Atmospheric Pollution Research最新文献

{"title":"Estimation and analysis of CO2 column concentrations (XCO2) in the Yangtze River Delta of China based on multi-source data and machine learning","authors":"Chunmei Chen ,&nbsp;Xiaomei Chen ,&nbsp;Qiong Liu ,&nbsp;Weiyu Zhang ,&nbsp;Yonghang Chen ,&nbsp;Yuhuan Ou ,&nbsp;Xin Liu ,&nbsp;Huiyun Yang","doi":"10.1016/j.apr.2025.102528","DOIUrl":"10.1016/j.apr.2025.102528","url":null,"abstract":"<div><div>Carbon dioxide (CO₂) is one of the most significant greenhouse gases in the atmosphere and plays a crucial role in global warming. Currently, the temporal resolution for XCO₂ from the satellite is low, and the ground-based XCO₂ observation station is limited. There is an urgent need for a XCO₂ dataset with high temporal and spatial resolution. Consequently, based on the random forest algorithm, we have developed an optimized model for predicting XCO₂ with a spatial resolution of 0.25° × 0.25° and a temporal resolution of 1 h for the Yangtze River Delta in 2020. The multi-source data, such as the ground-observation XCO₂ from the TCCON, as well as meteorological parameters, aerosols, surface vegetation index, and emission source factors from the ERA5, MERRA-2, MODIS, and MEIC datasets, were used in this study. The results indicate that the random forest model is well-suited for predicting XCO₂. Specifically, the model performs more optimally when utilizing 20 variables, including solar zenith angle, normalized vegetation index, and carbon emission data as input parameters with the prediction RMSE and R² of 1.031 × 10⁻⁶ and 0.940. The MAE for predicted XCO₂ at Xianghe and Hefei stations are 0.628 × 10⁻⁶ and 0.550 × 10⁻⁶, respectively, marking a substantial increase in accuracy compared to GOSAT data. In 2020, daily variations of XCO₂ follow a pattern of higher concentrations at night and lower concentrations during the day, negatively correlating with changes in the atmospheric boundary layer height. The inter-monthly and seasonal variations reveal smaller concentrations in summer and higher concentrations in winter. The minimum concentration occurs in July at 409.64 × 10⁻⁶, while the maximum concentration occurs in November at 413.11 × 10⁻⁶. Spatially, XCO₂ is higher in the northern areas and lower in the southern regions, showing a negative correlation with the NDVI and a positive correlation with anthropogenic carbon emissions. The XCO₂ dataset calculated in this study with continuous spatial and temporal resolutions could address the limitations of satellite products with low temporal resolution and a limited number of ground observation stations.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 7","pages":"Article 102528"},"PeriodicalIF":3.9,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143816431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating hourly surface PM2.5 concentrations with full spatiotemporal coverage in China using Himawari-8/9 AOD and a two-stage model","authors":"Shuyang Zhang ,&nbsp;Peng Chen ,&nbsp;Yuchen Zhang ,&nbsp;Chengchang Zhu ,&nbsp;Cheng Zhang ,&nbsp;Jierui Lu ,&nbsp;Mengyan Wu ,&nbsp;Xinyue Yang","doi":"10.1016/j.apr.2025.102519","DOIUrl":"10.1016/j.apr.2025.102519","url":null,"abstract":"<div><div>PM_2.5 (fine particulate matter with an aerodynamic diameter of less than <span><math><mrow><mn>2</mn><mo>.</mo><mn>5</mn><mspace></mspace><mi>μ</mi><mi>m</mi></mrow></math></span>) is a significant air pollutant, posing serious risks to both the atmospheric environment and human health. Satellite remote sensing Aerosol Optical Depth (AOD) data are often used to estimate surface PM_2.5 concentrations. However, satellite-derived AOD data are often affected by large-scale data gaps due to cloud contamination and high surface albedo, leading to discontinuities and incompleteness in surface PM_2.5 estimations based on AOD. PM_2.5 is influenced by natural and human activities, both of which show strong diurnal variations. Many previous studies have used AOD data from sun-synchronous orbiting satellites, whose coarser temporal resolution makes it difficult to capture these diurnal PM_2.5 variations. In this study, AOD products from the new generation of geostationary meteorological satellites, Himawari-8/9, are employed to estimate spatiotemporally continuous hourly seamless PM_2.5 grid data using a two-stage Random Forest (RF) model. This model integrates meteorological, surface, and demographic-economic factors. In the first stage, the RF model was used to fill the gaps in the satellite AOD data, achieving a good fit (<span><math><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>=</mo><mn>0</mn><mo>.</mo><mn>95</mn></mrow></math></span>), with a root mean square error (RMSE) and mean absolute error (MAE) of 0.05 and 0.03, respectively. In the second stage, the model estimates surface PM_2.5 grid data (5<!