检索结果-内蒙古大学图书馆

Estimation of economic costs of air pollution caused by motor vehicles in Iran (Isfahan)

ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2021年第47期28卷 66535-66555页

作者： Soleimani, Mozhgan Akbari, Nematollah Saffari, Babak Haghshenas, Hosein Univ Isfahan Dept Econ Esfahan Iran Isfahan Univ Technol Dept Transportat Engn Esfahan Iran

Since mobile sources are one of the most important sources of air pollution, this paper tries to estimate the health effects and economic burden due to fine particulate matter (PM2.5) concentrations from motor vehicles. In this regard, we calculate the economic costs of air pollution emitted by vehicles in Isfahan over the period from March 2018 to March 2020. The concentration of urban traffic pollution based on the generalized additive model (GAM) as well as spatial distribution of pollution is estimated. Health effects are evaluated using AirQ(+) updated by the WHO European Centre for Environment and Health. Economic burden of mortality attributable air pollution from traffic is calculated using value of a statistical life (VOSL), and the value of life years (VOLY) approach. The results indicated that the number of deaths attributable to PM2.5 from motor vehicles in these two consecutive years was 136 (95%CI: 89-179), and 147 cases (95%CI: 96-194), respectively. The number of years of life lost due to premature death from air pollution was 2079 years annually. The economic costs imposed under VOSL approach were on average USD 51.7 (95%CI: 43-75) million per year, and according to VOLY approach USD 11.5 (95%CI: 9-13) million per year. These results help to analyze the cost-benefit and prioritize control measures to reduce air pollution. In addition, combination of these results with other externality cost of road traffic can take account for urban transportation planning.

关键词： Economic valuation generalized additive model AirQ(+) Value of a statistical life Fine particulate matter Urban transportation

来源：评论

学校读者我要写书评

暂无评论

Investigating the roles of meteorological factors in COVID-19 transmission in Northern Italy

引用

ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2021年第35期28卷 48459-48470页

作者： Khursheed, Ambreen Mustafa, Faisal Akhtar, Ayesha Univ Cent Punjab UCP Business Sch Lahore Pakistan Univ Cent Punjab Lahore Pakistan

The novel COVID-19 is a highly invasive, pathogenic, and transmittable disease that has stressed the health care sector and hampered global development. Information of other viral respiratory diseases indicates that COVID-19 transmission could be affected by varying weather conditions;however, the impact of meteorological factors on the COVID-19 death counts remains unexplored. By investigating the impact of meteorological factors (absolute humidity, relative humidity, and temperature), this study will contribute both theoretically and practically to the concerned domain of pandemic management to be better prepared to control the spread of the disease. For this study, data is collected from 23 February to 31 March 2020 for Milan, Northern Italy, one of the badly hit regions by COVID-19. The generalized additive model (GAM) is applied, and a nonlinear relationship is examined with penalized spline methods. A sensitivity analysis is conducted for the verification of model results. The results reveal that temperature, relative humidity, and absolute humidity have a significant but negative relationship with the COVID-19 mortality rate. Therefore, it is possible to postulate that cool and dry environmental conditions promote virus transmission, leading to an increase in COVID-19 death counts. The results may facilitate health care policymakers in developing and implementing effective control measures in a timely and efficient way.

关键词： COVID-19 Temperature generalized additive model Humidity Air quality

来源：评论

学校读者我要写书评

暂无评论

Detecting the Feeding Habitat Zone of Albacore Tuna (Thunnus Alalunga) in the Southern Indian Ocean using Multisatellite Remote Sensing Data

引用

JOURNAL OF MARINE SCIENCE AND TECHNOLOGY-TAIWAN 2021年第6期29卷 795-809页

作者： Mondal, Sandipan Lan, Yang-Chi Lee, Ming-An Wang, Yi-Chen Semedi, Bambang Su, Wan-Ya Natl Taiwan Ocean Univ Dept Environm Biol & Fisheries Sci Keelung Taiwan Taiwan Fisheries Res Inst Coastal & Offshore Resources Res Ctr Kaohsiung Taiwan Natl Taiwan Ocean Univ Ctr Excellence Ocean Engn Keelung Taiwan Brawijaya Univ Fisheries & Marine Sci Fac Malang East Java Indonesia

