Uncertainties In Emissions Research Articles

A Measurement Device for the Normal Spectral Emissivity of Materials Under Low-Temperature and Vacuum Conditions

Based on parallel light, a device for measuring sample emissivity under vacuum and low temperature was established. In this paper, a new emissivity measurement formula was designed, which replaces the derivation of Kirchhoff’s law of thermal radiation. The device is designed with a light-weight blackbody, an improved cooling speed, and an improved PID temperature control system to achieve a good temperature stability, close to 1 mK. The device is capable of measuring within a wavelength range of 4 μm to 16 μm. The results of the uncertainty assessment show that the uncertainty of the normal emissivity is better than 1.6% in the range of 4 μm to 7 μm, and better than 0.6% in the range of 7 μm to 14 μm (k = 1) at 0 °C.

The Cosmological Optical Convergence: Extragalactic Background Light from TeV Gamma Rays

The intensity of the extragalactic background (EBL), the accumulated optical and infrared emissions since the first stars, is the subject of a decades-long tension in the optical band. These photons form a target field that attenuates the γ-ray flux from extragalactic sources. This Letter reports the first γ-ray measurement of the EBL spectrum at z = 0 that is purely parametric and independent of EBL evolution with redshift over a wavelength range from 0.18 to 120 μm. Our method extracts the EBL absorption imprint on more than 260 archival TeV spectra from the STeVECat catalog by marginalizing nuisance parameters describing the intrinsic emission and instrumental uncertainties. We report an intensity at 600 nm of 6.9 ± 1.9 nW m−2 sr−1 × h 70, which is indistinguishable from the intensity derived from integrated galaxy light (IGL) and compatible with direct measurements taken beyond Pluto’s orbit. We exclude with 95% confidence diffuse contributions to the EBL with an intensity relative to the IGL, f diff, greater than 20% and provide a measurement of the expansion rate of the Universe at z = 0, H0=67−6+7 km s−1 Mpc −1 × (1 + f diff), which is EBL-model independent. IGL, direct, and γ-ray measurements agree on the EBL intensity in the optical band, finally reaching a cosmological optical convergence.

Development of 1 ×1 km gridded emission inventory for air quality assessment and mitigation strategies from open biomass burning in Karnataka, India

In this work, we have developed a high-resolution (1 ×1 km grid) emission inventory of greenhouse gases and air pollutants from open biomass burning (OBB) in Karnataka for 2000, 2005, 2010, 2015, 2020, and 2022. Secondary and field datasets were used to validate the emission inventories. The GIS-based statistical model and activity data were used to estimate greenhouse and air pollutant gas emissions from OBB (Forest, grassland, and crop fires). The uncertainty analysis of the emissions were done using the Monte Carlo Simulation (MCS) model and Low Emission Analysis Platform (LEAP) tool was used for projecting the emissions to 2050 based on the annual average growth rate. According to our results, the total OBB burned areas (%) in Karnataka during the aforementioned years were 24.43, 15.69, 15.95, 11.41, 26.73, and 5.78. The results showed that total annual average OBB emissions in Karnataka for 2000–2022 in Gg were SO2 (6.67), NOx (9.48), NH3(9.80), CO (670.12), OC (59.78), BC (5.09), CO2 (11071 .11), CH4(33.68), PM10(84.29), PM2.5(81.08), NMVOC (74.13), and NO2 (4.80) respectively. The districts of Kalburgi, Raichur, Bagalkot, Uttara Kannada, and Shivamogga have the highest emissions from OBB in Karnataka from 2000 to 2022. The emission uncertainty results showed ±26.92, ±28.17, and ±30.27 % for the crop, grassland, and forest fires, respectively. Under the business-as-usual (BAU) scenarios, the CH4 emissions from forest, grassland, and crop fires are projected at 8823, 2.64, and 5262 metric tonnes by 2030 and 58046, 10, and 30700 metric tonnes by 2050, respectively. The developed high-resolution inventories are the most up-to-date surface emission datasets with hotspots in Karnataka from OBB emission. National Clean Air Programme (NCAP) and India’s Net-Zero Emission target by 2070, our high-resolution emission inventories and mitigation strategies may be helpful for air quality monitoring, health benefits analysis, and policy-making studies in Karnataka, India.

