Emission Inventory Research Articles

Source attribution of carbon monoxide over Northern India during crop residue burning period over Punjab

National Capital Territory of Delhi and its satellite cities suffer from poor air quality during the post-monsoon months of October–November. In this study, a novel attempt is made to estimate the contribution of different emission sources (industrial, residential, power generation, transportation, biomass burning, photochemical production, lateral transport, etc.) towards the criteria air pollutant carbon monoxide (CO) concentration over North India. Multiple simulations of the WRF-Chem model with a tagged tracer approach with different inputs (6 anthropogenic emission inventories and 3 biomass burning emission inventories) were used. The model performance was evaluated against the MOPITT retrieved CO surface concentration. Analysis of model simulated CO over North India suggests that anthropogenic emissions contribute around 32–49% to surface CO concentration while crop residue burning contributes 27–44% of which 80% originates from Punjab. For Delhi, the contribution from anthropogenic sources is dominant (53–77%) of which 10–28% is from the domestic sector and 14–55% is from the transport sector. Agricultural waste burning contributes about 15–30% to Delhi's surface CO concentration (of which 75% originates from Punjab). Crop residue burning emission is a chief source of CO over Punjab with a contribution of about 56–76%. The results suggest that industrial, transport, and domestic sector activities are more responsible for increased CO levels over New Delhi and surrounding regions than crop residue burning over Punjab. Furthermore, critical meteorological parameters like 10 m wind speed, boundary layer height, 2 m temperature, total precipitation, and relative humidity were evaluated against CO concentration to understand their impact on CO distribution. Results conclude that deteriorating air quality over the North Indian region is caused by a combination of prevailing meteorological factors (such as slow winds, shallow mixing layer, and cold temperatures) and man-made emissions.

Levelized Cost and Carbon Intensity of Solar Hydrogen Production from Water Electrolysis Using a Scalable and Intrinsically Safe Photocatalytic Z-Scheme Raceway System

The realization of an environmentally sustainable and widely-adopted hydrogen economy may require lowering hydrogen production costs of production pathways with ultra-low greenhouse gas emissions to $1/kg H2. The allocation of new or existing renewable electricity generation solely to hydrogen production remains contentious due to disputes regarding emissions accounting. Photoelectrochemical (PEC) hydrogen production technologies offer a unique solution, as hydrogen is produced directly from solar energy and water, without the need for electricity generation. However, cost projections for past photoelectrochemical designs have suggested that they are not cost competitive compared to conventional electrolysis systems manufactured at scale. Herein, we offer the first illustrative benchmark of cost and carbon intensity of hydrogen produced in a Type 2 Z-scheme photocatalytic reactor design that employs suspended semiconducting nanoparticles organized in two stacked baggies in a raceway design.To explore the near-term and future cost projection for hydrogen production via Type 2 photocatalytic Z-scheme raceways, the authors developed a bottom-up total installed capital cost model. This project cost model incorporates: 1) raceway reactor cost, derived from a Design for Manufacturing and Assembly (DFMA) process-based cost model, 2) mechanical balance of plant, including process equipment, piping, valves, and instrumentation, derived from equipment quotes, scaling, database values, and Aspen estimates; 3) electrical balance of plant, including wiring, derived from time and material cost correlations; 4) Site Preparation, focused on green field installation; and 5) Construction Overhead, including engineering, procurement, and construction (EPC) costs and project contingency. The capital cost model is fed into a Levelized Cost of Hydrogen (LCOH) discounted cash flow model that accounts for electricity and water consumption, in addition to other operating costs, including labor, maintenance, and raceway replacements.A hybrid life-cycle approach was used to develop an inventory of greenhouse gas emissions from all materials and energy flows associated with the facility life cycle. These flows are converted to emissions based on specific emission factors, or by using physical units-based input–output LCA models associated with the broader economic impacts of fuel and materials production, use, and end use.Estimates for levelized cost of hydrogen suggest that a 50 metric ton H2 per day (MTD) raceway plant can approach $2.50/kg H2 at an 8% solar-to-hydrogen efficiency with further cost improvements possible through increased performance and larger plant scales. Carbon intensity is estimated to be well under clean hydrogen targets for global warming potential of <2-4 kg CO2e/kg H2 produced. The results suggest a highly competitive and scalable technology, that justifies further experimental validation and prototyping in the field. Figure 1

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.

