Air Pollution And Mental Health
Identifying Short-Term Human Impacts of Air Pollution
@kaggle.thedevastator_air_pollution_and_mental_health
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About this Dataset
Air Pollution And Mental Health
Air Pollution and Mental Health
Identifying Short-Term Human Impacts of Air Pollution
By [source]
About this dataset
This dataset from the CitieS-Health project provides a unique insight into the impact of air pollution on humans. It is comprised of data collected in Barcelona, Spain, and examines various environmental variables, such as air pollution levels, and their effects on mental health and wellbeing. In addition to environmental factors, this dataset also captures self-reported survey data on mental health, physical activity, diet habits, and more. From performance in a Stroop test to information on total noise exposure at 55 dB - this comprehensive dataset will give you everything you need to understand the link between air pollution and human health so that we can begin finding better solutions for a cleaner future
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How to use the dataset
This dataset captures information on air pollution levels and variables related to mental health, such as performance in a Stroop test and self-reported surveys on mental health, physical activity, diet, and other factors. It can be used to answer the question “how does short-term exposure to air pollution affect human mental health?”
To use this dataset, start by understanding the variables you are interested in exploring. Look for correlations between environmental conditions (i.e., air pollution levels) and measures of wellbeing (i.e., performance in a Stroop test). Additionally pay special attention to any factors that may be associated with different levels of exposure (like access to green/blue spaces within 300m buffer).
Next you should examine any relevant self-reported surveys questions related to mental health or wellbeing (such as bienestar or sueno). For example consider looking at how responses vary based on age or gender; it is possible that some demographic groups are more sensitive than others when exposed to air pollutants.
Finally consider incorporating information from other external sources like local noise levels or traffic patterns into your analysis – these will help contextualise your findings even further.Using this dataset you can begin uncovering the impact of short-term exposure to air pollution on humans – by combining different variables together understanding what correlations exist between environment conditions and measures of wellbeing can help people make better decisions about their lifestyle choices like where they choose live, work or play!
Research Ideas
Analyzing the differences in response time in Stroop tests by age and gender. By looking at the accurate response time when it comes to completing a Stroop test from participants of different genders and ages, conclusions can be drawn about how our responses are affected by environmental factors like air pollution levels and noise exposure
Correlating green-space access with mental health outcomes over a period of time. This dataset can be used to analyze if access to green spaces has an impact on overall mental wellbeing indices like stress levels or perceived mood over a certain study period - allowing us to inform policies that leverage locations of urban green-spaces for better outcomes especially in heavily polluted cities
Acknowledgements
If you use this dataset in your research, please credit the original authors.
Data Source
License
License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.
Columns
File: CitieSHealth_BCN_DATA_PanelStudy_20220414.csv
| Column name | Description |
|---|---|
| date_all | Date of the survey. (Date) |
| year | Year of the survey. (Integer) |
| month | Month of the survey. (Integer) |
| day | Day of the survey. (Integer) |
| dayoftheweek | Day of the week of the survey. (String) |
| hour | Hour of the survey. (Integer) |
| mentalhealth_survey | Self-reported survey responses regarding mental health. (String) |
| occurrence_mental | Occurrence of mental health issues. (Integer) |
| bienestar | Self-reported survey responses regarding wellbeing. (String) |
| energia | Self-reported survey responses regarding energy levels. (String) |
| estres | Self-reported survey responses regarding stress levels. (String) |
| sueno | Self-reported survey responses regarding sleep quality. (String) |
| horasfuera | Self-reported survey responses regarding time spent outdoors. (String) |
| ordenador | Self-reported survey responses regarding computer use. (String) |
| dieta | Self-reported survey responses regarding diet. (String) |