Aerosol composition, air quality, and boundary layer dynamics in the urban background of Stuttgart in winter

Abstract. Aerosol distributions are of great relevance for air quality, especially for cities like Stuttgart, which has limited air exchange due to its location in a basin. We collected a comprehensive set of data from remote sensing and in situ methods including radiosondes for the urban background of downtown Stuttgart to determine the impact of boundary layer mixing processes on local air quality and to evaluate the simulation results of the high-resolution large eddy simulation (LES) model PALM-4U at 10 m grid spacing. Stagnant meteorological conditions caused accumulation of aerosols, and chemical composition analysis shows that ammonium nitrate (37 ± 9 %) and organic aerosol (OA; 34 ± 9 %) dominated during this winter study. Case studies show that clouds during previous nights can weaken temperature inversion and accelerate boundary layer mixing after sunrise by up to 3 h. This is important for ground-level aerosol dilution during the morning rush hour. Furthermore, our observations validate results of the LES model PALM-4U in terms of boundary layer heights and aerosol mixing for 48 h. The simulated aerosol concentrations follow the trend of our observations but are still underestimated by a factor of 4.5 ± 2.1 due to missing secondary aerosol formation processes and uncertainties of emissions and boundary conditions in the model. This paper firstly evaluates the PALM-4U model performance in simulating aerosol spatio-temporal distributions, which can help to improve the LES model and to better understand sources and sinks for air pollution as well as the role of horizontal and vertical transport.

Quantifying future carbon emissions uncertainties under stochastic modeling and Monte Carlo simulation: Insights for environmental policy consideration for the Belt and Road Initiative Region

This study critically examines future carbon (CO2) emissions in the Belt & Road Initiative (BRI) region, considering factors such as energy consumption, economic growth, population growth, and population density. The objective of this study is to identify critical areas of higher emissions, which require policy intervention capable of strengthening sustainability in the BRI compact. A combined approach of stochastic modeling and Monte Carlo simulations was employed, utilizing panel data from 45 countries in the BRI region from 1990 to 2021. Results confirm that emissions are higher in all scenarios in direct proportion to electric power consumption, population growth, and Gross Domestic Product (GDP) growth. In scenarios with high emissions, a continuous and significant upward trend in CO2 emissions was observe. The medium emissions scenario exhibited a more moderated rise in emissions, suggesting a balance between economic development and environmental considerations. Critical areas for future environmental policy-making resides in electric power consumption, population growth, and GDP growth. The study strongly recommends for a shift from the current focus on road and railway infrastructure to renewable energy infrastructure, green innovations and efficient technology transfer to member countries. Without this, the BRI region is likely to face increased emissions, posing significant challenges to future sustainable development and global environmental sustainability.

Solutions and insights for agricultural monitoring, reporting, and verification (MRV) from three consecutive issuances of soil carbon credits

Regenerative agricultural practice adoption on conventionally managed fields has gained momentum as a climate mitigation strategy, given the ability of these practices to sequester carbon or reduce greenhouse gas emissions. However, the geospatial and temporal variability of the impact of specific practices, such as cover cropping or no-till, pose challenges for scalable quantification of emissions reduction and deploying incentives to drive increased adoption. To quantify impact while accounting for variability and uncertainty at scale, Indigo Ag created a monitoring, reporting, and verification (MRV) pipeline to produce agricultural soil carbon credits produced at large scales (hundreds of thousands of hectares). The pipeline ingests field data from enrolled farmers, checks data quality, uses hybrid soil sampling and biogeochemical modeling to produce estimates of emissions reduction and uncertainty, and then applies deductions based on calculated uncertainty and leakage to quantify total project-wide carbon credits and monitor for durability of carbon. The implementation of a carbon project (CAR1459) from 2018 to 2022 on 553,743 ha of U.S. cropland utilizing the pipeline is estimated to have reduced emissions by 398,408.5 tCO2e, amounting to 296,662 tCO2e of soil carbon credits after uncertainty deductions. This paper explores the effect sizes associated with specific regenerative practice changes across the project domain. Cover cropping consistently resulted in a net positive climate impact and reduced emissions by 1.29 tCO2e per hectare per year, on average. Introduction of no-till was more common in the project, but it had a lower average emissions reduction of 0.38 tCO2e per hectare per year. Effect sizes for no-till vary spatiotemporally and are typically low in the first several years after adoption but increase in subsequent years. Agricultural carbon programs that capture and incentivize the nuance of outcomes of practices rather than the implementation of practices, can promote adoption of the right management practice to be deployed on the right field for maximum environmental benefit.

