Atmospheric Pollution Control Research Articles

The Role of Environmental Regulations in Shaping Companies' ESG Performance: Evidence from China's Prevention and Control of Atmospheric Pollution

The Role of Environmental Regulations in Shaping Companies' ESG Performance: Evidence from China's Prevention and Control of Atmospheric Pollution

Study on main sources of aerosol pH and new methods for additional reduction of PM2.5 during winter severe pollution: Based on the PMF-GAS model

In recent years, the air quality in urban areas of China has been a significant improvement. However, further advancements in air quality remain challenging, particularly in addressing the persistent problem of severe particulate matter (PM) pollution in winter. In-depth research on the aerosol acidity (pH) is beneficial for analyzing the pollution characteristics of PM and providing support for the continuous improvement of PM pollution. In this study, the sources of water-soluble inorganic ions (WSIIs) of PM2.5 in Zhengzhou were analyzed based on the PMF-GAS method, and the contribution of different sources to aerosol acidity was further investigated in conjunction with the ISORROPIA-II thermodynamic model. The research results indicate that secondary aerosols are the main source, and controlling the emissions of secondary aerosols and gaseous precursors is a crucial measure for reducing PM2.5 concentrations. Furthermore, the study revealed that targeted reductions of major pollution sources across varying pH ranges could significantly lower PM2.5 concentrations. In the range of pH < 4, when the contribution of secondary aerosol source is 23.9%, PM2.5 concentrations can be reduced by 40.6%, resulting in an additional emission reduction of 5.9 μg m−3. In the range of 4 < pH < 6, PM2.5 can be reduced by 36.9% at a contribution of 16.5% from secondary aerosol source, achieving an additional emission reduction of 7.0 μg m−3. This research provides important theoretical support for gaining a deeper understanding of the sources and control of atmospheric pollution, offering strategies to expedite the achievement of PM2.5 standards and further mitigating its impacts on human health and the environment.

Enhancing the Performance of BaxMnO3 (x = 1, 0.9, 0.8 and 0.7) Perovskites as Catalysts for CO Oxidation by Decreasing the Ba Content.

Mixed oxides featuring perovskite-type structures (ABO3) offer promising catalytic properties for applications focused on the control of atmospheric pollution. In this work, a series of BaxMnO3 (x = 1, 0.9, 0.8 and 0.7) samples have been synthesized, characterized and tested as catalysts for CO oxidation reaction in conditions close to that found in the exhausts of last-generation automotive internal combustion engines. All samples were observed to be active as catalysts for CO oxidation during CO-TPRe tests, with Ba0.7MnO3 (B0.7M) being the most active one, as it presents the highest amount of oxygen vacancies (which act as active sites for CO oxidation) and Mn (IV), which features the highest levels of reducibility and the best redox properties. B0.7M has also showcased a high stability during reactions at 300 °C, even though a slightly lower CO conversion is achieved during the second consecutive reaction cycle. This performance appears to be related to the decrease in the Mn (IV)/Mn (III) ratio.

Characteristics, sources and potential ecological risk of atmospheric microplastics in Lhasa city

Atmospheric microplastics are important contributors to environmental contamination in aquatic and terrestrial systems and pose potential ecological risks. However, studies on atmospheric microplastics are still limited in urban regions of the Tibetan Plateau, a sentinel region for climate and environmental change under a warming climate. In this study, the occurrence and potential ecological risk of atmospheric microplastics were investigated in samples of suspended atmospheric microplastics collected in Lhasa city during the Tibetan New Year in February 2023. The results show that the average abundance of atmospheric microplastics in Lhasa was 7.15 ± 2.46 MPs m−3. The sizes of the detected microplastics ranged from 20.34 to 297.18 μm, approximately 87% of which were smaller than 100 μm. Fragmented microplastics (95.76%) were the dominant shape, followed by fibres (3.75%) and pellets (0.49%). The primary polymer chemical components identified were polyamide (68.73%) and polystyrene (16.61%). The analysis of meteorological data and the backwards trajectory model indicated the air mass in Lhasa mainly controlled by westwards, and the atmospheric microplastics mainly originated from long-distance atmospheric transport. The potential ecological risk index assessment revealed that the atmospheric microplastic pollution in Lhasa was relatively low. This study provides valuable insights and a scientific foundation for future research on the prevention and control of atmospheric microplastic pollution in Lhasa and other ecologically sensitive cities.

