Atmospheric Pollution Research最新文献

{"title":"The influence of concentration and size on the error of particulate matter detection using charge induction method","authors":"Kai Zhang ,&nbsp;Yaqi Peng ,&nbsp;Hong Yu ,&nbsp;Pei Ning ,&nbsp;Xueyong Hou ,&nbsp;Ling Zhu ,&nbsp;Shengyong Lu","doi":"10.1016/j.apr.2024.102254","DOIUrl":"10.1016/j.apr.2024.102254","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"15 11","pages":"Article 102254"},"PeriodicalIF":3.9,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141843676","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":"Aliphatic and cyclic hydrocarbons in urban street dust from Riyadh city, Saudi Arabia: Levels, distribution, and sources","authors":"Hattan A. Alharbi ,&nbsp;Ahmed I. Rushdi ,&nbsp;Abdulqader Bazeyad ,&nbsp;Khalid F. Al-Mutlaq","doi":"10.1016/j.apr.2024.102261","DOIUrl":"10.1016/j.apr.2024.102261","url":null,"abstract":"<div><p>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 <u>n</u>-alkanes, methyl <u>n</u>-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 <u>n</u>-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 <u>n</u>-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.</p></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"15 11","pages":"Article 102261"},"PeriodicalIF":3.9,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141851517","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 change in winter aerosol composition and sources in Guiyang Southwest China (2003–2020)","authors":"Ziyun Chen ,&nbsp;Hui Guan ,&nbsp;Jing Tian","doi":"10.1016/j.apr.2024.102263","DOIUrl":"10.1016/j.apr.2024.102263","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"15 10","pages":"Article 102263"},"PeriodicalIF":3.9,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141844021","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":"Assessment of seasonal forecasting potential for springtime Asian dust in South Korea using the KMA global seasonal forecasting system","authors":"Misun Kang,&nbsp;Woojeong Lee","doi":"10.1016/j.apr.2024.102262","DOIUrl":"10.1016/j.apr.2024.102262","url":null,"abstract":"<div><p>This study aims to assess the potential of seasonal forecasting for springtime Asian dust days in South Korea using the Korea Meteorological Administration's Global Seasonal Forecasting System version 6 (KMA GloSea6). The system demonstrated similar predictive performance for springtime Asian dust days compared to GloSea5-ADAM, which incorporates the Asian Dust and Aerosol Model's emission algorithm into GloSea5. While KMA GloSea6 exhibited strong wind, cold, and moist biases in the Asian dust source region during the spring of the hindcast period, similar to GloSea5-ADAM, it showed an improved spatial distribution of ACC. The anomaly correlation coefficient for the annual variability of Asian dust occurrence frequency anomalies in the dust source region was also similar, with values of 0.41 for KMA GloSea6 and 0.43 for GloSea5-ADAM. Furthermore, in evaluating the seasonal forecasting of springtime Asian dust days using hindcast datasets, KMA GloSea6 accurately predicted 12 out of 24 instances, resulting in a forecast accuracy of 0.5, consistent with GloSea5-ADAM. When applying the criteria to spring 2022 and 2023 forecast data not used in the derivation, the predicted Asian dust days of 6.14 days in spring 2022 led to a “Near normal” prediction, differing from the observed “Below normal.” However, the spatial distribution showed “Below normal” conditions, aligning with observations in some areas. In spring 2023, the predicted 9.78 Asian dust days led to an “Above normal” prediction, consistent with observed conditions. These findings will contribute to advancing seasonal Asian dust forecasting in South Korea.</p></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"15 11","pages":"Article 102262"},"PeriodicalIF":3.9,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1309104224002277/pdfft?md5=009fa1a1bb33fc2e2fd038310bddac05&pid=1-s2.0-S1309104224002277-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141853864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simulation of the air quality and evaluation of a future district heating system in Valdivia, Chile","authors":"Ernesto Pino-Cortés ,&nbsp;Ismael Rabí ,&nbsp;Martín Muñoz ,&nbsp;Jonathan Acosta ,&nbsp;Fidel Vallejo ,&nbsp;Andrea Espinoza-Pérez ,&nbsp;Lorena Espinoza-Pérez ,&nbsp;Samuel