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Significance of geographical factors to the COVID-19 outbreak in India

modelING EARTH SYSTEMS AND ENVIRONMENT 2020年第4期6卷 2645-2653页

作者： Gupta, Amitesh Banerjee, Sreejita Das, Sumit Indian Inst Remote Sensing ISRO Marine & Atmospher Sci Dept Dehra Dun Uttarakhand India JIS Univ Dept Remote Sensing & GIS Kolkata India Savitribai Phule Pune Univ Dept Geog Pune 411007 Maharashtra India

Recently, the large outbreak of COVID-19 cases all over the world has whacked India with about 30,000 confirmed cases within the first 3 months of transmission. The present study used long-term climatic records of air temperature (T), rainfall (R), actual evapotranspiration (AET), solar radiation (SR), specific humidity (SH), wind speed (WS) with topographic altitude (E) and population density (PD) at the regional level to investigate the spatial association with the number of COVID-19 infections (NI). Bivariate analysis failed to find any significant relation (exceptSR) with the number of infected cases within 36 provinces in India. Variable Importance of Projection (VIP) through Partial Least Square (PLS) technique signified higher importance ofSR,T,RandAET. However, generalized additive model fitted with the log-transformed value of input variables and applying spline smoothening toPDandE, significantly found high accuracy of prediction (R-2 = 0.89), and thus well-explained complex heterogeneity among the association of regional parameters with COVID-19 cases in India. Our study suggests that comparatively hot and dry regions in lower altitude of the Indian territory are more prone to the infection by COVID-19 transmission.

关键词： COVID-19 Geographical factors Climatic influence generalized additive model India

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Air pollution and outpatient visits for cardiovascular and cerebrovascular diseases: A time-series analysis in Luoyang, China

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METEOROLOGICAL APPLICATIONS 2021年第6期28卷

作者： Guo, Hengliang Wang, Mengfei Wei, Haitao Gong, Xiyun Yang, Hong Zhengzhou Univ Henan Prov Supercomp Ctr Zhengzhou Henan Peoples R China Zhengzhou Univ Coll Chem Zhengzhou Henan Peoples R China Zhengzhou Univ Sch Geo Sci & Technol Zhengzhou Henan Peoples R China Zhengzhou Univ Joint Lab Ecol Meteorol Chinese Acad Meteorol Sci Zhengzhou Henan Peoples R China Zhengzhou Univ Zhengzhou Henan Peoples R China Zhengzhou Univ Dept Emergency Med Affiliated Hosp 1 Zhengzhou 450001 Henan Peoples R China

Previous studies have shown that air pollutants have a significant impact on cardiovascular and cerebrovascular diseases (CCD), but these studies have focused more on provincial capitals or large cities. This study was the first time to explore the effects of air pollutants on CCD in Luoyang. In this study, the generalized additive model (GAM) was used to establish a single-pollutant model, a multi-pollutant model and stratified modes of age, sex and season to evaluate the effects of PM2.5, PM10, SO2, NO2, CO and O-3 on outpatient visits for CCD within a week. The results of the single-pollutant model showed that PM2.5, PM10, SO2, NO2, CO and O-3 all had significant effects on the outpatient visits for CCD with a lag effect. The multi-pollutant model showed that there might be complex interactions among pollutants. The results of the stratified model showed that there was no significant difference in the effects of different pollutants on different genders and ages, and the results of seasonal stratification showed that PM2.5, PM10, SO2 and CO had a greater influence on the outpatient visits for CCD in spring and summer, while NO2 and O-3 had a greater influence on the outpatient visits for CCD in winter and summer, respectively. The results showed that air pollutants significantly affected outpatient visits for CCD, among which NO2 had the greatest influence, and seasonal effects and the combined effects of various pollutants should be considered in strategies for the prevention of CCD.

