Representative Emission Factors Research Articles

Full-scale determination of essential oil diffusion: Impact on indoor air quality

Essential oils have attracted increasing interest, as they inhibit the metabolic functions of microorganisms. Essential oils are claimed to improve indoor air quality through passive remediation and purifying activities. However, the impact of diffused essential oils on indoor air quality has not been clearly established from a VOC point of view. This study aims to assess the emissions produced by terpene molecules in tea tree essential oil by employing two types of widespread diffusers under real conditions using a full-scale experimental room (40 m3). The emissions generated by terpenoid volatile organic compounds (TerVOCs) are different depending on the type of diffuser. The relative contributions of individual terpenes in the gas phase vary throughout the diffusion process. The impact of tea tree essential oil diffusion on indoor air quality varies from 6 h to 51 days, depending on the diffusion device. Concentration levels can exceed European recommended exposure levels by more than one order of magnitude. Interestingly, the volume of the emission chamber directly impacts the emission dynamics of essential oil constituents. Small chambers may limit the volume of air to be contaminated and possibly impacts the magnitude of concentration gradients during diffusion. This work evidences the interest of using full-scale experimental facilities to determine accurate emission rates. A full-scale experimental methodology including data treatment is validated in this work. Through the proposed protocol, representative emission factors can be determined to assess exposure and perform indoor air modelling.

Impact of Active Diesel Particulate Filter Regeneration on Carbon Dioxide, Nitrogen Oxides and Particle Number Emissions from Euro 5 and 6 Vehicles under Laboratory Testing and Real-World Driving

Particulate mass concentration is a crucial parameter for characterising air quality. The diesel particulate filter (DPF) is the primary technology used to limit vehicle particle emissions, but it needs periodic cleaning, a process called regeneration. This study aims to assess the impact of active DPF regeneration on the performance and emissions of Euro 5 and 6 vehicles. The study examined both carbon dioxide (CO2) and pollutant (nitrogen oxides (NOx) and particle number (PN)) emissions for eight vehicles tested in the laboratory and on the road. Apart from the DPF, a wide range of emission control systems was covered in this experimental campaign, including exhaust gas recirculation (EGR), diesel oxidation catalyst (DOC), lean NOx trap (LNT) and selective catalytic reduction (SCR) catalyst, revealing the different impacts on NOx emissions. The regeneration frequency and duration were also determined and used to calculate the Ki factor, which accounts for the emissions with and without regeneration, weighted over the distance driven between two consecutive regeneration events. Based on these outcomes, representative emission factors (EF) were proposed for the regeneration phase only and the complete regeneration interval. In addition, the effect of regeneration on efficiency was estimated and compared with other energy consumers. The results indicated a significant impact of DPF regeneration on CO2, NOx and PN emissions, higher in the case of driving cycle testing in the laboratory. The relevant mechanisms behind the elevated emission levels were analysed, focusing on the regeneration period and the test phase following immediately after. The calculation of the Ki factor and the comparison with the official values revealed some weaknesses in its application in real-world conditions; to overcome these, new NOx EF values were calculated, depending on the emission control system. It was revealed that Euro 6 vehicles equipped with SCR could comply with the applicable limits when considering the complete regeneration interval. Finally, it was indicated that the DPF regeneration impact on vehicle efficiency is similar to that of driving with the air conditioning (A/C) system and headlights on.

Compilation of a city-scale black carbon emission inventory: Challenges in developing countries based on a case study in Brazil

