scholarly journals Optimizing a dynamic fossil fuel CO<sub>2</sub> emission model with CTDAS (v1.0) for an urban area using atmospheric observations of CO<sub>2</sub>, CO, NO<sub><i>x</i></sub>, and SO<sub>2</sub>

2019 ◽  
Author(s):  
Ingrid Super ◽  
Hugo A. C. Denier van der Gon ◽  
Michiel K. van der Molen ◽  
Stijn N. C. Dellaert ◽  
Wouter Peters
Keyword(s):  
Fossil Fuel ◽  
Covariance Structure ◽  
Traffic Density ◽  
Emission Model ◽  
Emission Rates ◽  
Activity Data ◽  
Modelling Framework ◽  
Novel Approach ◽  
Atmospheric Observations ◽  
Mean Differences

Abstract. We present a modelling framework for fossil fuel CO2 emissions in an urban environment, which allows constraints from emission inventories to be combined with atmospheric observations of CO2 and its co-emitted species CO, NOx, and SO2. Rather than a static assignment of average emission rates to each unit-area of the urban domain, the fossil fuel emissions we use are dynamic: they vary in time and space in relation to data that describe or approximate the activity within a sector, such as traffic density, power demand, 2 m temperature (as proxy for heating demand), and sunlight and wind speed (as proxies for renewable energy supply). Through inverse modelling, we optimize the relationships between these activity data and the resulting emissions of all species within the dynamic fossil fuel emission model, based on atmospheric mole fraction observations. The advantage of this novel approach is that the optimized parameters (emission factors and emission ratios, N = 44) in this dynamic model (a) vary much less over space and time, (b) allow a physical interpretation of mean and uncertainty, and (c) have better defined uncertainties and covariance structure. This makes them more suited to extrapolate, optimize, and interpret than the gridded emissions themselves. The merits of this approach are investigated using a pseudo-observation-based ensemble Kalman filter inversion setup for the Dutch Rijnmond area at 1 × 1 km resolution. We find that the dynamic fossil fuel model approximates the gridded emissions well (annual mean differences

scholarly journals Optimizing a dynamic fossil fuel CO<sub>2</sub> emission model with CTDAS (CarbonTracker Data Assimilation Shell, v1.0) for an urban area using atmospheric observations of CO<sub>2</sub>, CO, NO<sub><i>x</i></sub>, and SO<sub>2</sub>

2020 ◽  
Vol 13 (6) ◽  
pp. 2695-2721
Author(s):  
Ingrid Super ◽  
Hugo A. C. Denier van der Gon ◽  
Michiel K. van der Molen ◽  
Stijn N. C. Dellaert ◽  
Wouter Peters
Keyword(s):  
Co2 Emissions ◽  
Fossil Fuel ◽  
Covariance Structure ◽  
Traffic Density ◽  
Emission Model ◽  
Emission Rates ◽  
Activity Data ◽  
Atmospheric Observations ◽  
Fossil Fuel Emission ◽  
Emission Ratios

Abstract. We present a modelling framework for fossil fuel CO2 emissions in an urban environment, which allows constraints from emission inventories to be combined with atmospheric observations of CO2 and its co-emitted species CO, NOx, and SO2. Rather than a static assignment of average emission rates to each unit area of the urban domain, the fossil fuel emissions we use are dynamic: they vary in time and space in relation to data that describe or approximate the activity within a sector, such as traffic density, power demand, 2 m temperature (as proxy for heating demand), and sunlight and wind speed (as proxies for renewable energy supply). Through inverse modelling, we optimize the relationships between these activity data and the resulting emissions of all species within the dynamic fossil fuel emission model, based on atmospheric mole fraction observations. The advantage of this novel approach is that the optimized parameters (emission factors and emission ratios, N=44) in this dynamic emission model (a) vary much less over space and time, (b) allow for a physical interpretation of mean and uncertainty, and (c) have better defined uncertainties and covariance structure. This makes them more suited to extrapolate, optimize, and interpret than the gridded emissions themselves. The merits of this approach are investigated using a pseudo-observation-based ensemble Kalman filter inversion set-up for the Dutch Rijnmond area at 1 km×1 km resolution. We find that the fossil fuel emission model approximates the gridded emissions well (annual mean differences <2 %, hourly temporal r2=0.21–0.95), while reported errors in the underlying parameters allow a full covariance structure to be created readily. Propagating this error structure into atmospheric mole fractions shows a strong dominance of a few large sectors and a few dominant uncertainties, most notably the emission ratios of the various gases considered. If the prior emission ratios are either sufficiently well-known or well constrained from a dense observation network, we find that including observations of co-emitted species improves our ability to estimate emissions per sector relative to using CO2 mole fractions only. Nevertheless, the total CO2 emissions can be well constrained with CO2 as the only tracer in the inversion. Because some sectors are sampled only sparsely over a day, we find that propagating solutions from day-to-day leads to largest uncertainty reduction and smallest CO2 residuals over the 14 consecutive days considered. Although we can technically estimate the temporal distribution of some emission categories like shipping separate from their total magnitude, the controlling parameters are difficult to distinguish. Overall, we conclude that our new system looks promising for application in verification studies, provided that reliable urban atmospheric transport fields and reasonable a priori emission ratios for CO2 and its co-emitted species can be produced.

