Age-adjusted mortality rates for the contiguous United States in 2000–2005 were obtained from the Wide-ranging Online Data for Epidemiologic Research system of the U.S. Centers for Disease Control and Prevention (CDC) (2015). Age-adjusted mortality rates were weighted averages of the age-specific death rates, and they were used to account for different age structures among populations (Curtin and Klein 1995). The mortality rates for counties with < 10 deaths were suppressed by the CDC to protect privacy and to ensure data reliability; only counties with ≥ 10 deaths were included in the analyses. The underlying cause of mortality was specified using the World Health Organization’s International Statistical Classification of Diseases and Related Health Problems (10th revision; ICD-10). In this study, we focused on the all-cause mortality rate (A00-R99) and on mortality rates from the three leading causes: heart disease (I00-I09, I11, I13, and I20-I51), cancer (C00-C97), and stroke (I60- I69) (Heron 2013). We excluded mortality due to external causes for all-cause mortality, as has been done in many previous studies (e.g., Pearce et al. 2010, 2011; Zanobetti and Schwartz 2009), because external causes of mortality are less likely to be related to environmental quality. We also focused on the contiguous United States because the numbers of counties with available cause-specific mortality rates were small in Hawaii and Alaska. County-level rates were available for 3,101 of the 3,109 counties in the contiguous United States (99.7%) for all-cause mortality; for 3,067 (98.6%) counties for heart disease mortality; for 3,057 (98.3%) counties for cancer mortality; and for 2,847 (91.6%) counties for stroke mortality. The EQI includes variables representing five environmental domains: air, water, land, built, and sociodemographic (2). The domain-specific indices include both beneficial and detrimental environmental factors. The air domain includes 87 variables representing criteria and hazardous air pollutants. The water domain includes 80 variables representing overall water quality, general water contamination, recreational water quality, drinking water quality, atmospheric deposition, drought, and chemical contamination. The land domain includes 26 variables representing agriculture, pesticides, contaminants, facilities, and radon. The built domain includes 14 variables representing roads, highway/road safety, public transit behavior, business environment, and subsidized housing environment. The sociodemographic environment includes 12 variables representing socioeconomics and crime. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Human health data are not available publicly. EQI data are available at: https://edg.epa.gov/data/Public/ORD/NHEERL/EQI. Format: Data are stored as csv files. This dataset is associated with the following publication: Jian, Y., L. Messer, J. Jagai, K. Rappazzo, C. Gray, S. Grabich, and D. Lobdell. Associations between environmental quality and mortality in the contiguous United States 2000-2005. ENVIRONMENTAL HEALTH PERSPECTIVES. National Institute of Environmental Health Sciences (NIEHS), Research Triangle Park, NC, USA, 125(3): 355-362, (2017).

Air pollution is one of the leading causes of premature deaths across the world, and is often under monitored in developing countries. Mexico presents an interesting case study with greatly improved air pollution thanks to regulation since the 1990s in Mexico City while other places have continued to have dangerous air pollution levels, responsible for tens of thousands of deaths per year.

Public pollution datasets are crucial to research and policy-making regarding public health. Mexico's air quality information program is named Sinaica, but can be time-consuming to use as data can only be retrieved one month at a time manually. Using the rSinaica R package by Diego Valle-Jones, this dataset compiles all recorded hourly values for 28 pollution and weather variables from the years 2010-2021 for all stations in Mexico.

La contaminación del aire es uno de los más grandes causas de muertos prematuras por todo el mundo, aproximadamente 1 en 5 muertes, y es mucho menos monitoreado en naciones en desarrollo. México nos presenta un caso de estudio interesante, con muchas mejoradas niveles de contaminantes gracias a regulaciones desde los 1990s en la Ciudad de México mientras otras lugares han mantenidos niveles de contaminantes peligrosos. Estos son responsibles por decenas de miles de muertos prevenibles cada año en México.

Públicos conjuntos de datos de la contaminación del aire son cruciales para la investigación y la formulación de leyes que protegen la salud publica. En México, el programa llamada la 'Sistema Nacional de Información de la Calidad del Aire', o SINAICA, tiene maneras de conseguir datos, pero requiere mucho tiempo porque información solo se puede ser obtenido un mes, parámetro, y estación a la vez a mano. Usando el rSinaica paquete de R creador por Diego Valle-Jones, este conjunto de datos incluye todos medidos horarios por 28 polución y metorológico variables desde los años 2010-2021 por todos estaciones en México.

