Seasonal and diurnal variabilities of secondary organic aerosol in coastal and inland cities, north China: Impact of anthropogenic emission.

Impacts of land use/cover changes on local meteorology and air quality in the Yangtze River Delta region of China (2001-2021).

Bio-energy plants play a key role in the transition toward low-carbon energy systems, yet their large-scale deployment is constrained by variability in syngas quality, biofuel instability during storage, and fluctuating air pollutant emissions. This review examines how machine learning (ML) methods support improved decision-making across bio-energy value chains by linking multi-source data with predictive and adaptive control strategies. The study synthesizes recent advances in ML-based modelling for syngas composition prediction, biofuel stability assessment, and real-time emission monitoring and mitigation. Emphasis is placed on uncertainty-aware models and hybrid approaches that combine data-driven learning with process knowledge to address feedstock heterogeneity and dynamic operating conditions. The findings show that ML enhances operational efficiency, supports cleaner production, and improves system reliability by enabling proactive control rather than reactive adjustments. From a sustainability perspective, these outcomes directly contribute to SDG 7 (Affordable and Clean Energy) through higher efficiency and reliability of bio-energy systems, SDG 9 (Industry, Innovation, and Infrastructure) by promoting intelligent and resilient industrial processes, SDG 12 (Responsible Consumption and Production) via optimized resource use and reduced waste, and SDG 13 (Climate Action) through lower emissions and improved carbon performance. Overall, the review highlights ML as a practical decision-support tool for industry and policy stakeholders seeking resilient, data-driven pathways toward sustainable bio-energy deployment.

Isoprene, a major global precursor to secondary organic aerosol (SOA), affects air quality and radiative forcing. A key SOA formation pathway is the reactive uptake of isoprene epoxydiol (IEPOX), a process influenced by multiple environmental factors. Here, we simulate the spatial distribution and interannual trend of IEPOX-SOA over Eastern China from 2014 to 2019 using CMAQ and quantify the contributions of different factors. Our results indicate that ambient IEPOX-SOA concentrations are elevated in central and northeast China, averaging 135 ng·m-3 and with a recent declining trend between -19.1 and -34.8 ng·m-3·yr-1. Long-term trends of IEPOX-SOA are primarily driven by changes in sulfate (-50.5 ng·m-3·yr-1), which serve as seed aerosols, while a decrease in organic carbon reduces the coating effects and partially offsets (+16.1 ng·m-3·yr-1) the decline. Aerosol acidity plays a key role in governing its spatial distribution by affecting reactive rates. In Eastern China, high ammonia levels effectively neutralize acidity, thereby suppressing abundant IEPOX-SOA formation, resulting in lower concentrations compared to other regions, such as the United States. Sensitivity tests indicate that substantial future NH3 reductions in China could increase the IEPOX-SOA by up to 100%. This study clarifies the key chemical mechanisms governing IEPOX-SOA production and supports regional air pollution control strategies.

ABSTRACT This research investigates the presence and concentrations of microplastics and potentially toxic elements in dust from homes in urban and rural environments, revealing their hidden threats to human health and indoor air quality. Stereomicroscopic analysis showed that the fiber was dominant, and the nonfiber was less abundant. The average number of fibers and nonfibers in house dust samples in the city center was 61 ± 39 and 16 ± 8 per milligram, having an average size of 1206 ± 276 µm. While the average fibers and nonfibers count in house dust samples in rural areas was 57 ± 26 and 9 ± 3 per milligram, with an average size of microplastics in house dust samples in rural areas of 1068 ± 130 µm. The identified microplastics revealed different colours, mainly bright ones. The EDX analysis of microplastics revealed the existence of different elements (Ne, Na, C, S, Cl, O, Ca, Zn, Al, and Si). Seven potentially toxic elements (Ni, Cr, Cu, Pb, Zn, Cd, and Co) and Zn had the highest concentrations in all houses in the city center (1247.948 ± 907.904 mg/kg) and in rural areas (1164.394 ± 181.149 mg/kg). Average daily inhalation dose indices were calculated to assess the harmfulness of potentially toxic elements to human health via inhalation. Cd showed the lowest inhalation metal concentration, whereas Zn showed the highest. The findings show no significant differences in microplastics and potentially toxic elements' concentrations between urban and rural areas. The results emphasise the urgent need for better air quality management and pollution control strategies in residential settings.