scholarly journals Independent evaluation of point source fossil fuel CO2 emissions to better than 10%

2016 ◽  
Vol 113 (37) ◽  
pp. 10287-10291 ◽  
Author(s):  
Jocelyn Christine Turnbull ◽  
Elizabeth D. Keller ◽  
Margaret W. Norris ◽  
Rachael M. Wiltshire
Keyword(s):  
Point Source ◽  
Power Plants ◽  
Fossil Fuel ◽  
Transport Model ◽  
Sodium Hydroxide Solution ◽  
Time Integration ◽  
Emission Rates ◽  
Independent Evaluation ◽  
Atmospheric Observations ◽  
Better Than

Independent estimates of fossil fuel CO2 (CO2ff) emissions are key to ensuring that emission reductions and regulations are effective and provide needed transparency and trust. Point source emissions are a key target because a small number of power plants represent a large portion of total global emissions. Currently, emission rates are known only from self-reported data. Atmospheric observations have the potential to meet the need for independent evaluation, but useful results from this method have been elusive, due to challenges in distinguishing CO2ff emissions from the large and varying CO2 background and in relating atmospheric observations to emission flux rates with high accuracy. Here we use time-integrated observations of the radiocarbon content of CO2 (14CO2) to quantify the recently added CO2ff mole fraction at surface sites surrounding a point source. We demonstrate that both fast-growing plant material (grass) and CO2 collected by absorption into sodium hydroxide solution provide excellent time-integrated records of atmospheric 14CO2. These time-integrated samples allow us to evaluate emissions over a period of days to weeks with only a modest number of measurements. Applying the same time integration in an atmospheric transport model eliminates the need to resolve highly variable short-term turbulence. Together these techniques allow us to independently evaluate point source CO2ff emission rates from atmospheric observations with uncertainties of better than 10%. This uncertainty represents an improvement by a factor of 2 over current bottom-up inventory estimates and previous atmospheric observation estimates and allows reliable independent evaluation of emissions.

scholarly journals Novel approach to modeling high-frequency activity data to assess therapeutic effects of analgesics in chronic pain conditions

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zekun Xu ◽  
Eric Laber ◽  
Ana-Maria Staicu ◽  
B. Duncan X. Lascelles
Keyword(s):  
High Frequency ◽  
Activity Patterns ◽  
Treatment Strategies ◽  
Therapeutic Effects ◽  
Activity Levels ◽  
Activity Data ◽  
Novel Approach ◽  
High Frequency Activity ◽  
Using Data ◽  
Chronic Pain Conditions

AbstractOsteoarthritis (OA) is a chronic condition often associated with pain, affecting approximately fourteen percent of the population, and increasing in prevalence. A globally aging population have made treating OA-associated pain as well as maintaining mobility and activity a public health priority. OA affects all mammals, and the use of spontaneous animal models is one promising approach for improving translational pain research and the development of effective treatment strategies. Accelerometers are a common tool for collecting high-frequency activity data on animals to study the effects of treatment on pain related activity patterns. There has recently been increasing interest in their use to understand treatment effects in human pain conditions. However, activity patterns vary widely across subjects; furthermore, the effects of treatment may manifest in higher or lower activity counts or in subtler ways like changes in the frequency of certain types of activities. We use a zero inflated Poisson hidden semi-Markov model to characterize activity patterns and subsequently derive estimators of the treatment effect in terms of changes in activity levels or frequency of activity type. We demonstrate the application of our model, and its advance over traditional analysis methods, using data from a naturally occurring feline OA-associated pain model.