Context

We know that air pollution can cause health problems, like heart attacks, strokes, diabetes, and high blood pressure, which have been identified as the pre-existing medical conditions that raise the chances of death from COVID-19 infection. Emerging research finds that breathing more polluted air over many years may itself worsen the effects of COVID-19.

Content

These are the data for the Impact of Lockdown during the COVID-19 Outbreak on Global Air Quality.

Acknowledgements

Bray, Casey (2021), “Data for: Impact of Lockdown during the COVID-19 Outbreak on Global Air Quality”, Mendeley Data, V1, doi: 10.17632/wwjnw24xvk.1 Link-to-Datasource.

OBJECTIVES To estimate all-cause and cause-specific deaths attributable to fossil fuel-related air pollution and to assess potential health benefits from policies that replace fossil fuels with clean, renewable energy sources. METHODS An updated atmospheric composition model, a newly developed relative risk model and recent satellite-based data were used to determine exposure to ambient air pollution, estimate all-cause and disease-specific mortality, and attribute them to emission categories. DATA SOURCES Data of the Global Burden of Disease 2019, NASA satellite-based fine particulate matter and population data, and atmospheric chemistry, aerosol and relative risk modelling for 2019.

Air pollution globalization, as a combined effect of atmospheric transport and international trade, can lead to notable transboundary health impacts. Life expectancy reduction attribution analysis of transboundary pollution can reveal the effect of pollution globalization on the lives of individuals. This study coupled five state-of-the-art models to link the regional per capita life expectancy reduction to cross-boundary pollution transport attributed to consumption in other regions. Our results revealed that pollution due to consumption in other regions contributed to a global population-weighted PM2.5 concentration of 9 μg/m3 in 2017, thereby causing 1.03 million premature deaths and reducing the global average life expectancy by 0.23 year (≈84 days). Trade-induced transboundary pollution relocation led to a significant reduction in life expectancy worldwide (from 5 to 155 days per person), and even in the least polluted regions, such as North America, Western Europe, and Russia, a 12–61-day life expectancy reduction could be attributed to consumption in other regions. Our results reveal the individual risks originating from air pollution globalization. To protect human life, all regions and residents worldwide should jointly act together to reduce atmospheric pollution and its globalization as soon as possible.

This story map explores 19 years of particulate matter (PM 2.5) in the air we breathe. This is done by exploring a few different things:Recent PM 2.519-year Average PM 2.5Human ImpactTrendsThe collection of maps shown in the story highlight different patterns of air quality as seen by particulate matter (PM 2.5) concentrations. Maps were created from this Global Particulate Matter (PM 2.5) between 1998-2016) Living Atlas layer. The data is aggregated from NASA Socioeconomic Data and Applications Center (SEDAC) gridded data into country boundaries, administrative 1 boundaries, and 50 km hex bins. The unit of measurement is micrograms per cubic meter.PM 2.5 are small particulate matter of 2.5 microns or less in diameter that are in the air we breathe. These tiny particles can be ingested into your lungs and bloodstream, causing a great risk for cardiovascular and respiratory diseases. They can come from many different sources such as power plants, vehicles, fires, dust, and many others.According to the World Health Organization (WHO): “Ambient (outdoor) air pollution in both cities and rural areas was estimated to cause 4.2 million premature deaths worldwide per year in 2016; this mortality is due to exposure to small particulate matter of 2.5 microns or less in diameter (PM2.5), which cause cardiovascular and respiratory disease, and cancers.” Studying where PM 2.5 concentrations exist can help policymakers form new laws to help protect the health of their population.Some of the things we can learn from these maps:What is the average annual PM 2.5 value over 19 years? (1998-2016)What is the annual average PM 2.5 value for each year from 1998 to 2016?What is the statistical trend for PM 2.5 over the 19 years? (downward or upward)Are there hot spots (or cold spots) of PM 2.5 over the 19 years?How many people are impacted by the air quality in an area?What is the death rate caused by the joint effects of air pollution?Citations:van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2018. Global Annual PM2.5 Grids from MODIS, MISR and SeaWiFS Aerosol Optical Depth (AOD) with GWR, 1998-2016. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H4ZK5DQS. Accessed 1 April 2020van Donkelaar, A., R. V. Martin, M. Brauer, N. C. Hsu, R. A. Kahn, R. C. Levy, A. Lyapustin, A. M. Sayer, and D. M. Winker. 2016. Global Estimates of Fine Particulate Matter Using a Combined Geophysical-Statistical Method with Information from Satellites. Environmental Science & Technology 50 (7): 3762-3772. https://doi.org/10.1021/acs.est.5b05833.

