Dataset contains information on New York City air quality surveillance data.

Air pollution is one of the most important environmental threats to urban populations and while all people are exposed, pollutant emissions, levels of exposure, and population vulnerability vary across neighborhoods. Exposures to common air pollutants have been linked to respiratory and cardiovascular diseases, cancers, and premature deaths. These indicators provide a perspective across time and NYC geographies to better characterize air quality and health in NYC. Data can also be explored online at the Environment and Health Data Portal: http://nyc.gov/health/environmentdata.

United States US: Mortality Rate Attributed to Household and Ambient Air Pollution: Age-standardized: Male data was reported at 17.000 NA in 2016. United States US: Mortality Rate Attributed to Household and Ambient Air Pollution: Age-standardized: Male data is updated yearly, averaging 17.000 NA from Dec 2016 (Median) to 2016, with 1 observations. United States US: Mortality Rate Attributed to Household and Ambient Air Pollution: Age-standardized: Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Health Statistics. Mortality rate attributed to household and ambient air pollution is the number of deaths attributable to the joint effects of household and ambient air pollution in a year per 100,000 population. The rates are age-standardized. Following diseases are taken into account: acute respiratory infections (estimated for all ages); cerebrovascular diseases in adults (estimated above 25 years); ischaemic heart diseases in adults (estimated above 25 years); chronic obstructive pulmonary disease in adults (estimated above 25 years); and lung cancer in adults (estimated above 25 years).; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;

