Climate data analysis to assess resilience of wall assemblies to moisture loads arising from the effects of wind-driven rain

In North America, and abroad, there currently exist standard test protocols for assessing the watertightness of wall assemblies and fenestration components although most of these methods are not directly related to expectations of in-field conditions as might be experienced by a wall assembly over its intended service life. How useful might such test protocols be to help determine the longevity of wall assemblies to future climate loads? Existing walls may, depending on their geographic location, be vulnerable to future climate loads and thus risk premature deterioration. For the design of new wall assemblies consideration ought to given to the non-stationarity of the climate and implications on the moisture loads on walls and the expected performance over the long-term. To permit assessing the resilience of wall assemblies to the effects of a changing climate as may occur in the future, and indeed, perhaps heightened moisture loads, one requires sufficient information on the watertightness of the assembly in relation to specified wind-driven rain loads and wall air-leakage conditions from which wall moisture retention functions could readily be developed. Such moisture functions are the basis of input of moisture loads to hygrothermal models and from which the expected long-term wall moisture performance can subsequently be derived. In this paper, a description is provided of the strategies used to analyze the WDR load for generating experimental input for a watertightness test protocol under development to assess resilience of wall assemblies to moisture loads arising from the effects of wind-driven rain in consideration of both historical climate loads and those as may arise from a changing climate.

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Application of Support Vector Regression to the Prediction of the Long-Term Impacts of Climate Change on the Moisture Performance of Wood Frame and Massive Timber Walls

Buildings ◽

10.3390/buildings11050188 ◽

2021 ◽

Vol 11 (5) ◽

pp. 188

Author(s):

Naman Bansal ◽

Maurice Defo ◽

Michael A. Lacasse

Keyword(s):

Relative Humidity ◽

Full Range ◽

Support Vector ◽

Data Series ◽

Climate Variables ◽

Climate Data ◽

Moisture Performance ◽

Wood Frame ◽

Massive Timber

The objective of this study was to explore the potential of a machine learning algorithm, the Support Vector Machine Regression (SVR), to forecast long-term hygrothermal responses and the moisture performance of light wood frame and massive timber walls. Hygrothermal simulations were performed using a 31-year long series of climate data in three cities across Canada. Then, the first 5 years of the series were used in each case to train the model, which was then used to forecast the hygrothermal responses (temperature and relative humidity) and moisture performance indicator (mold growth index) for the remaining years of the series. The location of interest was the exterior layer of the OSB and cross-laminated timber in the case of the wood frame wall and massive timber wall, respectively. A sliding window approach was used to incorporate the dependence of the hygrothermal response on the past climatic conditions, which allowed SVR to capture time, implicitly. The variable selection was performed using the Least Absolute Shrinkage and Selection Operator, which revealed wind-driven rain, relative humidity, temperature, and direct radiation as the most contributing climate variables. The results show that SVR can be effectively used to forecast hygrothermal responses and moisture performance on a long climate data series for most of the cases studied. In some cases, discrepancies were observed due to the lack of capturing the full range of variability of climate variables during the first 5 years.

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Thermal comfort level in Chennai Metropolis under present and future climate scenarios

10.34154/2019-ejcc-0101-13-23/euraass ◽

2019 ◽

pp. 17-23 ◽

Cited By ~ 1

Author(s):

Anushiya Jeganathan ◽

Ramachandran Andimuthu ◽

Palanivelu Kandasamy

Keyword(s):