scholarly journals A Modelling Framework for the Conceptual Design of Low-Emission Eco-Industrial Parks in the Circular Economy: A Case for Algae-Centered Business Consortia

Water ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 69
Author(s):  
Aldric S. Tumilar ◽  
Dia Milani ◽  
Zachary Cohn ◽  
Nick Florin ◽  
Ali Abbas
Keyword(s):  
Circular Economy ◽  
Carbon Capture ◽  
Fossil Fuel ◽  
Production Systems ◽  
Flow Analysis ◽  
Biofuel Production ◽  
Industrial Symbiosis ◽  
Modelling Framework ◽  
Low Emission ◽  
Energy Exchanges

This article describes a unique industrial symbiosis employing an algae cultivation unit (ACU) at the core of a novel eco-industrial park (EIP) integrating fossil-fuel fired power generation, carbon capture, biofuel production, aquaculture, and wastewater treatment. A new modelling framework capable of designing and evaluating materials and energy exchanges within an industrial eco-system is introduced. In this scalable model, an algorithm was developed to balance the material and energy exchanges and determine the optimal inputs and outputs based on the industrial symbiosis objectives and participating industries. Optimizing the functionality of the ACU not only achieved a substantial emission reduction, but also boosted aquaculture, biofuel, and other chemical productions. In a power-boosting scenario (PBS), by matching a 660 MW fossil fuel-fired power plant with an equivalent solar field in the presence of ACU, fish-producing aquaculture and biofuel industries, the net CO2 emissions were cut by 60% with the added benefit of producing 39 m3 biodiesel, 6.7 m3 bioethanol, 0.14 m3 methanol, and 19.55 tons of fish products annually. Significantly, this article shows the potential of this new flexible modelling framework for integrated materials and energy flow analysis. This integration is an important pathway for evaluating energy technology transitions towards future low-emission production systems, as required for a circular economy.

scholarly journals Estimating US fossil fuel CO2emissions from measurements of14C in atmospheric CO2

2020 ◽  
Vol 117 (24) ◽  
pp. 13300-13307 ◽  
Author(s):  
Sourish Basu ◽  
Scott J. Lehman ◽  
John B. Miller ◽  
Arlyn E. Andrews ◽  
Colm Sweeney ◽  
...  
Keyword(s):  
Environmental Protection Agency ◽  
Fossil Fuel ◽  
Net Ecosystem Exchange ◽  
The United States ◽  
Reference Network ◽  
Random Errors ◽  
Atmospheric Measurements ◽  
Modeling Framework ◽  
Emission Data ◽  
Atmospheric Observations

We report national scale estimates of CO2emissions from fossil-fuel combustion and cement production in the United States based directly on atmospheric observations, using a dual-tracer inverse modeling framework and CO2andΔ14CO2measurements obtained primarily from the North American portion of the National Oceanic and Atmospheric Administration’s Global Greenhouse Gas Reference Network. The derived US national total for 2010 is 1,653 ± 30 TgC yr−1with an uncertainty (1σ) that takes into account random errors associated with atmospheric transport, atmospheric measurements, and specified prior CO2and14C fluxes. The atmosphere-derived estimate is significantly larger (>3σ) than US national emissions for 2010 from three global inventories widely used for CO2accounting, even after adjustments for emissions that might be sensed by the atmospheric network, but which are not included in inventory totals. It is also larger (>2σ) than a similarly adjusted total from the US Environmental Protection Agency (EPA), but overlaps EPA’s reported upper 95% confidence limit. In contrast, the atmosphere-derived estimate is within1σof the adjusted 2010 annual total and nine of 12 adjusted monthly totals aggregated from the latest version of the high-resolution, US-specific “Vulcan” emission data product. Derived emissions appear to be robust to a range of assumed prior emissions and other parameters of the inversion framework. While we cannot rule out a possible bias from assumed prior Net Ecosystem Exchange over North America, we show that this can be overcome with additionalΔ14CO2measurements. These results indicate the strong potential for quantification of US emissions and their multiyear trends from atmospheric observations.

