Feel free to FORK THIS NOTEBOOK in order to correctly load the data for your project!

Overview: This dataset offers a comprehensive collection of Daily weather readings from major cities around the world. In the first release, it included only capitals, but now it also adds main cities worldwide and hourly data as well, making up to ~1250 cities. Some locations provide historical data tracing back to January 2, 1833, giving users a deep dive into long-term weather patterns and their evolution.

Data License and Updates: This dataset is updated every Sunday using data from Meteostat API, ensuring access to the latest week's data without overburdening the data source.

Cities DataFrame (cities.csv)

This dataframe offers details about individual cities and weather stations. - Columns: - station_id: Unique ID for the weather station. - city_name: Name of the city. - country: The country where the city is located. - state: The state or province within the country. - iso2: The two-letter country code. - iso3: The three-letter country code. - latitude: Latitude coordinate of the city. - longitude: Longitude coordinate of the city.

Countries DataFrame (countires.csv)

This dataframe contains information about different countries, providing insights into their geographic and demographic characteristics. - Columns: - iso3: The three-letter code representing the country. - country: The English name of the country. - native_name: The native name of the country. - iso2: The two-letter code representing the country. - population: The population of the country. - area: The total land area of the country in square kilometers. - capital: The name of the capital city. - capital_lat: The latitude coordinate of the capital city. - capital_lng: The longitude coordinate of the capital city. - region: The specific region within the continent where the country is located. - continent: The continent to which the country belongs. - hemisphere: The hemisphere in which the country is located (e.g., Northern, Southern).

Daily Weather DataFrame (daily_weather.parquet)

This dataframe provides weather data on a daily basis. - Columns: - station_id: Unique ID for the weather station. - city_name: Name of the city where the station is located. - date: Date of the weather record. - season: Season corresponding to the date (e.g., summer, winter). - avg_temp_c: Average temperature in Celsius. - min_temp_c: Minimum temperature in Celsius. - max_temp_c: Maximum temperature in Celsius. - precipitation_mm: Precipitation in millimeters. - snow_depth_mm: Snow depth in millimeters. - avg_wind_dir_deg: Average wind direction in degrees. - avg_wind_speed_kmh: Average wind speed in kilometers per hour. - peak_wind_gust_kmh: Peak wind gust in kilometers per hour. - avg_sea_level_pres_hpa: Average sea-level pressure in hectopascals. - sunshine_total_min: Total sunshine duration in minutes.

These dataframes can be utilized for various analyses such as weather trend prediction, climate studies, geographic analysis, demographic insights, and more.

Dataset Image Source: Photo credits to 越过山丘. View the original image here.

Average wave height, wind speed and direction, and surfable days per month at Pacific City based on historical data since 1980.

Average wave height, wind speed and direction, and surfable days per month at Lincoln City based on historical data since 1980.

Wind speed and direction, interval average/max/min Parque Natural Metropolitano (PNM) crane station. Location: 8.994410 -79.543000 Established in 1995, the PNM canopy crane is located at the North Western edge of Panama City, at the eastern edge of the Parque Natural Metropolitano (Metropolitan Nature Park) and is surrounded by 80-year old, lowland semi-deciduous forest. The crane was 42 meters tall. The original monitoring station was located at approximately 25m. This level was about 1/3 the way from the canopy. In 2021 the original crane was dismantled and replaced with a new, 68m crane in Jan. 2022. The original met. station was located on the side of the tower at approximately 25m. The combination of a lower capability anemometer, and mid-canopy location of the station resulted in wind data that were of poor quality and unrepresentative of the conditions at the top of the canopy. This anemometer was abandoned in 2008. A new anemometer was installed close to the top (65m) of the new crane on March of 2022. Historical datasets can be located here: https://smithsonian.figshare.com/articles/dataset/Parque_Metropolitano_Crane_65m_Wind_speed_Direction/19795387

About the Dataset

Each year contains 8,760 weather datapoints recorded from a weather station located on the Toronto Island, in Toronto, Ontario, Canada. The CSVs contains 28 features. The description of each feature is listed below:

