Search Results (147)

Tropospheric ozone (O₃) is a harmful air pollutant and a short-lived greenhouse gas. To find effective O₃ reduction strategies, it is essential to understand the sensitivity of O₃ concentrations to its precursors, nitrogen oxides (NO_x), and volatile organic compounds (VOC). This study applied the Community Multi-Scale Air Quality model (CMAQ) to assess the effects of domestic anthropogenic emissions in 2015 and 2050. The emission scenarios were based on Japan’s CO₂ reduction targets, assuming an 80% decrease by 2050. Sensitivity analysis was performed by adjusting NO_x and VOC emissions by ±10% and ±20%, respectively, and examining seasonal and regional variations in the O₃ response. The results show that O₃ levels will decrease notably in spring and summer by 2050, although concentrations will still exceed the standards in some areas. NO_x reductions lead to significant O₃ decreases, while VOC reductions show limited benefits, except in urban regions such as Kanto and Kansai. In winter, NO_x reductions may even increase O₃ levels due to weakened titration. Overall, the findings highlight the importance of prioritizing NO_x control measures for effective O₃ mitigation in Japan’s future energy transition. Full article

Despite nationwide control efforts, central China experiences persistently high annual PM_2.5 concentrations (~50 μg/m³), which are particularly severe in January (exceeding 110 μg/m³). This study employs an integrated approach combining a Multiple Linear Regression (MLR) model derived from random forest analysis with the WRF-CMAQ chemical transport modeling system to quantitatively disentangle the driving factors of PM_2.5 concentrations in central China. Key findings reveal significant spatiotemporal heterogeneity in anthropogenic contributions, evidenced by consistently higher north–south gradients in regression residuals (reflecting emission impacts), linked to spatially varying industrial and transportation influences. Critically, the reduction in anthropogenic impacts over six years was substantially smaller in winter (January: 27 to 23 μg/m³) compared to summer (15 to −18 μg/m³, July), highlighting the profound role of emissions in driving severe January pollution events. Furthermore, WRF-CMAQ simulations demonstrated that adverse meteorological conditions in January 2020 counteracted emission controls, causing a net increase in PM_2.5 of +13 μg/m³ relative to 2016, thereby offsetting ~68% of the reductions achieved through emission abatement (−19 μg/m³). Significant regional transport, especially affecting northern and central Henan, further weakened local control efficacy. These quantitative insights into the mechanisms of PM_2.5 pollution, particularly the counteracting effects of meteorology on emission reductions in critical winter periods, provide a vital scientific foundation for designing more effective and targeted air quality management strategies in central China. Full article

Oil depots are continuous sources of volatile organic compounds (VOCs), which contribute to ground-level ozone (O₃) and secondary organic aerosol formation, posing threats to air quality and public health. This study investigated typical crude and refined oil depots in the Xigu District of Lanzhou by measuring VOC source profiles and establishing an emission inventory. The maximum incremental reactivity (MIR) method was applied to assess the chemical reactivity of VOCs; both the emission inventory and VOC profiles were incorporated into the WRF-CMAQ model for numerical simulations. Results showed that the average ambient VOC concentrations were 49.8 μg/m³ for the crude oil depot and 66.1 μg/m³ for the refined oil depot. The crude oil depot was dominated by alkanes (37.1%), aromatics (25.1%), and OVOCs (22.5%), while the refined oil depot was dominated by alkanes (57.3%) and OVOCs (16.7%), with isopentane identified as the most abundant species in both depots. The ozone formation potentials (OFPs) of the crude oil and refined oil depots were 153.1 μg/m³ and 178.3 μg/m³, respectively. Aromatics (47.0%) and OVOCs (29.0%) were the primary contributors at the crude oil depot, with isopentane, o-xylene, etc., as the dominant reactive species. In the refined oil depot, the main contributors were alkanes (27.8%), alkenes and alkynes (26.6%), OVOCs (24.5%), and aromatics (20.5%), among which isopentane, trans-2-butene, etc., were most prominent. In 2023, VOC emissions from the crude oil and refined oil depots were estimated at 1605.3 t and 1287.8 t, respectively, mainly from working loss (96.6%) in the crude oil depot and deck fitting loss (60.7%) and working loss (31.3%) in the refined oil depot. Numerical simulations indicated that oil depot emissions could increase regional MDA8 O₃ concentrations by up to 40.0 μg/m³. At the nearby Lanlian Hotel site, emissions contributed 15.1% of the MDA8 O₃, equivalent to a 6.1 μg/m³ increase, while the citywide average was 1.7 μg/m³. This study enriches the VOC source profile database for oil depots, reveals their significant role in regional O₃ formation, and provides a scientific basis for precise O₃ control and differentiated emission reduction strategies in Northwest China. Full article

