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Improved landslide prediction by considering continuous and discrete spatial dependency

LANDSLIDES 2025年第4期22卷 1107-1122页

作者： Fang, Zhice Wang, Jingjing Wang, Yi Du, Baoyu Liu, Guanting Henan Acad Sci Aerosp Informat Res Inst Zhengzhou 450046 Peoples R China China Univ Geosci Sch Geophys & Geomatics Wuhan 430074 Peoples R China Yango Univ Fujian Key Lab Spatial Informat Percept & Intellig Fuzhou 350015 Peoples R China China Univ Geosci Sch Future Technol Wuhan 430074 Peoples R China

Landslide spatial prediction studies predominantly focus on estimating the likelihood of landslide occurrence by considering a set of geo-environmental factors. Nevertheless, most of these studies fail to account for the spatial dependency between landslide occurrences across different terrain units. This study explored how the understanding of spatial dependency can enhance predictions of landslide occurrence across the entire Three Gorges Reservoir area, China. Specifically, we develop spatial binomial generalized additive models (GAMs) that incorporate Duchon spline (DS) and Markov random field (MRF) functions to represent continuous and discrete spatial dependencies, respectively. To test the validity of our proposed models, we compare them against the common GAM model as well as two popular machine learning models: the support vector machine (SVM) and the random forest (RF). The experimental results reveal that our models achieve superior predictive performance, with scores ranging from 0.929 to 0.938, compared to the benchmark methods, which scored between 0.912 and 0.914, based on a tenfold cross-validation procedure. These results demonstrate that incorporating spatial dependency significantly enhances the performance of landslide susceptibility prediction. Moreover, the comparison between GAM-DS and GAM-MRF models prove that the continuous smoothing of spatial dependency offers a more detailed and precise representation of landslide susceptibility. We believe that our approach, which integrates spatial dependency, will lay the foundation for the landslide community to assess landslide susceptibility from a spatially informed perspective.

关键词： Landslide susceptibility Spatial dependency Duchon spline Markov random fields generalized additive model

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Impact of coronavirus disease (COVID-19) on gaseous pollutants and particulate matter in a hot arid climate

KUWAIT JOURNAL OF SCIENCE

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KUWAIT JOURNAL OF SCIENCE 2025年第1期52卷

作者： Albanai, Jasem A. Shehab, Maryam Vatresia, Arie Jasim, Marium Al-Dashti, Hassan Yassin, Mohamed F. Univ Oxford Sch Geog & Environm Oxford England Environm Publ Author Safat Kuwait Univ Bengkulu Engn Fac Informat Engn Bengkulu Indonesia Kuwait Inst Sci Res Environm & Life Sci Ctr Safat Kuwait Directorate Gen Civil Aviat Meteorol Dept Al Farwaniyah Kuwait

The outbreak of the coronavirus disease (COVID-19) has had a significant impact on the global community, affecting various aspects of life. One of the unintended consequences of the pandemic is its effect on air quality. This study uses the state of Kuwait as a case study to investigate the impact of the COVID-19 pandemic on levels of gaseous pollutants (NO2, O3, SO2, H2S, CO) and particulate matter 2.5 and 10 mu m (PM2.5 and PM10) in a hot arid region. Hourly data for COVID-19 infected cases and gaseous pollutants and particulate matter were collected from January to December 2020. The Openair model with the R package was used to analyze gaseous pollutants and particulate matter data with an applied air quality index (AQI). Stochastic models with time series analysis - Kernel density were employed to investigate how COVID-19 infection can affect the changes of gaseous pollutants and particulate matter. In addition, the spatial interpolation approach was estimated using the inverse distance weighted (IDW). The generalized additive model (GAM) was used to analyze the daily data of the gasses and particle occurrences in a hot, dry climate. The results showed a significant decrease in levels of gaseous pollutants and particulate matter after implementing preventive measures to control the spread of the virus. This reduction benefits public health and the environment in the region, indicating that the preventive measures taken to address the COVID-19 pandemic have contributed to reducing environmental pollution and mitigating its adverse effects.