A mechanism of stratospheric O3 intrusion into the atmospheric environment: a case study of the North China Plain

Abstract. Stratosphere-to-troposphere transport results in the stratospheric intrusion (SI) of O3 into the free troposphere through the folding of the tropopause. However, the mechanism of SI that influences the atmospheric environment through the cross-layer transport of O3 from the stratosphere and free troposphere to the atmospheric boundary layer has not been elucidated thoroughly. In this study, an SI event over the North China Plain (NCP; 33–40° N, 114–121° E) during 19–20 May 2019 was chosen to investigate the mechanism of the cross-layer transport of stratospheric O3 and its impact on the near-surface O3 based on multi-source reanalysis, observation data, and air quality modeling. The results revealed a mechanism of stratospheric O3 intrusion into the atmospheric environment induced by an extratropical cyclone system. The SI with downward transport of stratospheric O3 to the near-surface layer was driven by the extratropical cyclone system, with vertical coupling of the upper westerly trough, the middle of the northeast cold vortex (NECV), and the lower extratropical cyclone, in the troposphere. The deep trough in the westerly jet aroused the tropopause folding, and the lower-stratospheric O3 penetrated the folded tropopause into the upper and middle troposphere; the westerly trough was cut off to form a typical cold vortex in the upper and middle troposphere. The compensating downdrafts of the NECV further pushed the downward transport of stratospheric O3 in the free troposphere; the NECV activated an extratropical cyclone in the lower troposphere; and the vertical cyclonic circulation governed the stratospheric O3 from the free troposphere across the boundary layer top, invading the near-surface atmosphere. In this SI event, the average contribution of stratospheric O3 to near-surface O3 was accounted for at 26.77 %. The proposed meteorological mechanism of the vertical transport of stratospheric O3 into the near-surface atmosphere, driven by an extratropical cyclone system, could improve the understanding of the influence of stratospheric O3 on the atmospheric environment, with implications for the coordinated control of atmospheric pollution.

Elucidating transport dynamics and regional division of PM2.5 and O3 in China using an advanced network model

Air pollution exhibits significant spatial spillover effects, complicating and challenging regional governance models. This study innovatively applied and optimized a statistics-based complex network method in atmospheric environmental field. The methodology was enhanced through improvements in edge weighting and threshold calculations, leading to the development of an advanced pollutant transport network model. This model integrates pollution, meteorological, and geographical data, thereby comprehensively revealing the dynamic characteristics of PM2.5 and O3 transport among various cities in China. Research findings indicated that, throughout the year, the O3 transport network surpassed the PM2.5 network in edge count, average degree, and average weighted degree, showcasing a higher network density, broader city connections, and greater transmission strength. Particularly during the warm period, these characteristics of the O3 network were more pronounced, showcasing significant transport potential. Furthermore, the model successfully identified key influential cities in different periods; it also provided detailed descriptions of the interprovincial spillover flux and pathways of PM2.5 and O3 across various time scales. It pinpointed major pollution spillover and receiving provinces, with primary spillover pathways concentrated in crucial areas such as the Beijing-Tianjin-Hebei (BTH) region and its surrounding areas, the Yangtze River Delta, and the Fen-Wei Plain. Building on this, the model divided the O3, PM2.5, and synergistic pollution transmission regions in China into 6, 7, and 8 zones, respectively, based on network weights and the Girvan Newman (GN) algorithm. Such division offers novel perspectives and strategies for regional joint prevention and control. The validity of the model was further corroborated by source analysis results from the WRF-CAMx model in the BTH area. Overall, this research provides valuable insights for local and regional atmospheric pollution control strategies. Additionally, it offers a robust analytical tool for research in the field of atmospheric pollution.