Tracking China's methane emissions from wastewater: Inventories, driving forces, and mitigation potential

China's rapid urbanization and burgeoning economy drove significant growth in sewage discharge, necessitating an examination of factors influencing wastewater methane emissions and mitigation measures. This study presented a comprehensive inventory of methane emissions in 31 provinces of China over a significant period. We developed a logarithmic mean divisor index (LMDI) model to reveal the driving forces behind emission changes. Methane emissions surged from approximately 1.4 Tg in 2000 to about 2.7 Tg in 2020, with treated domestic sewage being the fastest-growing source (49 Gg to 810 Gg). In 2020, the eastern, central, and southern regions contributed 61.7 % to total emissions. The increase in emissions was primarily driven by economic output, urbanization and population growth. This study forecasted future emission reduction potential, highlighting that reducing emission intensity (52.7 %) offered the most significant mitigation opportunities, followed by enhancing methane recovery (35.2 %). We recommended strengthening sewage discharge supervision and promoting biogas production.

Spatial and temporal analysis of scooter-induced traffic patterns and their environmental implications

Scooters are a popular means of transportation in urban areas. However, studies examining their spatial and temporal features are lacking. This study examined traffic patterns in New Taipei City from September 2021 to June 2023 using real-time surveillance camera images and the YOLOv4 algorithm. The focus of the study was to investigate the role of scooters in urban transportation and their effect on air quality. The findings revealed that sedans and scooters accounted for approximately 90% of the total vehicles, and their usage exhibited significant spatial and temporal variations across the city. Sedans were more prevalent in rural areas, whereas scooters were predominant in urban and suburban regions. An examination of diurnal patterns revealed that peak traffic occurred during early morning and evening rush hours, with distinct usage patterns between weekdays and weekends. Through hierarchical clustering, the city's stations were categorized into three types based on the dominant vehicle usage: sedan-dominant, sedan-prevailing, and scooter-dominant. The analysis also established a correlation between the number of vehicles and air pollution, highlighting the significant role of sedans and scooters as primary sources of emissions, particularly in areas where scooters were dominant. This suggests that the current emission inventories may underestimate the impact of scooters and buses on air quality while overestimating the trucks' contribution. Consequently, this study emphasizes the significance of scooters in determining urban air quality and advocates for improved monitoring and image identification technologies to accurately assess the numbers and speeds of scooters. These measures are essential for improving the accuracy of emissions inventories and forecasts, which are crucial for effective urban air quality management.

Air pollution emission inventory in Hanoi City, Vietnam

Abstract Hanoi is an economic-society centre of Vietnam but also faces the problem of air pollution with diverse sources including points, areas, and mobility sources. The emission inventory is a big project at the city level which determines a tabulation of compounds released into the atmosphere. The objectives of the study are to determine the status of air pollutant emissions and which activities are the main contributors to emissions in the sources in Hanoi city. By the combined methods, findings revealed that air pollution emitted from the sources in Hanoi included 10,570 tons SO2 per year, 65,963 tons NOx, 1,815,670 tons CO, 32,640 tons PM2.5 and 42,492 tons PM10 per year, respectively. Determined transport is an important source with accounted for over 90 per cent of the total NOx and CO emissions, and 66 per cent of the total PM. With SO2, point sources accounted for 57 per cent of total emissions in Hanoi. This research showed priority sources to manage and contribute the data for following the effort to reduce emissions.

