Emission Factor Ratios Research Articles

Self-consistent estimates of emission factors of carboncontaining pollutants from a typical gas flare

This study proposes an approach for estimating the emission of soot, carbon monoxide (CO) and carbondioxide (CO ) from a typical gas flare. The estimations depend on the quantity and varying composition of the 2 natural gas, flame dynamics (represented by the fire Froude number, Fr ) and the equivalence ratio, f, of the fuel- f air mixture. Soot emission estimates are presented as a function of fire Froude number for gases used in labbased test in order to validate the scheme and for two real-world fuel gas compositions. The mass-weighted carbon-hydrogen ratio (C:H) of the fuel gas compositions are 0.25 and 0.29 which are two extreme cases in terms of density. The soot yield of the lab-based test case was scaled up to estimate the soot yield of a full scale flare using the Richardson number as the scaling parameter. When all other variables are held constant at values characteristics of real-world flares, a difference of 16 % in the fuel-gas density, as indicated by the carbonhydrogen ratio, results in an increase of the emission factors (EF) of soot, CO and CO by factors of ~3, ~1.4 2 3 and ~1.7, measured in g/m , respectively. For both fuel gas compositions, the ratio of EF to EF at the fuel- soot CO lean region f < 1) is higher. The ratio lies in the range 0.031 – 0.13 and 0.0012 – 0.0055 for the fuel-lean (f < 1) and fuel-rich (f > 1) regions, respectively. The approach proposed and results obtained may be adopted to generate emissions inventories of emission species associated with gas flaring on regional and global scales.
 Keywords: gas flaring; soot; natural gas; emission factor; black carbon; equivalence ratio

Seasonal and Long-Term Trend of on-Road Gasoline and Diesel Vehicle Emission Factors Measured in Traffic Tunnels

Emissions of gaseous and particulate pollutants from on-road gasoline and diesel vehicles were measured in a traffic tunnel under real-world driving conditions. Emission factors were attributed to gasoline and diesel vehicles using linear regression against the fraction of fuel consumed by diesel vehicles (% fuelD). We measured 67% higher NOx emissions from gasoline vehicles in winter than in spring (2 versus 1.2 g NO2 kg fuel−1). Emissions of CO, NOx, and particulate matter from diesel vehicles all showed impacts of recent policy changes to reduce emissions from this source. Comparison of our measurements to those of a previous study ~10 years prior in a nearby traffic tunnel on the same highway showed that emission factors for both gasoline and diesel vehicles have fallen by 50–70%. To further confirm this long-term trend, we summarized emission factors measured in previous tunnel studies in the U.S. since the 1990s. More restrictive emission standards are effective at reducing emissions from both diesel and gasoline vehicles, and decreases in observed emissions can be mapped to specific vehicle control policies. The trend of diesel-to-gasoline emission factor ratios revealed changes in the relative importance of vehicle types, though fuel-specific emission factors of NOx and elemental carbon (EC) are still substantially larger (~5–10 times) for diesel vehicles than gasoline vehicles.

Estimating exposure to hydrogen sulfide from animal husbandry operations using satellite ammonia as a proxy: Methodology demonstration

Husbandry trace gases that have climate change implications such as carbon dioxide (CO2), methane (CH4) and ammonia (NH3) can be quantified through remote sensing; however, many husbandry gases with health implications such as hydrogen sulfide (H2S), cannot. This pilot study demonstrates an approach to derive H2S concentrations by coupling in situ and remote sensing data.Using AMOG (AutoMObile trace Gas) Surveyor, a mobile air quality and meteorology laboratory, we measured in situ concentrations of CH4, CO2, NH3, H2S, and wind at a southern California university research dairy. Emissions were 0.13, 1.93, 0.022 and 0.0064 Gg yr−1; emission factors (EF) were 422, 6333, 74, and 21 kg cow−1 yr−1, respectively, for the 306 head herd. Contributing to these strong EF were spillway emissions from a grate between the main cowshed and the waste lagoon identified in airborne remote sensing data acquired by the hyperspectral thermal infrared imager, Mako.NH3 emissions from the Chino Dairy Complex, also in southern California, were calculated from Infrared Atmospheric Sounding Interferometer (IASI) satellite data for 2008–2017 using average morning winds, yielding a flushing time of 2.7 h, and 8.9 Gg yr−1. The ratio of EF(H2S) to EF(NH3) for the research dairy from AMOG data were applied to IASI NH3 emissions to derive H2S exposure concentration maps for the Chino area, which ranged to 10–30 ppb H2S for many populated areas.Combining remote sensing with in situ concentrations of multiple emitted gases can allow derivation of emissions at the sub-facility, facility, and larger scales, providing spatial and temporal coverage that can translate into exposure estimates for use in epidemiology studies and regulation development. Furthermore, with high fidelity information at the sub-facility level we can identify best practices and opportunities to sustainably and holistically reduce husbandry emissions.