Gas-phase chemistry has been identified as a major computational bottleneck in both the forward and adjoint modes of chemical transport models (CTMs). Although previous studies have demonstrated the potential of deep learning models to simulate and accelerate this process, few studies have examined the applicability and performance of these models in adjoint sensitivity analysis. In this study, a deep learning emulator for gas-phase chemistry is developed and trained on a diverse set of forward-mode simulations from the Community Multiscale Air Quality (CMAQ) model. The emulator employs a residual neural network (ResNet) architecture referred to as FiLM-ResNet, which integrates Feature-wise Linear Modulation (FiLM) layers to explicitly account for photochemical and non-photochemical conditions. Validation within a single timestep indicates that the emulator accurately predicts concentration changes for 74% of gas-phase species with coefficient of determination (R²) exceeding 0.999. After embedding the emulator into the CTM, multi-timestep simulation over one week shows close agreement with the numerical model. For the adjoint mode, we compute the sensitivities of ozone (O₃) with respect to O₃, nitric oxide (NO), nitrogen dioxide (NO₂), hydroxyl radical (OH) and isoprene (ISOP) using automatic differentiation, with the emulator-based adjoint results achieving a maximum R² of 0.995 in single timestep evaluations compared to the numerical adjoint sensitivities. A 24 h adjoint simulation reveals that the emulator maintains spatially consistent adjoint sensitivity distributions compared to the numerical model across most grid cells. In terms of computational efficiency, the emulator achieves speed-ups of 80×–130× in the forward mode and 45×–102× in the adjoint mode, depending on whether inference is executed on Central Processing Unit (CPU) or Graphics Processing Unit (GPU). These findings demonstrate that, once the emulator is accurately trained to reproduce forward-mode gas-phase chemistry, it can be effectively applied in adjoint sensitivity analysis. This approach offers a promising alternative approach to numerical adjoint frameworks in CTMs. Full article

Aromatics and their key oxidation intermediate such as formaldehyde and dicarbonyl compounds (glyoxal and methyglyoxal) are crucial precursors for ozone (O₃) and secondary organic aerosols (SOA). However, the spatial–temporal variation in aromatics’ contribution to these intermediate species and O₃/SOA over Eastern China during the past decades remains insufficiently quantified. This study combines satellite observations of formaldehyde and glyoxal column densities (2008–2014) with an innovative tracer method implemented in the Community Multiscale Air Quality (CMAQ) modeling system to quantify aromatic-driven dicarbonyl chemistry. Simulations of summer and autumn in 2010, 2012, 2014, and 2016 are conducted to demonstrate the change in aromatics and its impact through the years. Estimated primary and intermediate VOCs show good consistency with measurements at a supersite; and the simulated vertical column density of formaldehyde and glyoxal agree with satellite observations in spatial distributions. The contribution of aromatic hydrocarbons to the columnar concentration of glyoxal has seen a significant increase since 2010, which can, to some extent, explain the interannual trend of glyoxal column concentrations in key regions of Beijing–Tianjin–Heibei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD). A cross-comparison reveals a good consistency between the observed glyoxal columnar concentrations to formaldehyde columnar concentration ratio (R_GF) from satellite measurements and the high contribution areas of aromatics to glyoxal: pronounced values are observed in the above three key regions in Eastern China. Additionally, the applicability of R_GF and its indicative nature in Eastern China was discussed, revealing notable seasonal and regional variations in R_GF. Revised R_GF thresholds ([0.015–0.03] for models vs. [0.04–0.06] for satellites) improve summer precursor classification, while a threshold of >0.04 could distinguish the areas with high anthropogenic impacts during autumn. These findings advance understanding of VOC oxidation pathways in polluted regions, providing critical insights for ozone and secondary organic aerosol mitigation strategies. The integrated satellite model approach demonstrates the growing atmospheric influence of aromatics amid changing emission patterns in Eastern China. Full article