Black carbon (BC) inventories for cities are scarce, especially in developing countries, despite their importance to tackle climate change and local air pollution. Here, we draw on results from a case study in a Brazilian city to discuss the challenges of compiling a BC inventory for different activity sectors. We included traditionally inventoried sectors, such as industries and on-road transportation, other less reported sectors (food establishments and aviation), and open burning of household solid waste (HSW), typically found in developing countries. We present a machine-learning technique (Random Forest) as a novel approach to obtain HSW burning activity using a set of spatial predictors. The BC inventory was based on PM2.5 emissions weighted by the fraction of PM2.5 emitted as BC and developed for the year 2018. We also reported the disaggregated spatial PM2.5 emissions for the same combustion sources, and documented the databases used for activity data and emission factors (EF). The total estimated BC and PM2.5 emissions amounted to 57.88 and 234.75 tons, respectively, with on-road vehicle exhaust emissions and industrial combustion as the main BC sources (63 and 22%, respectively). For PM2.5 emissions, on-road transportation (exhaust and non-exhaust) contributed 48%, followed by industrial combustion (21%) and food establishments (20%). Population density, number of vacant lots, and property tax values were identified as the most important features to predict the HSW fire activity. A comparison with other inventories revealed that the BC emission profile of Londrina is similar to the profile reported for Greater Mexico City, another Latin American city. Thus, the methodology used in this study could be extended to other cities with similar local BC sources. Finally, we highlight that the lack of local activity data, representative EF, and even methodology may undermine the development of reliable BC inventories, and intensive research should be conducted to characterize the emission sources.

Variability of Ammonia and Methane Emissions from Animal Feeding Operations in Northeastern Colorado.

Concentrated animal feeding operations (CAFOs) are major emitters of both ammonia (NH3) and methane (CH4). However, current emission inventories have limited temporal resolution and use data derived from a small subset of farms. To this end, we deployed three mobile laboratories during the DISCOVER-AQ campaign in summer 2014 with a focus on northeastern Colorado. Observations of NH3 and CH4 plumes downwind of 43 CAFOs were used to investigate the diurnal and site-to-site variability of emissions with an inverse area source plume modeling approach. Ammonia emissions scaled to all permitted animals in Weld, Morgan, and Larimer counties were estimated at 1.9 Gg month-1, 50% greater than the U.S. NEI 2014 and 360% greater than EDGAR for the month of August. Methane emissions were likewise estimated at 10.6 Gg month-1, consistent with the U.S. GHGI but 99% greater than EDGAR. Significant differences between individual CAFOs with repeat observations were also observed for both CH4 and NH3 emissions. The large subfarm, site-to-site, and diurnal variabilities observed show the importance of measurements taken across these scales in order to derive representative emission factors.

Improving the accuracy of emission inventories with a machine-learning approach and investigating transferability across cities

ABSTRACT This study presents a novel method for integrating the output of a microscopic emission modeling approach with a regional traffic assignment model in order to achieve an accurate greenhouse gas (GHG, in CO2-eq) emission estimate for transportation in large metropolitan regions. The CLustEr-based Validated Emission Recalculation (CLEVER) method makes use of instantaneous speed data and link-based traffic characteristics in order to refine on-road GHG inventories. The CLEVER approach first clusters road links based on aggregate traffic characteristics, then assigns representative emission factors (EFs), calibrated using the output of microscopic emission modeling. In this paper, cluster parameters including number and feature vector were calibrated with different sets of roads within the Greater Toronto Area (GTA), while assessing the spatial transferability of the algorithm. Using calibrated cluster sets, morning peak GHG emissions in the GTA were estimated to be 2,692 tons, which is lower than the estimate generated by a traditional, average speed approach (3,254 tons). Link-level comparison between CLEVER and the average speed approach demonstrates that GHG emissions for uncongested links were overestimated by the average speed model. In contrast, at intersections and ramps with more congested links and interrupted traffic flow, the average speed model underestimated GHG emissions. This proposed approach is able to capture variations in traffic conditions compared to the traditional average speed approach, without the need to conduct traffic simulation. Implications: A reliable traffic emissions estimate is necessary to evaluate transportation policies. Currently, accuracy and transferability are major limitations in modeling regional emissions. This paper develops a hybrid modeling approach (CLEVER) to bridge between computational efficiency and estimation accuracy. Using a k-means clustering algorithm with street-level traffic data, CLEVER generates representative emission factors for each cluster. The approach was validated against the baseline (output of a microscopic emission model), demonstrating transferability across different cities .