Climate Change ◽

Thermal Comfort ◽

City Planning ◽

Monsoon Season ◽

Future Climate ◽

Comfort Level ◽

Climate Scenarios ◽

Climate Data ◽

Post Monsoon

Climate change poses unprecedented challenges to urban inhabitants. Thermal comfort is one of the major issues in cities and it is expected to change in future due to climate change. The change of climate parameters particularly, temperature and relative humidity will affect the thermal comfort environments of people. Discomfort levels are largely preventable and requires prior assessment. In this study, the observed and projected thermal comfort level of Chennai Metropolis are calculated using Thermo-Hygrometric Index (THI) under present and future climate scenarios. The observed climate data of Chennai Metropolis for the period 1951-2010 procured from IMD are used to find the long term changes in observed thermal comfort. Monthly trends of THI are calculated for different periods to understand the thermal comfort behaviour in recent decades. On long term observation, high discomfort level is noticed during May and June months followed by July, August, April and September months. While there is a sharp increase in THI during winter months of recent decades. There is a considerable increase in discomfort level notice in post-monsoon season especially in December and November months. Future THI is calculated using high-resolution future climate scenarios developed using PRECIS. The deviations of THI from baseline to mid-century (2041-2070) and end-century period (2071-2099) are calculated and geospatially mapped using ArcGIS. There would be 2.0°C increase of THI is expected during winter and post monsoon months in mid-century scenario. Changes in future THI warrants the need for better cooling requirements and city planning to adapt with the future trends of external environment. Thus the study urges urban planners to evolve climate smart adaptation strategies to provide the congenial climate for a better living.

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In-Situ and Predicted Performance of a Certified Industrial Passive House Building under Future Climate Scenarios

Buildings ◽

10.3390/buildings11100457 ◽

2021 ◽

Vol 11 (10) ◽

pp. 457

Author(s):

Alison Conroy ◽

Phalguni Mukhopadhyaya ◽

Guido Wimmers

Keyword(s):

Energy Use ◽

Wood Products ◽

Future Climate ◽

Thick Wall ◽

Exterior Wall ◽

Climate Data ◽

Performance Models ◽

Passive House ◽

Hygrothermal Performance

The Wood Innovation Research Lab was designed as a low energy-use building to facilitate the construction and testing of engineered wood products by the faculty and staff of the Master of Engineering in Integrated Wood Design Program at the University of Northern British Columbia in Prince George, BC, Canada. Constructed using a 533 mm thick-wall and 659 mm flat roof assembly, it received certification as Canada’s first industrial facility built to the International Passive House standard. Temperature and humidity sensors were installed in the north and south exterior wall assemblies to measure long-term hygrothermal performance. Data collected between 2018–2020 shows no record of long-term moisture accumulation within the exterior assemblies. Data collected during this time period was used to validate hygrothermal performance models for the building created using the WUFI® Plus software. Long-term performance models created using future climate data for five cities across Canada under two global warming scenarios shows favorable results, with an increase in average annual temperatures resulting in lower average relative humidity values at the interior face of the exterior sheathing board in the exterior wall assemblies.

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Climate drivers of plague epidemiology in British India, 1898–1949

Proceedings of The Royal Society B Biological Sciences ◽

10.1098/rspb.2020.0538 ◽

2020 ◽

Vol 287 (1928) ◽

pp. 20200538

Author(s):

Warren S. D. Tennant ◽

Mike J. Tildesley ◽

Simon E. F. Spencer ◽

Matt J. Keeling

Keyword(s):

Predictive Models ◽

Control Measures ◽

Climate Data ◽

British India ◽

Middle Income ◽

Middle Income Countries ◽

Complex Interactions ◽

The World ◽

Spatio Temporal

Plague, caused by Yersinia pestis infection, continues to threaten low- and middle-income countries throughout the world. The complex interactions between rodents and fleas with their respective environments challenge our understanding of human plague epidemiology. Historical long-term datasets of reported plague cases offer a unique opportunity to elucidate the effects of climate on plague outbreaks in detail. Here, we analyse monthly plague deaths and climate data from 25 provinces in British India from 1898 to 1949 to generate insights into the influence of temperature, rainfall and humidity on the occurrence, severity and timing of plague outbreaks. We find that moderate relative humidity levels of between 60% and 80% were strongly associated with outbreaks. Using wavelet analysis, we determine that the nationwide spread of plague was driven by changes in humidity, where, on average, a one-month delay in the onset of rising humidity translated into a one-month delay in the timing of plague outbreaks. This work can inform modern spatio-temporal predictive models for the disease and aid in the development of early-warning strategies for the deployment of prophylactic treatments and other control measures.