Carrasco","doi":"10.1016/j.apr.2024.102255","DOIUrl":"10.1016/j.apr.2024.102255","url":null,"abstract":"<div><p>The city of Valdivia, located in southern Chile, has high levels of particulate matter, mainly due to the heating system based on wood used during winter. As a corrective measure, environmental authorities established the Air Pollution Management Plan for Valdivia in 2017, seeking to decrease the effects on the population's health and provide a better quality of life for inhabitants. An alternative to reduce indoor air quality problems in homes is to implement a district heating plant. This study evaluated the air quality if a district heating plant is implemented in the zone. The WRF-SMOKE-CMAQ model was applied in August 2018 to simulate the effect of this new system on air quality. An adequate fit was achieved for the actual case between the 24-h average concentrations of PM_2.5 and PM₁₀ from the simulated and observed values, obtaining correlation factor (r) values of 0.76 and 0.78, respectively. By simulating the scenario with a district heating plant (thus eliminating emissions from residences), it was found that if this plant is located in the southern area of the city, the daily concentrations would be reduced by an average of 53.52% and 51.40% for PM_2.5 and PM₁₀, respectively. The results of this study showed that the implementation of a district heating plant results in a reduction in air pollution problems compared to the current scenario, achieving the values established in the primary air quality standards.</p></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"15 10","pages":"Article 102255"},"PeriodicalIF":3.9,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141639236","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":"Exploring the impact of nocturnal boundary layer stability on wintertime air pollution in a highly polluted basin city using unsupervised learning classification","authors":"Jialin Yao ,&nbsp;Xingcan Jia ,&nbsp;Zhiheng Liao","doi":"10.1016/j.apr.2024.102253","DOIUrl":"10.1016/j.apr.2024.102253","url":null,"abstract":"<div><p>This study utilizes ten years of wintertime boundary layer meteorological and surface air quality observations to characterize the nocturnal boundary layer (NBL) stability and assess its relationship with air pollution in Taiyuan, a highly polluted basin city in China. An unsupervised learning feature extraction technique known as the self-organizing map (SOM) is applied to objectively classify nocturnal virtual potential temperature (VPT) profiles. The SOM-based classification scheme allows the representation of wintertime day-to-day NBL evolutions by just nine regimes. Special attention is given to four dominant regimes: weak to moderate stability regime (NBL1), cloudy moderate stability regime (NBL3), windy moderate stability regime (NBL7), and strong stability regime (NBL9). These dominant regimes have relatively higher occurrence frequencies (&gt;10%), with the highest frequency associated with the strong stability regime (NBL9) at 25.2%. The diurnal cycles of selected pollutants (CO, NO₂, SO₂, and PM_2.5) exhibit significant distinctions among the different NBL regimes. For instance, in the strong stability regime, CO, NO₂, and PM_2.5 show explosive growth in the evening due to the accumulation of primary pollutants. However, in the cloudy moderate stability regime, PM_2.5 exhibits persistent slow growth throughout the day, likely due to secondary particle formation under high humidity and high SO₂ conditions. These findings enhance our understanding of NBL meteorological impacts on surface air pollution in basin cities.</p></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"15 10","pages":"Article 102253"},"PeriodicalIF":3.9,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141639237","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":"Comparison of NMHC measurements between 2010 and 2020 in Wuxi City, Yangtze River Delta region: Levels, compositions, sources, and impacts","authors":"Chen Wang ,&nbsp;Yingyu Liu ,&nbsp;Baolin Wang ,&nbsp;Zhenguo Liu ,&nbsp;Yuchun Sun ,&nbsp;Na Yang ,&nbsp;Lei Sun ,&nbsp;Chuanyong Zhu ,&nbsp;Guihuan Yan ,&nbsp;Chongqing Xu ,&nbsp;Sihua Lu ,&nbsp;Min Shao","doi":"10.1016/j.apr.2024.102260","DOIUrl":"10.1016/j.apr.2024.102260","url":null,"abstract":"<div><p>Field observation analyses of volatile organic compounds (VOCs) have gained significant attention due to their potential