Aerosol Science and Engineering最新文献

{"title":"Impact of Sulphate Aerosols from Human Production and Consumption Activities in Different Regions on the Climate of Asia","authors":"Chunjiang Zhou,&nbsp;Gang Huang,&nbsp;Su Wang,&nbsp;Ruiqing Li,&nbsp;Min Zhang,&nbsp;Xueyi Xun","doi":"10.1007/s41810-025-00296-2","DOIUrl":"10.1007/s41810-025-00296-2","url":null,"abstract":"<div><p>Aerosols of anthropogenic origin exhibit significant spatial variability, leading to localized environmental pollution and climate effects. The increasing international trade has caused extensive cross-border transfer of pollution, rendering the traditional production-based perspective insufficient for assessing the climate impacts of human activities in specific regions. In this paper, using the Earth System Model, five numerical simulation experiments are designed to simulate the impacts of anthropogenic sources of sulfate, associated with both production and consumption activities, on the climate of East and South Asia in different affluent regions. The results show that sulfate associated with consumption activities in developed countries has a greater impact on climate than production activities, whereas the opposite is observed for developing countries. This is because products produced in developing countries are largely consumed by developed countries.</p></div>","PeriodicalId":36991,"journal":{"name":"Aerosol Science and Engineering","volume":"10 2","pages":"272 - 282"},"PeriodicalIF":2.0,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147738605","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":"Personal Exposure to Polycyclic Aromatic Hydrocarbon and Inhalation Risk Assessment among Asphalt Hot Mixed Plant and Road Paving Workers","authors":"B. Ravichandran,&nbsp;Somnath Sen,&nbsp;V. Dhananjayan,&nbsp;J. Narayana","doi":"10.1007/s41810-025-00291-7","DOIUrl":"10.1007/s41810-025-00291-7","url":null,"abstract":"<div><p>The workers working in hot mixing asphalt plants, road paving and tarring work are likely to be exposed to fumes and vapours containing hazardous substances such as polycyclic aromatic compounds (PAHs), which can cause respiratory irritation as well as carcinogenic and noncarcinogenic risks. The study estimated inhalation exposure to PAHs among asphalt workers in hot mixing plants and road paving areas using XAD-2 sorbent tubes and PTFE filter paper. A total of ninety-one personal air samples (paving 37 and plant 54) were collected from the workers’ breathing zone. The results were analyzed using HPLC following the NIOSH-5506 method. The total PAHs (TPAH) concentration range (composed of vapor and particulate phases) ranged from 0.02 to 184.71 µg/m³ (mean 21.07 ± 37.36 µg/m³), and the concentration of TPAHs among road paving workers was significantly greater (p &lt; 0.05) than that among hot mix asphalt plant workers. Among the individual PAH compounds, the mean concentration of benzo[ghi]perylene (BghiP) was 7.15 ± 17.05 µg/m³ it was the highest observed value. The second highest concentration was benzo[α]pyrene (BaP), with a mean concentration of 3.28 ± 8.46 µg/m³. The exposure levels of BghiP and BaP among the paving workers were significantly (p &lt; 0.05) greater than those among the plant workers. A health risk assessment suggested increased chances of developing cancer (3.44 × 10^–09 to 3.47 × 10^–04) in such populations if exposures are not controlled in the study area, whereas the percentage of noncancer hazard quotients and indices for BaP was 8.10 × 10⁰², which reflects that these contaminants have adverse effects on health. The results of our study demonstrate that asphalt workers are constantly exposed to PAHs and their derivatives at their work sites, and since PAHs are considered non-threshold carcinogens, our observed levels may still be harmful.</p></div>","PeriodicalId":36991,"journal":{"name":"Aerosol Science and Engineering","volume":"10 2","pages":"198 - 207"},"PeriodicalIF":2.0,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147738606","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":"Impact of Weather, Lockdown, and Fire on Air Quality: An Analysis of Particulate Matter in Kochi, India","authors":"Arun P. Thomas,&nbsp;K. Satheesan,&nbsp;Ravi Varma,&nbsp;Avinash Paul,&nbsp;M. Muhsin,&nbsp;H. Amarnath","doi":"10.1007/s41810-025-00294-4","DOIUrl":"10.1007/s41810-025-00294-4","url":null,"abstract":"<div><p>This study investigates Particulate Matter (PM) variations in Kochi, India, and its association with meteorological variables by analyzing five years of ground-based observational data. It focuses on PM pollution at the Vyttila Mobility Hub, using the hourly mean of PM_2.5 and PM₁₀ data