Remote sensing is an effective method for identifying potential feeding habitat zones. Spatial and temporal distribution patterns of albacore (ALB) tuna were studied using Taiwan longline fisheries data from 2009 to 2014. A generalized additive model (GAM) was used to compile a fishery database and statistically explore the relationship between distribution and environmental factors. Sea surface temperature (SST), sea surface chlorophyll-a concentration (SSC), sea surface salinity (SSS), sea surface height (SSH), mixed layer depth (MLD), and eddy kinetic energy (EKE) were examined. The results indicated SST as the largest contributor on ALB distribution, followed by SSS and SSC. Catches per unit effort (CPUE) values indicated that ALB tuna were primarily distributed at 20 degrees E-110 degrees E and 25 degrees S-40 degrees S. The SST and SSS ranges for higher ALB abundance were 17-19 degrees C and 35.1-35.5 psu, respectively. SSC was positively related to CPUE as a result of ALB tuna habits in the current location during the study period (0.1-0.25 mg m(-3)). The predicted CPUE indicated that the potential feeding habitat zones were 35 degrees S-40 degrees S and 25 degrees S-30 degrees S during March and April-August, respectively. These findings provide preliminary insight into the key environmental features affecting the ALB distribution in the southern Indian Ocean.

关键词： Albacore Sea surface temperature Sea color generalized additive model Long-line

来源：评论

学校读者我要写书评

暂无评论

An Alternative Method for Estimation of Stand-Level Biomass for Three Conifer Species in Northeast China

引用

FORESTS 2023年第6期14卷 1274-1274页

作者： Xin, Shidong Shahzad, Muhammad Khurram Mahardika, Surya Bagus Wang, Weifang Jiang, Lichun Northeast Forestry Univ Sch Forestry Dept Forest Management Harbin 150040 Peoples R China Punjab Forestry Wildlife & Fisheries Dept Multan 60000 Pakistan Northeast Forestry Univ Sch Forestry Key Lab Sustainable Forest Ecosyst Management Minist Educ Harbin 150040 Peoples R China

Accurate large-scale biomass prediction is crucial for assessing forest carbon storage and dynamics. It can also inform sustainable forest management practices and climate change mitigation efforts. However, stand-level biomass models are still scarce worldwide. Our study aims to introduce the generalized additive model (GAM) as a convenient and efficient approach for forest biomass estimation. Data from 311 sample plots of three conifer species in northeastern China were used to evaluate the performance of the GAM model and compare it with traditional nonlinear seemingly unrelated regression (NSUR) models in predicting stand biomass, including total, aboveground, and component biomass. The results indicated that the goodness of fit of GAM was better than that of NSUR in two model systems. In the majority of cases, the scatter plots and prediction performance revealed that the stand total and component biomass models utilizing GAM outperformed those based on NSUR. Disregarding heteroscedasticity and requiring fewer statistical assumptions provide additional support for the replacement of NSUR-based models with GAM-based models. This study implies that the GAM approach has greater potential for developing a system of stand biomass models.

关键词： conifer species generalized additive model nonlinear seemingly unrelated regression stand-level biomass

来源：评论

学校读者我要写书评

暂无评论

Assessment of non-linear models based on regional frequency analysis for estimation of hydrological quantiles at ungauged sites in South Korea

引用

JOURNAL OF HYDROLOGY-REGIONAL STUDIES 2024年 52卷

作者： Jung, Kichul An, Heejin Lee, Moonyoung Um, Myoung-Jin Park, Daeryong Korea Environm Inst Div Integrated Water Management Sejong 30147 South Korea Konkuk Univ Dept Civil Environm & Plant Engn Seoul 05029 South Korea Kyonggi Univ Dept Civil & Energy Syst Engn Suwon 16227 South Korea Konkuk Univ Dept Civil & Environm Engn 120 Neungdong Ro Seoul 05029 South Korea

Study region: The study region is comprised of 33 river networks, including hydrometric stations in South Korea. Study focus: The estimation of low -flow quantiles for ungauged sites has become increasingly important in the hydrological management of water resources and environmental preservation. Although hydrological processes exhibit nonlinear behaviors and complexity, low -flow quantile estimates in South Korea are typically derived using linear models, such as the drainage area ratio. This study explores the application of nonlinear models, particularly the ensemble artificial neural network (EANN) and generalized additive model (GAM), to determine whether the model is more effective in calculating low -flow quantiles. New hydrological insights for the region: Canonical correlation analysis is employed to enhance the accuracy and performance of regional frequency analysis. Thirty-three river networks in South Korea are used to estimate 2 -year and 5 -year low -flow quantiles for ungauged sites. Further, the jackknife resampling technique is used to validate the results of the proposed models based on statistical indices, including the Nash-Sutcliffe efficiency index, relative root mean squared error, and relative mean bias. The results indicate that the GAM outperforms the EANN model in estimating the low -flow quantiles for river networks in South Korea. This suggests that selecting an appropriate model can improve the accuracy and performance of regional estimates, offering significant insights into water resources and environmental management while accounting for hydrological nonlinearity. By enhancing the low -flow quantile estimates for ungauged sites, we can better manage water resources and aquatic ecosystems, ultimately promoting healthier streams.

