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Variations in polycyclic aromatic hydrocarbons concentrations and their contribution to aerosol optical depth during the COVID-19 outbreak: A study in Yokohama using generalized additive model

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ATMOSPHERIC POLLUTION RESEARCH 2025年第8期16卷

作者： Jing, Weidong Nakano, Kohei Mitani, Ryosuke Shen, Zhenxing Okuda, Tomoaki Xian Univ Architecture & Technol Sch Environm & Municipal Engn Xian 710055 Peoples R China Keio Univ Fac Sci & Technol Dept Appl Chem Yokohama Kanagawa 2238522 Japan Xi An Jiao Tong Univ Dept Environm Sci & Engn Xian 710049 Peoples R China

The severe social restrictions aiming at reducing the novel coronavirus disease (COVID-19) transmission during its outbreak significantly affected regional air quality. This study analyzed changes in polycyclic aromatic hydrocarbons (PAHs) concentrations in cyclone-collected powder form particulate matter (PM) samples collected in Yokohama, Japan, from 2018 to 2023. The annual mean & sum;PAHs concentration decreased significantly from 20.7 ng/mg in 2019 to 13.2 ng/mg in 2020 after the outbreak. However, the main sources of PAHs remained stable and were dominated by incomplete fossil fuel combustion. A generalized additive model was utilized to analyze the statistical relationships of air pollutants, namely, aerosol particulate matter (PM2.5), & sum;PAHs, ozone (O3), metal, and meteorological factors such as solar radiation and their impact on aerosol optical depth (AOD). AOD remote sensing data collected by the Terra/MODIS satellite reflects the degree of air turbidity and atmospheric aerosol loading. Despite the small sample size, the model fitting demonstrated a good fit and showed smoothing non-linear interaction effects among the target factors influencing secondary aerosol generation. In conclusion, intense solar radiation, high concentrations of oxidative gases, elevated levels of organic contaminants in aerosols, and the catalytic degradation of PAHs by transition metals contribute to increased atmospheric aerosol loads. This finding enhances the present understanding of the photochemical reaction processes of aerosol particles. However, longer-term observational studies are needed to confirm our findings due to the limited sample size in this study.

关键词： Cyclone PM-Bound PAHs Aerosol optical depth generalized additive model Multifactor interaction

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Phytoplankton-environment dynamics in a tropical estuary of the northeastern Arabian Sea: a generalized additive model (GAM) approach

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2025年第2期197卷 1-22页

作者： Akter, Sahina Wodeyar, K. Abhilash Nama, Suman Borah, Simanku Angmo, Sonam Deshmukhe, Geetanjali Nayak, Binaya Bhusan Ramteke, Karankumar ICAR Cent Inst Fisheries Educ Mumbai 400061 India ICAR Cent Inland Fisheries Res Inst Reg Ctr Gauhati 781006 Assam India

Phytoplankton are diverse photosynthetic organisms in estuarine ecosystems and sensitive indicators of environmental changes. This study employed generalized additive model (GAM) to explore the impact of environmental variables on the abundance of six dominant phytoplankton species in the tropical Karanja estuary, India. Data were collected from five sampling stations between January 2022 and March 2023. The GAM model explained >= 55% of the variability in species distribution, predicting that the Karanja estuary provides a suitable habitat for these phytoplankton. Spatiotemporal predictions revealed higher abundances of Asterionellopsis glacialis, Coscinodiscus sp., Pseudo-nitzschia pungens, Skeletonema costatum and Thalassionema frauenfeldii during post-monsoon and lower in pre-monsoon. Conversely, Odontella sinensis thrived during pre-monsoon. Optimal growth conditions included a water temperature range of 24-32 degrees C for O. sinensis, P. pungens and S. costatum, while chlorophyll-b concentrations between 1 and 20 mg/m3 favored A. glacialis, O. sinensis, P. pungens, S. costatum and T. frauenfeldii. P. pungens, S. costatum and T. frauenfeldii exhibited broad salinity tolerance ranging from 10-40 parts per thousand. Additionally, Coscinodiscus sp., P. pungens and T. frauenfeldii were found in environments with nitrate content of 0.1-1 mg/L, while O. sinensis preferred silicate levels of 1-10 mg/L. Notably, these species serve as indicators of eutrophication, providing insights into ecosystem health. This study represents the first application of GAM for exploring phytoplankton-environment interactions in India, offering critical data for water quality management, pollution control and food web preservation. Policymakers can use these findings to develop evidence-based regulations that support proactive coastal management, enhance estuarine resilience to climate stressors and ensure sustainable resource conservation.