This dataset shows the death rate attributed to air pollution caused by particulate matter in Cambodia, with the number of death, percentage, and the mortality rate per 100,000 population associated with death due to air pollution by ambient particulate matter, household, and ozone air pollution.

Additional file 1: Comparison of estimated annual average concentrations of air pollutants for the period 1980–2019 between districts of HPA and LPA.

BackgroundLittle is known about the effect of combined exposure to different air pollutants on mortality in dialysis patients. This study aimed to investigate the association of multiple exposures to air pollutants with all-cause and cause-specific death in dialysis patients.Materials and methodsThis registry-based nationwide cohort study included 90,373 adult kidney failure patients initiating maintenance dialysis between 2012 and 2020 identified from the French REIN registry. Estimated mean annual municipality levels of PM2.5, PM10, and NO2 between 2009 and 2020 were combined in different composite air pollution scores to estimate each participant’s exposure at the residential place one to 3 years before dialysis initiation. Adjusted cause-specific Cox proportional hazard models were used to estimate hazard ratios (HRs) per interquartile range (IQR) greater air pollution score. Effect measure modification was assessed for age, sex, dialysis care model, and baseline comorbidities.ResultsHigher levels of the main air pollution score were associated with a greater rate of all-cause deaths (HR, 1.082 [95% confidence interval (CI), 1.057–1.104] per IQR increase), regardless of the exposure lag. This association was also confirmed in cause-specific analyses, most markedly for infectious mortality (HR, 1.686 [95% CI, 1.470–1.933]). Sensitivity analyses with alternative composite air pollution scores showed consistent findings. Subgroup analyses revealed a significantly stronger association among women and fewer comorbid patients.DiscussionLong-term multiple air pollutant exposure is associated with all-cause and cause-specific mortality among patients receiving maintenance dialysis, suggesting that air pollution may be a significant contributor to the increasing trend of CKD-attributable mortality worldwide.

Polluted air is a major health hazard in developing countries. Improvements in pollution monitoring and statistical techniques during the last several decades have steadily enhanced the ability to measure the health effects of air pollution. Current methods can detect significant increases in the incidence of cardiopulmonary and respiratory diseases, coughing, bronchitis, and lung cancer, as well as premature deaths from these diseases resulting from elevated concentrations of ambient Particulate Matter (Holgate 1999). Scarce public resources have limited the monitoring of atmospheric particulate matter (PM) concentrations in developing countries, despite their large potential health effects. As a result, policymakers in many developing countries remain uncertain about the exposure of their residents to PM air pollution. The Global Model of Ambient Particulates (GMAPS) is an attempt to bridge this information gap through an econometrically estimated model for predicting PM levels in world cities (Pandey et al. forthcoming).The estimation model is based on the latest available monitored PM pollution data from the World Health Organization, supplemented by data from other reliable sources. The current model can be used to estimate PM levels in urban residential areas and non-residential pollution hotspots. The results of the model are used to project annual average ambient PM concentrations for residential and non-residential areas in 3,226 world cities with populations larger than 100,000, as well as national capitals. The study finds wide, systematic variations in ambient PM concentrations, both across world cities and over time. PM concentrations have risen at a slower rate than total emissions. Overall emission levels have been rising, especially for poorer countries, at nearly 6 percent per year. PM concentrations have not increased by as much, due to improvements in technology and structural shifts in the world economy. Additionally, within-country variations in PM levels can diverge greatly (by a factor of 5 in some cases), because of the direct and indirect effects of geo-climatic factors.The primary determinants of PM concentrations are the scale and composition of economic activity, population, the energy mix, the strength of local pollution regulation, and geographic and atmospheric conditions that affect pollutant dispersion in the atmosphere.

Abstract

Polluted air is a major health hazard in developing countries. Improvements in pollution monitoring and statistical techniques during the last several decades have steadily enhanced the ability to measure the health effects of air pollution. Current methods can detect significant increases in the incidence of cardiopulmonary and respiratory diseases, coughing, bronchitis, and lung cancer, as well as premature deaths from these diseases resulting from elevated concentrations of ambient Particulate Matter (Holgate 1999).

