Impacts of climate-driven vegetation changes on air quality over East Asia: Modulation of biogenic VOC emissions and secondary pollutants.

Operation-resolved VOC emissions from electronics manufacturing: Implications for ozone formation, toxicity, and risk-oriented management.

Atmospheric profiles and human exposure of industrial sector-specific emissions of antimony bound in fine particulate matter.

Differential patterns and controls of N2O emission from coastal rivers with varying land uses and salinity in northern China.

Role of n-alkanes in identifying biogenic and anthropogenic sources of ambient aerosols.

Improved O3 attribution and health impact assessment using RSM-based inverse emission inventories in the greater bay area.

Indoor air pollution during cooking and cleaning is influenced by complex interactions between direct emissions, ventilation-driven outdoor/indoor exchange, deposition and chemical reactions on surfaces, and indoor chemical processing. This study developed a flexible indoor single-box model (SBM-Flex) based on the INCHEM-Py indoor chemistry model to improve the representation of indoor chemistry and pollutant dynamics. The model contains chemical mechanisms of differing complexities, which can be chosen to balance computational efficiency and accuracy for specific applications. SBM-Flex was evaluated against a new observational dataset collected with reference instruments in a residential kitchen to evaluate the model's ability to simulate real-world conditions. The model qualitatively and quantitatively reproduces background conditions and episodic emission events, particularly for NOx, CO, and monoterpenes. We showed that a revised HONO formation scheme, incorporating relative humidity dependence, improves the process-level representation of indoor radical chemistry, resulting in a more realistic HONO, OH and HO2 description compared to a static HONO treatment. Simulated cooking and cleaning events highlight the importance of event-specific emissions and occupant-related effects, particularly enhanced surface deposition due to human presence, which influences O3 removal. Comparison between measurement-informed and inventory-based emissions reveals significant discrepancies, with inventory emissions often, though not uniformly, higher than real-world values. These findings underscore the need for activity-specific emission inventories and improved ventilation representation. SBM-Flex offers a promising approach for indoor air quality modelling providing valuable insights into the key processes that govern pollutant behaviour in residential environments and hence identifying priorities to reduce exposure and protect health.

Abstract. As the global automotive industry continues to grow rapidly, the increasing number of passenger vehicles has contributed to worsening air pollution. However, previous studies have insufficiently addressed nationwide hourly vehicle emissions. This study firstly utilized big data of ride-hailing services and traffic flow model to obtain nationwide hourly gridded speed and traffic volume. Then we established a high spatiotemporal resolution (0.01° × 0.01°; 1 h) emission inventory by using multiple correction factors. The annual amounts of CO, VOCs, NOx, PM and NH3 emitted from national passenger vehicles in 2019 were 4087.8, 1069.4, 211.7, 1.9, 77.5 kt, respectively. Despite occupying merely 0.8 % of the national territory, urban areas generated 35.3 % of the country's total vehicle emissions, due to high local traffic volumes and relatively low vehicle speeds. From a temporal perspective, passenger vehicle emissions exhibit significant holiday effect and weekend effect. In addition, hourly average emissions on workday exceeded those of weekend and holiday by 8 % and 5 % during the morning peak, with these differences increasing to 12 % and 18 % during the evening peak. Current traditional emission methodology might underestimate emissions by 31.5 %. We also used the WRF-Chem model for simulation validation. This hourly-scale inventory provides quantitative support for the precise implementation of pollution control and early warning.

Coastal communities neighboring port terminals are impacted by the air pollutants emitted by vessels during their activities. One of the sustainable management tools adopted by these cities is the inventories preparation of local pollutants and greenhouse gases (GHG) to quantify these atmospheric emissions. There are a variety of bottom-up methodologies developed to estimate air emissions from ships. Given this fact, the objective of this study was to evaluate the methodologies proposed by the United States Environmental Protection Agency (US EPA), the California Air Resources Board (CARB), and the European Environmental Agency (EEA), to highlight the assumptions, limitations, and uncertainties associated with the application of each one. Only the US EPA and CARB methodologies proposed estimation for GHG (CO2). In addition, the analysis showed that although the methodologies adopt the same approach (bottom-up methodology), the results can be quite different due to the input data that each one requested. This fact highlights the need for the development of experimental studies to obtain emission factors appropriate to the circulating vessels fleet, to reduce uncertainties in the estimates, since reliable emission inventories are a subsidy for the elaboration of environmental management policies and the creation of atmospheric emission control programs in port regions.