A new top-down approach for directly estimating biomass burning emissions and fuel consumption rates and totals from geostationary satellite fire radiative power (FRP)

Regional to global-scale biomass burning emissions inventories are primarily based on satellite-derived burned area or fire radiative power (FRP), and most rely on conversions to fuel consumption prior to the emissions estimation stage. This is generally considered the step introducing greatest uncertainty, and some apparently discrete inventories are not fully independent, as they have been cross-calibrated to aid this stage. We present a novel emissions inventory approach that bypasses the fuel consumption step, directly linking geostationary FRP measures to emission rates of total particulate matter (TPM), via coefficients derived from observations of smoke plume aerosol optical depth (AOD). The approach is fully ‘top-down’, being based on spaceborne observations alone, is performed at or close to the FRP data's original pixel resolution, and avoids the need to assume or model fuel consumption per unit area prior to the emissions calculation. Rates and totals of trace gas and carbon emission can be inferred from the TPM fluxes, and in combination with satellite burned area (BA) products the approach provides an innovative top down approach to mapping fuel consumption per unit area (kg·m−2) as a last step in the calculation. Using this innovative methodology, which we term ‘FREemissions’ (FREM), we generate a 2004–2012 fire emissions inventory for southern Africa, based on Meteosat FRP-PIXEL data. We find basic annual average TPM emissions 45% higher than the widely used GFASv1.2 inventory, with our higher totals in line with independent assessments that necessitate a significant upscaling of GFAS TPM emissions to match observed AODs. Our estimates are also 12% higher than GFEDv4.1s, which already includes a substantial upward adjustment for fires too small to be detected by the MODIS MCD64A1 BA product. If we adjust the FREM-derived emissions for SEVIRI's inability to detect the lower FRP component of the regions fire regime then the differences between FREM and GFAS/GFED grow further, to a mean of 64% with respect to GFED4.1s TPM emissions for example. These upwardly adjusted FREM estimates agree very well with FEER, an FRP- and AOD-based inventory driven by polar-orbiting MODIS FRP ‘snapshots’ rather than geostationary observations. Similarly higher totals are seen for FREM's fire-emitted trace gases, derived using the emission factor ratios of gases to particulates. Our exploitation of geostationary FRP requires fewer assumptions than use of polar orbiter FRP measures, avoids biases coming from incomplete sampling of the fire diurnal cycle, and enables the FREM approach to provide fire emissions and fuel consumption estimates at a higher spatio-temporal resolution than any inventory currently available (e.g. 0.05°, and hourly averages or better), including per km2 of area burned. The approach offers great potential to generate very high resolution fire emissions datasets for the tropics, sub-tropics and potentially temperate zones, with updates available in near real-time from the global suite of geostationary meteorological satellites operated by organisations such as EUMETSAT (Meteosat), NOAA (GOES) and JMA (Himawari).

New model for capturing the variations of fertilizer‐induced emission factors of N2O

AbstractAccumulating evidence indicates that N2O emission factors (EFs) vary with nitrogen additions and environmental variations. Yet the impact of the latter was often ignored by previous EF determinations. We developed piecewise statistical models (PMs) to explain how the N2O EFs in agricultural soils depend upon various predictors such as climate, soil attributes, and agricultural management. The PMs are derived from a new Bayesian Recursive Regression Tree algorithm. The PMs were applied to the case of EFs from agricultural soils in China, a country where large EF spatial gradients prevail. The results indicate substantial improvements of the PMs compared with other EF determinations. First, PMs are able to reproduce a larger fraction of the variability of observed EFs for upland grain crops (84%, n = 381) and paddy rice (91%, n = 161) as well as the ratio of EFs to nitrogen application rates (73%, n = 96). The superior predictive accuracy of PMs is further confirmed by evaluating their predictions against independent EF measurements (n = 285) from outside China. Results show that the PMs calibrated using Chinese data can explain 75% of the variance. Hence, the PMs could be reliable for upscaling of N2O EFs and fluxes for regions that have a phase space of predictors similar to China. Results from the validated models also suggest that climatic factors regulate the heterogeneity of EFs in China, explaining 69% and 85% of their variations for upland grain crops and paddy rice, respectively. The corresponding N2O EFs in 2008 are 0.84 ± 0.18% (as N2O‐N emissions divided by the total N input) for upland grain crops and 0.65 ± 0.14% for paddy rice, the latter being twice as large as the Intergovernmental Panel on Climate Change Tier 1 defaults. Based upon these new estimates of EFs, we infer that only 22% of current arable land could achieve a potential reduction of N2O emission of 50%.