关键词： Kuwait generalized additive model Air quality index GeoHealth Kernel density Inverse distance weighted

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Key environmental factors influencing eDNA quantitative detection of golden mussel (Limnoperna fortunei) in a long-distance water diversion project

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ENVIRONMENTAL TECHNOLOGY & INNOVATION 2025年 37卷

作者： Yang, Yao Liu, Kai Zhang, Jiahao Xu, Mengzhen Guo, Fang Zhou, Xinyi Wang, Congcong Zhou, Xiongdong Fu, Xudong Tsinghua Univ Dept Hydraul Engn State Key Lab Hydrosci & Engn Beijing 100084 Peoples R China China South North Water Divers Corp Ltd Beijing Peoples R China Chongqing Jiaotong Univ Natl Inland Waterway Regulat Engn Res Ctr Chongqing 400074 Peoples R China Chinese Acad Sci Inst Hydrobiol Wuhan 470002 Peoples R China

The golden mussel (Limnoperna fortunei) invasion and biofouling has caused huge economic and ecological damage worldwide, notably associated with inter-basin water diversion projects that bridge different biogeographical areas and facilitate dispersal of this species. Use of environmental DNA (eDNA) technology for monitoring golden mussel population has become increasingly prevalent but still restrained because of unclear quantitative relationship between eDNA concentration and densities of golden mussel. This study commenced with field surveys collecting the datasets of eDNA concentration, mussel density, and environmental parameters in the world's largest water diversion project. An exponential quantitative relationship between the eDNA concentration and density of golden mussel was successfully established. Meanwhile, water temperature and pH were identified as the critical environmental factors influencing this exponential relationship through recursive feature elimination. To further investigate these influences, generalized additive models were developed to capture variations in the quantitative relationship. The confirmation of a quantitative relationship by this study underscores the efficacy of eDNA as a tool for density quantification, paving the way for high efficiency and resolution detection of invasive species like golden mussel, and offering a proactive approach to ecological preservation and resource management.

关键词： Environmental DNA Invasive mussel Quantitative monitoring generalized additive model

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Meteorological and traffic effects on air pollutants using Bayesian networks and deep learning

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JOURNAL OF ENVIRONMENTAL SCIENCES 2025年 152卷 54-70页

作者： Lin, Yuan-Chien Lin, Yu-Ting Chen, Cai-Rou Lai, Chun-Yeh Natl Cent Univ Dept Civil Engn 300 Zhongda Rd Taoyuan 32001 Peoples R China Natl Cent Univ Res Ctr Hazard Mitigat & Prevent Taoyuan 32001 Taiwan Natl Cent Univ Grad Inst Environm Engn Taoyuan 32001 Taiwan

Traffic emissions have become the major air pollution source in urban areas. Therefore, understanding the highly non-stational and complex impact of traffic factors on air quality is very important for building air quality prediction models. Using real-world air pollutant data from Taipei City, this study integrates diverse factors, including traffic flow, speed, rainfall patterns, and meteorological factors. We constructed a Bayesian network probability model based on rainfall events as a big data analysis framework to investigate understand traffic factor causality relationships and condition probabilities for meteorological factors and air pollutant concentrations. generalized additive model (GAM) verified non-linear relationships between traffic factors and air pollutants. Consequently, we propose a long short term memory (LSTM) model to predict airborne pollutant concentrations. This study propose a new approach of air pollutants and meteorological variable analysis procedure by considering both rainfall amount and patterns. Results indicate improved air quality when controlling vehicle speed above 40 km/h and maintaining an average vehicle flow < 1200 vehicles per hour. This study also classified rainfall events into four types depending on its characteristic. Wet deposition from varied rainfall types significantly affects air quality, with Type I rainfall events (long-duration heavy rain) having the most pronounced impact. An LSTM model incorporating GAM and Bayesian network outcomes yields excellent performance, achieving correlation R2 > 0.9 and 0.8 for first and second order air pollutants, i.e., CO, NO, NO2 , and NOx;and O3 , PM10 , and PM2.5 , respectively. (c) 2024 The Research Center for Eco-Environmental Sciences, Chinese Academy of

关键词： Air quality Rainfall pattern Traffic emissions generalized additive model Bayesian networks LSTM model

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A quantitative approach to ranking management actions for an endangered pool-breeding amphibian

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JOURNAL OF ENVIRONMENTAL MANAGEMENT 2025年 373卷 123556页

作者： O'Brien, Aidan E. Cook, David G. Martinelli, Stacy Fawcett, Michael H. Searcy, Christopher A. Univ Miami Dept Biol 1301 Mem Dr 227 Cox Bldg Coral Gables FL 33146 USA 3003 Magowan Dr Santa Rosa CA 95405 USA Calif Dept Fish & Wildlife 3633 Westwind Blvd Santa Rosa CA 95403 USA Fawcett Environm Consulting 598 South First St Dunsmuir CA 96025 USA

