Atmospheric Pollution Research最新文献

Accurate forecasting of PM2.5 concentration is crucial for implementing effective protective measures and mitigating the adverse health impacts of air pollution. To address the complex spatial propagation dynamics and temporal variations of PM2.5, we developed the Temporal Enhanced Multisource Data Integration (TEMDI) model. This innovative approach combines spatial modeling by a Graph Neural Network (GNN) to capture the intricate spatial propagation patterns based on multi-source data fusion, and a novel Time Series Enhancement (TSE) module that includes Ensemble Empirical Mode Decomposition (EEMD), Gated Recurrent Units (GRUs), and a self-attention mechanism to adequately manage the time series’ short-term and long-term trends. Our results demonstrate TEMDI’s superior performance, achieving exceptionally high Probability of Detection (POD) rates of 96.15%, 80.28%, and 71.86% for forecast horizons of 3, 36, and 72 h, respectively. Furthermore, our feature importance analysis reveals that multi-scale features extracted by the EEMD component become increasingly crucial as the prediction horizon extends. The TEMDI model’s ability to provide accurate, reliable PM2.5 forecasts and its enhanced interpretability position it as a valuable tool for guiding environmental policy and management decisions to safeguard public health.

To mitigate haze impacts, three visibility simulation schemes were designed using decision tree and random forest algorithms, leveraging atmospheric boundary layer meteorological data, pollutant concentrations, and ground observations. The optimal approach was identified to investigate the boundary layer's effect on simulations. The results showed that the simulation effect of the random forest algorithm for two haze processes was better than that of the decision tree algorithm. In the first haze process, the random forest algorithm had a more significant reduction in root mean square error than the decision tree algorithm in the same visibility range (Scheme 3, visibility<200 m, mean absolute error reduced by 5.9%, root mean square error reduced by 19.1%). Simulation models significantly improved the accuracy of the models by adding atmospheric boundary layer observation data to the two fog-hazes process visibility. However, the addition of atmospheric boundary layer meteorological data in the first haze process had a better improvement effect (random forest: visibility<200 m, mean absolute errors of 25.0 (relative error<12.5%) and 25.5 m (relative error<12.8%) in Scheme 2 and 3, respectively). The addition of atmospheric boundary-layer pollutant concentrations data was more effective in the second haze process (random forest: visibility<200 m, scheme 2 and scheme 3 had mean absolute errors of 25.6 (relative error<12.8%) and 11.1 m (relative error<5.6%), respectively). The influence of atmospheric boundary layer meteorological data and pollutant data on the two fog processes is affected by the cause of the fog process.

Particulate sulfate (SO₄²⁻) is the major component of fine particles in the North China Plain (NCP). It plays an essential role in the entire atmosphere and climate system. Accurately reproducing the SO₄²⁻ levels is challenging for chemical transport model. Here, the major highlighted multiple phase SO₄²⁻ formation pathways are integrated into the WRF-Chem model and seasonal model performance of SO₄²⁻ are assessed by observed SO₄²⁻ at multiple sampling sites located in the NCP. The results show that the integration of these SO₄²⁻ formation pathways obviously narrow the gap between simulation and observation in autumn, winter, and summer at three sampling sites in the NCP, for high RH conditions facilitate the hygroscopic growth of PM_2.5 and promote the multiple phase reaction to form SO₄²⁻. The underestimation in autumn, winter, and summer may ascribe to the missed SO₄²⁻ source from photo-induced chemical route and missed hygroscopicity of organic aerosols. The obviously discrepancy between simulation and observation in spring may ascribe to the large underestimation of SO₂ levels and lack consideration of dust scheme. Source apportionment results show that the gas phase reaction played a vital role in the formation of SO₄²⁻ in each season. The contribution of aqueous phase SO₂ oxidation by H₂O₂ is higher in autumn and summer due to simultaneously high RH levels and stronger photochemical reaction activity. The Mn(II) catalytic SO₂ oxidation pathway at the particle interface is important in autumn and make greater contribution to SO₄²⁻ formation during winter severe haze events under extremely high RH levels.