Wildfire combustion emission inventory in Southwest China (2001–2020) based on MODIS fire radiative energy data

Wildfires, a persistent environmental menace, are a significant source of harmful gases and particulate emissions. This study leverages the fire radiative power (FRP) method to delineate a comprehensive wildfire emission inventory for Southwest China from 2001 to 2020. Daily fire radiative power data derived from 1 km MODIS Thermal Anomalies/Fire products (MOD14/MYD14) were used to calculate the FRE and combusted biomass. Available emission factors were assigned to three biomass burn types: forest, grass, and shrub fires. Over the span of two decades, we have compiled data and estimated the annual emissions of carbon dioxide (CO2), carbon monoxide (CO), methane (CH4), sulfur dioxide (SO2), ammonia (NH3), nitrogen oxides (NOx), total particulate matter (TPM), black carbon (BC), organic carbon (OC), and non-methane volatile organic compounds (NMVOCs) to be 9809.13, 566.82, 25.79, 5.37, 12.25, 16.67, 133.53, 4.16, 41.81, and 97.23 Gg per year (Gg yr−1), respectively. In terms of fire type, forest fires accounted for the largest portion of total CO2 emissions (59.23%), with grass fires and shrub fires coming in second and third, accounting for 20.41% and 20.36%, respectively. Geographically, Yunnan Province were identified as the major contributor in Southwest China, accounting for 69.67% of the total emissions. Temporally, the maximum emission occurred in 2010 (24263.33 Gg), and the minimum emission occurred in 2017 (2917.66 Gg). And the emissions were mainly concentrated in February (23.33%), March (25.52%), and April (22.61%), which accounted for nearly three-fourths of the total emissions. The results of this study are much higher than those obtained by the burned area method, almost three times as high. In contrast, the results of this study are close to the fire emission data from the GFED4s and GFASv1.2 and QFEDv2.5r1 databases.

Emission characterisation of motorcycles and the potential of co-benefits from selected development scenarios in the urban ecosystem of Hanoi, Vietnam

Abstract Motorcycles are the largest proportion of transport in Hanoi. This study focuses on the emission characteristics and initially evaluates the potential co-benefits of low emission zones (LEZ) applied to motorcycles in Hanoi. The International Vehicle Emissions (IVE) model was designed by the U.S. EPA to calculate emissions from motor vehicles and was used to find the emission factor, the important index for emission characteristics of motorcycle activities, in Hanoi. This study surveyed motorcycle technologies and activities in Hanoi with questionnaires, collected data from GPS devices, and counted traffic flow through video cameras and collaborators teams from 2021 to 2023. EFs for the typical pollutants and GHGs of motorcycles in Hanoi were determined. During running, EFruning is 0.053g/km with PM, 4.8 g/km for CO, 0.006 g/km for SO2, 0.13 g/km NOx, CO2 is 72.55 g/km and CH4 is 0.24 g/km, respectively. When starting, EFstart-up is 0.12 g/time with PM, CO is 12.24 g/time, SO2 is 0.0007 g/time, NOx is 1.05 g/time, CO2 is 6.96 g/time, CH4 is 0.53 g/time and N2O is 0.0009 g/time, respectively. Using the EMISENS model, inventory emissions were calculated at tier 3 for this source via the Bottom-up approach. The EMISENS model calculates emissions resulting from traffic, developed by Bang Ho et al in 2010 uses input data related to traffic activities and three emission factors found from the IVE model to calculate the emissions. The total emissions from motorcycle operations in Hanoi are approximately 5.3 thousand tons of PM and 4.6 million tons of CO2e. The proposed LEZ solution for motorcycles brings climate co-benefits of reducing 1.62 million tons CO2e in scenario 1 - limiting all day and 563.2 thousand tons CO2e in scenario 2 – limit operations from 3:00 p.m. to 8:00 p.m. in 2025. Co-benefits on the environment corresponding to the two scenarios with PM are 1990 and 692 tons. Reducing motorcycle operations during 5 peak hours at LEZ can reduce total emissions from motorcycles in Hanoi by 12%. Meanwhile, the proposed 24-hour LEZ for motorcycles in the area has the potential to reduce GHGs by 35%. These results show that reducing emissions from motorcycles brings climate and environmental co-benefits.