from January 2018 to March 2023. Annual PM_2.5, 41.78 μg/m³ and PM₁₀, 76.64 μg/m³ levels exceeded Indian National Ambient Air Quality Standards and World Health Organization limits, notably in winter and post-monsoon seasons. Inter-annual trends showed a decline from 2018 to 2021, followed by a rise in 2022, with the most significant decline observed between 2019 and 2020 possibly due to COVID-19 restrictions. Monthly variations revealed elevated PM levels in winter and lower levels during the monsoon months. Sunday consistently recorded the lowest levels in the weekly analysis. Diurnal patterns displayed two peaks during morning and evening rush hours. Additionally, the study included an examination of ratios across different time periods. Winter’s higher PM_2.5/PM₁₀ (0.65 ± 0.12) ratio indicated combustion emissions, while the monsoon’s lower ratio, (0.47 ± 0.15) resulted from rainout. Correlation analysis of PM and meteorological variables showed temperature positively correlates with PM, while humidity and wind speed negatively correlate. Fire at the Brahmapuram solid waste treatment plant in March 2023 led to elevated emissions, resulting in a surge of around 78% in PM_2.5 and 15% in PM₁₀ compared to the mean value of previous years and the observed ratio was 0.65 ± 0.15, indicating the increased presence of PM_2.5 due to the fire incident.</p></div>","PeriodicalId":36991,"journal":{"name":"Aerosol Science and Engineering","volume":"10 2","pages":"235 - 259"},"PeriodicalIF":2.0,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147738687","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":"Advancing Particulate Matter Chemical Composition Analysis: A Hybrid Machine-Learning Approach with UV-Vis Spectroscopy","authors":"Reza Bashiri Khuzestani,&nbsp;Ying Li,&nbsp;James J. Schauer,&nbsp;Yuanxun Zhang","doi":"10.1007/s41810-025-00282-8","DOIUrl":"10.1007/s41810-025-00282-8","url":null,"abstract":"<div><p>Fine particulate matter (PM_2.5) significantly impacts public health, necessitating detailed chemical composition analysis. Conventional PM_2.5 chemical analysis methods, including filter-based sampling and laboratory analysis, are time-consuming and resource-intensive. This study introduces a hybrid machine-learning approach combining Support Vector Machine (SVM) and Partial Least Squares (PLS) regression to estimate PM_2.5 chemical compositions using UV-Vis spectroscopy. The hybrid PLS-SVM model demonstrated superior performance across various PM_2.5 species, achieving R² values exceeding 0.9 with minimal RMSE and MAE values. Specifically, the model accurately estimated concentrations of organic carbon (OC) (R² = 0.96, RMSE = 2.37 µg/cm²) and elemental carbon (EC) (R² = 0.95, RMSE = 1.27 µg/cm²), outperforming optical color space methods. The model also illustrated superior performance for sulfate and nitrate (R² ~ 0.98, RMSE ~ 0.45 µg/m³) and other crustal metals (R² exceeding 0.9). The hybrid algorithm also allows us to identify essential spectral bands and their unique optical signatures for each PM_2.5 component, enhancing the interpretability of the results. For instance, OC and EC exhibited significant contributions across the UV, visible, and IR spectral regions, with distinct differences in their spectral profiles. Crustal elements and PM_2.5 mass showed higher contributions in the UV and visible regions, with unique peaks distinguishing different components. This detailed spectral profiling provides valuable insights into the optical properties and light-absorbing characteristics of PM_2.5 species. The hybrid model offers a robust, non-destructive, and cost-effective approach for PM_2.5 chemical characterizations, enhancing current PM monitoring techniques and offering significant potential for large-scale air pollution studies and mitigation strategies.</p></div>","PeriodicalId":36991,"journal":{"name":"Aerosol Science and Engineering","volume":"10 1","pages":"62 - 74"},"PeriodicalIF":2.0,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147642912","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":"Study of Aerosols Optical Depth and Ångström Exponent Over IGP: A Case Study","authors":"Pushpendra Singh Soni,&nbsp;Vikram Singh,&nbsp;Alok Sagar Gautam,&nbsp;Amar Deep,&nbsp;Rolly Singh,&nbsp;Alka Gautam,&nbsp;Sneha Gautam,&nbsp;Surendra Pratap Singh","doi":"10.1007/s41810-025-00290-8","DOIUrl":"10.1007/s41810-025-00290-8","url":null,"abstract":"<div><p>Examining Aerosol Optical Depth (AOD) and Ångström Exponent (AE) is essential in environmental and atmospheric science, providing insights into aerosol characteristics and their broader implications. This relevance is observed locally and globally, given