Carbon dioxide and methane emissions from the scale model of open dairy lots

To investigate the impacts of major factors on carbon loss via gaseous emissions, carbon dioxide (CO₂) and methane (CH₄) emissions from the ground of open dairy lots were tested by a scale model experiment at various air temperatures (15, 25, and 35 °C), surface velocities (0.4, 0.7, 1.0, and 1.2 m sec^?1), and floor types (unpaved soil floor and brick-paved floor) in controlled laboratory conditions using the wind tunnel method. Generally, CO₂ and CH₄ emissions were significantly enhanced with the increase of air temperature and velocity (P < 0.05). Floor type had different effects on the CO₂ and CH₄ emissions, which were also affected by air temperature and soil characteristics of the floor. Although different patterns were observed on CH₄ emission from the soil and brick floors at different air temperature-velocity combinations, statistical analysis showed no significant difference in CH₄ emissions from different floors (P > 0.05). For CO₂, similar emissions were found from the soil and brick floors at 15 and 25 °C, whereas higher rates were detected from the brick floor at 35 °C (P < 0.05). Results showed that CH₄ emission from the scale model was exponentially related to CO₂ flux, which might be helpful in CH₄ emission estimation from manure management.

Implications: Gaseous emissions from the open lots are largely dependent on outdoor climate, floor systems, and management practices, which are quite different from those indoors. This study assessed the effects of floor types and air velocities on CO₂ and CH₄ emissions from the open dairy lots at various temperatures by a wind tunnel. It provided some valuable information for decision-making and further studies on gaseous emissions from open lots.     

Influencing factors of carbon emissions and their trends in China and India: a machine learning method

China and India are the largest coal consumers and the most populated countries in the world. With industrial and population growth, the need for energy has increased, which has inevitably led to an increase in carbon dioxide (CO₂) emissions because both countries depend on fossil fuel consumption. This paper investigates the impact of energy consumption, financial development (FD), gross domestic product (GDP), population, and renewable energy on CO₂ emissions. The study applies the long short-term memory (LSTM) method, a novel machine learning (ML) approach, to examine which influencing driver has the greatest and smallest impact on CO₂ emissions; correspondingly, this study builds a model for CO₂ emission reduction. Data collected between 1990 and 2014 were analyzed, and the results indicated that energy consumption had the greatest effect and renewable energy had the smallest impact on CO₂ emissions in both countries. Subsequently, we increased the renewable energy coefficient by one and decreased the energy consumption coefficient by one while keeping all other factors constant, and the results predicted with the LSTM model confirmed the significant reduction in CO₂ emissions. Finally, this study forecasted a CO₂ emission trend, with a slowdown predicted in China by 2022; however, CO₂ emission’s reduction is not possible in India until 2023. These results suggest that shifting from nonrenewable to renewable sources and lowering coal consumption can reduce CO₂ emissions without harming economic development.

     

Feasibility of Landfill Gas as a Liquefied Natural Gas Fuel Source for Refuse Trucks

Abstract

The purpose of this paper is to develop a methodology to evaluate the feasibility of using landfill gas (LFG) as a liquefied natural gas (LNG) fuel source for heavy-duty refuse trucks operating on landfills. Using LFG as a vehicle fuel can make the landfills more self-sustaining, reduce their dependence on fossil fuels, and reduce emissions and greenhouse gases. Acrion Technologies Inc. in association with Mack Trucks Inc. developed a technology to generate LNG from LFG using the CO₂ WASH process. A successful application of this process was performed at the Eco Complex in Burlington County, PA. During this application two LNG refuse trucks were operated for 600 hr each using LNG produced from gases from the landfill. The methodology developed in this paper can evaluate the feasibility of three LFG options: doing nothing, electricity generation, and producing LNG to fuel refuse trucks. The methodology involved the modeling of several components: LFG generation, energy recovery processes, fleet operations, economic feasibility, and decision-making. The economic feasibility considers factors such as capital, maintenance, operational, and fuel costs, emissions and tax benefits, and the sale of products such as surplus LNG and food-grade carbon dioxide (CO₂).