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PERSIANN-CCS-CDR, a 3-hourly 0.04° global precipitation climate data record for heavy precipitation studies

Scientific Data ◽

10.1038/s41597-021-00940-9 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Mojtaba Sadeghi ◽

Phu Nguyen ◽

Matin Rahnamay Naeini ◽

Kuolin Hsu ◽

Dan Braithwaite ◽

...

Keyword(s):

Spatial Resolution ◽

Heavy Precipitation ◽

Climate Data ◽

Spatiotemporal Resolution ◽

Data Record ◽

Precipitation Estimation ◽

Precipitation Estimates ◽

Global Precipitation ◽

Climate Data Record

AbstractAccurate long-term global precipitation estimates, especially for heavy precipitation rates, at fine spatial and temporal resolutions is vital for a wide variety of climatological studies. Most of the available operational precipitation estimation datasets provide either high spatial resolution with short-term duration estimates or lower spatial resolution with long-term duration estimates. Furthermore, previous research has stressed that most of the available satellite-based precipitation products show poor performance for capturing extreme events at high temporal resolution. Therefore, there is a need for a precipitation product that reliably detects heavy precipitation rates with fine spatiotemporal resolution and a longer period of record. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR) is designed to address these limitations. This dataset provides precipitation estimates at 0.04° spatial and 3-hourly temporal resolutions from 1983 to present over the global domain of 60°S to 60°N. Evaluations of PERSIANN-CCS-CDR and PERSIANN-CDR against gauge and radar observations show the better performance of PERSIANN-CCS-CDR in representing the spatiotemporal resolution, magnitude, and spatial distribution patterns of precipitation, especially for extreme events.

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Waterlogging-Stress-Responsive LncRNAs, Their Regulatory Relationships with MiRNAs and Target Genes in Cucumber (Cucumis sativus L.)

International Journal of Molecular Sciences ◽

10.3390/ijms22158197 ◽

2021 ◽

Vol 22 (15) ◽

pp. 8197

Author(s):

Kinga Kęska ◽

Michał Wojciech Szcześniak ◽

Adela Adamus ◽

Małgorzata Czernicka

Keyword(s):

Target Genes ◽

Recovery Period ◽

Sufficient Information ◽

Low Oxygen ◽

Waterlogging Tolerance ◽

Cucumis Sativus L ◽

Waterlogging Stress ◽

Non Coding Rna

Low oxygen level is a phenomenon often occurring during the cucumber cultivation period. Genes involved in adaptations to stress can be regulated by non-coding RNA. The aim was the identification of long non-coding RNAs (lncRNAs) involved in the response to long-term waterlogging stress in two cucumber haploid lines, i.e., DH2 (waterlogging tolerant—WL-T) and DH4 (waterlogging sensitive—WL-S). Plants, at the juvenile stage, were waterlogged for 7 days (non-primed, 1xH), and after a 14-day recovery period, plants were stressed again for another 7 days (primed, 2xH). Roots were collected for high-throughput RNA sequencing. Implementation of the bioinformatic pipeline made it possible to determine specific lncRNAs for non-primed and primed plants of both accessions, highlighting differential responses to hypoxia stress. In total, 3738 lncRNA molecules were identified. The highest number (1476) of unique lncRNAs was determined for non-primed WL-S plants. Seventy-one lncRNAs were depicted as potentially being involved in acquiring tolerance to hypoxia in cucumber. Understanding the mechanism of gene regulation under long-term waterlogging by lncRNAs and their interactions with miRNAs provides sufficient information in terms of adaptation to the oxygen deprivation in cucumber. To the best of our knowledge, this is the first report concerning the role of lncRNAs in the regulation of long-term waterlogging tolerance by priming application in cucumber.