Ozone pollution in densely populated urban regions poses a great threat to public health, due to the intensive anthropogenic emissions of ozone precursors and is further aggravated by global warming and the urban heat island phenomenon. Air quality models have been utilized to formulate and evaluate air pollution control strategies. This study presents a comprehensive modeling assessment of ozone mitigation strategies during an ozone pollution episode in Changzhou, an industrial city in the Yangtze River Delta region. Utilizing the Community Multiscale Air Quality Modeling System (CMAQ), we quantified the contribution of ozone from different emission sectors and counties within Changzhou using the integrated source apportionment method (ISAM). During the pollution period, local emissions within Changzhou account for an average of 41.5% of MDA8 ozone, with particularly notable contributions from Jingkai (11.2%), Wujin (9.5%), and Liyang (7.8%). Upon these findings, we evaluated three sets of emission reduction scenarios: uniform, sector-specific, and county-specific reductions. Results show that industry and transportation are responsible for over 20% of ozone concentrations, and targeted reductions in these sources yielded the most significant decreases in ozone levels. Notably, reducing industrial emissions alone decreased ozone concentrations by 3.2 μg m⁻³ during the pollution episode. County-specific reductions revealed the importance of targeted strategies, with certain counties showing more pronounced responses to emission controls. On a daily basis, emission reductions in Xinbei contributed to a maximum ozone decrease of 4.4 μg m⁻³. This study provides valuable insights into the efficacy of different mitigation measures in Changzhou and offers a practical and useful framework for policymakers to implement strategies while addressing the complexities of urban air quality management. Full article

Accurately identifying the sources of fine particulate matter (PM_2.5) pollution is crucial for pollution control and public health protection. Taking the PM_2.5 pollution event that occurred in Suzhou in June 2023 as a typical case, this study analyzed the characteristics and components of PM_2.5, and quantified the contributions of meteorological conditions, regional transport, and local emissions to the summertime PM_2.5 surge in a typical Yangtze River Delta (YRD) city. Chemical composition analysis highlighted a sharp increase in nitrate ions (NO₃⁻, contributing up to 49% during peak pollution), with calcium ion (Ca²⁺) and sulfate ion (SO₄²⁻) concentrations rising to 2 times and 7.5 times those of clean periods, respectively. Results from the random forest model demonstrated that emission sources (74%) dominated this pollution episode, significantly surpassing the meteorological contribution (26%). The Weather Research and Forecasting model combined with the Community Multiscale Air Quality model (WRF–CMAQ) further revealed that local emissions contributed the most to PM_2.5 concentrations in Suzhou (46.3%), while external transport primarily originated from upwind cities such as Shanghai and Jiaxing. The findings indicate synergistic effects from dust sources, industrial emissions, and mobile sources. Validation using electricity consumption and key enterprise emission data confirmed that intensive local industrial activities exacerbated PM_2.5 accumulation. Recommendations include strengthening regulations on local industrial and mobile source emissions, and enhancing regional joint prevention and control mechanisms to mitigate cross-boundary transport impacts. Full article