This study introduces a new methodology aimed at predicting gross $\beta$ levels in the atmosphere. The methodology incorporates input data consisting of local meteorological and synoptic variables, alongside temporal lags and residence time of air masses, to predict gross $\beta$ activity concentration in the atmosphere. Weekly measurements conducted between January 2006 and December 2017 at various sampling sites across diverse locations with different climatic and geographical conditions in Spain were utilized. A high-performance Artificial Neural Network (ANN) model was constructed for this purpose. Across all locations, strong linear relationships are evident between predicted and actual values, with correlation coefficients (R) ranging from 0.86 to 0.92. Higher R values indicate a more robust correlation. Additionally, R-squared values, ranging from 0.7320 to 0.8502, further affirm the model’s ability to explain a significant proportion of the variance in gross $\beta$ activity. Moreover, the relatively low Mean Squared Error (MSE) values, spanning from 0.00966 to 0.11115, and Mean Absolute Error (MAE) values, ranging from 0.08176 to 0.11747, underscore the model’s accuracy in gross $\beta$ activity prediction. The predictive capabilities of the models are robustly demonstrated, showcasing promising results. According to the results of the sensitivity analysis, the category of synoptic parameters has the most important influence on the prediction of atmospheric $\beta$ radioactivity levels, namely air temperature, potential temperature and relative humidity. Regarding the residence time of the air masses, the periods spent over land or water have the most effect on gross $\beta$ levels.

With rapid urbanisation in China, PM2.5 has become a limiting factor for the sustainable development of cities. Taking the Yangtze River Delta as the experimental area, this study analysed the spatial and temporal changes of PM2.5 concentrations from 2001 to 2020. It also examined the variations, dispersion, and correlation with NDVI of PM2.5 concentrations in different vegetation zones at different temporal and spatial scales. The results showed that: (1) The PM2.5 concentration in the Yangtze River Delta showed an overall decreasing trend during 2001–2020, and the change was divided into two phases, starting with an increasing phase and entering a decreasing phase after 2013. (2) In terms of spatial distribution, PM2.5 concentrations in the Yangtze River Delta show a pattern of low in the south and high in the north, with the spatial focus shifting to the north over time. There is a concentration of high levels of particulate matter in the Hefei-Nanjing-Wuxi area. (3) The effect of natural vegetation on the reduction and stabilization of atmospheric particulate matter concentration is better than that of artificial vegetation. (4) Needleleaf forests, broadleaf forests, and shrubs in natural vegetation are more capable of reducing and stabilizing atmospheric particulate matter than grasses. The study can provide a reference for regional air pollution control and regional plant system construction.

Particulate nitrate pollution has emerged as a major contributor to haze events in urban environment, due to the rapid increase of vehicle emissions. However, a comprehensive formation mechanisms of PM_2.5 responses to vehicle emissions control still remains high uncertainties. In our study, hourly criteria air pollutants, meteorological parameters and chemical compositions of PM_2.5 were continuously measured with or without reduced on-road activity at the coastal city in southeast China. XG Boost-SHAP models analysis showed that increasing concentrations of NO₃⁻, NH₄⁺, and BC contribute to elevated PM_2.5 levels, due to the influence of vehicle emissions. Based on PMF model results, there was a notable increase in the contributions of traffic-related emissions, industrial activities, and dust sources to PM_2.5, with increments of 13%, 4%, and 7%, respectively. In addition, metal elements such as Mn emerged as the primary contributor to hazard quotient (HQ) values, originated from non-exhaust emissions of vehicles, which might cause the potential toxic risks on human health, particularly during haze events. Hence, this study improve the understanding of air quality and human health both direct and indirect responses to vehicle emissions control in future urban management.

New particle formation (NPF) process brings significant contribution to the global atmospheric particle number concentrations. Evidence on the formation and growth of NPF is reported for the first time at a Romanian urban site, i.e., Iasi, north-eastern Romania. Size-dependent aerosol number concentrations were obtained during two short-term campaigns undertaken in 2017 by using a scanning mobility particle sizer. The existence of two categories of events can be highlighted by investigating the net maximum increase in the nucleation mode particle number concentration and the maximum size of the geometric median diameter of new particles. The variability of meteorological parameters showed that the solar radiation peak was usually associated with NPF events, while relative humidity was anti-correlated with those events. Calculated nucleation rate values for events in May, median of 4.5 cm⁻³ s⁻¹, was lower than the corresponding median values of 8.6 cm⁻³ s⁻¹ for December. The particle growth rates showed a similar trend with a median of 4.0 nm h⁻¹ and 7.7 nm h⁻¹, in May and December, respectively. The obtained results suggest that regional emission sources might bring some contributions on the particle nucleation and growth processes, particularly over the cold season. Regarding the deposition of particles in the respiratory system, it appears that ultrafine particles are among the most significant contributors to the alveolar deposits. Moreover, evidences were obtained that the impact on the particle deposition at the alveolar region is not mandatory in direct relation to the intensity of the NPF event, the pollution burden of the particles most probably playing an important role.