Based on continuous field observations conducted at the Shangdianzi Regional Atmospheric Background Station from 21 October to 20 November 2024 and from 1 December 2024, to 2 January 2025, this study systematically analyzed the concentration levels, seasonal variations, diurnal patterns, and ozone formation potential (OFP) of 24 carbonyl compounds (OVOCs) in the atmosphere during autumn and winter. Source apportionment was further investigated using characteristic ratios, correlation analysis, and multiple linear regression. The results indicate that the average concentration of Σ24OVOCs during the observation period was 2.70 ± 1.55 ppb. Formaldehyde, acetone, and acetaldehyde were the dominant species, accounting for 94.5% of the total concentration in this background area. A significant seasonal difference in carbonyl concentrations was observed, with the average concentration in autumn (3.68 ± 1.66 ppb) being approximately 2.1 times higher than that in winter (1.78 ± 0.58 ppb). The diurnal variation in most carbonyls exhibited a pattern of nighttime accumulation and daytime depletion, which was consistent with the trend of NO2. The OFP results show that the average OFP of Σ24OVOCs was 30 ± 16 μg/m3, with formaldehyde contributing 86.9%, identifying it as a key precursor for ozone formation in the background region. Source analysis revealed that carbonyl compounds in autumn were influenced by combined natural, vehicular, and industrial sources, with significant secondary formation (27-36%) observed for C2 (acetaldehyde) and C3 (mainly acetone and propanal) species. In winter, anthropogenic contributions to carbonyls increased, with C2 and C3 species primarily originating from combustion sources, vehicle emissions, and industrial releases. This study provides the first insights into the pollution characteristics and source profiles of carbonyl compounds during autumn and winter at the Shangdianzi background site, offering a scientific basis for understanding regional atmospheric oxidative capacity and formulating integrated air pollution control strategies.

Under the fast development of urbanization, PM2.5 pollution has become a prominent issue affecting the urban ecological environment and residents’ health. To investigate the impact of urban landscape patterns on PM2.5 concentrations, this study applies the Local Climate Zone (LCZ) classification to Shanghai using the World Urban Database and Access Portal Tools (WUDAPT). LCZ-derived landscape metrics are adopted as predictor variables to focus on how urban form and spatial configuration affect PM2.5 distribution and to identify the key landscape categories and types influencing PM2.5 levels. The results reveal notable seasonal and spatial differences in the effects of different LCZ types and landscape metrics on PM2.5 concentrations; on average, over 69% of the spatial variation in PM2.5 across the four seasons can be explained by the Multi-scale Geographically Weighted Regression (MGWR) model. This research demonstrates that the LCZ framework effectively uncovers the seasonal and spatial mechanisms by which urban landscape patterns influence PM2.5 concentrations in Shanghai. It offers a novel perspective for understanding the interplay between urban landscape and atmospheric pollution, and provides scientific guidance for sustainable urban planning and precise air pollution control strategies in other cities.

Spatial heterogeneity and evolutionary pathways of PM2.5 pollution driven by urbanization in China to 2100.

Air pollution is one of the most pressing environmental issues, with direct impacts on human health and environmental quality. The increasing intensity of transportation activities, industrial operations, forest and land fires, and regional development in South Sumatra Province has increased the risk of air pollution, particularly from nitrogen dioxide (NO2) and sulfur dioxide (SO2). This study aims to analyze the concentrations and multi-year trends of NO2 and SO2 in South Sumatra based on passive sampler measurements conducted at 68 monitoring sites across 17 districts and municipalities during the period 2021–2024. The results show that the average concentrations of NO2 ranged from 6.654 to 9.944 micrograms per cubic meter, while SO2 concentrations ranged from 7.303 to 8.456 micrograms per cubic meter. All measured concentrations were below the National Ambient Air Quality Standards as well as the European Union guideline values. Trend analysis indicates a consistent decrease in NO2 and SO2 concentrations from 2021 to 2024. These findings contribute to the availability of long-term air quality data and enhance understanding of NO2 and SO2 dynamics, providing a scientific basis for the development of evidence-based strategies for air pollution control and prevention in South Sumatra Province. Keywords: nitrogen dioxide, sulfur dioxide, passive sampler, South Sumatra, air pollution trends.