• Longitude (x): The longitude geo-coordinate of the weather station
• Latitude (y): The longitude geo-coordinate of the weather station
• Station Name: The name of the weather station
• Climate ID: The Climate ID is a 7 digit number assigned by the Meteorological Service of Canada to a site where official weather observations are taken, and serves as a permanent, unique identifier.
• Date/Time: The date and time of when the weather data was collected.
• Year: The year the data was collected.
• Month: The month the data was collected.
• Day: The day the data was collected.
• Time: The time of day the data was collected.
• Temp (°C): The temperature of the air in degrees Celsius (°C).
• Temp Flag: Flag for unique events for temperature.
• Dew Point Temp (°C): The dew point temperature in degrees Celsius (°C), a measure of the humidity of the air, is the temperature to which the air would have to be cooled to reach saturation with respect to liquid water. Saturation occurs when the air is holding the maximum water vapour possible at that temperature and atmospheric pressure.
• Dew Point Temp Flag: Flag for unique events for dew point temp.
• Rel Hum (%): Relative humidity in percent (%) is the ratio of the quantity of water vapour the air contains compared to the maximum amount it can hold at that particular temperature.
• Rel Hum Flag: Flag for unique events for relative humidity.
• Wind Dir (10s deg): The speed of motion of air in kilometres per hour (km/h) usually observed at 10 metres above the ground. It represents the average speed during the one-, two- or ten-minute period ending at the time of observation.
• Wind Dir Flag: Flag for unique events for wind direction.
• Wind Spd (km/h): The speed of motion of air in kilometres per hour (km/h) usually observed at 10 metres above the ground. It represents the average speed during the one-, two- or ten-minute period ending at the time of observation.
• Wind Spd Flag: Flag for unique events for wind speed.
• Visibility (km): Visibility in kilometres (km) is the distance at which objects of suitable size can be seen and identified.
• Visibility Flag: Flag for unique events for visibility.
• Stn Press (kPa): The atmospheric pressure in kilopascals (kPa) at the station elevation. Atmospheric pressure is the force per unit area exerted by the atmosphere as a consequence of the mass of air in a vertical column from the elevation of the observing station to the top of the atmosphere.
• Stn Press Flag: Flag for unique events for station atmospheric pressure.
• Hmdx: Humidex is an index to indicate how hot or humid the weather feels to the average person. It is derived by combining temperature and humidity values into one number to reflect the perceived temperature.
• Hmdx Flag: Flag for unique events for humidex.
• Wind Chill: Wind chill is an index to indicate how cold the weather feels to the average person. It is derived by combining temperature and wind velocity values into one number to reflect the perceived temperature.
• Wind Chill Flag: Flag for unique events for wind chill index.
• Weather: Observations of atmospheric phenomenon including the occurrence of weather and obstructions to vision have been taken at many hourly reporting stations.

Reference Source

Government of Canada Government of Canada has a catalogue of historical weather data throughout various weather stations across Canada.

Context

I prepared this dataset for a project on rainfall forecasting. Met Éireann is the Irish Meteorological Service and a scientific organisation that undertakes research in numerous fields such as Numerical Weather Prediction and Climate Modelling. Their short-term predictions work very well. I was curious to see how Machine Learning and Deep Learning models would handle these types of tasks. Care to join me?

Description

This dataset contains: * Folder with Individual CSV files for 24 Met Éireann weather stations in Ireland capable to record hourly weather data (the start date of individual time series depends on when the particular station was opened, the end date is 2022-02-01); * Aggregated hourly weather data from 24 stations in Ireland for the period of time from 2007-12-31 to 2022-02-01 (with station names and locations added); * The list of stations.

