Atmospheric Pollution Control Research Articles

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.

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.

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.

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.

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.

Effect of Evaluation of Various Emission Control Policies on PM2.5 Reduction.

In previous work, a methodology was developed to discuss the influence of meteorological factors, policies, and surrounding cities on PM2.5 concentrations in a city. Two models were constructed using Zibo City, Shandong Province, as the target city. Initially, we improved the established PM2.5-Meteorological-Policy (PMP) model and applied it to six other target cities in Shandong Province. Concurrently, a novel model named the PM2.5-Interregional (PI) model was further developed in each city to directly express the influence of surrounding cities on the target cities. The model construction period was from January 2014 to August 2022, with the extended prediction period until November 2022. The results confirmed that disparities in the spatial distribution in seasons became smaller after the implementation of environmental policies. Moreover, two models in each city revealed good interpretation with high adjusted R2 values (>0.7) and lower MAPE and RMSE values (the lowest was 5.53% and 2.57), suggesting reasonable short-term prediction. Additionally, meteorological factors and the combined implementation of different policy types played crucial roles in reducing PM2.5 concentrations in all cities. Specifically, the temperature and wind speed were negatively correlated with PM2.5 concentrations in all models, with temperature having a stronger influence. The Law of the People's Republic of China on the Prevention and Control of Atmospheric Pollution (LAPAP), implemented in 2016, had a clear influence on reducing PM2.5 concentrations, with the highest absolute fitted coefficient in most cities (-0.166 to -0.344). On the contrary, the influence of temperature seemed to be more significant compared to policies, due to the larger standardized coefficient in each city (-0.606 to -0.864).

Atmospheric Nitrogen Pollution Control Benefits the Coastal Environment.

Nitrogen is an essential nutrient and a major limiting element for the ocean ecosystem. Since the preindustrial era, substantial amounts of nitrogen from terrestrial sources have entered the ocean via rivers, groundwater, and atmospheric deposition. China serves as a key hub in the global nitrogen cycle, but the pathways, sources, and potential mitigation strategies for land-ocean nitrogen transport are unclear. By combining the CHANS, WRF-Chem, and WNF models, we estimated that 8 million tonnes (Tg) of nitrogen was transferred into the ocean in 2017 in China, with atmospheric deposition contributing 1/3. About half variation of the offshore chlorophyll concentration was explained by atmospheric deposition. The Bohai Sea was the hot spot of nitrogen input, estimated at 214 kg N ha-1, while other areas were around 25-51 kg N ha-1. The largest contributors are agricultural systems (4 Tg, 55%), followed by domestic sewage (2 Tg, 21%). Abatement measures could reduce nitrogen export to the ocean by 43%, and mitigating ammonia and nitrogen oxide emissions accounts for 33% of this reduction, highlighting the importance of addressing air pollution in resolving ocean pollution. The cost-benefit analysis suggests the priority of nitrogen reduction in cropland and transport systems for the ocean environment.

Adsorption of NOx by graphene oxide loaded with CeO2 as a catalyst for atmospheric pollution control

In this study, we utilized graphene oxide (GO) loaded with cerium oxide (CeO2) to synthesize GO-CeO2 catalysts using a hydrothermal method and high-temperature calcination. The performance of the catalyst was evaluated by characterization of the catalyst material and testing for nitrogen oxide (NOx) conversion rates and nitrogen gas adsorption isotherm in mixed flue gas. The results demonstrated that increasing the ratio of NO2 in the NO+NO2 mixture significantly enhanced the conversion rate of NOx. Notably, when the proportion of NO2 reached 0.4, the denitrified gas displayed a remarkable increase in NOx conversion rate, exceeding 98%. This finding highlighted that even low concentrations of NO2 can accelerate denitrification reactions. Moreover, increasing the oxygen concentration in the gas exhibited considerable potential to elevate the NOx conversion rate. The oxygen concentration played a vital role in the process of denitrification, and the GO-CeO2 catalyst exhibited a high oxygen storage capacity. Moreover, the GO-CeO2 catalyst maintained a high denitrification efficiency even under high-temperature conditions and demonstrated excellent NOx conversion performance. The catalyst also demonstrated good stability and strong water resistance under varying proportions and temperatures while maintaining a consistent denitrification efficiency. These findings indicated the significant adsorption effect of the graphene oxide loaded with CeO2 catalyst on NOx, suggesting promising potential for application and widespread use.