Accurate air quality forecasting is essential for environmental management and health protection. However, conventional air quality models often exhibit systematic biases and underpredict pollution events due to uncertainties in emissions, meteorology, and atmospheric processes. Addressing these limitations, this study introduces a hybrid deep learning model that integrates convolutional neural networks (CNNs) and Long Short-Term Memory (LSTM) for ozone forecast bias correction. The model is trained here, using data from ten stations in Texas, enabling it to capture both spatial and temporal patterns in atmospheric behavior. Performance evaluation shows notable improvements, with a Root Mean Square Error (RMSE) reduction ranging from 34.11% to 71.63%. F1 scores for peak detection improved by up to 37.38%, Dynamic Time Warping (DTW) distance decreased by 72.77%, the Index of Agreement rose up to 90.09%, and the R² improved by up to 188.80%. A comparison of four loss functions—Mean Square Error (MSE), Huber, Asymmetric Mean Squared Error (AMSE), and Quantile Loss—revealed that MSE offered balanced performance, Huber Loss achieved the highest reduction in systematic RMSE, and AMSE performed best in peak detection. Additionally, four deep learning architectures were evaluated: baseline CNN-LSTM, a hybrid model with attention mechanisms, a transformer-based model, and an End-to-End framework. The hybrid attention-based model consistently outperformed others across metrics while maintaining lower computational demands. Full article

Firstly, this study investigates the spatiotemporal distribution characteristics of the ozone (O₃) pollution in Liaoyuan City using monitoring data from 2015 to 2024. Then, three machine learning models (ML)—random forest (RF), support vector machine (SVM), and artificial neural network (ANN)—are employed to quantify the influence of meteorological and non-meteorological factors on O₃ concentrations. Finally, the HYSPLIT clustering method and CMAQ model are utilized to analyze inter-regional transport characteristics, identifying the causes of O₃ pollution. The results indicate that O₃ pollution in Liaoyuan exhibits a distinct seasonal pattern, with the highest concentrations found in spring and summer, peaking in the afternoon. Among the three ML models, the random forest model demonstrates the best predictive performance (R² = 0.9043). Feature importance identifies NO₂ as the primary driving factor, followed by meteorological conditions in the second quarter and land surface characteristics. Furthermore, regional transport significantly contributes to O₃ pollution, with approximately 80% of air mass trajectories in heavily polluted episodes originating from adjacent industrial areas and the sea. The combined effects of transboundary precursors and O₃ transport with local emissions and meteorological conditions further increase the O₃ pollution level. This study highlights the need to strengthen coordinated NO_X and VOCs emission reductions and enhance regional joint prevention and control strategies in China. Full article

Air quality in Guangdong Province has improved in recent years, but progress varies across different provincial sub-regions, particularly between Pearl River Delta (PRD) and non-PRD (NPRD) regions. To unveil possible causes of this, this study established a high-resolution gridded emission inventory for Guangdong (2006–2020) by integrating multi-year Point of Interest (POI) data and road network information. The spatiotemporal evolutions of anthropogenic sulfur dioxide (SO₂), nitrous oxide (NO_X), and particulate matter (PM₁₀ and PM_2.5) emissions were analyzed, with a focus on their impacts on PM_2.5 pollution using the CMAQ model. Spatial shifts in emission sources were quantified using spatial statistical methods, including the average nearest neighbor index (ANNI), kernel density analysis (KDA), standard deviational ellipse (SDE), and mean center (MC). From 2006 to 2020, emissions decreased significantly for SO₂ (88%), NO_X (26%), PM₁₀ (64%), and PM_2.5 (68%). Emission hotspots shifted toward NPRD regions, driven by stricter environmental policies and industrial restructuring, lowering PRD-to-NPRD emission ratios for SO₂ (from 1.25 to 0.87), NO_X (1.67–1.51), and PM₁₀ (0.94–0.89). The spatial evolution of emissions varied across sources. For example, the emission share of industrial sources in the PRD declined despite an increase in enterprises, whereas vehicle emissions remained concentrated in the PRD. CMAQ modeling results revealed that overall emission reductions from 2012 to 2020 lowered provincial PM_2.5 concentrations by 9.2–10.5 μg/m³. Accounting for spatial evolution further enhanced PM_2.5 reductions in the PRD by 1.4 μg/m³ (April) and 1.1 μg/m³ (October). Conversely, PM_2.5 improvements in NPRD regions weakened, with reductions declining by 0.2–3.2 μg/m³ (April) and 0.1–1.4 μg/m³ (October). These findings provide guidance for formulating region-specific strategies, aiming for more equitable air quality improvements across Guangdong. Full article

