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In August 2021, the U.S. Environmental Protection Agency (EPA) released draft models to estimate daily NH3, H2S, PM10, PM2.5, and TSP emissions from egg-layer houses (high-rise and manure-belt) and manure storage using inputs of daily mean ambient temperature, relative humidity (RH), and hen inventory. These models were developed from refined datasets generated by the National Air Emissions Monitoring Study fieldwork completed in 2009. Notably, they do not include data for cage-free housing. Currently, 66% of U.S. laying hens are housed in cages; thus, these models, if adopted, will have a substantial impact on the U.S. egg industry. This study evaluated the EPA draft models' robustness and assessed model outputs for egg production systems under differing climate scenarios. The EPA draft models distort emission factors for bird inventories to be lower or higher than those used to develop the models. With inventory held constant, the marginal influence of ambient temperature and RH on daily emissions varied substantially, with some values falling below the measurement detection threshold while others exceeding literature findings. For twelve representative U.S. locations representing differing climates, substantial differences in emission factors were found for bird inventories outside the range in the database. Annual emissions estimated from inventories used to develop the EPA models also varied by location. We conclude that the current draft EPA emission models cannot be used to the degree of precision that is suitable to apply to a wide range of layer facilities, particularly cage-free systems. Revisions are suggested to accommodate a greater range of climates, laying hen facility types, and inventories for practical emission estimations. [ABSTRACT FROM AUTHOR]

Woodwork has functions as the versatile and sustainable furniture material to decorate buildings in our low-carbon society, while commonly giving significant risks of releasing volatile organic compound (VOC). Thus, how to accurately identify and quantify the VOC of woodwork raw materials is critical. Herein, we designed an easy-to-handle, processable, time-saving strategy of coupling headspace (HS) and gas chromatography (GC) to effectively and precisely investigate the emission behavior of VOC of Cinnamomum camphora (C. camphora). The effects of operation parameters of HS-GC on the VOC emission are analyzed in detail, where the optimize treatment conditions are 140 °C of equilibrium temperature, 25 h of equilibrium time, and 20–40 mesh of particle size. In addition, camphor and eucalyptol are the main chemical components of VOC, which shows the high content, more than 50 % of harvesting VOC. Moreover, a semi-empirical model for VOC emission of C. camphora is established, in which the predicted VOC emission demonstrates a strongly linear relationship with the experimental results (R2 > 0.99). This study enables the comprehensive understanding of VOC emission of raw materials of woodwork, beyond C. camphora, and provides references to effectively reduce VOC in sustainably building and construction. • VOC emission of C. camphora was investigated based on HS-GC. • The VOC main components of include camphor, α-terpineol, nerolidol, caryophyllene, α-pinene, and eucalyptol. • Semi-empirical model for VOC emission is established. [ABSTRACT FROM AUTHOR]

SUMMARY: Plant cuticles are a mixture of crystalline and amorphous waxes that restrict the exchange of molecules between the plant and the atmosphere. The multicomponent nature of cuticular waxes complicates the study of the relationship between the physical and transport properties. Here, a model cuticle based on the epicuticular waxes of Petunia hybrida flower petals was formulated to test the effect of wax composition on diffusion of water and volatile organic compounds (VOCs). The model cuticle was composed of an n‐tetracosane (C24H50), 1‐docosanol (C22H45OH), and 3‐methylbutyl dodecanoate (C17H34O2), reflecting the relative chain length, functional groups, molecular arrangements, and crystallinity of the natural waxes. Molecular dynamics simulations were performed to obtain diffusion coefficients for compounds moving through waxes of varying composition. Simulated VOC diffusivities of the model system were found to highly correlate with in vitro measurements in isolated petunia cuticles. VOC diffusivity increased up to 30‐fold in completely amorphous waxes, indicating a significant effect of crystallinity on cuticular permeability. The crystallinity of the waxes was highly dependent on the elongation of the lattice length and decrease in gap width between crystalline unit cells. Diffusion of water and higher molecular weight VOCs were significantly affected by alterations in crystalline spacing and lengths, whereas the low molecular weight VOCs were less affected. Comparison of measured diffusion coefficients from atomistic simulations and emissions from petunia flowers indicates that the role of the plant cuticle in the VOC emission network is attributed to the differential control on mass transfer of individual VOCs by controlling the composition, amount, and dynamics of scent emission. Significance Statement: To assess the barrier properties of crystalline cuticular waxes found on aerial plant surfaces, we formulated an in silico model cuticle representing the physicochemical properties of Petunia hybrida flower petal waxes. Atomistic simulations of the compositional variants of the model cuticle show that changes in relative cuticle crystallinity exert control on both the rate of water loss and the dynamics of volatile emissions. [ABSTRACT FROM AUTHOR]