关键词： air pollution analysis of time series cardiovascular and cerebrovascular diseases daily outpatient visits generalized additive model meteorological factors

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Distributional Regression Forests Approach to Regional Frequency Analysis With Partial Duration Series

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WATER RESOURCES RESEARCH 2021年第10期57卷 e2021WR029909-e2021WR029909页

作者： Kiran, K. G. Srinivas, V. V. Indian Inst Sci Dept Civil Engn Bangalore Karnataka India Indian Inst Sci Interdisciplinary Ctr Water Res ICWaR Bangalore Karnataka India

Regional flood frequency analysis (RFFA) is widely used to quantify flood risk at ungauged and sparsely gauged locations. There are minimal attempts to use partial duration series (PDS) for RFFA, though the use of PDS instead of widely used annual maximum series (AMS) can offer some advantages. This article contributes two novel random/regression forests (RFs)-based methodologies, namely generalized pareto distribution (GPD)-based distributional RFs (DRFs) and multivariate RFs (MVRFs), for RFFA with PDS. The RFs facilitate modeling interactions between predictors and their complex relationships with the predictands without explicitly specifying them. The DRFs and MVRFs comprise an ensemble of corresponding regression trees, each constructed by recursive binary partitioning of the feature space into meaningful segments. The proposed DRFs account for the sampling uncertainty of PDS in the partitioning and parameter estimation. In DRFs (MVRFs), quantile estimates for an ungauged site are obtained using maximum likelihood estimates (expected values) of GPD parameters corresponding to the segments to which the site belongs. The potential of DRFs and MVRFs relative to two recently proposed techniques (univariate RFs-based quantile regression, generalized additive model based on GPD) is demonstrated through Monte-Carlo simulation experiments and a study on 1,031 watersheds in the United States. The key features influencing scale and shape parameters of GPD fitted to PDS of the watersheds are identified as drainage area and 24-hr rainfall intensity corresponding to 2-year return period, respectively. Those identified for shape parameter differ from key features known based on analysis with AMS and generalized extreme value distribution.

关键词： regional flood frequency analysis partial duration series distributional random forests multivariate random forests generalized additive model

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Abundance and habitats of marine cladocerans in the Sea of Japan over two decades

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PROGRESS IN OCEANOGRAPHY 2021年 194卷 102561-102561页

作者： Kodama, Taketoshi Ohshimo, Seiji Tanaka, Hiroshige Ashida, Hiroshi Kameda, Takahiko Tanabe, Toshiyuki Okazaki, Makoto Ono, Tsuneo Tanaka, Yosuke Japan Fisheries Res & Educ Agcy Fisheries Resources Inst Yokohama Kanagawa Japan

Marine cladocerans are secondary producers in marine neritic ecosystems. To investigate the population dynamics and species-specific habitats of marine cladocerans in the Sea of Japan, marine cladoceran abundance was evaluated in the southern Sea of Japan and the East China Sea-Kuroshio using three mesh-size (0.06, 0.10, and 0.33 mm) plankton nets at several depths (n = 4674) over two decades from 1997 to 2018. We observed seven out of eight species, including Penilia avirostris, Evadne nordmanni, E. spinifera, Pseudevadne tergestina, and Pleopis schmackeri, from 10% of samples, and Pleopis polyphemoides and Podon leuckartii from < 2% of samples. Abundance and diversity were higher in the Sea of Japan than in the East China Sea-Kuroshio during summer. In the East China Sea-Kuroshio, E. spinifera and Ps. tergestina abundances were high and they were distributed widely;however, other species were rare and were sparsely distributed. These suggested that the environmental conditions in the Sea of Japan are optimal for the reproduction of marine cladocerans because horizontal advective transport of marine cladocerans is not expected from the East China Sea-Kuroshio to the Sea of Japan. The habitats of the five common species in the Sea of Japan were estimated using generalized additive models (GAMs), revealing different optimal temperature ranges. Evadne nordmanni performed optimally under cold water, and the other four performed optimally under warm water. Penilia avirostris, E. spinifera, Ps. tergestina, and Pl. schmackeri coexisted in time and space in the Sea of Japan, although the vertical habitats were potentially different. Based on the estimated habitats, steep and shallow thermocline was responsible for the high marine cladoceran abundance and rich diversity in the southern Sea of Japan during summer.

