Air Quality Atmosphere and Health最新文献

{"title":"Correction: Carcinogenic and non-carcinogenic risk assessment of heavy metals in PM2.5 air pollutant in Ulaanbaatar, Mongolia during the wintertime","authors":"Odbaatar Enkhjargal, Munkhnasan Lamchin, Xue Yi You, Jonathan Chambers, Davaagatan Tuyagerel, Renchinmyadag Tovuudorj, Zolzaya Khurelsukh, Enkhmaa Sarangerel, Nyamgerel Enkhtuya","doi":"10.1007/s11869-025-01705-8","DOIUrl":"10.1007/s11869-025-01705-8","url":null,"abstract":"","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"18 2","pages":"631 - 631"},"PeriodicalIF":2.9,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143622257","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"High-resolution vehicle emission inventory and emission reduction effect evaluation in Pingdingshan City","authors":"Wenjuan Zhang,&nbsp;Changsong Zhou,&nbsp;Dong Chen,&nbsp;Zhaohui Du,&nbsp;Yujia Song,&nbsp;Biao Liu,&nbsp;Hao Wu,&nbsp;Zhen Zhang,&nbsp;Hongmin Yang","doi":"10.1007/s11869-024-01684-2","DOIUrl":"10.1007/s11869-024-01684-2","url":null,"abstract":"<div><p>The COPERT model and ArcGIS were utilized to construct a high-resolution vehicle emission inventory of 1 km × 1 km in Pingdingshan City in 2021, evaluating the emission reduction effects under various measures. According to the findings, Pingdingshan City's vehicle emissions in 2021 were 1.668, 10.267, 0.023, 0.455, and 3735.940 Gg of VOCs, NO_x, SO₂, PM_2.5, and CO₂. Among them, the biggest contributors of VOCs, SO₂, and CO₂ were LDPVs (69.2%, 49.63%, and 50.78%, respectively); the greatest sources of NO_x and PM_2.5, on the other hand, were HDTs (71.23% and 43.83%, respectively). China III vehicles were the primary sources of VOCs, NO_x, and PM_2.5 emissions, and the emissions of China II and below vehicles were also significant. Gasoline and diesel vehicles exhibited similar emission characteristics as light and heavy vehicles, respectively. NEVs achieved almost zero emissions. Heavy emission intensity regions were primarily found in places with a dense road network, while the temporal distribution was mainly influenced by the frequency of residential trips. Between 2018 and 2021, SO₂ and CO₂ emissions continued to rise, but at a gradually slower pace. In addition, an assessment of several emission reduction measures revealed that the government needs to adopt diversified control strategies to maximize emission reductions because the effectiveness of single measures to reduce emissions is limited. The core of future pollution control lies in optimizing the structure of road traffic models, especially in increasing the market share of new energy and strict emission standard vehicles.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"18 3","pages":"911 - 925"},"PeriodicalIF":2.9,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Effect of butanol addition in diesel and biodiesel fuels on OC, EC, particulate PAH, and alkyl-PAH emissions from a common-rail diesel engine","authors":"Xinling Li,&nbsp;Pengcheng Zhao","doi":"10.1007/s11869-024-01682-4","DOIUrl":"10.1007/s11869-024-01682-4","url":null,"abstract":"<div><p>Characteristics of carbonaceous particle substances, i.e., organic carbon (OC), elemental carbon (EC), particulate polycyclic aromatic hydrocarbon (PAH), and their derivatives, emitted from modern common-rail diesel engine fueled with high-chain alcohol are ambiguous. In this study, OC, EC, PAH, and alkyl-PAH emissions from a common-rail diesel engine fueled with diesel (D100), biodiesel (B100), 15% and 30% butanol addition in diesel (D85Bu15 and D70Bu30), and biodiesel (B85Bu15 and B70Bu30) at four engine loads were analyzed comprehensively. Compared with D100 samples, the reduction in EC emission for B100, B85Bu15, and B70Bu30 samples is approximately 90% due to their oxygenated compounds with OH that populate even locally fuel-rich zones, while the variation of OC emissions with butanol addition is related with the engine operation condition and the proportion of butanol in the blends. For D100 samples, similar PAH emission profiles at different engine loads and several 2-ring to 4-ring PAHs are the most abundant compounds. The abundant of two predominant alkyl-PAH compounds (1-methylphenanthrene and 2-methylfluoranthene) accounts for 10–20% fractions of total PAHs for D100 samples, while they sharply decrease to less than 5% for D85Bu15 and D70Bu30 samples. Butanol addition into diesel slightly affects PAH profile distribution characteristics, and Pyr, Flt, and Nap are the three most abundant PAH species