Several studies, in the past, have used chassis dynamometer and remote-sensing method to describe effects of speeds on pollutant emissions. These studies reasonably lacked data on important modal events such as acceleration, deceleration, speed, and their effects on emissions. Present study includes on-road experiments carried out to examine the impacts of car speed, acceleration, and deceleration on their tailpipe emissions. The study was carried out on cars, with and without catalytic converter on a two-lane roadway in engine operating modes of acceleration and deceleration. The power to weight ratio of the cars was 0.03hp/lb. The relationships of pollutant emissions with the speeds were examined at two acceleration levels (a = 1.0 m/s² and a = 1.6 m/s²). A prominent relationship of tailpipe emission with the averaged speed was seen at both accelerations. Further, the pollutant emissions were different at different speed ranges of 0-3 m/s (0-10.8 km/h), 3-6 m/s (10.8-21.6 km/h) and above 6 m/s (21.6 km/h). A second-order statistical emission - speed model has been presented and discussed. The effect of deceleration on tailpipe emission was not clear in the study. [ABSTRACT FROM AUTHOR]

Measurements of BVOC emissions, meteorological parameters, and solar radiation were carried out in a temperate forest, China during the summer seasons in 2010 and 2011. Terpenoid emissions were measured using the Relaxed Eddy Accumulation (REA) technique on an above–canopy tower. Isoprene contributed 79.1% and 82.0% of terpenoid emissions in 2010 and 2011 summer. The monoterpene emissions were dominated by α–pinene, contributing 6.3% and 12.2% of the total terpenoid emissions in 2010 and 2011 summer. Terpenoid emissions exhibited strong diurnal variations. Isoprene and monoterpene emissions maxima typically occurred a few hours after the noon PAR peak and coincided with the daily temperature maximum. During 2011 summer, the mean isoprene emission flux (mg m–2 h–1) was 0.889, mean total monoterpene emission flux was 0.143. Emission factors, representing the emission expected at a temperature of 30 °C, for this site were 0.32 mg m–2 h–1 for total monoterpenes and 4.3 mg m–2 h–1 for isoprene. The observations were used to evaluate the isoprene and monoterpene emission magnitude and variability predicted by the MEGANv2.1 model. Canopy scale isoprene and monoterpene emission factors based on these observations fall within the range of emission factors assigned to locations within 50 km of the site by the MEGANv2.1 emission model. When using the site specific landcover data for the site, the measured emission factors are 12% for isoprene and 20% for monoterpenes lower than the MEGANv2.1 emission factors. MEGANv2.1 predicts that variations in light intensity should result in significant changes in isoprene emissions during the study but this was not evident in the observations. Observed diurnal, seasonal and interannual variations in isoprene and monoterpene emissions were strongly correlated with air temperature which was the dominant driving variable for MEGANv2.1 during the study period. The observed temperature response for isoprene and monoterpenes is similar to the temperature sensitivity of the MEGANv2.1 response functions.

• A web – based platform for methane emission modelling from landfills is proposed. • Model integration technology with databases and GIS are described. • The inverse algorithm is built to determine the emission source capacity. • To quantify CH 4 emissions for SKER about 158,977.77 tons in the period 2019–2030. • The results of CH 4 pollution dispersion simulation is performed. This study developed a platform using a modelling and web technology approach to estimate methane emissions from landfills to assess methane emissions across the region. The web technology-based software EnLandFill, which was developed, allows users to log in, interact with landfill databases, and document and extract information regarding landfill emissions. Models that integrate web technology with databases and geographic information systems (GIS) are described. One of the achievements of this study was the development of an inverse algorithm to determine the waste source capacity according to a dispersion model, accounting for complex terrain and meteorological time-series data extracted from the Weather Research and Forecasting (WRF) model. EnLandFill software was applied to quantify CH 4 emissions for key developing regions, predicting approximately 158,977 tonnes, equivalent to 167,786,878 m3 of CH 4 for the period of 2019 – 2030. The software also allows the evaluation of the scope and level of impacts of landfill emissions under given meteorological conditions. [ABSTRACT FROM AUTHOR]