Management of vulnerable amphibian populations requires a better understanding of the habitat factors that will make the greatest difference in their preservation. We set out to develop a predictive model of amphibian abundance based on habitat characteristics that may influence their survival and persistence. Our study system was the Sonoma County California tiger salamander (Ambystoma californiense;SCTS), an amphibian threatened by habitat loss and fragmentation. In 2019, 298 ponds (almost every breeding pond in this federally endangered Distinct Population Segment) were surveyed to determine SCTS larval densities, a proxy for population wellbeing. We used model selection to identify which factors of pool morphology, the surrounding pool neighborhood, and terrestrial upland habitat best predict variation in larval densities. Our best model showed a significant positive relationship between larval SCTS densities and three variables: pool depth, number of breeding pools within 500 m of a focal pool, and amount of suitable and accessible upland habitat within 1500 m of a pool. To further refine this model, we then examined interactions and non-linearities using a generalized additive model. Based on this refined model, we simulated a variety of management scenarios to identify which management practices would most increase larval densities across the endangered Distinct Population Segment and where on the landscape employing each mitigation action would yield the greatest return. This provides the basis for a county-wide Habitat Conservation Plan for SCTS, and highlights habitat characteristics likely critical to the conservation of other pond-breeding amphibians.

关键词： Ambystoma Habitat conservation plan generalized additive model Amphibian conservation

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Optical Remote Sensing Analysis of Exhaust Emissions During Aircraft Taxiing at Hefei Xinqiao International Airport

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REMOTE SENSING 2025年第4期17卷 664-664页

作者： Qin, Yusheng Han, Xin Li, Xiangxian Gui, Huaqiao Xue, Weiwei Gao, Minguang Tong, Jingjing Zhang, Yujun Shi, Zheng Chinese Acad Sci Hefei Inst Phys Sci Anhui Inst Opt & Fine Mech Hefei 230031 Anhui Peoples R China Anhui Civil Aviat Airport Grp Co Ltd Hefei 230088 Peoples R China China Eastern Airlines Co Ltd Anhui Branch Hefei 230088 Peoples R China

The taxiing stage of an aircraft is characterized by its long duration, low operating thrust, and low combustion efficiency, resulting in substantial emissions of CO, CO2, and VOCs, which adversely affect air quality near airports. This study has developed an open-path Fourier transform infrared spectroscopy (OP-FTIR) monitor with second-level time resolution to enable the optical remote monitoring of pollutants during taxiing. Measurements of CO, CO2, and VOCs were conducted over one month at Hefei Xinqiao International Airport (HXIA). The generalized additive model (GAM) is used for data analysis to reveal complex nonlinear relationships between aircraft emission concentrations and meteorological factors, aircraft models, and their corresponding registration numbers. The GAM analysis shows that among meteorological factors, humidity, and atmospheric pressure have the most significant impact on aircraft exhaust monitoring, with a relative average contribution value as high as approximately six. The explanatory power of aircraft models for emissions is low (R-2 < 0.18), whereas that of registration numbers is high (R-2 > 0.6), suggesting that individual differences between aircrafts play a crucial role in emission concentration variations. Furthermore, a noticeable correlation was found between the CO/CO2 ratio and volatile organic compound (VOC) concentrations (R-2 > 0.63), indicating that combustion efficiency significantly affects VOC emissions. This study not only advances the real-time remote sensing monitoring of pollutants during aircraft taxiing but also underscores the crucial role of the GAM in identifying the key drivers of emissions, providing a scientific basis for precise environmental protection management and policy-making.

