This repository contains data to reconstruct the exposure-response functions (ERF) of temperature-related mortality by five 5 age groups in 854 cities in Europe. These ERFs have been derived in the study by Masselot et al. 2023, Excess mortality attributed to heat and cold: a health impact assessment study in 854 cities in Europe, The Lancet Planetary Health (https://doi.org/10.1016/S2542-5196(23)00023-2). An associated semi-replicable GitHub repository is available at https://github.com/PierreMasselot/Paper--2023--LancetPH--EUcityTRM to reproduce part of the analysis and the full results, as well as to provide technical details on the derivation of these ERFs. Note: This updated version contains revised data after the correction of an error in the code related to the computation of the age-specific baseline mortality rates. Details about the error can be found in the GitHub repository linked above. This correction only affects the figures of excess mortality (found in the results.zip archive) while the ERFs are negligibly affected. The originally published results can be found in V1.0.0 of this repository. Extraction of the ERFs The ERFs are provided as coefficients of B-spline functions that can be used to reconstruct the ERFs, along with variance-covariance matrices and quantiles from location-specific temperature distributions. The parametrisation associated with these coefficients is a quadratic B-spline (degree 2), with knots located at the 10th, 75th and 90th percentiles of the temperature distribution. In R, the associated basis can be constructed using the dlnm package, with a temperature series x, as follows:

library(dlnm) basis <- onebasis(x, fun = "bs", degree = 2, knots = quantile(x, c(.1, .75, .9))) The main files associated with ERFs are the following: coefs.csv: The B-spline coefficients for each age group and city. vcov.csv: The variance-covariance matrix of the coefficients in each city and age group. It is provided here as the lower triangular part of the matrix with names indicating the position of each value (v[row][column]). In R, assuming x is a row of this file, the matrix can be reconstructed using xpndMat(x) after loading the mixmeta package. coef_simu.csv: 1000 simulations from the distribution of each city and age-specific coefficients. Useful to derive empirical confidence intervals for derived measures such as excess deaths or attributable fractions. tmean_distribution.csv: The city-specific temperature percentiles representing the distribution of the data derived from the ERA5-Land dataset. Health impact assessment results results.zip: A summary of the results from the health impact assessment reported in the analysis. The dataset includes several impact measures provided in files representing different geographical levels, including city, country and regional level. Different files are also provided for age-group specific or all age results. Additional data We provide additional data that are useful to reproduce or extend the analysis. Please note that due to restrictive data-sharing agreements for the mortality series, only a part of the code is reproducible. See the associated GitHub repository for more details. metadata.csv: City-specific metadata used to create the ERFs and perform the health impact assessment. additional_data.zip: contains further data used to replicate the second stage of the analysis and the final health impact assessment. It includes the full city-level daily temperature series (era5series.csv), the detail of extracted metadata for available years (metacityyear.csv), a description of the city-level characteristics (metadesc.csv), and the first-stage ERF coefficients for all available city and age-groups (stage1res.csv). Additionally, the file meta-model.RData contains R object defining the second-stage model that can be used to predict new ERFs.

This repository contains the data and results from the paper Estimating future heat-related and cold-related mortality under climate change, demographic and adaptation scenarios in 854 European cities published in Nature Medicine (https://doi.org/10.1038/s41591-024-03452-2).

It provides projections of excess death rates and burden for the period 2015-2099 for five age groups in 854 cities across 30 countries, under three Shared Socioeconomic Pathway (SSP) scenarios, and four adaptation scenarios. The results include point estimates for five-year periods and four global warming levels, along with 95% empirical confidence intervals.

The fully reproducible analysis code using the data and producing the results included in this repository is provided in GitHub. The results can be visualised and explored in a dedicated Shiny app.

Content

This repository contains three zip files, each with an internal codebook:

• data.zip: contains the input data necessary to run the analysis. It includes historical and projected daily temperature at the city level, age-group specific projections of population and survival rates at the country level, and exposure-response functions extracted from another Zenodo repository (https://doi.org/10.5281/zenodo.10288665). This file also include a script showing how each dataset was extracted for the purpose of this projection study.
• results_csv.zip: contains the full results from the health impact projections. It includes one file for each combination of geographical level (city, country, region or European wide) and scale of reporting (five year periods or global warming levels).
• results_parquet.zip: contains the same information as the results_csv.zip but in a parquet format. This allows for more efficient storage and data reading.

It is recommended to only download results_csv.zip for a quick exploration of the results, or only results_parquet.zip when the results are to be loaded into a software for deeper analysis.

Contains hourly weather, air quality and uv data in Malaysia from 1996 - present across a variety of locations.

Includes weather factors like temperature, wind speed and uv index. Air quality data includes pollutant_value.

Where is the data from and how is it extracted? quality-of-life repo

Data files

full_weather.csv - Contains main weather data from 1996-2023 July

air_quality_indicators.csv - Info about air quality indicators

air_quality_warnings.csv - Info about air quality warning levels

uv_info - Info about uv indexes and their dangers

full_locations.csv - Info about each location extracted

The dataset is accessible using BigQuery/SQL or CSV . The cloud dataset is updated everyday.

BigQuery (SQL)

Try out the public notebook: https://www.kaggle.com/code/shahmirvarqha/weather-bigquery

There are several fact tables (main data is here):

prod.air_quality - All air quality data

prod.weather - Only airport weather stations

prod.personal_weather - Only personal weather stations

prod.uv - Merges UV data with warnings and labels

prod.full_weather_places - Similar to full_weather.csv file

The following tables have additional information:

prod.state_locations

prod.full_locations - Similar to full_locations.csv file

prod.city_states

prod.city_places

prod.air_quality_warnings

prod.air_quality_indicators

prod.uv_info

*Not all days and hours are available (especially earlier on, there is a lot of missing data).

Context

I made this dataset for project Hourly electricity consumption prediction of Homestead city(https://github.com/ajtheb/Homestead-Electric-Consumption). You can find the data extraction details in this link and can change dates for your use. The dataset is created using two APis.

Content

It contains hourly electricity consumption and weather features like temperature, humidity, rain, windspeed etc.

Acknowledgements

wwo hist api.eia.gov

Inspiration

Electricity with weather data not available on internet.