Variables measured by stations (available variables may vary depending on the station): * date: Date and Time of observation * ind: Encoded Rainfall Indicators (see KeyHourly.txt for details) * rain: Precipitation Amount, mm * ind.1: Encoded Temperature Indicators (see KeyHourly.txt for details) * temp: Air Temperature, °C * ind.2: Encoded Wet Bulb Indicators (see KeyHourly.txt for details) * wetb: Wet Bulb Air Temperature, °C * dewpt: Dew Point Air Temperature, °C * vappr: Vapour Pressure, hPa * rhum: Relative Humidity, % * msl: Mean Sea Level Pressure, hPa * ind.3: Encoded Wind Speed Indicators (see KeyHourly.txt for details) * wdsp: Mean Hourly Wind Speed, knot * ind.4: Encoded Wind Direction Indicators (see KeyHourly.txt for details) * wddir: Predominant Hourly wind Direction, degree * ww: Synop Code Present Weather (see KeyHourly.txt for details) * w: Synop Code Past Weather (see KeyHourly.txt for details) * sun: Sunshine duration, hours * vis: Visibility, m * clht: Cloud Ceiling Height (if none value is 999), 100s of feet * clamt: Cloud Amount, okta

"Wind direction is usually reported in cardinal (or compass) direction, or in degrees. Consequently, a wind blowing from the north has a wind direction referred to as 0° (360°); a wind blowing from the east has a wind direction referred to as 90°, etc." Wikipedia page for "Wind direction"

Table: Common Cardinal (or compass) direction vs degrees

Information on the stations: * county: County the station is losated in * st_id: Station number * st_name: Station name * st_height: Station Height, m * st_lat: Station Latitude, sexagesimal degrees (degrees, minutes, and seconds - DMS notation) * st_long: Station Longitude, sexagesimal degrees (degrees, minutes, and seconds - DMS notation)

Latitude and longitude are presented in sexagesimal degrees (degrees, minutes, and seconds - DMS notation). To convert them into decimal degrees (DD) which are used in GIS and GPS apply the following formula: DD = D + M/60 + S/3600. More details can be found here.

Acknowledgements

Data were obtained from the Met Éireann website.

Copyright statement: Copyright Met Éireann Source: www.met.ie Licence Statement: This data is published under a Creative Commons Attribution 4.0 International (CC BY 4.0). Disclaimer: Met Éireann does not accept any liability whatsoever for any error or omission in the data, their availability, or for any loss or damage arising from their use.

Hourly weather data for 24 stations were downloaded and aggregated into one dataframe. Station names and locations were added.

Photo by Nils Nedel on Unsplash was used as a Banner image.

Inspiration

For EDA and Data Visualization: 1. What are the most prominent seasonal weather patterns in Ireland? 2. How does the weather conditions affect city life? * Pedestrian footfall * Bikeshare sevices * Road accidents * Taxi

For ML and Neural Networks modelling: 1. Can you predict the probability of rain using weather data obtained from a single station in the previous 24, 36 or 48 hours? 2. How does the addition of data recorded by neighbouring stations affect the accuracy of the model?

Articles for ideas: 1. Streamflow and rainfall forecasting by two long short-term memory-based models 2. [Short-Term Rainfall Forecasting Using Multi-Layer Perceptron](https://ieeexplore.ieee.org/document/8468...

Consists of historical daily weather data for the cities of Warsaw and Cracow. The data includes a variety of weather-related variables such as temperature (minimum, maximum, and average), precipitation, humidity, wind speed, and atmospheric pressure. The dataset is presented in a tabular format with each row representing a day and each column representing a weather variable.

The date range covered by this dataset spans multiple years, providing a comprehensive view of the weather patterns in these two cities. This dataset can be useful for researchers, data scientists, and enthusiasts who are interested in analyzing weather trends, conducting climate studies, or developing predictive models for weather forecasting.

Users can access the data on Kaggle, where they can download it in CSV format, explore the data using Kaggle's built-in tools, or use their preferred tools for data analysis. Additionally, users can engage with the Kaggle community to share insights, discuss analyses, and collaborate on projects related to this dataset.

Explore trends in Bergen city bike usage by weather, location, and time. This dataset includes bike trips made between January 1 to December 31, 2023.

