The present study deals with demographic survey around Jaitapur Nuclear Power Plant proposed site which comes in Ratnagiri and part of Sindhudurg District. Demography study is important for creating base line data. The study area includes 121 villages which come into 30 km radial range around JNPP. The present study focuses on the demographic characteristics and socio-economic conditions of this region. The household survey was carried out around the proposed project site

IntroductionAlthough people spend most of the day in their home environment, the focus of research in environmental psychology to date has been on factors outside the home. However, it stands to reason that indoor quality likewise has an impact on psychological well-being. Therefore, the present study addresses the question of whether the subjective evaluation of home environmental parameters are related to self-reported anxiety and whether they can additionally explain variance beyond the usual sociodemographic and general lifestyle variables.MethodsData from the Hamburg City Health Study (first 10,000 participants) was analyzed. A subsample of N = 8,886 with available GAD-7 anxiety data was selected, and hierarchical regression models were computed, with demographic data entered first, followed by variables concerning lifestyle/habits and finally variables of the subjective evaluation of home environment.ResultsUsing the integrated model, we were able to explain about 13% of the variance in self-reported anxiety scores. This included both the demographic, lifestyle, and subjective evaluation of home environment variables. Protection from disturbing night lights, a greater sense of security, less disturbing noises, brighter accommodations, and a satisfactory window view explained almost 6% of the variance and was significantly associated with lower anxiety scores.ConclusionThe home as a place of refuge plays an increasingly important role as home office hours rise. It is therefore crucial to identify domestic factors contributing to people's mental well-being. The subjective evaluation of one's home environment has proven influential over and above modifiable lifestyle variables.

Background: Efforts to support disadvantaged communities have been prioritized through initiatives like Justice40, the Inflation Reduction Act (IRA), and the Bipartisan Infrastructure Law (BIL). Identifying disadvantaged communities involves several datasets with associated variables related to vulnerability indicators and scores. There are three key datasets:

1. Climate and Economic Justice Screening Tool (CEJST) from the White House Council on Environmental Quality (CEQ).
2. Energy Justice Mapping Tool from the Department of Energy (DOE) Office of Economic Impact & Diversity.
3. Environmental Justice Screening Tool from the Environmental Protection Agency (EPA) Office of Environmental Justice and External Civil Rights.

Problem:

1. Each dataset employs similar but distinct criteria and outcomes.
2. The unique IDs or GEOIDs differ across datasets. CEJST uses the 2010 census boundaries, while the Energy Justice Mapping Tool and the Environmental Justice Screening Tool are based on the 2019 and 2021 census boundaries, respectively.

To address these issues, this dataset consolidates information on disadvantaged communities and their associated variables by combining the three distinct datasets:

• CEJST: Provides binary data indicating whether a tract is a disadvantaged community. A community is classified as disadvantaged if it meets any of the following thresholds: 1) one or more indicators within categories such as climate change, energy, health, housing, pollution, transportation, and water & wastewater, coupled with low income; 2) one or more indicators in workforce development category and education; or 3) tribal lands. Environment and pollution indicators come from the EPA, while socio-demographic indicators are from the American Community Survey (ACS) for 2015-2019.

• Energy Justice Mapping Tool: Offers a DAC score, a continuous variable representing the sum of the 36 indicator percentiles. It includes environment, pollution, and socio-demographic indicators from the EPA and ACS (2015-2019).

• Environmental Justice Screening Tool: Includes the 13 Environmental Justice (EJ) Index and Supplemental Index. These continuous variables are weighted with socio-demographic indicators from ACS (2017-2021).

Data Descriptions

• Unit of Analysis: Census tract
• Geometry: 2021 census boundaries
• Columns:
  • Socio-demographic indicators
  • 13 Environmental Justice (EJ) index values
  • 13 Supplemental index values
  • Binary indicator for disadvantaged community classification
  • Disadvantaged Community (DAC) scores
• Datasets:
  • results/DAC.csv: Contains all columns from the three datasets.
  • results/DAC_s.csv: A shorter version, including socio-demographic indicators and EJ and Supplemental indices (Environmental Justice Screening Tool), disadvantaged community classification (CEJST), and DAC scores (Energy Justice Mapping Tool).
• Code:
  • syntax/code.R: This script illustrates the methodology for merging the three datasets, culminating in the creation of the two CSV files located in the results directory.

