Set Of Emission Factors Research Articles

Emission performance of major ship classes: An estimation based on a new set of emission factors and realistic activity profile data

The maritime sector significantly contributes on the major environmental problems that humanity is being confronted with their consequences. The Greenhouse Gases (GHGs) emitted from the sector, which are responsible for the global phenomenon of climate change, are estimated in 2,89% of total anthropogenic GHGs. Ships are also an important source of local air-quality degradation in coastal areas by emitting major quantities of pollutants such as Nitrogen Oxides (NOx), Sulphur Oxides (SOx) and Particulate Matter (PM). The overall emitted quantities of the sector seem not to be equally allocated to the major ship classes (containers, dry and liquid bulk carriers, cruise ships, ro-ro ships etc.), even though the engine technologies that are being used in these classes are approximately the same (slow speed, medium speed, high speed diesel engines). A factor of differentiation among the ship types is the activity profile. Depending on the ship type, engines (main, auxiliary, boilers) present different power needs and therefore are being operated at different load points which among others are related with the sailing profile (cruising, maneuvering, hoteling), the cargo type and weight conditions (laden, ballast). In this context the target of the present paper is to evaluate the emission performance of the major ship classes. This evaluation is performed by using a new set of engine load-dependent Emission Factors for ships, which have been derived by a statistical analysis of emission rates found in literature, in combination with average activity profiles per ship type as these are found in dedicated shipping inventory databases and in literature. These activity data concern a global scale of consideration. Results aim to highlight the differences and similarities in the emission performance of ship types, enhancing the understanding of policy makers and ship operators, on the principle of tackling pollutants especially at ports, close to cities.

Environmental benefit analysis of “road-to-rail” policy in China based on a railway tunnel measurement

The substitution of long-distance road transportation by railways, so-called the “road-to-rail” policy, is one of the major national regulatory policies for atmospheric pollution mitigation in China. However, owing to the lack of real-world diesel locomotive emission measurements, the environmental benefits of the “road-to-rail” policy have not been well evaluated. In this study, emissions from 245 in-use line-haul diesel locomotives (including three types of DF4, HX3, and HX5) were measured under real-world conditions via a railway tunnel observation conducted in Beijing on September 7–13, 2017. The fuel-specific and distance-specific emission factors (EFs) of NOx, CO, SO2, and BC were determined by using the carbon balance and mass conservation methods, respectively. The measured EFs of NOx and CO for the HX series locomotives were 41% and 30% higher than the Tire 2 emission standard limits respectively. Based on the two sets of EFs and corresponding activity levels, total locomotive emissions of NOx and CO in China in 2018 were approximately 17.36–19.23 × 104 t and 4.76–5.69 × 104 t respectively. Scenario analysis based on the fuel life cycle shows that all “road-to-rail” scenarios reduce the total energy consumption and pollutant emissions in the short term. For the scenario maintaining the current proportion (20%) of diesel locomotives, the “road-to-rail” policy may reduce NOx and BC emissions by 7.03–70.33 × 104 t/year and 0.10–1.02 × 104 t/year respectively. Considering the upgradation of emission standards with heavy-duty diesel vehicles, the air quality improvement benefit of the “road-to-rail” policy may only be sustained for 2–3 years and 8 years respectively, under the full diesel locomotive scenario and the current diesel locomotive scenario. We highlight the necessity of a more careful and accurate estimation of “road-to-rail” policy environmental benefits to obtain better air quality and the greatest health benefits.

