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Effects of Local Vegetation and Regional Controls in Near-Surface Air Temperature for Southeastern Brazil

ATMOSPHERE 2022年第11期13卷 1758-1758页

作者： de Abreu, Rafael Cesario Hallak, Ricardo da Rocha, Humberto Ribeiro Inst Astron Geofis & Ciencias Atmosfer IAG USP BR-05508090 Sao Paulo Brazil

The spatial range of near-surface air temperature average and trends for Southeast Brazil in recent decades motivated us to investigate the causality of local vegetation and other geophysical controls at the regional scale to explain the spatial variability of the average maximum and minimum temperature (Tmax and Tmin). We used measurements from 52 weather stations between 1985 and 2010. Using linear regression, NDVI and cloud cover were significant to explain spatial variability of Tmax and Tmin. With the generalized additive model (GAM), we improved temperature-dependent relationships with regional geophysical controls, and local scale NDVI. The modeling of Tmax and Tmin showed non-linear and combined relationships with geographical position (lat,lon) jointly expressing the effects of zonality and continentality, and NDVI at distances of 300 m and 3000 m. For Tmin, geographical position and altitude responded with an amplitude of similar or equal to 5 degrees C each, and NDVI with similar or equal to 3 degrees C. Similarly, the geographical position and altitude were significant for Tmax, with an amplitude of similar or equal to 5 degrees C each, and cloud cover with similar or equal to 3.5 degrees C. Our findings help to clarify the local scale controls of near-surface air temperature and stress the need to increase resilience against adversities of global climate change and increasing urbanization, by providing metrics to predict the effects of nature-based solutions within the urban space.

关键词： near-surface air temperature land cover change generalized additive model climate change urban heat island regional climate Brazil weather stations

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Ambient Particulate Air Pollution and Daily Hospital Admissions in 31 Cities in Poland

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ATMOSPHERE 2022年第2期13卷 345页

作者： Adamkiewicz, Lukasz Maciejewska, Katarzyna Rabczenko, Daniel Drzeniecka-Osiadacz, Anetta European Clean Air Ctr PL-31104 Krakow Poland Warsaw Univ Technol Fac Bldg Serv Hydro & Environm Engn PL-00653 Warsaw Poland Natl Inst Hyg Natl Inst Publ Hlth PL-00791 Warsaw Poland Univ Wroclaw Dept Climatol & Atmosphere Protect Inst Geog & Reg Dev PL-51621 Wroclaw Poland

A strong and consistent association has been observed between morbidity or mortality rates and PM concentration, and is well documented in many countries. In Poland, despite poor air quality, studies concerning the evaluation of acute health effects of ambient air pollution on morbidity from respiratory or cardiovascular diseases are rare. We examined the short-term impact of PMx concentration on hospital admission in 31 Polish cities based on the daily PM10, PM2.5 concentration, meteorological variables, and hospital data. The generalized additive model (GAM) and a random-effects meta-analysis were used to assess the impact of air pollution on morbidity within the exposed population. Almost 1.6 million cardiovascular admissions and 600 thousand respiratory disorders were analyzed. The RR values for PM10-related cardiovascular and respiratory hospital admissions in Poland are equal to 1.0077 (95% confidence interval, 1.0062 to 1.0092) and 1.0218 (95% confidence interval, 1.0182 to 1.0253), respectively, while for PM2.5 1.0088 (95% confidence interval, 1.0072 to 1.0103) and 1.0289 (95% confidence interval, 1.0244 to 1.0335), respectively. Moreover, a moderate heterogeneity of RR estimates was observed between the analyzed cities (I-2 values from 27% to 45%). The presented analysis confirms the significant association between hospital admission and PMx concentration, especially during heating seasons.