Texas was used as a case study. The 96 landfills in Texas were prioritized and 17 landfills were identified that showed potential for converting LFG to LNG for use as a refuse truck fuel. The methodology was applied to a pilot landfill in El Paso, TX. The analysis showed that converting LFG to LNG to fuel refuse trucks proved to be the most feasible option and that the methodology can be applied for any landfill that considers this option.     

Compositional data analysis of smoke emissions from debris piles with low-density polyethylene

ABSTRACT

Data describing the composition of smoke are inherently multivariate and always non-negative parts of a whole. The data are relative and the information is contained in the ratios between parts of the composition. A prior analysis of smoke emissions produced from the burning of manzanita wood mixed with low-density polyethylene plastic applied traditional statistical methods to the compositional data and found no effect. The current paper applies compositional data techniques to these smoke emissions to determine if the prior analysis was accurate. Analysis of variance of the isometric log-ratios showed that LDPE significantly affected the CO₂ emission ratio for 8 of the 191 trace gases; this analysis showed none of the gases identified in the previous analysis were affected by LDPE. LDPE did not affect the CO₂ emission ratios for the alkanes, alkenes, alkynes, aldehydes, cycloalkanes, cycloalkenes, diolefins, ketones, MAHs, and PAHs. Compositional data analysis should be used to analyze smoke emissions data. Burning contaminant-free LDPE should produce emissions like wood.     

Spatiotemporal analysis of traffic emissions in over 5000 municipal districts in Brazil

Exposure to traffic emission is harmful to human health. Emission inventories are essential to public health policies aiming at protecting human health, especially in areas with incomplete or nonexistent air pollution monitoring networks. In Brazil, for example, only 1.7% of municipal districts have a monitoring network, and only a few studies have reported data on vehicle emission inventories. No studies have presented emission inventories by municipality. In this study, we predicted vehicular emissions for 5570 municipal districts in Brazil during the period 2001–2012. We used a top-down method to estimate emissions. Carbon dioxide (CO₂) is the pollutant with the highest emissions, with approximately 190 million tons per year during the period 2001–2012). For the other traffic-related pollutants, we predicted annual emissions of 1.5 million tons for carbon monoxide (CO), 1.2 million tons of nitrogen oxides (NO_x), 209,000 tons of nonmethane hydrocarbons (NMHC), 58,000 tons of particulate matter (PM), and 42,000 tons for methane (CH₄). From 2001 to 2012, CO, NMHC, and PM emissions decreased by 41, 33, and 47%, respectively, whereas those CH₄, NO_x, and CO₂ increased by 2, 4, and 84%, respectively. We estimated uncertainties in our study and found that NO_x was the pollutant with the lowest percentage difference, 8%, and NMHC with the highest one, 30%. For CO, CH₄, CO₂, and PM, the values were 22, 14, 21, and 20%, respectively. Finally, we found that during 2001 and 2012 emissions increased in the Northwest and Northeast. In contrast, pollutant emissions, except for CO₂, decreased in the Southeast, South, and part of Midwest. Our predictions can be critical to efforts developing cost-effective public policies tailored to individual municipal districts in Brazil.

Implications: Emission inventories may be an alternative approach to provide data for air quality forecasting in areas where air quality data are not available. This approach can be an effective tool in developing spatially resolved emission inventories.     

Carbon dioxide emission tallies for 210 U.S. coal-fired power plants: A comparison of two accounting methods

Annual CO₂ emission tallies for 210 coal-fired power plants during 2009 were more accurately calculated from fuel consumption records reported by the U.S. Energy Information Administration (EIA) than measurements from Continuous Emissions Monitoring Systems (CEMS) reported by the U.S. Environmental Protection Agency. Results from these accounting methods for individual plants vary by ± 10.8%. Although the differences systematically vary with the method used to certify flue-gas flow instruments in CEMS, additional sources of CEMS measurement error remain to be identified. Limitations of the EIA fuel consumption data are also discussed. Consideration of weighing, sample collection, laboratory analysis, emission factor, and stock adjustment errors showed that the minimum error for CO₂ emissions calculated from the fuel consumption data ranged from ± 1.3% to ± 7.2% with a plant average of ± 1.6%. This error might be reduced by 50% if the carbon content of coal delivered to U.S. power plants were reported.