N2O emission in full-scale wastewater treatment: Proposing a refined monitoring strategy

Nitrous oxide (N2O) emissions from wastewater treatment contribute significantly to greenhouse gas emissions. They have been shown to exhibit a strong seasonal and daily profile in previously conducted monitoring campaigns. However, only two year-long online monitoring campaigns have been published to date. Based on three monitoring campaigns on three full-scale wastewater treatment plants (WWTPs) with different activated sludge configurations, each of which lasted at least one year, we propose a refined monitoring strategy for long-term emission monitoring with multiple flux chambers on open tanks. Our monitoring campaigns confirm that the N2O emissions exhibited a strong seasonal profile and were substantial on all three plants (1–2.4% of the total nitrogen load). These results confirm that N2O is the most important greenhouse gas emission from wastewater treatment. The temporal variation was more distinct than the spatial variation within aeration tanks. Nevertheless, multiple monitoring spots along a single lane are crucial to assess representative emission factors in flow-through systems. Sequencing batch reactor systems were shown to exhibit comparable emissions within one reactor but significant variation between parallel reactors. The results indicate that considerable emission differences between lanes are to be expected in cases of inhomogeneous loading and discontinuous feeding. For example, N2O emission could be shown to depend on the amount of treated reject water: lanes without emitted <1% of the influent load, while parallel lanes emitted around 3%. In case of inhomogeneous loading, monitoring of multiple lanes is required. Our study enables robust planning of monitoring campaigns on WWTPs with open tanks. Extensive full-scale emission monitoring campaigns are important as a basis for reliable decisions about reducing the climate impact of wastewater treatment. More specifically, such data sets help us to define general emission factors for wastewater treatment plants and to construct and critically evaluate N2O emission models.

Particle and VOC emission factor measurements for anthropogenic sources in West Africa

Abstract. A number of campaigns have been carried out to establish the emission factors of pollutants from fuel combustion in West Africa, as part of work package 2 (“Air Pollution and Health”) of the DACCIWA (Dynamics-Aerosol-Chemistry-Cloud Interactions in West Africa) FP7 program. Emission sources considered here include wood (hevea and iroko) and charcoal burning, charcoal making, open trash burning, and vehicle emissions, including trucks, cars, buses and two-wheeled vehicles. Emission factors of total particulate matter (TPM), elemental carbon (EC), primary organic carbon (OC) and volatile organic compounds (VOCs) have been established. In addition, emission factor measurements were performed in combustion chambers in order to reproduce field burning conditions for a tropical hardwood (hevea), and obtain particulate emission factors by size (PM0.25, PM1, PM2.5 and PM10). Particle samples were collected on quartz fiber filters and analyzed using gravimetric method for TPM and thermal methods for EC and OC. The emission factors of 58 VOC species were determined using offline sampling on a sorbent tube. Emission factor results for two species of tropical hardwood burning of EC, OC and TPM are 0.98 ± 0.46 g kg−1 of fuel burned (g kg−1), 11.05 ± 4.55 and 41.12 ± 24.62 g kg−1, respectively. For traffic sources, the highest emission factors among particulate species are found for the two-wheeled vehicles with two-stroke engines (2.74 g kg−1 fuel for EC, 65.11 g kg−1 fuel for OC and 496 g kg−1 fuel for TPM). The largest VOC emissions are observed for two-stroke two-wheeled vehicles, which are up to 3 times higher than emissions from light-duty and heavy-duty vehicles. Isoprene and monoterpenes, which are usually associated with biogenic emissions, are present in almost all anthropogenic sources investigated during this work and could be as significant as aromatic emissions in wood burning (1 g kg−1 fuel). EC is primarily emitted in the ultrafine fraction, with 77 % of the total mass being emitted as particles smaller than 0.25 µm. The particles and VOC emission factors obtained in this study are generally higher than those in the literature whose values are discussed in this paper. This study underlines the important role of in situ measurements in deriving realistic and representative emission factors.

