Heleen C. Vos, Kaukurauee I. Kangueehi, René Toesie, Frank D. Eckardt, Grant Ravenscroft, Susanne Fietz
{"title":"Spatial variability of dust concentration and deposition around an industrial port in South Africa emphasises the complexity of sources and transport","authors":"Heleen C. Vos, Kaukurauee I. Kangueehi, René Toesie, Frank D. Eckardt, Grant Ravenscroft, Susanne Fietz","doi":"10.1007/s11869-024-01581-8","DOIUrl":"10.1007/s11869-024-01581-8","url":null,"abstract":"<div><p>The port and industrial zone of Saldanha Bay in South Africa accommodates activities related to the transport, processing, and production of commodities such as iron ore, manganese ore, and steel. The visible emission of dust from this area raised concerns for public health and to address this, the municipality has monitored the fine particulate matter (PM<sub>2.5</sub>) concentration and dust deposition since 2015. Here, this monitoring data served to assess spatial and temporal changes and to evaluate the potential contribution of industrial and meteorological processes to these changes. We observed high temporal variability in both PM<sub>2.5</sub> concentration and dust deposition, and high spatial variation in dust depositions. Dust originated from local sources such as industry and traffic, but industrial activities could not explain the observed spatial variability, and concentration and deposition fluxes did not significantly increase over the years despite the extension of industrial activities. Meteorological factors such as rain, wind speed, wind direction, as well as topography exerted an important influence, but could also only partially explain the observed variability in both dust concentration and deposition. Furthermore, the PM<sub>2.5</sub> concentration and dust deposition are not significantly correlated, which highlights the challenges in appropriate dust monitoring. It follows that such monitoring efforts, though meeting national standards, require improvement to assess risks accurately. Our study illustrates that in areas with such high complexity of industrial activities, the high variability of dust load and deposition must be considered to evaluate implications for public and environmental health, adherence to guidelines, and mitigation strategies.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"17 11","pages":"2445 - 2459"},"PeriodicalIF":2.9,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11869-024-01581-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140980704","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}
Andrew Nguyen, Keita Ebisu, Rupa Basu, Nico Schulte, Scott A. Epstein, Xiangmei Wu
{"title":"Subdaily PM2.5 exposure and cardiorespiratory risks: data and findings from Southern California, 2018–2020","authors":"Andrew Nguyen, Keita Ebisu, Rupa Basu, Nico Schulte, Scott A. Epstein, Xiangmei Wu","doi":"10.1007/s11869-024-01583-6","DOIUrl":"10.1007/s11869-024-01583-6","url":null,"abstract":"<div><p>As hourly PM<sub>2.5</sub> measurements become more accessible, health impacts from subdaily exposures can be evaluated to develop health guidance. We obtained hourly PM<sub>2.5</sub> concentrations covering Southern California from May 2018 through March 2020 and daily emergency department visits (EDVs) for cardiorespiratory-related conditions at ZIP Code Tabulation Area (ZCTA) levels. ZCTAs were aggregated into 35 clusters based on similar geographic and sociodemographic features. Daily exceedance concentration hours (DECH) above 9, 12, and 15 µg/m<sup>3</sup>, daily maximum, and average PM<sub>2.5</sub> concentrations were calculated for each cluster-day. Two-stage time-series analyses were conducted to estimate excess risks of daily EDVs. DECH metrics exhibited the same direction but smaller effects on cardiovascular and respiratory EDVs compared to daily average metrics. Excess risks for cardiovascular EDVs were 1.77% (95% CI: 1.20, 2.34), 1.04% (0.61, 1.47), and 2.67% (1.98, 3.37) per interquartile range increase of DECH-9, DECH-12, and daily average PM<sub>2.5</sub> during 7-day lag period, respectively. Excess risks of respiratory EDVs increased by 6.34% (4.25, 8.48), 4.39% (2.85, 5.95), and 6.61% (4.78, 8.47) per IQR increase of DECH-9, DECH-12, and daily average PM<sub>2.5</sub> during a 3-day lag period, respectively. Elevated excess risks were observed among older adults (65+), children (0–17), and low-poverty neighborhoods on both subdaily and daily metrics. In summary, subdaily PM<sub>2.5</sub> exposures above the current standards exhibited excess risks in cardiorespiratory-related EDVs but no greater than those derived from the daily average metric. Health guidance based on the daily average metric provides sensible protection to the public in Southern California.