Data Sources

• Bike sharing data: Bergen City Bike https://bergenbysykkel.no/en/open-data/historical
  • Data licensed under the Norwegian Licence for Open Government Data (NLOD) 2.0.
• Weather data: Norwegian Center for Climate Services by The Norwegian Meteorological Institute (MET Norway) https://seklima.met.no/
  • Weather symbol codes (from Yr.no): https://nrkno.github.io/yr-weather-symbols/
  • Data licensed under the Norwegian License for Open Government Data (NLOD) 2.0 and Creative Commons 4.0 BY.
• Public holidays in Norway: https://publicholidays.no/2023-dates/

Metadata

The data folder contains two CSV files. - bergen_merged.csv: Original dataset merging Bergen bike sharing and weather data. Contains 467862 items. Contains 19 22 variables (updated 2025-08-17): - start_time: Timestamp of when the trip started (YYYY-MM-DD HH:00:00) - end_time: Timestamp of when the trip ended (YYYY-MM-DD HH:00:00) - duration: Duration of trip (s) - start_station_id: Unique ID of start station - start_station_name: Name of start station - start_station_latitude: Latitude of start station - start_station_longitude: Longitude of start station - end_station_id: Unique ID of end station - end_station_name: Name of end station - end_station_latitude: Latitude of end station - end_station_longitude: Longitude of end station - temperature: Air temperature at time of observation (°C) - max_temperature: Highest recorded air temperature per hour (°C) (added 2025-08-17) - min_temperature: Lowest recorded air temperature per hour (°C) (added 2025-08-17) - wind_speed: Maximum mean wind speed per hour (m/s) - precipitation: Amount of precipitation per hour (mm) - humidity: Relative air humidity per hour (%) (added 2025-08-17) - weather: Weather symbol code from Yr.no (0-50; see legend: https://nrkno.github.io/yr-weather-symbols/) - sunshine: Sunshine duration per hour (min) - season: Season code (0=Spring, 1=Summer, 2=Fall, 3=Winter) - is_holiday: Public holiday in Norway - is_weekend: Weekend - Saturday and Sunday - bergen_sampled.csv: Lite version of the bergen_merged dataset, suitable for visualization in Vega Altair. Contains 5000 items randomly sampled from May 01 - Dec 31 2023. Contains the same 19 22 variables as the original dataset.

Data Processing

• Bike sharing data:
  • Data from January - December 2023 were downloaded.
  • Each item is a bike ride with a duration of at least one minute, made during Bergen City Bike regular opening hours (04:00-22:00).
  • Start and end times were rounded down to the hour.
• Weather data:
  • Data from January 01 - December 31 2023 were downloaded.
  • Sunshine duration was taken from Flesland station.
  • Precipitation, Air temperature, Maximum air temperature, Minimum air temperature, Maximum mean wind speed, Relative air humidity, and Weather symbol codes (from Yr.no) were taken from the Bergen-Florida station.
  • Weather symbol code data was forward filled to cover missing values.
  • Weather data is mapped to the start time of bike trip.

License

Norwegian License for Open Government Data (NLOD) 2.0

Header image: Bergen Bysykkel

These data are in addition to "Madison Wisconsin Daily Meteorological Data 1869-current." Additional variables added  include: daily cloud cover, wind, solar radiation, vapor pressure, dew point temperature, total atmospheric pressure, and average relative humidity for Madison, Wisconsin.  In addition, the adjustment factors which were applied on a given date to calculate the adjusted parameters in "Madison Wisconsin Daily Meteorological Data 1869-current" are also included in these data. Raw data, in English units, were assembled by Douglas Clark - Wisconsin State Climatologist. Data were converted to metric units and adjusted for temporal biases by Dale M. Robertson. For adjustments applied to various parameters see Robertson, 1989 Ph.D. Thesis UW-Madison. Adjusted data represent the BEST estimated daily data and may be raw data. Data collected at Washburn observatory, 8-1-1883 to 9-30-1904. Data collected at North Hall, 10-1-1904 to 12-31-1947 Data collected at Truax Field (Admin BLDG), 1-1-1948 to 12-31-1959. Data collected at Truax Field, center of field, 1-1-1960 to Present. Much of the data after 1990 were obtained in digital form from Ed Hopkins, UW-Meteorology. Data starting in 2002-2005 were obtained from Sullivan at http://www.weather.gov/climate/index.php?wfo=mkx%20 ,then go to CF6 and download monthly data to Madison_sullivan_conversion. Relative humidity data was obtained from 1986 to 1995 from CD's at the State Climatologist's Office. Since Robertson (1989) adjusted all historical data to that collected prior to 1989; no adjustments were applied to the recent data except for wind and estimated vapor pressure. Wind after January 1997, and only wind from the southwest after November 2007, was extended by Dale M. Robertson and Yi-Fang "Yvonne" Hsieh, see methods. Estimated vapor pressure after April 2002 was updated by Yvonne Hsieh, see methods.