Scarce public resources have limited the monitoring of atmospheric particulate matter (PM) concentrations in developing countries, despite their large potential health effects. As a result, policymakers in many developing countries remain uncertain about the exposure of their residents to PM air pollution. The Global Model of Ambient Particulates (GMAPS) is an attempt to bridge this information gap through an econometrically estimated model for predicting PM levels in world cities (Pandey et al. forthcoming).

The estimation model is based on the latest available monitored PM pollution data from the World Health Organization, supplemented by data from other reliable sources. The current model can be used to estimate PM levels in urban residential areas and non-residential pollution hotspots. The results of the model are used to project annual average ambient PM concentrations for residential and non-residential areas in 3,226 world cities with populations larger than 100,000, as well as national capitals.

The study finds wide, systematic variations in ambient PM concentrations, both across world cities and over time. PM concentrations have risen at a slower rate than total emissions. Overall emission levels have been rising, especially for poorer countries, at nearly 6 percent per year. PM concentrations have not increased by as much, due to improvements in technology and structural shifts in the world economy. Additionally, within-country variations in PM levels can diverge greatly (by a factor of 5 in some cases), because of the direct and indirect effects of geo-climatic factors.

The primary determinants of PM concentrations are the scale and composition of economic activity, population, the energy mix, the strength of local pollution regulation, and geographic and atmospheric conditions that affect pollutant dispersion in the atmosphere.

Geographic coverage

The database covers the following countries: Afghanistan Albania Algeria Andorra Angola
Antigua and Barbuda Argentina
Armenia Australia
Austria Azerbaijan
Bahamas, The
Bahrain Bangladesh
Barbados
Belarus Belgium Belize
Benin
Bhutan
Bolivia Bosnia and Herzegovina
Brazil
Brunei
Bulgaria
Burkina Faso
Burundi Cambodia
Cameroon
Canada
Cayman Islands
Central African Republic
Chad
Chile
China
Colombia
Comoros Congo, Dem. Rep.
Congo, Rep. Costa Rica
Cote d'Ivoire
Croatia Cuba
Cyprus
Czech Republic
Denmark Dominica
Dominican Republic
Ecuador Egypt, Arab Rep.
El Salvador Eritrea Estonia Ethiopia
Faeroe Islands
Fiji
Finland France
Gabon
Gambia, The Georgia Germany Ghana
Greece
Grenada Guatemala
Guinea
Guinea-Bissau
Guyana
Haiti
Honduras
Hong Kong, China
Hungary Iceland India
Indonesia
Iran, Islamic Rep.
Iraq
Ireland Israel
Italy
Jamaica Japan
Jordan
Kazakhstan
Kenya
Korea, Dem. Rep.
Korea, Rep. Kuwait
Kyrgyz Republic Lao PDR Latvia
Lebanon Lesotho Liberia Liechtenstein
Lithuania
Luxembourg
Macao, China
Macedonia, FYR
Madagascar
Malawi
Malaysia
Maldives
Mali
Mauritania
Mexico
Moldova Mongolia
Morocco Mozambique
Myanmar Namibia Nepal
Netherlands Netherlands Antilles
New Caledonia
New Zealand Nicaragua
Niger
Nigeria Norway
Oman
Pakistan
Panama
Papua New Guinea
Paraguay
Peru
Philippines Poland
Portugal
Puerto Rico Qatar
Romania Russian Federation
Rwanda
Sao Tome and Principe
Saudi Arabia
Senegal Sierra Leone
Singapore
Slovak Republic Slovenia
Solomon Islands Somalia South Africa
Spain
Sri Lanka
St. Kitts and Nevis St. Lucia
St. Vincent and the Grenadines
Sudan
Suriname
Swaziland
Sweden
Switzerland Syrian Arab Republic
Tajikistan
Tanzania
Thailand
Togo
Trinidad and Tobago Tunisia Turkey
Turkmenistan
Uganda
Ukraine United Arab Emirates
United Kingdom
United States
Uruguay Uzbekistan
Vanuatu Venezuela, RB
Vietnam Virgin Islands (U.S.)
Yemen, Rep. Yugoslavia, FR (Serbia/Montenegro)
Zambia
Zimbabwe