Provincial emission inventory of atmospheric polycyclic aromatic hydrocarbons (PAHs) in China

China, as the largest global emitter of atmospheric polycyclic aromatic hydrocarbons (PAHs), exhibits significant variation in energy structure and technological capabilities among provinces. It is vital to construct a provincial-level emission inventory of PAHs, fully considering these provincial disparities, for assessing the environmental budgets and instituting effective mitigation measures. This study constructed a dataset of provincial PAHs emission factors based on downscaling estimates by incorporating the Human Development Index (HDI). These emission factors were combined with the latest activity rate data to estimate PAHs emissions from individual provinces in China from 2007 to 2017. The national total PAHs emissions in our study were 11.8–12.3% higher than the previous emission inventory results. The validation results indicated that the inventory-based PAHs emissions were significantly correlated with the simulated emissions by an atmospheric box model and watershed model. From 2007 to 2017, nearly three-quarters of provinces declined in PAHs emissions resulting from technological advancements and environmental standard improvements, leading to a 34.4% reduction (from 118.5 to 77.8 Gg) of the national total. The industrial sector was consistently the dominant contributor (51.9–66.4%) with a growing trend in its share. This growth was dominantly contributed by less developed provinces, such as Heilongjiang, Ningxia, and Qinghai, indicating that there was a shift of industrial sectors and associated PAHs emissions from highly concentrated industrial regions to nascent industrial regions. This study provides essential data support for a comprehensive understanding of each province's emission responsibilities regarding PAHs and for assessing the potential risks that PAHs pose to human health and ecosystems in these provinces.

Estimating global 0.1° scale gridded anthropogenic CO2 emissions using TROPOMI NO2 and a data-driven method

Satellite remote sensing is a promising approach for monitoring global CO2 emissions. However, existing satellite-based CO2 observations are too coarse to meet the requirements of fine-scale global mapping. We propose a novel data-driven method to estimate global anthropogenic CO2 emissions at a 0.1° scale, which integrates emissions inventories and satellite data while bypassing the inadequate accuracy of CO2 observations. Due to the co-emitted anthropogenic emissions of nitrogen oxides (NOx = NO + NO2) and CO2, high-resolution NO2 measurements from the TROPOspheric Monitoring Instrument (TROPOMI) are employed to map the global anthropogenic emissions at a global 0.1° scale. We construct the driving features from NO2 data and also incorporate gridded CO2/NOx emission ratios and NOx/NO2 conversion ratios as driving data to describe co-emissions. Both ratios are predicted using a long short-term memory (LSTM) neural network (with an R2 of 0.984 for the CO2/NOx emission ratio and an R2 of 0.980 for the NOx/NO2 conversion ratio). The data-driven model for estimating anthropogenic CO2 emissions is implemented by random forest regression (RFR) and trained using the Emissions Database for Global Atmospheric Research (EDGAR). The satellite-based anthropogenic CO2 emission dataset at a global 0.1° scale agrees well with the national CO2 emission inventories (an R2 of 0.998 with Global Carbon Budget (GCB) and an R2 of 0.996 with EDGAR) and consistent with city-level emission estimates from Carbon Monitor Cities (CMC) with the R2 of 0.824. This data-driven method based on satellite-observed NO2 provides a new perspective for fine-resolution anthropogenic CO2 emissions estimation.