关键词： aircraft emissions optical monitoring meteorological factors generalized additive model

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Meteorology Driven Ozone Variability

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OZONE-SCIENCE & ENGINEERING 2025年

作者： Santra, Samrat Patra, Aditya Kumar Penchala, Abhishek Mishra, Namrata Pradhan, Dhruti Sundar Indian Inst Technol Kharagpur Sch Environm Sci & Engn Kharagpur 721302 India Indian Inst Technol Kharagpur Dept Min Engn Kharagpur 721302 India

Meteorology plays a significant role in influencing ground-level ozone (O-3) concentrations that can adversely affect public health through exposure to elevated O-3 levels. Currently, regulatory compliance measurements rely on large, expensive instruments that provide O-3 averaged data over extended periods. In recent years, continuous air quality monitoring stations have gained popularity for real-time O-3 monitoring in outdoor environments (mostly in metropolitan cities). However, there is a lack of comprehensive studies evaluating the impact of local meteorological conditions on O-3 levels in both road and rural settings. This study aims to assess the interaction between major meteorological variables and O-3 concentrations at a high-traffic road site and no-traffic rural site. A comparative analysis was conducted using high-precision reference instruments in uncontrolled outdoor environments. Single-factor GAM (generalized additive model) demonstrated strong performance (R-2 = 0.95-0.96) and deviance explained (95-96%). When paired variables were incorporated, the multi-factor GAM further improved performance and explained 97-99% of the variability in O-3 concentrations. The multi-factor GAM outperformed the GLM (generalized linear model) (R-2 = 0.88-0.89) and achieved superior results (R-2 = 0.979-0.987;deviance explained = 98.2-98.8%) in accurately predicting O-3 levels and can be applied in various occupational environments like personal exposure monitoring among industry workers.

关键词： generalized additive model meteorological Influence nonlinear effects O-3 Concentration paired Interaction

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Time series analysis of the impact of air pollutants on influenza-like illness in Changchun, China

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BMC PUBLIC HEALTH 2025年第1期25卷 1-13页

作者： Pan, Yang Yao, Laishun Huang, Biao He, Yinghua Xu, Changxi Yang, Xianda Ma, Yingying Wang, Zhidi Wang, Xingyu Zhu, Hong Wang, Man Song, Lijun Liu, Xiao Yu, Guiping Ye, Lin Zhou, Liting Jilin Prov Acad Prevent Med Sci Jilin Prov Ctr Dis Control & Prevent Changchun Jilin Peoples R China Changchun Ctr Dis Control & Prevent Changchun Jilin Peoples R China First Hosp Jilin Univ Changchun Jilin Peoples R China Changchun Childrens Hosp Changchun Jilin Peoples R China Jilin Univ Sch Publ Hlth Changchun Jilin Peoples R China

Background Emerging evidence links air pollution to respiratory infections, yet systematic assessments in cold regions remain limited. This study evaluates the short-term effects of six major air pollutants on influenza-like illness (ILI) incidence in Changchun, Northeast China, with implications for air quality management and respiratory disease prevention. Methods ILI surveillance data from Changchun were extracted from "China Influenza Surveillance Network" and the ambient air quality monitoring data of the city were collected from 2017 to 2022. A generalized additive model (GAM) with quasi-Poisson regression analysis was employed to quantify pollutant-ILI associations, adjusting for meteorological factors and temporal trends. Results Among 84,010 ILI cases, immediate exposure effects were observed: each 10 mu g/m(3) increase in PM2.5 (ER = 1.00%, 95% CI: 0.63-1.37%), PM10 (0.90%, 0.57-1.24%), and O-3 (1.05%, 0.44-1.67%) significantly elevated ILI risks. Young and middle-aged individuals (25-59 years old) exhibited the highest susceptibility to five pollutants (PM2.5, PM10, SO2, O-3, and CO), and age subgroups under 15 years old exhibited susceptibility to NO2. Post-COVID-19 outbreak showed amplified effects across all pollutants (p < 0.05 vs. pre-outbreak). The effects of PM2.5, PM10, SO2 and O-3 on ILI cases were greater in the cold season (October to March) (p < 0.05). Conclusions PM2.5, PM10, and O-3 exposure significantly increases ILI risks in Changchun, particularly among young/middle-aged populations during cold seasons and post-pandemic periods. These findings underscore the urgency for real-time air quality alerts and targeted protection strategies during high-risk periods to mitigate respiratory health burdens.