关键词： particulate matter hospital admission respiratory diseases cardiovascular diseases time-series generalized additive model Poland

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Behavioural response of brown trout (Salmo trutta) to total dissolved gas supersaturation in a regulated river

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ECOHYDROLOGY 2022年第1期15卷

作者： Lennox, Robert J. Thiemer, Kirstine Vollset, Knut Wiik Pulg, Ulrich Stranzl, Sebastian Nilsen, Cecilie Iden Haugen, Thrond Oddvar Velle, Gaute NORCE Norwegian Res Ctr Lab Freshwater Ecol & Inland Fisheries Nygardsgaten 112 N-5008 Bergen Norway Norwegian Univ Life Sci Dept Ecol & Nat Resource Management As Norway Norwegian Inst Water Res Sect Freshwater Ecol Oslo Norway Univ Bergen Dept Biol Sci Bergen Norway

Total dissolved gas supersaturation from dams and power stations is a chronic freshwater pollutant that is toxic to animals with aquatic respiration. Laboratory ecotoxicology experiments have revealed capacity for captive fishes to saturoregulate by moving deeper, but field ecotoxicology research is largely lacking. We instrumented 94 brown trout in the Rysstad basin of the Otra River, Norway, with depth sensor acoustic transmitters and monitored their movements for 10 months. We found that the depths used by the trout largely protected them from the effects of total dissolved gas supersaturation, which ranged from 96% to 133% total gas pressure during the study. The depth use of fish was affected by sun position, lunar phase and spatial position in the river (i.e., available depth), and there was an extremely weak effect of total dissolved gas supersaturation that was counterintuitive (i.e., positive slope, movement toward surface). Depth traces of the fish revealed that nine fish died during the study, mostly coinciding with the first wave of supersaturation, consistent with observations of untagged dead fish with signs of gas bubble trauma found on the river bottom during this period (May-June). Overall, tagged trout exposure to total dissolved gas supersaturation depended on their use of depth, but responses to waves of extreme supersaturation at supraphysiological levels were weak and biologically insignificant, with individual variation and spatial position in the river most important in the model. Exposure to total dissolved gas supersaturation was mediated by individual differences in habitat use, which may be linked to activity and other traits that determine overall vulnerability to exposure to total dissolved gas supersaturation.

关键词： acoustic telemetry generalized additive model hydropower pollution river regulation

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Quantitative assessment of the risk of human activities on landscape fragmentation: A case study of Northeast China Tiger and Leopard National Park

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SCIENCE OF THE TOTAL ENVIRONMENT 2022年第Part2期851卷 158413页

作者： Zhang, Xiaoyu Ning, Xiaogang Wang, Hao Zhang, Xiaoyuan Liu, Yafei Zhang, Weiwei Chinese Acad Surveying & Mapping Beijing 100036 Peoples R China Liaoning Tech Univ Sch Geomat Fuxin 123000 Liaoning Peoples R China Shandong Univ Sci & Technol Sch Geomat & Spatial Informat Qingdao 266590 Shandong Peoples R China Suzhou Univ Sci & Technol Sch Geog Sci & Geomat Engn Suzhou 215009 Peoples R China

Risk assessment of human activities on landscape fragmentation in nature reserves can effectively balance the conflict between wildlife conservation and human development. However, previous studies had been unable to quantitatively assess the risk of human activities on landscape fragmentation. Thus, we constructed a risk assessment methodology to quantitatively assess the risk of different human activities on the Landscape Fragmentation Composite Index (LFCI) in the Northeast China Tiger and Leopard National Park (NCTLNP). First, we fitted the relationship curve between LFCI and different human activity factors based on the generalized additive model (GAM) to determine the impact patterns of each factor on LFCI. Secondly, we identified impact risk areas of each human activity factor on LFCI by the location of threshold points in the curve and analyzed their spatiotemporal variation characteristics from 2015 to 2020. The results show that the relationship between LFCI and Land Use Intensity (LUI) showed an inverted "U" shape, the relationship with Population Density (POPD) showed a "rising-flat-rising" trend, and the relationship with Traffic Accessibility (TA) and Industrial and Mining Activity (IMA) showed a positive correlation after a flat interval. In addition, we found that the LUI and IMA impact risk areas were widely distributed and remained stable for five years. But the POPD impact risk area was mainly distributed around settlements and expanded by 6.6 % from 2015 to 2020. The TA impact risk area was distributed in strips and expanded by 16.38 % from 2015 to 2017 due to the construction of the G331 national road. And the joint impact risk area of these four factors expanded by 1.55 times in five years. Our research can provide a reference for ecological risk assessment under the impact of human activities on other nature reserves in the world.