Purpose:

The dataset aims to help researchers identify overall disadvantaged communities or determine which specific communities are classified as disadvantaged. By consolidating these datasets, researchers can more effectively analyze and compare the various criteria used to define disadvantaged communities, enhancing the comprehensiveness of their studies.

Note

For complete data descriptions and sources, please refer to the original datasets.

• Climate and Economic Justice Screening Tool (CEJST)
• Energy Justice Mapping Tool
• Environmental Justice Screening Tool

Environment Canada has identified a need for sound information on current youth opinion pertaining to climate change in order to direct policy and communications efforts. In light of this need, Ipsos-Reid was commissioned to conduct opinion research among young Canadians (from 16 to 25 years of age) to establish a baseline measurement of their awareness, receptivity and behaviour on issues related to climate change. The methodology for this research involved conducting a telephone survey of young Canadians. Respondents were identified through the Ipsos-Reid Household panel, and the Ipsos-Reid Express panel. Both panels are recruited using the random digit dialling technique, and the sample records were selected for inclusion in the study on a random basis. The survey took place between March 11 and March 18, 2004. A total of 1,228 young Canadians between the ages of 16 and 25 were surveyed. The sample of 1,228 respondents has been weighted by region, age and gender to be representative of Canada’s population of 16-25 year olds in accordance with Census data. The overall margin of error for a sample of this size is ±2.2 percentage points, 19 times out of 20. Margins of error are higher within demographic and attitudinal subgroups. Youth Perceptions of Climate Change [2004] - Final Report: http://www.queensu.ca/cora/_files/YPCC-2004.pdf Copyright (c) 2004 - Environment Canada and Ipsos Reid

Natural Environment White Paper (2012, updated 2014) - United Kingdom

Background: Living near, recreating in, and feeling psychologically connected to nature are all associated with better overall mental health. This study aims to better understand people’s feelings towards different types of natural and built green space environments in the highly urbanized ‘garden city’ of Singapore. The key research question addresses the matter of what types of green space elicit positive (Eudemonic) or negative (Apprehensive) affective responses. Type of environment (natural and built), frequency of experience (high and low) and childhood location (urban, suburban, rural) were tested for effects of Eudemonia and Apprehension. 288 adults and university students residing in Singapore completed a survey that asked them to report affective states in response to images of 10 locally different environment types and to complete measures of nature connectedness, childhood location, frequency of visit to natural/built environments, and dispositional anxiety, as well as demographic items for age and gender.

This data record contains:

• Qualtrics survey data in SPSS (.spss), tab delimited (.dat) and open document (.ods) format.
• Supplementary material in PDF format (.pdf) containing the Mean (sd) ratings of Apprehension (A, anxious, isolated, lonely) and Eudemonia (E, alive, awe, connected, contemplative, empathy, freedom, fun, refreshed, relaxed, serene, talkative) for 10 types of environment.

The Qualtrics survey included the following:

• Participant demographics:
  • Age in years (continuous)
  • Gender (categorical: Male, Female, Nonbinary)
• Categorisation of urban green space in Singapore:
  • 20 photographs of urban green spaces in Singapore (stimuli).
  • 10 categories of urban green spaces consisted of: beach, forest, grassy field, heritage street, modern city street, rooftop garden, river, town park, wetland, and woodland.
  • Two photographs that were best suited to each category according to participant responses (i.e., highest frequency of category selection) were used as stimuli for the study, with a total of 20 photographs selected.
• Experiential feeling states (Eudemonia & Apprehension) (interval) (20 x 14 items).
  • “Imagine yourself in the environment shown above. To what extent would you feel the following?”
  • Responses were recorded on a 7-point scale ranging from not at all (1) to extremely (7).
• Frequency of experience in green space (interval) (20 x 1 item).
  • “On average, how often do you visit or experience the type of environment as the one shown above?” Responses were recorded on a 5-point scale ranging from never (1) to very often (5).
• Childhood location (categorical) (1 item). “In what sort of location did you spend the majority of your childhood?” Urban (Modernised city, city-centre, many buildings with few trees, high traffic), Suburban (More greenery than city-centre but still developed, outside the main city area, neighbourhood towns, moderate traffic), Rural (Mostly greenery, few facilities, low traffic, “kampung” environment).
• Nature Connectedness Index (NCI) (interval) (6 items). "The next items will help us understand how you feel about nature and natural environments. Remember, this is not a test so there are no 'right' or 'wrong' answers. We want to understand how you feel about nature." The six items draw on five pathways to nature connectedness: emotion, beauty, contact, meaning and compassion. Participants respond using a 7-point scale ranging from completely agree (1) to completely disagree (7). Raw scores were transformed using a weighted points index ranging from zero to 100.
• Brief State-Trait Anxiety Inventory (STAIT-5) (interval) (5 items). “A number of statements which people have used to describe themselves are given below. Read each statement and then select the number at the end of the statement that indicates how you generally feel.” Responses are recorded on a 4-point scale ranging from not at all (1) to very much so (4).