Emission factor ratios, SOA mass yields, and the impact of vehicular emissions on SOA formation

Abstract. The underprediction of ambient secondary organic aerosol (SOA) levels by current atmospheric models in urban areas is well established, yet the cause of this underprediction remains elusive. Likewise, the relative contribution of emissions from gasoline- and diesel-fueled vehicles to the formation of SOA is generally unresolved. We investigate the source of these two discrepancies using data from the 2010 CalNex experiment carried out in the Los Angeles Basin (Ryerson et al., 2013). Specifically, we use gas-phase organic mass (GPOM) and CO emission factors in conjunction with measured enhancements in oxygenated organic aerosol (OOA) relative to CO to quantify the significant lack of closure between expected and observed organic aerosol concentrations attributable to fossil-fuel emissions. Two possible conclusions emerge from the analysis to yield consistency with the ambient data: (1) vehicular emissions are not a dominant source of anthropogenic fossil SOA in the Los Angeles Basin, or (2) the ambient SOA mass yields used to determine the SOA formation potential of vehicular emissions are substantially higher than those derived from laboratory chamber studies.

Development of real-world driving cycles and estimation of emission factors for in-use light-duty gasoline vehicles in urban areas

This investigation adopts vehicle tracking manner to establish real-world driving patterns and estimates emission factors with dynamometers with 23 traffic-driving variables for 384 in-use light-duty passenger vehicles during non-rush hour. Adequate numbers of driving variables were decided with factor analysis and cluster analysis. The dynamometer tests were performed on FTP75 cycle and five local driving cycles derived from real-world speed profiles. Results presented that local driving cycles and FTP75 cycle were completely different in driving characteristic parameters of typical driving cycles and emission factors. The highest values of emission factor ratios of local driving cycle and FTP75 cycle for CO, NMHC, NO x , CH4, and CO2 were 1.38, 1.65, 1.58, 1.39, and 1.14, respectively.

Estimating Diesel Vehicle Emission Factors at Constant and High Speeds for Short Road Segments

Vehicle emission estimates with high spatial and temporal resolution are needed to estimate near-roadway air quality and human exposure to emissions. The U.S. Environmental Protection Agency's (EPA) MOBILE6 emission factor model is based on test cycles with transient speeds and durations on the order of 10 min. MOBILE6 does not report emission factors for average speeds higher than 65 mph. However, for near-roadway studies, emission factors are needed for short highway segments that represent only a few seconds of vehicle travel time at approximately constant speed and speeds greater than 65 mph. Constant-speed and high-speed correction factors for nitrogen oxides, hydrocarbons, carbon dioxide (CO2), and carbon monoxide were developed on the basis of 59,286 s of EPA dynamometer emissions data for diesel vehicles from which speed–acceleration modal models were derived. The constant speed correction factor is the ratio of the emission factor at constant speed to the emissions factor during transient cycles with an equivalent average speed. For example, the constant speed correction factor for CO2 increases from 0.71 to 1 as speed increases from 31 to 78 mph. High-speed correction factors are based on the ratio of emission factors at a constant speed greater than 65 mph to emission factors at a constant speed of 65 mph. For example, at 80 mph, the high-speed correction factor is approximately 1.5 for CO2. Sensitivity analysis of emission estimates to these correction factors was conducted for speeds of 40 to 80 mph.