Carbon emission calculation and uncertainty analysis of asphalt concrete during the production stage

Asphalt concrete is the main material consumed by road engineering. At present, there is no uniform standard for determining the carbon emissions of different types of asphalt concrete, and the calculation process is uncertain. In this study, a carbon emission calculation method that combines theory with practice is proposed. First, a theoretical carbon emission model based on the heat balance equation and an actual carbon emission model based on actual energy data are established, and the key links in the carbon emissions of asphalt concrete are determined. Second, the carbon emission correction factors of five different types of asphalt concrete are obtained to estimate the actual carbon emissions more accurately. Finally, the uncertainty of the parameters is evaluated and characterized via the DQI-Monte Carlo simulation method. The results show that the carbon emissions from large to small are modified asphalt concrete (MAC) > ordinary asphalt concrete (AC) > stone matrix asphalt (SMA) > recycled asphalt concrete (ZAC) > warm mix asphalt concrete (WAC). The aggregate heating stage is the key link in the carbon emission of asphalt concrete. Controlling the aggregate heating temperature and moisture content is the most direct and efficient energy savings and emission reduction measure.

Uncertainties of biogenic VOC emissions caused by land cover data and implications on ozone mitigation strategies for the Yangtze river Delta region

Biogenic volatile organic compounds (BVOC) play a crucial role in ground-level ozone formation, yet emission quantification faces considerable uncertainties stemming from input data. Land cover data, which determines the distribution of growth forms, is an essential driving input of BVOC emissions. This study explores the uncertainty in BVOC emission estimates arising from using two land cover datasets, specifically the MODIS and ESA land cover data, and the implications for ozone mitigation strategies in the Yangtze River Delta (YRD) region. By employing the MEGAN model for the year 2021, we estimated that ESA land cover data, with a finer 10 m resolution, yields a total BVOC emission estimate of 2299 Gg, which is over 52.9% higher than the MODIS dataset with a resolution of 500 m, attributed to the higher tree coverage (33.9% in ESA vs. 17.6% in MODIS) identified in the ESA dataset. We then simulated the ozone concentrations by both emission estimates with the WRF-CMAQ model. The elevated BVOC emissions based on the ESA data improved the underestimation of simulated isoprene concentrations and consequently resulted in more than 30% higher domain-averaged MDA8 ozone concentration compared to the MODIS-derived BVOC emission. Regarding ozone mitigation strategy, simulation with the ESA-based BVOC emissions results in 0.3–1.2 μg/m3 less ozone reductions compared to that with the MODIS-based emissions when reducing anthropogenic VOC emissions, but 0.7–2.9 μg/m3 higher ozone reductions when controlling anthropogenic NOx emissions. These disparities are more pronounced at the city level, highlighting the importance of accurate BVOC emission estimates for effective ozone control strategies.

Prediction of building carbon emission based on grey Markov theory

The increase in carbon emissions in the construction industry is one of the factors that lead to environmental problems and hinder the sustainable development of society and the construction industry. Therefore, there is an urgent need to rationally control carbon emissions by formulating scientific and effective energy-saving and emission reduction policies so as to safeguard the development of the industry. However, because of the uncertainty and complexity of CO2 emissions, more reliable prediction and assessment tools are needed to comprehensively analyze and predict CO2 emissions. This study combines the gray system theory and Markov principle to give full play to the advantages of the two methods, using Markov theory to determine the state transition probability, find out the characteristics of carbon emissions in the construction industry, and form a suitable gray Markov prediction model. The feasibility of the prediction model is demonstrated by calculating and examining the prediction model using data related to carbon emissions from the U.S. buildings from 2002 to 2022, which greatly improves the accuracy of CO2 emissions prediction.