explaining atmospheric temporal trends. This study measured non-methane hydrocarbons (NMHCs) in Wuxi City, located in the Yangtze River Delta (YRD) of China, in 2010 and 2020. The aim was to gain a better understanding of the ten-year change of VOCs in terms of their mixing ratios, sources, role in ozone, secondary organic aerosol (SOA) formation, and health risks. The average NMHC level 2020 was 20.67 ppbv, which indicated a 20% drop from 2010. A notable shift was observed in the chemical composition of NMHCs. The contribution of alkanes increased from 45% to 62% in 2020, while that of the aromatics decreased from 25% in 2010 to 21% in 2020. Ratio analysis and receptor modelling for 2010 and 2020 were used to identify the source changes of VOC species. The source apportionment model showed that the contribution of vehicle exhaust decreased from 34.5% to 20.9% from 2010 to 2020, and that of the solvent increased from 15.1% to 25.3%. The chemical reactivity results suggested that aromatics played significant roles in photochemical reactions in Wuxi City. The ozone formation potential (OFP) and SOA potential of VOCs also showed decreasing trends from 2010 to 2020. The total OFP of alkenes decreased by 34.0%, from 30.6 to 20.2 ppbvO₃/ppbv. The total non-carcinogenic risk value for the key VOC species was 0.034 in 2010 and 0.024 in 2020. Although the carcinogenic risks for benzene have decreased significantly, the value is still above the acceptable carcinogenic risk level. The results of this study demonstrate the effectiveness of air pollution control measures in Wuxi City and highlight the positive impacts on reducing secondary pollution and health risks in this region over one decade.</p></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"15 10","pages":"Article 102260"},"PeriodicalIF":3.9,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141736392","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":"Different contributions of meteorological conditions and emission reductions to the ozone pollution during Shanghai’s COVID-19 lockdowns in winter and spring","authors":"Xuedan Dou ,&nbsp;Mengying Li ,&nbsp;Yaping Jiang ,&nbsp;Zhe Song ,&nbsp;Pengfei Li ,&nbsp;Shaocai Yu","doi":"10.1016/j.apr.2024.102252","DOIUrl":"10.1016/j.apr.2024.102252","url":null,"abstract":"<div><p>Shanghai experienced the COVID-19 lockdowns (LCD) in the winter of 2020 and the spring of 2022, respectively. In this paper, the Weather Research and Forecasting-Community Multiscale Air Quality (WRF-CMAQ) modeling system was used to simulate ozone (O₃) in Shanghai, and the contributions of meteorological conditions and emission reductions to the increases of the ground-level O₃ concentrations in Shanghai during the two LCD periods were analyzed. The relationships between O₃ concentrations and meteorological factors were quantified by the multiple linear regression model (MLR). The results showed that the increases in the concentrations of daily maximum 8-h moving average O₃ (MDA8 O₃) were caused by both meteorological changes and emission reductions, but their relative contributions to O₃ concentrations were different in winter and spring. Unfavorable meteorological conditions and the emission reductions during the 2020 winter LCD period increased MDA8 O₃ by 2.2 μg/m³ (2.2%) and 2.8 μg/m³ (2.8%), respectively, relative to the same period in the previous year. On the other hand, the meteorological conditions during the 2022 spring LCD period increased MDA8 O₃ by 7.9 μg/m³ (6.6%), while the emission reductions only increased MDA8 O₃ by 1.1 μg/m³ (0.9%). The O₃ formation in Shanghai urban areas was under the VOC-limited conditions during two lockdowns in winter and spring. Therefore, the VOCs emission reductions were more effective for the control of O₃ in Shanghai urban areas. The results emphasized the complex relationship between emission reductions, meteorological conditions and short-term O₃ changes.</p></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"15 10","pages":"Article 102252"},"PeriodicalIF":3.9,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141732039","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":"Spatial weighting EMD-LSTM based approach for short-term PM2.5 prediction research","authors":"Qian Yu ,&nbsp;Hong-wu Yuan ,&nbsp;Zhao-long Liu ,&nbsp;Guo-ming Xu","doi":"10.1016/j.apr.2024.102256","DOIUrl":"10.1016/j.apr.2024.102256","url":null,"abstract":"<div><p>Given the significant health and environmental risks posed by atmospheric <span><math><msub><mrow><mi>PM</mi></mrow><mrow><mn>2</mn><mo>.