Evaluation of OSPM against air quality measurements in Brazil – the case study of Fortaleza, Ceará

ABSTRACT The contribution of vehicle emissions to air pollution is considered a large environmental and health problem in big Brazilian cities caused, among other factors, by slow renewal of the old vehicle fleet. Brazilian studies usually only consider traffic-related issues in transportation analysis, with minor assessments of emissions and close to non-existent assessment of air quality. On this background, this research aimed to calibrate and evaluate the Operational Street Pollution Model (OSPM®) to Brazilian conditions by implementing Brazilian emission factors. The urban background concentrations were modeled with the Urban Background Model (UBM) as part of the air quality system (THOR–AirPAS). In this case, we used meteorological data from a ground meteorological station outside Fortaleza processed by meteorological pre-processor and regional background concentrations from the Integrated Forecast System (IFS) as input to UBM. New air quality measurements were collected in busy streets of the city of Fortaleza during the year of 2017. The study collected samples of daily NO2 and PM10 concentrations to evaluate OSPM daily estimations. In addition, a transportation travel demand model (TRANUS) has been calibrated to the case study area with observed traffic data collected, in order to provide Annual Average Daily Traffic (AADT) as inputs to OSPM®. Two sets of emission factors were evaluated. Official Brazilian emission factors were applied to OSPM®, as well as adjusted emission factors derived in the project based on calibration that were higher than the official emission factors. Data showed that concentrations are significantly influenced by meteorological factors (such as temperature, wind speeds, wind directions), and especially precipitation for PM10 concentrations. OSPM® simulated results showed concentration levels and patterns close to air quality measurements with default emission factors and calibrated emission for UBM but large underestimations if official emissions were used for both UBM and OSPM. Implications: Busy urban streets in Brazilian cities with intense flow of diesel vehicles (such as buses and trucks) can significantly increase air pollution, especially for NO2 and PM10. With OSPM calibrated and evaluated to Brazilian conditions, the model system can be used by authorities to assess the impact of policy measures, such as vehicle access restrictions in Low Emission Zones, in order to consider not only traffic related issues, but also air pollution due to mobile sources with outdated emission technologies.

A new database of on-road vehicle emission factors for Colombia: A case study of Bogotá

Mobile sources contribute directly or indirectly with most of the atmospheric emissions in Colombian cities. Quantification of mobile source emissions rely on emission factors (EF) and vehicle activity. However, EF for vehicles in the country have not evolved at the same time as fleet renovation and fuel composition changes in the last few years. In fact, estimated EF before 2010 may not reflect the reduction of sulfur content in diesel and the renovation and deterioration of passenger vehicles; therefore, emission levels may be over or under estimated. To account for these changes, we have implemented the MOVES model in Bogota and obtained a new database of on-road vehicle emission factors. For this purpose, local information of activity rates, speed profiles, vehicle population distribution and age, meteorology and fuel composition was used. Emissions were estimated with these new set of EF and compared with previous inventories. We observed large reductions in SO2 (-87%), CO (-65%) and VOC (-62%) emissions from mobiles sources and lower reductions in NOx (-20%). Other pollutants such as PM2.5 (+15%) and CO2 (+28%) reported increases. This paper includes a new database of on-road vehicle emission factors for Bogota, which can be applied in other Colombian cities in the absence of local data.