Analysis of anthropogenic CO2 emission uncertainty and influencing factors at city scale in Yangtze River Delta region: One of the world's largest emission hotspots

Cities occupy only 3% of the earth's land area, yet they contribute over 70% of anthropogenic CO2 emissions. Accurately quantifying the corresponding uncertainty of CO2 emissions at the city scale is the first step in simulating global/regional greenhouse gas concentration and implementing effective emission reduction policies. As the world's largest emitter, China has committed to reaching its CO2 emissions peak by 2030, and cities are facing substantial pressure to reduce CO2 emissions. The Yangtze River Delta (YRD) region is treated as the largest urban agglomeration in China and globally. Despite the availability of multiple emission inventories, comprehensive assessments of city-level CO2 emissions uncertainty within this region are still lacking. This study focused on the YRD region, compared six inventories and used nighttime light intensity, GDP, population, and satellite-based xCO2 concentrations as proxy data to identify potential sources of emission bias. The findings are as follows: (1) The city-level CO2 emissions in the YRD region ranged from tens of Mt to approximately 600 Mt. From 2010 to 2019, emissions in the region increased slowly. However, emission intensity (calculated by dividing the CO2 emissions by the Gross Domestic Product) showed a declining trend. The relatively low proportion of point source emissions in EDGAR inventory is attributed to its reliance on the point source CARMA, which overlooks smaller point sources. (2) The average uncertainties for low emission (0–50 Mt), medium emission (50–100 Mt) and high emission (>100 Mt) cities were 51.1%, 34.0%, and 45.0%, respectively. The absolute errors of emissions showed strong positive correlations with proxy data. There exists a logarithmic relationship between them and uncertainty, which can assist in estimating emission uncertainty in other cities in the future. (3) The ratios of biological CO2 flux to the ten-year average of CO2 emissions varied between-6.79% and −0.02%, which are much smaller than the uncertainty of anthropogenic emissions, the comparisons indicate that recent large biases in estimating anthropogenic CO2 emissions hinder the evaluation of carbon neutrality ability.

Evaluation of modelled versus observed non-methane volatile organic compounds at European Monitoring and Evaluation Programme sites in Europe

Abstract. Atmospheric volatile organic compounds (VOCs) constitute a wide range of species, acting as precursors to ozone and aerosol formation. Atmospheric chemistry and transport models (CTMs) are crucial to understanding the emissions, distribution, and impacts of VOCs. Given the uncertainties in VOC emissions, lack of evaluation studies, and recent changes in emissions, this work adapts the European Monitoring and Evaluation Programme Meteorological Synthesizing Centre – West (EMEP MSC-W) CTM to evaluate emission inventories in Europe. Here we undertake the first intensive model–measurement comparison of VOCs in 2 decades. The modelled surface concentrations are evaluated both spatially and temporally, using measurements from the regular EMEP monitoring network in 2018 and 2019, as well as a 2022 campaign. To achieve this, we utilised the UK National Atmospheric Emissions Inventory to derive explicit emission profiles for individual species and employed a tracer method to produce pure concentrations that are directly comparable to observations. The degree to which the modelled and measured VOCs agree varies depending on the specific species. The model successfully captures the overall spatial and temporal variations of major alkanes (e.g. ethane, n-butane) and unsaturated species (e.g. ethene, benzene) but less so for propane, i-butane, and ethyne. This discrepancy underscores potential issues in the boundary conditions for the latter species and in their primary emissions from, in particular, the solvent and road transport sectors. Specifically, potential missing propane emissions and issues with its boundary conditions are highlighted by large model underestimations and smaller propane-to-ethane ratios compared to the measurement. Meanwhile, both the model and measurements show strong linear correlations among butane isomers and among pentane isomers, indicating common sources for these pairs of isomers. However, modelled ratios of i-butane to n-butane and i-pentane to n-pentane are approximately one-third of the measured ratios, which is largely driven by significant emissions of n-butane and n-pentane from the solvent sector. This suggests issues with the speciation profile of the solvent sector, underrepresented contributions from transport and fuel evaporation sectors in current inventories, or both. Furthermore, the modelled ethene-to-ethyne and benzene-to-ethyne ratios differ significantly from measured ratios. The different model performance strongly points to shortcomings in the spatial and temporal patterns and magnitudes of ethyne emissions, especially during winter. For OVOCs, the modelled and measured concentrations of methanal and methylglyoxal show a good agreement, despite a moderate underestimation by the model in summer. This discrepancy could be attributed to an underestimation of contributions from biogenic sources or possibly a model overestimation of their photolytic loss in summer. However, the insufficiency of suitable measurements limits the evaluation of other OVOCs. Finally, model simulations employing the CAMS inventory show slightly better agreements with measurements than those using the Centre on Emission Inventories and Projections (CEIP) inventory. This enhancement is likely due to the CAMS inventory's detailed segmentation of the road transport sector, including its associated sub-sector-specific emission profiles. Given this improvement, alongside the previously mentioned concerns about the model's biased estimations of various VOC ratios, future efforts should focus on a more detailed breakdown of dominant emission sectors (e.g. solvents) and the refinement of their speciation profiles to improve model accuracy.