Open biomass burning plays an important role in the formation of heavy pollution events during harvest seasons in China by releasing gases and particulate matters into the atmosphere. A better understanding of open biomass burning in China is required to assess its impacts on the air quality and especially on heavy haze pollution.By using datasets of MODIS fire spot, land cover, vegetation cover, biomass loading and emission factors, a biomass emission model is developed, which is then embedded to an air quality model (WRF-CUACE) to quantitatively assess impacts of biomass burning on surface PM2.5 concentration in China through sensitivity tests. Three simulation scenarios are designed to ensure that simulation results of revised scenarios are closer to actual atmospheric conditions according to the model evaluation. Results show that in October 2014, Northeast, South and Southwest China are regions of the largest contribution to biomass burning with the average monthly increased concentration of PM2.5 up to 30-60 μg·m-3, and even more than 100 μg·m-3 at local regions. In North, East and South China, biomass burning generally provides a contribution of PM2.5 concentration of 5-20 μg·m-3. In terms of the percentage of relative contribution, the value in Northeast China exceeds 50% in most regions. In South China, the relative contribution of biomass burning reaches 20%-50%, and even exceeds 60% in parts of Southwest China. While in North, Central and East China, the relative contribution of biomass burning is generally 10%-20%. In addition, the contribution of secondary aerosols in PM2.5 from biomass burning is also estimated. A group of sensitivity experiments are set up, with and without the gas emission from biomass burning. In Northeast China, the contribution concentration of secondary aerosols is only 0-10 μg·m-3, significantly lower than that in North, Central, East and South China, where the contribution concentration of secondary aerosols could reach 5-15 μg·m-3. In terms of the percentage of contribution to secondary aerosols in PM2.5 from biomass burning, the value in Northeast China is the lowest, which is less than 30% in most regions. And in South and Southwest China, the contribution percentage is relatively larger, which can reach 30%-50%. While in North, Central, East China and vast remote areas, the contribution percentage almost exceed 70%. Based on the above analysis, it is found that the percentage of secondary aerosols in PM2.5 from biomass burning drops when the biomass burning grows.

Rice is a staple crop that meets 21% of worldwide human calorie requirements. Eighty percent of produced rice in Iran comes from the northern part of Iran. Considering the importance of northern regions in meeting the demands for rice, and increased consumer awareness on environmental sustainability, this aspect should be considered and investigated. Life cycle assessment (LCA) approach is a potential tool to quantify the environmental sustainability of agri-food systems. But modeling the agrochemical emissions is a challenge for LCA-practitioners to build a reliable inventory for fertilizers and pesticides. This study aimed to investigate the impact of agrochemical emission models on the environmental impacts of paddy rice production in paddy fields in three major regions in the north of Iran. Emission models (Margni and PestLCI for pesticides and IPCC and Brentrup for fertilizers) were selected based on the availability of site-specific information and based on that two scenarios were defined. Scenario I was defined for the cases in which there is no enough site-specific information and scenario II is defined based on the availability of on-site information. ReCiPe2016 (H) methodology on the midpoint level was used for quantifying the environmental impacts. Results showed that rice seed production, diesel fuel, urea, phosphate fertilizer, and Diazinon are the major environmental hotspots in paddy rice production. Results revealed that the general emission models used in scenario I overestimated the impact scores in marine eutrophication (90%), freshwater ecotoxicity (87%), marine ecotoxicity (44%), non-cancer human toxicity (26%), and terrestrial ecotoxicity (10%), while underestimation was found in some impact categories such as ozone depletion (115%), terrestrial acidification (21%) and global warming (4%). It shows the importance of emission models on achieving reliable impact scores in each environmental impact category. About rice straw management strategy, utilizing straw as livestock feed can mitigate the emissions of greenhouse gases to the air by 30% to 38% compared to burning paddy residue on the farm. [ABSTRACT FROM AUTHOR]

Poultry production significantly contributes to atmospheric ammonia (NH 3) emissions, causing concerns over their environmental impacts and the effects on human and bird health inside the poultry houses. A model is needed to estimate NH 3 emissions from poultry facilities in order to develop effective mitigation strategies and science-based regulations. The laboratory-scale mechanistic NH 3 emission model developed by Tong, Zhao, Heber, and Ni (2020) requires extensive monitoring and laboratory work for the inputs and therefore cannot be conveniently used by producers or regulatory agencies. Based on this mechanistic model, a farm-scale model was developed for estimating dynamic NH 3 emission rates from commercial manure-belt layer houses. Sub-models were developed to estimate the inputs of the mechanistic model from farm-scale conditions that are commonly known during poultry production. Computational fluid dynamics (CFD) models and analyses of manure characteristics were used to estimate air conditions, manure properties, and NH 3 emission areas inside layer houses. The model estimated daily NH 3 emission from typical commercial manure-belt layer houses for given dates with input data consisting of hen inventory, ambient air temperature, house ventilation mode and rate, and manure management practices. Model performance was evaluated by comparison with NH 3 emission data from four commercial manure-belt layer houses with different ventilation systems. Uncertainties of 26–32% relative to the mechanistic model were obtained based on the uncertainties of individual sub-models along with their corresponding sensitivities. The model had an acceptable performance during ordinary egg production (p-value = 0.10–0.61), but it needs further development for special events such as moulting and introduction of new flocks. • A farm-scale model was developed for estimating NH 3 emission from US layer houses. • Cage air conditions were estimated using CFD simulations and linear interpolations. • Manure properties were estimated using polynomial regressions based on measurement. • The model had good performance for estimating daily NH 3 emission, but not hourly. • The 26–32% model uncertainty can be reduced with better manure pH estimations. [ABSTRACT FROM AUTHOR]