As cities face rising temperatures, increased frequency of extreme weather events, and altered precipitation patterns, buildings are subjected to increasing energy demand, heat stress, thermal comfort issues, and decreased service life. Therefore, evaluating building performance under changing climate conditions is essential for building sustainable and resilient communities. Unique climate characteristics of cities, such as the urban heat island effect, are not well simulated by global or regional climate models, and is therefore often not included in typical building analyses. Consequently, a computationally efficient approach is used to generate “urbanized” climate data, derived from regional climate models, to prepare building simulation climate data that incorporate urban effects. We demonstrate this process using existing climate data for Toronto airport’s weather station and extend it to prepare projections for scenarios where nature-based solutions, such as increased greenery and albedo, were implemented. We find significant improvements in the representation of the urban heat island and subsequent cooling effects of nature-based solutions in the urbanized climate data. This dataset allows building practitioners to evaluate building performance under historical and potential future changes in climate, considering the complex interactions within the urban canopy and the implementation of mitigation efforts such as nature-based solutions.

This dataset contains hourly historical and future weather files for use in building simulations for the city of Toronto, Canada. While similar weather files are usually based on measurements taken at a city's nearby airport, the current dataset utilizes a novel statistical-dynamical downscaling technique which involves the use of the dynamical Weather Research and Forecasting (WRF) model combined with a statistical approach and climate projections from an ensemble of 15 Canadian Regional Climate Model 4 (CanRCM4) to generate urban climate data which includes the effects of the urban heat island and different nature-based solutions (NBS) as mitigation strategies (such as increasing surface albedo and greenery). Additionally, different levels of implementation of these mitigation strategies were produced, for example, when the albedo is increased to 0.40 (ALBD40) and 0.80 (ALBD80), and similarly for the green and combined scenarios, GRN40, GRN80, COMB40, and COMB80. The URBAN scenario is considered the control case where the urban heat island effects are accounted for in the data, but the NBS scenarios are not yet implemtned.

The data are stored in large CSV files, where the rows consists of all 15 realizations of the CanRCM4 ensemble and the variables make up the columns. For example, each 31-year period is repeated 15 times, once for each of the RCM realizations. Therefore, there are 4,073,400 (15x31x8760) rows in each file. We recommend viewing the data using packages from Python or R.

The historical and future global warming thresholds and their corresponding time periods are as follows:

Global Warming Scenario

Time Period

Historical

1991-2021

Global Warming 0.5ºC

2003-2033

Global Warming 1.0ºC

2014-2044

Global Warming 1.5ºC

2024-2054

Global Warming 2.0ºC

2034-2064

Global Warming 2.5ºC

2042-2072

Global Warming 3.0ºC

2051-2081

Global Warming 3.5ºC

2064-2094

The following variables are included in the files:

Variable Description

RUN Run number (R1-R15) of Canadian Regional Climate Model, CanRCM4 large ensemble associated with the selected reference year data

YEAR Year associated with the record

MONTH Month associated with the record

DAY Day of the month associated with the record

HOUR Hour associated with the record

YDAY Day of the year associated with the record

DRI_kJPerM2 Direct horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)

DHI_kJperM2 Diffused horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)

DNI_kJperM2 Direct normal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)

GHI_kJperM2 Global horizontal irradiance in kJ/m2 (total from previous HOUR to the HOUR indicated)

TCC_Percent Instantaneous total cloud cover at the HOUR in % (range: 0-100)

RAIN_Mm Total rainfall in mm (total from previous HOUR to the HOUR indicated)

WDIR_ClockwiseDegFromNorth Instantaneous wind direction at the HOUR in degrees (measured clockwise from the North)

WSP_MPerSec Instantaneous wind speed at the HOUR in meters/sec

RHUM_Percent Instantaneous relative humidity at the HOUR in %

TEMP_K Instantaneous temperature at the HOUR in Kelvin

ATMPR_Pa Instantaneous atmospheric pressure at the HOUR in Pascal

SnowC_Yes1No0 Instantaneous snow-cover at the HOUR (1 - snow; 0 - no snow)

SNWD_Cm Instantaneous snow depth at the HOUR in cm

The Sri Lanka Weather Dataset is a comprehensive collection of weather data for 30 prominent cities in Sri Lanka, covering the period from January 1, 2010, to January 1, 2023. The dataset offers a wide range of meteorological parameters, enabling detailed analysis and insights into the climate patterns of different regions in Sri Lanka.