Implications:

Potentially, this study might inform efforts to regulate CO₂ emissions (such as CO₂ performance standards or taxes) and more immediately, the U.S. Greenhouse Gas Reporting Rule where large coal-fired power plants currently use CEMS to measure CO₂ emissions. Moreover, if, as suggested here, the flue-gas flow measurement limits the accuracy of CO₂ emission tallies from CEMS, then the accuracy of other emission tallies from CEMS (such as SO₂, NO_x, and Hg) would be similarly affected. Consequently, improved flue gas flow measurements are needed to increase the reliability of emission measurements from CEMS.     

Development and Preliminary Evaluation of a Particulate Matter Emission Factor Model for European Motor Vehicles

ABSTRACT

Although modeling of gaseous emissions from motor vehicles is now quite advanced, prediction of particulate emissions is still at an unsophisticated stage. Emission factors for gasoline vehicles are not reliably available, since gasoline vehicles are not included in the European Union (EU) emission test procedure. Regarding diesel vehicles, emission factors are available for different driving cycles but give little information about change of emissions with speed or engine load. We have developed size-specific speed-dependent emission factors for gasoline and diesel vehicles. Other vehicle-generated emission factors are also considered and the empirical equation for re-entrained road dust is modified to include humidity effects. A methodology is proposed to calculate modal (accelerating, cruising, or idling) emission factors. The emission factors cover particle size ranges up to 10 um, either from published data or from user-defined size distributions.

A particulate matter emission factor model (PMFAC), which incorporates virtually all the available information on particulate emissions for European motor vehicles, has been developed. PMFAC calculates the emission factors for five particle size ranges [i.e., total suspended particulates (TSP), PM₁₀, PM₅, PM₂₅, and PM₁] from both vehicle exhaust and nonexhaust emissions, such as tire wear, brake wear, and re-entrained road dust. The model can be used for an unlimited number of roads and lanes, and to calculate emission factors near an intersection in user-defined elements of the lane. PMFAC can be used for a variety of fleet structures. Hot emission factors at the user-defined speed can be calculated for individual vehicles, along with relative cold-to-hot emission factors. The model accounts for the proportions of distance driven with cold engines as a function of ambient temperature and road type (i.e., urban, rural, or motorway).

A preliminary evaluation of PMFAC with an available dispersion model to predict the airborne concentration in the urban environment is presented. The trial was on the A6 trunk road where it passes through Loughborough, a medium-size town in the English East Midlands. This evaluation for TSP and PM₁₀ was carried out for a range of traffic fleet compositions, speeds, and meteorological conditions. Given the limited basis of the evaluation, encouraging agreement was shown between predicted and measured concentrations.     

Investigation of aerosol and gas emissions from a coal-fired power plant under various operating conditions

The concentrations of fine particles and selected gas pollutants in the flue gas entering the stack were measured under several common operation modes in an operating coal power plant producing electricity. Particle size distributions in a diameter range from 10 nm to 20 μm were measured by a scanning mobility particle sizer (SMPS), and the flue gas temperature and concentrations of CO₂ and SO₂ were monitored by a continuous emission monitoring system (CEMS). During the test campaign, five plant operating modes were studied: soot blowing, bypass of flue-gas desulfurization (FGD), reheat burner operating at 0% (turned off), 27%, and 42% (normal condition) of its full capacity. For wet and dry aerosols, the measured mode sizes were both around 40 nm, but remarkable differences were observed in the number concentrations (#/cm³, count per square centimeter). A prototype photoionizer enhanced electrostatic precipitator (ESP) showed improved removal efficiency of wet particles at voltages above +11.0 kV. Soot blowing and FGD bypass both increased the total particle number concentration in the flue gas. The temperature was slightly increased by the FGD bypass mode and varied significantly as the rating of reheat burner changed. The variations of CO₂ and SO₂ emissions showed correlations with the trend of total particle number concentration possibly due to the transitions between gas and particle phases. The results are useful in developing coal-fired power plant operation strategies to control fine particle emissions and developing amine-based CO₂ capture technologies without operating and environmental concerns associated with volatile amine emissions.