scholarly journals A Novel Approach of Traffic Density Estimation Using CNNs and Computer Vision

2021 ◽  
Vol 5 (4) ◽  
pp. 80-84
Author(s):  
Luong Anh Tuan Nguyen ◽  
Thanh Xuan Ha
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Traffic Flow ◽  
Density Estimation ◽  
Traffic Density ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Modern Life ◽  
Traffic Jam ◽  
Novel Approach

In modern life, we face many problems, one of which is the increasingly serious traffic jam. The cause is the large volume of vehicles, inadequate infrastructure and unreasonable distribution, and ineffective traffic signal control. This requires finding methods to optimize traffic flow, especially during peak hours. To optimize traffic flow, it is necessary to determine the traffic density at each time in the streets and intersections. This paper proposed a novel approach to traffic density estimation using Convolutional Neural Networks (CNNs) and computer vision. The experimental results with UCSD traffic dataset show that the proposed solution achieved the worst estimation rate of 98.48% and the best estimation rate of 99.01%.

Quantifying the Uncertainty of Energy Creation from Solar and Wind Farms in Different Locations

2016 ◽  
Vol 5 (2) ◽  
pp. 13-46 ◽  
Author(s):  
Roy Nersesian ◽  
Kenneth David Strang
Keyword(s):  
Large Scale ◽  
Wind Farm ◽  
Fossil Fuel ◽  
Wind Farms ◽  
System Stability ◽  
Novel Approach ◽  
Plant Operations ◽  
Generating Capacity ◽  
Demand Fluctuations ◽  
Battery Technology

This paper illustrates how to assess the risk associated with solar and wind farm energy creation by identifying the critical operational factors and then developing multivariate models. The study reveals that a dependence on solar and wind could place consumers at risk of interrupted service given the state of contemporary battery technology. Large scale electricity storage is not currently available which places a contingency risk on electricity generating capacity. More so, maintaining system stability where solar and wind play a significant role in generating electricity is a growing challenge facing utility operators. Therefore, the authors demonstrate how to build a model that quantifies uncertainty by matching uncontrollable supply to uncontrollable demand where a gravity battery may be installed as a buffer. This novel approach generalizes to fossil fuel and nuclear plant operations because demand fluctuations could be managed by storing surplus energy into a gravity battery to meet high peak periods.

scholarly journals Emissions of halocarbons from India inferred through atmospheric measurements

2019 ◽  
Vol 19 (15) ◽  
pp. 9865-9885 ◽  
Author(s):  
Daniel Say ◽  
Anita L. Ganesan ◽  
Mark F. Lunt ◽  
Matthew Rigby ◽  
Simon O'Doherty ◽  
...  
Keyword(s):  
Rapid Growth ◽  
Central India ◽  
Fold Increase ◽  
Montreal Protocol ◽  
Emission Rates ◽  
Atmospheric Measurements ◽  
Modelling Framework ◽  
Global Emission ◽  
Atmospheric Measurement ◽  
Ozone Depleting Substances