This United States Environmental Protection Agency (US EPA) feature layer represents monitoring site data, updated hourly concentrations and Air Quality Index (AQI) values for the latest hour received from monitoring sites that report to AirNow.Map and forecast data are collected using federal reference or equivalent monitoring techniques or techniques approved by the state, local or tribal monitoring agencies. To maintain "real-time" maps, the data are displayed after the end of each hour. Although preliminary data quality assessments are performed, the data in AirNow are not fully verified and validated through the quality assurance procedures monitoring organizations used to officially submit and certify data on the EPA Air Quality System (AQS).This data sharing, and centralization creates a one-stop source for real-time and forecast air quality data. The benefits include quality control, national reporting consistency, access to automated mapping methods, and data distribution to the public and other data systems. The U.S. Environmental Protection Agency, National Oceanic and Atmospheric Administration, National Park Service, tribal, state, and local agencies developed the AirNow system to provide the public with easy access to national air quality information. State and local agencies report the Air Quality Index (AQI) for cities across the US and parts of Canada and Mexico. AirNow data are used only to report the AQI, not to formulate or support regulation, guidance or any other EPA decision or position.About the AQIThe Air Quality Index (AQI) is an index for reporting daily air quality. It tells you how clean or polluted your air is, and what associated health effects might be a concern for you. The AQI focuses on health effects you may experience within a few hours or days after breathing polluted air. EPA calculates the AQI for five major air pollutants regulated by the Clean Air Act: ground-level ozone, particle pollution (also known as particulate matter), carbon monoxide, sulfur dioxide, and nitrogen dioxide. For each of these pollutants, EPA has established national air quality standards to protect public health. Ground-level ozone and airborne particles (often referred to as "particulate matter") are the two pollutants that pose the greatest threat to human health in this country.A number of factors influence ozone formation, including emissions from cars, trucks, buses, power plants, and industries, along with weather conditions. Weather is especially favorable for ozone formation when it’s hot, dry and sunny, and winds are calm and light. Federal and state regulations, including regulations for power plants, vehicles and fuels, are helping reduce ozone pollution nationwide.Fine particle pollution (or "particulate matter") can be emitted directly from cars, trucks, buses, power plants and industries, along with wildfires and woodstoves. But it also forms from chemical reactions of other pollutants in the air. Particle pollution can be high at different times of year, depending on where you live. In some areas, for example, colder winters can lead to increased particle pollution emissions from woodstove use, and stagnant weather conditions with calm and light winds can trap PM2.5 pollution near emission sources. Federal and state rules are helping reduce fine particle pollution, including clean diesel rules for vehicles and fuels, and rules to reduce pollution from power plants, industries, locomotives, and marine vessels, among others.How Does the AQI Work?Think of the AQI as a yardstick that runs from 0 to 500. The higher the AQI value, the greater the level of air pollution and the greater the health concern. For example, an AQI value of 50 represents good air quality with little potential to affect public health, while an AQI value over 300 represents hazardous air quality.An AQI value of 100 generally corresponds to the national air quality standard for the pollutant, which is the level EPA has set to protect public health. AQI values below 100 are generally thought of as satisfactory. When AQI values are above 100, air quality is considered to be unhealthy-at first for certain sensitive groups of people, then for everyone as AQI values get higher.Understanding the AQIThe purpose of the AQI is to help you understand what local air quality means to your health. To make it easier to understand, the AQI is divided into six categories:Air Quality Index(AQI) ValuesLevels of Health ConcernColorsWhen the AQI is in this range:..air quality conditions are:...as symbolized by this color:0 to 50GoodGreen51 to 100ModerateYellow101 to 150Unhealthy for Sensitive GroupsOrange151 to 200UnhealthyRed201 to 300Very UnhealthyPurple301 to 500HazardousMaroonNote: Values above 500 are considered Beyond the AQI. Follow recommendations for the Hazardous category. Additional information on reducing exposure to extremely high levels of particle pollution is available here.Each category corresponds to a different level of health concern. The six levels of health concern and what they mean are:"Good" AQI is 0 to 50. Air quality is considered satisfactory, and air pollution poses little or no risk."Moderate" AQI is 51 to 100. Air quality is acceptable; however, for some pollutants there may be a moderate health concern for a very small number of people. For example, people who are unusually sensitive to ozone may experience respiratory symptoms."Unhealthy for Sensitive Groups" AQI is 101 to 150. Although general public is not likely to be affected at this AQI range, people with lung disease, older adults and children are at a greater risk from exposure to ozone, whereas persons with heart and lung disease, older adults and children are at greater risk from the presence of particles in the air."Unhealthy" AQI is 151 to 200. Everyone may begin to experience some adverse health effects, and members of the sensitive groups may experience more serious effects."Very Unhealthy" AQI is 201 to 300. This would trigger a health alert signifying that everyone may experience more serious health effects."Hazardous" AQI greater than 300. This would trigger a health warnings of emergency conditions. The entire population is more likely to be affected.AQI colorsEPA has assigned a specific color to each AQI category to make it easier for people to understand quickly whether air pollution is reaching unhealthy levels in their communities. For example, the color orange means that conditions are "unhealthy for sensitive groups," while red means that conditions may be "unhealthy for everyone," and so on.Air Quality Index Levels of Health ConcernNumericalValueMeaningGood0 to 50Air quality is considered satisfactory, and air pollution poses little or no risk.Moderate51 to 100Air quality is acceptable; however, for some pollutants there may be a moderate health concern for a very small number of people who are unusually sensitive to air pollution.Unhealthy for Sensitive Groups101 to 150Members of sensitive groups may experience health effects. The general public is not likely to be affected.Unhealthy151 to 200Everyone may begin to experience health effects; members of sensitive groups may experience more serious health effects.Very Unhealthy201 to 300Health alert: everyone may experience more serious health effects.Hazardous301 to 500Health warnings of emergency conditions. The entire population is more likely to be affected.Note: Values above 500 are considered Beyond the AQI. Follow recommendations for the "Hazardous category." Additional information on reducing exposure to extremely high levels of particle pollution is available here.

The AQS Data Mart is a database containing all of the information from AQS. It has every measured value the EPA has collected via the national ambient air monitoring program. It also includes the associated aggregate values calculated by EPA (8-hour, daily, annual, etc.). The AQS Data Mart is a copy of AQS made once per week and made accessible to the public through web-based applications. The intended users of the Data Mart are air quality data analysts in the regulatory, academic, and health research communities. It is intended for those who need to download large volumes of detailed technical data stored at EPA and does not provide any interactive analytical tools. It serves as the back-end database for several Agency interactive tools that could not fully function without it: AirData, AirCompare, The Remote Sensing Information Gateway, the Map Monitoring Sites KML page, etc.

AQS must maintain constant readiness to accept data and meet high data integrity requirements, thus is limited in the number of users and queries to which it can respond. The Data Mart, as a read only copy, can allow wider access.