Pan-tropical daily L-band microwave land surface emissivity retrieval from GNSS-R observations

ABSTRACT The uncertainties in microwave land surface emissivity (MLSE) measurements have long limited the use of spaceborne microwave radiometer data. As an emerging observation method, Global Navigation Satellite System Reflectometry (GNSS-R) has demonstrated great potential in several land and ocean applications. In this study, a method for obtaining daily MLSE dataset in the pan-tropical region from Cyclone GNSS (CYGNSS) observations is presented and evaluated. The CYGNSS observations are first aggregated into the Equal-Area-Scalable-Earth (EASE) 2.0 36 km grid by a combined weight function of distance, time, and signal-to-noise ratio variance. Then, the method employs a pixel-by-pixel regression algorithm to conduct the daily MLSE retrieval using reference emissivity derived from the Soil Moisture Active Passive (SMAP) brightness temperature. The CYGNSS MLSE shows good agreement with SMAP MLSE, delivering an overall root-mean-square error (RMSE) of 0.022 and 0.017 for horizontal and vertical polarization, respectively, during the training set spanning the whole year of 2018. Furthermore, on the test set from January 2019 to May 2019, the RMSE values amounted to 0.030 and 0.023 for horizontal and vertical polarization, respectively. Temperature records from the International Soil Moisture Network are employed to calculate the emissivity and for in-situ validation, which yield an RMSE of 0.034 and 0.026 for the two polarizations, respectively. The proposed algorithm provides an encouraging approach to obtain accurate daily MLSE dataset for microwave remote sensing. Compared to the SMAP MLSE, the CYGNSS MLSE has a remarkable improvement of 86% in temporal resolution, greatly complementing the existing microwave emissivity datasets.

Constraining non-methane VOC emissions with TROPOMI HCHO observations: impact on summertime ozone simulation in August 2022 in China

Abstract. Non-methane volatile organic compounds (NMVOC), serving as crucial precursors of O3, have a significant impact on atmospheric oxidative capacity and O3 formation. However, both anthropogenic and biogenic NMVOC emissions remain subject to considerable uncertainty. Here, we extended the Regional multi-Air Pollutant Assimilation System (RAPAS) using the ensemble Kalman filter (EnKF) algorithm to optimize NMVOC emissions in China in August 2022 by assimilating TROPOspheric Monitoring Instrument (TROPOMI) HCHO retrievals. We also simultaneously optimize NOx emissions by assimilating in situ NO2 observations to address the chemical feedback among VOCs–NOx–O3. Furthermore, a process-based analysis was employed to quantify the impact of NMVOC emission changes on various chemical reactions related to O3 formation and depletion. NMVOC emissions exhibited a substantial reduction of 50.2 %, especially in the middle and lower reaches of the Yangtze River, revealing a prior overestimation of biogenic NMVOC emissions due to an extreme heat wave. Compared to the forecast with prior NMVOC emissions, the forecast with posterior emissions significantly improved HCHO simulations, reducing biases by 75.7 %, indicating a notable decrease in posterior emission uncertainties. The forecast with posterior emissions also effectively corrected the overestimation of O3 in forecasts with prior emissions, reducing biases by 49.3 %. This can be primarily attributed to a significant decrease in the RO2+NO reaction rate and an increase in the NO2+OH reaction rate in the afternoon, thus limiting O3 generation. Sensitivity analyses emphasized the necessity of considering both NMVOC and NOx emissions for a comprehensive assessment of O3 chemistry. This study enhances our understanding of the effects of NMVOC emissions on O3 production and can contribute to the development of effective emission reduction policies.

