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Assessing soil quality dynamics in northern Iran: a spatial modeling approach to predict and project future trends

ENVIRONMENTAL EARTH SCIENCES 2024年第19期83卷 576页

作者： Aghalari, Fatemeh Chavoshi, Elham Borujeni, Sattar Chavoshi Islamic Azad Univ Coll Agr Dept Soil Sci Isfahan Khorasgan Branch Esfahan Iran AREEO Isfahan Agr & Nat Resources Res & Educ Ctr Soil Conservat & Watershed Management Res Dept Esfahan *** Iran

The soil in Northern Iran exhibits significant variability, showing notable responses to variations in climatic and environmental conditions. In a representative area covering 936.7 km(2), we measured 18 soil properties at 73 sampling locations. These properties were then screened using Principal Component Analysis (PCA) which identified five PCs with eigenvalues greater than 1.0 and correlation analysis to construct the Nemro Soil Quality Index (SQI) with a mean of 0.27 +/- 0.04. The predictability of SQI was modeled using the generalized additive model (R-2 = 0.669, explained deviance = 69.7%), indicating that elevated Normalized Difference Vegetation Index (NDVI;p value = 0.000) and daytime Land Surface Temperature (LST;p value = 0.000) enhance SQI, while higher slopes (p value = 0.020) have a diminishing effect. The model was also utilized to illustrate potential future alterations in SQI for the year 2040. For this purpose, the 2040 MODIS data (NDVI and LST) were projected using various regression models (0.09 < R-2 < 0.69) applied to historical mean annual MODIS data spanning from 2002 to 2022. The majority of projected changes in SQI exhibited a negative trend, primarily attributed to the depletion of vegetation cover at the peripheries of forest borders. These findings underscore the imperative need for strategic future management plans, emphasizing the preservation of soil in marginal forest lands.

关键词： Soil quality index generalized additive model NDVI MODIS Iran

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modelling vegetation phenology in alpine grasslands of North-East Italy integrating microclimatic and meteorological Drivers: A 23-Years Time-Series analysis

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COMPUTERS AND ELECTRONICS IN AGRICULTURE 2025年 232卷

作者： Pinna, Daniele Sozzi, Marco Pornaro, Cristina Macolino, Stefano Pezzuolo, Andrea Marinello, Francesco Univ Padua Dept Land Environm Agr & Forestry I-35020 Legnaro Italy Univ Padua Dept Agron Food Nat Resources Anim & Environm I-35020 Legnaro Italy

Grasslands in mountainous regions often exhibit unique variations in the amplitude and timing of their growing seasons in response to microclimatic and meteorological variability. This study investigates the environmental drivers of vegetation phenology in the Alpine grasslands of North-East Italy using a 23-year MODIS NDVI time series (2001-2023), harmonic regression, and generalized additive models (GAMs). Results highlight a strong negative correlation between altitude and NDVI metrics, with higher elevations exhibiting lower vegetation cover, greater seasonal variability, and shorter growing seasons. Slope similarly constrains vegetation growth, while aspect influences microclimatic conditions but shows weaker overall effects. Temperature and solar radiation positively influence NDVI, enhancing vegetation vigor and extending the growing season, while precipitation plays a more limited role, with limited variability across the region. Wind exposure shortens the growing season, reducing NDVI values, while snowfall significantly delays and shortens vegetation growth, increasing seasonal variability. Soil properties, particularly water-holding capacity, pH, and texture, strongly influence vegetation dynamics, with silt-rich soils favoring longer and more stable growing periods. The developed GAMs demonstrated high predictive accuracy (R2 = 0.70-0.91), effectively estimating phenological transitions, including the start and end of the vegetative season, with an accuracy of approximately 11 days. These findings enhance our understanding of the complex interactions shaping alpine pasture ecosystems and provide valuable tools for sustainable management, conservation, and climate adaptation strategies.

关键词： Grassland generalized additive model Harmonic regression NDVI MODIS Pasture Time-Series

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Improved confidence intervals for nonlinear mixed-effects and nonparametric regression models

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ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS 2025年第1期77卷 105-126页

作者： Zheng, Nan Cadigan, Noel Mem Univ Newfoundland Dept Math & Stat St John NF A1C 5S7 Canada Mem Univ Newfoundland Fisheries & Marine Inst Ctr Fisheries Ecosyst Res St John NF A1C 5R3 Canada

Statistical inference for high-dimensional parameters (HDPs) can leverage their intrinsic correlations, as spatially or temporally close parameters tend to have similar values. This is why nonlinear mixed-effects models (NMMs) are commonly used for HDPs. Conversely, in many practical applications, the random effects (REs) in NMMs are correlated HDPs that should remain constant during repeated sampling for frequentist inference. In both scenarios, the inference should be conditional on REs, instead of marginal inference by integrating out REs. We summarize recent theory of conditional inference for NMM, and then propose a bias-corrected RE predictor and confidence interval (CI). We also extend this methodology to accommodate the case where some REs are not associated with data. Simulation studies indicate our new approach leads to substantial improvement in the conditional coverage rate of RE CIs, including CIs for smooth functions in generalized additive models, compared to the existing method based on marginal inference.