The most commonly requested aggregation levels of data (and key metrics in each) are:

Sample Values (2.4 billion values back as far as 1957, national consistency begins in 1980, data for 500 substances routinely collected) The sample value converted to standard units of measure (generally 1-hour averages as reported to EPA, sometimes 24-hour averages) Local Standard Time (LST) and GMT timestamps Measurement method Measurement uncertainty, where known Any exceptional events affecting the data NAAQS Averages NAAQS average values (8-hour averages for ozone and CO, 24-hour averages for PM2.5) Daily Summary Values (each monitor has the following calculated each day) Observation count Observation per cent (of expected observations) Arithmetic mean of observations Max observation and time of max AQI (air quality index) where applicable Number of observations > Standard where applicable Annual Summary Values (each monitor has the following calculated each year) Observation count and per cent Valid days Required observation count Null observation count Exceptional values count Arithmetic Mean and Standard Deviation 1st - 4th maximum (highest) observations Percentiles (99, 98, 95, 90, 75, 50) Number of observations > Standard Site and Monitor Information FIPS State Code (the first 5 items on this list make up the AQS Monitor Identifier) FIPS County Code Site Number (unique within the county) Parameter Code (what is measured) POC (Parameter Occurrence Code) to distinguish from different samplers at the same site Latitude Longitude Measurement method information Owner / operator / data-submitter information Monitoring Network to which the monitor belongs Exemptions from regulatory requirements Operational dates City and CBSA where the monitor is located Quality Assurance Information Various data fields related to the 19 different QA assessments possible

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.epa_historical_air_quality.[TABLENAME]. Fork this kernel to get started.

Acknowledgements

Data provided by the US Environmental Protection Agency Air Quality System Data Mart.

Air pollution results from the introduction of a range of substances into the atmosphere from a wide variety of sources. It can cause both short term and long term effects on health, but also on the wider environment. The air quality in Northern Ireland is generally better now than it has been at any time since before the Industrial Revolution.

These improvements have been achieved through the introduction of legislation enforcing tighter controls on emissions of pollutants from key sources, notably industry, domestic combustion and transport. However, despite the improvements made, air pollution is still recognised as a risk to health, and many people are concerned about pollution in the air that they breathe.

Government statistics estimate that air pollution in the UK reduces the life expectancy of every person by an average of 7-8 months, with an associated cost of up to £20 billion each year. Legislation and Policies aiming to further minimise and track the impact of air pollution on health and the environment have been introduced in Europe, the UK and Northern Ireland.

Air Pollution Health Impacts in London Boroughs

Incidence and 95% Confidence intervals

By data.world's Admin [source]

About this dataset

This dataset reveals the long-term health impacts of air pollution in London's boroughs. Home to over 8 million people, London's air pollution is a growing health concern and this study provides invaluable insights into the devastating effects of exposure.

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For more datasets, click here.

Featured Notebooks

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How to use the dataset

How to Use this Dataset:

This dataset provides detailed analysis of the long-term health impacts of air pollution. It includes estimated cases and costs associated with each borough, as well as projections for each scenario used in modelling the effects. This dataset is useful for learners who want to learn about how various factors, such as population growth or new technologies, may affect future health outcomes related to air pollution in London.

The columns included are ‘Scenario’ (the scenario used), ‘Year’ (the year modelled), ‘Disease’ (the type of disease modelled), ‘AgeGroup’ (the age group of the population modelled) and ‘95% CL’ (confidence level).

To understand these columns further we recommend looking at the original source report. This will provide additional detail about each element considered when modelling.

To get started with analysing this data set we recommend exploring how estimates differ between scenarios and considering which ages benefit most from different interventions proposed by London Environment Strategy for reducing diseases caused by air pollution. Additionally you could look at different diseases separately, or consider disease costs versus number of cases across different age groups and scenarios

Research Ideas

• Analyzing the long-term impact of air pollution on London's NHS and social care system by borough.
• Comparing the health impacts of different scenarios related to air pollution in different years and age groups to inform effective policymaking.
• Modeling how changes in air pollution levels might affect different diseases or health outcomes over time in a particular area or community

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: newham-no2-xlsm-18.csv | Column name | Description | |:--------------|:-----------------------------------------------------------------------------------------| | Scenario | The scenario used to model potential long-term health impacts of air pollution. (String) | | Year | Year of modelling which ranges from 2016 - 2050. (Integer) | | Disease | The type of disease attributable to air pollution. (String) | | AgeGroup | Age range which data relates to. (String) | | 95% CL | 95% Confidence Level based on modeling techniques used in study. (Float) |

File: bromley-pm25-xlsm-35.csv | Column name | Description | |:--------------|:-----------------------------------------------------------------------------------------| | Scenario | The scenario used to model potential long-term health impacts of air pollution. (String) | | Year | Year of modelling which ranges from 2016 - 2050. (Integer) | | Disease | The type of disease attributable to air pollution. (String) | | AgeGroup | Age range which data relates to. (String) | | 95% CL | 95% Confidence Level based on modeling techniques used in study. (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 data.world's Admin.