Software/equipment used to create/collect the data: Qualtrics Online Survey Software through JCU licence

Software/equipment used to manipulate/analyse the data: SPSS, Microsoft Excel

The Smart Location Database (SLD) summarizes over 80 demographic, built environment, transit service, and destination accessibility attributes for every census block group in the United States. Future updates to the SLD will include additional attributes which summarize the relative location efficiency of a block group when compared to other block groups within the same metropolitan region. EPA also plans to periodically update attributes and add new attributes to reflect latest available data. A log of SLD updates is included in the SLD User Guide. See the user guide for a full description of data sources, data currency, and known limitations: https://edg.epa.gov/data/Public/OP/SLD/SLD_userguide.pdf

Datasets, conda environments and Softwares for the course "Population Genomics" of Prof Kasper Munch. This course material is maintained by the health data science sandbox. This webpage shows the latest version of the course material.

1. Data.tar.gz Contains the datasets and executable files for some of the softwares
   You can unpack by simply doing
   tar -zxf Data.tar.gz -C ./
   This will create a folder called Data with the uncompressed material inside
2. Course_Env.packed.tar.gz Contains the conda environment used for the course. This needs to be unpacked to adjust all the prefixes (Note this environment is created on Ubuntu 22.10). You do this in the command line by
   1. creating the folder Course_Env: mkdir Course_Env
   2. untar the file: tar -zxf Course_Env.packed.tar.gz -C Course_Env
   3. Activate the environment: conda activate ./Course_Env
   4. Run the unpacking script (it can take quite some time to get it done): conda-unpack
3. Course_Env.unpacked.tar.gz The same environment as above, but will work only if untarred into the folder /usr/Material - so use the version above if you are using it in another folder. This file is mostly to execute the course in our own cloud environment.
4. environment_with_args.yml The file needed to generate the conda environment. Create and activate the environment with the following commands:
   1. conda env create -f environment_with_args.yml -p ./Course_Env
   2. conda activate ./Course_Env

The data is connected to the following repository: https://github.com/hds-sandbox/Popgen_course_aarhus. The original course material from Prof Kasper Munch is at https://github.com/kaspermunch/PopulationGenomicsCourse.

Description

The participants will after the course have detailed knowledge of the methods and applications required to perform a typical population genomic study.

The participants must at the end of the course be able to:

• Identify an experimental platform relevant to a population genomic analysis.
• Apply commonly used population genomic methods.
• Explain the theory behind common population genomic methods.
• Reflect on strengths and limitations of population genomic methods.
• Interpret and analyze results of population genomic inference.
• Formulate population genetics hypotheses based on data

The course introduces key concepts in population genomics from generation of population genetic data sets to the most common population genetic analyses and association studies. The first part of the course focuses on generation of population genetic data sets. The second part introduces the most common population genetic analyses and their theoretical background. Here topics include analysis of demography, population structure, recombination and selection. The last part of the course focus on applications of population genetic data sets for association studies in relation to human health.

Curriculum

The curriculum for each week is listed below. "Coop" refers to a set of lecture notes by Graham Coop that we will use throughout the course.