Greenhouse gas emissions from liquid dairy manure: Prediction and mitigation

The handling and use of manure on livestock farms contributes to emissions of the greenhouse gases (GHG) CH4 and N2O, especially with liquid manure management. Dairy farms are diverse with respect to manure management, with practices ranging from daily spreading to long-term storage for more efficient recycling of manure nutrients for crop production. Opportunities for GHG mitigation will depend on the baseline situation with respect to handling and storage, and therefore prediction and mitigation at the farm level requires a dynamic description of housing systems and storage conditions, and use of treatment technologies. Also, effects of treatment and handling on the properties of field-applied manure must be taken into account. Storage conditions and manure composition importantly define carbon and nitrogen transformations, and the resulting emissions of CH4 and N2O, as well as CO2 and NH3, which are all important for the GHG balance. Currently, inventories for CH4 and N2O emissions from manure are based on emission factors for a limited number of production systems, together with average annual temperature, but the inherent uncertainty of this approach is a barrier toward prediction and mitigation. Although more representative emission factors may be determined at country level, this is both challenging and costly, and effects of management changes for GHG mitigation are not easily quantified. An empirical model of CH4 emissions during storage is discussed that is based on daily time steps, and a parameterization based on measurements. A distinction between emissions from manure in barns and outside storage facilities is important for assessing effects of treatment technologies, such as anaerobic digestion, where only posttreatment emissions are affected. Upon field application, manure and soil together define the equilibrium distribution of labile carbon and nitrogen between bulk soil and manure hotspots. This introduces heterogeneity with respect to potential for N2O emissions, which is not represented in existing prediction models. Manure treatment and management options for GHG mitigation are discussed with emphasis on effects on manure volatile solids and N availability. Anaerobic digestion and acidification represent treatment technologies that are relevant for GHG mitigation on dairy farms.

Recommended Revisions of Norwegian Emission Factors for Wood Stoves

Abstract In this paper we present recommended revisions of emission factors for Norwegian wood stoves. Emission factors exist for a number of emissions to air, and vary in a wide range between individual studies depending on the technology, operating condition and fuel studied. This is a challenge when authorities need to decide on emission factors for inclusion in national emission inventories. In these inventories, emission factors need to be representative on a national level and include firing habits and technology in use. Herein lies a considerable challenge, resulting in large uncertainties for a number of the emission factors. In addition, value chain analysis is becoming increasingly common to assess the influence of the different elements in the value chain on different impact factors or stressors. For the wood stove value chain, the conversion element, i.e. the combustion process and the resulting emissions, is most important for most of the impact factors. It is essential for the outcome of such analyses that the best available emission factors are used to be able to assess the current impact of these as well as the improvement potential. Hence, it becomes increasingly important to select or derive representative emission factors, at a sufficient detailing level. In this work, recommended emission factors for wood stoves to be used in Norwegian value chain analysis are proposed.

Ammonia and Methane Emission Factors from Cattle Operations Expressed as Losses of Dietary Nutrients or Energy

The objective of this study was to conduct a systematic review of published literature on ammonia (NH3) and enteric methane (CH4) emissions from beef and dairy cattle operations to obtain statistically representative emission factors based on dietary intakes of nutrients or energy, and to identify major causes of emission variations. NH3 emissions from lagoon or other manure storage facilities were not included in this review. The NH3 and CH4 emission rates, expressed as a percentage losses of dietary nutrients or energy, demonstrated much less variation compared with emission rates expressed in g/animal/day. Air temperature and dietary crude protein (CP) content were identified as two major factors that can affect NH3 emission rates in addition to farm type. Feed digestibility and energy intake were identified as two major factors that can affect CH4 emission rates expressed as a percentage losses of dietary energy. Generally, increasing productivity and feed efficiency represented the greatest opportunity for mitigating NH3 or CH4 emissions per unit of livestock product. Expressing CH4 loss on a digestible energy basis rather than a gross energy intake basis can better represent the large variation among diets and the effects of varying dietary emission mitigation strategies.