A physical-mathematical model has been designed to estimate the emission of dust from the surface of granular materials exposed to wind erosion. The emission model implements the Monte Carlo probabilistic approach, which for a given wind velocity (i.e., shear stress velocity) ascribes the probability of saltation to the particle aggregates composing the erodible surface and calculates the emission of dust aerosol based on the main laws governing the physics of wind-blown particles. The article discusses the application of the emission code to the surfaces of two metal sulphides (PbS and ZnS), which are typically stored in stockpiles in the open yards of industrial plants that operate in the commodity sector, to be used as raw materials for the production of lead and zinc (non-ferrous metals). The results of the simulation were found to be in agreement with the indication provided by the technical literature about the emission potential of the two metal sulphides. The emission model hereby proposed intends to provide an analytical integration to the experimental and empirical Emission Factors (EF) already suggested by the technical and scientific literature about industrial wind erosion.

This article proposes a methodology for the estimation of emissions in real driving conditions, based on board diagnostics data and machine learning, since it has been detected that there are no models for estimating pollutants without large measurement campaigns. For this purpose, driving data are obtained by means of a data logger and emissions through a portable emissions measurement system in a real driving emissions test. The data obtained are used to train artificial neural networks that estimate emissions, having previously estimated the relative importance of variables through random forest techniques. Then, by the application of the K-means algorithm, labels are obtained to implement a classification tree and thereby determine the selected gear by the driver. These models were loaded with a data set generated covering 1218.19 km of driving. The results generated were compared to the ones obtained by applying the international vehicle emissions model and with the results of the real driving emissions test, showing evidence of similar results. The main contribution of this article is that the generated model is stronger in different traffic conditions and presents good results at the speed interval with small differences at low average driving speeds because more than half of the vehicle’s trip occurs in urban areas, in completely random driving conditions. These results can be useful for the estimation of emission factors with potential application in vehicular homologation processes and the estimation of vehicular emission inventories.

The monoterpene composition (emission and tissue internal concentration) of major forest tree species in the United States is discussed. Of the 14 most commonly occurring compounds (α-pinene, β-pinene, Δ3-carene, d-limonene, camphene, myrcene, α-terpinene, β-phellandrene, sabinene, ρ-cymene, ocimene, α-thujene, terpinolene, and γ-terpinene), the first six are usually found to be most abundant. Expected regional variability based on the monoterpene composition fingerprints and corresponding tree species distribution and abundance is examined. In the southeast, a-pinene and β-pinene seem to dominate monoterpene emissions, while in the northern forests emissions are distributed more evenly among the six major compounds. In some parts of western forests, β-pinene and Δ3-carene can be more abundant than α-pinene. Among the other eight compounds, β-phellandrene and sabinene occasionally are significant percentages of expected local monoterpene emissions. Ocimene and ρ-cymene are estimated to be more common in regions dominated by deciduous broadleaf forests, although total emission rates are generally lower for these forests relative to those dominated by conifers. These percentages are compared with monoterpene composition measured in ambient air at various sites. Estimated monoterpene emission composition based on local forest species composition agrees fairly well with ambient measurements for the six major compounds. The past assumption that α-pinene composes approximately 50% of total monoterpene emissions appears reasonable for many areas, except for possibly the northern coniferous forests and some areas in the west dominated by true firs, spruce, and western pines (lodgepole and ponderosa pines). The oxygenated monoterpenes such as camphor, bornyl acetate, and cineole often compose high percentages of the monoterpenes within plant tissues, but are much less abundant in emission samples. Even after adjusting for lower vapor pressures of these compounds, emission rates relative to the hydrocarbon monoterpenes are often lower than would be expected from their internal concentrations. More study is warranted on monoterpene emission rates and composition, especially from the spruces, true firs, hemlocks, cedars, and some deciduous species such as the maples. Non-invasive canopy level and whole ecosystem flux studies are also needed to establish uncertainty estimates for monoterpene emission models. © 2000 Elsevier Science Ltd. All rights reserved.