Analysis of ‘Daily Air Pollution Data - India & USA’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sumandey/daily-air-quality-dataset-india on 30 September 2021.

--- Dataset description provided by original source is as follows ---

Context

Air Pollution is a major health concern of many. However, the COVID-19 pandemic might have some role to play in bringing some changes to the overall quality of air.

Content

The dataset consists of pm2.5 measurements from Jan 2019 to May 2021 of the Major Cities of India & the United States. You also need to understand how pm2.5 classifies Air Quality.

Acknowledgements

Special thanks go to https://aqicn.org for making the data open-source and use it for research purposes.

Inspiration

This data could be used to answer several questions -

• How the air quality been pre and post-Covid.
• How Air Quality varies across different cities.
• Can you forecast the values for the next 1 month.

You are open to coming up with your own analysis as well.

--- Original source retains full ownership of the source dataset ---

The AQI describes the five main types of air pollution regulated by the Clean Air Act: sulfur dioxide, nitrogen dioxide, carbon monoxide, ground-level ozone, and particle pollution. The EPA and its partners take regular readings of these pollutants and converts the results into a number ranging from 0 to 500, along with a specific color corresponding to a level of health concern. Generally, if the air quality is good, the air quality index is low (0 to 50) or moderate (51-100), and the color associated with it is green or yellow. As the air quality gets worse, the numbers go up, and the color linked with it goes from orange, to red, to purple, all the way to a dark shade of maroon for hazardous (300+).

This dataset contains the AQI scores by metropolitant area (CBSA) during 2017. I've enhanced some publically available data from the EPA's airnow website with census data, to be able to provide context about the number of people who are actually impacted when an AQI score is high or low in a given area.

Related datasets on relative composition of air pollution by source: typical distribution and during wildfire season are available here.

Context

I am developing my data science skills in areas outside of my previous work. An interesting problem for me was to identify which factors influence life expectancy on a national level. There is an existing Kaggle data set that explored this, but that information was corrupted. Part of the problem solving process is to step back periodically and ask "does this make sense?" Without reasonable data, it is harder to notice mistakes in my analysis code (as opposed to unusual behavior due to the data itself). I wanted to make a similar data set, but with reliable information.

This is my first time exploring life expectancy, so I had to guess which features might be of interest when making the data set. Some were included for comparison with the other Kaggle data set. A number of potentially interesting features (like air pollution) were left off due to limited year or country coverage. Since the data was collected from more than one server, some features are present more than once, to explore the differences.

Content

A goal of the World Health Organization (WHO) is to ensure that a billion more people are protected from health emergencies, and provided better health and well-being. They provide public data collected from many sources to identify and monitor factors that are important to reach this goal. This set was primarily made using GHO (Global Health Observatory) and UNESCO (United Nations Educational Scientific and Culture Organization) information. The set covers the years 2000-2016 for 183 countries, in a single CSV file. Missing data is left in place, for the user to decide how to deal with it.

Three notebooks are provided for my cursory analysis, a comparison with the other Kaggle set, and a template for creating this data set.

Inspiration

There is a lot to explore, if the user is interested. The GHO server alone has over 2000 "indicators". - How are the GHO and UNESCO life expectancies calculated, and what is causing the difference? That could also be asked for Gross National Income (GNI) and mortality features. - How does the life expectancy after age 60 compare to the life expectancy at birth? Is the relationship with the features in this data set different for those two targets? - What other indicators on the servers might be interesting to use? Some of the GHO indicators are different studies with different coverage. Can they be combined to make a more useful and robust data feature? - Unraveling the correlations between the features would take significant work.

OpenAQ is an open-source project to surface live, real-time air quality data from around the world. Their “mission is to enable previously impossible science, impact policy and empower the public to fight air pollution.” The data includes air quality measurements from 5490 locations in 47 countries.