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"17 11","pages":"2431 - 2444"},"PeriodicalIF":2.9,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140936932","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":"OzoneNet:A spatiotemporal information attention encoder model for ozone concentrations prediction with multi-source data","authors":"Wei Tian, Zhongqi Ge, Jianjun He","doi":"10.1007/s11869-024-01568-5","DOIUrl":"10.1007/s11869-024-01568-5","url":null,"abstract":"<div><p>Surface ozone (<span>(O_3)</span>) pollution is a serious environmental problem that endangers human health, and it is also an increasingly prominent environmental problem in the World. Existing works focus on how to directly improve the accuracy of predicting the target sequence from the input sequence while ignoring the inherent uncertainty of ozone in the atmosphere during the modeling process. Therefore, we utilize data fusion techniques to integrate ground observation data, satellite data, and reanalysis data for simulating atmospheric dynamics and enhancing prediction accuracy. We developed a sequence to sequence using a unit embedded with spatiotemporal information self attention mechanism as its encoder (OzoneNet) predict ozone concentration in the future. In the proposed method, we utilize the LSTM model with Spatiotemporal information self-attention mechanism to extract fixed Spatiotemporal data features, and the temporal dimension characteristics in long-term series are modeled by sequence-to-sequence network. Results show that the model has higher reliability and validity, outperforming benchmark models in simulating future changes in <span>(O_3)</span> concentrations. The progeress of this method can help the public take corresponding protective measures, provide scientific guidance for the government’s coordinated control of regional pollution, and can also provide important references for environmental protection and climate change research</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"17 10","pages":"2223 - 2234"},"PeriodicalIF":2.9,"publicationDate":"2024-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140937278","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":"Analyses of industrial air pollution and long-term health risk using different dispersion models and WRF physics parameters","authors":"Omer Mert Bayraktar, Atilla Mutlu","doi":"10.1007/s11869-024-01573-8","DOIUrl":"10.1007/s11869-024-01573-8","url":null,"abstract":"<div><p>This study consists of three main sections. The first section delves into a performance analysis centered around modeling PM<sub>10</sub>, NOx, and CO emissions from a cement factory. It examines the effectiveness of various factors, including meteorological data, physics models, and air quality dispersion models, in producing accurate results for atmospheric simulations. The second section covers the dispersion direction and concentrations obtained by visualizing the dispersion maps. The third section covers an analysis of heavy metals emitted from the facility, taking into account potential risks in the region such as cancer, acute and chronic effects, and long-term respiratory risks. This study made use of meteorological models (WRF, AERMET, and CALMET), air quality dispersion models (AERMOD and CALPUFF), a health risk analysis model (HARP), and various sub-models (MMIF and CALWRF). Satellite meteorological data were obtained from NCEP and ERA, with the majority of meteorological data based on the Global Data Assimilation System (GDAS)/Final Operational Global Analysis (FNL) from Global Tropospheric Analyses and Forecast Grids used for the WRF model. In the daily results, AERMOD showed the highest concentration values, but CALPUFF had greater concentrations throughout the annual period. The winter season had the highest concentrations of pollutants. Although there are differences among the physics models used in this research, the conclusions produced are consistent. Analysis of the data from the HARP model suggested that cancer risk levels exceeded the threshold of one person per million. However, the proportion of exceedance instances is rather small in comparison to the receptor points.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"17 10","pages":"2277 - 2305"},"PeriodicalIF":2.9,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11869-024-01573-8.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140936985","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":"Pseudo-total metal loads in dusts and soils of the steel city and coal capital of India: source apportionment and assessment of human health and ecological risks","authors":"Arpita Roy, Abhishek Kumar, Jayanta Kumar Biswas, Tanushree Bhattacharya","doi":"10.1007/s11869-024-01580-9","DOIUrl":"10.1007/s11869-024-01580-9","url":null,"abstract":"<div><p>The study examined metal concentrations in indoor dust, street dust, and soils of Bokaro, known as the ‘Steel City,’ and Dhanbad, recognized as the ‘Coal Capital’ of India, across summer, monsoon, and winter seasons in 2019. In Bokaro, the highest concentrations (mg/kg) of metals including Al (7755.12), Mg (8525), Mn (370.26), Fe (27882.75), Cu (738.83), Cr (44.57), Ni (31.33), Pb (29.67), and Zn (683.42) were observed during winter, whereas in Dhanbad, higher concentrations were noted