On the uncertainty of anthropogenic aromatic volatile organic compound emissions: model evaluation and sensitivity analysis

On the uncertainty of anthropogenic aromatic volatile organic compound emissions: model evaluation and sensitivity analysis

Diagnosing uncertainties in global biomass burning emission inventories and their impact on modeled air pollutants

Abstract. Large uncertainties persist within current biomass burning (BB) inventories, and the choice of these inventories can substantially impact model results when assessing the influence of BB aerosols on weather and climate. We evaluated discrepancies among BB emission inventories by comparing carbon monoxide (CO) and organic carbon (OC) emissions from seven major BB regions globally between 2013 and 2016. Mainstream bottom-up inventories, including the Fire INventory from NCAR 1.5 (FINN1.5) and Global Fire Emissions Database version 4s (GFED4s), along with the top-down inventories Quick Fire Emissions Dataset 2.5 (QFED2.5) and Visible Infrared Imaging Radiometer (VIIRS-)based Fire Emission Inventory version 0 (VFEI0), were selected for this study. Global CO emissions range from 252 to 336 Tg, with regional disparities reaching up to a 6-fold difference. Dry matter is the primary contributor to the regional variation in CO emissions (50 %–80 %), with emission factors accounting for the remaining 20 %–50 %. Uncertainties in dry matter often arise from biases in calculating bottom fuel consumption and burned area, influenced by vegetation classification methods and fire detection products. In the tropics, peatlands contribute more fuel loads and higher emission factors than grasslands. At high latitudes, increased cloud fraction amplifies the discrepancy in estimated burned area (or fire radiative power) by 20 %. The global OC emissions range from 14.9 to 42.9 Tg, exhibiting higher variability than CO emissions due to the corrected emission factors in QFED2.5, with regional disparities reaching a factor of 8.7. Additionally, we applied these BB emission inventories to the Community Atmosphere Model version 6 (CAM6) and assessed the model performance against observations. Our results suggest that the simulations based on the GFED4s agree best with the Measurements of Pollution in the Troposphere (MOPITT-)retrieved CO. While comparing the simulation with the Moderate Resolution Imaging Spectroradiometer (MODIS) and AErosol RObotic NETwork (AERONET) aerosol optical depth (AOD), our results reveal that there is no global optimal choice for BB inventories. In the high latitudes of the Northern Hemisphere, using GFED4s and QFED2.5 can better capture the AOD magnitude and diurnal variation. In equatorial Asia, GFED4s outperforms other models in representing day-to-day changes, particularly during intense burning. In Southeast Asia, we recommend using the OC emission magnitude from FINN1.5 combined with daily variability from QFED2.5. In the Southern Hemisphere, the latest VFEI0 has performed relatively well. This study has implications for reducing the uncertainties in emissions and improving BB emission inventories in further studies.

Multiagency Ensemble Forecast of Wildfire Air Quality in the United States: Toward Community Consensus of Early Warning

Abstract Wildfires pose increasing risks to human health and properties in North America. Due to large uncertainties in fire emission, transport, and chemical transformation, it remains challenging to accurately predict air quality during wildfire events, hindering our collective capability to issue effective early warnings to protect public health and welfare. Here, we present a new real-time Hazardous Air Quality Ensemble System (HAQES) by leveraging various wildfire smoke forecasts from three U.S. federal agencies (NOAA, NASA, and Navy). Compared to individual models, the HAQES ensemble forecast significantly enhances forecast accuracy. To further enhance forecasting performance, a weighted ensemble forecast approach was introduced and tested. Compared to the unweighted ensemble mean, the multilinear regression weighted ensemble reduced fractional bias by 34% in the major fire regions, false alarm rate by 72%, and increased hit rate by 17%. Finally, we improved the weighted ensemble using quantile regression and weighted regression methods to enhance the forecast of extreme air quality events. The advanced weighted ensemble increased the PM2.5 exceedance hit rate by 55% compared to the ensemble mean. Our findings provide insights into the development of advanced ensemble forecast methods for wildfire air quality, offering a practical way to enhance decision-making support to protect public health.

The essential role of climate policy uncertainty in carbon emissions: a fresh insight.

This study assesses the influence of climate policy uncertainty (CPU) on carbon emissions (CE) against the backdrop of economic policy uncertainty (EPU) in the US. The wavelet analysis provides a comprehensive understanding of correlations in the time and frequency domains. The results demonstrate a significant correlation between CPU and CE, which varies across different time periods and frequencies. In the time domain, the results indicate that the CPU and CE move together during certain subperiods. Moreover, there are observable comovements in the frequency domain, particularly in the short to medium range. However, the correlation becomes stronger in the short term when there is no EPU, suggesting a closer interaction between CPU and CE. Therefore, it is crucial for governments to prioritize improving the clarity, credibility, and consistency of climate policies. They should also consider potential economic shocks when designing these policies.