Scientists, researchers, developers, and citizens can use this data to understand the quality of air near them currently. The dataset only includes the most current measurement available for the location (no historical data).

Update Frequency: Weekly

Querying BigQuery tables

You can use the BigQuery Python client library to query tables in this dataset in Kernels. Note that methods available in Kernels are limited to querying data. Tables are at bigquery-public-data.openaq.[TABLENAME]. Fork this kernel to get started.

Acknowledgements

Dataset Source: openaq.org

Use: This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source and is provided "AS IS" without any warranty, express or implied.

API operated by Louisville Metro that returns AQI information from local sensors operated by APCD. Shows the latest hourly data in a JSON feed.The Air Quality Index (AQI) is an easy way to tell you about air quality without having to know a lot of technical details. The “Metropolitan Air Quality Index” shows the AQI from the monitor in Kentuckiana that is currently detecting the highest level of air pollution. See: https://louisvilleky.gov/government/air-pollution-control-district/servi...See the air quality map (Louisville Air Watch) for more details: airqualitymap.louisvilleky.gov/#Read the FAQ for more information about the AQI data: https://louisvilleky.gov/government/air-pollution-control-district/louis...If you'd prefer air quality forecast data (raw data, maps, API) instead, please see AIRNow: https://www.airnow.gov/index.cfm?action=airnow.local_city&zipcode=40204&...See the Data Dictionary section below for information about what the AQI numbers mean, their corresponding colors, recommendations, and more info and links.To download daily snapshots of AQI for the last 25 years, visit the EPA website, set your year range, and choose, Louisville KY. Then download with the CSV link at the bottom of the page.IFTTT integration trigger that fires and after retrieving air quality from Louisville Metro air sensors via the APIGives a forecast instead of the current conditions, so you can take action before the air quality gets bad.The U.S. EPA AirNow program (www.AirNow.gov) protects public health by providing forecast and real-time observed air quality information across the United States, Canada, and Mexico. AirNow receives real-time air quality observations from over 2,000 monitoring stations and collects forecasts for more than 300 cities.Sign up for a free account and get started using the RSS data feed for Louisville. https://docs.airnowapi.org/feedsAir Quality Forecast via AirNowAQI Level - Value and Related Health Concerns LegendGood 0-50 GreenAir quality is considered satisfactory, and air pollution poses little or no risk.Moderate 51-100 YellowAir quality is acceptable; however, for some pollutants there may be a moderate health concern for a very small number of people who are unusually sensitive to air pollution.Unhealthy for Sensitive Groups 101-150 OrangeMembers of sensitive groups may experience health effects. The general public is not likely to be affected.Unhealthy 151-200 RedEveryone may begin to experience health effects; members of sensitive groups may experience more serious health effects.Very Unhealthy 201-300 PurpleHealth alert: everyone may experience more serious health effects.Hazardous > 300 Dark PurpleHealth warnings of emergency conditions. The entire population is more likely to be affected.Here are citizen actions APCD recommends on air quality alert days, that is, days when the forecast is for the air quality to reach or exceed the “unhealthy for sensitive groups” (orange) level:Don’t idle your car. (Recommended all the time; see the second link below.)Put off mowing grass with a gas mower until the alert ends.“Refuel when it’s cool” (pump gasoline only in the evening or night).Avoid driving if possible. Share rides or take TARC.Check on neighbors with breathing problems.Here are some links in relation to the recommendations:KAIRE, www.helptheair.org/Idle Free Louisville, www.helptheair.org/idle-freeTARCTicket to Ride, tickettoride.org/Lawn Care for Cleaner Air (rebates)Contact:Bryan FrazerBryan.Frazar@louisvilleky.gov