during summer and monsoon months. Among the metals, Zn concentrations exceeded the World Health Organisation permissible limit (50 mg/kg) in both cities across all seasons. During summer, monsoon, and winter, concentrations (mg/kg) in Bokaro were 77.07, 102.53, 683.42, and in Dhanbad were 139.69, 541.36, and 361.39, respectively. Indoor dust generally exhibited higher metal concentrations than street dust and soil, indicating either its indoor origin or accumulation over time. Moderate contaminations, according to geo-accumulation index values, were contributed by Cu (1.21–1.24), Pb (1.06–1.21), and Zn (1.07–1.80). Ecological risk indices were highest in Bokaro's street dust during summer (33.66 ± 27.27) and Dhanbad's soils during monsoons (46.06 ± 10.90), but no significant ecological danger detected. However, carcinogenic risks were evident for children in Dhanbad due to Cr, in both street dust (1.18E-06) and soils (1.25E-06) during summers. The principal component analysis identified the metals originating from mixed sources in different matrices and seasons. Seasonal variations in indoor dust, street dust, and soil demonstrated the dominance of anthropogenic activities, such as coal/metal mining and traffic load. Overall, the study underscores the necessity for ongoing monitoring and mitigation of anthropogenic environmental impacts to safeguard human health.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"17 10","pages":"2405 - 2429"},"PeriodicalIF":2.9,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140937211","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}
Muhammad Khan, Salman Tariq, Zia Ul Haq, Mehnaz Rashid
{"title":"Understanding the spatiotemporal distribution of aerosols and their association with natural and anthropogenic factors over Saudi Arabia using multi-sensor remote sensing data","authors":"Muhammad Khan, Salman Tariq, Zia Ul Haq, Mehnaz Rashid","doi":"10.1007/s11869-024-01578-3","DOIUrl":"10.1007/s11869-024-01578-3","url":null,"abstract":"<div><p>Air quality is becoming a serious public health issue, affecting millions of people globally. In support of this fact, the World Health Organization predicts that approximately 2.4 million people die per year as a result of the health impacts of air pollution. So, to recognize the impacts of air pollution, we must first investigate their physical properties. In this article, we used the Ultraviolet Aerosol Index (UVAI) and Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol optical depth (AOD) from January 2005 to December 2021 obtained by the Ozone Monitoring Instrument (OMI) and MODIS respectively to investigate the Spatio-temporal patterns, annually and seasonal variations of absorbing aerosols, and interaction of aerosols with various meteorological parameters (rainfall, temperature, wind speed, e.g.) over Saudi Arabia (SA). Using the Hybrid Single Particle Langrangian Integrated Trajectory (HYSPLIT) model, we also identified pollution sources in SA's main cities. We also go through the natural and manmade factors that influence absorbing aerosols. Significant UVAI and MAIAC AOD values were observed high in the eastern and central regions of SA and low in the northern and western regions. Over SA, the average UVAI and MAIAC AOD are increasing at 0.93% and 0.83% per year respectively. UVAI has a favorable relationship with temperature in SA's eastern regions. In SA, UVAI has a positive and negative correlation with energy consumption and secondary industries of 0.787 and -0.52, respectively. Therefore, this study will help policymakers to identify the major hotspots and variability of aerosols in SA. Moreover, the contribution of different anthropogenic activities in polluting the atmosphere will also be analyzed in this study. Furthermore, depending on the findings of this study, various techniques such as plantation promotion, excellent fuel efficiency, a ban on the use of old and outdated vehicles, and so on can be employed to minimize the concentration of particle pollution.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"17 10","pages":"2365 - 2394"},"PeriodicalIF":2.9,"publicationDate":"2024-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140936860","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}
Md. Arfan Ali, Mazen E. Assiri, M. Nazrul Islam, Muhamad Bilal, Ayman Ghulam, Zhongwei Huang