| alcohol | Self-reported survey responses regarding alcohol consumption. (String) |
| drogas | Self-reported survey responses regarding drug use. (String) |
| enfermo | Self-reported survey responses regarding illness. (String) |
| otrofactor | Self-reported survey responses regarding other factors. (String) |
| stroop_test | Performance in a Stroop test. (Integer) |
| occurrence_stroop | Occurrence of Stroop test. (Integer) |
| mean_incongruent | Mean of incongruent responses in the Stroop test. (Float) |
| correct | Number of correct responses in the Stroop test. (Integer) |
| response_duration_ms | Response duration in milliseconds in the Stroop test. (Integer) |
| performance | Performance in the Stroop test. (Float) |
| mean_congruent | Mean of congruent responses in the Stroop test. (Float) |
| inhib_control | Inhibition control in the Stroop test. (Float) |
| z_performance | Z-score of performance in the Stroop test. (Float) |
| z_mean_incongruent | Z-score of mean incongruent responses in the Stroop test. (Float) |
| z_inhib_control | Z-score of inhibition control in the Stroop test. (Float) |
| no2bcn_24h | Nitrogen dioxide (NO2) levels in Barcelona over 24 hours. (Float) |
| no2bcn_12h | Nitrogen dioxide (NO2) levels in Barcelona over 12 hours. (Float) |
| no2gps_24h | Nitrogen dioxide (NO2) levels in GPS locations over 24 hours. (Float) |
| no2gps_12h | Nitrogen dioxide (NO2) levels in GPS locations over 12 hours. (Float) |
| no2bcn_12h_x30 | Nitrogen dioxide (NO2) levels in Barcelona over 12 hours multiplied by 30. (Float) |
| no2bcn_24h_x30 | Nitrogen dioxide (NO2) levels in Barcelona over 24 hours multiplied by 30. (Float) |
| no2gps_12h_x30 | Nitrogen dioxide (NO2) levels in GPS locations over 12 hours multiplied by 30. (Float) |
| no2gps_24h_x30 | Nitrogen dioxide (NO2) levels in GPS locations over 24 hours multiplied by 30. (Float) |
| min_gps | Minimum GPS location. (Float) |
| district | District of Barcelona where the survey was conducted. (String) |
| education | Educational level of the participant. (String) |
| maxwindspeed_12h | Maximum wind speed over 12 hours. (Float) |
| noise_total_LDEN_55 | Total noise level in decibels (dB) over 55 minutes. (Float) |
| access_greenbluespaces_300mbuff | Access to green and blue spaces within a 300m buffer. (String) |
| µgm3 | Micrograms per cubic meter. (Float) |
| start_day | Start day of the survey. (Integer) |
| start_month | Start month of the survey. (Integer) |
| start_year | Start year of the survey. (Integer) |
| start_hour | Start hour of the survey. (Integer) |
| end_day | End day of the survey. (Integer) |
| end_month | End month of the survey. (Integer) |
| end_year | End year of the survey. (Integer) |
| end_hour | End hour of the survey. (Integer) |
| Totaltime | Total time of the survey. (Integer) |
| Totaltime_estimated | Estimated total time of the survey. (Integer) |
| Houron | Hour on of the survey. (Integer) |
| Houroff | Hour off of the survey. (Integer) |
| age_yrs | Age of the participant in years. (Integer) |
| yearbirth | Year of birth of the participant. (Integer) |
| smoke | Self-reported survey responses regarding smoking status. (String) |
| psycho | Self-reported survey responses regarding psychological state. (String) |
| gender | Gender of the participant. (String) |
| hour_gps | Hour of the GPS location. (Integer) |
| pm25bcn | Particulate matter (PM2.5) levels in Barcelona. (Float) |
| BCμg | Black carbon (BC) levels in micrograms. (Float) |
| sec_noise55_day | Seconds of noise over 55 minutes in a day. (Integer) |
| sec_noise65_day | Seconds of noise over 65 minutes in a day. (Integer) |
| sec_greenblue_day | Seconds of access to green and blue spaces in a day. (Integer) |
| hours_noise_55_day | Hours of noise over 55 minutes in a day. (Integer) |
| hours_noise_65_day | Hours of noise over 65 minutes in a day. (Integer) |
| hours_greenblue_day | Hours of access to green and blue spaces in a day. (Integer) |
| tmean_24h | Mean temperature over 24 hours. (Float) |
| tmean_12h | Mean temperature over 12 hours. (Float) |
| humi_24h | Humidity over 24 hours. (Float) |
| humi_12h | Humidity over 12 hours. (Float) |
| pressure_24h | Pressure over 24 hours. (Float) |
| pressure_12h | Pressure over 12 hours. (Float) |
| precip_24h | Precipitation over 24 hours. (Float) |
| precip_12h | Precipitation over 12 hours. (Float) |
| precip_12h_binary | Binary value for precipitation over 12 hours. (Integer) |
| precip_24h_binary | Binary value for precipitation over 24 hours. (Integer) |
| maxwindspeed_24h | Maximum wind speed over 24 hours. (Float) |
Acknowledgements
If you use this dataset in your research, please credit the original authors.