Implications: The measurement of the fine particle size distributions in the exhaust gas under several common operating conditions of a coal-fired power plant revealed different response relations between aerosol number concentration and the operating condition. A photo-ionizer enhanced ESP was demonstrated to capture fine particles with higher efficiency compared to conventional ESPs, and the removal efficiency increased with the applied voltage. The characteristic information of aerosols and main gaseous pollutants in the exhaust gas is extremely important for developing and deploying CO₂ scrubbers, whose amine emissions and operating effectiveness depends greatly on the upstream concentrations of fine particles, SO₂, from the power plant.     

The impact of CO2 capture in the power and heat sector on the emission of SO2, NOx,particulate matter,volatile organic compounds and NH3 in the European Union

This study quantifies the trade-offs and synergies between climate and air quality policy objectives for the European power and heat (P&H) sector. An overview is presented of the expected performance data of CO₂ capture systems implemented at P&H plants, and the expected emission of key air pollutants, being: SO₂, NO_X, NH₃, volatile organic compounds (VOCs) and particulate matter (PM). The CO₂ capture systems investigated include: post-combustion, oxyfuel combustion and pre-combustion capture.For all capture systems it was found that SO₂, NO_x and PM emissions are expected to be reduced or remain equal per unit of primary energy input compared to power plants without CO₂ capture. Increase in primary energy input as a result of the energy penalty for CO₂ capture may for some technologies and substances result in a net increase of emissions per kWh output. The emission of ammonia may increase by a factor of up to 45 per unit of primary energy input for post-combustion technologies. No data are available about the emission of VOCs from CO₂ capture technologies.A simple model was developed and applied to analyse the impact of CO₂ capture in the European P&H sector on the emission level of key air pollutants in 2030. Four scenarios were developed: one without CO₂ capture and three with one dominantly implemented CO₂ capture system, varying between: post-combustion, oxyfuel combustion and pre-combustion.The results showed a reduction in GHG emissions for the scenarios with CO₂ capture compared to the baseline scenario between 12% and 20% in the EU 27 region in 2030. NO_x emissions were 15% higher in the P&H sector in a scenario with predominantly post-combustion and lower when oxyfuel combustion (?16%) or pre-combustion (?20%) were implemented on a large scale. Large scale implementation of the post-combustion technology in 2030 may also result in significantly higher, i.e. increase by a factor of 28, NH₃ emissions compared to scenarios with other CO₂ capture options or without capture. SO₂ emissions were very low for all scenarios that include large scale implementation of CO₂ capture in 2030, i.e. a reduction varying between 27% and 41%. Particulate Matter emissions were found to be lower in the scenarios with CO₂ capture. The scenario with implementation of the oxyfuel technology showed the lowest PM emissions followed by the scenario with a significant share allocated to pre-combustion, respectively ?59% and ?31%. The scenario with post-combustion capture resulted in PM emissions varying between 35% reduction and 26% increase.     

The effects of economic growth and innovation on CO2 emissions in different regions

Economic growth and economic energy consumption have received greater attention due to its contribution to global CO₂ emissions in recent decades. The literature on CO₂ emissions and innovation for regional differences is very scanty as there is not enough study that considered different regions in a single analysis. We adopt a holistic approach by incorporating different regions so as to assess how innovation contributes to emission reduction. The study, therefore, examined the effects of innovation and economic growth on CO₂ emissions for 18 developed and developing countries over the period of 1990 to 2016. The study used panel technique capable of dealing with cross-section dependence effects: panel cross-sectional augmented Dickey-Fuller (CADF) unit root to determine the order of integration, Westerlund cointegration tests confirmed that the variables are co-integrated. We employed panel fully modified ordinary least square (FMOLS) and panel dynamic ordinary least square (DOLS) to estimate the long-run relationship. The results show that energy consumption increases CO₂ emissions at all panel levels. However, innovation reduces CO₂ emissions in G6 while it increases emissions in the MENA and the BRICS countries. Environmental Kuznets curve (EKC) hypothesis is valid for the BRICS. The pollution haven hypothesis (PHH) and pollution halo effect were confirmed at different panel levels. Based on the findings different policy recommendations are proposed.