关键词： Landscape fragmentation Risk assessment Human footprint generalized additive model Northeastern China Tiger and Leopard National Park

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Calibrating COVID-19 susceptible-exposed-infected-removed models with time-varying effectivecontact rates

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PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY A-MATHEMATICAL PHYSICAL AND ENGINEERING SCIENCES 2022年第2214期380卷 20210120-20210120页

作者： Gleeson, James P. Murphy, Thomas Brendan O'Brien, Joseph D. Friel, Nial Bargary, Norma O'Sullivan, David J. P. Univ Limerick Dept Math & Stat MACSI Limerick V94 T9PX Ireland Univ Coll Dublin Sch Math & Stat Dublin D04 V1W8 Ireland Insight Ctr Data Analyt Dublin Ireland Confirm Ctr Smart Mfg Limerick Ireland Irish Epidemiol Modelling Advisory Grp IEMAG Dublin Ireland

We describe the population-based susceptible-exposed-infected-removed (SEIR) model developed by the Irish Epidemiological modelling Advisory Group (IEMAG), which advises the Irish government on COVID-19 responses. The model assumes a time-varying effective contact rate (equivalently, a time-varying reproduction number) to model the effect of non-pharmaceutical interventions. A crucial technical challenge in applying such models is their accurate calibration to observed data, e.g. to the daily number of confirmed new cases, as the history of the disease strongly affects predictions of future scenarios. We demonstrate an approach based on inversion of the SEIR equations in conjunction with statistical modelling and spline-fitting of the data to produce a robust methodology for calibration of a wide class of models of this type. This article is part of the theme issue 'Data science approaches to infectious disease surveillance'.

关键词： epidemic modelling differential equations calibration generalized additive model thin-plate splines

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PREDICTION ENHANCEMENT OF MACHINE LEARNING USING TIME SERIES modelING IN GAS TURBINES

PREDICTION ENHANCEMENT OF MACHINE LEARNING USING TIME SERIES...

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ASME Turbomachinery Technical Conference and Exposition (Turbo Expo) - Sustainable Energy - Accelerating the Transition by Advancing Turbine Technology

作者： Goyal, Vipul Xu, Mengyu Kapat, Jayanta Vesely, Ladislav Univ Cent Florida Ctr Adv Turbomachinery & Energy Res Dept Mech & Aerosp Engn Orlando FL 32816 USA Univ Cent Florida Dept Stat & Data Sci Ctr Adv Turbomachinery & Energy Res Orlando FL USA

ISBN: (纸本)9780791884966

Blade-path temperature can serve as a precursor of anomalies in combustion system and/or cooling system. Given observations from blade-path temperature sensors of a power plant, we consider prediction of the temperature for each sensor. The only extraneous predictor is the combustion turbine fuel flow, while measurements of other potential predictors are unavailable. Long-memory behavior and heterogeneous variance are observed from the residuals of the generalized additive model. Autoregressive Fractionally Integrated Moving Average (ARFIMA) and generalized Autoregressive Conditional Heteroskedasticity (GARCH) models are employed to fit the residual process, which significantly improve the prediction. Rolling one-step-ahead forecast is studied for each of the sixteen univariate blade-path temperature sensors. Their conditional variances are also estimated. Numerical experiments are performed with manually generated perturbation to evaluate the specificity and sensitivity of the prediction. Abrupt changes in the temperature are considered in the numerical study with various jump sizes. We also consider slowly increasing trend in the blade-path temperature with different slopes. Our prediction is sensitive given reasonable signal-to-noise ratio. It also has a much lower false positive rate than the generalized additive model prediction from the combustion turbine fuel flow. Difference between the real-time forecast and observation can be deployed to test for anomalies.