关键词： Confidence interval Nonlinear mixed-effects model Prediction error State-space model generalized additive model Template model builder

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Spatial distributions, driving factors, and threshold effects of soil organic carbon stocks in the Tibetan Plateau

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SOIL & TILLAGE RESEARCH 2025年 248卷

作者： Sun, Zheng Liu, Feng Yang, Fei Wang, Decai Zhang, Gan-Lin Chinese Acad Sci Inst Soil Sci State Key Lab Soil & Sustainable Agr Nanjing 211135 Peoples R China Henan Agr Univ Coll Forestry Zhengzhou 450002 Peoples R China Nanjing Hydraul Res Inst Natl Key Lab Water Disaster Prevent Nanjing 210029 Peoples R China Nanjing Hydraul Res Inst Ctr Ecoenvironm Res Nanjing 210029 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Qinghai Normal Univ Sch Geog Sci Key Lab Phys Geog & Environm Proc Qinghai Prov Xining 312366 Peoples R China Chinese Acad Sci Nanjing Inst Geog & Limnol Key Lab Watershed Geog Sci Nanjing 210008 Peoples R China

The Tibetan Plateau (TP), known as the "Earth's Third Pole", has a fragile ecological environment, and is sensitive to global changes, which can easily lead to fluctuations of soil organic carbon (SOC). The spatial variations of soil organic carbon stocks (SOCS), and their driving factors in TP remain unclear. Here. we used quantile regression forest (QRF) model to map soil organic carbon density (SOCD) in TP at 90 m spatial resolution, and estimated the spatial uncertainty of the mapping. generalized additive model (GAM) was used to analyze the nonlinear responses of SOCD to the driving factors. The results showed that the QRF model can explain about 32 %-51 % of SOCD variation, and the explanatory power decreased with increasing depth. The SOCD decreased gradually from southeast to northwest, and showed a decreasing trend with increasing depth. The SOCS of 0-100 cm soil was 37.26 Pg C of the entire TP, where the grassland occupied 54.59 % of the total stock. Vegetation and land surface temperature were important environmental covariates at all depths. SOCD has obvious nonlinear responses and threshold effects on temperature (MAAT), precipitation (MAP) and aridity (1-AI). The results are of great significance for understanding the status of SOC sequestration, and the response of SOCS in TP to climate conditions.

关键词： Digital soil mapping Carbon pools Quantile regression forest generalized additive model

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A statistical framework for district energy long-term electric load forecasting

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APPLIED ENERGY 2025年 384卷

作者： Royal, Emily Bandyopadhyay, Soutir Newman, Alexandra Huang, Qiuhua Tabares-Velasco, Paulo Cesar Colorado Sch Mines Dept Elect Engn 1500 Illinois St Golden CO 80401 USA Colorado Sch Mines Dept Appl Math & Stat 1500 Illinois St Golden CO 80401 USA Colorado Sch Mines Dept Mech Engn 1500 Illinois St Golden CO 80401 USA

An accurate forecast of electric demand is essential for the optimal design of a generation system. For district installations, the projected lifespan may extend one or two decades. The reliance on a single-year forecast, combined with a fixed load growth rate, is the current industry standard, but does not support a multi-decade investment. Existing work on long-term forecasting focuses on annual growth rate and/or uses time resolution that is coarser than hourly. To address the gap, we propose multiple statistical forecast models, verified over as long as an 11-year horizon. Combining demand data, weather data, and occupancy trends results in a hybrid statistical model, i.e., generalized additive model (GAM) with a seasonal autoregressive integrated moving average (SARIMA) of the GAM residuals, a multiple linear regression (MLR) model, and a GAM with ARIMA errors model. We evaluate accuracy based on: (i) annual growth rates of monthly peak loads;(ii) annual growth rates of overall energy consumption;(iii) preservation of daily, weekly, and month-to-month trends that occur within each year, known as the "seasonality"of the data;and, (iv) realistic representation of demand fora full range of weather and occupancy conditions. For example, the models yield an 11-year forecast from a one-year training data set with a normalized root mean square error of 9.091%, a six-year forecast from a one-year training data set with a normalized root mean square error of 8.949%, and a one-year forecast from a 1.2-year training data set with a normalized root mean square error of 6.765%.