</mo><mn>5</mn></mrow></msub></math></span> pollution, accurately predicting its concentration changes is especially important. Current models fall short in researching time-series feature extraction from pollutants and spatial correlations among monitoring stations. In this study, a spatiotemporal prediction model is introduced to address these issues. The model combines spatial weighting, empirical mode decomposition (EMD), and a long short-term memory (LSTM) network. First, weights are allocated to sites using Pearson correlation analysis and distance weighting methods. Next, the pollutant time series is decomposed using the EMD method. The highly correlated intrinsic mode function (IMF) component is selected for signal reconstruction, enhancing denoising. Finally, the model uses an LSTM network to capture nonlinear and dynamic time series traits, which significantly improves the <span><math><msub><mrow><mi>PM</mi></mrow><mrow><mn>2</mn><mo>.</mo><mn>5</mn></mrow></msub></math></span> prediction accuracy. The model utilizes data collected from 10 monitoring stations across Hefei city during 2018-2019, employing the previous 24 h of observations to forecast <span><math><msub><mrow><mi>PM</mi></mrow><mrow><mn>2</mn><mo>.</mo><mn>5</mn></mrow></msub></math></span> concentrations for the subsequent hour. By comparing with RNN, HPO-RNN, GRU, LSTM, and CBAM-CNN-Bi LSTM, the results show that our model surpasses five benchmark models in terms of prediction accuracy. Relative to the best-performing CBAM-CNN-Bi LSTM model, our model reduces RMSE and MAE by 73.91% and 72.99%, respectively, and improves <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> by 8.15%. In summary, the proposed spatial weighting EMD-LSTM model offers an efficient new approach for predicting atmospheric <span><math><msub><mrow><mi>PM</mi></mrow><mrow><mn>2</mn><mo>.</mo><mn>5</mn></mrow></msub></math></span> pollution. It integrates spatial and time series analysis, significantly enhancing the prediction accuracy.</p></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"15 10","pages":"Article 102256"},"PeriodicalIF":3.9,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141736599","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":"Decreasing trends in PM2.5 and BC concentrations observed on central and southwestern Japanese Islands","authors":"Yayoi Inomata ,&nbsp;Atsushi Matsuki ,&nbsp;Mizuo Kajino ,&nbsp;Naoki Kaneyasu ,&nbsp;Takafumi Seto","doi":"10.1016/j.apr.2024.102258","DOIUrl":"10.1016/j.apr.2024.102258","url":null,"abstract":"<div><p>The interannual trends in fine particulate matter (PM_2.5) and black carbon (BC) concentrations at remote stations in central (Noto) and southwestern (Fukue) Japan were investigated using statistical trend analysis and the chemical transport model, Regional Air Quality Model 2 (RAQM2ver3). The concentrations of PM_2.5 and BC in Noto and Fukue exhibited seasonal variations and decreased from 2013 to 2020. Higher concentrations of PM_2.5 and BC were observed during the spring season (April) in both locations. The PM_2.5 concentrations peaked in Fukue approximately one month earlier than in Noto, while the BC concentration peak in Noto occurred one month earlier than that of PM_2.5. The total reductions in PM_2.5 concentrations in Noto and Fukue were 7.5 ± 5.0 and 3.8 ± 0.95 μg m⁻³, corresponding to reduction rates 1.1 ± 0.7 and 0.5 ± 0.95 μg m⁻³year⁻¹, respectively, compared to the concentrations in 2013. The total reduction in BC concentrations in Noto from 2013 to 2019 was estimated to be 0.13 ± 0.1 μg m⁻³, amounting to 0.018 ± 0.014 μg m⁻³year⁻¹ reduction rate. The results of the source–receptor relationships analysis suggest that the decreases in PM_2.5 and BC concentrations in Fukue and Noto were significantly influenced by reductions in amounts transported from central China (CCHN, 30–40°N), northern China (NCHN, &gt;40°N) and Japan (JPN) after 2015/2016. The decrease in emissions from these three regions accounted for the observed reductions in particulate concentrations.</p></div>","PeriodicalId":8604,"journal":{"name":"Atmospheric Pollution Research","volume":"15 10","pages":"Article 102258"},"PeriodicalIF":3.9,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141841876","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}