Low-temperature sintering of multifunctional SiC catalytic membrane for synergistic emission control of toluene and dust

SiC catalytic membranes have shown great potential in dust and atmospheric pollutant emission control. However, their high sintering temperature (up to 1850 °C) and the ‘trade-off’ effect between its gas permeance and catalytic performance have hindered their widespread application. In this study, a high-performance CuO-loaded SiC catalytic membrane was fabricated to address the above issues using a liquid phase sintering and surficial reconstruction (LSSR) strategy. The low melting point of CuO enables the liquid phase sintering of CuO-SiC membrane at a significantly reduced temperature of 1040 °C, and the formation of CuO nanoparticles (CuO-NPs) on bulk CuO in CuO-SiC membrane (CuO-NPs/CuO-SiC) is achieved through a surficial reconstruction process. The resulting CuO-NPs/CuO-SiC catalytic membrane exhibits a high gas permeance of 275.8 m3·m−2·h−1·kPa−1 and a bending strength of 14.2 MPa. The CuO-NPs/CuO-SiC catalytic membrane also displays complete (100 %) toluene oxidation and PM2.5 filtration efficiencies at 280 °C, as well as enhanced water vapor resistance and long-term stability. The LSSR was successfully applied to fabricate an CuO-NPs/CuO-Al2O3 catalytic membrane, which shows complete toluene oxidation and PM2.5 filtration efficiency at 300 °C, demonstrating that LSSR is readily extended to fabricating other ceramic and metal-ceramic-based catalytic membranes.

The Spatiotemporal Distribution of NO2 in China Based on Refined 2DCNN-LSTM Model Retrieval and Factor Interpretability Analysis

With the advancement of urbanization in China, effective control of pollutant emissions and air quality have become important goals in current environmental management. Nitrogen dioxide (NO2), as a precursor of tropospheric ozone and fine particulate matter, plays a significant role in atmospheric chemistry research and air pollution control. However, the uneven ground monitoring stations and low temporal resolution of polar-orbiting satellites set challenges for accurately assessing near-surface NO2 concentrations. To address this issue, a spatiotemporal refined NO2 retrieval model was established for China using the geostationary satellite Himawari-8. The spatiotemporal characteristics of NO2 were analyzed and its contribution factors were explored. Firstly, seven Himawari-8 channels sensitive to NO2 were selected by using the forward feature selection based on information entropy. Subsequently, a 2DCNN-LSTM network model was constructed, incorporating the selected channels and meteorological variables as retrieval factors to estimate hourly NO2 in China from March 2018 to February 2020 (with a resolution of 0.05°, per hour). The performance evaluation demonstrates that the full-channel 2DCNN-LSTM model has good fitting capability and robustness (R2 = 0.74, RMSE = 10.93), and further improvements were achieved after channel selection (R2 = 0.87, RMSE = 6.84). The 10-fold cross-validation results indicate that the R2 between retrieval and measured values was above 0.85, the MAE was within 5.60, and the RMSE iwas within 7.90. R2 varied between 0.85 and 0.90, showing better validation at mid-day (R2 = 0.89) and in spring and fall transition seasons (R2 = 0.88 and R2 = 0.90). To investigate the cooperative effect of meteorological factors and other air pollutants on NO2, statistical methods (beta coefficients) were used to test the factor interpretability. Meteorological factors as well as other pollutants were analyzed. From a statistical perspective, PM2.5, boundary layer height, and O3 were found to have the largest impacts on near-surface NO2 concentrations, with each standard deviation change in these factors leading to 0.28, 0.24, and 0.23 in standard deviations of near-surface NO2, respectively. The findings of this study contribute to a comprehensive understanding of the spatiotemporal distribution of NO2 and provide a scientific basis for formulating targeted air pollution policies.