Long-term evolution of carbonaceous aerosols in PM2.5 during over a decade of atmospheric pollution outbreaks and control in polluted central China

Against the backdrop of an uncertain evolution of carbonaceous aerosols in polluted areas over the long term amid air pollution control measures, this 11-year study (2011–2021) investigated fine particulate matter (PM2.5) and carbonaceous components in polluted central China. Organic carbon (OC) and elemental carbon (EC) averaged 16.5 and 3.4 μg/m3, constituting 16 and 3 % of PM2.5 mass. Carbonaceous aerosols dominated PM2.5 (35 and 27 %) during periods of excellent and good air quality, while polluted days witnessed other components as dominants, with a significant decrease in primary organic aerosols and increased secondary pollution. From 2011 to 2021, OC and EC decreased by 53 and 76 %, displaying a high-value oscillation phase (2011–2015) and a low-value fluctuation phase (post-2016). A substantial reduction in high OC and EC concentrations in 2016 marked a milestone in significant air quality improvement attributed to effective control measures, especially targeting OC and EC, evident from their decreased proportion in PM2.5. Primary OC (POC) in winter exhibited the most pronounced reduction (8 % per year), and the seasonal disparities in PM2.5 and carbonaceous components were reduced, showcasing the effectiveness of control measures. Contrary to the more pronounced reduction of EC, which decreased in proportion to PM2.5, secondary OC (SOC) in PM2.5 exhibited an increasing trend. Along with rising OC/EC, SOC/OC, and SOC/EC ratios, this indicates a growing prominence of secondary pollution compared to the decrease in primary pollution. SOC shows an increasing trend with NO2 rise (r = 0.53), without O3 promoting SOC. Positive correlations of SOC with SO2, CO (r = 0.41, 0.59), also highlight their influence on atmospheric conditions, oxidative capacity, and chemical reactions, indirectly impacting SOC formation. The implementation of precise precursor emission reduction measures holds the key to future efforts in mitigating SOC pollution and reducing PM2.5 concentrations, thereby contributing to improved air quality.

Toward better atmospheric polycyclic aromatic hydrocarbons pollution control in the Northern Hemisphere: Process analysis based on interpretable deep learning models

The accurate prediction of atmospheric polycyclic aromatic hydrocarbons (PAHs) concentrations in the Arctic is crucial in guiding the development of pollution control measures for atmospheric PAHs. However, the complex formation process and strong time-dependence of PAHs in the Arctic increase the difficulty of prediction. Herein, deep learning (DL) models were developed to predict monthly air concentrations of 16 priority PAHs at Alert monitoring station by incorporating the inherent periodicity of PAH concentrations and ancillary data including PAH emissions, meteorological information, fire emissions, and sea ice area. The prediction performance of long short-term memory (LSTM), gated recurrent unit (GRU) and bidirectional LSTM (Bi LSTM) algorithms were compared by multiple model evaluation metrics. The results showed that Bi LSTM models outperformed LSTM and GRU in predicting PAHs concentrations because it exhibited higher accuracy in terms of average R2ext (0.09) and errors (average MAPEext = 1.45). The DL interpretation based on SHAP values suggested that the main drivers of PAHs concentrations were PAHs emissions and sea ice area, with a more prominent contribution from meteorological conditions. The interpretable deep learning approach provides a potential shortcut for predicting time-delayed PAHs concentrations in the Arctic, promoting the management and control of global PAHs pollution.