--> <!-->km <span><math><mo>×</mo></math></span> 5<!--> <!-->km) at hourly intervals during the daytime, based on the gap-filled AOD data, actual PM_2.5 measurements from ground stations, and auxiliary data. The final hourly PM_2.5 estimates were well-fitted to the ground station measurements (<span><math><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>=</mo><mn>0</mn><mo>.</mo><mn>92</mn></mrow></math></span>), with RMSE and MAE values of 7.14 and <span><math><mrow><mn>4</mn><mo>.</mo><mn>90</mn><mspace></mspace><mi>μ</mi><mi>g</mi></mrow></math></span>/m<span><math><msup><mrow></mrow><mrow><mn>3</mn></mrow></msup></math></span>, respectively. This study provides a valuable approach for estimating complete, hourly-level spatial and temporal distributions of PM_2.5 from incomplete satellite remote sensing AOD data, which is crucial for air quality management and assessing short-term exposure risks.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 7","pages":"Article 102519"},"PeriodicalIF":3.9,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Long-term reconstruction of NO2 photolysis rate coefficients using machine learning and its impact on secondary pollution: A case study in a megacity of the Sichuan Basin, China","authors":"Tong Li ,&nbsp;Song Liu ,&nbsp;Dongyang Chen ,&nbsp;Ruirui Zhang ,&nbsp;Hefan Liu ,&nbsp;Danlin Song ,&nbsp;Qinwen Tan ,&nbsp;Hongbin Jiang ,&nbsp;Li Zhou ,&nbsp;Fumo Yang","doi":"10.1016/j.apr.2025.102526","DOIUrl":"10.1016/j.apr.2025.102526","url":null,"abstract":"<div><div>The NO₂ photolysis rate coefficient (<em>J</em>_<em>NO2</em>) is a critical parameter for assessing the intensity of atmospheric photochemical reactions. However, continuous long-term measurements of <em>J</em>_<em>NO2</em> are scarce. In this study, we developed a machine learning-based method to reconstruct hourly <em>J</em>_<em>NO2</em> values, applying it to a megacity in the Sichuan Basin from 2015 to 2023. The model exhibited strong performance with cross-validation R² = 0.854 and RMSE = 8.15 × 10⁻⁴ s⁻¹. Utilizing the Shapley Additive Explanations (SHAP) method, we identified solar activity and pollutant levels both as significant predictors for <em>J</em>_<em>NO2</em>. Our long-term <em>J</em>_<em>NO2</em> reconstructions indicate a strong correlation between <em>J</em>_<em>NO2</em> and ozone concentration, highlighting its important role in secondary pollution. This study illustrates the effectiveness of machine learning in reconstructing hourly <em>J</em>_<em>NO2</em> values, providing a valuable enhancement to traditional models. The findings are crucial for understanding regional photochemical processes and for analyzing trends and causes of ozone and aerosol pollution.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 7","pages":"Article 102526"},"PeriodicalIF":3.9,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Long-term exposure to ambient air pollution and incident nephritis: A prospective cohort study in the UK Biobank","authors":"Qiong Duan ,&nbsp;Cheng Zhou ,&nbsp;Haifeng Chen ,&nbsp;Jie Zhang ,&nbsp;Zhaohui Ruan ,&nbsp;Hongfei Cao ,&nbsp;Zixing Zhang ,&nbsp;Xihai Xu ,&nbsp;Xinyu Fang","doi":"10.1016/j.apr.2025.102524","DOIUrl":"10.1016/j.apr.2025.102524","url":null,"abstract":"<div><div>Substantial studies have highlighted the implications of air pollution in relation to several kidney diseases. However, studies on the relationships of long-term exposure to NO₂, NO_x, PM_2.5, PM_2.5-10, PM₁₀ with the incidence of nephritis are relatively scarce. In our prospective cohort study, 446,626 participants from the UK Biobank who had no kidney diseases at baseline were enrolled. Annual concentrations of particulate matter (PM) with diameters ≤2.5 μm (PM_2.5), between 2.5 and 10 μm (PM_2.5–10), and ≤10 μm (PM₁₀), as well as nitrogen dioxide (NO₂) and nitrogen oxides (NO_x) were gauged by land-use regression models. We employed Cox proportional hazards models to examine the associations of air pollutants with the incidence of nephritis, adjusted for potential covariates. We applied restricted cubic spline (RCS) analysis to find the exposure-response relationship. 3,455 cases were observed through a median follow-up duration of 13.58 years. Our results showed the enhanced risk of nephritis was linked to per interquartile range (IQR) increase in NO₂ (hazard ratio (HR): 1.09, 95 % confidence intervals (95 %CI): 1.04–1.14) and in NO_x (1.05, 1.01–1.08). We found nonlinear relationships between the levels of NO_x, PM_2.5, and PM_2.5-10 and incident nephritis. They all displayed a tendency of initial rapid increase followed by a subsequent gradual growth. We didn't find nonlinear relationships between NO₂ and PM₁₀ concentrations and incident nephritis. Thus, exposure to air pollution may induce the incidence of nephritis, emphasizing the importance of controlling ambient air pollution for its prevention.