关键词： Anomaly detection long-range dependence generalized additive model Fractional difference GARCH

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Association between short-term exposure to air pollution and COVID-19 infection: Evidence from China

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SCIENCE OF THE TOTAL ENVIRONMENT 2020年 727卷 138704-138704页

作者： Zhu, Yongjian Xie, Jingui Huang, Fengming Cao, Liqing Univ Sci & Technol China Sch Management Hefei Peoples R China Univ Sci & Technol China Affiliated Hosp USTC 1 Div Life Sci & Med Hefei Peoples R China Brunel Univ London Brunel Business Sch Uxbridge Middx England

The novel coronavirus pneumonia, namely COVID-19, has become a global public health problem. Previous studies have found that air pollution is a risk factor for respiratory infection by carrying microorganisms and affecting body's immunity. This study aimed to explore the relationship between ambient air pollutants and the infection caused by the novel coronavirus. Daily confirmed cases, air pollution concentration and meteorological variables in 120 cities were obtained from January 23, 2020 to February 29, 2020 in China. We applied a generalized additive model to investigate the associations of six air pollutants (PM2.5, PM10, SO2, CO, NO2 and O-3) with COVID-19 confirmed cases. We observed significantly positive associations of PM2.5, PM10, NO2 and O-3 in the last two weeks with newly COVID-19 confirmed cases. A 10-mu g/m(3) increase (lag0-14) in PM2.5, PM10, NO2, and O-3 was associated with a 2.24% (95% CI: 1.02 to 3.46), 1.76% (95% CI: 0.89 to 2.63), 6.94% (95% CI: 2.38 to 11.51), and 4.76% (95% CI: 1.99 to 7.52) increase in the daily counts of confirmed cases, respectively. However, a 10-mu g/m(3) increase (lag0-14) in SO2 was associated with a 7.79% decrease (95% CI: -14.57 to -1.01) in COVID-19 confirmed cases. Our results indicate that there is a significant relationship between air pollution and COVID-19 infection, which could partially explain the effect of national lockdown and provide implications for the control and prevention of this novel disease. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Air pollution Novel coronavirus pneumonia COVID-19 generalized additive model

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Optimization of environmental variables in habitat suitability modeling for mantis shrimp Oratosquilla oratoria in the Haizhou Bay and adjacent waters

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Acta Oceanologica Sinica 2020年第6期39卷 36-47页

作者： Yunlei Zhang Huaming Yu Haiqing Yu Binduo Xu Chongliang Zhang Yiping Ren Ying Xue Lili Xu College of Fisheries Ocean University of ChinaQingdao 266003China Laboratory for Marine Fisheries Science and Food Production Processes Pilot National Laboratory for Marine Science and Technology(Qingdao)Qingdao 266237China College of Oceanic and Atmospheric Sciences Ocean University of ChinaQingdao 266100China Institute of Oceanology Chinese Academy of SciencesQingdao 266071China

Habitat suitability index(HSI)models have been widely used to analyze the relationship between species abundance and environmental factors,and ultimately inform management of marine *** response of species abundance to each environmental variable is different and habitat requirements may change over life history stages and ***,it is necessary to determine the optimal combination of environmental variables in HSI *** this study,generalized additive models(GAMs)were used to determine which environmental variables to be included in the HSI *** variables were retained and weighted in the HSI model according to their relative contribution(%)to the total deviation explained by the boosted regression tree(BRT).The HSI models were applied to evaluate the habitat suitability of mantis shrimp Oratosquilla oratoria in the Haizhou Bay and adjacent areas in 2011 and 2013–*** and seasonal variations in HSI models of mantis shrimp were also *** the four models(non-optimized model,BRT informed HSI model,GAM informed HSI model,and both BRT and GAM informed HSI model),both BRT and GAM informed HSI model showed the best *** environmental variables(bottom temperature,depth,distance offshore and sediment type)were selected in the HSI models for four groups(spring-juvenile,spring-adult,falljuvenile and fall-adult)of mantis *** distribution of habitat suitability showed similar patterns between juveniles and adults,but obvious seasonal variations were *** study suggests that the process of optimizing environmental variables in HSI models improves the performance of HSI models,and this optimization strategy could be extended to other marine organisms to enhance the understanding of the habitat suitability of target species.