Vertical distributions of VOCs in the Tibetan Plateau background region

Exploring the vertical variation in volatile organic compounds (VOCs) in background regions can provide information on the spatial distribution of pollutants, providing a scientific basis for atmospheric pollution prevention and control strategies. From 15 August to 5 September 2023, at the Southeast Tibet Mountain Comprehensive Environmental Observation Station (SETS), a tethered balloon was used to sample VOCs every 100 m from the ground to 1000 m. A total of 403 air bag samples were collected, and 39 vertical profiles of VOCs were obtained. Ninety-two VOC species were detected. The VOC concentration at the SETS did not change significantly vertically, and the average VOC concentration was 11.1 ± 2.4 ppbv. The main components were alkanes (51.4 %), alkenes (18.7 %), and halohydrocarbons (18.1 %). There was no obvious diurnal change in VOCs and no significant difference between the different layers. When the surface VOC concentration was less than 10 ppbv, the concentrations, components, and sources of VOCs were evenly distributed vertically, and the main sources of VOCs at different heights were vehicle exhaust and background. When the surface VOC concentration exceeded 10 ppbv, the VOC concentration gradually decreased with height. The proportion of alkanes in surface VOCs increased, and the source was mainly vehicle exhaust. This study confirmed that VOCs are vertically homogeneous in the background of the Tibetan Plateau, emphasizing the importance of vehicle emissions as a potential source of VOCs.摘要研究背景区域挥发性有机物 (VOCs) 的垂直变化规律, 可以提供污染物的空间分布信息, 为制定大气污染防治策略提供科学依据. 2023年夏季, 利用系留气艇对藏东南背景区域VOCs垂直分布规律进行研究, 共获取92种VOC的39条垂直廓线. 结果表明, VOC浓度垂直分布均一, 平均浓度为11.1 ± 2.4 ppbv; 主要成分为烷烃 (51.4 %), 烯烃 (18.7 %) 和卤代烃 (18.1 %). 地表VOC浓度小于10 ppbv时, 不同高度VOCs的主要来源为机动车尾气和背景贡献. 而地表VOC浓度超过10 ppbv时, 烷烃占比增加, 来源以机动车尾气为主. 该研究证实了青藏高原VOC在垂直方向上分布均一, 强调了机动车排放作为VOCs潜在来源的重要性.

Relationships among six urban air pollutants and identification of pollution types – A case study of Chinese cities above prefecture level

Urban air pollution is caused by the interplay of urban development and natural conditions, but it's unclear which factor has a greater impact. Studying the link between air pollutants and identifying the types of pollution causes is crucial for understanding the pollution mechanisms and formulating efficient management measures. During the research period from 2015 to 2020, this study analyzed the relationships between six air pollutants, calculated the city capacity (CC) and natural condition (NC) scores of 285 cities with the help of the index system and entropy weight TOPSIS model, and employed Gaussian Mixture Model (GMM) and random forest regression model to identify air pollution types. The results show: During the study period, the concentrations of most pollutants decreased, with SO2 experiencing the largest decline, reflecting the significant effectiveness of China's recent atmospheric pollution control efforts. In terms of spatial distribution, the North China Plain (NCP) and the Loess Plateau (LP) remain focal areas of pollution. The correlation analysis results indicate a significant positive correlation among all air pollutants except O3, underscoring the necessity of coordinated pollution control efforts, but the complexity of O3 pollution should not be overlooked. High-value agglomerations of CC scores calculated by the model are predominantly located in the eastern coastal region, while NC scores exhibit two high-value agglomerations in the north and south. Utilizing the GMM, the 285 cities were classified into three categories, and the random forest regression model was employed to identify them as CC dominate, NC dominate, and intermediate types. This paper proposes a novel approach for categorizing city air pollution types, aiming to tailor prevention measures and foster sustainable urban development.

A Review on Economic Dispatch of Power System Considering Atmospheric Pollutant Emissions

The environmental/economic dispatch (EED) of power systems addresses the environmental pollution problems caused by power generation at the operational level, offering macroscopic control without requiring additional construction and remediation costs, garnering widespread attention in recent years. This paper undertakes a comprehensive review of existing EED models, categorizing them according to the control of atmospheric pollutants into total air pollutant control (TAPC) and control considering the spatial and temporal diffusion (STD) of atmospheric pollutants. In addition, various methods employed to address the EED problems, as well as the current state of research on multi-area EED models, are presented. Finally, this paper analyzes and summarizes the literature on existing EED models, highlighting the deficiencies of the current work and future research directions. Through these explorations, the authors find that controlling the EED model by considering TAPC is more suitable for general macro planning, whereas the EED model considering the STD of air pollutant emissions enables more precise and effective control. Summarizing such models and techniques is conducive to developing dispatch plans adapted to local conditions, which is significantly beneficial for public welfare and government management, promoting sustainable and environmentally friendly power system dispatch methods.