in D100, D85Bu15, and D70Bu30 samples. The abundant of heavier compounds (from cyclopenta[cd]pyrene to coronene) significantly increases with butanol addition into diesel and biodiesel, especially for D70Bu30 and B70Bu30 samples, indicating the contribution of pyrogenic origination instead of fuel origination PAH for these samples derived from fuels with high proportion of butanol in the blends. Compared with D100 samples, total PAH emissions approximately decrease up to 60% for B70Bu30, while a slight reduction in PAH emissions for 30% pentanol addition in biodiesel and even sharp increase for the high blend ratios of diesel/<i>n</i>-butanol was observed by Yang et al. (Fuel 209: 132–140, 2017) and Yilmaz and Davis (Process Saf. Environ. 166: 430–439, 2022b). The discrepancy is probably associated with the different fuel and engine properties. On average of the four engine loads, the particulate toxicity decreases 50–80% for B100 and butanol content samples compared with D100 samples, which is ascribed to the low PAH emissions, although the relative abundance of high cyclic PAH (4–6 rings) with high toxicity dominates in these samples.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"18 3","pages":"899 - 910"},"PeriodicalIF":2.9,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Radon (222Rn) as a tracer of cave air exchange","authors":"Lidia FIJAŁKOWSKA–LICHWA,&nbsp;Andrzej TYC,&nbsp;Tadeusz A. PRZYLIBSKI","doi":"10.1007/s11869-024-01670-8","DOIUrl":"10.1007/s11869-024-01670-8","url":null,"abstract":"","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"18 3","pages":"867 - 897"},"PeriodicalIF":2.9,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11869-024-01670-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Global environmental sustainability: the role of economic, social, governance (ECON-SG) factors, climate policy uncertainty (EPU) and carbon emissions","authors":"Cem Işık,&nbsp;Serdar Ongan,&nbsp;Hasibul Islam","doi":"10.1007/s11869-024-01675-3","DOIUrl":"10.1007/s11869-024-01675-3","url":null,"abstract":"<div><p>Raising ESG standards and reducing climate policy uncertainty can directly determine CO₂ emissions. Therefore, this study examines the effects of economic, social, governance (ECON-SG), and climate policy uncertainty on CO₂ emissions (CO₂E) on a global scale from 2001 to 2020. The ARDL and ARDL bound tests are employed. The <i>F-statistics</i> suggest a stable long-run relationship among the variables, indicating that the estimated coefficients are robust. Empirical findings reveal that while economic factors increase CO₂E, social factors mitigate. Based on these results, the interactions of economic, social, governance, and climate policy with global (total) environmental problems can be interpreted as follows. <i>(i)</i> Energy consumption driven by global economic growth and industrial production based mainly on fossil fuels increases CO₂E. This result may also indicate unsustainable economic growth. <i>(ii)</i> Despite unsustainable global economic growth, increasing social awareness, education level, and life expectancy can be interpreted as improving society's environmental awareness and reducing CO₂E. Therefore, policymakers must harmonize their global economic and social policies conflicting with CO₂E. In fact, this result may indicate that economic growth that does not support and develop social policies cannot positively contribute to the environment because the ultimate point of economic policies and growth is people in social life. <i>(iii)</i> The insignificant impact of climate policy uncertainty on CO₂E can be interpreted because of an international lack of coordination in global climate policies.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"18 3","pages":"851 - 866"},"PeriodicalIF":2.9,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Identification of potential regional sources of ambient criteria air pollutants at a national-controlled air quality observational site in Guiyang","authors":"Zhenxing Shen,&nbsp;Haiyan Sun,&nbsp;Jinjuan Li,&nbsp;Yuan Yang,&nbsp;Peng Xu,&nbsp;Fengming Zhang","doi":"10.1007/s11869-024-01676-2","DOIUrl":"10.1007/s11869-024-01676-2","url":null,"abstract":"<div><p>Comprehensively characterizing air pollutant cross-boundary transport is indispensable for determining effective control measures to further improve air quality. Taking advantage of the datasets of criteria pollutants (i.e., PM₁₀, PM_2.5, SO₂, CO, NO, NO₂, NO_x and O₃) and meteorological parameters, we comprehensively characterized