关键词： Tropical ecosystems Estuary Phytoplankton Environmental variables Species distribution generalized additive model

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Interactions Between Serotonin Transporter Gene and Adverse Childhood Experience in a generalized additive model: A Pilot Study

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JOURNAL OF THE AMERICAN PSYCHIATRIC NURSES ASSOCIATION 2025年第1期31卷 55-63页

作者： Niitsu, Kosuke Lee, Chiyoung Rice, Michael J. Univ Washington Bothell Bothell WA USA Univ Arizona Tucson AZ USA Univ Colorado Anschutz Med Campus Aurora CO USA Univ Washington Bothell Sch Nursing & Hlth Studies Box 35853218115 Campus Way NE Bothell WA 98011 USA

BACKGROUND: While most people experience potentially traumatic events (PTEs), including Adverse Childhood Experiences (ACEs), the stress reactions to PTEs on mental health outcomes are highly heterogeneous. Resilience is influenced by a complex biopsychosocial ecological system, including gene serotonin transporter-linked promoter region or 5-HTTLPR /rs25531 by ACEs interactions. AIMS: This pilot study investigated the gene-by-environment interactions on mental health outcomes in adults enrolled in a health care profession program using a generalized additive model (GAM). METHODS: Seventy health care college students (mean age = 27.4 years, 67.1% women) participated in this cross-sectional study. Saliva samples were collected from students to analyze 5-HTTLPR/rs25531. Participants completed the ACE Questionnaire and the Mental Health Inventory. GAMs with different interaction terms were built adjusting for age, gender, and race. The value of the effective degree of freedom (EDF) quantifies the curvature of the relationship. RESULTS: Among participants with the long allele of 5-HTTLPR/rs25531, a linear pattern was found between the total ACE score and mental health outcomes (EDF = 1). Conversely, among participants with the short allele, EDF was approximately 2, indicating a curved association suggesting that mental health worsens in individuals exposed to up to four types of ACEs. CONCLUSIONS: The impact of up to four ACEs on mental health was stronger among individuals with the short allele of 5-HTTLPR/rs25531 than those with the long allele. Although this study does not claim to provide a definite approach to analyzing gene-by-environment interactions, we offer a different perspective to explore the relationship.

关键词： psychological resilience serotonin-transporter-linked promoter region (5-httlpr/rs25531) gene by environmental interaction adverse childhood experiences generalized additive model

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Application of a delta-generalized additive model to assess the impact of environmental changes on the spatial distribution of bigeye tuna (Thunnus obesus) in the Indian Ocean

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FISHERIES RESEARCH 2025年 281卷

作者： Lurkpranee, Supatcha Kitakado, Toshihide Tokyo Univ Marine Sci & Technol Dept Marine Biosci 4-5-7 Konan Minato Tokyo 1080075 Japan

This study estimated the spatial distribution of bigeye tuna (Thunnus obesus) in the Indian Ocean under changing environmental conditions through habitat suitability modeling. Fisheries data from the Indian Ocean Tuna Commission and environmental satellite data from COPERNICUS were used in a delta-generalized additive model approach. This involved separately modeling the probability of presence or absence using binomial logistic regression and the positive catch using lognormal distribution. Results showed that sea surface temperature, mixed layer depth, and chlorophyll-a significantly influenced the presence of bigeye tuna in the Indian Ocean, whereas sea surface height, mixed layer depth, and chlorophyll-a exerted a substantial impact on their density. Adjusted simulated environmental data from 2020 to 2100 under three climate change scenarios (SSP126, SSP245, and SSP585) from Coupled model Intercomparison Project Phase 6 were utilized to predict the future spatial distribution pattern of bigeye tuna in the Indian Ocean. Bigeye tuna is expected to variedly shift its spatial distribution under the three different climate change scenarios, with SSP585 having the most pronounced changes. Generally, bigeye tuna is predicted to spread poleward from the equator as a result of tropicalization in the higher latitudes.

关键词： Climate change Delta model Future prediction generalized additive model Spatial distribution

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Assessment of factors affecting infant mortality in Sudan: application of generalized additive model

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BMC Pediatrics 2025年第1期25卷 1-10页

作者： Ayele, Dawit Getnet. Mohammed, Mohammed Omar Musa Abdallah, Ahmed Saied Rahama Center for Policy Planning and Evaluation (CPPE) DC Health Washington USA College of Business Administration in Hawtat Bani Tamim Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia

The evaluation of the factors influencing infant mortality entails examining the various factors contributing to the high rate of infant deaths in Sudan. These factors encompass access to healthcare services, nutritional status, sanitation, maternal health, vaccination, and socioeconomic circumstances. This study aims to assess the factors affecting infant mortality in the state of Gezira in Sudan. The study conducted in Gezira state, Sudan, was a cross-sectional analytical household cluster survey carried out from July 2021 to December 2021. This survey used a questionnaire divided into two parts: the first section collected basic demographic information about each participant to identify the general characteristics of the population. In the second section, detailed demographic, socioeconomic, and geographic information was gathered regarding factors influencing infant mortality in Gezira. The generalized additive models was used to identify factors affecting infant mortality. The findings indicate a statistically significant correlation between the child’s sex (AOR = 1.108, P-value = 0.0227), breastfeeding exclusively (AOR = 0.883, P-value = 0.0294), maternal follow-up (AOR = 0.883, P-value = 0.0294), a child suffering from malaria (AOR = 0.883, P-value = 0.0294), a child experiencing malnourishment (AOR = 1.44, P-value < 0.0001), a child suffering from septicemia (AOR = 0.883, P-value = 0.0294), and infant mortality. These results support WHO recommendations and other studies showing that exclusive breastfeeding is a major preventive factor against infant morbidity and mortality from respiratory and gastrointestinal tract illnesses. This emphasizes how crucial it is to provide early childhood primary healthcare, especially in Sudan where infant death rates are high because of common illnesses including blood poisoning, malaria, and other ailments. Thus, the government needs to adopt measures and policies to minimize this mortality and save lives. Good prenatal

关键词： Assessment Infant Factors generalized additive model Mortality Sudan

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generalized additive model and regression tree analyses of blue shark (Prionace glauca) catch rates by the Hawaii-based commercial longline fishery

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FISHERIES RESEARCH 2001年第2期53卷 115-131页

作者： Walsh, WA Kleiber, P Univ Hawaii Joint Inst Marine & Atmospher Res Honolulu HI 96822 USA

generalized additive model (GAM) and regression tree analyses were conducted with blue shark, Prionace glauca, catch rates (catch per set) as reported by National Marine Fisheries Service observers serving aboard Hawaii-based commercial longline vessels from March 1994 through December 1997 (N=2010 longline sets). The objective was to use GAM and regression tree methodology to relate catch rates to a tractable suite of readily measured or computed variables. Because the predictor variables are also either provided in or easily computed from the logbooks that commercial vessels submit upon landing fish for sale, it is likely that a model or models fitted to accurate observer data could then be applied on a fleet-wide basis to serve as a standard of comparison for the logbooks. The GAM included nine spatio-temporal, environmental, and operational variables and explained 72.1% of the deviance of blue shark catch rates. Latitude exerted the strongest effects of any individual variable;longitude was the most influential variable when adjusted for the effects of all other factors. Relatively cold sea surface temperatures were associated with high catch rates. The initial regression tree included 68 terminal nodes and I I predictors. It was refined to a final tree with 42 terminal nodes, which reduced the root mean deviance by 65.3%. The tree was partitioned first on latitude 26.6 degreesN, and then branched out to reach terminal nodes after 2-8 additional partitionings. Sets south of this latitude were characterized by lower catch rates and partitionings on a greater number and variety of predictors. Northerly sets were characterized by higher and more variable blue shark catch rates. Predictions from the two analyses were highly correlated (r=0.903. P much less than0.001). Moreover, use of these methods in combination aided greatly in the interpretation of results. We conclude that GAM and regression tree analyses can be usefully employed in the assessment of blue shark

关键词： blue shark catch rates generalized additive model regression tree

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Electrical Energy Prediction of Combined Cycle Power Plant Using Gradient Boosted generalized additive model

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IEEE ACCESS 2022年 10卷 24566-24577页

作者： Pachauri, Nikhil Ahn, Chang Wook Gwangju Inst Sci & Technol Artificial Intelligence Grad Sch Gwangju 61005 South Korea

A combined cycle power plant (CCPP) employs gas and steam turbines to generate 50% more power while utilizing the same fuel as a normal single cycle plant. The performance of a CCPP under full load is affected by a variety of factors such as weather, process interactions, and coupling, which makes it challenging to operate. Therefore, a reliable assessment of the maximum output power of a CCPP is required to improve plant reliability and monetary performance. In this paper, a predictive model based on a generalized additive model (GAM) is proposed for the electrical power prediction of a CCPP at full load. In GAM, a boosted tree and gradient boosting algorithm are considered as shape function and learning technique for modeling a non-linear relationship between input and output attributes. Furthermore, predictive models based on linear regression (LR), Gaussian process regression (GPR), multilayer perceptron neural network (MLP), support vector regression (SVR), decision tree (DT), and bootstrap-aggregated tree (BBT) are also designed for comparison purposes. Results reveal that GAM improves the RMSE by 74%, 68.8%, 70.3%, 54.8%, 21.2%, and 17.3% compared to LR, GPR, MLP, SVR, DT, and BBT, respectively. Furthermore, the results of the Man-Whitney U test and rank analysis also confirm the effectiveness of GAM for energy prediction of CCPP. Finally, it can be concluded that the proposed method is effective, robust, and accurate for the assessment of the maximum output power of a CCPP to improve plant consistency and financial performance.

