken from the scientific literature.

Semi-empirical process-based models for ammonia emissions from beef, swine, and poultry operations in the United States

Farm-level ammonia emissions factors in the literature vary by an order of magnitude due to variations in manure management practices and meteorology, and it is essential to capture this variability in emission inventories used for atmospheric modeling. Loss of ammonia to the atmosphere is modeled here through a nitrogen mass balance with losses controlled by mass transfer resistance parameters, which vary with meteorological conditions and are tuned to match literature-reported emissions factors. Variations due to management practices are captured by having tuned parameters that are specific to each set of management practices. The resulting farm emissions models (FEMs) explain between 20% and 70% of the variability in published emissions factors and typically estimate emission factors within a factor of 2. The r2 values are: 0.53 for swine housing (0.67 for shallow-pit houses); 0.48 for swine storage; 0.29 for broiler chickens; 0.70 for layer chickens; and 0.21 for beef feedlots (0.36 for beef feedlots with more farm-specific input data). Mean fractional error was found to be 22–44% for beef feedlots, swine housing, and layer housing; fractional errors were greater for swine lagoons (90%) and broiler housing (69%). Unexplained variability and errors result from model limitations, measurement errors in reported emissions factors, and a lack of information about measurement conditions.

Evaluating near highway air pollutant levels and estimating emission factors: Case study of Tehran, Iran

A field sampling campaign was implemented to evaluate the variation in air pollutants levels near a highway in Tehran, Iran (Hemmat highway). The field measurements were used to estimate road link-based emission factors for average vehicle fleet. These factors were compared with results of an in tunnel measurement campaign (in Resalat tunnel). Roadside and in-tunnel measurements of carbon monoxide (CO) and size-fractionated particulate matter (PM) were conducted during the field campaign. The concentration gradient diagrams showed exponential decay, which represented a substantial decay, more than 50–80%, in air pollutants level in a distance between 100 and 150meters (m) of the highway. The changes in particle size distribution by distancing from highway were also captured and evaluated. The results showed particle size distribution shifted to larger size particles by distancing from highway. The empirical emission factors were obtained by using the roadside and in tunnel measurements with a hypothetical box model, floating machine model, CALINE4, CT-EMFAC or COPERT. Average CO emission factors were estimated to be in a range of 4 to 12g/km, and those of PM10 were 0.1 to 0.2g/km, depending on traffic conditions. Variations of these emission factors under real working condition with speeds were determined.

Reduced carbon emission estimates from fossil fuel combustion and cement production in China.

Nearly three-quarters of the growth in global carbon emissions from the burning of fossil fuels and cement production between 2010 and 2012 occurred in China. Yet estimates of Chinese emissions remain subject to large uncertainty; inventories of China's total fossil fuel carbon emissions in 2008 differ by 0.3 gigatonnes of carbon, or 15 per cent. The primary sources of this uncertainty are conflicting estimates of energy consumption and emission factors, the latter being uncertain because of very few actual measurements representative of the mix of Chinese fuels. Here we re-evaluate China's carbon emissions using updated and harmonized energy consumption and clinker production data and two new and comprehensive sets of measured emission factors for Chinese coal. We find that total energy consumption in China was 10 per cent higher in 2000-2012 than the value reported by China's national statistics, that emission factors for Chinese coal are on average 40 per cent lower than the default values recommended by the Intergovernmental Panel on Climate Change, and that emissions from China's cement production are 45 per cent less than recent estimates. Altogether, our revised estimate of China's CO2 emissions from fossil fuel combustion and cement production is 2.49 gigatonnes of carbon (2 standard deviations = ±7.3 per cent) in 2013, which is 14 per cent lower than the emissions reported by other prominent inventories. Over the full period 2000 to 2013, our revised estimates are 2.9 gigatonnes of carbon less than previous estimates of China's cumulative carbon emissions. Our findings suggest that overestimation of China's emissions in 2000-2013 may be larger than China's estimated total forest sink in 1990-2007 (2.66 gigatonnes of carbon) or China's land carbon sink in 2000-2009 (2.6 gigatonnes of carbon).

Measured and estimated benzene and volatile organic carbon (VOC) emissions at a major U.S. refinery/chemical plant: Comparison and prioritization

Estimates of emissions for processes and point sources at petroleum refineries and chemical plants provide the foundation for many other environmental evaluations and policy decisions. The most commonly used method, based on emission factors, results in unreliable estimates. More information regarding the actual emissions within a facility is necessary to provide a foundation for improving emission factors and prioritizing which emission factors most need improvement. Identification of which emission factors both perform poorly and introduce the largest error is needed to provide such a prioritization. To address this need, benzene and volatile organic compound (VOC) emissions within a major chemical plant/refinery were measured and compared with emission factor estimates. The results of this study indicate estimated emissions were never higher and commonly lower than the measured emissions. At one source location, VOC emissions were found to be largely representative of those measured (i.e., the catalytic reformer), but more often, emissions were significantly underestimated (e.g., up to 448 times greater than estimated at a floating roof tank). The sources with both the largest relative error between the estimate and the measurement and the largest magnitude of emissions in this study were a wastewater treatment process, an aromatics concentration unit and benzene extraction unit process area, and two sets of tanks (sets 7 and 8). Emission factors for these sources are priorities for further evaluation and improvement in this chemical plant/refinery. This study presents empirical data that demonstrate the need to validate and improve emission factors. Emission factors needing improvement are prioritized by identifying those that are weak models and introduce the largest error in magnitude of emissions. The results can also be used to prioritize evaluations of the emissions sources and controls, and any operational conditions or erroneous assumptions that may be contributing to the error.Implications: Emissions measured at processes and point sources at a major petrochemical plant complex were compared to emission factor estimates. The results of this study indicate estimated emissions were never higher and commonly lower than the measured emissions. The comparison was used to prioritize emission factors needing improvement by identifying those that were weak representations and that introduce the largest error in emissions. The results can also be used to prioritize evaluations of the emissions sources and controls, and any operational conditions or erroneous assumptions that may be contributing to the error.