that atmospheric processes are closely tied to aerosol concentrations. This study focuses on the analysis of AOD and AE data obtained from Moderate Resolution Imaging Spectrometer (MODIS) satellite observations, concentrating on the Indo-Gangetic Plain (IGP), which includes the regions of Punjab, Haryana, Delhi, and Agra in Uttar Pradesh from 2018 to 2022. The dataset was rigorously analyzed on a daily, monthly, seasonal, and annual basis. The highest seasonal average AOD values were recorded during the monsoon season, with Delhi showing an average of 0.90 ± 0.13 in 2019, Agra at 0.876 ± 0.002 in 2019, Haryana at 0.75 ± 0.22 in 2018, and Punjab at 0.83 ± 0.16 in 2019. The highest mean AOD values were observed in Agra (0.652 ± 0.17 in 2021), Haryana (0.597 ± 0.21 in 2018), and Punjab (0.642 ± 0.14 in 2021) during the study period. The highest mean AE values were recorded in Delhi (1.566 ± 0.2 in 2021), Agra (1.54 ± 0.24 in 2021), Haryana (1.28 ± 0.33 in 2022), and Punjab (1.282 ± 0.45 in 2022). During the months of May, June, July, and August (MJJA) across several years, AOD values ranged from 0.3 to 0.55, while AE values varied between 1.3 and 1.5. Additionally, the mean AOD values were found to be highest in Agra (0.652 ± 0.17 in 2021), followed by Haryana (0.597 ± 0.21 in 2018) and Punjab (0.642 ± 0.14 in 2021). A comprehensive analysis of AOD and AE parameters from MODIS data significantly enhances our understanding of aerosol dynamics, laying the groundwork for addressing climatic implications in the IGP region.</p></div>","PeriodicalId":36991,"journal":{"name":"Aerosol Science and Engineering","volume":"10 2","pages":"171 - 183"},"PeriodicalIF":2.0,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147738217","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":"Dynamics, Reactive Chemistry and Short-Term Risk of Indoor Pollutants in a General Public Toilet","authors":"M. E. Emetere,&nbsp;T. J. Ayua,&nbsp;O. T. Olapade,&nbsp;A. I. Salami,&nbsp;J. O. Popoola,&nbsp;F. O. Akinluyi,&nbsp;T. O. Adeleke,&nbsp;M. N. Eze,&nbsp;J. O. Fadiji,&nbsp;N. O. Adeniji,&nbsp;I. E. Olaniyan,&nbsp;O. O. Jegede,&nbsp;B. B. Deji-Jinadu","doi":"10.1007/s41810-025-00287-3","DOIUrl":"10.1007/s41810-025-00287-3","url":null,"abstract":"<div><p>Air pollution from indoor sources is increasing due to massive participation of reactive chemistry and microbial transformations on existing pollutants. Since 90% of the world population spends their time indoors, the subject matter becomes salient. The mode of participation of outdoor-indoor interactions, reactive chemistry and microbial transformations within an indoor system had not been examined holistically. This research made use of a low cost indoor air sampler to measure selected pollutants in a general public toilet at peak and off times. The descriptive statistics was used to investigate the time series variation of the indoor air pollutants. The reactivity of the pollutants and dynamics of the indoor air pollutant were investigated using the wavelet transform concept and IAQX software respectively. The diversity of the pollutants and its implications in the indoor environment were investigated using diversity profile. The statistical relevance of indoor pollutants was carried out using beta diversity indices and the risk analysis of indoor pollutants was carried out using AirQ + software. It was discovered that the outdoor-indoor interactions, reactive chemistry and microbial transformations played significant roles in indoor pollutants depending on the ventilation of the building. Hence, for the public toilet considered, the reactive chemistry, microbial transformation and outdoor-indoor plays significant roles in descending order.</p></div>","PeriodicalId":36991,"journal":{"name":"Aerosol Science and Engineering","volume":"10 1","pages":"127 - 156"},"PeriodicalIF":2.0,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147642513","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":"Variation in PM2.5 Chemical Composition During the Heating Seasons from 2018–2019 to 2020–2021 in Harbin: Response to Mitigation Strategies","authors":"Qin-Qin Yu,&nbsp;Jiu-Meng Liu,&nbsp;Yuan Cheng","doi":"10.1007/s41810-025-00283-7","DOIUrl":"10.1007/s41810-025-00283-7","url":null,"abstract":"<div><p>To better understand the changes in chemical components and potential source regions of PM_2.5 in cold region of Northeast China, a field study was conducted during 2018–2019 and 2020–2021 heating seasons at an urban site in Harbin. On average, PM_2.5 concentration decreased from 55.57 ± 44.45 μg/m³ in 2018–2019 to 49.70 ± 32.14 μg/m³ in 2020–2021, reflecting a notable improvement in air quality. Overall, a