Global fire emissions estimates during 1997–2016

Abstract. Climate, land use, and other anthropogenic and natural drivers have the potential to influence fire dynamics in many regions. To develop a mechanistic understanding of the changing role of these drivers and their impact on atmospheric composition, long-term fire records are needed that fuse information from different satellite and in situ data streams. Here we describe the fourth version of the Global Fire Emissions Database (GFED) and quantify global fire emissions patterns during 1997–2016. The modeling system, based on the Carnegie–Ames–Stanford Approach (CASA) biogeochemical model, has several modifications from the previous version and uses higher quality input datasets. Significant upgrades include (1) new burned area estimates with contributions from small fires, (2) a revised fuel consumption parameterization optimized using field observations, (3) modifications that improve the representation of fuel consumption in frequently burning landscapes, and (4) fire severity estimates that better represent continental differences in burning processes across boreal regions of North America and Eurasia. The new version has a higher spatial resolution (0.25°) and uses a different set of emission factors that separately resolves trace gas and aerosol emissions from temperate and boreal forest ecosystems. Global mean carbon emissions using the burned area dataset with small fires (GFED4s) were 2.2 × 1015 grams of carbon per year (Pg C yr−1) during 1997–2016, with a maximum in 1997 (3.0 Pg C yr−1) and minimum in 2013 (1.8 Pg C yr−1). These estimates were 11 % higher than our previous estimates (GFED3) during 1997–2011, when the two datasets overlapped. This net increase was the result of a substantial increase in burned area (37 %), mostly due to the inclusion of small fires, and a modest decrease in mean fuel consumption (−19 %) to better match estimates from field studies, primarily in savannas and grasslands. For trace gas and aerosol emissions, differences between GFED4s and GFED3 were often larger due to the use of revised emission factors. If small fire burned area was excluded (GFED4 without the s for small fires), average emissions were 1.5 Pg C yr−1. The addition of small fires had the largest impact on emissions in temperate North America, Central America, Europe, and temperate Asia. This small fire layer carries substantial uncertainties; improving these estimates will require use of new burned area products derived from high-resolution satellite imagery. Our revised dataset provides an internally consistent set of burned area and emissions that may contribute to a better understanding of multi-decadal changes in fire dynamics and their impact on the Earth system. GFED data are available from http://www.globalfiredata.org.

Particulate mass and number emission factors for road vehicles based on literature data and relevant gap filling methods

This work presents a new set of exhaust particulate emission factors for light and heavy duty vehicles and different driving conditions (urban, rural and highway). The emission factors, building upon COPERT methodology, are expressed in terms of particulate mass (PM), particle number (PN) and as particle size distributions, addressing current and future vehicle technologies, as well as conventional and alternative fuels. All emission factors correspond to fine particles (PM2.5), as the coarse fraction (PM2.5-10) is negligible in primary vehicle emissions. PN emission factors refer to both total and solid particles to cover typical engine exhaust. The set of emission factors is a consistent set of information and can be useful to inventory compilers, air quality researchers and as input to impact assessment studies for policy options.

Particulate Matter Emission Factors for Biomass Combustion

Emission factor is a relative measure and can be used to estimate emissions from multiple sources of air pollution. For this reason, data from literature on particulate matter emission factors from different types of biomass were evaluated in this paper. Initially, the main sources of particles were described, as well as relevant concepts associated with particle measurements. In addition, articles about particle emissions were classified and described in relation to the sampling environment (open or closed) and type of burned biomass (agricultural, garden, forest, and dung). Based on this analysis, a set of emission factors was presented and discussed. Important observations were made about the main emission sources of particulate matter. Combustion of compacted biomass resulted in lower particulate emission factors. PM2.5 emissions were predominant in the burning of forest biomass. Emission factors were more elevated in laboratory burning, followed by burns in the field, residences and combustors.

Disaggregating the Power Generation Sector for Input‐Output Life Cycle Assessment

SummaryThe electric power industry plays a critical role in the economy and the environment, and it is important to examine the economic, environmental, and policy implications of current and future power generation scenarios. However, the tools that exist to perform the life cycle assessments are either too complex or too aggregated to be useful for these types of activities. In this work, we build upon the framework of existing input‐output (I‐O) models by adding data about the electric power industry and disaggregating this single sector into additional sectors, each representing a specific portion of electric power industry operations. For each of these disaggregated sectors, we create a process‐specific supply chain and a set of emission factors that allow calculation of the environmental effects of that sector's output. This new model allows a much better fit for scenarios requiring more specificity than is possible with the current I‐O model.