An Improved Deep Learning Approach Considering Spatiotemporal Heterogeneity for PM2.5 Prediction: A Case Study of Xinjiang, China

Prediction of fine particulate matter with particle size less than 2.5 µm (PM2.5) is an important component of atmospheric pollution warning and control management. In this study, we propose a deep learning model, namely, a spatiotemporal weighted neural network (STWNN), to address the challenge of poor long-term PM2.5 prediction in areas with sparse and uneven stations. The model, which is based on convolutional neural network–bidirectional long short-term memory (CNN–Bi-LSTM) and attention mechanisms and uses a geospatial data-driven approach, considers the spatiotemporal heterogeneity effec It is correct.ts of PM2.5. This approach effectively overcomes instability caused by sparse station data in forecasting daily average PM2.5 concentrations over the next week. The effectiveness of the STWNN model was evaluated using the Xinjiang Uygur Autonomous Region as the study area. Experimental results demonstrate that the STWNN exhibits higher performance (RMSE = 10.29, MAE = 6.4, R2 = 0.96, and IA = 0.81) than other models in overall prediction and seasonal clustering. Furthermore, the SHapley Additive exPlanations (SHAP) method was introduced to calculate the contribution and spatiotemporal variation of feature variables after the STWNN prediction model. The SHAP results indicate that the STWNN has significant potential in improving the performance of long-term PM2.5 prediction at the regional station level. Analyzing spatiotemporal differences in key feature variables that influence PM2.5 provides a scientific foundation for long-term pollution control and supports emergency response planning for heavy pollution events.

2019–2020年西安城市PM2.5、气体污染物及气象数据集

Air pollution in China has improved in recent years, but Central Shaanxi region continues to face great pressure and challenges in pollution control due to its unique geographical location and climatic conditions. The regional and complex air pollution, characterized by ozone (O3) and fine particulate matter (PM2.5), is becoming increasingly serious. More meticulous observation data are needed to clarify the causes of air pollution and provide scientific support for control policies. This dataset comprises the data on the concentrations of nitrogen oxides, sulfur dioxide, ozone and carbon monoxide in the atmosphere obtained by gas analyzer in Gaoxinzi Observation Site of the National Observation and Research Station of Regional Ecological Environment Change and Comprehensive Management in Guanzhong Plain. Weather stations collected data on temperature, humidity, air pressure, solar radiation, wind speed, wind direction and other meteorological parameters around the station. Offline PM2.5 filter membranes were measured and the mass concentrations were analyzed. The observation site is located in the urban area of Xi 'an, Shaanxi Province. The data covers the period from January 2019 to December 2020 with a resolution of 5 minutes for pollution gas data, one minute for meteorological data, and PM2.5 collected every 24 hours. Online instruments are calibrated monthly to ensure data quality, while the offline PM2.5 sampler is calibrated and maintained weekly to ensure its data quality. This dataset can provide fundamental data records for the atmospheric environment in Central Shaanxi Province and offer data support for further pollution evaluation, control, and treatment. Moreover, it can give researchers scientific insights into the processes, casual mechanisms, as well as prevention and control of atmospheric pollution in Central Shaanxi Province.

The variability of NO2 concentrations over China based on satellite and influencing factors analysis during 2019–2021

The variation of tropospheric nitrogen dioxide (NO2) vertical column densities (VCDs) indirectly reflects the difference in pollution emissions from industrial production and transportation. Accurately analyzing its pollution sources and driving factors plays an important role in energy conservation, emission reduction, and air pollution reduction. NO2 concentration products of Sentinel-5P (Sentinel-5 Precursor) TROPOMI (TROPOspheric Monitoring Instrument) from 2019 to 2021 and Aura OMI (Ozone Monitoring Instrument) from 2009 to 2021, combined with China’s main energy consumption, the growth value of the industry, Gross Domestic Product (GDP), and other data were used to analyze the influencing factors of NO2 variations. Firstly, NO2 tropospheric vertical column densities (NO2 TVCDs) of China increased by 14.72% and 3.26% in 2021 and 2020 compared with the 2019. The secondary and tertiary industry and the national energy consumption increased synchronously, which was highly related to the increase in NO2 TVCDs. Secondly, the impact of COVID-19 (coronavirus disease 2019) on China’s industrial production and residents was mainly concentrated in the first quarter of 2020, which leading to a decline in the annual average NO2 concentration in densely populated areas in 2020 compared to the same period in 2019. The industrial production scale and production capacity has gradually recovered since April 2020, and the NO2 concentration has gradually reached or exceeded the level of the same period of 2019. Finally, atmospheric pollution prevention and control measures played a positive role in the decline of NO2 of China.