Dust emissions due to aeolian erosion of exposed granular materials are strongly influenced by grain size distribution. Non-erodible particles that are too heavy to be lifted into the air play a protective role in the aeolian erosion process attenuating emission, which is known as the pavement phenomenon. To date, there is no approach that reliably predicts the reduction in emissions caused by their presence on an aggregate surface. In this work, an analytical model was developed to quantify emissions from particle beds with a wide size distribution. As non-erodible particles accumulate, changes in surface characteristics create increasing shelter for the erodible portion of the bed until the shear on the erodible surface reaches a minimum and emissions cease. The proposed emission model describes the relationship between this minimum value of wind shear and the eroded depth of the bed after the pavement, which in turn gives the emitted mass. In addition, wind tunnel experiments were carried out in order to broaden knowledge of the pavement phenomenon and validate the modelling. A bimodal particle size distribution of sand with erodible and non-erodible particles was used for the tested velocities. Three experimental measurements were carried out: (i) continuous weighing of the emitted mass, (ii) eroded depth of the bed at regular time intervals and (iii) final cover rates of the non-erodible particles using digital analysis of sand bed pictures after experiments. Good agreement between the modelling and experimental results was found. The emission model proposed herein is a simple algebraic expression that demands low computational effort. This approach may serve as a base for an emission model for application in granular materials stockpiles. [ABSTRACT FROM AUTHOR]

Aeolian erosion of granular materials is investigated by means of a mathematical emission model and experimental wind tunnel measurements. Main model input data are: friction velocity, relationship between cover rate (CR) and eroded height (H) and particle properties (density, size distribution). It is proposed: (1) to evaluate the linearity of the relation between CR and H considering the presence of a multimodal distribution of particle sizes, (2) to validate the mathematical model with wind tunnel data, (3) to evaluate the protective effect of non-erodible particles and (4) to qualitatively evaluate the final stage of erosion through experimental photographs of the oblong stockpile. The relationship between CR and H may still be considered linear for the tested mixture of particles. The modelled emission, when compared with experimental data, showed that the physical tendency of the aeolian erosion phenomenon was well predicted. The model showed to be useful in comparative analysis between scenarios but not in absolute values due to errors found. It is valid for the monitoring of air quality degradation due to aeolian erosion of open yards of storage piles. Detailed analysis of emitted mass explained that the smallest diameters among the non-erodible particles create a less effective protection effect leading to higher emissions. The qualitative analysis of high-quality photographs of the experiments showed that the non-erodible particle agglomeration on the stockpile surface can be well explained if one evaluates simultaneously, on the pile, the angle of velocity vectors (which influences the threshold friction velocity value) and shear stress. [ABSTRACT FROM AUTHOR]

Abstract The combustion process in engines is highly influenced by the combined effect of various parameters. As far as the internal combustion engines are concerned, emission is the important parameters for which the other design and operating parameters have to be optimized. This paper studies the use of RSM (Response Surface Methodology) to optimize the performance parameters of a 4-stroke spark ignition engine. In this work, a description of the facilities developed for conducting experimental work on the test engine experiment was conducted. Theoretical evaluation, experimental evaluation, prediction of performance parameters using RSM and statistical evaluation of SI engine were performed. The study also explained how to build an analytical model for the complicated problem. The development of the incombustible gases concentration (part per million-ppm) as a function of engine speed (rpm), loading condition (%) and operating time (seconds) was done via 23 factorial designs of the experiment (DoE)and RSM. The results obtained from HC, CO and NO x emission models showed that the engine speed, loading condition and time were found to have significant influence on the emission. The HC, CO and NO x emission models have also proved positive response from the regression analysis of actual and predicted responses. In the error estimation with 95% confidence interval the equations are within the ranges. Thus, the response surface methodology provides useful information required for the experiment and also useful in predicting the response of engine parameters to engine emissions. [ABSTRACT FROM AUTHOR]