关键词： Marine cladocerans Western North Pacific Sea of Japan generalized additive model Habitats Kuroshio East China Sea

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Evaluation of four regression techniques for stem taper modeling of Dahurian larch (Larix gmelinii) in Northeastern China

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FOREST ECOLOGY AND MANAGEMENT 2021年 494卷 119336-119336页

作者： He, Pei Hussain, Amna Shahzad, Muhammad Khurram Jiang, Lichun Li, Fengri Northeast Forestry Univ Sch Forestry Dept Forest Management Harbin 150040 Peoples R China Northeast Forestry Univ Sch Forestry Key Lab Sustainable Forest Ecosyst Management Minist Educ Harbin 150040 Peoples R China

Estimating stem volume and biomass in forests is fundamental to both economic and ecological assessments of forest ecosystem structure and function. Stem taper models were widely used to calculate stem volume and biomass, but it can be challenging to get an accurate and convenient technique for taper models in practice. This study evaluated ordinary nonlinear least squares (ONLS), fixed-effects model (FIXED), quantile regression (QR), and generalized additive model (GAM) to predict tree diameter, volume, and merchantable height of Dahurian larch (Larix gmelinii) in Northeastern China. As far as we know, a comprehensive analysis of these four techniques is limited for taper data. Therefore, our main objectives were to compare these four techniques at an equitable level without calibration and select a single and widely applied technique for the taper model. The dataset comprising 1372 felled-trees from Dahurian larch natural forest were used to evaluate the techniques with a leave-one-out cross-validation approach. Evaluation statistics and box plots showed that the GAM performed better than other techniques for stem profile description and volume estimation. Results also revealed that all techniques had a bias in estimating merchantable height. However, this limitation does not significantly affect the overall performance and applied use of the GAM for diameter and volume prediction. When intuitive interpretations are not needed, the GAM can serve as an accurate and convenient technique for prediction.

关键词： Taper model Fixed-effects model Quantile regression generalized additive model

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The influence of environmental variables on the abundance and temporal distribution of Octopus cyanea around Mafia Island, Tanzania

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FISHERIES RESEARCH 2021年 241卷 105991-105991页

作者： Chande, Muhaji A. Mgaya, Yunus D. Benno, Lusato B. Limbu, Samwel M. Univ Dar Es Salaam Sch Aquat Sci & Fisheries Technol Aquaculture Technol Dept POB 60091 Dar Es Salaam Tanzania Tanzania Fisheries Res Inst POB 78850 Dar Es Salaam Tanzania

Abundance and distribution of short-lived species like Octopus cyanea depends on favorable environmental conditions, which are essential for growth and successful recruitment. However, the influence of environmental variables on the abundance and distribution of O. cyanea remains unknown, particularly in the Western Indian Ocean (WIO) region, despite its importance as a fishery resource. This study investigated the role of environmental variables (sea surface temperature [SST], chlorophyll-a [Chl-a], rainfall and wind) on the abundance and temporal distribution of O. cyanea around Mafia Island in Tanzania for a period of 60 months (2013-2017). Octopus catch data were obtained from TANPESCA fish processing factory, while environmental data were obtained from the National Aeronautics and Space Administration - NASA satellite (SST and Chl-a) and the Tanzania Meteorological Agency - TMA (rainfall and wind). The results for the O. cyanea CPUE showed two peaks, between March and October, suggesting successful recruitment and stable environmental conditions during these periods. The additive effect of wind speed and Chl-a with a 4-month lag, was the best model for predicting the abundance and temporal distribution of O. cyanea around Mafia Island followed by the additive effect of SST, wind and Chl-a. Temperature influences octopus growth, while strong wind facilitates the mixing of water, which brings the bottom nutrient rich water to the euphotic zone for increased photosynthesis. In conclusion, the abundance and temporal distribution of O. cyanea are influenced by the additive effect of wind, Chl-a and SST with time-lagged effect of four months. This study recommends seasonal closure of the octopus fishery between April and July and reopening in March and October for sustainable harvesting of this valuable resource.