Unveiling the Spatial-Temporal Characteristics and Driving Factors of Greenhouse Gases and Atmospheric Pollutants Emissions of Energy Consumption in Shandong Province, China

As the largest energy-consuming province in China, Shandong faces the dual task of greenhouse gas (GHG) reduction and atmospheric pollution control. Based on the latest activity data and updated emission factors, this study establishes a high-resolution emission inventory (5 km × 5 km) for GHGs and main atmospheric pollutants from the energy consumption sectors of Shandong Province from 2010 to 2021, quantifies the relationship between social economic factors and GHGs and atmospheric pollutants emissions using the expanded stochastic environmental impact assessment (STIRPAT) model, and forecasts the future emission trend with the help of the scenario analysis method. Results indicate that the electricity and transportation sectors are the main contributors to all pollutants. Spatially, the high value of pollutants is mainly concentrated in the urban agglomerations of central and eastern Shandong. Up to 72% of GHGs and 50% of air pollution emissions are attributed to the top 10% of emission grids. Emission peaks occur mainly in summer and winter due to straw burning, increased utilization of temperature-controlled facilities, and expansion of plant capacity. Population, energy consumption, the proportion of secondary industry, and energy consumption intensity are the most significant influencing factors for pollutant emissions. Scenario analysis results indicate Shandong province can reach its carbon peak in 2027 without sacrificing population growth or economic progress.

Long-Term Spatiotemporal Characteristics and Influencing Factors of Dust Aerosols in East Asia (2000–2022)

The Taklamakan Desert Region (TDR) and the Gobi Desert Region (GDR) in East Asia significantly impact air quality, human health, and climate through dust aerosols. Utilizing the MERRA-2 dataset’s long-term dust aerosol optical depth (DAOD) at 550 nm from 2000 to 2022, we systematically monitored the spatiotemporal dynamics of DAOD. Our analysis covered annual, seasonal, and monthly scales, employing geographical detector analyses to investigate the impact of eight factors on DAOD distribution. Over the 23-year period, the interannual variability in DAOD across East Asia was not pronounced, but a discernible decreasing trend was observed, averaging an annual decrease of −0.0002. The TDR had higher DAOD values (0.337) than the GDR (0.103). The TDR showed an average annual increase of 0.004, while the GDR exhibited an average annual decrease of −0.0003. The spatial distribution displayed significant seasonal variations, with peak values in spring, although the peak months varied between the TDR and GDR. The driving factor analysis revealed that relative humidity and soil moisture significantly impacted the DAOD spatial distribution in East Asia, which were identified as common driving factors for both the region and the major dust sources. Complex mechanisms influenced the variation in DAOD, with interactions between variables having a greater impact than individual effects. The geodetector-derived interaction q-value identified the collective impact of soil temperature and relative humidity (0.896) as having the highest impact on the spatial and temporal DAOD distribution. The overall spatial pattern exhibited a nonlinear enhancement trend, with the TDR and GDR showing bilinear enhancement patterns. These findings contribute to a better understanding of the factors influencing DAOD, offering a theoretical basis for atmospheric pollution control in East Asia.

Evaluation, Driving Mechanism and Spatial Correlation Analysis of Atmospheric Environmental Efficiency in the “2+26” Cities Based on the Nonradial MEA Model