关键词： Air pollutants Influenza-like illness generalized additive model Infectious disease

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Understanding the coupling effect of multiple urban features on land surface temperature in Europe

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URBAN FORESTRY & URBAN GREENING 2025年 105卷

作者： Ren, Yaxue Feng, Fei Elia, Mario Giannico, Vincenzo Sanesi, Giovanni Lafortezza, Raffaele Univ Bari Aldo Moro Dept Soil Plant & Food Sci Via Amendola 165-A I-70126 Bari Italy Beijing Forestry Univ Res Ctr Urban Forestry Key Lab Silviculture & Forest Ecosyst State Forest Beijing 100083 Peoples R China

Cities in Europe are facing significant challenges as rising temperatures exacerbate health risks, energy consumption, and environmental degradation. This study investigates the multiple features affecting land surface temperature (LST) across 780 European cities, categorized into eight macro-regions. The methodology involved utilizing SHAP values to interpret the results of a Random Forest model that evaluated the impact of individual urban features on LST. Additionally, the generalized additive model (GAM) was employed to explore non-linear relationships between key urban features and LST, offering a deeper understanding of how specific features influence urban temperatures. The results highlight the important cooling effects of ecological attributes like tree height and evapotranspiration, particularly in warm regions (e.g., the Iberian Peninsula and Turkey). Conversely, urban structure elements like built-up volume were shown to increase LST. GAM analysis further revealed nonlinear relationships, such as the diminishing returns of evapotranspiration (ET) on cooling in the Iberian Peninsula, where LST decreased sharply at ET < 3-4.5 mm/d but plateaued at higher values. These results emphasize the complexity of urban climate regulation, where ecological and built environments interact in diverse ways across different regions. This study underscores the importance of region-specific urban planning strategies that integrate both ecological and urban structure features to effectively mitigate the effects of urban heat. The findings offer actionable insights for policymakers seeking to enhance urban climate resilience and manage the growing challenges posed by global warming in European cities.

关键词： SHapley additive exPlanations generalized additive model Land surface temperature Urban structure Urban landscape Ecological attributes

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Optimizing potassium management for enhanced cotton yields in China's diverse agro-ecological regions

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FIELD CROPS RESEARCH 2025年 323卷

作者： Liang, Hongbang Yin, Feihu Zhang, Jinzhu Zhang, Jihong Zhao, Yue Zhao, Tao Li, Deyi Wang, Zhenhua Shihezi Univ Coll Water Conservancy & Architectural Engn Shihezi 832000 Xinjiang Peoples R China Shihezi Univ Key Lab Modern Water Saving Irrigat Xinjiang Prod & Construct Grp Shihezi 832000 Xinjiang Peoples R China Minist Agr & Rural Affairs Key Lab Northwest Oasis Water Saving Agr Shihezi 832000 Xinjiang Peoples R China Technol Innovat Ctr Agr Water & Fertilizer Efficie Shihezi 832000 Xinjiang Peoples R China Xinjiang Acad Agr & Reclamat Sci Shihezi 832000 Peoples R China

Context: Potassium (K) plays a crucial role in the growth and yield of cotton. As the world's leading cottonproducing country, China faces challenges such as soil K depletion and rising costs of K fertilizers. Current research primarily focuses on K application at specific locations, which limits its applicability to broader agroecological regions. Therefore, there is an urgent need to conduct K fertilizer management research on a regional scale. Objective: This study aims to employ a novel approach by integrating meta-analysis with K-yield relationship models to assess the effects of K fertilization in China's major cotton-growing areas from 1980 to 2024, and to combine this with an economic benefit model to determine the ideal K application rates (KR) for different ecological cotton-growing regions in China. Methods: The study utilizes generalized additive models (GAM) to model seed cotton yield and economic benefits as functions of KR. Data from various studies were compiled to analyze the relationship between K application and cotton yield across different regions. Results: The findings indicate that K fertilization enhances cotton yields by an average of 11.05 % in the Northwest Arid Zone (NW), 19.43 % in the Yellow River Basin (YR), and 20.36 % in the Yangtze River Basin (YT). K application significantly influences cotton yield, particularly in areas with mean annual temperatures (MAT) of 15-25 degrees C, mean annual precipitation (MAP) of 600-1000 mm, low soil available K (SAK), high pH, and soil organic matter (SOM) content above 20 g/kg, especially in coarse soils. Recommendations: For different cotton regions in China, ideal KR recommendations are 85.7-93.0 kg K2O/ha for the NW, 117.5-121.5 kg K2O/ha for the YR, and 163.1-172.1 kg K2O/ha for the Yangtze River Basin. The corresponding planting densities are > 6 plants/m(2) for the NW, 0-4 plants/m(2) for the YR, and 4-6 plants/m(2) for the Yangtze River Basin. Significance This study offers important insigh

关键词： Meta-analysis generalized additive model Soil properties Cotton yield K fertilizer

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