关键词： habitat suitability index mantis shrimp generalized additive model boosted regression tree Haizhou Bay

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Short-term associations between ambient fine particulate matter pollution and hospital visits for chronic obstructive pulmonary disease in Yinzhou District, China

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2020年第17期27卷 21647-21653页

作者： Lin, Cheng-Yi Li, Die Lu, Jie-Ming Yu, Zhe-Bin Zhu, Yao Shen, Peng Tang, Meng-Ling Jin, Ming-Juan Lin, Hong-Bo Shui, Li-Ming Chen, Kun Wang, Jian-Bing Zhejiang Univ Sch Publ Hlth Dept Epidemiol & Biostat Hangzhou Zhejiang Peoples R China Yinzhou Dist Ctr Dis Control & Prevent Dept Chron Dis & Hlth Promot Ningbo Zhejiang Peoples R China Yinzhou Dist Hlth Bur Ningbo Ningbo Zhejiang Peoples R China Zhejiang Univ Dept Epidemiol & Biostat Sch Med 866 Yuhangtang Rd Hangzhou 310058 Zhejiang Peoples R China Zhejiang Univ Affiliated Hosp 2 Sch Med 866 Yuhangtang Rd Hangzhou 310058 Zhejiang Peoples R China Zhejiang Univ Childrens Hosp Natl Clin Res Ctr Child Hlth Dept Epidemiol & BiostatSch Med 866 Yuhangtang Rd Hangzhou 310058 Zhejiang Peoples R China

Ambient particulate matter is one of the main risk factors of chronic obstructive pulmonary disease (COPD) in developing countries. However, the studies were scant in China concerning the health effects of the fine particulate matter (PM2.5;particulate matter <= 2.5 mu m in diameter) on hospital visits for COPD. We applied a generalized additive model (GAM) to calculate relative risks (RRs) with 95% confidence intervals (CIs) for the associations between hospital visits for COPD and an interquartile range (24.50 mu g/m(3)) increment of ambient PM2.5 concentrations in Yinzhou District between 2016 and 2018. The ambient PM2.5 concentration was positively associated with hospital visits for COPD at a distributed lag of 0-7 days (RR = 1.073, 95% CI, 1.016, 1.133). In the stratified analysis, we found that the association between ambient PM2.5 and COPD was stronger during the warm season (April to September) than that during the cold season (October to March), but we did not observe statistically significant differences in age groups (< 60 years and >= 60 years) or gender groups (male and female) related to the effects of PM2.5. The associations between ambient PM2.5 and COPD became partially attenuated after the adjustment for gaseous pollutants in subgroups. Our findings could provide evidence that regulations for controlling both PM2.5 and gaseous pollutants should be implemented to protect the overall population.

关键词： PM2 5 Hospital visits COPD Air pollution generalized additive model Time-series study

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Impacts of Fractional Hot-Deck Imputation on Learning and Prediction of Engineering Data

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2020年第12期32卷 2363-2373页

作者： Song, Ikkyun Yang, Yicheng Im, Jongho Tong, Tong Ceylan, Halil Cho, In Ho Iowa State Univ Dept Civil Engn Ames IA 50011 USA Yonsei Univ Dept Appl Stat Seoul 03722 South Korea

In broad engineering fields, missing data is a common issue which often causes undesired bias and sparseness impeding rigorous data analyses. To tackle this problem, many imputation theories have been proposed and widely used. However, prior methods often require distributional assumptions and prior knowledge regarding data which may cause some difficulty for engineering research. Essentially, the fractional hot-deck imputation (FHDI) is an assumption-free imputation method, holding broad applicability in the engineering domains. FHDIs internal parameters and impact on statistical and machine learning methods, however, have been rarely understood. Thus, this study investigates the behavior and impacts of FHDI on prediction methods including generalized additive model, support vector machine, extremely randomized trees, and artificial neural network, for which four practical datasets (appliance energy, air quality, phenotypes, and weather) are used. Results show that FHDI performs better for improving the prediction accuracy compared to a simple naive method which cures missing data using the mean value of attributes, and FHDI has an asymptotically positive effect on prediction accuracy with decreasing response rates. Regarding an optimal setting, 30 to 35 is recommended for the FHDIs internal categorization number while 5 is recommended for the FHDI donors, which is aligned with Rubins recommendation.

关键词： Fractional hot-deck imputation missing data data curing generalized additive model machine learning regression

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