Search Results (121)

Air pollution is an important environmental issue that affects social development and human life. Atmospheric fine particulate matter (PM_2.5) is the primary pollutant affecting the air quality of most cities in the authors’ country. It can cause severe haze, reduce air visibility and cleanliness, and affect people’s daily lives and health. Therefore, it has become a primary research object. Ground monitoring and satellite remote sensing are currently the main ways to obtain PM_2.5 data. Satellite remote sensing technology has the advantages of macro-scale, dynamic, and real-time functioning, which can make up for the limitations of the uneven distribution and high cost of ground monitoring stations. Therefore, it provides an effective means to establish a mathematical model—based on atmospheric aerosol optical thickness data obtained through satellite remote sensing and PM_2.5 concentration data measured by ground monitoring stations—in order to estimate the PM_2.5 concentration and temporal and spatial distribution. This study takes the Yangtze River Delta region as the research area. Based on the measured PM_2.5 concentration data obtained from 184 ground monitoring stations in 2023, the newly released sixth version of the MODIS aerosol optical depth product obtained via the US Terra and Aqua satellites is used as the main prediction factor. Dark-pixel AOD data with a 3 km resolution and dark-blue AOD data with a 10 km resolution are combined with the European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis meteorological, land use, road network, and population density data and other auxiliary prediction factors, and XGBoost and LSTM models are used to achieve high-precision estimation of the spatiotemporal changes in PM_2.5 concentration in the Yangtze River Delta region. Full article

Despite recent improvements in particulate matter (PM) pollution, haze events still frequently occur in many regions of China. Volatile organic compounds (VOCs), as key precursors in atmospheric photochemistry, play a crucial role in haze formation. To elucidate their contributions, high-resolution hourly VOC measurements were conducted in Shaoxing, an industrial city in eastern China, during a winter field campaign from 1 December 2023 to 15 January 2024. The VOC groups were dominated by alkanes (31.5–53.8%), followed by alkenes (7.1–15.1%) and aromatics (6.7–14.1%). Positive Matrix Factorization (PMF) analysis resolved six major VOC sources: vehicle emissions (VE, 33.8%), combustion sources (CS, 20.0%), industrial emissions (IE, 13.4%), gasoline evaporation (GE, 14.6%), solvent usage (SU, 6.9%), and biogenic activities (BA, 12.6%). Based on the PMF results, we further evaluated the source-specific contributions of VOCs to OH radical loss rate (L_OH), ozone formation potential (OFP), and secondary organic aerosol potential (SOAP). During the haze episode, GE was the dominant driver of L_OH (33%), while IE (23%), GE (22%), and VE (20%) were major SOAP contributors. In contrast, during the other periods, CS contributed most to both OFP (24%) and SOAP (28%), followed by VE (22–23%). Overall, our study highlights the critical role of anthropogenic activities in driving secondary pollution and suggests that sector-specific mitigation strategies hold significant potential for local haze abatement. Full article

The COVID-19 pandemic emerging in early 2020 triggered global responses. In China, stringent lockdown measures were implemented to suppress the rapid spread of infection, resulting in substantial reductions in anthropogenic emissions. However, several atmospheric haze episodes still occurred. Previous studies have investigated the cause of these haze events predominantly based on the average concentration obtained from bulk analysis, while the micro-scale structure and composition of the haze particles remain poorly understood. In this study, we analyzed the morphology and elemental composition of individual airborne particles collected from an urban area of Beijing in early 2020 using high-resolution transmission electron microscopy equipped with Energy Dispersive X-ray Spectroscopy. The results show that sulfur-dominant, ultrafine, and mixed particles were the most abundant types during the pollution process. Reduced human activities corresponded with a lower percentage of anthropogenic-derived soot, organic particles, and metal-containing particles. Atmospheric aging analysis demonstrated that secondary aerosols were the most significant component during the haze events. The proportion of core–shell particles increased with the intensification of the pollution, while the core/shell ratio of the particles decreased, suggesting a substantial contribution of secondary aerosols to the haze formation. Despite reductions in anthropogenic emissions, larger proportions of secondary aerosol formation enhanced aerosol aging and thereby caused episodic haze pollution during the lockdown period. Full article