Dynamic biomass burning emission factors and their impact on atmospheric CO mixing ratios

AbstractBiomass burning is a major source of trace gases and aerosols, influencing atmospheric chemistry and climate. To quantitatively assess its impact, an accurate representation of fire emissions is crucial for the atmospheric modeling community. So far, most studies rely on static emission factors (EF) which convert estimates of dry matter burned to trace gas and aerosol emissions. These EFs are often based on the arithmetic mean of field measurements stratified by biome, neglecting the variability in time and space. Here we present global carbon monoxide (CO) emission estimates from fires based on six EF scenarios with different spatial and temporal variability, using dry matter emission estimates from the Global Fire Emissions Database (GFED). We used the TM5 model to transport these different bottom‐up estimates in the atmosphere and found that including spatial and temporal variability in EFs impacted CO mixing ratios substantially. Most scenarios estimated higher CO mixing ratios (up to 40% more CO from fires during the burning season) over boreal regions compared to the GFED standard run, while a decrease (~15%) was estimated over the continent of Africa. A comparison to atmospheric CO observations showed differences of 10–20 ppb between the scenarios and systematic deviations from local observations. Although temporal correlations of specific EF scenarios improved for certain regions, an overall “best” set of EFs could not be selected. Our results provide a new set of emission estimates that can be used for sensitivity analyses and highlight the importance of better understanding spatial and temporal variability in EFs for atmospheric studies in general and specifically when using CO or aerosols concentration measurements to top‐down constrain fire carbon emissions.

Development of emission factors and emission inventories for motorcycles and light duty vehicles in the urban region in Vietnam

This paper reports on a 2-year emissions monitoring program launched by the Centre for Environmental Monitoring of the Vietnam Environment Administration which aimed at determining emission factors and emission inventories for two typical types of vehicle in Hanoi, Vietnam. The program involves four major activities. A database for motorcycles and light duty vehicles (LDV) in Hanoi was first compiled through a questionnaire survey. Then, two typical driving cycles were developed for the first time for motorcycles and LDVs in Hanoi. Based on this database and the developed driving cycles for Hanoi, a sample of 12 representative test vehicles were selected to determine vehicle specific fuel consumption and emission factors (CO, HC, NOx and CO 2). This set of emission factors were developed for the first time in Hanoi with due considerations of local driving characteristics. In particular, it was found that the emission factors derived from Economic Commission for Europe (ECE) driving cycles and adopted in some previous studies were generally overestimated. Eventually, emission inventories for motorcycles and LDVs were derived by combining the vehicle population data, the developed vehicle specific emission factors and vehicle kilometre travelled (VKT) information from the survey. The inventory suggested that motorcycles contributed most to CO, HC and NOx emissions while LDVs appeared to be more fuel consuming.

Emission Inventory for PFOS in China: Review of Past Methodologies and Suggestions

Perfluorooctane sulfonate (PFOS) is a persistent, bioaccumulative, and toxic chemical that has the potential for long-range transport in the environment. Its use in a wide variety of consumer products and industrial processes makes a detailed characterization of its emissions sources very challenging. These varied emissions sources all contribute to PFOS' existence within nearly all environmental media. Currently, China is the only country documented to still be producing PFOS, though there is no China PFOS emission inventory available. This study reviews the inventory methodologies for PFOS in other countries to suggest a China-specific methodology framework for a PFOS emission inventory. The suggested framework combines unknowns for PFOS-containing product penetration into the Chinese market with product lifecycle assumptions, centralizing these diverse sources into municipal sewage treatment plants. Releases from industrial sources can be quantified separately using another set of emission factors. Industrial sources likely to be relevant to the Chinese environment are identified.

Estimation of vegetative mercury emissions in China

Vegetative mercury emissions were estimated within the framework of Biogenic Emission Inventory System (BEIS3 V3.11). In this estimation, the 19 categories of U.S. Geological Survey landcover data were incorporated to generate the vegetation-specific mercury emissions in a 81-km Lambert Conformal model grid covering the total Chinese continent. The surface temperature and cloud-corrected solar radiation from a Mesoscale Meteorological model (MM5) were retrieved and used for calculating the diurnal variation. The implemented emission factors were either evaluated from the measured mercury flux data for forest, agriculture and water, or assumed for other land fields without available flux data. Annual simulations using the MM5 data were performed to investigate the seasonal emission variation. From the sensitivity analysis using two sets of emission factors, the vegetative mercury emissions in China domain were estimated to range from a lower limit of 79 x 10(3) kg/year to an upper limit of 177 x 10(3) kg/year. The modeled vegetative emissions were mainly generated from the eastern and southern China. Using the estimated data, it is shown that mercury emissions from vegetation are comparable to that from anthropogenic sources during summer. However, the vegetative emissions decrease greatly during winter, leaving anthropogenic sources as the major sources of emission.