{"title":"Identification of NO2 and SO2 over China: Characterization of polluted and hotspots Provinces","authors":"Md. Arfan Ali, Mazen E. Assiri, M. Nazrul Islam, Muhamad Bilal, Ayman Ghulam, Zhongwei Huang","doi":"10.1007/s11869-024-01565-8","DOIUrl":"10.1007/s11869-024-01565-8","url":null,"abstract":"<div><p>Increasing emissions of aerosol and trace gases (e.g. nitrogen dioxide: NO<sub>2</sub> and sulfur dioxide: SO<sub>2</sub>) have resulted in severe air pollution in China due to its rapid industrialization, economic growth, and urbanization. This resulted in numerous environmental and health problems, and poor air quality mainly in industrial areas and major cities. This study identifies long-term (2005‒2020) Ozone Monitoring Instrument (OMI) based NO<sub>2</sub> and SO<sub>2</sub> pollution hotspots across China by analyzing spatiotemporal distributions and variations, with characterization of polluted provinces, SO<sub>2</sub>/NO<sub>2</sub> ratio, trend, and assessing how effective China’s Air Pollution Control Policy (APCP) is on NO<sub>2</sub> and SO<sub>2</sub>. Results show that NO<sub>2</sub> and SO<sub>2</sub> pollution hotspots were seen in China's central (Hubei), eastern (Anhui, Jiangsu, Shandong, Zhejiang), northern (Beijing, Hebei, Henan, Shanxi, Tianjin), northeast (Liaoning, Jilin), northwestern (Urumqi), southern (Guangdong, Hong Kong), and southwest (Chongqing, Sichuan). However, the pollution level was higher in winter, followed by autumn, spring, and summer. China’s eight provinces (Tianjin, Shanghai, Shandong, Jiangsu, Beijing, Hebei, Hong Kong, and Henan) were identified as extremely polluted with high NO<sub>2</sub> levels ranging from 16.86 − 9.75 (10<sup>15</sup> molecules/cm<sup>2</sup>), whereas Shandong, Tianjin, Hebei, Beijing, Henan, Shanxi, Jiangsu, Shanghai, Anhui, and Liaoning were deemed to extremely polluted provinces with high SO<sub>2</sub> levels ranging from 20.62 − 14.30 (10<sup>15</sup> molecules/cm<sup>2</sup>). Moreover, the SO<sub>2</sub>/NO<sub>2</sub> ratio for 27 Chinese provinces fluctuates between 1.02 to 4.98, indicating industries emit more SO<sub>2</sub> than NO<sub>2</sub>. Finally, China’s air pollution control policies (APCP) led to the largest annual reductions in NO<sub>2</sub> during the 12th five-year plan (FYP) (6%‒94%) and SO<sub>2</sub> during the 11th FYP (6%‒74%). The present study concludes, however, that China’s APCP improved air quality by easing NO<sub>2</sub> and SO<sub>2</sub> emissions. This study recommends that the Chinese government may adopt a comprehensive strategy to reduce air pollution, including investing in clean energy, promoting electric vehicles, enforcing strict emission standards for industries, implementing green building practices, and raising public awareness about pollution reduction.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"17 10","pages":"2203 - 2221"},"PeriodicalIF":2.9,"publicationDate":"2024-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140833037","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}
R. C. Temple, J. May, P. F. Linden, B. Latter, S. F. Wilson, M. Morelli
{"title":"A satellite-based, near real-time, street-level resolution air pollutants monitoring system using machine learning for personalised skin health applications","authors":"R. C. Temple, J. May, P. F. Linden, B. Latter, S. F. Wilson, M. Morelli","doi":"10.1007/s11869-024-01577-4","DOIUrl":"10.1007/s11869-024-01577-4","url":null,"abstract":"<div><p>Skin exposome encapsulates all internal and environmental exposures that affect skin health. Of these, photo-pollution refers to the combined effect on human skin of the simultaneous exposure to solar radiation (especially UV) and air pollution. Providing personalised photo-pollution exposure warnings and dose monitoring to an individual through a smartphone app could help in reducing skin ageing and degradation as well as in managing skin conditions (for example Atopic Dermatitis). However, accurate monitoring is challenging without a potentially expensive or cumbersome sensor device. In this work we present an innovative satellite-based air pollutant monitoring software service, ExpoPol, developed by siHealth Ltd. ExpoPol synthesises several inputs including live satellite imagery in real-time into an artificial intelligence (AI) model to provide assessment of the exposure of a smartphone user to relevant air pollutants, such as nitrogen oxides (NO<sub>x</sub>), poly-aromatic hydrocarbons (PAH) and ozone (O<sub>3</sub>). When combined with siHealth’s patented technology HappySun® for solar radiation monitoring, ExpoPol can effectively provide a sensor-less personal skin photo-pollution dosimetry. By downscaling satellite data using local geographic data, ExpoPol is capable of monitoring pollutants with street-level resolution and global coverage in near real-time. We evaluate the accuracy of ExpoPol against ground-station monitoring data for three pollutants across three continental regions (Europe, Asia, North America) and find R<sup>2</sup> values of 0.62, 0.65, 0.74 for PM<sub>10</sub>, PM<sub>2.5</sub>, NO<sub>2</sub> respectively. ExpoPol is shown to be significantly more accurate than a state-of-the-art global atmospheric forecasting system (CAMS) over the same ground-station dataset and provide data on much finer spatial resolutions. The presented system can support the real-time automatic assessment of the user’s skin exposome, anywhere and anytime. This paves the way for the development of mobile applications empowering users and clinicians to make informed decisions about skin health, or assisting dermocosmetic manufacturers in the creation of personalised products for personal care (e.g., skin ageing prevention or hair care).</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"17 10","pages":"2353 - 2364"},"PeriodicalIF":2.9,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140808861","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}