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 7","pages":"Article 102524"},"PeriodicalIF":3.9,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808018","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of COVID-19 pandemic on air pollution and hospitalization risk for cardiovascular and respiratory diseases in Suzhou, China","authors":"Fan Liu ,&nbsp;Xikun Liu ,&nbsp;Shuhua Yu ,&nbsp;Xiang Liu ,&nbsp;Jingguang Li ,&nbsp;Chongyang Zhang ,&nbsp;Chanjuan Sun ,&nbsp;Hua Qian ,&nbsp;Xinyi Zhu","doi":"10.1016/j.apr.2025.102525","DOIUrl":"10.1016/j.apr.2025.102525","url":null,"abstract":"<div><div>During the COVID-19 pandemic in China, a notable reduction in ambient air pollution levels has been documented. The risk of exposure to pollutants for the population is influenced by various factors, including the types of pollutants, seasonal variations, and demographic characteristics. However, it remains unclear whether the effects of these factors differ when comparing the periods before and during the COVID-19 pandemic. This study aims to evaluate the relationships between specific air pollutants (PM_2.5, NO₂ and SO₂) and hospitalization risk for cardiovascular and respiratory diseases in Suzhou, China, both prior to and during the pandemic. A time-series analysis was conducted utilizing a Distributed Lagged Nonlinear Model, incorporating data from 95,235 hospital admissions in Suzhou spanning from 2018 to 2022. The study also accounted for the influences of seasonal variations, gender, and age on these associations. The findings reveal a positive correlation between exposure to air pollution and hospitalization risk, with significant variations based on seasonal factors, gender, and age. Specifically, the risk of hospitalization is markedly increased during cold seasons, while in warm seasons during the pandemic, exposure to NO₂ also contributes to increased risk. Furthermore, female individuals exposed to NO₂ exhibit a higher hospitalization risk compared to males during the pandemic. Notably, elderly individuals aged 65 and above are at a higher risk of hospitalization due to air pollution exposure, highlighting the necessity for careful consideration in the design of environments that are conducive to the well-being of older adults.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 7","pages":"Article 102525"},"PeriodicalIF":3.9,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental and numerical characterization of the concentration distribution of particulate pollutants inside a full-scale car cabin","authors":"Mokhtar Djeddou ,&nbsp;Amine Mehel ,&nbsp;Georges Fokoua ,&nbsp;Anne Tanière ,&nbsp;Patrick Chevrier","doi":"10.1016/j.apr.2025.102516","DOIUrl":"10.1016/j.apr.2025.102516","url":null,"abstract":"<div><div>We report an investigation of particle dynamics through measurements of particle concentrations inside a full-scale car cabin and comparing the results to numerical predictions obtained using the ”Diffusion-Inertia Model” (DIM) for particle transport, coupled with the RANS approach for single-phase flow. Measurements were conducted by placing the vehicle in a closed chamber where a homogenized atmosphere was generated and controlled, enabling the study of fine and ultrafine particle infiltration by measuring the particle mass concentration distribution inside the vehicle’s cabin. A comparison between numerical and experimental results for particle concentration profiles of PM₁ and PM₁₀ showed that the numerical model reasonably reproduces the experimental results, particularly for low-inertia particles. Both numerical and experimental analyses revealed a tendency toward particle concentration homogeneity within the compartment. Additionally, the influence of ventilation velocity on the dynamics of <span><math><mrow><mn>1</mn><mspace></mspace><mi>μ</mi><mi>m</mi></mrow></math></span> and <span><math><mrow><mn>10</mn><mspace></mspace><mi>μ</mi><mi>m</mi></mrow></math></span> particles was investigated numerically. The results indicate that increasing airflow velocity accelerates the homogenization of particle concentrations, while inertia effects become more pronounced, leading to lower concentration levels due to particle deposition on cabin surfaces. The effect of thermal buoyancy on particle transport was also examined. While the overall dispersion patterns remained largely unchanged, localized variations were observed, particularly in the passenger breathing zone, where thermal effects reduced particle concentration.