Course plan

1. Course intro and overview:
   • Coop chapters 1, 2, 3, Paper: Genome Diversity Project
2. Drift and the coalescent:
   • Coop chapter 4; Paper: Platypus
   • Exercise: Read mapping and base calling
3. Recombination:
   • Lecture: Review: Recombination in eukaryotes, Review: Recombination rate estimation
   • Exercise: Phasing and recombination rate
4. Population strucure and incomplete lineage sorting:
   • Lecture: Coop chapter 6, Review: Incomplete lineage sorting
   • Exercise: Working with VCF files
5. Hidden Markov models:
   • Lecture: Durbin chapter 3, Paper: population structure
   • Exercise: Inference of population structure and admixture
6. Ancestral recombination graphs:
   • Lecture: Paper: Approximating the ARG, Paper: Tree inference
   • Exercise: ARG dashboard exercises + Inference of trees along sequence
7. Past population demography:
   • Lecture: Coop chapter 4, Paper: PSMC, revisit Paper: Tree inference
   • Exercise: Inferring historical populations
8. Direct and linked selection:
   • Lecture: Coop chapters 12, 13, revisit Paper: Tree inference
9. Admixture:
   • Lecture: Review: Admixture, Paper: Admixture inference
   • Exercise: Detecting archaic ancestry in modern humans
10. Genome-wide association study (GWAS):
    • Lecture: Coop lecture notes 99-120
    • Exercise: GWAS quality control
11. Heritability:
    • Lecture: Coop Lecture notes Sec. 2.2 (p23-36) + Chap. 7 (p119-142)
    • Exercise: Association testing
12. Evolution and disease:
    • Lecture: Coop Lecture notes Sec. 11.0.1 (p217-221)
    • Exercise: Estimating heritability

BackgroundThe design of social programs at the environmental level such as in schools, parks, bicycle paths, or workspaces generates changes in the behavior of individuals and modifies lifestyles by increasing physical activity (PA) levels.ObjectiveTo determine the effectiveness of environmental interventions based on social programs by changing the population's level of PA.MethodologyNatural experiment studies that involved environmental intervention programs at a social level were included. The primary outcome was PA levels with consideration of both objective and subjective measurements. An electronic search was carried out in Medline/Pubmed, SCIENCE DIRECT, WEB OF SCIENCE, and CINAHL databases up to January 2022 with two reviewers screening titles and abstracts and selecting studies for full-text reading. Two reviewers also acquired relevant data and evaluated study quality using the ROBINS I tool. A qualitative analysis was performed.ResultsThree thousand eight hundred and sixty-five articles were found in the 4 consulted databases. After eliminating duplication (200), two reviewers screened 3,665 titles and abstracts and excluded 3,566 that did not meet the inclusion criteria, leaving 99 articles to be read in full text. The 99 full texts were reviewed of which 24 papers met the eligibility criteria. All were natural experiments published between 2011 and 2020 and all evaluated environmental social programs revealing that social programs at the environmental level promoted PA in various populations at the community level worldwide.ConclusionThe 24 reviewed studies suggest innovative proposals for social programs that seek to increase PA and promote healthy lifestyles related to public activity policies developed in the countries in which they were generated. Environmental social programs can positively impact PA levels among children and adults.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/display_record.php?RecordID=229718, identifier: CRD42021229718.

Geospatial data about Policies Natural Environment. Export to CAD, GIS, PDF, CSV and access via API.

This data set offers a territorial view of the socioeconomic, health, demographic, educational, quality of life, housing, environment, municipal facilities, citizen participation and budget of the districts of Madrid. This information is also included at the city level and, when available, at the neighborhood level. The information has been obtained mainly from the data that the Madrid City Council offers, from a territorial perspective. In the 'Downloads' section, the annual versions of this study are available in XLS format and a CSV version that collects the data of all the annual versions. The section ' Associated documentation ' includes an explanatory document on the preparation of the panel and the annual studies in PDF format. Other datasets with more information on districts and wards are also available on this portal: surface and perimeter, census, budgets, etc. Through the Visualization Portal ' Visualize Madrid with Open Data ' , the City Council of Madrid puts at your disposal a visualization made with open data of the Panel of indicators of districts and neighborhoods of Madrid . Access View District and Neighborhood Indicators (full screen)

VITAL SIGNS INDICATOR Greenhouse Gas Emissions (EN3)

FULL MEASURE NAME Greenhouse gas emissions from primary sources

LAST UPDATED August 2017

DESCRIPTION Greenhouse gas emissions refer to carbon dioxide and other chemical compounds that contribute to global climate change. Vital Signs tracks greenhouse gas emissions linked to consumption from the three largest sources in the region: surface transportation, electricity consumption, and natural gas consumption. This measure helps track progress towards achieving regional greenhouse gas reduction targets, including the region's per-capita greenhouse gas target for surface transportation under Senate Bill 375. This dataset includes emissions estimates on the regional and county levels.