reduction in the fraction of organic matter (OM) and an increase in the fraction of secondary inorganic aerosol (SNA) in PM_2.5 were observed. Indeed, the decrease in OM was primarily attributed to the reduction in emissions from open straw burning. However, during the heavily polluted period of 2020–2021, the influence of biomass burning significantly increased. This indicated that, although the impact of biomass burning emissions was mitigated by stricter straw burning prohibition policies compared to 2018–2019, it still played a crucial role in haze pollution. Additionally, the proportion of SNA increased due to the rise in the sulfur oxidation ratio (SOR), nitrogen oxidation ratio (NOR) and a slight increase in NO₂ concentration. The study revealed that liquid-phase reactions played a significant role in sulfate formation, while photochemical processes were more critical for nitrate formation. Meanwhile, higher SOR and NOR levels were observed during pollution episodes, highlighting the enhanced secondary formation on polluted days. Moreover, backward trajectory cluster, potential source contribution function (PSCF) and weighted concentration weighted trajectory (CWT) analyses identified the northwest and central-western regions of Jilin Province, including cities such as Changchun and Songyuan, as main potential source regions influencing air quality in Harbin.</p></div>","PeriodicalId":36991,"journal":{"name":"Aerosol Science and Engineering","volume":"10 1","pages":"75 - 89"},"PeriodicalIF":2.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147642315","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":"Simulation of Ground-Level Particulate Matter Concentration with AOD and Related Meteorological Parameters: A Case Study on Kolkata by Random Forest and ANN","authors":"Eeshan Basu,&nbsp;Chalantika Laha Salui,&nbsp;Indranil Mukherjee,&nbsp;Debasis Sau","doi":"10.1007/s41810-025-00289-1","DOIUrl":"10.1007/s41810-025-00289-1","url":null,"abstract":"<div><p>Particulate matter (PM), particularly those with an aerodynamic diameter of less than 2.5µm (PM_2.5), is linked to various health issues, including respiratory and cardiovascular diseases. With its dense population, extensive diesel-dominated vehicle fleet, expanding commercial sectors, and numerous industrial facilities, Kolkata faces severe air quality challenges. The city frequently experiences ‘very unhealthy’ AQI levels, primarily driven by elevated PM_2.5 concentrations during winter. Despite the availability of ground-based PM measurements, their spatial and temporal limitations hinder comprehensive air quality assessment, especially in a rapidly urbanizing city like Kolkata. This study estimates ground-level PM_2.5 and PM₁₀ concentrations from 2015 to 2022 using Aerosol Optical Depth (AOD) derived from MODIS MOD04_L2 product with a spatial resolution of 10 km. This study also examines the influence of meteorological variables (temperature, relative humidity, precipitation, wind speed, and cloud cover) on the AOD-PM relationship. Two machine learning models were developed: an Artificial Neural Network (ANN) with three hidden layers optimized through backpropagation and a Random Forest Regression (RF) model utilizing ensemble learning. Model performance was validated using ground-based PM and independent test data from October 2023. Quantitative results show that both models performed well in predicting PM concentrations. For PM_2.5, the RF model achieved an R² of 0.961 for training and 0.952 for testing, with a lower RMSE of 12.4 µg/m³, while the ANN model achieved an R² of 0.97 for training and 0.95 for testing, with a higher RMSE of 14.8 µg/m³. For PM₁₀, the RF model produced an R² of 0.965 for training and 0.932 for testing, with an RMSE of 18.2 µg/m³, while the ANN model showed an R² of 0.987 for training and 0.947 for testing, with an RMSE of 21.5 µg/m³. Although both models demonstrated strong predictive capabilities, the RF model was selected due to its lower RMSE values and greater consistency across training and testing phases. Among meteorological factors, temperature emerged as the most significant predictor, followed by wind speed, in influencing PM concentrations. The findings demonstrate the successful integration of machine learning and geospatial technology for PM estimation, overcoming the limitations of ground-based measurements. While the study highlights location-specific challenges in Kolkata, the methods employed provide a transferable framework for improving air quality assessments in other urban regions globally.