the regional transport of air pollutants in Guiyang using BPPs (Bivariate polar plots), HYSPLIT 4 (Hybrid Single Particle Lagrangian Integrated Trajectory), PSCF (potential source contribution function) and CWT (concentration weighted trajectory) models. The average mass concentrations of PM₁₀, PM_2.5, SO₂, CO, NO₂ and O₃ were lower than that the CNAAQ Class I standards, and were much lower than these in the key air pollution control regions for the “Blue Sky Protection Campaign”. Decreasing trends of PM₁₀, PM_2.5, SO₂, NO, NO₂ and NO_x, and increasing trend of O₃ were observed, confirming the effectiveness of air pollution control policies, and suggesting continuous and effective emission control measures should be implemented for further improving air quality. The BPPs revealed that all pollutants, except O₃, had higher mass concentrations when wind speeds were low, and in the high wind speed scenario, the dependence of air pollutants concentrations on wind speed and wind direction was more varied seasonally. High concentration back trajectories, PSCF and CWT analysis demonstrated that both tailored local emissions reduction and regional cooperative control, which should be taken when suitable, are crucial for controlling multiple pollutants and hence further improving air quality in Guiyang in the upcoming years.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"18 3","pages":"827 - 850"},"PeriodicalIF":2.9,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Prediction of heavy metal and PM2.5 concentrations in atmospheric particulate matter using key magnetic parameters","authors":"Guan Wang,&nbsp;Zhenxiang Ji,&nbsp;Xun Tian,&nbsp;Yumei Hou,&nbsp;Fan Yang,&nbsp;Feifan Ren","doi":"10.1007/s11869-024-01680-6","DOIUrl":"10.1007/s11869-024-01680-6","url":null,"abstract":"<div><p>Heavy metal pollution is harmful to the human health and the environment, and it is of great significance to accurately predict the concentration of heavy metals in atmospheric particulate matter. However, the prediction of heavy metals in atmospheric particulate matter has not yet been reported, and traditional geochemical methods are inefficient and time-consuming. In this study, environmental magnetic parameters were introduced as independent variables of machine learning to predict the concentration of heavy metals in atmospheric particulate matter and classify PM_2.5 concentrations. Four popular models were constructed to predict heavy metal concentrations. Moreover, using magnetic parameters and PM_2.5 concentrations as feature values, the correlation between magnetic parameters, PM_2.5 and heavy metal concentrations were explored. The results show that all heavy metals are positively correlated with χ_lf, SIRM, HIRM and χ_ARM, and the GA-SVM model has the best prediction performance. Additionally, the optimal GA-SVM model was used to perform sensitivity analysis on Fe heavy metal concentration and to conduct PM_2.5 concentration classification prediction, it was found that SIRM, HIRM and χ_ARM have a significant effect on the prediction results, and the prediction results are highly accurate. The research results have reference significance for the prediction of pollutant concentrations in the future.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"18 3","pages":"815 - 825"},"PeriodicalIF":2.9,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 indoor air quality and characterization of indoor settled dust in schools of Tezpur, Northeast India","authors":"Anamika Nath,&nbsp;Dimpi Saikia,&nbsp;Mebaaibok L. Nonglait,&nbsp;Pratibha Deka","doi":"10.1007/s11869-024-01679-z","DOIUrl":"10.1007/s11869-024-01679-z","url":null,"abstract":"<div><p>This study aims to assess the indoor air quality along with the elemental concentrations of indoor settled classroom dust across nineteen schools in Tezpur, Northeast India. The average indoor temperature and relative humidity were 24.53 ͦ C and 60.61%, respectively which is within the American Society of Heating, Refrigerating, and Air-Conditioning Engineers (ASHRAE’s) recommended comfort limits. The overall average PM_2.5 concentrations were 134.69 ± 70.71 µg/m³ indoors and 122.89 ± 61.55 µg/m³ outdoors, significantly exceeding the WHO’s 24-hour recommended guideline of 15 µg/m³. However, CO₂ concentrations were within the standards established by ASHRAE 62.1. The elemental concentrations in decreasing order were: Fe &gt; Al &gt; Mg &gt; Mn &gt; Zn &gt; Ni &gt; Cr &gt; Pb &gt; Cu &gt; Co &gt; Cd. The average Enrichment Factor (EF) values were 16.01 (Zn), 