Kind of data

Observation data/ratings [obs]

Mode of data collection

Other [oth]

Air pollution is the contamination of air due to the presence of substances in the atmosphere that are harmful to the health of humans and other living beings, or cause damage to the climate or to materials. There are many different types of air pollutants, such as gases (including ammonia, carbon monoxide, sulfur dioxide, nitrous oxides, methane, carbon dioxide and chlorofluorocarbons), particulates (both organic and inorganic), and biological molecules. Air pollution can cause diseases, allergies, and even death to humans; it can also cause harm to other living organisms such as animals and food crops, and may damage the natural environment (for example, climate change, ozone depletion or habitat degradation) or built environment (for example, acid rain). Both human activity and natural processes can generate air pollution.

dataset is scrapped from the IQAir website. almost 6k most polluted cities are covered in it.

All values are average.

https://www.sparetheair.com/assets/aqi/PM2017.png">

Several observational studies from locations around the globe have documented a positive correlation between air pollution and the severity of COVID-19 disease. Observational studies cannot identify the causal link between air quality and the severity of COVID-19 outcomes, and these studies face three key identification challenges: 1) air pollution is not randomly distributed across geographies; 2) air-quality monitoring networks are sparse spatially; and 3) defensive behaviors to mediate exposure to air pollution and COVID-19 are not equally available to all, leading to large measurement error bias when using rate-based COVID-19 outcome measures (e.g., incidence rate or mortality rate). Using a quasi-experimental design, we explore whether traffic-related air pollutants cause people with COVID-19 to suffer more extreme health outcomes in New York City (NYC). When we address the previously overlooked challenges to identification, we do not detect causal impacts of increased chronic concentrations of traffic-related air pollutants on COVID-19 death or hospitalization counts in NYC census tracts.

IntroductionGlobally, air pollution is the leading environmental cause of disease and premature death. Raising awareness through environmental education and adequate communication on air quality could reduce the adverse effects. We aimed to assess the knowledge, attitudes, and practices (KAP) regarding air pollution and health and determine the factors associated with these KAP in children and adolescents.MethodsIn 2019–2020, a cross-sectional study was conducted on 6th–11th grade high school students in five municipalities in Colombia. Variables collected included: age, sex, private or public school, any medical history, emergency room visits due to respiratory symptoms in the last year, and whether students played sports. The main exposure was the School Environmental Project. The outcomes were the KAP scale [0% (the lowest score) to 100% (the highest score)]. The factors associated with KAP levels were evaluated with independent mixed regressions due to the multilevel structure of the study (level 1: student; level 2: school), and the exponential coefficients (95% confidence interval-CI) were reported.ResultsAmong 1,676 students included, 53.8% were females. The median knowledge score about air pollution and its health effects was 33.8% (IQR: 24.0–44.9), 38.6% knew the air quality index, 30.9% knew the air quality alerts that occurred twice a year in these municipalities and 5.3% had high self-perceived knowledge. Positive attitudes, pro-environmental practices, being female, grade level, attending a private school, having respiratory diseases, and the school environmental project importance were associated with higher knowledge scores. The median attitudes score was 78.6% (IQR: 71.4–92.9). Pro-environmental attitudes were associated with knowledge-increasing, being female, attending a private school, and the school environmental project. The median pro-environmental practices score was 28.6% (IQR: 28.6–42.9). During air quality alerts, 11.6% had worn masks, 19% had reduced the opening time of windows and 15.9% avoided leaving home. Pro-environmental practices were associated with knowledge-increasing and attitudes-increasing, and lower practices with higher grade levels, visiting a doctor in the last year, and practicing sports.DiscussionChildren and adolescents have low knowledge scores and inadequate pro-environmental practices scores regarding air pollution. However, they demonstrate positive attitudes towards alternative solutions and express important concerns about the planet’s future.