Study on the establishment of air pollutant and carbon emission inventory and collaborative emission reduction potential of China's coking industry from 2012 to 2022

Coking industry is usually regarded as a high pollution and high energy consumption industry. China is accelerating its efforts to reduce pollution and carbon emissions in the industrial sector, which has received little attention as the world's largest producer of coke. Therefore, in this study, the trend of air pollution and carbon emissions in China's coking industry and the path of coordinated emission reduction were studied. The results indicate that the average annual emissions of PM, SO2, NOx, VOCs, and CO2 in China's coking industry from 2012 to 2022 amount to 205.98, 69.47, 193.45, 599.80 Gg and 191.10 Tg, respectively. The main sources of PM, SO2, NOx, VOCs and CO2 in coking industry were coal preparation (51.5 %), charge and pushing (39.5 %), coke oven gas (99.8 %), byproduct recovery (47.0 %) and fuel combustion (87.5 %). The emissions from coking plants in central and southern Shanxi, eastern and southern Hebei, and central Shandong are the most concentrated. Ultra-low emission transformation and deep treatment of VOCs have greatly reduced pollutant emissions in key areas of air pollutant control, but the actual emission reduction effect of these measures has been weakened by the additional emissions caused by the increase of coke production in other non-key areas. The research on synergetic emission reduction path shows that there is a great synergistic benefit between air pollutants and CO2 emission reduction in coking industry. It is estimated that the APeq (air pollutants and carbon equivalent) of China's coking industry in 2025, 2028 and 2030 will decrease by 38.2 %, 63.5 % and 70.8 % respectively compared with 2022. With the continuous promotion of pollution reduction and carbon reduction measures, the emission reduction potential of China's coking industry will gradually shift from key areas to non-key areas.

Evaluation of WRF-Chem PM2.5 simulations in Thailand with different anthropogenic and biomass-burning emissions

Thailand experiences severe air quality issues, predominantly due to PM2.5 pollution that surpasses WHO guidelines. The main sources are attributed to energy production, industrial activities, vehicular emissions, agricultural burning, and transboundary transport of pollutants. Understanding the transport and transformation of these pollutants is necessary for addressing air quality issues. The Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem) provides information about meteorology, chemical reactions, and transport of trace gases and aerosols. The accuracy of WRF-Chem simulations greatly depends on the choice of anthropogenic and biomass burning emissions inventories. This study provides a detailed evaluation of these inventories to model PM2.5 concentrations in Thailand during both haze and off-haze seasons in 2019. We evaluated WRF-Chem using four anthropogenic emission inventories—CAMS-GLOB-ANT, ECLIPSE, HTAP, and REAS—and four biomass burning emissions inventories—FINN1.5, FINN2.5 MOD, FINN2.5 MODVAR, and QFED—using data from ground-based air quality stations, MODIS AOD, and MOPITT CO satellite data. Our findings suggest CAMS-GLOB-ANT performs optimally for North Thailand, while HTAP and REAS are more effective in Eastern Thailand. For biomass burning, FINN1.5 shows superior performance. The study also highlights the challenge in capturing PM2.5 diurnal variability, particularly due to inaccuracies in simulating the planetary boundary layer height during nighttime in complex terrains. Moreover, our analysis exhibits moderate model performances during the off-haze season while using global and regional anthropogenic emissions in Thailand, emphasizing the need for improving anthropogenic inventories for reliable air quality prediction. For biomass burning emissions, updating emission factors to reflect Thailand's specific vegetation types is recommended to improve WRF-Chem's representation of PM2.5 levels.

Estimation of Ammonia Emission Inventory Using Life Cycle Assessment Based on Livestock Manure Flow: A Case Study of the Manure Management Sector in Korea

Ammonia is one of the precursor gases in the formation of particulate matter (PM) that reacts with nitrogen oxides and sulfur oxides in the atmosphere. Based on the Clean Air Policy Support System (CAPSS) of Korea, the annual ammonia emissions amounted to 261,207 tons in 2020 and the agricultural source (manure management sector) contributes the highest proportion of the ammonia inventory. However, the methodology for the study of ammonia emissions in Korea has some limitations regarding the representativeness of the sites selected and the reliability of the measurement method. In this study, we aimed to recalculate the ammonia emissions from the livestock industry in Korea using the UK’s estimation method, which uses the life cycle assessment of livestock manure. Three major animal types, i.e., cattle (beef cattle and dairy cows), pigs and chickens, and three major processes based on the manure flow, i.e., housing, manure storage and treatment and land application processes, were considered. The total ammonia emissions were estimated to be approximately 33% higher than the official ammonia emissions stated by the CAPSS. For the manure flow, the ammonia emissions were the highest from land application processes. The ammonia emissions from dairy cow and poultry manure were much higher than those stated by the CAPSS, while the emissions from beef cattle and pig manure showed similar levels. The methodology used in this study can offer an alternative approach to the ammonia emission estimation of the manure management sector in the agriculture industry of Korea. Korean emission factors based on the manure flow should be developed and applied in the future.