Abstract. As the second most populous country and third fastest growing economy, India has emerged as a global economic power. As such, its emissions of greenhouse and ozone-depleting gases are of global significance. However, unlike neighbouring China, the Indian sub-continent is very poorly monitored by atmospheric measurement networks. India's halocarbon emissions, here defined as chlorofluorocarbons (CFCs), hydrochlorofluorocarbons (HCFCs), hydrofluorocarbons (HFCs) and chlorocarbons, are not well-known. Previous measurements from the region have been obtained at observatories many hundreds of kilometres from source regions, or at high altitudes, limiting their value for the estimation of regional emission rates. Given the projected rapid growth in demand for refrigerants and solvents in India, emission estimates of these halocarbons are urgently needed to provide a benchmark against which future changes can be evaluated. In this study, we report atmospheric-measurement-derived halocarbon emissions from India. With the exception of dichloromethane, these top-down estimates are the first for India's halocarbons. Air samples were collected at low altitude during an aircraft campaign in June and July 2016, and emissions were derived from measurements of these samples using an inverse modelling framework. These results were evaluated to assess India's progress in phasing out ozone-depleting substances under the Montreal Protocol. India's combined CFC emissions are estimated to be 54 (27–86) Tg CO2 eq. yr−1 (5th and 95th confidence intervals are shown in parentheses). HCFC-22 emissions of 7.8 (6.0–9.9) Gg yr−1 are of similar magnitude to emissions of HFC-134a (8.2 (6.1–10.7) Gg yr−1). We estimate India's HFC-23 emissions to be 1.2 (0.9–1.5) Gg yr−1, and our results are consistent with resumed venting of HFC-23 by HCFC-22 manufacturers following the discontinuation of funding for abatement under the Clean Development Mechanism. We report small emissions of HFC-32 and HFC-143a and provide evidence to suggest that HFC-32 emissions were primarily due to fugitive emissions during manufacturing processes. A lack of significant correlation among HFC species and the small emissions derived for HFC-32 and HFC-143a indicate that in 2016, India's use of refrigerant blends R-410A, R-404A and R-507A was limited, despite extensive consumption elsewhere in the world. We also estimate emissions of the regulated chlorocarbons carbon tetrachloride and methyl chloroform from northern and central India to be 2.3 (1.5–3.4) and 0.07 (0.04–0.10) Gg yr−1 respectively. While the Montreal Protocol has been successful in reducing emissions of many ozone-depleting substances, growth in the global emission rates of the unregulated very short-lived substances poses an ongoing threat to the recovery of the ozone layer. Emissions of dichloromethane are found to be 96.5 (77.8–115.6) Gg yr−1, and our estimate suggests a 5-fold increase in emissions since the last estimate derived from atmospheric data in 2008. We estimate perchloroethene emissions from India and chloroform emissions from northern–central India to be 2.9 (2.5–3.3) and 32.2 (28.3–37.1) Gg yr−1 respectively. Given the rapid growth of India's economy and the likely increase in demand for halocarbons such as HFCs, the implementation of long-term atmospheric monitoring in the region is urgently required. Our results provide a benchmark against which future changes to India's halocarbon emissions may be evaluated.

EMISSION MODELLING OF HAZARDOUS AIR POLLUTANTS FROM ROAD TRANSPORT AT URBAN SCALE

Transport ◽  
2012 ◽  
Vol 27 (3) ◽  
pp. 299-306 ◽  
Author(s):  
Oxana Tchepel ◽  
Daniela Dias ◽  
Joana Ferreira ◽  
Richard Tavares ◽  
Ana Isabel Miranda ◽  
...  
Keyword(s):  
Urban Areas ◽  
Air Pollutants ◽  
Road Traffic ◽  
Probabilistic Approach ◽  
Cold Start ◽  
Spatial And Temporal Variability ◽  
Emission Model ◽  
Hazardous Air Pollutants ◽  
Transport Activity ◽  
Activity Data

This study is focused on the development of a modelling approach to quantify emissions of traffic-related hazardous air pollutants in urban areas considering complex road network and detailed data on transport activity. In this work a new version of the Transport Emission Model for line sources has been developed for hazardous pollutants (TREM-HAP). Emission factors for benzene, 1,3-butadiene, formaldehyde, acetaldehyde, acrolein, naphthalene and also particulate matter (PM2.5) were implemented and the model was extended to integrate a probabilistic approach for the uncertainty quantification using Monte-Carlo technique. The methodology has been applied to estimate road traffic emissions in Porto Urban Area, Portugal. Hourly traffic counts provided by an automatic counting system were used to characterise the spatial and temporal variability of the number of vehicles, vehicle categories and average speed at different road segments. The data for two summer and two winter months were processed to obtain probability density functions of the input parameters required for the uncertainty analysis. For quantification of cold start excess emissions, Origin-Destination matrix for daily trips was used as additional input information. Daily emissions of hazardous air pollutants from road traffic were analysed for the study area. The uncertainty of the emission estimates related to the transport activity factors range from as small as −2 to +1.7% for acrolein and acetaldehyde on highways, to as large as −33 to +70% for 1,3-butadiene considering urban street driving. An important contribution of cold start emissions to the total daily values was estimated thus achieving 45% in case of benzene. The uncertainty in transport activity data on resulting urban emission inventory highlights the most important parameter and reveals different sensitivity of the emission quantification to the input data. The methodology presented in this work allows the development of emission inventories for hazardous air pollutants with high spatial and temporal resolution in complex urban areas required for air quality modelling and exposure studies and could be used as a decision support tool.

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