Abstract. Lidar-derived particle backscatter coefficient is commonly used to assess air pollution levels; however, hygroscopic growth can amplify particle backscatter and hinder accurate assessment of particle concentration. This study investigated the hygroscopic growth characteristics of urban anthropogenic aerosols in Wuhan (30.5° N, 114.4° E), central China, using ground-based 532 nm polarization lidar observations during 2010–2024. A total of 192 cases were identified based on the following criteria: (1) the presence of a layer thicker than 300 m; (2) a lidar-derived backscatter coefficient that increases monotonically with simultaneously-measured relative humidity (RH) from radiosonde, and (3) limited variations in key meteorological parameters, including water vapor mixing ratio, potential temperature, and wind speed and direction. Using the Hänel parameterization method, the hygroscopic growth parameter γ was estimated as 0.62 (±0.24), corresponding to a backscatter coefficient enhancement factor of 2.36 at 85 % RH. No evident differences in γ were observed between the boundary layer (0.63 ± 0.25) and free troposphere (0.60 ± 0.24). The annual mean γ increased from 0.49 in 2015 to 0.63 in 2017 and stabilized within 0.6–0.7 after 2018, closely following the evolution of the annual mean NO2-to-SO2 concentration ratio. The minimum seasonal average γ occurred in winter (0.56), while the maximum was observed in autumn (0.64). These results provide a comprehensive characterization of the long-term and seasonal hygroscopicity of pollutants over central China, enhancing our understanding of the influence of hygroscopic growth on lidar-observed particle backscatter coefficients and offering valuable insights for urban air pollution control strategies.

VOCs are significant precursors for the formation of O3 and SOA, directly impacting human health. This study employs multiple approaches to analyzing atmospheric VOCs by focusing on OVOCs including aldehydes, ketones, and phenols, with a case study in Beijing, China. We analyzed the concentration levels and compositions of VOCs and their atmospheric activities, offering a new perspective on VOCs. This analysis was conducted through offline measurements of volatile phenols and carbonyl compounds, complemented by online VOC observations during the summer period of high O3 levels. The total atmospheric VOCs concentration was found to be 51.29 ± 10.01 ppbv, with phenols contributing the most (38.87 ± 11.57%), followed by carbonyls (34.91 ± 6.85%), and aromatics (2.70 ± 1.03%, each compound is assigned to only one category based on its primary functional group, with no double counting). Carbonyls were the largest contributors to the OFP at 59.03 ± 14.69%, followed by phenols (19.94 ± 4.27%). The contribution of phenols to the SOAFP (43.37 ± 9.53%) and the LOH (67.74 ± 16.72%) is dominant. Among all quantified VOC species, phenol and formaldehyde exhibited the highest species-level contributions to atmospheric reactivity metrics, including LOH, OFP and SOAFP, owing to their combination of elevated concentrations and large kinetic or MIR coefficients. Using the PMF model for source analysis, six main sources of volatile organic compounds were identified. Solvent use and organic chemicals production were found to be the primary contributors, accounting for 31.76% of the total VOCs emissions, followed by diesel vehicle exhaust (17.80%) and biogenic sources (15.51%). This study introduces important OVOCs such as phenols, re-evaluates the importance of OVOCs and their role in atmospheric chemical processes, and provides new insights into atmospheric VOCs. These findings are crucial for developing effective air pollution control strategies and improving air quality. This study emphasizes the importance of OVOCs, especially aldehydes and phenols, in the mechanism of summer O3 generation.