Emission Characterization of In-Use Diesel and Gasoline Euro 4 to Euro 6 Passenger Cars Tested on Chassis Dynamometer Bench and Emission Model Assessment

ABSTRACTThis paper focuses on CO2 and regulated pollutants (NOx, HC, CO, PM) emitted by eight Euro 4–6 gasoline and diesel vehicles with six different technologies. The emission factors were repeatedly measured on a chassis dynamometer bench using Artemis Urban with cold and hot start, Road and Motorway, WLTC and NEDC driving conditions. The influence of driving conditions and approved driving cycles on pollutant emissions was also investigated. The measured emission factors for regulated compounds were compared to the corresponding emission factors of the COPCETE emission model developed by the French Ministry of Ecology. The results indicate that the NEDC cycle, used for type-approval of emissions of regulated compounds, leads to underestimation of CO2 (9–23%) and NOx (1.2 to 2 times) emissions and overestimation of CO and HC (2 to 5 times) in relation to the Artemis cycles, which are real-world simulation driving cycles. The WLTC cycle for the worldwide harmonization of vehicle emissions shows similar HC, NOx and CO emissions with the Artemis average cycle within uncertainty of the measurements. The NOx emissions measured were 1.6 to 8 times greater than the type-approval limits. These high NOx emissions produced by all the diesel vehicles tested under real-world driving conditions could serve as particle precursors and increase secondary organic aerosol formation. They are also indicative of the significant cause for concern regarding urban air quality and the increase in the portion of Euro 5 and 6 diesel vehicles in France's vehicle fleet. Regarding emission factor assessments, the emission levels measured are overall in fair agreement with the COPCETE predictions within uncertainties for CO2 and regulated pollutants. Updating the database is vital in order to be able to produce more representative emission factors and better evaluate the health and environmental effects from vehicle emissions.

Emissions of NOx, particle mass and particle numbers from aircraft main engines, APU's and handling equipment at Copenhagen Airport

This paper presents a detailed emission inventory for NOx, particle mass (PM) and particle numbers (PN) for aircraft main engines, APU's and handling equipment at Copenhagen Airport (CPH) based on time specific activity data and representative emission factors for the airport. The inventory has a high spatial resolution of 5 m × 5 m in order to be suited for further air quality dispersion calculations. Results are shown for the entire airport and for a section of the airport apron area (“inner apron”) in focus. The methodology presented in this paper can be used to quantify the emissions from aircraft main engines, APU and handling equipment in other airports. For the entire airport, aircraft main engines is the largest source of fuel consumption (93%), NOx, (87%), PM (61%) and PN (95%). The calculated fuel consumption [NOx, PM, PN] shares for APU's and handling equipment are 5% [4%, 8%, 5%] and 2% [9%, 31%, 0%], respectively. At the inner apron area for handling equipment the share of fuel consumption [NOx, PM, PN] are 24% [63%, 75%, 2%], whereas APU and main engines shares are 43% [25%, 19%, 54%], and 33% [11%, 6%, 43%], respectively. The inner apron NOx and PM emission levels are high for handling equipment due to high emission factors for the diesel fuelled handling equipment and small for aircraft main engines due to small idle-power emission factors. Handling equipment is however a small PN source due to the low number based emission factors. Jet fuel sulphur-PM sensitivity calculations made in this study with the ICAO FOA3.0 method suggest that more than half of the PM emissions from aircraft main engines at CPH originate from the sulphur content of the fuel used at the airport. Aircraft main engine PN emissions are very sensitive to the underlying assumptions. Replacing this study's literature based average emission factors with “high” and “low” emission factors from the literature, the aircraft main engine PN emissions were estimated to change with a factor of 14.

Speed-dependent representative emission factors for catalyst passenger cars and influencing parameters

This paper presents speed-dependent hot emission factors for passenger cars equipped with a three-way catalyst. The emission factors were developed using emission data from in-use vehicles based on chassis dynamometer tests conducted over real-world driving cycles. The variability of the emission data and the use of cycles with different characteristics resulted in a low correlation between emission factors and mean travelling speed. Analysis of the data identified differences in vehicle emission performance as the main source of variability. The potential for decreasing emission variability by classifying vehicles according to their engine capacity and mileage was studied by means of an analysis of variance test. Results showed that CO, NO x and HC emissions were dependent on mileage but not on engine size. Emissions of CO 2 were found to be strongly dependent on engine size, but insensitive to vehicle mileage.