The present study aimed to observe the Air pollution status of Milk city - Anand and its impact on the prevalent vegetation in the Industrial area, Urban area, and Commercial area during a post-monsoon season in 2022. SO2, NO2, NH3, PM2.5, and PM10 were measured using High Volume Air Sampler (HVAS). As we know air pollution is a serious problem in India, more than 90% of India's population lives in places where air quality is below WHO criteria, as it is mixed with road dust, vehicular emission, domestic fuel burning, open waste burning, construction activities, industrial emissions, and anthropogenic activity. Exceeding amounts of gases and particulate matter may lead to various health effects like respiratory infections, Chronic obstructive pulmonary disease (COPD), lung cancer and also affect vegetation. According to the World Health Organization's report(year), exposure to fine particulate outdoor air pollution over a long period causes about 4.2 million premature deaths annually. The concentration of SO2, NO2, NH3, PM2.5, and PM10 varied between 9.31 to 12.16 µg/m3, 7.08 to 10.42 µg/m3, 1.65 to 1.93 µg/m3, 34.75 to 58.30 µg/m3 and 42.90 to 86.64 µg/m3 respectively (Fig. 1). The Air Quality Index (AQI) is a significant tool for measuring ambient air quality and environmental health. AQI ranged from 65.54 to 112.64. The highest AQI recorded was at the industrial site (112.64) and the lowest at Urban site (65.54) (Fig. 2). During the study period, the industrial area was highly polluted due to more release of hazardous gases and particulate matter from various industries. At the commercial and Urban area lower values of AQI represents a satisfactory category as per CPCB. APTI plays a significant role to determine the resistivity and susceptibility of plant species against pollution levels. Plant that has a higher index value are tolerant to air pollution. Fig. 3 shows the APTI for an industrial, urban, and commercial site. All three plant species (Alstonia scholaris, Azardirachta indica and polyalthia longifolia) growing in industrial area showed higher APTI value compared to Urban and Commercial area. The APTI values for plant species were as Alstonia scholaris > Azardirachta indica > polyalthia longifolia. All the species were found in the range of 1-16, hence these species fall under the category of sensitive species.

Lung Cancer Prediction

Air Pollution, Alcohol, Smoking & Risk of Lung Cancer

About this dataset

This dataset contains information on patients with lung cancer, including their age, gender, air pollution exposure, alcohol use, dust allergy, occupational hazards, genetic risk, chronic lung disease, balanced diet, obesity, smoking, passive smoker, chest pain, coughing of blood, fatigue, weight loss ,shortness of breath ,wheezing ,swallowing difficulty ,clubbing of finger nails and snoring

How to use the dataset

Lung cancer is the leading cause of cancer death worldwide, accounting for 1.59 million deaths in 2018. The majority of lung cancer cases are attributed to smoking, but exposure to air pollution is also a risk factor. A new study has found that air pollution may be linked to an increased risk of lung cancer, even in nonsmokers.

The study, which was published in the journal Nature Medicine, looked at data from over 462,000 people in China who were followed for an average of six years. The participants were divided into two groups: those who lived in areas with high levels of air pollution and those who lived in areas with low levels of air pollution.

The researchers found that the people in the high-pollution group were more likely to develop lung cancer than those in the low-pollution group. They also found that the risk was higher in nonsmokers than smokers, and that the risk increased with age.

While this study does not prove that air pollution causes lung cancer, it does suggest that there may be a link between the two. More research is needed to confirm these findings and to determine what effect different types and levels of air pollution may have on lung cancer risk

Research Ideas

• predicting the likelihood of a patient developing lung cancer
• identifying risk factors for lung cancer
• determining the most effective treatment for a patient with lung cancer

Acknowledgements

License

See the dataset description for more information.

Columns

File: cancer patient data sets.csv | Column name | Description | |:-----------------------------|:--------------------------------------------------------------------| | Age | The age of the patient. (Numeric) | | Gender | The gender of the patient. (Categorical) | | Air Pollution | The level of air pollution exposure of the patient. (Categorical) | | Alcohol use | The level of alcohol use of the patient. (Categorical) | | Dust Allergy | The level of dust allergy of the patient. (Categorical) | | OccuPational Hazards | The level of occupational hazards of the patient. (Categorical) | | Genetic Risk | The level of genetic risk of the patient. (Categorical) | | chronic Lung Disease | The level of chronic lung disease of the patient. (Categorical) | | Balanced Diet | The level of balanced diet of the patient. (Categorical) | | Obesity | The level of obesity of the patient. (Categorical) | | Smoking | The level of smoking of the patient. (Categorical) | | Passive Smoker | The level of passive smoker of the patient. (Categorical) | | Chest Pain | The level of chest pain of the patient. (Categorical) | | Coughing of Blood | The level of coughing of blood of the patient. (Categorical) | | Fatigue | The level of fatigue of the patient. (Categorical) | | Weight Loss | The level of weight loss of the patient. (Categorical) | | Shortness of Breath | The level of shortness of breath of the patient. (Categorical) | | Wheezing | The level of wheezing of the patient. (Categorical) | | Swallowing Difficulty | The level of swallowing difficulty of the patient. (Categorical) | | Clubbing of Finger Nails | The level of clubbing of finger nails of the patient. (Categorical) |