The dataset includes information such as: - Time: The timestamp of each weather observation. - Weather Code: A numerical code representing the weather conditions at the given time. - Temperature: Maximum, minimum, and mean values of 2-meter temperature. - Apparent Temperature: Maximum, minimum, and mean values of apparent temperature, which takes into account factors like wind chill or heat index. - Sunrise and Sunset: The times of sunrise and sunset for each day. - Shortwave Radiation: Sum of shortwave radiation received during the observation period. - Precipitation: Total sum of precipitation, including rainfall and snowfall. - Precipitation Hours: The duration of time with measurable precipitation. - Wind Speed and Gusts: Maximum values of wind speed and wind gusts at 10 meters above ground level. - Wind Direction: Dominant wind direction at 10 meters above ground level. - Evapotranspiration: Reference evapotranspiration (ET0) based on the FAO Penman-Monteith equation. - Latitude, Longitude, and Elevation: Geographic coordinates and elevation of each city. - Country and City: Names of the country and city corresponding to each weather observation.

This dataset was sourced from Open-Meteo and simplemaps, and the data was collected using a basic Python script. The collected data was pre-processed to ensure cleanliness and readability before being stored in CSV format.

Average wave height, wind speed and direction, and surfable days per month at Cape Town based on historical data since 1980.

Long term monthly mean winds which Sadler derived from his collection of aircraft data (which we have in DS365.0. [https://rda.ucar.edu/datasets/ds365.0/]) and average rawinsonde data. The aircraft data were obtained from two sources: operational GTS reports collected in Honolulu and the FHWF; and aircraft logs from many routes which often were not reported over the GTS. The aircraft winds were summarized for each month in 5-degree latitude-longitude squares. The average monthly rawinsonde data were then combined with these in manual analyses of streamlines and isotachs. The 2.5- degree grids of wind speed and direction were manually read from these.

Unless you have a special interest in this dataset, DSS recommends that you use one of our reanalysis datasets, such as ds090.2 [https://rda.ucar.edu/datasets/ds090.2/]. That's because those are based on a more complete collection of observations, improved analysis methods, and are provided in a common modern format (GRIB).

In 2022, there were 78 fatalities due to hurricanes reported in the United States. Since the beginning of the century, the highest number of fatalities was recorded in 2005, when four major hurricanes – including Hurricane Katrina – resulted in 1,518 deaths. The worst hurricanes in U.S. history Hurricane Katrina, which made landfall in August 2005, ranked as the third deadliest hurricane in the U.S. since records began. Affecting mainly the city of New Orleans and its surroundings, the category 3 hurricane caused an estimated 1,500 fatalities. Katrina was also the costliest tropical cyclone to hit the U.S. in the past seven decades, with damages amounting to roughly 186 billion U.S. dollars. Hurricanes Harvey and Maria, both of which made landfall in 2017, ranked second and third, resulting in damage costs of 149 and 107 billion dollars, respectively. How are hurricanes classified? According to the Saffir-Simpson scale, hurricanes can be classified into five categories, depending on their maximum sustained wind speed. Most of the hurricanes that have made landfall in the U.S. since 1851 are category 1, the mildest of the five. Hurricanes rated category 3 or above are considered major hurricanes and can cause devastating damage. In 2021, there were 38 hurricanes recorded across the globe, of which 17 were major hurricanes.

The Air Quality Dataset provides a comprehensive overview of atmospheric pollution levels across various locations in Poland from 2017 to 2023. It features extensive measurements of numerous air pollutants captured through an extensive network of air quality monitoring stations throughout the country. The dataset includes both hourly (1g) and daily (24g) averages of the recorded pollutants, offering detailed temporal resolution to study short-term peaks and long-term trends in air quality.