     

Total life cycle emissions of post-Panamax containerships powered by conventional fuel or natural gas

This study proposes an easy-to-apply method, the Total Life Cycle Emission Model (TLCEM), to calculate the total emissions from shipping and help ship management groups assess the impact on emissions caused by their capital investment or operation decisions. Using TLCEM, we present the total emissions of air pollutants and greenhouse gases (GHGs) during the 25-yr life cycle of 10 post-Panamax containerships under slow steaming conditions. The life cycle consists of steel production, shipbuilding, crude oil extraction and transportation, fuel refining, bunkering, and ship operation. We calculate total emissions from containerships and compare the effect of emission reduction by using various fuels. The results can be used to differentiate the emissions from various processes and to assess the effectiveness of various reduction approaches. Critical pollutants and GHGs emitted from each process are calculated. If the containerships use heavy fuel oil (HFO), emissions of CO₂ total 2.79 million tonnes (Mt), accounting for 95.37% of total emissions, followed by NO_x and SO_x emissions,which account for 2.25% and 1.30%, respectively.The most significant emissions are from the operation of the ship and originate from the main engine (ME).When fuel is switched to 100% natural gas (NG), SO_x, PM₁₀, and CO₂ emissions show remarkable reductions of 98.60%, 99.06%, and 21.70%, respectively. Determining the emission factor of each process is critical for estimating the total emissions. The estimated emission factors were compared with the values adopted by the International Maritime Organization (IMO).The proposed TLCEM may contribute to more accurate estimates of total life cycle emissions from global shipping.

Implications: We propose a total life cycle emissions model for 10 post-Panamax container ships. Using heavy fuel oil, emissions of CO₂ total 2.79 Mt, accounting for approximately 95% of emissions, followed by NO_x and SO_x emissions. Using 100% natural gas, SO_x, PM₁₀, and CO₂ emissions reduce by 98.6%, 99.1%, and 21.7%, respectively. NO_x emissions increase by 1.14% when running a dual fuel engine at low load in natural gas mode.     

Power models and average ship parameter effects on marine emissions inventories

Maritime greenhouse gas emissions are projected to increase significantly by 2050, highlighting the need for reliable inventories as a first step in analyzing ship emission control policies. The impact of ship power models on marine emissions inventories has garnered little attention, with most inventories employing simple, load-factor-based models to estimate ship power consumption. The availability of more expansive ship activity data provides the opportunity to investigate the inventory impacts of adopting complex power models. Furthermore, ship parameter fields can be sparsely populated in ship registries, making gap-filling techniques and averaging processes necessary. Therefore, it is important to understand of the impact of averaged ship parameters on ship power and emission estimations. This paper examines power estimation differences between results from two complex, resistance-based and two simple, load-factor-based power models on a baseline inventory with unique ship parameters. These models are additionally analyzed according to their sensitivities toward average ship parameters. Automated Identification System (AIS) data from a fleet of commercial marine vessels operating over a 6-month period off the coast of the southwestern United States form the basis of the analysis. To assess the inventory impacts of using averaged ship parameters, fleet-level carbon dioxide (CO₂) emissions are calculated using ship parameter data averaged across ship types and their subtype size classes. Each of the four ship power models are used to generate four CO₂ emissions inventories, and results are compared with baseline estimates for the same sample fleet where no averaged values were used. The results suggest that a change in power model has a relatively high impact on emission estimates. They also indicate relatively little sensitivity, by all power models, to the use of ship characteristics averaged by ship and subtype.

Implications: Commercial marine vessel emissions inventories were calculated using four different models for ship engine power. The calculations used 6 months of Automated Identification System (AIS) data from a sample of 248 vessels as input data. The results show that more detailed, resistance-based models tend to estimate a lower propulsive power, and thus lower emissions, for ships than traditional load-factor-based models. Additionally, it was observed that emission calculations using averaged values for physical ship parameters had a minimal impact on the resulting emissions inventories.     