The results of the research in the field of benzene, toluene, ethylbenzene and xylene isomers (BTEX) concentrations in exhaust gases of spark ignition engines under different operating conditions are presented in this paper. The aim of this paper is to gain a clearer insight into the impact of different engine working parameters on the concentrations of BTEX. The experimental investigation has been performed on the SCHENCK 230 W test stand with the controlled IC engine. The engine operating points have been chosen based on the results of a simulation and they are considered as the typical driving conditions according to the New European Driving Cycle. Concentration levels of BTEX compounds in exhaust gas mixtures have been determined by gas chromatography technique by using the combination of Supelcowax 10-Polyethylene glycol column and the PID detector. Based on the experimental research results, the emission model of BTEX compounds has been defined by the simulation of movement of a Fiat Punto Classic passenger car in accordance with the NEDC cycle. Using the results obtained within the simulation, the official statistics on the number of gasoline-powered cars on the territory of the Republic of Serbia and the European Commission data on the annual distance traveled by car, the amounts of BTEX compounds emitted annually per car have been estimated, as well as the emissions of the entire Serbian car fleet. [ABSTRACT FROM AUTHOR]

Abstract Concern about high formaldehyde emission from specific models of laminate flooring (LF) products has recently drawn wide public attention in the U.S. This raises questions about how to best test emissions of LF products and how to make appropriate model assumptions when conducting relevant lifetime exposure and risk assessments. This paper presents a preliminary study focused on these questions. Formaldehyde emissions were measured in 20-L environmental chambers for 6 months for two LF products made with low- and high-emitting composite wood core. Tests were conducted for specimens with and without exposed seams (“click-joints” at the sides and ends of each flooring plank) and perimeter cut edges. Results demonstrate that exposed seams and cut edges can contribute significantly to overall emissions, especially for the product with a high-emitting core, and this effect could last long after flooring installation. Testing with no exposed seams and perimeter cut edges might allow a finished flooring product with a high-emitting core and high emissions after installation to meet low-emitting labelling criteria inappropriately. Highlights • We compared laminate flooring products containing low- or high-emitting core. • We measured the impact of seams and perimeter cut edges on formaldehyde emission. • Exposing seams and cut edges significantly increased overall formaldehyde emission. • The impact was long term, and much stronger for the product with high-emitting core. • These results suggest a way to make protocols for VOC emission tests more realistic. [ABSTRACT FROM AUTHOR]

This paper examines the environmental impact of potential coordination on supply chains. A decentralized two-node supply chain is studied, in which one node is a buyer ordering from a second node, who is a supplier operating under the lot-for-lot policy. The supplier is allowed to use a quantity discount to manipulate the buyer’s decision reducing both his individual cost and system’s operational costs. This results in decreasing the frequency of deliveries. We demonstrate that environmentally friendly policies could be also cost saving. The crucial factor about the environmental benefits is the total distance travelled rather than the vehicle loads. We establish the magnitude of the environmental benefits using numerical examples under specific operational parameters. Complete and incomplete information cases are investigated, where the buyer and the supplier make their decisions to optimize their own business operations. [ABSTRACT FROM AUTHOR]

This study presents a simulation framework for estimating on-road CO 2 emissions of compressed natural gas (CNG) and diesel tractor-trailer heavy-duty trucks under various operational conditions. A second-by-second component-level model was developed and then used to simulate seven distinct drive cycles. This paper specifically considers road grade, and develops a novel technique to pair road grade profiles with given speed vs. time data when gradient data are not available. Six routes around the Canadian province of British Columbia were used as case study drive cycles, including an extreme hill climb route. Results showed that omission of road grade under-estimates CO 2 emissions by as much as 24% for both CNG and diesel drivetrains. Simulations indicated that CNG trucks emit 13–15% less CO 2 than comparable diesel trucks, depending on weight class and drive cycle. Sensitivity analyses highlighted the importance of aerodynamic drag, rolling friction, and engine efficiency for all cycles. An assessment of advanced vehicle technology options for CNG trucks showed achievable CO 2 reductions of 28–35% in the near-term and 41–51% over the longer term, compared to current diesel technology. The same advanced technology options would reduce diesel drivetrain CO 2 emissions by 17–23% and 31–42% over the near and long-term respectively. It is worthwhile to emphasize that with commensurate technology developments, CNG drivetrains offer the same 13–15% CO 2 reductions compared to diesels over the near and long term. The results demonstrate that CO 2 reductions in heavy-duty trucks depend primarily on drivetrain technology, while operational conditions play a less significant role. [ABSTRACT FROM AUTHOR]