Comparison of Exhaust Emissions Resulting from Cold- and Hot-Start Motorcycle Driving Modes

This study investigated the emissions of criteria air pollutants (carbon monoxide [CO], hydrocarbons [HCs], and oxides of nitrogen [NOx]) from motorcycle exhaust at cold- and hot-start driving cycles on a chassis dynamometer. Seven four-stroke carburetors and two fuel-injection motorcycles were tested. As expected, the emission factors (g/km) of CO and HCs increased during cold-start driving. The ratio of emission factors (g/km) for cold- and hot-start driving cycles ranged from 1.1–1.5 (for CO) to 1.2–2.8 (for HCs). However, the difference of NOx emissions between the cold- and hot-start cycles was not pronounced. Further, the cold-/hot-start ratios of CO and HCs from 50-cm3 motorcycles were higher than those of 100- and 125-cm3 motorcycles; however, the carbon dioxide (CO2) emission was the lowest for the four-stroke motorcycles. High engine temperature and poor combustion efficiency of smaller cylinder-capacity motorcycles may contribute a significant amount of exhaust emission. Additionally, the fuel-base emission factor (g/L-fuel) ratios were low compared with the distance-base emission factor (g/km) in cold- and hot-start driving. This indicates that the effect of catalyst efficiency was greater than the effect of fuel combustion in the tested motorcycles. A comparison of emission ratios of motorcycles and passenger cars shows that the warm-up may be more important for cars, especially under low-temperature conditions. However, the motorcycle contributes a large proportion of CO and HC emissions in many Asian counties. The difference between cold- and hot-start emissions may affect inventory accuracy, especially in HC emissions. For those tropical or subtropical countries in which motorcycles are one of the dominant air pollution sources, optimizing the engine combustion efficiency and developing new catalyst technology of motorcycles at cold-start conditions are the key issues to be improved.

Development of a local real world driving cycle for motorcycles for emission factor measurements

The development of driving cycles is important for the success of a mobile source management program. A comparison between a localized driving cycle (Kaohsiung Driving Cycle, KHM), European (Economic Commission of Europe Driving Cycle, ECE), USA, Japanese and Hong Kong cycles was investigated in this study. The CO, THC, NO x and CO 2 emission factors of the entire KHM and ECE cycles as well as the speed–time relationship between these two cycles were also compared in this study. Results indicated that the KHM and ECE as well as worldwide harmonized motorcycle emissions certification/test procedure (WMTC) cycles were completely different in driving characteristic parameters, emission factors and fuel consumption. For example, the time percentages of acceleration and deceleration of ECE cycle are much less than those values in Kaohsiung city. In contrast, the duration of cruising mode (8.6%) in the Kaohsiung area driving cycle is lower than those of ECE (32.3%), WMTC (27–53%) Japanese 10–15 mode (21.2%) and Hong Kong (9.4%). Results further indicated the emission factor ratios of the KHM and ECE driving cycles of 2stroke new/in-use motorcycles for CO, THC, NO x and CO 2 were 2.5/1.5, 1.5/1.3, 2.0/1.4 and 1.0/1.0, respectively. In addition, the emission factors in the KHM cycle for 4-stoke cycles are higher than those of the ECE. For fuel consumption, the KHM cycle was 10% higher than ECE cycle for the same engine type. Consequently, the KHM pattern developed in the present study for the Kaohsiung metropolitan area should be used to truly represent the traffic pattern, emission factor and fuel consumption.

Particulate Emissions from Construction Activities

Although it has long been recognized that road and building construction activity constitutes an important source of particulate matter (PM) emissions throughout the United States, until recently only limited research has been directed to its characterization. This paper presents the results of PM10 and PM2.5 (particles ≤10 μm and ≤2.5 μm in aerodynamic diameter, respectively) emission factor development from the onsite testing of component operations at actual construction sites during the period 1998 –2001. Much of the testing effort was directed at earthmoving operations with scrapers, because earthmoving is the most important contributor of PM emissions across the construction industry. Other sources tested were truck loading and dumping of crushed rock and mud and dirt carryout from construction site access points onto adjacent public paved roads. Also tested were the effects of watering for control of scraper travel routes and the use of paved and graveled aprons at construction site access points for reducing mud and dirt carryout. The PM10 emissions from earthmoving were found to be up to an order of magnitude greater than predicted by AP-42 emission factors drawn from other industries. As expected, the observed PM2.5:PM10 emission factor ratios reflected the relative importance of the vehicle exhaust and the resuspended dust components of each type of construction activity. An unexpected finding was that PM2.5 emissions from mud and dirt carryout were much less than anticipated. Finally, the control efficiency of watering of scraper travel routes was found to closely follow a bilinear moisture model.