The maritime sector is a significant contributor to greenhouse gases and air pollutant emissions, accounting for approximately 3% of global CO 2 emissions in 2018, a figure expected to rise to 17% by 2050. To address these challenges, the authorities put forward initiatives for reducing emissions through technical and operational strategies. Accurate emissions estimation is critical for evaluating the impact of these measures and technologies on emissions reductions. The main method used in the EU for maritime emissions inventorying and projections is the European Monitoring and Evaluation Programme/European Environment Agency (EMEP/EEA) air pollutant emission inventory guidebook methodology. This study identifies key limitations in the current European shipping guidebook methodology, which has not been updated since 2010, and proposes enhancements improving accuracy and relevance. Utilizing a dataset of around 15,000 vessels trading in European ports, the analysis reveals the need to expand ship categories from nine to nineteen to reflect the current fleet profile. The study proposes updates to technical and operational parameters, like main and auxiliary engine specifications, speed profiles, engine loads and operational phase durations. The inclusion of the anchorage phase in emissions calculations is also recommended to address emissions near port areas. Validation against MRV-reported emissions demonstrates that the recommended approach reduces CO 2 estimation deviations from 52.1% to 12.3% compared to the original methodology, with improvements for other harmful air pollutants. Such updates will assist stakeholders in devising better targeted emissions reduction strategies and align the European guidebook with advancements in maritime technologies and operational practices. • Fleet in European ports during 2022: 15,000 ships analyzed • Proposed ship categories for introduction in methodology expanded from 9 to 19 • Updates reduce CO 2 deviations from 52.1% to 12.3% compared to official database • Anchorage phase inclusion in emission calculations is recommended • Better estimates of climate-relevant and air pollutant emissions

Although the adverse health effects of particulate matter (PM) are often considered in terms of mass and particle size, a growing number of studies now focus on new health metrics such as the oxidative potential (OP) of PM, highlighting the role of certain metals, among them copper (Cu), iron (Fe), and manganese (Mn) for their capacity to impact OP levels in ambient air. In this study, we built on a previously-developed European anthropogenic emission inventory for Cu, Fe and Mn to simulate PM<inf>10</inf> concentrations of these metals within PM<inf>10</inf> during a two-year period with the WRF-CHIMERE chemistry transport model, applying a source-tagging method. We also estimated the contribution of erosion dust (mostly of African origin) and urban dust (mostly road and construction dust) as significant sources for each of these metals. For validation purpose, we compared the simulation results with a large database of measurements collected from various sources and locations. The medians of the fractional mean biases for Cu are close to zero for both rural and urban background sites, indicating that the emission in the inventory adequately represent atmospheric Cu concentrations. However, the biases are highly negative for Fe and Mn in a simulation when only considering anthropogenic emissions. The correlation coefficients obtained for (rural and urban) background stations range from 0.4 to 0.6, with urban sites showing slightly higher correlations for Fe but slightly lower ones for Cu and Mn. The inclusion of desert dust sources improves the correlation for some rural stations, while including local urban dust has a less pronounced effect. However, neither source fully explains the significant biases for Fe and Mn concentrations. Overall, this study calls for further works to try and elucidate the missing sources of Fe and Mn, revisit our previously developed emission inventory, and refine the share of PM resuspension to ambient air concentrations of key metallic compounds.

Neglected but potentially significant emissions of unintentional persistent organic pollutants from primary copper smelting industry.

Deepening insights into the spatiotemporal dynamics and multi-scenario projections of Pb, Hg, Cr, Cd, and As emissions from China's copper-lead-zinc smelting industry.

Abstract Reliable city-level energy use and energy-related CO₂ inventories are essential for evidence-based climate governance, yet city energy statistics by energy type and sector remain unavailable for most Chinese prefecture-level cities. Conventional top-down allocation (TDA) method based on province-average energy intensity are therefore widely used, but they assume that all cities within the same province have the same energy intensity because city-level energy intensity data were previously unavailable. This may lead to substantial deviations between the estimated results and actual city-level energy use. To address this problem, this study develops a modified top-down allocation (MTDA) method by introducing the ratio of city-level to provincial energy intensity into the allocation coefficient. Applied to Guangdong Province from 2010 to 2022, the method constructs continuous city-level energy-related CO2 inventories for 21 cities. Benchmark validation against published city statistics shows strong agreement between the MTDA estimates and observed values (R2 = 0.99), supporting the reliability of the reconstructed inventory. The difference between MTDA and TDA increases with the deviation of city-level energy intensity from the provincial average (R2 = 0.908), indicating that the intensity adjustment captures inter-city heterogeneity. Based on the joint pattern of emission scale and carbon intensity, the 21 cities are classified into three categories: high emission scale-dominated cities, high emission intensity-dominated cities, and moderate emission cities. The results further show that, in Guangdong, urban energy system optimization, production-side industrial decarbonization, and low-carbon development-path guidance are the key mitigation directions for these three categories, respectively.

VOC emissions from asphalt: Laboratory oxidation, ultrafine particle formation, and urban air quality implications.

Future trends and driving factors of ozone pollution in China under the carbon neutrality target using an ensemble machine learning approach.