The “2+26” cities are 26 cities in Beijing, Tianjin and the surrounding cities, constituting a region characterized by economic prosperity and diverse industries but plagued by severe atmospheric pollution. As a focal area for atmospheric pollution control, a scientific assessment of atmospheric environmental efficiency in the “2+26” cities that measures the degree of coordination between the economy and air pollution is very important for winning the battle of blue sky defense. Based on this, this study comprehensively used the nonradial multi-directional efficiency analysis (MEA) model, Global Reference Malmquist Model and spatial correlation analysis to evaluate the atmospheric environmental efficiency, calculate the driving factors and explore the spatiotemporal distribution characteristics of the “2+26” cities from 2009 to 2018. The research findings indicate the following: (1) Atmospheric environmental efficiency showed a trend of first decreasing and then increasing, with a significant improvement potential of 26.7% in the future. (2) There was a significant discrepancy between the best- and worst-performing cities, with the best being 0.910 and the worst being 0.573, demonstrating imbalanced development between cities. The relatively low-efficiency cities were mainly located in Hebei, Shanxi and Henan provinces. (3) A value of technological efficiency change (EC) less than 1 was the main restrictive factor for improving atmospheric environmental efficiency, whereas a value of technological change (TC) greater than 1 enhanced it. (4) The atmospheric environmental efficiency presented a distinct spatial distribution pattern of high–high and low–low aggregation, forming high-value areas centered in the Beijing–Tianjin region and along the Zibo–Zhengzhou line. The western and central regions were relatively low, whereas the northern and eastern regions were relatively high, with significant regional differences in spatial distribution. The conclusions from this article’s empirical analysis can help concerned developing countries determine key factors to improve their atmospheric environmental efficiency and then formulate policies for sustainable economic and environmental development.

Scenario simulation and synergistic effect analysis of CO2 and atmospheric pollutant emission reduction in urban transport sector: A case study of Chengdu, China

Realizing coordinated control of CO2 and atmospheric pollutants in comprehensive transportation in large cities is an important way to promote sustainable urban development. Based on the LEAP framework, this study established a transport CO2 and atmospheric pollutant emission analysis model for Chengdu from 2021 to 2040, set up three main scenarios for the study of carbon peaking trend and 12 sub-scenarios for the study of the synergistic effect of various emission reduction measures. In addition, the synergistic effects were analyzed by radar charts, grading charts of synergistic effects and the synergetic equivalence index. The results show that under the Green Scenario (GS) and the Strengthen Green Scenario (SGS), the CO2 emissions from comprehensive transportation in Chengdu will peak at 26.774 × 106 t and 23.507 × 106 t in 2032 and 2029, respectively. The emissions of other atmospheric pollutants show a general downward trend. Under SGS, The measures that play the greatest role in reducing CO2, CO, VOCs, and NOx emissions by 2040 are NEV (New energy conversion of vehicles), Among them, the CO2 emission reduction rate relative to BS scenario is 20.7%. While the measures that play the greatest role in reducing PM and SO2 emissions are IES (Improvement of emission standards) and SRT (Strengthen railway transportation), respectively. Compared with other measures, the synergistic effect of SRT is the most highest. The emission reduction effect of NEV is most significant, with a synergistic effect level above level II. Under the joint regulation of multiple measures by GS and SGS, the synergistic equivalence index based on air quality standards has been effectively controlled, showing a downward trend. Overall, Chengdu faces challenges due to a large number of motor vehicles and air passenger throughput. Therefore, vigorously developing hydrogen vehicles and electric vehicles, strengthening railway construction in Chengdu, transferring the pressure of civil aviation and road transportation to railway, can make significant contributions to the pollution reduction and carbon reduction of Chengdu's transportation sector.

“Dual Carbon” Strategy Motivated Teaching Reform of Atmospheric Pollution Control Engineering

“Dual Carbon” Strategy Motivated Teaching Reform of Atmospheric Pollution Control Engineering

Direct reduction of NO into N2 catalyzed by fullerene-supported rhodium clusters.

Catalytic conversion of NO has long been a focus of atmospheric pollution control and diesel vehicle exhaust treatment. Rhodium is one of the most effective metals for catalyzing NO reduction, and understanding the nature of the active sites and underlying mechanisms can help improve the design of Rh-based catalysts towards NO reduction. In this work, we investigated the detailed catalytic mechanisms for the direct reduction of NO to N2 by fullerene-supported rhodium clusters, C60Rh4+, with density functional theory calculations. We found that the presence of C60 facilitates the smooth reduction of NO into N2 and O2, as well as their subsequent desorption, recovering the catalyst C60Rh4+. Such a process fails to be completed by free Rh4+, emphasizing the critical importance of C60 support. We attribute the novel performance of C60Rh4+ to the electron sponge effect of C60, providing useful guidance for designing efficient catalysts for the direct reduction of NO.