Forest degradation is widespread around the world, due to multiple factors such as unsustainable logging, agriculture, invasive species, fire, fuelwood gathering, and livestock grazing. In the Brazilian Amazon forest degradation from August 2006 to July 2016 reached 1,1 869 800 ha. The processes of forest degradation are still poorly understood, being a missing component in anthropogenic CO _2 emission estimates in tropical forests. In this work, we analyzed temporal trajectories of forest degradation from August 2006 to July 2016 in the Brazilian Amazon and assessed their impact on the regional carbon balance. We combined the degradation process with deforestation-related processes (clear-cut deforestation and secondary vegetation dynamics), using the spatially-explicit INPE-EM carbon emission model. The trajectory analysis showed that 13% of the degraded area ended up being cleared and converted in the period and 61% of the total degraded area experienced only one event of degradation throughout the whole period. Net emissions added up to 5.4 GtCO _2 , considering the emissions from forest degradation and deforestation, absorption from degraded forest recovery, and secondary vegetation dynamics. The results show an increase in the contribution of forest degradation to net emissions towards the end of the period, related to the decrease in clear-cut deforestation rates, decoupled from the forest degradation rates. The analysis also indicates that the regeneration of degraded forests absorbed 1.8 GtCO2 from August 2006 and July 2016—a component typically overlooked in the regional carbon balance.

In this paper, the equivalent \textCO\text{2} emission models for fossil-fuel-based distributed generator units are developed considering their individual emission characteristic and fuel consumption. These models are then integrated within a microgrid energy management system (EMS) model. Constant energy, demand shifting load models are further integrated in the EMS to examine the possible impact of demand response (DR) on the total system emissions and economics of a microgrid. Thus, the impacts of including the developed emission models on the operation of an isolated microgrid, equivalent \textCO\text{2} emissions, and costs are examined considering five different operating strategies. The proposed operating strategies are validated on a modified CIGRE medium voltage benchmark system. The results obtained highlight the effectiveness of the proposed EMS and also demonstrate the impact of DR on emissions and costs. [ABSTRACT FROM PUBLISHER]

Melting snow provides an essential source of water in many regions of the world and can also contribute to devastating, wide-scale flooding. Global datasets of recorded passive microwave emissions provide non-destructive, daily information on snow processes including the presence of liquid water in the snow, which can be an indicator of snowmelt. The objective of this research is to test the sensitivity of the emission signal as it relates to the spatial distribution of liquid water content in the snowpack. This signal response was evaluated over an area approximately the size of a microwave pixel to assess whether a relationship exists between the aerial extent of wet snow and the magnitude of the T B response. A sensitivity analysis was performed using a high-resolution, physically based snow-emission model to simulate microwave emissions. The signal response to wet snow was evaluated given a range of spatially distributed snowpack conditions. Daily snow states were simulated for a 9-year period using a high-resolution (50 m) energy balance snow model over a 34 × 34 km domain. These data were fed into a microwave emission model to simulate brightness temperatures. A near-linear relationship was found between the T B signal response over a spatially heterogeneous snowpack and the percent area with liquid water content (LWC) present. The results were confirmed by evaluating actual wet snow events over a 9-year period. The model output was also compared to AMSR-E passive microwave satellite data and discharge data at a basin outlet within the study area. The results are used to help understand the impact of spatially distributed snowmelt as detected by passive microwave data. [ABSTRACT FROM AUTHOR]

The traffic noise source intensity emission model is the basis of traffic noise prediction. In addition, the presence of water on the road is an important factor of road traffic noise. However, to date, none of the traffic noise source intensity emission models account for a situation in which the road surface is wet. Therefore, the main objective of this paper is to build a traffic noise source intensity emission model on a wet asphalt road based on traffic noise measurements, in which speed, sound pressure level and frequency spectrum were recorded as a single vehicle passed by. By using statistical analysis on the measurement data, a traffic noise source intensity emission model on a wet asphalt road was acquired, which conforms to correlation test, F-test and t -test. The result shows that the sound pressure level increases considerably with the presence of water on asphalt road for all types of vehicles; the mean difference in the sound pressure level between the wet and dry asphalt roads for light, middle-size, and heavy vehicles are 10.09 dB(A), 5.56 dB(A), and 4.26 dB(A), respectively. In addition, the noise difference between a wet and dry asphalt road decreases as the speed and vehicle size grow. Furthermore, the frequency characteristics of the road traffic noise were analysed through the sound pressure level and noise energy percentage in the 1/3 octave band spectrum. The result shows that for the wet asphalt road, the sound pressure level is high at high frequency and low at low frequency, which is completely different from the response on a dry asphalt road. In addition, for a dry asphalt road, the vehicle’s noise energy percentage at low frequency decreases as the speed increases, whereas at high frequency, the percentage increases as the speed grow. However, for a wet asphalt road, the noise energy percentage changes little with speed. The findings can be applied to the accurate traffic noise prediction and noise control, especially in the rainy regions. [ABSTRACT FROM AUTHOR]