Managing Uncertainty in Urban Road Traffic Emissions Associated with Vehicle Fleet Composition: From the Perspective of Spatiotemporal Sampling Coverage

With pronounced differences in emission factors among vehicle types and marked spatiotemporal heterogeneity of vehicle fleet composition, extrapolating fleet composition from insufficient sample hour periods and road segments will introduce significant uncertainty in calculating regional daily road traffic emissions. We proposed a framework to manage uncertainty in urban road traffic emissions associated with vehicle fleet composition from the perspective of spatiotemporal sampling coverage. Initially, the respective relationships of the temporal and spatial sampling coverages of fleet composition with the resulting regional daily road traffic emission uncertainties were determined, using the core area of a typical small and medium-sized city in China with the widely-used International Vehicle Emissions (IVE) model as example. Subsequently, function models were developed to explore the determination of the spatiotemporal sampling coverage of fleet composition. These results of emission uncertainties and function models implied that gases with larger emission factor discrepancies between vehicle types, such as NOx, required greater spatiotemporal sampling coverage than gases with smaller discrepancies, such as CO2, under the same uncertainties target. Therefore, sampling efforts should be prioritized for gases with larger emission factor discrepancies. Additionally, increasing sampling coverage in one dimension (either spatial or temporal) can reduce the minimum required coverage in the other dimension. To further reduce uncertainty, enhancing both spatial and temporal sampling coverage of the fleet composition is more effective than enhancing one type of coverage alone. The framework and results proposed in this work can reduce the uncertainty of emissions calculations caused by insufficient sampling coverage and contribute to more accurate transport emission reduction policy formulation.

More applicable quantification of non-CO2 greenhouse gas emissions from wastewater treatment plants by on-site plant-integrated measurements

Wastewater treatment is an important source of non-CO2 greenhouse gases (GHGs). However, current quantification of these GHG emissions mainly employs unit-based measurements, where emissions from individual process units are identified, leading to large uncertainties of overall emissions. Here we introduce plant-integrated measurements, where emissions from the whole plant are measured through the off-gas pipelines of the enclosed facility, to quantify methane (CH4) and nitrous oxide (N2O) emissions from an underground municipal wastewater treatment plant (WWTP) in southern China. Our results show that the primary oxic tank contributes the largest in total CH4 and N2O emissions, with an average fraction of over 80 % and over 90 %, respectively. This can be attributed to the vigorous aeration process, which facilitates the transfer of dissolved CH4 and N2O from the liquid phase to the atmosphere through intensive air stripping. The plant-integrated measurements yield around 3–9 times higher emission factors of CH4 and N2O than the unit-based measurements. This difference in emission accounting is attributed to both varying survey durations of the two approaches and the omission of uncertain emission sources during unit-based measurements. The comparison between these two approaches indicates that plant-integrated measurements are more applicable for emission quantification of the whole plant whereas unit-based measurements provide insights into the emission characteristics of individual process units. More plant-integrated measurements are needed in the future for more accurate emission accounting of WWTPs.

Demand response of an Electric Vehicle charging station using a robust-explicit model predictive control considering uncertainties to minimize carbon intensity

This paper presents a novel approach to address uncertainties and enable demand response in Electric Vehicle (EV) charging station optimization. A two-stage optimization strategy is proposed, integrating Robust Optimization and explicit Model Predictive Control (eMPC). The first stage involves day-ahead planning using Robust Optimization technique to limit the hourly power consumption of EVs, considering worst-case scenarios caused by uncertainties in EV consumption and CO2 emissions. The objective is to minimize environmental impact by reducing CO2 emissions. An Explicit Model Predictive Control strategy is developed in the second stage for real-time operation. The explicit solution, calculated offline, models uncertainties such as the initial state of charge of the battery energy storage, photovoltaic power production, and EV power consumption. During real-time operation, the explicit solution is accessed using measured data from the charging station, refining the schedule derived from the first stage. The proposed solution is implemented and evaluated at an EV charging station in Trieste, Italy. The results demonstrate a significant 69% reduction in CO2 emissions compared to a deterministic approach while maintaining a real-time computation time of less than 0.1 s.