DATA SOURCE California Energy Commission: Retail Fuel Outlet Annual Reporting 2010-2012, 2015 Form CEC-A15 http://www.energy.ca.gov/almanac/transportation_data/gasoline/piira_retail_survey.html

Energy Information Administration: CO2 Conversion Data 2015 conversion purposes only; consistent over time http://www.eia.gov/tools/faqs/faq.cfm?id=307&t=11

California Energy Commission: Electricity Consumption by County 2003-2015 http://www.ecdms.energy.ca.gov/elecbycounty.aspx

Pacific Gas & Electric Company: Greenhouse Gas Emission Factors 2003-2013 audited by the Climate Registry; conversion purposes only https://www.pge.com/includes/docs/pdfs/shared/environment/calculator/pge_ghg_emission_factor_info_sheet.pdf

Pacific Gas & Electric Company: Greenhouse Gas Emission Factors 2014-2015 audited by the Climate Registry; conversion purposes only http://www.pgecurrents.com/2017/02/09/pge-cuts-carbon-emissions-with-clean-energy-2/

California Energy Commission: Natural Gas Consumption by County 1990-2015 http://www.ecdms.energy.ca.gov/gasbycounty.aspx

Pacific Gas & Electric Company: Climate Footprint Calculator 2015 conversion purposes only; consistent over time https://www.pge.com/includes/docs/pdfs/about/environment/calculator/assumptions.pdf

California Department of Finance: Population and Housing Estimates 1990-2015 http://www.dof.ca.gov/research/demographic/

CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator) For surface transportation, the dataset is based on a survey of fueling stations, the vast majority of which respond to the survey; the Energy Commission corrects for non-response bias by imputing the remaining share of fuel sales. Note that 2014 data was excluded to data abnormalities for several counties in the region; methodology improvements in 2012 affected estimated by +/- 5% according to CEC estimates. For years 2013 and 2014, a linear trendline assumption was used instead between 2012 and 2015 data points. Greenhouse gas emissions are calculated based on the gallons of gasoline and diesel sales, relying upon standardized Energy Information Administration conversion rates for E10 fuel (gasoline with 10% ethanol) and standard diesel. Per-capita greenhouse gas emissions are calculated simply by dividing emissions attributable to fuel sold in that county by the total number of county residents; there may be a slight bias in the data given that a fraction of fuel sold in a given county may be purchased by non-residents.

For electricity consumption, the dataset is based on electricity consumption data for the nine Bay Area counties; note that this is different than electricity production as the region imports electricity. Because such data is not disaggregated by utility provider, a simple assumption is made that electricity consumed has the greenhouse gas emissions intensity (on a kilowatt-hour basis) of Pacific Gas & Electric, the primary electricity provider in the Bay Area. For this reason, with the small but growing market share of low- and zero-GHG community choice aggregation (CCA) providers, the greenhouse gas emissions estimate in more recent years may be slightly overestimated. Per-capita greenhouse gas emissions are calculated simply by dividing emissions attributable to fuel sold in that county by the total number of county residents; data is disaggregated between residential and non-residential customers.

For natural gas consumption, the dataset is based on natural gas consumption data for the nine Bay Area counties; note that this is different than natural gas production as the region imports electricity. Certain types of liquefied natural gas shipped into the region or "makegas" produced at oil refineries during their production process may not be fully reflected in this data. Therms are converted to metric tons of greenhouse gas emissions using standardized assumptions that align with federal guidance. Per-capita greenhouse gas emissions are calculated simply by dividing emissions attributable to fuel sold in that county by the total number of county residents; data is disaggregated between residential and non-residential customers.