关键词： Ensemble Artificial Neural Network generalized additive model Low-Flow Quantile Regional Estimates

来源：评论

学校读者我要写书评

暂无评论

Unveiling the nonlinear dynamics of player performance in China's super league as a function of age

引用

SCIENTIFIC REPORTS 2024年第1期14卷 1-6页

作者： Cao, Jun Zhang, Wei Zhou, Changjing Shanghai Univ Sport Sch Athlet Performance Shanghai Peoples R China

To explore the dynamics in physical and technical performance of professional football players and changes across age groups. Match statistics were collected from 1900 games across ten seasons (2012-2021) in the Chinese Super League. generalized additive models visualized age-related trends in 12 key performance indicators including technical and physical variables. Revealed nonlinear trajectories characterized by rapid early declines, stable peak periods and accelerated late decreases. Physical indicators decreased progressively from the early 20 s before stabilizing briefly then declining further after 30. Conversely, technical metrics gradually improved into the late 20 s and early 30 s prior to decreasing again. This study provides novel evidence that football performance changes nonlinearly across age. Targeted training and development strategies should be tailored to the specific needs of different career stages.

关键词： Football Performance Age generalized additive model Nonlinearity

来源：评论

学校读者我要写书评

暂无评论

Effect of wind variability on the recruitment of anchovy Engraulis ringens in the southern Humboldt upwelling ecosystem

引用

FISHERIES OCEANOGRAPHY 2024年第5期33卷 e12677-e12677页

作者： Arteaga, Marcos Vasquez, Sebastian I. Neira, Sergio Cubillos, Luis Inst Invest Pesquera INPESCA Avda Cristobal Colon 2780 Talcahuano Chile Univ Concepcion Dept Oceanog Programa Doctorado Ciencias Menc Manejo Recursos A Concepcion Chile Univ Concepcion Programa Doctorado Oceanog Dept Oceanog Concepcion Chile Univ Concepcion Dept Oceanog Programa COPAS COASTAL ANID FB210021 Concepcion Chile

Recruitment is a critical process in the dynamics of fish populations since it represents the abundance of new fish that enter a population each year. In Ekman-type upwelling ecosystems, wind is a critical factor for small pelagic fish recruitment, as it controls food availability and physical constraints such as turbulence and offshore advection. In this study, we evaluate the effect of wind, chlorophyll-a, and spawning biomass as independent variables on anchovy recruitment. Using generalized additive models (GAMs), we tested (i) the significance of each factor, (ii) the shape of the relationship between recruitment and predictors variables, (iii) recruitment regimes in the series, and (iv) whether extended windy events are associated with low recruitment levels potentially related to offshore transport of early life stages. The study period spanned from 1990 to 2021, focusing on the austral spring in south-central Chile (35-40 degrees S) when both upwelling and anchovy spawning are maximized. GAMs revealed a parabolic relationship between the velocity of upwelling-favorable winds and anchovy recruitment. Maximized recruitment occurred at wind speeds around 6 m s(-1) but decreased with winds above 7 m s(-1) and below 5 m s(-1), and during periods of extended windy events (i.e., those lasting more than 15 days). Chlorophyll-a showed no significance on anchovy recruitment, suggesting that phytoplankton were not a limiting factor for anchovy early survival. We also found that anchovy recruitment regimes were associated with specific wind intensities. We discussed the pertinence of incorporating a recruitment index based on wind in the fishery management of the anchovy in south-central Chile.

关键词： advection anchovy generalized additive model recruitment spawning biomass upwelling wind

来源：评论

学校读者我要写书评

暂无评论

The Impact of Atmospheric Pollutants on Human Health and Economic Loss Assessment

引用

ATMOSPHERE 2021年第12期12卷 1628页

作者： Zhang, Houli You, Shibing Zhang, Miao Liu, Difei Wang, Xuyan Ren, Jingru Yu, Chuanhua Wuhan Univ Econ & Management Sch Wuhan 430072 Peoples R China Huazhong Univ Sci & Technol Union Hosp Tongji Med Coll Wuhan 430022 Peoples R China Shenzhen Inst Informat Technol Shenzhen 518172 Peoples R China Wuhan Univ Sch Hlth Sci Dept Epidemiol & Hlth Stat Wuhan 430071 Peoples R China