Air pollution, particularly fine particulate matter (PM_2.5), poses significant environmental and public health challenges in South Korea. The National Institute of Environmental Research (NIER) currently relies on numerical models such as the Community Multiscale Air Quality (CMAQ) model for PM_2.5 forecasting. However, these models exhibit inherent uncertainties due to limitations in emission inventories, meteorological inputs, and model frameworks. To address these challenges, this study evaluates and compares the forecasting performance of two alternative models: Long Short-Term Memory (LSTM), a deep learning model, and Seasonal Auto Regressive Integrated Moving Average with Exogenous Variables (SARIMAX), a statistical model. The performance evaluation was focused on Seoul, South Korea, and took place over different forecast lead times (D00–D02). The results indicate that for short-term forecasts (D00), SARIMAX outperformed LSTM in all statistical metrics, particularly in detecting high PM_2.5 concentrations, with a 19.43% higher Probability of Detection (POD). However, SARIMAX exhibited a sharp performance decline in extended forecasts (D01–D02). In contrast, LSTM demonstrated relatively stable accuracy over longer lead times, effectively capturing complex PM_2.5 concentration patterns, particularly during high-concentration episodes. These findings highlight the strengths and limitations of statistical and deep learning models. While SARIMAX excels in short-term forecasting with limited training data, LSTM proves advantageous for long-term forecasting, benefiting from its ability to learn complex temporal patterns from historical data. The results suggest that an integrated air quality forecasting system combining numerical, statistical, and machine learning approaches could enhance PM_2.5 forecasting accuracy. Full article

PM_2.5 concentrations in major port cities in the Republic of Korea, such as Incheon and Busan, are as serious as those in land-based metropolises, such as Seoul, and fine dust generated in port cities is mainly emitted from ships. To identify the specific substances influencing local air quality, the occurrence and effects of high concentrations of PM_2.5 at the ports of Incheon and Busan were analyzed. To analyze the effects of improving air quality based on the Republic of Korea’s port and ship-related reduction measures, we calculated an emissions forecast for 2025 following the implementation/non-implementation of these measures and analyzed all impacts using the WRF-SMOKE-CMAQ modeling system. The ratio of ionic components constituting PM_2.5 at the ports of Incheon and Busan was generally high in nitrate composition; however, the ratio of sulfate was high on high PM_2.5 concentration days. When implementing measures to reduce emissions related to ports and ships, forecasted PM_2.5 and SO₂ emissions showed substantial decreases in port areas as well as nearby land and sea areas. The associated decrease in the PM_2.5 concentration was highly influential in reducing the concentration of sulfate. Full article

Due to the “One City, One Policy” for air pollution prevention and control measures, Tai’an City was the only city in Shandong Province with a year-on-year decrease in O₃ concentrations in 2022. In this study, the WRF-CMAQ model was used to simulate the O₃ concentrations in Tai’an and other inland cities in Shandong Province in September 2022, and the model evaluation method was applied to discover the differences in the O₃ concentrations between Tai’an and other cities. During the periods of high maximum daily 8 h average O₃ (MDA8 O₃), the model only overestimated the O₃ concentrations in Tai’an by 3.4% and underestimated those in other inland cities by −11.0% to −2.2%. Dozens of O₃ simulation scenarios were designed on the basis of the control of O₃ precursor emissions, and the results indicate that the O₃ precursor emissions in Tai’an were at a lower level. On this basis, the impacts of meteorological conditions and O₃ precursor emission changes on O₃ concentrations in Tai’an were quantified. Adverse meteorological conditions and changes in emissions from other inland cities led to a 49.5 µg/m³ increase in the mean MDA8 O₃ in Tai’an during the study period. However, the local emission reduction measures in Tai’an, to some extent, offset these adverse effects, reducing the mean MDA8 O₃ by 5.8 µg/m³. In summary, the Tai’an City might implement effective emission reduction measures during periods of high MDA8 O₃, thereby achieving a reduction in overall O₃ concentrations. This effort secured its leading position in Shandong Province’s O_3–8h-90per ranking in 2022. Full article