Comparative analysis of the growth impact of pollution and energy use in selected West African nations

We adopt the FMOLS and Granger causality technique to analyse the effect of energy use and carbon emissions on output growth in selected West African economies, which includes Nigeria, Gambia and Ghana, from 1970 to 2019. Findings confirm that energy use enhances growth in the three selected West African economies. But in terms of significance, energy consumption is significant in Nigeria and Gambia at a 1% level of significance while it is insignificant for the Gambia. CO₂ emission positively and significantly propels economic growth for the three selected West African economies. For Nigeria, causality evidence shows no direct influence among the variables. For Ghana, we find a bi-causal association between output growth and carbon emissions and a unidirectional causality from pollution to energy consumption. For Gambia, economic growth causes CO₂ emissions. We recommend that the West African government reinforce their stand on a sustainable growth path through energy conservation.

     

Cost of lower NOx emissions: Increased CO2 emissions from heavy-duty diesel engines

This paper highlights the effect of emissions regulations on in-use emissions from heavy-duty vehicles powered by different model year engines. More importantly, fuel economy data for pre- and post-consent decree engines are compared.The objective of this study was to determine the changes in brake-specific emissions of NO_x as a result of emission regulations, and to highlight the effect these have had on brake-specific CO₂ emission; hence, fuel consumption. For this study, in-use, on-road emission measurements were collected. Test vehicles were instrumented with a portable on-board tailpipe emissions measurement system, WVU's Mobile Emissions Measurement System, and were tested on specific routes, which included a mix of highway and city driving patterns, in order to collect engine operating conditions, vehicle speed, and in-use emission rates of CO₂ and NO_x. Comparison of on-road in-use emissions data suggests NO_x reductions as high as 80% and 45% compared to the US Federal Test Procedure and Not-to-Exceed standards for model year 1995–2002. However, the results indicate that the fuel consumption; hence, CO₂ emissions increased by approximately 10% over the same period, when the engines were operating in the Not-to-Exceed region.     

Estimating and comparing greenhouse gas emissions with their uncertainties using different methods: A case study for an energy supply utility

Energy supply utilities release significant amounts of greenhouse gases (GHGs) into the atmosphere. It is essential to accurately estimate GHG emissions with their uncertainties, for reducing GHG emissions and mitigating climate change. GHG emissions can be calculated by an activity-based method (i.e., fuel consumption) and continuous emission measurement (CEM). In this study, GHG emissions such as CO₂, CH₄, and N₂O are estimated for a heat generation utility, which uses bituminous coal as fuel, by applying both the activity-based method and CEM. CO₂ emissions by the activity-based method are 12–19% less than that by the CEM, while N₂O and CH₄ emissions by the activity-based method are two orders of magnitude and 60% less than those by the CEM, respectively. Comparing GHG emissions (as CO₂ equivalent) from both methods, total GHG emissions by the activity-based methods are 12–27% lower than that by the CEM, as CO₂ and N₂O emissions are lower than those by the CEM. Results from uncertainty estimation show that uncertainties in the GHG emissions by the activity-based methods range from 3.4% to about 20%, from 67% to 900%, and from about 70% to about 200% for CO₂, N₂O, and CH₄, respectively, while uncertainties in the GHG emissions by the CEM range from 4% to 4.5%. For the activity-based methods, an uncertainty in the Intergovernmental Panel on Climate Change (IPCC) default net calorific value (NCV) is the major uncertainty contributor to CO₂ emissions, while an uncertainty in the IPCC default emission factor is the major uncertainty contributor to CH₄ and N₂O emissions. For the CEM, an uncertainty in volumetric flow measurement, especially for the distribution of the volumetric flow rate in a stack, is the major uncertainty contributor to all GHG emissions, while uncertainties in concentration measurements contribute a little to uncertainties in the GHG emissions.

Implications:Energy supply utilities contribute a significant portion of the global greenhouse gas (GHG) emissions. It is important to accurately estimate GHG emissions with their uncertainties for reducing GHG emissions and mitigating climate change. GHG emissions can be estimated by an activity-based method and by continuous emission measurement (CEM), yet little study has been done to calculate GHG emissions with uncertainty analysis. This study estimates GHG emissions and their uncertainties, and also identifies major uncertainty contributors for each method.     