Sarmite Kernchen, Holger Schmalz, Martin G. J. Löder, Christoph Georgi, Andrej Einhorn, Andreas Greiner, Anke C. Nölscher, Christian Laforsch, Andreas Held
{"title":"Atmospheric deposition studies of microplastics in Central Germany","authors":"Sarmite Kernchen, Holger Schmalz, Martin G. J. Löder, Christoph Georgi, Andrej Einhorn, Andreas Greiner, Anke C. Nölscher, Christian Laforsch, Andreas Held","doi":"10.1007/s11869-024-01571-w","DOIUrl":"10.1007/s11869-024-01571-w","url":null,"abstract":"<div><p>Emission of microplastics (MP) to the atmosphere, airborne transport, and subsequent deposition are now recognized. However, the temporal and spatial resolution of data on MP pollution and knowledge of their atmospheric behaviour and fate is still very limited. Hence, we investigated MP wet and dry deposition in Central Germany and examined the role of weather conditions on MP contamination levels. Monthly samples of dry and wet deposition were taken over an eight-month period (05/2019-12/2019) and analysed by micro-Fourier-Transform Infrared spectroscopy (µFTIR) down to 11 μm particle size and one dry deposition sample was subjected to Raman analysis to determine plastic particles down to a size of 0.5 μm. MP in a size range from 11 μm to 130 μm were detected in all wet deposition samples and in 4 out of 8 dry deposition samples by µFTIR. Polypropylene particles were found most frequently and accounted for 62% and 54% of all particles in wet and dry deposition samples, respectively. Over the eight-month period, wet deposition of MP slightly dominated at the study site and comprised 59% of the total MP deposition. The MP mean total (wet + dry) deposition flux (DF) was 17 ± 14 MP m<sup>− 2</sup> day<sup>− 1</sup>. Extensive Raman analyses of an exemplary dry deposition sample revealed additional plastic particles in the extended size range from 1 to 10 μm resulting in a deposition flux of 207 MP m<sup>− 2</sup> day<sup>− 1</sup>. Our results suggest that MP analysis by µFTIR down to 11 μm may underestimate DF at least by an order of magnitude. More comprehensive studies on submicron plastics and nanoplastics are needed to fully assess air pollution by plastic particles.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"17 10","pages":"2247 - 2261"},"PeriodicalIF":2.9,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11869-024-01571-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140663539","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":"Investigation of emissions from passenger flights Denizli Çardak Airport, Türkiye","authors":"Mehmet Ali Çil, Cevahir Tarhan","doi":"10.1007/s11869-024-01579-2","DOIUrl":"10.1007/s11869-024-01579-2","url":null,"abstract":"<div><p>Due to developing aviation sector, number of aircraft in the world is increasing. Along with this development, problems such as the decrease in air quality in and around the airport also arise. In this study, it is tried to calculate pollutant emissions occurring in 2022 during the LTO cycles of Denizli Çardak Airport in Turkey. These calculations are based on the information obtained from ICAO Engine Emission Data Bank and flight information published by the General Directorate of State Airports Authority (GDSAA). As a result of the data obtained, 74.64 ton/year pollutants (NOx-37.148 t/y, CO-35.398 t/y and HC-2.094 t/y) were calculated for 2022 at Denizli Çardak Airport. Of all emissions, NOx accounted for 50%, CO 47% and HC 3%. In the LTO cycle, the most fuel is burned in taxi cycle and pollutant emissions produced in this cycle are greater. With a 2 min reduction in taxi time, there will be an approximate 6.8% reduction in the total emission rate in the LTO cycle. Similarly, with a 4 min reduction in taxi time, there will be a 13.72% reduction in the whole emission rate in the LTO cycle. Unlike other studies, in this study the emission rates of various engines were compared. It has been calculated that the amount of pollutant emissions produced by the new generation Boeing 737 MAX LEAP-1B powered aircraft in LTO cycle is 25% less than the amount of pollutant emissions produced by the Airbus A320 NEO LEAP-1 A powered aircraft. The biggest factor here is that the emission of CO pollutants is less. Considering the emission rates produced by these four different engines (B737-800 CFM56-7B, A320 V2500-A1, B737 MAX LEAP-1B, A320 NEO LEAP-1 A), the Airbus A320 V2500-A1 engine is a more environmentally friendly engine than the other engines.</p></div>","PeriodicalId":49109,"journal":{"name":"Air Quality Atmosphere and Health","volume":"17 10","pages":"2395 - 2403"},"PeriodicalIF":2.9,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s11869-024-01579-2.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140662998","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}