Participants completed an online survey about their preferences over ways of reducing their risks of dying over time such that they obtained gains in life expectancy. The dataset includes the options they faced and their choices. It also includes some demographic information and other related preference questions (e.g. time preferences, risk preferences, sequence preferences).A key role of the UK government is to address causes of premature fatality. In the UK, air pollution leads to the loss of 340,000 years of life each year and workplace cancers led to the loss of over 140,000 years of life in 2010. Government policies can address the many causes of premature fatality, but these policies need to be evaluated to ensure they make the best use of public money. The question then becomes: what is the value of increasing a person's life expectancy? To address this question, researchers have introduced the concept of the Value Of a Life Year (VOLY). This VOLY is used in government policy evaluations as a measure of the benefits of policies including air pollution mitigation and workplace safety regulation, and thus it is crucial it is measured accurately. The VOLY is estimated using surveys of members of the public, in which people state how much they would pay for a given reduction in their risk of dying, or for a given increase in their life expectancy. The benefits being valued occur in the future. Crucially then, a key component of the VOLY is the effect of timing. Put simply, the further in the future something is, the less we tend to care about it. So a reduction in our risk of dying this year might be more valuable than a reduction in our risk of dying in the future, even if the effect on our overall life expectancy is the same. Unless we understand the influence of this 'discounting' for changes in life expectancy, we cannot accurately disentangle it from the true VOLY. This is the problem we aim to solve with our research. To solve it, our team of experimental economists will use an innovative mixture of experiments and surveys. Participants will play experimental games designed to include simplified models of the air pollution policies, so our team can learn the best ways to describe and measure discounting as it relates to delayed changes in risk. The survey will use the insights from the experiment and elicit individuals' preferences for reductions in their risks at different points in the future. Taken together, the experiments and survey will provide the first major investigation into how people discount their future life expectancy in the context of the VOLY. Our results will be important for policymakers in two ways. First, unless we can account for the effects of discounting on the VOLY, then policy estimates of the VOLY taken from current surveys might be wrong. If these incorrect estimates are used in the evaluation of policies aimed at improving life expectancy, then the value of the policies will be over- or under-estimated, which means public money is likely to be spent on the wrong policies. Second, when the government is evaluating policies where improvements in life expectancy happen in the future, as is the case for air pollution policies, they have to apply discounting to the value of the benefits. Our research will provide evidence about how governments should discount future gains in life expectancy, to make sure that public preferences are reflected in policymaking. Our research is also academically cutting-edge. It combines models from economics with insights from psychology to generate new methodological and empirical evidence about how discounting influences preferences for changes in risk, both for money outcomes (in the experiments) and for fatality risks (in the surveys). It also forges a new methodological agenda, which is the incorporation of incentivised experiments into policy-driven research projects. Overall, our research aims to provide the basis for changing the VOLY used in government policy, challenge existing guidance for discounting fatality risk reductions, and ultimately change how government money is spent, so that the policies implemented are those that improve the wellbeing of society. Survey programmed by the research team in o-tree and conducted online using a sample of respondents recruited on prolific.ac. The sample sex and age band distribution was selected to match those of the UK population (although the respondents were not restricted to be UK residents).

The datasets contains date- and state-wise historically compiled data on air quality (by pollution level) in rural and urban areas of India from the year 2015 , as measured by Central Pollution Board (CPCB) through its daily (24 hourly measurements, taken at 4 PM everyday) Air Quality Index (AQI) reports.

The CPCB measures air quality by continuous online monitoring of various pollutants such as Particulate Matter10 (PM10), Particulate Matter2.5 (PM2.5), Sulphur Dioxide (SO2), Nitrogen Oxide or Oxides of Nitrogen (NO2), Ozone (O3), Carbon Monoxide (CO), Ammonic (NH3) and Lead (Pb) and calculating their level of pollution in the ambient air. Based on the each pollutant load in the air and their associated health impacts, the CPCB calculates the overall Air Pollution in Air Quality Index (AQI) value and publishes the data. This AQI data is then used by CPCB to report the air quality status i.e good, satisfactory, moderate, poor, very poor and severe, etc. of a particular location and their related health impacts because of air pollution.

Air pollution affects billions of people worldwide, yet ambient pollution measurements are limited for much of the world. Urban air pollution concentrations vary sharply over short distances (≪1 km) owing to unevenly distributed emission sources, dilution, and physicochemical transformations. Accordingly, even where present, conventional fixed-site pollution monitoring methods lack the spatial resolution needed to characterize heterogeneous human exposures and localized pollution hotspots. Here, we demonstrate a measurement approach to reveal urban air pollution patterns at 4–5 orders of magnitude greater spatial precision than possible with current central-site ambient monitoring. We equipped Google Street View vehicles with a fast-response pollution measurement platform and repeatedly sampled every street in a 30-km2 area of Oakland, CA, developing the largest urban air quality data set of its type. Resulting maps of annual daytime NO, NO2, and black carbon at 30 m-scale reveal stable, persistent pollution patterns with surprisingly sharp small-scale variability attributable to local sources, up to 5–8× within individual city blocks. Since local variation in air quality profoundly impacts public health and environmental equity, our results have important implications for how air pollution is measured and managed. If validated elsewhere, this readily scalable measurement approach could address major air quality data gaps worldwide.