</p></div>","PeriodicalId":36991,"journal":{"name":"Aerosol Science and Engineering","volume":"10 2","pages":"184 - 197"},"PeriodicalIF":2.0,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147737438","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 Enhancement and Variation in the Carbon Dioxide Concentration in a Typical Industrial Park in the Northern China","authors":"Qixiang Cai,&nbsp;Zhaojun Wang,&nbsp;Pengfei Han,&nbsp;Ning Zeng,&nbsp;Xi Nie,&nbsp;Xiaoyu Yang,&nbsp;Zhenyang Wang,&nbsp;Sulian Gao","doi":"10.1007/s41810-025-00286-4","DOIUrl":"10.1007/s41810-025-00286-4","url":null,"abstract":"<div><p>Industrial parks facilitate efficient industrial manufacturing by concentrate various types of industrial sectors and activities, which is accompanied by significant carbon emissions. More than 15,000 industrial parks have been established in China since their introduction, accounting for approximately 31% of the national CO₂ emissions. In the context of low-carbon industrial park pilot projects, we selected a typical chemical industrial park for our research on monitoring the carbon dioxide concentration and enhancement. We deployed an observation network encompassing eight sites in this industrial park, as well as a reference site located several kilometers away from the park, and conducted continuous observations for a year. The observation results indicated that the annual enhancement reached approximately 16 ppm and exhibited a spatial pattern of high values in the west and low values in the east of this park. The CO₂ concentration and enhancement in autumn were much greater than those in other seasons, with an increase of 28 ppm, whereas the smallest increase was observed in winter, with a value of only 5 ppm. The duration of extremely high-concentration events caused by significant carbon emissions ranged from several hours to half a day, resulting in local concentrations ranging from 1500 to 2000 ppm. In this study, we provided the basic spatiotemporal pattern of the CO₂ concentration and enhancements in an industrial park, laying a foundation for inversion simulations and inventory accounting.</p></div>","PeriodicalId":36991,"journal":{"name":"Aerosol Science and Engineering","volume":"10 1","pages":"114 - 126"},"PeriodicalIF":2.0,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147643084","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":"Analyzing Maximum Temperature Trends and Extremes in Brazil: A Study of Climate Variability and Anthropogenic Influences from 1960 to 2020","authors":"Amaury de Souza,&nbsp;Elias Silva de Medeiros,&nbsp;José Francisco de Oliveira-Júnior,&nbsp;Vikram Kumar,&nbsp;Sneha Gautam,&nbsp;Aline Pessoa Bezerra","doi":"10.1007/s41810-025-00288-2","DOIUrl":"10.1007/s41810-025-00288-2","url":null,"abstract":"<div><p>This study examines trends and variability in maximum annual temperatures across 27 Brazilian capitals from 1960 to 2020, revealing significant temperature variations with Teresina experiencing the highest temperatures and São Paulo and Curitiba the lowest. Equatorial cities like Fortaleza and João Pessoa exhibit narrower temperature ranges, while Cuiabá shows broader fluctuations. Statistical analysis using Mann–Kendall (MK) and Sen Slope (SS) tests indicates a significant increase in temperatures in 24 of the 27 capitals, with rates varying from 0.004 °C per year in Aracaju to 0.027 °C per year in Teresina. Pettitt's test identifies abrupt changes in 21 time series, particularly between 1989 and 1994. The study further explores maximum temperature modeling in Brazil using Gumbel (GUM) and Generalized Extreme Value (GEV) probability functions to understand extreme weather events such as heat waves, droughts, and fires. It highlights the role of seasonality and the impact of anthropogenic factors like deforestation and poor environmental management on temperature variability and extremes. The influence of global climate change, especially the southern El Niño phenomenon (ENSO), on interannual temperature variability is also examined. Key findings underscore the effectiveness of Extreme Value Theory and probability distributions (GEV, GUM) in modeling and predicting extreme temperatures. Comparative analyses from other regions confirm the global trend of increasing extreme temperatures. The study emphasizes the need for practical adaptation strategies and climate policies to address the challenges posed by rising temperatures and climate change, with important implications for urban and agricultural resilience in Brazil.</p></div>","PeriodicalId":36991,"journal":{"name":"Aerosol Science and Engineering","volume":"10 2","pages":"157 - 170"},"PeriodicalIF":2.0,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147738691","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}