Air Pollution And Mental Health by Kaggle | Healthcare

About this Dataset

Air Pollution And Mental Health

Air Pollution and Mental Health

Identifying Short-Term Human Impacts of Air Pollution

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

631.42 KB
3348 rows
95 columns


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,
  "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
);