Areas of the state where people who are vulnerable to health, social, and environmental inequities are also highly impacted by criteria air pollution.Using multiple sources of air quality data and environmental justice information, Ecology identified 16 areas of the state containing multiple overburdened communities, neighborhoods, and towns that are highly impacted by criteria air pollution. The places are a mix of urban, suburban, and rural. They vary greatly in population, from about 1,500 to more than 200,000 people. They also range vastly in area, from less than 3 square miles to 173 square miles. Collectively, they represent more than 1.2 million people, or about 15.5% of Washington’s population. We heard from Tribes, the public, members of the Environmental Justice Council, and other environmental justice advocates about identifying the people and places in Washington that are overburdened and highly impacted by criteria air pollution. We have not yet included any communities on Tribal land. More information about how communities were identified can be found on our website: ecology.wa.gov/overburdenedDISCLAIMER: This is not a diagnostic tool. These are communities identified for a specific purpose under the Climate Commitment Act.

Information on hospitalizations of COPD patients from electronic health records linked to air pollution concentrations for the study period. 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: Can be requested through NCTracts https://tracs.unc.edu/index.php/services/comparative-effectiveness-research/data-linkage. Format: Data used in this analysis include electronic health records from the UNC healthcare system. This dataset is associated with the following publication: Cowan, K., L. Wyatt, T. Luben, J. Sacks, C. Ward-Caviness, and K. Rappazzo. Effect measure modification of the association between short-term exposures to PM2.5 and hospitalizations by longs-term PM2.5 exposure among a cohort of people with Chronic Obstructive Pulmonary Disease (COPD) in North Carolina, 2002–2015. ENVIRONMENTAL HEALTH. Academic Press Incorporated, Orlando, FL, USA, 22: 49, (2023).

BackgroundWhile a great number of papers have been published on the short-term effects of air pollution on mortality, few have tried to assess whether this association varies according to the neighbourhood socioeconomic level and long-term ambient air concentrations measured at the place of residence. We explored the effect modification of 1) socioeconomic status, 2) long-term NO2 ambient air concentrations, and 3) both combined, on the association between short-term exposure to NO2 and all-cause mortality in Paris (France).MethodsA time-stratified case-crossover analysis was performed to evaluate the effect of short-term NO2 variations on mortality, based on 79,107 deaths having occurred among subjects aged over 35 years, from 2004 to 2009, in the city of Paris. Simple and double interactions were statistically tested in order to analyse effect modification by neighbourhood characteristics on the association between mortality and short-term NO2 exposure. The data was estimated at the census block scale (n=866).ResultsThe mean of the NO2 concentrations during the five days prior to deaths were associated with an increased risk of all-cause mortality: overall Excess Risk (ER) was 0.94% (95%CI=[0.08;1.80]. A higher risk was revealed for subjects living in the most deprived census blocks in comparison with higher socioeconomic level areas (ER=3.14% (95%CI=[1.41-4.90], p