Abstract (en): This survey is part of a continuing series designed to monitor trends in a wide range of social attitudes in Great Britain. The British Social Attitudes Survey (BSA) is similar to the General Social Survey carried out by the National Opinion Research Center (NORC) in the United States. The BSA questionnaire has two parts, one administered by an interviewer and the other completed by the respondent. The 1993 self-enumerated questionnaire was devoted to a series of questions on a range of social, economic, political, and moral issues. Topics covered (by section) are: (1) government spending, the National Health Service, (2) labor market participation, the workplace, redundancy, employee decision-making, (3) AIDS, the countryside, (4) primary and secondary school education, transportation, the environment, (5) Northern Ireland, the European Community, (6) charitable giving, economic issues and policies (including income and taxation), (7) illegal drugs, social security benefits, child maintenance, (8) sexual relations, (9) housing, (10) religious denomination and attendance, and (11) ethnic origin. Beginning in 1985, an international initiative funded by the Nuffield Foundation, the International Social Survey Program (ISSP), also contributed a module to the BSA. The topic of the ISSP module in this collection was the environment. Additional demographic data included age, education, income, marital status, and religious and political affiliations. All adults aged 18 or over living in private households in Britain whose addresses were included in the electoral registers (excluding the "crofting counties" north of the Caledonian Canal). Multistage stratified random sample consisting of four stages. From 1993 the sample was drawn from the Postcode Address File, whereas in previous years it had been drawn from the electoral register. 2005-07-22 The data and documentation were resupplied by the United Kingdom Data Archive (UKDA). The data are now available as an SPSS portable file and the documentation has been converted to PDF by the UKDA. Funding insitution(s): Sainsbury Family Charitable Trusts (Great Britain). Nuffield Foundation (United Kingdom). Economic and Social Research Council (United Kingdom). Department of Employment. Department of Health (United Kingdom). Home Office (United Kingdom). Department of Social Security (United Kingdom). Department of Education. Scottish Office Education Department. Countryside Commission (Great Britain). Charities Aid Foundation (United Kingdom). European Commission. face-to-face interview, computer-assisted personal interview (CAPI), self-enumerated questionnaire (1) In 1999, Social and Community Planning Research (SCPR) became the National Centre for Social Research. (2) Under agreement with the UKDA, the data are disseminated as they were received, without additional processing by ICPSR. This agreement also provides that ICPSR will disseminate the data only for use within its member institutions. Persons from nonmember institutions may request these data directly from the UKDA. (3) The data are provided as an SPSS portable file. (4) The documentation was converted to Portable Document Format (PDF) by the UKDA. The PDF documentation can also be downloaded from the UKDA Web site. (5) The formats for some variables in the SPSS portable file (e.g., REMPLOYE) are not wide enough to accommodate the missing value specifications. For some procedures SPSS will display these missing values as asterisks. Users can widen the formats to display the actual missing value codes. (6) The documentation contains information for two different studies: British Social Attitudes, 1993, and Northern Ireland Social Attitudes, 1993. However, only the British Social Attitudes dataset is provided in this collection. (7) The British Social Attitudes Survey series began in 1983 and was conducted every year since, except in 1988 and 1992 when the core funding from the Sainsbury Family Charitable Trusts was devoted to conducting post-election studies of political attitudes and voting behavior in the British Election Study (BES) Survey series. (8) In 1993 a split-sample experiment was carried out whereby a random half of the sample points was allocated to computer-assisted personal interviewing (CAPI) and the rest to pencil and paper interviewing (PAPI).

IntroductionSub-Saharan Africa has the highest under-five mortality rate and is among the regions where people have the least access to adequate Water, Sanitation, and Hygiene (WASH) services. The work aimed to investigate the effects of WASH conditions faced by children on under-five mortality in Sub-Saharan Africa.MethodsWe carried out secondary analyses using the Demographic and Health Survey datasets of 30 countries in Sub-Saharan Africa. The study population consisted of children born within 5 years preceding the selected surveys. The dependent variable was the child’s status (1 = deceased versus 0 = alive) on the survey day. The individual WASH conditions in which children live were assessed in their immediate environment, i.e., at the level of their households of residence. The other explanatory variables were related to the child, mother, household, and environment. Following a description of the study variables, we identified the predictors of under-five mortality using a mixed logistic regression.ResultsThe analyses involved 303,985 children. Overall, 6.36% (95% CI = 6.24–6.49) of children died before their fifth birthday. The percentage of children living in households with access to individual basic WASH services was 58.15% (95% CI = 57.51–58.78), 28.18% (95% CI = 27.74–28.63), and 17.06% (95% CI = 16.71–17.41), respectively. Children living in households using unimproved water facilities (aOR = 1.10; 95% CI = 1.04–1.16) or surface water (aOR = 1.11; 95% CI = 1.03–1.20) were more likely to die before five than those coming from households with basic water facilities. The risk of under-five mortality was 11% higher for children living in households with unimproved sanitation facilities (aOR = 1.11; 95% CI = 1.04–1.18) than for those with basic sanitation services. We found no evidence to support a relationship between household access to hygiene services and under-five mortality.ConclusionInterventions to reduce under-five mortality should focus on strengthening access to basic water and sanitation services. Further studies are needed to investigate the contribution of access to basic hygiene services on under-five mortality.