Emission Control in Expressway Systems: Vehicle Emission Inventory and Policy Scenario Analysis

Expressway systems play a vital role in facilitating intercity travels for both passengers and freights, which are also a significant source of vehicle emissions within the transportation sector. This study investigates vehicle emissions from expressway systems using the COPERT model to develop multi-year emission inventories for different pollutants, covering the past and future trends from 2005 to 2030. Thereinto, an integrated SARIMA-SVR method is designed to portray the temporal variation of vehicle population, and the possible future trends of expressway vehicle emissions are predicted through policy scenario analysis. The Jiang–Zhe–Hu Region of China is taken as the case study to analyze emission control in expressway systems. The results indicate that (1) carbon monoxide (CO) and volatile organic compounds (VOCs) present a general upward trend primarily originating from passenger vehicles, while nitrogen oxides (NOx) and inhalable particles (PM) display a slowing upward trend with fluctuations mainly sourcing from freight vehicles; (2) vehicle population constraint is an effective emission control policy, but upgrading the medium- and long-haul transportation structure is necessary to meet the continuous growth of intercity trips. Expressway vehicle emission reduction effectiveness can be further enhanced by curtailing the update frequency of emission standards, along with the scrapping of high-emission vehicles.

A bottom-up approach for greenhouse gas emission estimation at the community level: A case study in Japan

Greenhouse gas (GHG) emission mitigation and climate change adaptation in urban communities are urgent. There is enormous potential to improve the GHG inventory at the community level. This research aims to develop a community-scale GHG emission inventory and improve its accuracy and consistency through a bottom-up approach. This study covers direct and indirect emissions categorized into three scopes and selects the Honjo Waseda community in Japan as a case study community. Energy consumption from different sectors (the residential and commercial, transport, and waste management sectors) is evaluated and calculated through Residential Energy Estimation based on the Daily Activities (REEDA) database, National Census, and first-hand field data. The outcome revealed that the residential and commercial sector emissions rank highest in the total GHG emissions of the community, with a value of 39 %. Translated into the scope concept, the emissions under scopes 2 and 3 account for 39 % and 61 %, respectively. Furthermore, a sensitivity analysis of the implementation of green technologies to reduce GHG emissions is also conducted. Mechanical biological treatment (MBT) implementation could yield the largest emission reduction (18 %–22 %), followed by implementing electric vehicles, ultralightweight vehicles, photovoltaic systems, and ground heat utilization.

Revising VOC emissions speciation improves the simulation of global background ethane and propane.

Non-Methane Volatile Organic Compounds (NMVOCs) generate ozone (O3) when they are oxidized in the presence of oxides of nitrogen, modulate the oxidative capacity of the atmosphere and can lead to the formation of aerosol. Here, we assess the capability of a chemical transport model (GEOS-Chem) to simulate NMVOC concentrations by comparing ethane, propane and higher alkane observations in remote regions from the NOAA Flask Network and the World Meteorological Organization's Global Atmosphere Watch (GAW) network. Using the Community Emissions Data System (CEDS) inventory we find a significant underestimate in the simulated concentration of both ethane (35%) and propane (64%), consistent with previous studies. We run a new simulation where the total mass of anthropogenic NMVOC emitted in a grid box is the same as that used in CEDS, but with the NMVOC speciation derived from regional inventories. For US emissions we use the National Emissions Inventory (NEI), for Europe we use the UK National Atmospheric Emissions Inventory (NAEI), and for China, the Multi-resolution Emission Inventory for China (MEIC). These changes lead to a large increase in the modelled concentrations of ethane, improving the mean model bias from -35% to -4%. Simulated propane also improves (from -64% to -48% mean model bias), but there remains a substantial model underestimate. There were relatively minor changes to other NMVOCs. The low bias in simulated global ethane concentration is essentially removed, resolving one long-term issue in global simulations. Propane concentrations are improved but remain significantly underestimated, suggesting the potential for a missing global propane source. The change in the NMVOC emission speciation results in only minor changes in tropospheric O3 and OH concentrations.