Particulate matters generated from waste incineration carry charge due to collision and friction. By using charge induction method, it becomes feasible to detect particulate matter concentration by capturing the electrical signals emitted by particulate matters. In this study, a new charge induction device was constructed and tested. The investigation revealed a linear relationship between the sine wave eigenvalues of the electrical signals and the concentration of particulate matters. The corresponding formulas for peak-to-peak value, root mean square, and standard deviation were calculated, with R² values greater than 0.98. Additionally, the influence of concentration and size on detection error was studied. The results showed that as the concentration increased or the size decreased, the detection error decreased. Furthermore, the study found that the impact of particulate matter concentration on detection results mitigated that of particulate matter size. The detection device, correlation formulas and influencing factors proposed in this study are expected to provide technical support and theoretical basis for particulate matter detection, offering significant value in the field of air pollution control.

Dust particles contain diverse natural and anthropogenic organic compounds and act as local collectors of pollutants, particularly in urban settings. Samples of street dust were collected from various sites in Riyadh city in 2023. These samples were extracted using a dichloromethane–methanol mixture, and the resulting extracts were subjected to analysis through gas chromatography–mass spectrometry (GC–MS). The primary compounds identified included n-alkanes, methyl n-alkanoates (FAMEs), hopanes, steranes, polycyclic aromatic hydrocarbons (PAHs), plasticizers, tobacco miscellanies, and an unresolved complex mixture (UCM). Vegetation detritus constituted the primary natural source of organic compounds, ranging from 7.4 ± 3.5% to 15.0 ± 4.0%, and included fractional n-alkanes and FAMEs. Petroleum-related products from vehicular emissions, oil combustion, and spills were predominant, accounting for 73.3 ± 5.1% to 87.5 ± 4.8%, and included partial n-alkanes, hopanes, steranes, PAHs, and UCMs. Litterings from discarded plastics and tobacco smoking varied from 5.2 ± 1.3% to 12.0 ± 5.3%, and included phthalates, nicotine, and cotinine, as well as recreational drinks (coffee and tea beverages containing caffeine). The occurrence and distribution of natural and anthropogenic extractable organic matter in this arid urban area were influenced by local vegetation and human activities. The prevalence of anthropogenic organic compounds in Riyadh city's street dust depended on the location and type of urban activity, with elevated levels observed in high-traffic and industrial zones. Consequently, further investigations are necessary to understand the potential health effects of anthropogenic organic matter on city residents.

This study presents a comprehensive analysis of air quality in China, Southwest China, over four winter seasons: 2003–2004, 2004–2005, 2017–2018 and 2019–2020. We initially collected Total Suspended Particles (TSP) samples during the earlier periods and PM_2.5 samples during the later periods. Our goal was to illustrate the changes in sources of atmospheric pollutants over time. By focusing on the chemical composition of water-soluble inorganic ions (WSIIs), we highlighted significant long-term changes in air quality and pollution sources in Guiyang alongside the effectiveness of recent pollution treatment strategies. Historically affected by acid rain and acid pollution, Guiyang has shown notable improvements in air quality. Notably, sulfate pollution, primarily from coal combustion, has significantly decreased, with the sulfates concentration declining from an estimated 19.04 μg m⁻³ to 30.46 μg m⁻³ during the winter of 2003–2004, to just 7.37 μg m⁻³ in PM_2.5 during the winter of 2019–2020. Additionally, the mean mass concentration of PM_2.5 dropped by 18% between the 2017–2018 and 2019–2020 winters. An increasing ratio of nitrate to sulfate in the aerosols indicates a shift in pollution sources, with secondary nitrate pollution, largely from vehicle emissions, becoming increasingly prevalent. Positive Matrix Factorization (PMF) model analysis identified five major pollution sources, highlighting a transition from secondary sulfate to secondary nitrate as the primary contributors to air pollution in Guiyang, and secondary nitrate pollution mainly from vehicles emission was increasingly severe meanwhile the significance of ammonium should not be overlooked. The results stress the importance of local pollution sources and suggest a need for revised pollution control policies that address the evolving characteristics of aerosols and prioritize the major pollutants in Guiyang, especially during winter months.