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Digital Technologies To Enhance Social Connectedness in Long-Term Care Facilities During COVID-19: A Review

Innovation in Aging ◽

10.1093/geroni/igaa057.3451 ◽

2020 ◽

Vol 4 (Supplement_1) ◽

pp. 942-943

Author(s):

Shannon Freeman ◽

Aderonke Abgoji ◽

Alanna Koopmans ◽

Christopher Ross

Keyword(s):

Social Isolation ◽

Rural Areas ◽

Social Connectedness ◽

Long Term Care ◽

Care Facility ◽

Well Being ◽

Sufficient Information ◽

Term Care ◽

Care Facilities

Abstract A consequence of the strict visitor restrictions implemented by many Long-term Care Facilities (LTCFs), during the COVID-19 pandemic, was the exacerbation of loneliness and social isolation felt by older adult residents. While there had been a shift by some persons to utilize digital solutions to mitigate the effects of the imposed social isolation, many facilities did not have sufficient information regarding available solutions to implement institutional strategies to support social connectedness through digital solutions. To support our partners in evidence-based policy-making we conducted a scoping review to identify existing virtual technology solutions, apps, and platforms feasible to promote social connectedness among persons residing in a long-term care facility context during times of lockdown such as experienced during the COVID-19 pandemic. Initial identification of relevant literature involved a combination of keywords and subject headings searches within 5 databases (PubMed, CINAHL EBSCO, PsychINFO EBSCO, Embase OVIDSP, and Web of Science ISI). DistillerSR was used to screen, chart and summarize the data. There is growth in the availability of technologies focused on promoting health and well-being in later life for persons in long-term care facilities however a gap remains in widespread uptake. We will describe the breadth of technologies identified in this review and discuss how they vary in utility in smaller scale facilities common in rural areas. Of the technologies that can be used to mitigate the impacts of social isolation felt by long-term care residents, many “solutions” depend on stable highspeed internet, which remains a challenge in rural and northern areas.

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The Passive Microwave Neural Network Precipitation Retrieval Algorithm for Climate Applications (PNPR-CLIM): Design and Verification

Remote Sensing ◽

10.3390/rs13091701 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1701

Author(s):

Leonardo Bagaglini ◽

Paolo Sanò ◽

Daniele Casella ◽

Elsa Cattani ◽

Giulia Panegrossi

Keyword(s):

Neural Network ◽

Passive Microwave ◽

Retrieval Algorithm ◽

Climate Data ◽

High Quality ◽

Data Record ◽

Global Precipitation ◽

Climate Data Record

This paper describes the Passive microwave Neural network Precipitation Retrieval algorithm for climate applications (PNPR-CLIM), developed with funding from the Copernicus Climate Change Service (C3S), implemented by ECMWF on behalf of the European Union. The algorithm has been designed and developed to exploit the two cross-track scanning microwave radiometers, AMSU-B and MHS, towards the creation of a long-term (2000–2017) global precipitation climate data record (CDR) for the ECMWF Climate Data Store (CDS). The algorithm has been trained on an observational dataset built from one year of MHS and GPM-CO Dual-frequency Precipitation Radar (DPR) coincident observations. The dataset includes the Fundamental Climate Data Record (FCDR) of AMSU-B and MHS brightness temperatures, provided by the Fidelity and Uncertainty in Climate data records from Earth Observation (FIDUCEO) project, and the DPR-based surface precipitation rate estimates used as reference. The combined use of high quality, calibrated and harmonized long-term input data (provided by the FIDUCEO microwave brightness temperature Fundamental Climate Data Record) with the exploitation of the potential of neural networks (ability to learn and generalize) has made it possible to limit the use of ancillary model-derived environmental variables, thus reducing the model uncertainties’ influence on the PNPR-CLIM, which could compromise the accuracy of the estimates. The PNPR-CLIM estimated precipitation distribution is in good agreement with independent DPR-based estimates. A multiscale assessment of the algorithm’s performance is presented against high quality regional ground-based radar products and global precipitation datasets. The regional and global three-year (2015–2017) verification analysis shows that, despite the simplicity of the algorithm in terms of input variables and processing performance, the quality of PNPR-CLIM outperforms NASA GPROF in terms of rainfall detection, while in terms of rainfall quantification they are comparable. The global analysis evidences weaknesses at higher latitudes and in the winter at mid latitudes, mainly linked to the poorer quality of the precipitation retrieval in cold/dry conditions.