Polluted air is a major health hazard in developing countries. Improvements in pollution monitoring and statistical techniques during the last several decades have steadily enhanced the ability to measure the health effects of air pollution. Current methods can detect significant increases in the incidence of cardiopulmonary and respiratory diseases, coughing, bronchitis, and lung cancer, as well as premature deaths from these diseases resulting from elevated concentrations of ambient Particulate Matter (Holgate 1999).

Scarce public resources have limited the monitoring of atmospheric particulate matter (PM) concentrations in developing countries, despite their large potential health effects. As a result, policymakers in many developing countries remain uncertain about the exposure of their residents to PM air pollution. The Global Model of Ambient Particulates (GMAPS) is an attempt to bridge this information gap through an econometrically estimated model for predicting PM levels in world cities (Pandey et al. forthcoming).

The estimation model is based on the latest available monitored PM pollution data from the World Health Organization, supplemented by data from other reliable sources. The current model can be used to estimate PM levels in urban residential areas and non-residential pollution hotspots. The results of the model are used to project annual average ambient PM concentrations for residential and non-residential areas in 3,226 world cities with populations larger than 100,000, as well as national capitals.

The study finds wide, systematic variations in ambient PM concentrations, both across world cities and over time. PM concentrations have risen at a slower rate than total emissions. Overall emission levels have been rising, especially for poorer countries, at nearly 6 percent per year. PM concentrations have not increased by as much, due to improvements in technology and structural shifts in the world economy. Additionally, within-country variations in PM levels can diverge greatly (by a factor of 5 in some cases), because of the direct and indirect effects of geo-climatic factors.

The primary determinants of PM concentrations are the scale and composition of economic activity, population, the energy mix, the strength of local pollution regulation, and geographic and atmospheric conditions that affect pollutant dispersion in the atmosphere.

This indicator provides information about the mortality rate from motor vehicle crashes and traffic-related injuries, including among pedestrians. Death rate has been age-adjusted to the 2000 U.S. standard population. Single-year data are only available for Los Angeles County overall, Service Planning Areas, Supervisorial Districts, City of Los Angeles overall, and City of Los Angeles Council Districts.Motor vehicle crashes are a leading cause of death from unintentional injury both in Los Angeles County and in the US. While many factors contribute to motor vehicle crash mortality, the built environment plays a critical role. Communities that are exposed to heavy traffic or that lack adequate walking infrastructure for pedestrians have higher rates of motor vehicle crash-related injuries and deaths. They are also more impacted by traffic-related environmental hazards, such as vehicle emissions and air pollution. In Los Angeles County, many of these communities are also home to a large number of low-income residents. Thus, motor vehicle crash mortality can be viewed as an environmental justice issue.For more information about the Community Health Profiles Data Initiative, please see the initiative homepage.

This dataset provides geolocated information about the following pollutants:

• NO2 : nitrogen dioxide is one of the several nitrogen oxides. It is introduced into the air by natural phenomena like entry from stratosphere, or lighting. At the surface level, however, NO2 forms from cars, trucks and buses emissions, power plants and off-road equipment. Exposure over short periods can aggravate respiratory diseases, like asthma. Longer exposures may contribute to develoment of asthma and respiratory infections. People with asthma, children and the elderly are at greater risk for the health effects of NO2.
• O3 : the ozone molecule is harmful for outdoor air quality (if outside of the ozone layer). At surface level, ozone is created by chemical reactions between oxides of nitrogen and volatile organic compounds (VOC). Differently from the good ozone located in the upper atmosphere, ground level ozone can provoke several health problems like chest pain, coughing, throat irritation and airway inflammation. Furthermore it can reduce lung function and worsen bronchitis, emphysema, and asthma. Ozone affects also vegetation and ecosystems. In particular, it damages sensitive vegetation during the growing season.
• CO : carbon monoxide is a colorless and odorless gas. Outdoor, it is emitted in the air above all by cars, trucks and other vehicles or machineries that burn fossil fuels. Such items like kerosene and gas space heaters, gas stoves also release CO affecting indoor air quality.
  Breathing air with a high concentration of CO reduces the amount of oxygen that can be transported in the blood stream to critical organs like the heart and brain. At very high levels, which are not likely to occur outdoor, but which are possible in enclosed environments, CO can cause dizziness, confusion, unconsciousness and death.
• PM10 : atmospheric particulate matter (PM), also known as atmospheric aerosol particles, are complex mixtures of small solid and liquid matter that get into the air. If inhaled they can cause serious heart and lungs problem. They have been classified as group 1 carcinogen by the International Agengy for Research on Cancer (IARC). PM10 refers to those particules with a diameter of 10 micrometers or less.
• PM2.5 : they refer to those particles with a diameter of 2.5 micrometers or less.