Achieving sustainable air quality improvements in rapidly industrializing regions requires a clear understanding of the emission sources that drive the formation of PM_2.5 pollution. This study identified the sources of PM_2.5 and its organic carbon (OC) in Zibo, a typical industrial city in Northern China Plain, using the Positive Matrix Factorization (PMF) model during five pollution episodes (P1–P5) from 26 November 2022 to 9 February 2023. A high-temporal-resolution online observation of 61 organic molecular tracers was conducted using an Aerodyne TAG stand-alone system combined with a gas chromatograph–mass spectrometer (TAG-GC/MS) system. The results indicate that during pollution episodes, PM_2.5 was contributed by 32.4% from coal combustion and 27.1% from inorganic secondary sources. Moreover, fireworks contributed 13.1% of PM_2.5, primarily due to the extensive fireworks during the Gregorian and Lunar New Year celebrations. Similarly, coal combustion was the largest contributor to OC, followed by mobile sources and secondary organic aerosol (SOA) sources, accounting for 16.2% and 15.3%, respectively. Although fireworks contributed significantly to PM_2.5 concentrations (31.6% in P4 of 20–24 January 2023), their impact on OC was negligible. Overall, a combination of local and regional industrial combustion emissions, mobile sources, extensive residential heating during cold weather, and unfavorable meteorological conditions led to elevated secondary aerosol concentrations and the occurrence of this haze episode. The high-temporal-resolution measurements obtained using the TAG-GC/MS system, which provided more information on source-indicating organic molecules (tracers), significantly enhanced the source apportionment capability of PM_2.5 and OC. The findings provide science-based evidence for designing more sustainable emission control strategies, highlighting that the coordinated management of coal combustion, mobile emissions, and wintertime heating is essential for long-term air quality and public health benefits. Full article

Fine particulate matter (PM2.5) is a critical environmental and health concern in northern Thailand, where haze episodes are strongly influenced by biomass burning, meteorological variability, and complex topography. This study aims to (1) analyze and select input variables for PM2.5 prediction by integrating WRF-Chem outputs, satellite data, and ground observations, and (2) evaluate the predictive performance of four machine learning (ML) algorithms—Random Forest (RF), XGBoost, CNN3D, and ConvLSTM—during the 2024 haze season (January–May). The dataset included hourly PM2.5 observations from 54 stations, the WRF-Chem-simulated PM2.5 and meteorological variables, satellite-based fire data, and geographical data. To improve consistency with ground-based data, WRF-Chem PM2.5 values were bias-corrected for the training and validation phases prior to ML learning. Among Linear Regression, RF, XGBoost, Artificial Neural Network (ANN), and Convolutional Neural Network (CNN) tested for bias correction, RF achieved the best performance (R = 0.78, RMSE = 29.28 µg/m³); the RF-corrected WRF-Chem PM2.5 was then used as an input to the forecasting stage. Variable selection was supported by correlation, VIF, feature importance, and SHAP analyses. The results indicate that RF provided the most reliable predictions, achieving a correlation of R = 0.867 and the lowest RMSE of 27.6 µg/m³ when using the SHAP+VIF-selected input set (seven variables: PM2.5_lag1, PM2.5_lag24, T2, RH2, Precip, Burned Area, NDVI). Notably, RF remained the top performer, predicting PM2.5 more accurately than the other algorithms during high-pollution conditions, specifically Air Quality Index (AQI) “Unhealthy for Sensitive Groups” (high) and “Unhealthy” (very high). Taken together, RF set the performance bar across both stages, with XGBoost ranked second, whereas CNN3D and ConvLSTM performed considerably worse. These findings emphasize the effectiveness of ensemble tree-based algorithms combined with bias-corrected WRF-Chem outputs and strategic variable selection in supporting accurate hourly PM2.5 predictions for air quality management in biomass burning regions. Full article