As a critical core node of the “Belt and Road” Initiative and a representative arid-zone urban agglomeration in Northwest China, the Urumqi-Changji-Shihezi (U-Chang-Shi) region faces severe air pollution, posing significant threats to ecological security and public health. Leveraging the 2000–2022 China High-Resolution Air Quality (CHAP) dataset and multi-source meteorological data, this study systematically investigates the spatiotemporal evolution of PM2.5, PM10, and ozone (O3) alongside their driving mechanisms. Results reveal distinct seasonal patterns: PM2.5 and PM10 concentrations peak in winter due to coal combustion emissions and unfavorable static meteorological conditions, while dropping below 30 µg/m3 in summer as photochemical reactions weaken. The Mann–Kendall (MK) trend test, combined with spatial-temporal analysis methods, elucidates the complex pollution dynamics. The U-Chang-Shi industrial belt acts as a pollution hotspot, with Dabancheng District exhibiting elevated PM10 levels attributed to pollutant transport and terrain effects. O3 pollution intensifies in spring and summer, surging post-2016 across regional cities, with Shihezi showing a 16.7% annual increase. Key drivers include unfavorable static meteorology and sparse vegetation for particulate pollutants, while precipitation (P) wet deposition enhances their removal. O3 production is modulated by potential evapotranspiration (PET) and wind speed (WIND), with high temperatures (T) accelerating photochemical reactions, although counteracted by particulate matter. Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT) simulations indicate that Eurasian mid-latitude winter circulation and cross-border dust contribute to winter PM10 variability. Although the “coal-to-gas” project mitigated particulate pollution, its efficacy is constrained by Shihezi’s lagging industrial restructuring. This study provides critical insights for optimizing air pollution control strategies in ecologically vulnerable regions of Northwest China and arid-zone urban agglomerations under the Belt and Road Initiative, emphasizing the need for region-specific emission reduction measures and cross-border collaboration.

This study investigates the release characteristics and influencing factors of volatile organic compounds (VOCs) in sludge drying exhaust gases from two urban sewage treatment plants and one sludge treatment plant in Zhengzhou, Henan Province. Through on-site sampling and dynamic simulation experiments, 99 VOC components were analyzed. Results show that aromatic hydrocarbons (28.2%–31.8%), particularly benzene-related compounds, dominate in sewage plants, while volatile oxygenated organic compounds (OVOCs) (22.9%–36.1%) are more prominent in sludge plants. Peak VOC concentrations of 780, 1220, and 1720 μg/m³ were observed 7−10 minutes after aeration, stabilizing thereafter. Higher drying temperatures and aeration intensities increase VOC release, with composting fermentation producing the highest emissions. These findings provide insights into selecting drying methods and exhaust gas treatment strategies for effective air pollution control.

Air pollution remains a critical global challenge due to its profound impacts on human health and the environment. In Africa, the need for effective air quality management has become increasingly urgent. This systematic review evaluates advanced strategies for air pollution control across the continent. Using the Web of Science and ProQuest, 10 peer-reviewed articles published between 2020 and 2025 were screened and selected for analysis. The studies were grouped into two categories: (1) preventive policies targeting emissions from energy consumption, transportation, and industry, and (2) control strategies and technologies designed to mitigate specific pollutants. Across the reviewed studies, mean annual PM2.5 concentrations ranged from 19.3 µg/m3 in Bamako to 63.5 µg/m3 in Kano, highlighting substantial variability in air quality. Preventive policies were found to reduce emissions by 10–35% in energy and industrial sectors, while targeted control measures lowered concentrations of PM2.5, SO2, NO2, VOCs, and ozone by 5–20%, depending on implementation context. These findings demonstrate measurable improvements in air quality associated with both policy and technological interventions, while underscoring the need for integrated, context-specific strategies to effectively combat air pollution in African cities.

Sand and dust storms exacerbate the toxicity of particle pollution on mortality: A cohort study among 1.5 million Chinese older adults.