Detection of PM2.5 spatiotemporal patterns and driving factors in urban agglomerations in China

As important urban gathering places for new urbanization construction, urban agglomerations (UAs) have long suffered from high concentrations of PM2.5 pollution. By analyzing the changes of the temporal and spatial distribution patterns and the natural and human drivers of PM2.5 in UAs in China can provide an effective reference basis for atmospheric pollution prevention and control. In this study, based on PM2.5 concentration data from 2000 to 2020, PM2.5 pollution in 19 UAs in China was investigated. First, the annual average PM2.5 concentration was used to analyze the temporal variation, spatial distribution, and agglomeration characteristics of PM2.5 pollution in each UA. Then, factor detection was used to analyze the driving mechanisms of the effects of natural and human factors on PM2.5 pollution in UAs in China. Finally, the explanatory intensities of the interactions between different factors of PM2.5 pollution were analyzed based on interaction detection. The results show that from 2000 to 2020, the PM2.5 concentration exhibited an evolutionary trend of “M”-shape. An inflection point occurred in 2007, and 2020 had the lowest value for the study period. PM2.5 of high concentration took the Central Plains and the Beijing-Tianjin-Hebei UAs as the core of pollution. 2013 was an important turning point with a spatial pattern of expansion to the surrounding areas and then contraction to the center. UAs presented an elliptical spatial distribution, and outliers occurred in individual years. Humidity and temperature were the dominant natural factors for the variation of the PM2.5 concentration in 2013, 2018, and 2020, and their q-values were 0.276(humidity), 0.429(temperature), and 0.375(temperature), respectively. The total population and energy consumption were the dominant human factors for these three years, with q-values of 0.150(total population), 0.259(total population), and 0.196(energy consumption), respectively. The explanatory effect of the two-factor interaction was higher than that of a singular factor. Furthermore, the factor and interaction detection of natural factors found them to be more significant driving mechanisms than human factors. The results of this study not only allow for the understanding of the spatiotemporal variation of the PM2.5 concentration in UAs and the effects of different influencing factors, but also provide a scientific reference for the steady development of UAs and air pollution prevention and management in China.

High-Resolution Daily Spatiotemporal Distribution and Evaluation of Ground-Level Nitrogen Dioxide Concentration in the Beijing–Tianjin–Hebei Region Based on TROPOMI Data

This study utilized TROPOMI remote sensing data, MODIS remote sensing data, ground observation data, and other ancillary data to construct a high-resolution spatiotemporal distribution and evaluation of ground-level NO2 concentrations in the Beijing–Tianjin–Hebei (BTH) region using the Geographic Temporal Neural Network Weighted Regression (GTNNWR) model. Through this model, we obtained the daily distribution of ground-level nitrogen dioxide (NO2) concentrations in the Beijing–Tianjin–Hebei region at a resolution of 500 m for the period of 2019–2022. The research results exhibited higher accuracy and more detailed features compared to other models, enabling a more accurate reflection of the spatial distribution and temporal variations of ground-level NO2 concentrations in the region, while retaining more details and trends and excluding the influence of noisy data. Furthermore, we conducted an evaluation analysis considering important events such as public health incidents and the Winter Olympics. The results demonstrated that the GTNNWR model outperformed the Random Forest (RF), Convolutional Neural Network (CNN), and Geographic Neural Network Weighted Regression (GNNWR) models in performance metrics such as R2, RMSE, MAE, and MAPE, showcasing greater reliability when considering spatiotemporal heterogeneity and spatiotemporal non-stationarity. This study provides crucial data support and reference for atmospheric environmental management and pollution prevention and control in the Beijing–Tianjin–Hebei region.