Context

Air Pollution is contamination of the indoor or outdoor environment by any chemical, physical or biological agent that modifies the natural characteristics of the atmosphere. Household combustion devices, motor vehicles, industrial facilities and forest fires are common sources of air pollution. Pollutants of major public health concern include particulate matter, carbon monoxide, ozone, nitrogen dioxide and sulfur dioxide. Outdoor and indoor air pollution cause respiratory and other diseases and are important sources of morbidity and mortality.

This dataset provides geolocated information about the following pollutants:

• Nitrogen Dioxide [NO2] : Nitrogen Dioxide is one of the several nitrogen oxides. It is introduced into the air by natural phenomena like entry from stratosphere or lighting. At the surface level, however, NO2 forms from cars, trucks and buses emissions, power plants and off-road equipment. Exposure over short periods can aggravate respiratory diseases, like asthma. Longer exposures may contribute to develoment of asthma and respiratory infections. People with asthma, children and the elderly are at greater risk for the health effects of NO2.

• Ozone [O3] : The Ozone molecule is harmful for outdoor air quality (if outside of the ozone layer). At surface level, ozone is created by chemical reactions between oxides of nitrogen and volatile organic compounds (VOC). Differently from the good ozone located in the upper atmosphere, ground level ozone can provoke several health problems like chest pain, coughing, throat irritation and airway inflammation. Furthermore it can reduce lung function and worsen bronchitis, emphysema, and asthma. Ozone affects also vegetation and ecosystems. In particular, it damages sensitive vegetation during the growing season.

• Carbon Monoxide [CO] : Carbon Monoxide is a colorless and odorless gas. Outdoor, it is emitted in the air above all by cars, trucks and other vehicles or machineries that burn fossil fuels. Such items like kerosene and gas space heaters, gas stoves also release CO affecting indoor air quality. Breathing air with a high concentration of CO reduces the amount of oxygen that can be transported in the blood stream to critical organs like the heart and brain. At very high levels, which are not likely to occur outdoor but which are possible in enclosed environments. CO can cause dizziness, confusion, unconsciousness and death.

• Particulate Matter [PM2.5] : Atmospheric Particulate Matter, also known as atmospheric aerosol particles, are complex mixtures of small solid and liquid matter that get into the air. If inhaled they can cause serious heart and lungs problem. They have been classified as group 1 carcinogen by the International Agengy for Research on Cancer (IARC). PM10 refers to those particules with a diameter of 10 micrometers or less. PM2.5 refers to those particles with a diameter of 2.5 micrometers or less.

Content

There is one dataset here.

global air pollution dataset.csv

• Country : Name of the country
• City : Name of the city
• AQI Value : Overall AQI value of the city
• AQI Category : Overall AQI category of the city
• CO AQI Value : AQI value of Carbon Monoxide of the city
• CO AQI Category : AQI category of Carbon Monoxide of the city
• Ozone AQI Value : AQI value of Ozone of the city
• Ozone AQI Category : AQI category of Ozone of the city
• NO2 AQI Value : AQI value of Nitrogen Dioxide of the city
• NO2 AQI Category : AQI category of Nitrogen Dioxide of the city
• PM2.5 AQI Value : AQI value of Particulate Matter with a diameter of 2.5 micrometers or less of the city
• PM2.5 AQI Category : AQI category of Particulate Matter with a diameter of 2.5 micrometers or less of the city

Acknowledgement

These datas are collected from elichens. Cover photo from VectorStock.