This study examined air quality data collected from 2015 to 2023 across Shenyang, Dalian, Changchun, and Harbin to assess interannual and monthly variations in PM_2.5, PM₁₀, SO₂, NO₂, and O₃, along with their correlations, seasonal meteorological influences, and potential source regions. Annual mean concentrations of PM_2.5, PM₁₀, SO₂, and NO₂ declined substantially (by 39.9–79.3%), whereas O₃ showed a fluctuating pattern, remaining persistently high in the coastal city of Dalian. Seasonally, PM_2.5, PM₁₀, SO₂, and NO₂ concentrations peaked in winter and decreased in summer, while O₃ displayed the opposite trend. Particulate levels in Liaoning rebounded earlier in spring than in Jilin and Heilongjiang. Correlation analysis revealed strong positive relationships among particulate and gaseous pollutants, but O₃ generally exhibited negative correlations with other species. Haze events occurred mainly in winter, whereas complex pollution episodes were more frequent in summer. Meteorological analysis indicated that relative humidity was negatively correlated with PM_2.5, PM₁₀, SO₂, and NO₂ in summer but positively correlated in winter. Elevated temperatures outside the winter months promoted NO₂ dispersion and enhanced O₃ formation. Strong winds in spring and winter markedly reduced PM_2.5 and SO₂ levels, though this effect was less evident in Shenyang. WPSCF results identified significant cross-regional transport from the southwest contributing to PM_2.5, PM₁₀, and NO₂ during spring and winter, while O₃ was primarily affected by long-range transport in spring and only marginally in winter. In Dalian, sea–land breeze circulation further intensified transport processes in summer and autumn. Overall, this work provides an integrated, multi-year, and multi-city assessment of pollution dynamics, meteorological drivers, and transboundary transport in Northeast China, offering new insights into regional air quality improvement and its spatial heterogeneity relative to other regions of China. Full article

South Asia’s worsening air pollution crisis represents one of the most urgent public health and environmental challenges of the 21st century. Nearly two billion people—over one-quarter of the global population—reside in this region, where air quality levels routinely exceed World Health Organization (WHO) guidelines by factors of 10 to 15. This has translated into an unprecedented health burden, with approximately two million premature deaths annually, widespread chronic respiratory and cardiovascular disease, and rising economic losses. According to recent World Bank estimates, welfare losses amount to over 5% of regional GDP, a figure far exceeding the projected costs of coordinated mitigation. Despite this, South Asia continues to lack a binding regional framework capable of addressing its shared airshed. Existing cooperative efforts—such as the Malé Declaration on Control and Prevention of Air Pollution (1998)—have provided a useful platform for dialog and pilot monitoring, but they remain voluntary, under-resourced, and insufficient to manage the transboundary nature of the crisis. National-level programs, including India’s National Clean Air Programme (NCAP), Bangladesh’s National Air Quality Management Plan (NAQMP), and Nepal’s National Air Quality Management Action Plan (AQMAP), demonstrate domestic commitment but are constrained by fragmentation, limited financing, and lack of regional integration. This gap represents the central knowledge and governance challenge that prompted the present commentary. To address it, we propose a dual-track architecture designed to institutionalize binding regional cooperation. Track A would establish a United Nations-anchored South Asian Transboundary Air Pollution Protocol, under the auspices of the United Nations Environment Programme, the World Health Organization (WHO), and the United Nations Economic and Social Commission for Asia and the Pacific (UNESCAP). This protocol would codify legally enforceable emission standards, compliance committees, financial mechanisms, and harmonized monitoring. Track B would establish a South Asian Association for Regional Cooperation (SAARC) Prime Ministers’ Council on Air Quality (SPMCAQ) to provide political leadership, align domestic implementation, and authorize rapid responses to cross-border haze events. Lessons from the Indian Ocean Experiment, the ASEAN Agreement on Transboundary Haze Pollution, and Europe’s Convention on Long-Range Transboundary Air Pollution demonstrate that legally binding agreements combined with high-level political ownership can achieve durable reductions in pollution despite geopolitical tensions. By situating South Asia within these global precedents, the proposed framework provides a pragmatic, enforceable, and politically resilient pathway to protect health, reduce economic losses, and deliver cleaner air for nearly one-quarter of humanity. Full article