A study on the evaluations of emission factors and uncertainty ranges for methane and nitrous oxide from combined-cycle power plant in Korea

In this research, in order to develop technology/country-specific emission factors of methane (CH₄) and nitrous oxide (N₂O), a total of 585 samples from eight gas-fired turbine combined cycle (GTCC) power plants were measured and analyzed. The research found that the emission factor for CH₄ stood at “0.82 kg/TJ”, which was an 18 % lower than the emission factor for liquefied natural gas (LNG) GTCC “1 kg/TJ” presented by Intergovernmental Panel on Climate Change (IPCC). The result was 8 % up when compared with the emission factor of Japan which stands at “0.75 kg/TJ”. The emission factor for N₂O was “0.65 kg/TJ”, which is significantly lower than “3 kg/TJ” of the emission factor for LNG GTCC presented by IPCC, but over six times higher than the default N₂O emission factor of LNG. The evaluation of uncertainty was conducted based on the estimated non-CO₂ emission factors, and the ranges of uncertainty for CH₄ and N₂O were between ?12.96 and +13.89 %, and ?11.43 and +12.86 %, respectively, which is significantly lower than uncertainties presented by IPCC. These differences proved that non-CO₂ emissions can change depending on combustion technologies; therefore, it is vital to establish country/technology-specific emission factors.     

Quantification of Variability and Uncertainty in Lawn and Garden Equipment NOx and Total Hydrocarbon Emission Factors

Abstract

Variability refers to real differences in emissions among multiple emission sources at any given time or over time for any individual emission source. Variability in emissions can be attributed to variation in fuel or feedstock composition, ambient temperature, design, maintenance, or operation. Uncertainty refers to lack of knowledge regarding the true value of emissions. Sources of uncertainty include small sample sizes, bias or imprecision in measurements, nonrepresentativeness, or lack of data. Quantitative methods for characterizing both variability and uncertainty are demonstrated and applied to case studies of emission factors for lawn and garden (L&G) equipment engines. Variability was quantified using empirical and parametric distributions. Bootstrap simulation was used to characterize confidence intervals for the fitted distributions. The 95% confidence intervals for the mean grams per brake horsepower/hour (g/hp-hr) emission factors for two-stroke engine total hydrocarbon (THC) and NO_x emissions were from -30 to +41% and from -45 to +75%, respectively. The confidence intervals for four-stroke engines were from -33 to +46% for THCs and from -27 to +35% for NO_x. These quantitative measures of uncertainty convey information regarding the quality of the emission factors and serve as a basis for calculation of uncertainty in emission inventories (Els).     

Regional and sectoral assessment of greenhouse gas emissions in India

In this paper the authors have estimated for 1990 and 1995 the inventory of greenhouse gases CO₂, CH₄ and N₂O for India at a national and sub-regional district level. The district level estimates are important for improving the national inventories as well as for developing sound mitigation strategies at manageable smaller scales. Our estimates indicate that the total CO₂, CH₄ and N₂O emissions from India were 592.5, 17, 0.2 and 778, 18, 0.3 Tg in 1990 and 1995, respectively. The compounded annual growth rate (CAGR) of these gases over this period were 6.3, 1.2 and 3.3%, respectively. The districts have been ranked according to their order of emissions and the relatively large emitters are termed as hotspots. A direct correlation between coal consumption and districts with high CO₂ emission was observed. CO₂ emission from the largest 10% emitters increased by 8.1% in 1995 with respect to 1990 and emissions from rest of the districts decreased over the same period, thereby indicating a skewed primary energy consumption pattern for the country. Livestock followed by rice cultivation were the dominant CH₄ emitting sources. The waste sector though a large CH₄ emitter in the developed countries, only contributed about 10% the total CH₄ emission from all sources as most of the waste generated in India is allowed to decompose aerobically. N₂O emissions from the use of nitrogen fertilizer were maximum in both the years (more than 60% of the total N₂O). High emission intensities, in terms of CO₂ equivalent, are in districts of Gangetic plains, delta areas, and the southern part of the country. These overlap with districts with large coal mines, mega power plants, intensive paddy cultivation and high fertilizer use. The study indicates that the 25 highest emitting districts account for more than 37% of all India CO₂ equivalent GHG emissions. Electric power generation has emerged as the dominant source of GHG emissions, followed by emissions from steel and cement plants. It is therefore suggested, to target for GHG mitigation, the 40 largest coal-based thermal plants, five largest steel plants and 15 largest cement plants in India as the first step.     