Proportional change in effective area of similar ecological environments for Mammals as a function of land clearing within the present long term (30 year average) climate (1990 centred) based on Generalised Dissimilarity Modelling (GDM) of compositional turnover.

This metric describes the effects of land clearing on the area of similar environments to each grid cell as a proportion. Each cell is compared with a sample of 60,000 points in both uncleared landscape and degraded landscape (pairwise similarities summed (e.g. a completely similar cell will contribute 1, a dissimilar cell 0, with a range of values in between). The contribution of each cell is then multiplied by a 0 (cleared) to 1 (intact) condition index based on the natural areas layer. By dividing the test area by the current area, we are able to quantify the reduction in area as a function of land use/climate change. Values less than one indicate a reduction, values of 1 no change, and values greater than 1 (rare cases in the north) show an increase in similar environments.

This metric was developed along with others for use in an assessment of the efficacy of the protected area system for biodiversity under climate change at continental and global scales, presented at the IUCN World Parks Congress 2014. It is described in the AdaptNRM Guide “Implications of Climate Change for Biodiversity: a community-level modelling approach”, available online at: www.adaptnrm.org.

Data are provided in two forms: 1. Zipped ESRI float grids: Binary float grids (.flt) with associated ESRI header files (.hdr) and projection files (.prj). After extracting from the zip archive, these files can be imported into most GIS software packages, and can be used as other binary file formats by substituting the appropriate header file. 2. ArcGIS layer package (.lpk): These packages contain can be unpacked by ArcGIS as a raster with associated legend.

Additionally a short methods summary is provided in the file 9sMethodsSummary.pdf for further information.

Layers in this 9s series use a consistent naming convention: BIOLOGICAL GROUP _ FROM BASE_ TO SCENARIO_ ANALYSIS e.g. A_90_CAN85_S or R_90_MIR85_L where BIOLOGICAL GROUP is A: amphibians, M: mammals, R: reptiles and V: vascular plants

Lineage: Proportional change in the area of similar ecological environments was calculated using the highly parallel bespoke CSIRO Muru software running on a LINUX high-performance-computing cluster, taking GDM model transformed environmental grids as inputs. Proportional change was calculated by taking the area of baseline ecological environments similar to each present cell as the denominator and the area of present cells with their contribution scaled by the natural areas condition index (0 degraded to 1 intact) as the numerator. More detail of the calculations and methods are given in the document “9sMethodsSummary.pdf” provided with the data download. GDM Model: Generalised dissimilarity model of compositional turnover in reptile species for continental Australia at 9 second resolution using ALA data extracted 28 February 2014 (GDM: REP_r3_v2) Climate data. Models were built and projected using: a) 9-second gridded climatology for continental Australia 1976-2005: Summary variables with elevation and radiative adjustment b) 9-second gridded climatology for continental Australia 2036-2065 CanESM2 RCP 8.5 (CMIP5): Summary variables with elevation and radiative adjustment Natural Areas Layer (intact to degraded land) Australian Government Department of the Environment (2014) Natural areas of Australia - 100 metre (digital dataset and metadata). Available at http://www.environment.gov.au/metadataexplorer/explorer.jsp and up to date information for Western Australia were provided at 25m Albers projection were reprojected to GDA94, merged and aggregated to a continuous measure of proportion of intact area per grid cell at 9s.