关键词： Abundance Distribution Environmental variables Octopus cyanea Catch per unit effort generalized additive model

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Effects and interaction of air pollution and meteorological factors on pertussis incidence in ***

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HYGIENE AND ENVIRONMENTAL HEALTH ADVANCES 2022年 4卷

作者： Luo, Yizhe Zhang, Longyao Zhang, Simin Ai, Lele Lv, Heng Zhu, Changqiang Wu, Jiahong Tan, Weilong Nanjing Med Univ Sch Publ Hlth Dept Epidemiol Nanjing 211166 Peoples R China Nanjing Bioengn Gene Technol Ctr Med Nanjing 210002 Peoples R China Nanjing Med Univ Sch Publ Hlth Dept Biostat Nanjing 211166 Jiangsu Peoples R China Guizhou Med Univ Guiyang 550025 Guizhou Peoples R China

Background: Previous studies on the risk of pertussis exposure to atmospheric pollutants are still inconclusive. Methods: Air pollutant, meteorological data and epidemiological distribution of pertussis cases in China during 2004-2018 were concluded in this study. A distributed lag nonlinear model (DLNM) for a maximum lag of 15 months was developed to evaluate the lag effects of monthly air pollutants and meteorological factors on pertussis incidence. Then a generalized additive model (GAM) was constructed to explore the interaction effect among air pollutants, meteorological factors and pertussis incidence and the stratified effect of selected variables. Results: A total of 74,249 cases of pertussis were included during 2004-2018 in China. Long-term exposure to NO2 was positively associated with the risk of pertussis at 32-94 mu g/m(3). Interaction and stratified analyses showed that there were certain correlations between 4 air pollutants (PM2.5, SO2, NO2, and O-3) and 3 meteorological factors (temperature, sunlight and wind speed). In the high PM2.5 environment, a unit increment in NO2 contributed to a 2.52% (95% CI: 2.13%-2.92%) increase in pertussis incidence risk, while in a low PM2.5 environment, a unit increment of NO2 contributed to a 2.16% (95% CI: 1.64%-2.69%) increase in pertussis incidence risk. Conclusions: Our study indicated that air pollutants and meteorological factors have delayed effects on the occurrence of pertussis in China, and the effect of NO2 can be modified by PM2.5, SO2, and O-3. In the prevention and control of pertussis, the additive effect of different factors on pertussis and the variability of weather should be considered.

关键词： Air pollution Meteorological factors Pertussis Distributed lag nonlinear models generalized additive model

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E-scooters and sustainability: Investigating the relationship between the density of E-scooter trips and characteristics of sustainable urban development

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SUSTAINABLE CITIES AND SOCIETY 2021年 66卷 102624-102624页

作者： Hosseinzadeh, Aryan Algomaiah, Majeed Kluger, Robert Li, Zhixia Univ Louisville Dept Civil & Environm Engn WS Speed Louisville KY 40292 USA

Shared E-Scooters have become popular in many cities around the world. E-Scooters provide convenient and quick rides for short distances and can act as a connection for first/last mile trips. This study aims to identify spatial factors associated with scooter trips. To accomplish this, a generalized additive modeling (GAM) approach was applied to analyze 159 TAZs in Louisville, Kentucky based on data between November 2018 and February 2020. Contributing factors investigated included demographics, land use, connectivity and urbanism scores. Peak-hours of E-scooter use was studied in addition to all time periods. According to the model, commercial land use percent, industrial land use percent, Walk Score and Bike Score influenced E-Scooter trip density at a TAZ level. Results were compared to studies in other cities in the US. Distributing E-scooters and implementing policies based on the findings of this study can improve the quality of micromobility services by providing an alternative solution for first-mile-last-mile (FMLM) while taking sustainability of urban development into consideration.