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 7","pages":"Article 102516"},"PeriodicalIF":3.9,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143746894","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Influence of meteorological variables and human activities on precipitation chemistry in the Guanzhong Plain, Northwest China","authors":"Rui Sun ,&nbsp;Xiaofei Li ,&nbsp;Huayu Huang ,&nbsp;Chi Zhou ,&nbsp;Yibo Wang","doi":"10.1016/j.apr.2025.102523","DOIUrl":"10.1016/j.apr.2025.102523","url":null,"abstract":"<div><div>Precipitation chemistry can reflect the impacts of both anthropogenic and natural sources on air quality and provide insights into material cycles between the Earth's surface and atmosphere. We explored the chemical characteristics of precipitation in relation to meteorological and environmental factors in Weinan, a key hub for agriculture and ecological protection on the Guanzhong Plain in Northwest China. Precipitation samples (n = 291) collected in Weinan from 2021 to 2022 were analyzed for their chemical compositions using chemometric analysis, correlation analysis, the positive matrix factorization (PMF) model, and the backward trajectory model. The findings revealed that the primary ions in the precipitation were Ca²⁺, NH₄⁺, SO₄²⁻ and NO₃⁻. The concentrations of most ions were higher in winter and lower in summer due to changes in precipitation amount, humidity, PM_2.5 and PM₁₀. The PMF analysis identified six ion sources in precipitation, including crustal sources (24.9 %), secondary formation (20.7 %), waste incineration (16 %), marine sources (15 %), industrial emissions (11.8 %), and biomass burning (11.5 %). The backward trajectory analysis showed that water vapor transport varies seasonally and is primarily influenced by westerly, monsoonal, and regional circulations. The westerly circulation predominantly affects ion concentrations by transporting dust and anthropogenic pollutants to Weinan. The monsoonal circulation carries large amounts of water vapor and contributes the most to precipitation (54.38 %). This study reveals the impacts of natural factors, human activities, and water vapor sources on precipitation chemistry and offers decision support for air quality management and pollution control in Northwest China.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 7","pages":"Article 102523"},"PeriodicalIF":3.9,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143799689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Meteorological and climatological conditions supportive for windblown dust formation in Poland","authors":"Filip Skop,&nbsp;Ewa Bednorz","doi":"10.1016/j.apr.2025.102521","DOIUrl":"10.1016/j.apr.2025.102521","url":null,"abstract":"<div><div>Windblown dust is considered a type of severe weather phenomena, causing low horizontal visibility, high particulate matter concentrations and economic loss. Although dust events mostly occur in arid and semiarid climates, they are also being reported in Poland during dry spells. Currently there are no comprehensive studies releted to windblown dust climatology of Poland, despite their abundance in the recent years. In order to identify significant windblown dust events in Poland, compiled data from meteorological stations, air quality stations and media/social media platforms was used. Hourly observations from 50 Polish meteorological stations were obtained in order to gather all windblown dust related reports. Hourly mean PM₁₀ concentrations were obtained in order to estimate the impact of windblown dust on air quality as well as to identify cases away from meteorological stations. Lastly, media and social media reports, depicting intense windblown dust, were included in the study in order to make the database more detailed. A total of 65 days with a windblown dust were identified for a period between 2001 and 2022. Each case was examined based on a type of a meteorological disturbance causing it (synoptic or convective).</div><div>Meteorological conditions present during windblown dust cases, including near-surface relative humidity, wind speed and visibility were also analyzed along with surface soil moisture and Standarized Precipitation Evapotranspiration Index (SPEI). Additionaly, atmospheric soundings and vertical tropospheric relative humidity profiles were simulated for convective windblown dust cases, based on ECMWF ERA5 Reanalysis. It was found that central and western regions of Poland are most prone to windblown dust, with April being by far the most active month for dust activity. Significant differences were also noted between the intensity of recorded windblown dust occurrences, with most cases being local and lasting less than 1 h to some covering large area of a Country and lasting for over 10 h. Recorded convective windblown dust most commonly formed as a result of thunderstorm's outflow, connected to cold fronts and low tropospheric convergence zones. High Lifted Condensation Level and low humidity in the lower troposphere strongly supported this type of events.