Abstract. Nitrous acid (HONO) is a key precursor of atmospheric hydroxyl radicals (OH) and significantly influences the formation of secondary pollutants, making it essential for understanding and controlling air pollution. While many studies have focused on its formation mechanisms, few have explored the impact of variations in anthropogenic activities on HONO formation. Therefore, we investigated the impact of variations in anthropogenic activities on HONO formation based on comprehensive observations conducted in urban Beijing during autumn and winter of 2022. During clean periods with a 53 % drop in Traffic Performance Index, HONO, CO, and NO2 levels decreased by 2–3 times compared to polluted periods and significantly lower than previously reported wintertime levels in Beijing. Source apportionment revealed that NO2 heterogeneous reaction on ground was the dominant HONO source across all periods. Vehicle emissions contributed more to HONO during clean periods, suggesting that reducing anthropogenic activities has a stronger influence on secondary HONO formation. Particulate nitrate (pNO3) photolysis contributed more to HONO during polluted periods, due to higher pNO3 fractions in PM2.5 under more polluted conditions. Despite including all known formation pathways in the model, unidentified HONO sources still remained. This was strongly associated with intense solar radiation and high OH concentrations at daytime, as well as elevated NH3 concentrations at nighttime. Emission reduction simulations further revealed that a 50 % NOx reduction during polluted periods could lower HONO by up to 46.3 %, directly demonstrating that reducing anthropogenic activities significantly suppresses HONO formation and provides a scientific basis for the development of air pollution control strategies.

Air quality significantly impacts public health, industrial stability, and timely responses to environmental hazards, all of which are essential for sustainable development. Accurate forecasting of the Air Quality Index (AQI) is therefore crucial for effective environmental monitoring and management. In this study, we develop a hybrid deep learning model that integrates a Transformer encoder with a Bidirectional Long Short-Term Memory (BiLSTM) network. The model is trained and validated using daily air quality data collected from Shijiazhuang, Beijing and Tianjin, spanning November 2013 to February 2025. Experimental results demonstrate that the proposed Transformer-BiLSTM model delivers stable and reliable predictive performance, with root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) of 3.0012 ug/m^3, 1.7928 ug/m^3, and 3.3646%, respectively. Compared with conventional baseline models, the hybrid model improves accuracy and generalization capability. This approach offers a reliable and interpretable tool for AQI forecasting and provides quantitative support for data-driven air pollution control strategies.

Background: Understanding the long-term,multiscaletemporal dynamics of particulate matter (PM2.5 and PM10) and its complex interactions with meteorological factorsis critical for developing effective air pollution control strategies,particularly in cities with unique topographical characteristics.This study investigates the decadal evolution of PM pollution in Guiyang,a typical karst highland city, to unravel its underlying mechanismsand inform targeted mitigation policies. Methods: We analyzed continuous daily PM2.5 and PM10 concentration data from 2015 to 2024 in Guiyang, along with concurrentmeteorological data (temperature, relative humidity, wind speed, precipitation,and air pressure). Multiscale wavelet analysis, employing Daubechies6 and Morlet wavelets, was used to decompose the time series, identifydominant periodicities, and quantify the scale-dependent correlationsbetween PM concentrations and meteorological drivers. Results: Over the last decade, Guiyang has achieved significant improvementin air quality, with the annual average concentrations of PM2.5 and PM10 decreasing by 51.05% and 49.60%, respectively,compared to 2015. The annual average PM2.5 concentrationexceeded the grade II of the National Ambient Air Quality Standard(35 μg/m3) in 2015 and 2016. A stable “U”-shapedannual cycle (winter > spring > autumn > summer) was identified,whichwavelet analysis further resolved into three distinct dynamic stagesand attempt to analyze its dominant factors: a winter–springfluctuation period (January–May) dominated by atmospheric inversionsand anthropogenic emissions, a summer stabilization period (May–October)driven by clean air masses and enhanced wet deposition, and an autumn–winteraccumulation period (October–January) triggered by regionalbiomass burning and stagnant weather. Extreme pollution events, suchas the 2024 Chinese New Year, caused PM2.5 and PM10 to surge by over 130% on a single day. The PM–meteorologycoupling exhibited strong scale dependence: at short scales (<16d), relative humidity and precipitation were the primary drivers;at medium scales (16–64 d), wind speed and temperature (promotingsecondary aerosol formation) became dominant; while at large scales(>64 d), correlations weakened. Conclusion: Whilelong-term policies have been effective, Guiyang’s air qualityis challenged by persistent winter pollution and extreme events, governedby a multistage annual pattern and constrained by its unique karsttopography. We recommend a three-pronged strategy: (1) prioritizingwinter–spring controls focusing on clean heating, (2) implementinga dynamic “festival–meteorology” early warningsystem for extreme events, and (3) developing terrain-adapted strategiesto optimize emission source layouts and enhance urban ventilation.These findings also provide crucial insights for improving air qualitymodels in mountainous regions by incorporating multiscale dynamics,particle-specific responses, and terrain-specific parametrizations.