Measurements of BVOC emissions, ozone concentration and environmental parameters were carried out from May 2013 to January 2016 in a subtropical Pinus plantation in China. Isoprene and monoterpene emissions were measured using a relaxed eddy accumulation (REA) system and a gradient technique on an above-canopy tower. In 2013, isoprene comprised 21.2% of total terpenoid emissions, while α-pinene, camphene, β-pinene and limonene constituting 51.5%, 2.4%, 9.1%, and 13.0% of total emissions, respectively. Monoterpenes together were the dominant VOCs measured contributing 71.6%. α-pinene, camphene, β-pinene and limonene constituted 67.7%, 3.2%, 11.9%, 17.2% of total monoterpene emissions. Isoprene and monoterpene emissions displayed strong diurnal variations, with lower emissions in the morning and late evening, and the highest emissions around noon. BVOC peak emissions typically occurred a few hours after the noon PAR peak. Isoprene and monoterpene emissions varied with season and were the highest in summer, contributing more than half of the total annual emission, and the lowest emissions were in winter. Evident interannual variations of isoprene, monoterpenes and total BVOCs were observed. Compared to 2013, annual BVOC emissions decreased in 2015, associated with decreases of PAR, Temperature, water vapor, and an increase of all substances in gas, liquid and solid phases in the atmospheric column (e.g., S/Q, the ratio of solar scattered radiation to global radiation). Ozone concentration showed clear diurnal variation with PAR, higher around noon and lower in the early morning and late evening. Generally, there were no evident correlations between ozone concentrations and BVOC emissions, or the vertical gradients of ozone concentrations and BVOC concentrations. Under all sky conditions (including cloudy skies), no strong correlations at a high confidence level or very similar variation patterns were observed between any two following parameters, BVOC emissions, PAR, temperature, water vapor, and S/Q. The major factors controlling BVOC emissions were PAR and temperature but biomass burning smoke and phenology (pine florescence) may also play a role. The mean emission factors at standard conditions determined using the MEGAN model emission algorithms and empirical model of BVOC emissions were 0.71 and 1.19 mg m −2 h −1 for isoprene and 1.39 and 1.65 mg m −2 h −1 for total monoterpenes, respectively. [ABSTRACT FROM AUTHOR]

Energy shortage and atmospheric pollution problems are getting more serious in China, and transportation is the main source of energy consumption, pollutants, and carbon emissions. This study combined the activity-based analysis method with emission models, and investigated the influence mechanism of people's activity travel scheduling on transportation energy consumption and emissions on holidays. Based on the holiday travel behavior survey data, the multinomial logistic regression model was first applied to explore the decision mechanisms of individual travel-mode choices in holidays. Next, the emission model was integrated with an activity-based travel demand model to calculate and compare transportation energy consumption and emissions under different policy scenarios. The results showed that socio-demographic characteristics had significant effects on holiday activity--travel patterns, and combined mode chains had a larger number of activity points than single mode chains. With an increase in the trip time of cars, and decrease of travel distance and the number of activity points, transportation energy consumption and emissions could be reduced greatly with an adjustment of holiday activity--travel patterns. The reduced portion is mainly attracted by slow traffic and public transport. However, the effects of a single policy strategy are very limited, thus portfolio policies need to be considered by policy makers. [ABSTRACT FROM AUTHOR]

The objectives of this study were to identify the sources of variations in reported methane (CH4) conversion factor (Ym) for dairy and beef cattle through a meta-analytical approach, and to specifically investigate the effects of feed digestibility and intake level on Ym, as well as the alternative expression of methane conversion factor on a digestible energy basis (Dm). Results from 89 peer-reviewed journal articles published from 1992 to 2015 were compiled, and data across studies were analyzed statistically using the MIXED procedures of SAS. For housed cattle, Ym increased significantly with forage-to-concentrate ratio of feed and was also affected by breed. For grazing cattle, Ym varied by geographic region. The observed effect of forage-to-concentrate ratio of feed for housed cattle and the effect of geographic region for grazing cattle on enteric CH4 emissions could both be related to the effect of feed digestibility. Dm was affected significantly by both energy intake level and energy digestibility of feed as well as their interaction. Higher energy digestibility of feed and higher energy intake level of cattle resulted in a lower percentage of digestible energy intake being converted to CH4. Results of this study improved estimation of CH4 conversion factor according to animal husbandry practices. Compared with the Ym approach, the use of CH4 conversion factor on a digestible energy basis can better represent the large variation among diets and the effects of different dietary emission mitigation strategies. [ABSTRACT FROM AUTHOR]