12.43 (Pb), 9.52(Cd), 7.27 (Ni), 1.63 (Cu), 1.54 (Mn), 1.31 (Cr), 0.92 (Co), 0.73 (Mg), and 0.48 (Al). Urban schools had the highest average EF for traffic-related elements (TREs) followed by suburban schools and rural schools. The degree of contamination (C_degree) values indicated moderate contamination levels, while all schools had pollution load index (PLI) values below 1, signifying low to negligible pollution and acceptable classroom environmental quality. A strong significant correlation at <i>p</i> &lt; 0.05 was found between Mg-Mn (0.55), Mg-Fe (0.54), Mg-Ni (0.56), Mg-Co (0.48), Mg-Cu (0.63), Al-Cr (0.79), Al-Mn (0.79), Al-Fe (0.60), Al-Ni (0.54), Al-Co (0.80), Cr-Fe (0.78), Cr-Ni (0.74), Cr-Co (0.81), Mn-Fe (0.48), Mn-Co (0.64), Mn-Cu (0.64), Fe-Ni (0.99), Fe-Co (0.80), Ni-Co (0.75), Cu-Zn (0.50), and Cd-Pb (0.62). Hierarchical cluster analysis (HCA) and principal component analysis (PCA) identified consistent pollutant distribution patterns and their probable sources. Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD) analyses of classroom dust samples showed that quartz, calcite, and haematite were the most common minerals. This suggests that the source of classroom dust could be soil, chalk dust, and anthropogenic activities. The health risk assessment indicated that non-cancerous risks from heavy metals were within acceptable ranges. However, the total lifetime cancer risk (TLCR) for rural (1.37E-04), suburban (1.09E-04), and urban (1.08E-04) areas slightly exceeded the acceptable limits.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"18 3","pages":"793 - 814"},"PeriodicalIF":2.9,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 and source apportionment of PM2.5 in a major Latin American port: elevated concentrations from traffic in the Great Atlantic Forest Reserve","authors":"Bruno Martins Gurgatz,&nbsp;Camila Arielle Bufato Moreira,&nbsp;Luiza Natalino,&nbsp;Julia Stefany Chagas Albrecht,&nbsp;Marina Reback Garcia,&nbsp;Emerson Joucoski,&nbsp;Carlos Itsuo Yamamoto,&nbsp;César de Castro Martins,&nbsp;Rodrigo Arantes Reis,&nbsp;Ricardo Henrique Moreton Godoi","doi":"10.1007/s11869-024-01677-1","DOIUrl":"10.1007/s11869-024-01677-1","url":null,"abstract":"<div><p>Ports are pivotal to the global economy but contribute significantly to environmental impacts, notably air pollution from local sources. Long-term exposure to atmospheric fine particulate matter (PM_2.5) poses severe health risks, including respiratory and cardiovascular diseases. This study investigates the source apportionment of PM_2.5 Paranaguá, a major Latin American port situated in a sensitive ecosystem and a marine protected area in the South Atlantic. In 2017, the mean PM_2.5 concentration at Paranaguá port was 15.3 ± 7.5 µg m^− 3, and 10% (<i>n</i> = 34) of the samples exceeding Brazil’s 24-hour environmental quality standard of (25 µg m^− 3). This level of PM_2.5 correlates with an 8% increase in the risk of general mortality risk in the port region. Four diagnostic tools were employed to estimate PM_2.5 sources from soluble ions, trace and major metal compositions, and black carbon (BC) fraction: (1) Polar plots using meteorological data; (2) Correlation analysis with daily ship and truck counts; (3) Enrichment factors; and (4) Positive matrix factorization (PMF). The results indicate that traffic is the predominant source of PM_2.5, primarily due to the extensive road transport of soy to the port. Given the anticipated continued dominance of road transport emissions, Brazil must implement measures to reduce traffic-related emissions. Aligning with the United Nations’ Sustainable Development Goal (SDG) 3.9, we recommend adopting environmentally responsible production models, such as agroecology and local productive systems to mitigate PM_2.5-related health risk.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"18 3","pages":"775 - 791"},"PeriodicalIF":2.9,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"The role of environmental mitigation technology and energy productivity in reducing air pollution-related premature deaths: insights from the top 20 polluted economies","authors":"Yasir Khan,&nbsp;Xiangdong Li","doi":"10.1007/s11869-024-01674-4","DOIUrl":"10.1007/s11869-024-01674-4","url":null,"abstract":"","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"18 3","pages":"755 - 773"},"PeriodicalIF":2.9,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143786425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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}