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 7","pages":"Article 102521"},"PeriodicalIF":3.9,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143737950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-scale characteristics and statistically associated factors of ozone pollution in Hangzhou based on machine learning","authors":"Yu-ting He ,&nbsp;Tao Ding ,&nbsp;Ru Yi ,&nbsp;Yuan-yuan Wang ,&nbsp;Yi Fu ,&nbsp;Cheng-kai Tu ,&nbsp;Hong-yan Fang ,&nbsp;Jin-ye Li ,&nbsp;Ming Zhang","doi":"10.1016/j.apr.2025.102522","DOIUrl":"10.1016/j.apr.2025.102522","url":null,"abstract":"<div><div>Ozone pollution poses a significant air quality challenge in Hangzhou in recent years. This study investigated the multi-scale characteristics and statistically associated factors of ozone pollution in Hangzhou based on O₃ data from 12 monitoring stations in the city from 2018 to 2022, along with data on other pollutants and meteorology. The analysis utilized the PAM (Partitioning Around Medoids) clustering method, KZ (Kolmogorov–Zurbenko) filtering method, and XGBoost (eXtreme Gradient Boosting) combined with the SHAP (Shapley additive explanations) model. The results indicate that: (1) Clustering of the MDA8-O₃ (Maximum Daily 8-Hour Average O₃) data from monitoring stations using PAM reveals that Hangzhou can be divided into three sub-regions: west, central, and east, with varying degrees of ozone pollution from low in the west to high in the east. (2) Decomposition of the MDA8-O₃ concentration time series into long-term, seasonal, and short-term components highlights that the short-term components primarily drive the fluctuations in the original sequence. (3) At both temporal and spatial scales, disparities in the statistically associated factors of ozone pollution exist. Temporally, temperature and relative humidity dominate seasonal and short-term components, while long-term components are statistically associated with both temperature and long-term emissions. Spatially, temperature is the main factor in the west, but diminishes in the central and eastern regions, where other pollutants become more influential. Regional differences in emission sources near monitoring sites also affect statistically associated factors. The findings of this study can offer valuable insights for developing targeted strategies for ozone pollution control in Hangzhou.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 7","pages":"Article 102522"},"PeriodicalIF":3.9,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a representative transient cycle for evaluating real driving emissions of heavy-duty diesel engines","authors":"Gengfei Liu ,&nbsp;Xiuhua Yang ,&nbsp;Bin Pei ,&nbsp;Huaimin Xu ,&nbsp;Binyang Wu ,&nbsp;Wanhua Su","doi":"10.1016/j.apr.2025.102520","DOIUrl":"10.1016/j.apr.2025.102520","url":null,"abstract":"<div><div>Accurately assessing real driving emissions is crucial for effectively controlling vehicle exhaust pollution. However, significant discrepancies exist between the World Harmonized Transient Cycle (WHTC) used for emission certification and real driving conditions of heavy-duty diesel engines in China. To address this issue, this study introduces a two-step method for developing representative transient cycles. In the first step, short strokes are classified using the k-means clustering algorithm with adaptive particle swarm optimization to identify key kinematic scenarios for heavy-duty diesel vehicles. The Markov Chain Monte Carlo method is then applied to simulate driving patterns for these scenarios, thereby constructing the heavy-duty real driving cycle (HRDC). In the second step, the heavy-duty real transient cycle (HRTC) for diesel engines is generated by integrating typical transmission system and gear matching rules based on the HRDC. The emission test results indicate that compared to WHTC, NOx, PM, and PN emissions under HRTC increased by 36.69 %, 4.57 %, and 78.73 %, respectively. Additionally, transient soot emissions under HRTC are 155.74 % higher than those predicted by steady-state interpolation. The primary factor leading to transient soot emission deterioration is a sudden torque increase exceeding 40 %/s, observed during idle or motoring conditions. These findings provide a solid foundation for reliably evaluating the road emission performance of heavy-duty diesel vehicles.</div></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"16 7","pages":"Article 102520"},"PeriodicalIF":3.9,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}