Food environments, or interfaces between consumers and their food systems, are a useful lens for assessing global dietary change. Growing inclusivity of nature-dependent societies in lower-and middle-income countries is driving recent developments in food environment frameworks. Downs et al. (2020) propose a food environment typology that includes: wild, cultivated, informal and formal market environments, where wild and cultivated are “natural food environments.” Drawing from transdisciplinary perspectives, this paper argues that wild and cultivated food environments are not dichotomous, but rather exist across diverse landscapes under varying levels of human management and alteration. The adapted typology is applied to a case study of Indigenous Pgaz K’Nyau food environments in San Din Daeng village, Thailand, using the Gallup Poll’s Thailand-adapted Diet Quality Questionnaire with additional food source questions. Wild-cultivated food environments, as classified by local participants, were the source of more food items than any other type of food environment (37% of reported food items). The case of Indigenous Pgaz K’Nyau food environments demonstrates the importance of understanding natural food environments along a continuum from wild to cultivated.

Evidence supporting the benefits of immersive virtual reality (VR) and exposure to nature for the wellbeing of individuals is steadily growing. So-called digital forest bathing experiences take advantage of the immersiveness of VR to make individuals feel like they are immersed in nature, which has led to documented improvements in mental health. The majority of existing studies have relied on conventional VR experiences, which stimulate only two senses: auditory and visual. However, the principle behind forest bathing is to have one stimulate all of their senses to be completely immersed in nature. As recent advances in olfactory technologies have emerged, multisensory immersive experiences that stimulate more than two senses may provide additional benefits. In this systematic literature review, we investigate the multisensory digital nature setups used and their psychological and psychophysiological outcomes; particular focus is placed on the inclusion of smells as the third sensory modality. We searched papers published between 2016 and April 2023 on PubMed, Science Direct, Web of Science, Scopus, Google Scholar, and IEEE Xplore. Results from our quality assessment revealed that the majority of studies (twelve) were of medium or high quality, while two were classified as low quality. Overall, the findings from the reviewed studies indicate a positive effect of including smells to digital nature experiences, with outcomes often comparable to conventional exposure to natural environments. The review concludes with a discussion of limitations observed in the examined studies and proposes recommendations for future research in this domain.

The insecticidal compound pyrethrin is synthesized in Dalmatian pyrethrum (Tanacetum cinerariifolium (Trevis.) Sch.Bip.; Asteraceae), a plant species endemic to the eastern Mediterranean. Pyrethrin is a mixture of six compounds, pyrethrin I and II, cinerin I and II, and jasmolin I and II. For this study we sampled 15 natural Dalmatian pyrethrum populations covering the entire natural distribution range of the species; Croatian coastal regions and the islands, inland Bosnia and Herzegovina and Montenegro. The plants were grown in a field experiment under uniform growing conditions to exclude a short-term response to environmental factors and instead observe variation in pyrethrin content and composition among and within populations due to genetic adaptation to the native environment. The drivers of local adaptation were explored by examining the role of bioclimatic factors as a cause of population differentiation. Pyrethrins were extracted by ultrasound-assisted extraction, and the extracts were analyzed by HPLC-UV-DAD. The populations differed significantly in the content and composition of pyrethrins. The highest levels of total pyrethrins (1.27% flower DW), were found in population P14 Budva and the significantly highest levels of pyrethrin I in population P14 Vranjske Njive, Podgorica (66.47% of total pyrethrin). Based on bioclimatic conditions of the sampling sites, populations were grouped into five bioclimatic groups (A, B, C, D, and E), which showed qualitative and quantitative variability in pyrethrin content. The most abundant bioclimatic group was bioclimatic group E, which was characterized by the highest average values for pyrethrin I (53.87% of total pyrethrin), total pyrethrin content (1.06% flower DW) and the ratio of pyrethrin I and II (1.85). The correlation analysis between the pyrethrin compounds and some of the bioclimatic variables (e. g., BIO03 Isothermality and BIO04 Temperature seasonality) showed their significant contribution in explaining the variation of pyrethrins in T. cinerariifolium. The differences in pyrethrin content and composition may be partly due to genetic adaptation to the ecological conditions of the native environment. The obtained data would enable the selection of source populations for breeding programs aimed at producing cultivars with desirable biochemical properties and adaptation to different bioclimatic conditions.