If you use this dataset in your research, please credit .
Tables
Citieshealth Bcn Data Panelstudy 20220414
@kaggle.thedevastator_air_pollution_and_mental_health.citieshealth_bcn_data_panelstudy_20220414
- 646.57 kB
- 3,348 rows
- 95 columns
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CREATE TABLE citieshealth_bcn_data_panelstudy_20220414 (
"id_zenodo" BIGINT,
"date_all" BIGINT,
"year" BIGINT,
"month" BIGINT,
"day" BIGINT,
"dayoftheweek" BIGINT,
"hour" BIGINT,
"mentalhealth_survey" VARCHAR,
"occurrence_mental" DOUBLE,
"bienestar" DOUBLE,
"energia" DOUBLE,
"estres" DOUBLE,
"sueno" DOUBLE,
"horasfuera" DOUBLE,
"actividadfisica" VARCHAR,
"ordenador" VARCHAR,
"dieta" VARCHAR,
"alcohol" VARCHAR,
"drogas" VARCHAR,
"bebida" VARCHAR,
"enfermo" VARCHAR,
"otrofactor" VARCHAR,
"stroop_test" VARCHAR,
"occurrence_stroop" DOUBLE,
"mean_incongruent" DOUBLE,
"correct" DOUBLE,
"response_duration_ms" DOUBLE,
"performance" DOUBLE,
"mean_congruent" DOUBLE,
"inhib_control" DOUBLE,
"z_performance" DOUBLE,
"z_mean_incongruent" DOUBLE,
"z_inhib_control" DOUBLE,
"no2bcn_24h" DOUBLE,
"no2bcn_12h" DOUBLE,
"no2gps_24h" DOUBLE,
"no2gps_12h" DOUBLE,
"no2bcn_12h_x30" DOUBLE,
"no2bcn_24h_x30" DOUBLE,
"no2gps_12h_x30" DOUBLE,
"no2gps_24h_x30" DOUBLE,
"min_gps" DOUBLE,
"hour_gps" DOUBLE,
"pm25bcn" DOUBLE,
"bc_g" DOUBLE,
"sec_noise55_day" DOUBLE,
"sec_noise65_day" DOUBLE,
"sec_greenblue_day" DOUBLE,
"hours_noise_55_day" DOUBLE,
"hours_noise_65_day" DOUBLE,
"hours_greenblue_day" DOUBLE,
"tmean_24h" DOUBLE,
"tmean_12h" DOUBLE,
"humi_24h" DOUBLE,
"humi_12h" DOUBLE,
"pressure_24h" DOUBLE,
"pressure_12h" DOUBLE,
"precip_24h" DOUBLE,
"precip_12h" DOUBLE,
"precip_12h_binary" BIGINT,
"precip_24h_binary" BIGINT,
"maxwindspeed_24h" DOUBLE,
"maxwindspeed_12h" DOUBLE,
"noise_total_lden_55" DOUBLE,
"access_greenbluespaces_300mbuff" VARCHAR,
"n__gm3" DOUBLE -- Μgm3,
"incidence_cat" VARCHAR,
"start_day" DOUBLE,
"start_month" DOUBLE,
"start_year" DOUBLE,
"start_hour" DOUBLE,
"end_day" DOUBLE,
"end_month" DOUBLE,
"end_year" DOUBLE,
"end_hour" DOUBLE,
"totaltime" DOUBLE,
"totaltime_estimated" VARCHAR,
"houron" VARCHAR,
"houroff" VARCHAR,
"age_yrs" DOUBLE,
"yearbirth" DOUBLE,
"smoke" VARCHAR,
"psycho" VARCHAR,
"gender" VARCHAR,
"district" VARCHAR,
"education" VARCHAR,
"covid_work" VARCHAR,
"covid_mood" VARCHAR,
"covid_sleep" VARCHAR,
"covid_espacios" VARCHAR,
"covid_aire" VARCHAR,
"covid_motor" VARCHAR,
"covid_electric" VARCHAR,
"covid_bikewalk" VARCHAR,
"covid_public_trans" VARCHAR
);