Bisphenol A (BPA) has been listed as a substance of very high concern (SVHC) due to its endocrine-disrupting properties according to REACH in 2017. European competent authorities have prepared a REACH restriction proposal to reduce BPA levels in the environment. The proposed limit for the concentration of free BPA and other bisphenols in articles is 10 mg kg⁻¹. If exceeded, migration testing can demonstrate that no more than 0.04 mg L⁻¹ is released from the product or material over its lifetime. German authorities are drafting a new restriction proposal after the original was temporarily withdrawn. The residual and migration limits mentioned above were key requirements from the previous restriction proposal. Numerous national and international standards exist for assessing how environmental factors affect the physical and chemical properties of products and materials—such as notch impact strength and tensile strength—but these standards do not cover the release of pollutants. A standardised procedure that covers all aspects of artificial weathering and monitors the subsequent release of pollutants is necessary, especially in the context of the regulation of these substances. An accelerated weathering procedure was established for non-protected samples. This material was not intended for outdoor applications. The testing procedure applied a typical weathering scenario that represents Central European climate conditions. The procedure was validated and applied to samples under distinct quality assurance aspects. Released BPA is quantified via an organic isotope dilution LC-MS/MS method. In parallel, identical samples were weathered outdoors on a weathering rack. Haze and yellowness index are measured to compare outdoor and weathering chamber results. Full article

Cloud detection is an indispensable step in satellite remote sensing of cloud properties and objects under the influence of cloud occlusion. Nevertheless, interfering targets such as snow and haze pollution are easily misjudged as clouds for most of the current algorithms. Hence, a robust cloud detection algorithm is urgently needed, especially for regions with high latitudes or severe air pollution. This paper demonstrated that the passive satellite detector Advanced Geosynchronous Radiation Imager (AGRI) onboard the FY-4A satellite has a great possibility to misjudge the dense aerosols in haze pollution as clouds during the daytime, and constructed an algorithm based on the spectral information of the AGRI’s 14 bands with a concise and high-speed calculation. This study adjusted the previously proposed cloud mask rectification algorithm of Moderate-Resolution Imaging Spectroradiometer (MODIS), rectified the MODIS cloud detection result, and used it as the accurate cloud mask data. The algorithm was constructed based on adjusted Fisher discrimination analysis (AFDA) and spectral spatial variability (SSV) methods over four different underlying surfaces (land, desert, snow, and water) and two seasons (summer and winter). This algorithm divides the identification into two steps to screen the confident cloud clusters and broken clouds, which are not easy to recognize, respectively. In the first step, channels with obvious differences in cloudy and cloud-free areas were selected, and AFDA was utilized to build a weighted sum formula across the normalized spectral data of the selected bands. This step transforms the traditional dynamic-threshold test on multiple bands into a simple test of the calculated summation value. In the second step, SSV was used to capture the broken clouds by calculating the standard deviation (STD) of spectra in every 3 × 3-pixel window to quantify the spectral homogeneity within a small scale. To assess the algorithm’s spatial and temporal generalizability, two evaluations were conducted: one examining four key regions and another assessing three different moments on a certain day in East China. The results showed that the algorithm has an excellent accuracy across four different underlying surfaces, insusceptible to the main interferences such as haze and snow, and shows a strong detection capability for broken clouds. This algorithm enables widespread application to different regions and times of day, with a low calculation complexity, indicating that a new method satisfying the requirements of fast and robust cloud detection can be achieved. Full article

Northern Thailand experiences seasonal surges in PM₂.₅ pollution, posing significant respiratory health risks. This cross-sectional study aimed to evaluate associations between PAHs exposure and early health biomarkers. In April 2024, 127 rural residents in Chiang Mai were recruited during a high-exposure period (mean monthly PM_2.5 = 41.7 μg/m³). Participants reporting eye irritation and pneumonia showed significantly higher 8-iso-PGF₂α levels (p = 0.010 and 0.012, respectively). Smokers exhibited elevated CC16 levels (130.0 ± 65.3 ng/mL) compared to non-smokers (96.3 ± 39.9 ng/mL, p < 0.05). CC16 was also significantly associated with self-reported symptoms, including fatigue, poor sleep quality, and activity limitation. For example, participants who reported difficulty performing daily activities (i.e., disagreed with the statement “I can do things at home without any restrictions”) had significantly higher CC16 levels (108 ± 47 ng/mL) than those without such limitations (74 ± 35 ng/mL; p < 0.001). A weak but significant positive correlation was observed between respiratory rate and CC16 (R² = 0.334, p = 0.001). Interestingly, serum 8-iso-PGF₂α was inversely associated with diabetes (OR = 0.965; 95% CI: 0.935–0.997; p = 0.033), potentially indicating a compensatory or phenotype-specific oxidative stress response. In addition, CC16 levels were positively associated with diabetes (p = 0.022), suggesting altered epithelial responses in individuals with metabolic disease. CC16 and 8-iso-PGF₂α demonstrated significant associations with respiratory symptoms and metabolic status, suggesting their potential as early indicators for environmental health surveillance in haze-affected populations. Full article