Deriving fuel-based emission factor thresholds to interpret heavy-duty vehicle roadside plume measurements

Remote sensing devices have been used for decades to measure gaseous emissions from individual vehicles at the roadside. Systems have also been developed that entrain diluted exhaust and can also measure particulate matter (PM) emissions. In 2015, the California Air Resources Board (CARB) reported that 8% of in-field diesel particulate filters (DPF) on heavy-duty (HD) vehicles were malfunctioning and emitted about 70% of total diesel PM emissions from the DPF-equipped fleet. A new high-emitter problem in the heavy-duty vehicle fleet had emerged. Roadside exhaust plume measurements reflect a snapshot of real-world operation, typically lasting several seconds. In order to relate roadside plume measurements to laboratory emission tests, we analyzed carbon dioxide (CO₂), oxides of nitrogen (NO_X), and PM emissions collected from four HD vehicles during several driving cycles on a chassis dynamometer. We examined the fuel-based emission factors corresponding to possible exceedances of emission standards as a function of vehicle power. Our analysis suggests that a typical HD vehicle will exceed the model year (MY) 2010 emission standards (of 0.2 g NO_X/bhp-hr and 0.01 g PM/bhp-hr) by three times when fuel-based emission factors are 9.3 g NO_X/kg fuel and 0.11 g PM/kg using the roadside plume measurement approach. Reported limits correspond to 99% confidence levels, which were calculated using the detection uncertainty of emissions analyzers, accuracy of vehicle power calculations, and actual emissions variability of fixed operational parameters. The PM threshold was determined for acceleration events between 0.47 and 1.4 mph/sec only, and the NO_X threshold was derived from measurements where after-treatment temperature was above 200°C. Anticipating a growing interest in real-world driving emissions, widespread implementation of roadside exhaust plume measurements as a compliment to in-use vehicle programs may benefit from expanding this analysis to a larger sample of in-use HD vehicles.

Implications: Regulatory agencies, civil society, and the public at large have a growing interest in vehicle emission compliance in the real world. Leveraging roadside plume measurements to identify vehicles with malfunctioning emission control systems is emerging as a viable new and useful method to assess in-use performance. This work proposes fuel-based emission factor thresholds for PM and NOx that signify exceedances of emission standards on a work-specific basis by analyzing real-time emissions in the laboratory. These thresholds could be used to prescreen vehicles before roadside enforcement inspection or other inquiry, enhance and further develop emission inventories, and potentially develop new requirements for heavy-duty inspection and maintenance (I/M) programs, including but not limited to identifying vehicles for further testing.     

Emissions from India's transport sector: Statewise synthesis

A decentralized emission inventories are prepared for road transport sector of India in order to design and implement suitable technologies and policies for appropriate mitigation measures. Globalization and liberalization policies of the government in 90's have increased the number of road vehicles nearly 92.6% from 1980–1981 to 2003–2004. These vehicles mainly consume non-renewable fossil fuels, and are a major contributor of green house gases, particularly CO₂ emission. This paper focuses on the statewise road transport emissions (CO₂, CH₄, CO, NO_x, N₂O, SO₂, PM and HC), using region specific mass emission factors for each type of vehicles. The country level emissions (CO₂, CH₄, CO, NO_x, N₂O, SO₂ and NMVOC) are calculated for railways, shipping and airway, based on fuel types. In India, transport sector emits an estimated 258.10 Tg of CO₂, of which 94.5% was contributed by road transport (2003–2004). Among all the states and Union Territories, Maharashtra's contribution is the largest, 28.85 Tg (11.8%) of CO₂, followed by Tamil Nadu 26.41 Tg (10.8%), Gujarat 23.31 Tg (9.6%), Uttar Pradesh 17.42 Tg (7.1%), Rajasthan 15.17 Tg (6.22%) and, Karnataka 15.09 Tg (6.19%). These six states account for 51.8% of the CO₂ emissions from road transport.