Development of a processor in BEIS3 for estimating vegetative mercury emission in the continental United States

We have developed a regression-based processor for estimating vegetative mercury emission within the framework of Biogenic Emission Inventory System Version 3.11 (BEIS3 V3.11). In this development, we incorporated the 230 categories of USGS landcover data to generate the vegetation-specific mercury emission in a 36-km Lambert Conformal model grid covering the continental United States (CONUS). The surface temperature and cloud-corrected solar radiation from a Mesoscale Meteorological model (MM5) were retrieved and used for calculating the diurnal variation. The implemented emission factors were either evaluated from the measured mercury flux data for selected tree species, wetland and water, or assumed for the tree species without available flux data. Annual simulations using the 2001 USEPA MM5 data were performed to investigate the seasonal emission variation. From our sensitivity analysis using three sets of emission factors, we estimated that the vegetative mercury emission in the CONUS domain ranges from a lower limit of 31 ton yr −1 to an upper limit of 140 ton yr −1, with the best estimate at 44 ton yr −1. The modeled vegetative emission was mainly contributed from southeast US. Using the best estimate data, it is shown that mercury emission from vegetation is comparable to that from anthropogenic sources in summer (nearly half of the total emission). However, the vegetative emission decreases greatly in winter, leaving anthropogenic sources as the major emission source (>90% in winter months). Modeling assessment indicates that including vegetative emission (44 ton yr −1) can force an increase of ambient mercury concentration of up to 0.2 ng m −3 in summer midday, but has little impact on dry deposition of mercury. Additional emission factors can be implemented in the model once further mercury flux data become available.

Comparison of road traffic emission factors and testing by comparison of modelled and measured ambient air quality data

This paper describes a comparison of three different sets of road traffic emission factors released by the UK government for use in air quality review and assessment. The air quality management process of review and assessment began in 1997 in the UK. During this period of ongoing review and assessment, a number of changes have been made to the emission factors provided by the government. The use of different sets of emission factors during the assessment process has lead to some inconsistencies between results from neighbouring local authorities and also between different modelling exercises undertaken by the same local authorities. One purpose of this study has been to compare three different sets of emission factors, including the most recent set, and to some degree highlight the uncertainty associated with the use of factors, such as the shift of emphasis in terms of emissions from cars to heavy goods vehicles. The most recently released emission factors are the most comprehensive to date, and theoretically more accurate than previous sets due to the larger database of emission measurements that they have been based on. Therefore, the most recent set of emission factors have been additionally used in a validation exercise between modelled and monitored data. Comparison has been undertaken with monitoring data at a variety of urban background, urban centre and roadside sites. This work has shown some differences between the predicted trends in emission factors and measured trends in ambient air pollution levels, especially at roadside sites, indicating an under-prediction of the air pollution contribution from road traffic.

A MODEL TO DETERMINE PRIMARY AIR POLLUTANT EMISSIONS FROM URBAN BUSES IN SANTIAGO DE CHILE

This paper presents a methodology for estimating emissions form urban buses using passive traffic data. A software simulation of the physical road network and traffic activity is linked to a set of emission factors for estimating emissions of particulate matter (PM), carbon monoxide (CO), unburned hydrocarbons (HC), oxides of nitrogen (NOx), sulphur dioxide(SO2), methane (CH4), nitrous oxide (N2O), ammonia (NH3), and fuel consumption (FC). Observed traffic data is compared to simulated data from a travel demand model in the metropolitan area of Santiago de Chile. Urban buses are one of the largest sources of primary particulate matter and nitrogen oxides in Santiage.