Accurate air quality prediction (AQP) is crucial for safeguarding public health and guiding smart city management. However, reliable assessment remains challenging due to complex emission patterns, meteorological variability, and chemical interactions, compounded by the limited coverage of ground-based monitoring networks. To address this gap, we propose Vision-AQ (Visual Integrated Operational Network for Air Quality), a novel multi-modal deep learning framework that classifies Air Quality Index (AQI) levels by integrating environmental imagery with pollutant data. Vision-AQ employs a dual-input neural architecture: (1) a pre-trained ResNet50 convolutional neural network (CNN) that extracts high-level features from city-scale environmental photographs in India and Nepal, capturing haze, smog, and visibility patterns, and (2) a multi-layer perceptron (MLP) that processes tabular sensor data, including ${PM}_{2.5}$, ${PM}_{10}$, and AQI values. The fused representations are passed to a classifier to predict six AQI categories. Trained on a comprehensive dataset, the model achieves strong predictive performance with high accuracy, precision, recall and F1-score of 99%, with 23.7 million parameters. To ensure interpretability, we use Grad-CAM visualization to highlights the model’s reliance on meaningful atmospheric features, confirming its explainability. The results demonstrate that Vision-AQ is a reliable, scalable, and cost-effective approach for localized AQI classification, offering the potential to augment conventional monitoring networks and enable more granular air quality management in urban South Asia. Full article

In this study, typical haze pollution influenced by biomass burning (BB) activities in Changsha in the autumn of 2024 was investigated through the mixing state and evolution process of BB particles via the real-time measurement of single-particle aerosol mass spectrometry (SPAMS). From the clean period to the haze period, the PM_2.5 concentration increased from 25 μg·m⁻³ at 12:00 to 273 μg·m⁻³ at 21:00 on 12 October, and the proportion of total BB single particles in the total detected particles increased from 17.2% to 54%. This indicates that the rapid increase in PM_2.5 concentration was accompanied by a concurrent increase in the contribution of particles originating from BB sources. The detected BB particles were classified into two types based on their mixing states and temporal variations: BB1 and BB2, which accounted for 71.7% and 28.3% of the total BB particles, respectively. The analysis of backward trajectories and fire spots suggested that BB1 particles originated from straw burning emissions at northern Changsha, while BB2 particles were primarily related to local nighttime cooking emissions in Changsha. In addition, a special type of K-containing single particles without K cluster ions was found closely associated with BB1 type particles, which were designated as secondarily processed BB particles (BB-sec). The BB-sec particles contained abundant sulfate and ammonium signals and showed lagged appearance after the peak of BB1-type particles, which was possibly due to the aging and formation of ammonium sulfate on the freshly emitted particles. In all, this study provides insights into understanding the substantial impact of BB sources on regional air quality during the crop harvest season and the appropriate disposal of crop straw, including conversion into high-efficiency fuel through secondary processing or clean energy via biological fermentation, which is of great significance for the mitigation of local haze pollution. Full article

Nitrogen oxides (NO_X) are important precursors of ozone and secondary aerosols. Accurate and timely NO_X emission estimates are essential for formulating measures to mitigate haze and ozone pollution. Bottom–up and satellite–constrained top–down methods are commonly used for emission inventory compilation; however, they have limitations of time lag and high computational demands. Here, we propose a machine learning model, WOA-XGBoost (Whale Optimization Algorithm–Extreme Gradient Boosting), to retrieve NO_X emissions. We constructed a dataset incorporating satellite observations and conducted model training and validation in the Shandong region with severe NO_X pollution to retrieve high spatiotemporal resolution of NO_X emission rates. The 10–fold cross–validation coefficient of determination (R²) for the NO_X emission retrieval model was 0.99, indicating that WOA-XGBoost has high accuracy. Validation of the model for the other year (2019) showed high agreement with MEIC (Multi–resolution Emission Inventory for China), confirming its strong robustness and good temporal transferability. The retrieved NO_X emissions for 2021–2022 revealed that emission rate hotspots were located in areas with heavy traffic flow. Among 16 prefecture–level cities in Shandong, Zibo exhibited the highest NO_X rate (>1 μg/m²/s), explaining its high NO₂ pollution levels. In the future, priority areas for emission reduction should focus on heavy industry clusters such as Zibo and high traffic urban centers. Full article