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A method for simulating risk profiles of wheat yield in data-sparse conditions

The Journal of Agricultural Science ◽

10.1017/s0021859621000253 ◽

2021 ◽

pp. 1-12

Author(s):

G. Bracho-Mujica ◽

P.T. Hayman ◽

V.O. Sadras ◽

B. Ostendorf

Keyword(s):

Reference Site ◽

Climate Impacts ◽

Weather Data ◽

Climate Data ◽

High Quality ◽

Risk Profiles ◽

Record Length ◽

Daily Weather Data ◽

Study Sites

Abstract Process-based crop models are a robust approach to assess climate impacts on crop productivity and long-term viability of cropping systems. However, these models require high-quality climate data that cannot always be met. To overcome this issue, the current research tested a simple method for scaling daily data and extrapolating long-term risk profiles of modelled crop yields. An extreme situation was tested, in which high-quality weather data was only available at one single location (reference site: Snowtown, South Australia, 33.78°S, 138.21°E), and limited weather data was available for 49 study sites within the Australian grain belt (spanning from 26.67 to 38.02°S of latitude, and 115.44 to 151.85°E of longitude). Daily weather data were perturbed with a delta factor calculated as the difference between averaged climate data from the reference site and the study sites. Risk profiles were built using a step-wise combination of adjustments from the most simple (adjusted series of precipitation only) to the most detailed (adjusted series of precipitation, temperatures and solar radiation), and a variable record length (from 10 to 100 years). The simplest adjustment and shortest record length produced bias of modelled yield grain risk profiles between −10 and 10% in 41% of the sites, which increased to 86% of the study sites with the most detailed adjustment and longest record (100 years). Results indicate that the quality of the extrapolation of risk profiles was more sensitive to the number of adjustments applied rather than the record length per se.

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The Use of Primary Cultured Rat Hepatocytes for the Assessment of Xenobiotic Effects on Biotransformation

Alternatives to Laboratory Animals ◽

10.1177/026119299101900211 ◽

1991 ◽

Vol 19 (2) ◽

pp. 209-213

Author(s):

Gabi Schepers ◽

Christiane Aschmann ◽

Sabine Mörchel

Keyword(s):

Cell Viability ◽

Rat Hepatocytes ◽

Standard Test ◽

In Vitro Test ◽

Chlorinated Phenols ◽

Test Protocol ◽

Primary Cultured Rat Hepatocytes ◽

Cultured Rat Hepatocytes ◽

Different Response

An in vitro test protocol is reported, which, using primary cultured rat hepatocytes, allows for the screening of xenobiotic effects on biotransformation as well as on basal cellular functions. O-Deethylation of 7-ethoxycoumarin (7-EC) and subsequent conjugation of the metabolite 7-hydroxycoumarin (7-HC) with sulphate or glucuronic acid are determined, as representative parameters for the hepatic biotransformation. Cell viability is examined by measuring cellular ATP content and leakage of lactate dehydrogenase. With respect to immediate and delayed effects on biotransformation reactions, the standard test protocol includes exposure to xenobiotics for 1, 24 and 48 hours. Different response patterns could be demonstrated for the solvents dimethylformamide (DMF) and dimethylsulphoxide (DMSO), and the chlorinated phenols, pentachlorophenol (PCP) and hexachlorophene (HCP), which are known to uncouple mitochondrial respiration. Short-term incubation with the solvents resulted in decreased 7-EC- O-deethylation without signs of cytotoxicity. PCP and HCP inhibited 7-EC- O-deethylation and 7-HC-conjugation, affecting sulphate and glucuronide formation differently. 24-hour exposures to PCP and HCP resulted in decreased 7-ethoxycoumarin- O-deethylase activity, which correlated with diminished cell viability, while DMSO and DMF enhanced 7-EC- O-deethylation at sub-cytotoxic concentrations. After exposure for 48 hours to the solvents, enzyme induction was even more pronounced.

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