This layer shows particulate matter in the air sized 2.5 micrometers of smaller (PM 2.5). 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.The layer shows the annual average PM 2.5 from 1998 to 2016, highlighting if the overall mean for an area meets the World Health Organization guideline of 10 micrograms per cubic meter annually. Areas that don't meet the guideline and are above the threshold are shown in red, and areas that are lower than the guideline are in grey.The data is averaged for each year and over the the 19 years to provide an overall picture of air quality globally. Some of the things we can learn from this layer: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?Choose a different attribute to symbolize in order to reveal any of the patterns above.A space time cube was performed on a multidimensional mosaic version of the data in order to derive an emerging hot spot analysis, trends, and a 19-year average. The country and administrative 1 layers provide a population-weighted PM 2.5 value to emphasize which areas have a higher human impact. 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.Boundaries and population figures:Antarctica is excluded from all maps because it was not included in the original NASA grids.50km hex bins generated using the Generate Tessellation tool - projected to Behrmann Equal Area projection for analysesPopulation figures generated using Zonal Statistics from the World Population Estimate 2016 layer from ArcGIS Living Atlas.Administrative boundaries from World Administrative Divisions layer from ArcGIS Living Atlas - projected to Behrmann Equal Area projection for analyses and hosted in Web MercatorSources: Garmin, CIA World FactbookPopulation figures generated using Zonal Statistics from the World Population Estimate 2016 layer from ArcGIS Living Atlas.Country boundaries from Esri 2019 10.8 Data and Maps - projected to Behrmann Equal Area projection for analyses and hosted in Web Mercator. Sources: Garmin, Factbook, CIAPopulation figures attached to the country boundaries come from the World Population Estimate 2016 Sources Living Atlas layer Data processing notes:NASA's GeoTIFF files for 19 years (1998-2016) were first brought into ArcGIS Pro 2.5.0 and put into a multidimensional mosaic dataset.For each geography level, the following was performed: Zonal Statistics were run against the mosaic as a multidimensional layer.A Space Time Cube was created to compare the 19 years of PM 2.5 values and detect hot/cold spot patterns. To learn more about Space Time Cubes, visit this page.The Space Time Cube is processed for Emerging Hot Spots where we gain the trends and hot spot results.The layers are hosted in Web Mercator Auxillary Sphere projection, but were processed using an equal area projection: Behrmann. If using this layer for analysis, it is recommended to start by projecting the data back to Behrmann.The country and administrative layer were dissolved and joined with population figures in order to visualize human impact.The dissolve tool ensures that each geographic area is only symbolized once within the map.Country boundaries were generalized post-analysis for visualization purposes. The tolerance used was 700m. If performing analysis with this layer, find detailed country boundaries in ArcGIS Living Atlas. To create the population-weighted attributes on the country and Admin 1 layers, the hex value population values were used to create the weighting. Within each hex bin, the total population figure and average PM 2.5 were multiplied.The hex bins were converted into centroids and the PM2.5 and population figures were summarized within the country and Admin 1 boundaries.The summation of the PM 2.5 values were then divided by the total population of each geography. This population value was determined by summarizing the population values from the hex bins within each geography.Some artifacts in the hex bin layer as a result of the input NASA rasters. Because the gridded surface is created from multiple satellites, there are strips within some areas that are a result of satellite paths. Some areas also have more of a continuous pattern between hex bins as a result of the input rasters.Within the country layer, an air pollution attributable death rate is included. 2016 figures are offered by the World Health Organization (WHO). Values are offered as a mean, upper value, lower value, and also offered as age standardized. Values are for deaths caused by all possible air pollution related diseases, for both sexes, and all age groups. For more information visit this page, and here for methodology. According to WHO, the world average was 95 deaths per 100,000 people.To learn the techniques used in this analysis, visit the Learn ArcGIS lesson Investigate Pollution Patterns with Space-Time Analysis by Esri's Kevin Bulter and Lynne Buie.

The NEPA air quality assessment and the BOEM AQRP analysis are separate and distinct evaluations required before BOEM can approve plans for oil and gas activities proposed for the Arctic OCS. When these two evaluations are used together, they provide a holistic assessment of Arctic air pollution transport and show how new emission sources, both onshore and offshore, might impact air quality on the North Slope and over near shore areas. In addition, the combined evaluation determines the extent of cumulative effects when considering other emission sources affecting the North Slope (e.g., onshore and in near shore state waters). Results of this study may be used by various entities in support of environmental justice initiatives and permit applications, and the study would serve the public seeking a direct and reliable accounting of air pollution effects on the people and natural environment of the NSB. BOEM uses air quality dispersion modeling to assess the potential onshore impacts of emissions from proposed Arctic OCS oil and gas activities. The accuracy of the modeling predictions depends on several factors, including the rate of emissions and a representative meteorological dataset. Thus, the air quality impact analysis is only as comprehensive as the emissions inventory on which the analysis is based, and only as accurate as the meteorological dataset applied to simulate dispersion and transport of the pollutants. While the EPA, the Alaska Department of Environmental Conservation (ADEC), and various potential OCS operators have prepared emissions inventories of sources located on the North Slope for purposes of air permitting and other regulatory needs, research is needed to bring together data from these resources that will contribute to a comprehensive accounting of annual emissions.