China’s accelerating industrialization has led to worsening air pollution, characterized by recurrent haze episodes. The accurate quantification of PM_2.5 distribution is crucial for air quality assessment and public health management. Although traditional prediction models can effectively identify PM_2.5 concentration fluctuations with moderate accuracy, their dependence relies heavily on extensive ground-based monitoring station data, limiting their applicability in areas with sparse monitoring coverage. To address this limitation, this study proposes a novel algorithm for high-precision PM_2.5 concentration prediction, termed VDMS (Vision Transformer with DLSTM Multi-Head Self-Attention and Self-supervision). Based on the traditional Vision Transformer (ViT) architecture, VDMS incorporates a Double-Layered Long Short-Term Memory (DLSTM) network and a Multi-Head Self-Attention mechanism to enhance the model’s capacity to capture temporal sequence features and global dependencies. These enhancements contribute to greater stability and robustness in feature representation, ultimately improving prediction performance. Cross-validation experimental results show that the VDMS model outperforms benchmark models in PM_2.5 concentration prediction tasks, achieving a coefficient of determination (R²) of 0.93, a root mean square error (RMSE) of 4.05 μg/m³, and a mean absolute error (MAE) of 3.23 μg/m³. Furthermore, experiments conducted in areas with sparse ground monitoring stations demonstrate that the model maintains high predictive accuracy, further validating its applicability and generalization capability in data-limited scenarios. Moreover, the VDMS model adopts a modular design, offering strong scalability that allows its architecture to be adjusted according to specific requirements. This adaptability renders it suitable for monitoring various atmospheric pollutants, providing essential technical support for precise environmental management and air quality forecasting. Full article

Why is fog-haze pollution very serious in Hebei province, where there are many pollution-intensive industries, and in Guangdong province, where it is not so serious? This paper uses the spatial Durbin model, the threshold effect model, and relevant local city data, etc., to explore the effect of the atmospheric environment’s self-purification capacity on haze pollution from the perspective of green technology innovation. We found that the great haze outbreak in China is due to the large amount of ultrafine-particle low-cost emissions caused by the haze detection by weight method implemented in 2011 and 2012. This study also found that haze pollution in China has a significant impact on the atmospheric environment’s self-purification capacity. The atmospheric environment’s self-purification capacity has an inhibitory effect on haze pollution. When green technology innovation reaches the first threshold, the atmospheric self-purification capacity can significantly reduce the impact of haze pollution. When green technology innovation reaches the second threshold, the atmospheric self-purification capacity to reduce haze pollution is significantly enhanced. China’s local haze pollution is serious due to the industrial layout being unreasonable, caused by high-pollution industries emitting particles beyond the limits of atmospheric environment self-purification capacity. Industries in Hebei Province and Guangdong Province are more pollution-intensive, and haze pollution in Hebei Province is serious due to the weak self-purification capacity of the atmospheric environment. Guangdong Province’s atmospheric environment self-purification capacity is strong, and its haze pollution is not serious. Given the scientific use of atmospheric environment self-purification capacity and regional differences in green technology innovation, the development of targeted green input and atmospheric self-purification capacity enhancement policies in areas with serious air pollution, along with green technology innovations based on a region with less pollution, would be beneficial. To increase the amount of green technology innovation investment in regions where the atmospheric environment is not seriously polluted and green technology innovation is based on a bad region, more green funds should be invested in the atmospheric environment’s self-purification capacity. In regions where the atmospheric environment is not seriously polluted and the foundation of green technology innovation needs improvement, more green funds should be invested into atmospheric environment self-purification capacity to fully harness its inhibition of haze pollution. This should be accompanied by scientific planning and adjustments to the high-pollution industrial layout, etc., to effectively enhance the self-purification capacity of the regional atmospheric environment. In addition, the gradient transfer of high-pollution industries should be implemented based on atmospheric environment self-purification capacity to effectively reduce the impact of haze pollution. Full article