关键词： E-Scooters Built environment First-mile-last-mile generalized additive model Spatial analysis Micromobility

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Correlation does not imply geomorphic causation in data-driven landslide susceptibility modelling ? Benefits of exploring landslide data collection effects

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SCIENCE OF THE TOTAL ENVIRONMENT 2021年 776卷 145935-145935页

作者： Steger, Stefan Mair, Volkmar Ko, Christian Pittore, Massimiliano Zebisch, Marc Schneiderbauer, Stefan Eurac Res Inst Earth Observat Viale Druso 1 Drususallee 1 I-39100 Bolzano Italy Off Geol & Bldg Mat Testing Cardano Kardaun Autonomous Prov Italy UNU EHS GLOMOS Programme Bolzano Italy

Data-driven landslide susceptibility models formally integrate spatial landslide information with explanatory environmental variables that describe predisposing factors of slope instability. Well-performing models are commonly utilized to identify landslide-prone terrain or to understand the causes of slope instability. In most cases, however, the available landslide data is affected by spatial biases (e.g. underrepresentation of landslides far from infrastructure or in forests) and does therefore not perfectly represent the spatial distribution of past slope instabilities. Literature shows that implications of such data flaws are frequently ignored. This study was built upon landslide information that systematically relates to damage-causing and infrastructure-threatening events in South Tyrol, Italy (7400 km2). The created models represent three conceptually different strategies to deal with biased landslide information. The aims were to demonstrate why an inference of geomorphic causation from apparently well-performing models is invalid under common landslide data bias conditions (model 1), to test a novel bias-adjustment approach (model 2) and to exploit the underlying data bias to model areas likely affected by potentially damaging landslides (model 3;intervention index), instead of landslide susceptibility. The study offers a novel perspective on how biases in landslide data can be considered within data-driven models by focusing not only on the process under investigation (landsliding), but also on the circumstances that led to the registration of landslide information (data collection effects). The results were evaluated in terms of statistical relationships, variable importance, predictive performance, and geomorphic plausibility. The results revealed that none of the models reflected landslide susceptibility. Despite partly high predictive per-formances, the models were unable to create geomorphically plausible spatial predictions. The impact-oriented int

关键词： generalized additive model Landslide inventory Validation South Tyrol Landslide exposure

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Elemental composition of biochars is affected by methods used for its determination

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JOURNAL OF ANALYTICAL AND APPLIED PYROLYSIS 2021年 156卷 105174-105174页

作者： Nzediegwu, Christopher Naeth, M. Anne Chang, Scott X. Univ Alberta Dept Renewable Resources Edmonton AB T6G 2E3 Canada Univ Alberta Land Reclamat Int Grad Sch Edmonton AB T6G 2E3 Canada

Ultimate analysis, energy dispersive X-ray spectroscopy (EDX), and X-ray photoelectron spectroscopy (XPS) are widely used methods for the determination of elemental composition of biomaterials such as biochar. However, models that describe the relationship in elemental composition between ultimate and spectroscopic methods are lacking. Elemental composition of biochars determined using ultimate analysis, EDX, and XPS methods was used to develop relationships among these methods using linear regression and generalized additive modelling. The relationships were evaluated using min/max accuracy and cross-validation. Based on Akaike information criterion and generalized cross-validation scores, the generalized additive model with a thin plate spline (TPS) best described the relationships. model performance significantly improved by adding a feedstock-dependent grouping variable. Elemental carbon concentration determined by the different methods was significantly related (R2 = 0.82-0.88) by the TPS. Although not detected by EDX, nitrogen concentration was significantly related by TPS (R2 = 0.77) between the ultimate and XPS methods. For oxygen concentration, a strongly significant relationship (R2 = 0.83-0.86) was found between the ultimate and both spectroscopic methods, but a weak one (R2 < 0.54) between the XPS and EDX methods. Min/max accuracy, reaching 80 % and above during testing, confirmed that the best-fit models would predict carbon, nitrogen and oxygen concentrations with small variations for several biochars. We conclude that the elemental composition of biochars is affected by the method used for its determination and the generalized additive models developed can be used to calibrate the elemental composition analyzed using different methods.

关键词： Energy dispersive X-ray spectroscopy generalized additive model K-fold cross-validation Linear regression X-ray photoelectron spectroscopy Ultimate analysis

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