This study examines the relationship between ship traffic and ambient air pollutant concentrations in Quintero Bay, Chile, an industrial coastal area characterized by intense port activity. The analysis integrates air quality measurements from the national monitoring network (SINCA) with vessel movement data derived from the AIS, combined with advanced time–frequency analysis techniques. Unlike traditional emission inventories, which aim to quantify emissions at the source, SINCA provides real-time ambient pollutant concentrations expressed in µg/m 3 and ppb. Therefore, the objective of this study is not to directly estimate ship emissions, but to evaluate how variations in maritime activity co-vary with observed pollutant levels at air quality monitoring stations. The investigation focuses on pollutants commonly associated with shipping activities, including PM 10 and PM 2.5 , NO x , SO 2 , and CO. A detailed characterization of the vessel fleet operating in the study area was performed using both static and dynamic AIS data. This included ship type classification, estimation of main engine power based on gross-tonnage models, and calculation of engine load distributions derived from AIS-reported vessel speeds. Although these parameters allow the estimation of emission factors, the present work emphasizes identifying temporal associations between ship movements and ambient pollutant concentrations rather than quantifying emissions. Additionally, pollution roses and Conditional Probability Functions (CPF) were used to evaluate the directional origin of high-concentration episodes in relation to prevailing wind conditions. The results show that several episodes of elevated NO x , SO 2 , and PM 10 concentrations exhibit significant coherence with ship activity, particularly at daily time scales influenced by local meteorology. Among the analyzed pollutants, PM 10 displays the strongest and most recurrent association with maritime traffic, highlighting the influence of port operations on air quality in Quintero Bay.

Abstract. India has high sulfur dioxide (SO2) emissions, primarily due to its extensive coal-fired power sector. SO2 column observations from Sentinel-5P Tropospheric Monitoring Instrument (TROPOMI) enables observation-based emission estimates using inversion techniques. Among inversion methods, the flux-divergence method is particularly sensitive to point source emissions and well-suited for estimating SO2 emissions in India. However, when applied to satellite observations, this method tends to spatially spread calculated emissions into neighboring grid cells around the source. This spreading effect weakens the emission signal at the exact source location, making precise quantification of emissions more difficult. In this paper, we design a sharpening algorithm to reverse the spreading and sharpen the emission signals while conserving total mass of the emissions. We apply the algorithm on gridded SO2 emissions at a high spatial resolution of 0.025° × 0.025° (≈ 2.5 km × 2.5 km) derived from TROPOMI observations that have a typical mean footprint size of 6.0 km × 6.0 km. After sharpening, the effective spatial resolution of the emissions matches the grid cell resolution. Emissions from point sources increase at their exact locations, while emissions in neighboring grid cells decrease. In the resulting SO2 emission inventory, about 80 % of coal-fired power plants with capacities above 100 MW are detected at their correct location, while the remaining 20 % fall below the detection threshold. The detected power plants account for 99 % of India's total coal-based power generation. We also identify twenty two previously unreported SO2 point sources, including coal-based thermal power plants, cement factories, crude oil production facilities, chemical fertilizers factory, and copper, steel, and aluminum industries. This sharpening algorithm improves emission detection and can also be extended to other pollutants emitted by point sources to enhance the accuracy of emission inventories.

Abstract. Nitrogen oxides are one of the most important air pollutants with a large impact on human health. Their emissions are monitored by national emission inventories that are the basis for emission related policies. Because of their large impact on policies these emission data should ideally be verified against independent data, such as emission estimates derived from atmospheric observations. Here, we present a detailed comparison of NOx emissions from the Dutch national emission inventory with completely independent emission data derived with the DECSO algorithm from satellite observations by TROPOMI on board of sentinel 5-P. This is enabled by the introduction of a new high-resolution DECSO version DECSO-HR 6.5. We find good agreement in overall emission levels, the spatial emission pattern, the 5-year emission trend, and regional emissions, with deviations in the yearly variation of emissions and at large point sources. Our results demonstrate the robustness of the national inventory and the satellite-derived emissions. This approach might serve as a use-case for the adoption of similar methods in other countries.

Due to urbanization and industrialization, there are significant regional differences in carbon emissions, making it increasingly urgent and necessary to conduct an in-depth examination of carbon emission trends from energy consumption across various sectors at the provincial level. Taking Hebei Province, a major carbon-emitting province in China, as a case study, we analyzed carbon emissions from three perspectives: historical emissions, influencing factors, and scenario projections. First, we established a carbon emission inventory for energy consumption. Second, using the integrated LMDI-SD-MC framework, we constructed four subsystems economy, society, energy, and technology and employed three scenarios for forecasting. The results show that: (1) Carbon emissions in Hebei Province from 2003 to 2021 exhibited increased trend year by year, with the share of coal and coke decreasing and the share of natural gas increasing. The industry, residential, and transportation sectors accounted for more than 95% of total carbon emissions. (2) In terms of influencing factors, energy intensity and the level of economic development contributed the most significantly, with contribution rates of −75.97% and 195.97%, respectively. (3) Among the scenario projections, the low-carbon development scenario is the most suitable for Hebei Province, enabling the province to achieve its “Dual Carbon” goals as scheduled. Under the baseline development scenario, the peak is reached in 2040. Under the rapid development scenario, carbon emissions will reach 1130.86 106 tons by 2060. (4) Uncertainty analysis using Monte Carlo simulation for all three scenarios showed errors within ±10%, indicating that the model results are robust and interpretable. This study provides a provincial level emission reduction perspective for China to achieve its “Dual Carbon” goals and sustainable development.