关键词： Electric demand forecasting District energy Renewable energy technologies Long-term load forecasting generalized additive model SARIMA

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Improved landslide prediction by considering continuous and discrete spatial dependency

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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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Effect of zinc intake on association between fluoride exposure and abnormal sex steroid hormones among US pubertal males: NHANES, 2013-2016

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2024年第2期31卷 2759-2772页

作者： Niu, Zeyuan Duan, Leizhen Du, Yuhui Yu, Fangfang Chen, Ruiqin Li, Zhiyuan Ba, Yue Zhou, Guoyu Zhengzhou Univ Sch Publ Hlth Dept Environm Hlth Zhengzhou 450001 Henan Peoples R China Zhengzhou Univ Zhengzhou Cent Hosp Zhengzhou Henan Peoples R China Jinshui Dist Ctr Dis Control & Prevent Zhengzhou Henan Peoples R China Zhengzhou Univ Yellow River Inst Ecol Protect & Reg Coordinated Zhengzhou Henan Peoples R China Henan Key Lab Populat Defects Prevent Natl Hlth Commiss Key Lab Birth Defects Prevent Zhengzhou Henan Peoples R China

Excessive fluoride exposure can disturb the balance of sex hormones. Zinc is essential for sex hormone synthesis and spermatogenesis. But it is not clear how zinc affects the relationship of fluoride exposure with abnormal sex steroid hormones. Here, a total of 1008 pubertal males from the National Health and Nutrition Examination Survey (NHANES) in two cycles (2013-2014, 2015-2016) were enrolled. The concentrations of water fluoride and plasma fluoride and the levels of serum testosterone, estradiol, and sex hormone binding globulin (SHBG) were measured. Two 24-h dietary recall interviews were conducted to assess the dietary zinc intake. The relationships of fluoride exposure and zinc intake with sex hormones were examined using linear regression and logistic regression models, while the generalized additive model was used to evaluate their non-linear relationship. Our findings revealed that for every two-fold increase in plasma fluoride concentration, testosterone levels decreased by 7.27% (95% CI - 11.49%, - 2.86%) and estradiol levels decreased by 8.73% (95% CI - 13.61%, - 3.57%). There was also significant non-linear association observed between zinc intake and SHBG levels. Being in the first tertile of plasma fluoride had a 60% lower risk of high SHBG (OR = 0.40, 95% CI 0.18, 0.89) compared with being in the second tertile. When compared to the first tertile, being in the second tertile of zinc intake was associated with a 63% (OR = 0.37, 95% CI 0.14, 0.98) lower risk of high SHBG. Furthermore, we observed an interactive effect between the plasma fluoride and zinc intake on estradiol and SHBG, as well as the risk of high SHBG (P-interaction < 0.10). These findings suggest that fluoride exposure and zinc intake can affect sex steroid hormone levels and the risk of high SHBG. Notably, zinc intake may alleviate the increased risk of high SHBG and the abnormal changes of estradiol and SHBG caused by higher fluoride exposure.

关键词： Fluoride Zinc intake Sex steroid hormones generalized additive model Pubertal males National Health and Nutrition Examination Survey

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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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Do sterile males and females synergistically suppress pests targeted by the sterile insect technique?

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APPLIED ENTOMOLOGY AND ZOOLOGY 2024年第2期59卷 185-193页

作者： Himuro, Chihiro Ikegawa, Yusuke Okinawa Prefectural Plant Protect Ctr Naha Okinawa Japan Ryukyu Sankei Co Ltd Naha Okinawa Japan Univ Ryukyus Fac Agr Nishihara Okinawa Japan

The sterile insect technique (SIT) is a pest control method that releases a large number of sterile pests into the field to inhibit mating between wild pests, ultimately resulting in the eradication of wild pest populations. It has often been discussed whether releasing one sex (mainly males) of sterile insects enhances the pest control efficiency of SIT compared to releasing both sexes (bisexual release). The control efficiency of sterile females may be negatively affected by their release, because sterile males lose the opportunity to mate with wild females. In this study, we investigated whether sterile females have a positive effect on the control efficiency of SIT and whether there is an additive or synergistic effect of releasing both sexes using two weevil species, Cylas formicarius and Euscepes postfasciatus, which are serious pests of the sweet potato Ipomoea batatas and targets of SIT in Okinawa. The results showed that in both species, the reproduction rate of wild pests was significantly suppressed in the case of the sterile female-only release. Interestingly, the most effective method varied between male-only and bisexual release depending on the density of sterile individuals and weevil species. Synergistic effects of both sexes on bisexual release were observed but varied with the density of sterile individuals and weevil species. High-density bisexual release was most effective for the SIT of at least two weevil species.

关键词： Cylas formicarius Euscepes postfasciatus generalized additive model Bisexual release

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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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