Pollutants Measured:

1. Gaseous Pollutants: Carbon Monoxide (CO), Nitrogen Dioxide (NO2), Nitric Oxide (NO), Nitrogen Oxides (NOx), Sulfur Dioxide (SO2), Ozone (O3), and Benzene (C6H6).
2. Particulate Matter: PM10, PM2.5; and specific elements and compounds bound to PM10 such as Lead (Pb), Arsenic (As), Cadmium (Cd), Nickel (Ni), among others.
3. Polycyclic Aromatic Hydrocarbons (PAHs) associated with PM10: Benzo[a]anthracene (BaA), Benzo[b]fluoranthene (BbF), Benzo[j]fluoranthene (BjF), Benzo[k]fluoranthene (BkF), Benzo[a]pyrene (BaP), Indeno[1,2,3-cd]pyrene (IP), Dibenzo[a,h]anthracene (DBahA).
4. Additional Chemicals: Including various volatile organic compounds (VOCs) like ethylene, toluene, xylene variants, aldehydes, and hydrocarbons.

Features of the Dataset:

Locations: Data from numerous air quality monitoring stations distributed across various urban, suburban, and rural areas in Poland.
Time Resolution: Measurements are provided in both hourly and daily intervals, catering to different analytical needs.
Coverage Period: This dataset encompasses data from 2017 to the year, 2023, enabling analysis over multiple years to discern trends and assess the effectiveness of air quality management policies.
Deployment of Deposition Sampling: Concentrations of certain pollutants in wet and dry deposition forms, noted with 'cdepoz' (cumulative deposition), providing insights into the deposition rates of airborne pollutants.

Potential Applications:

Environmental Research: Study the impact of various pollutants on air quality, health, and the environment.
Policy Making: Assist policymakers in evaluating the effectiveness of past regulations and planning future actions to improve air quality.
Public Health: Correlate pollutant exposure levels with health outcomes, helping public health professionals to mitigate risks associated with poor air quality.

Data Format:

The dataset is structured in a tabular format with each row representing an observation time (either hourly or daily) and columns representing different pollutants and their concentrations at various monitoring stations.

This dataset is an essential resource for researchers, policymakers, environmental agencies, and health professionals who need a detailed and robust dataset to understand and combat air pollution in Poland.

Source of data: Chief Inspectorate of Environmental Protection (GIOS)

The historic weather dataset for Cracow and Warsaw

The historic weather dataset for Cracow and Warsaw with suburbs, covering daily observations from 2019 to August 2024, would encompass a range of atmospheric and meteorological data points collected over the defined time period and locations. Here’s a description of what such a dataset might include and signify: Key Characteristics:

Locations: The cities of Cracow and Warsaw, along with their suburbs. The dataset would likely specify the exact areas or measurements stations.
Time Frame: Daily records from January 1, 2019, to August, 2024, providing a comprehensive view of weather variations through different seasons and years.
Data Granularity: Daily data would allow trends such as temperature fluctuations, precipitation patterns, and weather anomalies to be studied in considerable detail.

Likely Data Fields:

Each record in the dataset might contain:

DATE_VALID_STD: Representing each day within the date range specified (from 2019-01-01 to 2024-08-20 for Cracow and Warsaw suburbs).
Temperature Fields (Min, Max, Avg): Temperature readings at specified intervals, likely in Celsius, providing insight into daily and seasonal temperature patterns and extremes.
Humidity Fields (Min, Max, Avg): Relative and specific humidity readings to assess moisture levels in the air, which have implications for weather conditions, comfort levels, and health.
Precipitation: Data on rainfall, snowfall, and total snow depth, essential for understanding water cycle dynamics, agricultural planning, and urban water management in these areas.
Wind Measurements: May include minimum, average, and maximum speeds and perhaps prevailing directions, useful in sectors like aviation, construction, and event planning.
Pressure and Tendency: Barometric pressure readings at different measurement standards to help predict weather changes.
Radiation and Cloud Cover: D...