Evaluating CHASER V4.0 global formaldehyde (HCHO) simulations using satellite, aircraft, and ground-based remote-sensing observations

Abstract. Formaldehyde (HCHO), a precursor to tropospheric ozone, is an important tracer of volatile organic compounds (VOCs) in the atmosphere. Two years (2019–2020) of HCHO simulations obtained from the global chemistry transport model CHASER at a horizontal resolution of 2.8° × 2.8° have been evaluated using the Tropospheric Monitoring Instrument (TROPOMI) and multi-axis differential optical absorption spectroscopy (MAX-DOAS) observations. In situ measurements from the Atmospheric Tomography Mission (ATom) in 2018 were used to evaluate the HCHO simulations for 2018. CHASER reproduced the TROPOMI-observed global HCHO spatial distribution with a spatial correlation (r) of 0.93 and a negative bias of 7 %. The model showed a good capability to reproduce the observed magnitude of the HCHO seasonality in different regions, including the background conditions. The discrepancies between the model and satellite in the Asian regions were related mainly to the underestimated and missing anthropogenic emission inventories. The maximum difference between two HCHO simulations based on two different nitrogen oxide (NOx) emission inventories was 20 %. TROPOMI's finer spatial resolution than that of the Ozone Monitoring Instrument (OMI) sensor reduced the global model–satellite root-mean-square error (RMSE) by 20 %. The OMI- and TROPOMI-observed seasonal variations in HCHO abundances were consistent. The simulated seasonality showed better agreement with TROPOMI in most regions. The simulated HCHO and isoprene profiles correlated strongly (R=0.81) with the ATom observations. However, CHASER overestimated HCHO mixing ratios over dense vegetation areas in South America and the remote Pacific region (background condition), mainly within the planetary boundary layer (&lt; 2 km). The simulated seasonal variations in the HCHO columns showed good agreement (R&gt;0.70) with the MAX-DOAS observations and agreed within the 1σ standard deviation of the observed values. However, the temporal correlation (R∼0.40) was moderate on a daily scale. CHASER underestimated the HCHO levels at all sites, and the peak occurrences in the observed and simulated HCHO seasonality differed. The coarseness of the model's resolution could potentially lead to such discrepancies. Sensitivity studies showed that anthropogenic emissions were the highest contributor (up to ∼ 35 %) to the wintertime regional HCHO levels.

An Analysis of Primary Contributing Sources to the PM2.5 Composition in a Port City in Canada Influenced by Traffic, Marine, and Wildfire Emissions

Ambient air quality, specifically fine particulate matter (PM2.5) pollution, poses a global health concern due to its associations with adverse health effects and persistent presence in urban settings. This study investigates PM2.5 exposure in a traffic-influenced residential area in Vancouver, employing chemical speciation data sampled in the vicinity of a major road throughout 2018. Using receptor-oriented models, Positive Matrix Factorization (PMF), and Principal Component Analysis (PCA), major emission sources contributing to the total PM2.5 mass were identified. The Organic and Elemental carbon concentration time series analysis reveals impacts from traffic and episodic influences from wildfires and residential heating. Applying PMF, we attribute identified factors to specific sources: traffic (33%), secondary aerosols (27%), dust (14%), Marine Heavy Fuel Combustion (HFC) (12%), Local (9%), and rare metallic elements (5%), respectively. This apportioning is subsequently verified by PCA, which introduces a sea salt factor to the analysis. Applying the conditional probability function analysis on the integrated wind speed and direction, and the PM concentration data further confirms the partial PM mass apportionment to marine HFC, rare metallic elements, and local factors. Lastly, comparing the results with the 2015 emission inventory of Vancouver shows similarities in apportioning the share of primary sources while overlooking secondary emissions in the latter, emphasizing the importance of field sampling for accurate source characterization. This study highlights the necessity of receptor modeling coupled with field sampling in quantifying the contribution and chemical characteristics of secondary emission sources, particularly near residential neighborhoods. Precise characterization aids health exposure studies that contribute to informed air quality management and policy regulations.