Ammonia (NH₃) is an important precursor to airborne fine particulate matter (PM_2.5) which causes significant health issues and can significantly impact terrestrial and aquatic ecosystems through deposition. The largest source of NH₃ emissions in the UK is agriculture, including animal husbandry and NH₃-based fertilizer applications. This study investigates the impact of mitigation measures targeting UK NH₃ emissions from farming activities, focusing on their implications for air quality and nitrogen deposition in 2030. A series of mitigation scenarios—low2030, medium2030, and high2030—were developed through engagement with stakeholders, including farmers, advisers, and researchers, and their impact was modelled using the CMAQ air quality model. These scenarios represent varying levels of the uptake of mitigation measures compared to a baseline (base2030). The results indicate that reductions in total NH₃ emissions across the UK could reach up to 13% under the high2030 scenario (but reaching nearly 20% for some regions). These reductions can lead to significant decreases in NH₃ concentrations in some parts of the UK (up to 22%, ~1.2 µg/m³) but with a mean reduction of 8% across the UK. However, the reductions have a limited effect on fine ammonium particulate (${\mathrm{N}\mathrm{H}}_4^{+}$) concentrations, achieving only modest reductions of up to 4%, with mean reductions of 1.6–1.9% due to a NH₃-rich atmosphere. Consequently, the mitigation measures have minimal impact on secondary inorganic aerosol formation and PM_2.5 concentrations, aligning with findings from other studies in Europe and beyond. These results suggest that addressing the primary sources of PM_2.5 or other PM_2.5 precursors, either alone or in combination with NH₃, may be necessary for more substantial air quality improvements. In terms of nitrogen (N) deposition, reductions in NH₃ emissions primarily affect NH₃ dry deposition, which constitutes approximately two-thirds of reduced nitrogen deposition. Total N deposition declines by 15–18% in source regions depending on the scenario, but national average reductions remain modest (~4%). While the study emphasizes annual estimates, further analyses focusing on finer temporal scales (e.g., daily or seasonal) could provide additional insights into exposure impacts. This research highlights the need for integrated mitigation strategies addressing multiple pollutants to achieve meaningful reductions in air pollution and nitrogen deposition. Full article

The accurate quantification of anthropogenic carbon dioxide (CO₂) emissions in urban areas is hindered by high uncertainties in emission inventories. We assessed the spatial distributions of three anthropogenic CO₂ emission inventories in Shanghai, China—MEIC (0.25° × 0.25°), ODIAC (1 km × 1 km), and a local inventory (LOCAL) (4 km × 4 km)—and compared simulated CO₂ column concentrations (XCO₂) from WRF-CMAQ against OCO-3 satellite Snapshot Mode XCO₂ observations. Emissions differ by up to a factor of 2.6 among the inventories. ODIAC shows the highest emissions, particularly in densely populated areas, reaching 4.6 and 8.5 times for MEIC and LOCAL in the central area, respectively. Emission hotspots of ODIAC and MEIC are the city center, while those of LOCAL are point sources. Overall, by comparing the simulated XCO₂ values driven by three emission inventories and the WRF-CMAQ model with OCO-3 satellite XCO₂ observations, LOCAL demonstrates the highest accuracy with slight underestimation, whereas ODIAC overestimates the most. Regionally, ODIAC performs better in densely populated areas but overestimates by around 0.22 kt/d/km² in relatively sparsely populated districts. LOCAL underestimates by 0.39 kt/d/km² in the center area but is relatively accurate near point sources. Moreover, MEIC’s coarse resolution causes substantial regional errors. These findings provide critical insights into spatial variability and precision errors in emission inventories, which are essential for improving urban carbon inversion. Full article