The impact of air pollution on human health is becoming increasingly severe, and economic losses are a significant impediment to economic and social development. This paper investigates the impact of air pollutants on the respiratory system and its action mechanism by using information on inpatients with respiratory diseases from two IIIA (highest) hospitals in Wuhan from 2015 to 2019, information on air pollutants, and meteorological data, as well as relevant demographic and economic data in China. This paper describes the specific conditions of air pollutant concentrations and respiratory diseases, quantifies the degree of correlation between the two, and then provides a more comprehensive assessment of the economic losses using descriptive statistical methods, the generalized additive model (GAM), cost of illness approach (COI), and scenario analysis. According to the findings, the economic losses caused by PM2.5, PM10, SO2, NO2, and CO exposure are USD 103.17 million, USD 70.54 million, USD 98.02 million, USD 40.35 million, and USD 142.38 million, for a total of USD 454.46 billion, or approximately 0.20% of Wuhan's GDP in 2019. If the government tightens control of major air pollutants and meets the WHO-recommended criterion values, the annual evitable economic losses would be approximately USD 69.4 million or approximately 0.03% of Wuhan's GDP in 2019. As a result, the relevant government departments must strengthen air pollution control to mitigate the impact of air pollution on population health and the associated economic losses.

关键词： air pollution respiratory disease generalized additive model scenario analysis assessment of economic losses

来源：评论

学校读者我要写书评

暂无评论

LOGISTIC REGRESSION BASED ON STATISTICAL LEARNING model WITH LINEARIZED KERNEL FOR CLASSIFICATION

引用

COMPUTING AND INFORMATICS 2021年第2期40卷 298-317页

作者： Guan, Xiaochun Zhang, Jianhua Chen, Shengyong Zhejiang Univ Technol Sch Comp Sci & Technol Hangzhou 310023 Peoples R China

In this paper, we propose a logistic regression classification method based on the integration of a statistical learning model with linearized kernel preprocessing. The single Gaussian kernel and fusion of Gaussian and cosine kernels are adopted for linearized kernel pre-processing respectively. The adopted statistical learning models are the generalized linear model and the generalized additive model. Using a generalized linear model, the elastic net regularization is adopted to explore the grouping effect of the linearized kernel feature space. Using a generalized additive model, an overlap group-lasso penalty is used to fit the sparse generalized additive functions within the linearized kernel feature space. Experiment results on the Extended Yale-B face database and AR face database demonstrate the effectiveness of the proposed method. The improved solution is also efficiently obtained using our method on the classification of spectra data.

关键词： Elastic net generalized additive model kernel lasso regression spectra data

来源：评论

学校读者我要写书评

暂无评论

Essays in Transformation models

Essays in Transformation Models

引用

作者： Zhang, Jian The University of Wisconsin - Madison

学位级别：Ph.D., Doctor of Philosophy

In Chapter 1, I study the estimation and inference of transformation models in the presence of a high dimensional set of control variables. In the study, I consider a generalized form of the transformed model, which includes the traditional transformed model, binary choice model, and generalized accelerated failure time model as special cases. I include both low dimensional covariates of interest and high dimensional control variables in this model. The estimation of high dimension nuisance parameters could lead to substantial bias and thus incorrect inference on parameters of interest. I provide a double-machine learning estimator to reduce this substantial bias and obtain a root-n-consistent and asymptotically normal results. According to the simulation study, I compare the performance of our estimator with the classical estimator based on average partial derivatives, it turns out that our estimator has less bias and provides correct inference results. Finally, I use an empirical example to illustrate the performance of our estimator in real data. In Chapter 2, I study the specification test for a generalized additive model (a.k.a. GAM) with an unknown link function. GAM is widely used to reduce the curse of dimensionality in nonparametric estimation. additive model is a special case when the link function is known by econometricians to be an identity. Under some regular conditions, I derive a sufficient and necessary condition when a function can be written as a GAM, which turns out to be a partial differential equation. This equation implies countably many restrictions on the coefficients from a simple polynomial series estimation, which forms the base of our test. Therefore, our test doesn't need to run a GAM estimation. Instead, I use an ``unrestricted'' series regression estimation with polynomial basis functions and make a statistical inference on its coefficients. The asymptotic properties of the test statistics are derived. The asymptotic distribution is t

关键词： Double machine kearning generalized additive model Minimax efficiency Partial differential equation Transformation model

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：