High-Resolution Modeling of Summertime Biogenic Isoprene Emissions in New York City.

As cities strive for ambitious increases in tree canopy cover and reductions in anthropogenic volatile organic compound (AVOC) emissions, accurate assessments of the impacts of biogenic VOCs (BVOCs) on air quality become more important. In this study, we aim to quantify the impact of future urban greening on ozone production. BVOC emissions in dense urban areas are often coarsely represented in regional models. We set up a high-resolution (30 m) MEGAN (The Model of Emissions of Gases and Aerosols from Nature version 3.2) to estimate summertime biogenic isoprene emissions in the New York City metro area (NYC-MEGAN). Coupling an observation-constrained box model with NYC-MEGAN isoprene emissions successfully reproduced the observed isoprene concentrations in the city core. We then estimated future isoprene emissions from likely urban greening scenarios and evaluated the potential impact on future ozone production. NYC-MEGAN predicts up to twice as much isoprene emissions in NYC as the coarse-resolution (1.33 km) Biogenic Emission Inventory System version 3.61 (BEIS) on hot summer days. We find that BVOCs drive ozone production on hot summer days, even in the city core, despite large AVOC emissions. If high isoprene emitting species (e.g., oak trees) are planted, future isoprene emissions could increase by 1.4-2.2 times in the city core, which would result in 8-19 ppbv increases in peak ozone on ozone exceedance days with current NOx concentrations. We recommend planting non- or low-isoprene emitting trees in cities with high NOx concentrations to avoid an increase in the frequency and severity of future ozone exceedance events.

Combining Top‐Down and Bottom‐Up Approaches to Evaluate Recent Trends and Seasonal Patterns in UK N2O Emissions

AbstractAtmospheric trace gas measurements can be used to independently assess national greenhouse gas inventories through inverse modeling. Atmospheric nitrous oxide (N2O) measurements made in the United Kingdom (UK) and Republic of Ireland are used to derive monthly N2O emissions for 2013–2022 using two different inverse methods. We find mean UK emissions of 90.5 ± 23.0 (1σ) and 111.7 ± 32.1 (1σ) Gg N2O yr−1 for 2013–2022, and corresponding trends of −0.68 ± 0.48 (1σ) Gg N2O yr−2 and −2.10 ± 0.72 (1σ) Gg N2O yr−2, respectively, for the two inverse methods. The UK National Atmospheric Emissions Inventory (NAEI) reported mean N2O emissions of 73.9 ± 1.7 (1σ) Gg N2O yr−1 across this period, which is 22%–51% smaller than the emissions derived from atmospheric data. We infer a pronounced seasonal cycle in N2O emissions, with a peak occurring in the spring and a second smaller peak in the late summer for certain years. The springtime peak has a long seasonal decline that contrasts with the sharp rise and fall of N2O emissions estimated from the bottom‐up UK Emissions Model (UKEM). Bayesian inference is used to minimize the seasonal cycle mismatch between the average top‐down (atmospheric data‐based) and bottom‐up (process model and inventory‐based) seasonal emissions at a sub‐sector level. Increasing agricultural manure management and decreasing synthetic fertilizer N2O emissions reduces some of the discrepancy between the average top‐down and bottom‐up seasonal cycles. Other possibilities could also explain these discrepancies, such as missing emissions from NH3 deposition, but these require further investigation.