Abstract. Understanding the urban-rural patterns and driving drivers behind the recent decrease in particulate matter (PM) pollution across eastern China is essential for assessing the efficacy of environmental policies and ensuring equitable health co-benefits. By employing an interpretable, end-to-end machine learning framework integrating satellite observations, meteorological factors, and auxiliary datasets, this study reveals changes in urban and rural PM pollution and the underlying drivers. During the period 2015–2023, the average decrease rates of PM10 and PM2.5 in eastern China were −4.02 ± 1.29 and −2.41 ± 0.91 µg m−3 yr−1, respectively. The rate of decrease in urban areas was higher than that in rural areas, which played a dominant role in PM reduction. Significant reductions in PM concentrations were observed in urban core areas, suburbs, towns and regions with high agricultural pressure. The interpretability analysis showed that temperature and interannual variability were the main drivers of PM pollution reduction. However, only interannual variability showed a significant decreasing trend in its effect on PM pollution, while other driving factors showed periodic variations. Furthermore, there were differences in the drivers of PM reduction between urban and rural areas, particularly with interannual variability in particular contributing to PM pollution reduction in urban areas, but having a lesser impact in most rural areas. This study reveals the urban-rural patterns of PM pollution reduction in eastern China, and highlights the need for differentiated air pollution control strategies in urban and rural areas.

Utilizing China’s air quality monitoring data from 2013 to 2023, this study employs spatial autocorrelation analysis and health impact assessment methodologies to quantify the health economic costs and temporal trends of PM2.5 and O3 pollution across mainland China. Results indicate a substantial decline in PM2.5-attributable mortality over the study period. With 0 and 15 μg/m3 taken as the reference concentrations, all-cause mortality decreased by 47.41% (233,173 cases) and 65.55% (240,448 cases), respectively. Cardiovascular disease mortality declined by 47.71% (56,086 cases) and 65.75% (57,504 cases), while respiratory disease mortality reduced by 46.97% (38,519 cases) and 65.27% (40,055 cases). Conversely, O3-related mortality exhibited a significant upward trend. At reference concentrations of 0 and 60 μg/m3, all-cause mortality increased by 24.25% (365,084 cases) and 79.70% (372,724 cases), respectively. Cardiovascular mortality rose by 25.43% (80,056 cases) and 81.52% (77,497 cases), and respiratory mortality increased by 15.46% (98,620 cases) and 64.54% (163,165 cases). Spatially, health economic costs and their GDP proportions for both pollutants followed a high distribution in the east and a low distribution in the west. Shandong, Henan and Jiangsu were the top regions for PM2.5-related economic costs, with Henan, Hebei and Tianjin exhibiting the highest GDP ratios. For O3, Guangdong, Jiangsu and Shandong incurred the greatest economic costs, while Henan, Hebei and Gansu showed the highest GDP proportions. These findings underscore the divergent trends in PM2.5 and O3 health impacts and provide critical evidence for targeted air pollution control strategies in China.