关键词： Predictive models Power generation Prediction algorithms Load modeling Artificial neural networks additives Radio frequency Combined cycle power plant electrical energy generalized additive model linear regression decision tree Man-Whitney U test

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Rock glacier inventory and predictive modeling in the Mackenzie Mountains: predicting rock glacier likelihood with a generalized additive model

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ARCTIC SCIENCE 2024年第4期10卷 653-672页

作者： Thiessen, Rabecca Bonnaventure, Philip P. Lapalme, Caitlin M. Univ Lethbridge Dept Geog & Environm Lethbridge AB T1K 3M4 Canada Queens Univ Dept Geog & Planning Kingston ON K7L 3N6 Canada

Rock glaciers have been the subject of extensive research in recent years due to their potential to serve as indicators of past and present climate conditions and their potential impacts on water resources. Location and descriptive rock glacier data within the Mackenzie Mountains were used to build a rock glacier inventory that will serve as a valuable resource for future research and monitoring efforts. Additionally, this study maps the likelihood of rock glacier presence using extracted variables in a generalized additive model (GAM). The model incorporates attribute data, including potential incoming solar radiation (PISR), topographic position index (TPI), slope, elevation, and lithology as controls for rock glacier development. Topographic data were compiled for three study regions of the Mackenzie Mountains from a 30 m digital elevation model (DEM). The analysis of the GAM showed that the most significant explanatory variables were PISR, elevation, slope, and TPI. The GAM model had an accuracy of 0.87 with a sensitivity of 0.92. This study provides important insights into the controls, distribution, and dynamics of rock glaciers in the Mackenzie Mountains, as well as both the limitations and the potential of statistical models in predicting their occurrence.

关键词： rock glacier permafrost Mackenzie Mountains generalized additive model

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Semiparametric stochastic frontier models: A generalized additive model approach

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2017年第2期258卷 761-777页

作者： Ferrara, Giancarlo Vidoli, Francesco Univ Palermo Dept Econ Business & Stat Viale Sci Ed 13 Palermo Italy Univ Roma Tre Dept Polit Sci Via G Chiabrera 199 Rome Italy

The choice of the functional form of the frontier into a stochastic frontier model is typically neglected in applications and canonical functions are usually considered. This paper introduces a semiparametric approach for stochastic frontier estimation that extends previous works based on pseudo-likelihood estimators allowing flexibility in model selection and capability of imposing monotonicity and concavity constraints. For these purposes the present work introduces a generalized additive framework that moreover permits to model the influence of contextual environmental factors to the hypothesized production process by the relative extension given by generalized additive models for location, scale and shape. Through some Monte Carlo simulations and an application to European agricultural data the flexibility of the proposed framework in analyzing efficiency is illustrated. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Stochastic frontier Semiparametric generalized additive model Splines Efficiency

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Wildfire Impacts on O3 in the Continental United States Using PM2.5 and a generalized additive model (2018-2023)

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ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024年第33期58卷 14764-14774页

作者： Lee, Haebum Jaffe, Daniel A. Univ Washington Sch Sci Technol Engn & Math Bothell WA 98011 USA Univ Washington Dept Atmospher Sci Seattle WA 98195 USA

We examined PM2.5 and Hazard Mapping System smoke plume satellite data at similar to 600 United States (US) air monitoring stations to identify surface smoke on 14.0% of all May-September days for 2018-2023, with large influences in 2020 and 2021, due to California fires, and 2023, due to Canadian fires. Days with smoke have an average of 11 mu g m(-3) more PM2.5 and 8 ppb higher maximum daily 8 h average (MDA8) O-3 concentrations than nonsmoke days, and they also account for 94% of all days that exceed the daily PM2.5 health standard (35 mu g m(-3)) and 36% of all days that exceed the O-3 health standard (70 ppb). To estimate the smoke contributions to the O-3 MDA8, generalized additive models (GAMs) were built for each site using the nonsmoke day data and up to 8 predictors. The mean and standard deviation of the residuals from the GAMs were 0 +/- 6.1 ppb for the nonsmoke day data and 4.3 +/- 7.9 ppb for the smoke day data, indicating a significant enhancement in the MDA8 O-3 on smoke days. We found positive residuals on 72% of the smoke days and for these days, we calculate an average smoke contribution to the O-3 MDA8 of 7.8 +/- 6.0 ppb. Over the 6 year period, the percentage of exceedance days due to smoke in the continental US was 25% of all exceedance days, and the highest was in 2023 (38%). In 2023, the Central US experienced an unusually high number of exceedance days, 1522, with 52% of these impacted by smoke, while the Eastern US had fewer exceedance days, 288, with 78% of these impacted by smoke. Our results demonstrate the importance of wildland fires as contributors to exceedances of the health-based national air quality standards for PM2.5 and O-3.

关键词： generalized additive model ozone concentrations PM2.5 concentrations wildfire smoke

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