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Creating the Next Generation of Student Engagement

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Embark on 30 adventures that will help you understand and celebrate our natural world, while learning about how dependent we are on our planet.

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Earth School - The information and inspiration we need to get to net-zero by 2050.

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How much do you already know about the basics of climate change?

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How do we address this in our classroom?

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DK Find Out! | Fun Facts for Kids on Animals, Earth, History and more!

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Visit DK Find Out! to learn about dinosaurs, space, animals, nature & more. Fun learning activities for kids with videos, quizzes & galleries at DKFindOut.com

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Earth science elementary resources| National Oceanic and Atmospheric Administration

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National Oceanic and Atmospheric Administration

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Creating the Next Generation of Student Engagement

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Question Formulation Technique (QFT) What do I do with the questions once students ask them??

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Question Formulation Technique (QFT) What do I do with the questions once students ask them??

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Lessons cover how dirt works, the role of pollinators, how nature filters water, and more

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All Resources for Ages 5-11 (U.S Grades K-5)

Download our educational resources for students in elementary school. Lessons cover how dirt works, the role of pollinators, how nature filters water, and more! Each lesson plan comes with a free teacher's guide and video.

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All Resources for Ages 5-11 (U.S Grades K-5) Download our educational resources for students in elementary school. Lessons cover how dirt works, the role of pollinators, how nature filters water, and more! Each lesson plan comes with a free teacher's guide and video.

# data[].attributes.attachment_link.readable_text
Share the Strategy and Source RQI Our materials are available under a Creative Commons Sharealike license. To comply with this license, you must include the following language on all materials you use, share, adapt, or create: "Source: The Question Formulation Technique (QFT) was created by the Right Question Institute (rightquestion.org)." As long as you include this source language, you are welcome to use, adapt, and share our strategies and materials for noncommercial use. You may also create new materials that reference the Question Formulation Technique (QFT) and/or other Right Question Institute strategies as long as you include the source language above. If you are interested in sharing RQI's resources for commercial purposes you must contact us by selecting the "Permission for Use" option on the contact form.

# data[].attributes.attachment_link.description
Share the Strategy and Source RQI Our materials are available under a Creative Commons Sharealike license. To comply with this license, you must include the following language on all materials you use, share, adapt, or create: "Source: The Question Formulation Technique (QFT) was created by the Right Question Institute (rightquestion.org)."

# data[].attributes.attachment_link.title
DK Find Out! | Fun Facts for Kids on Animals, Earth, History and more!

# data[].attributes.attachment_link.description
Visit DK Find Out! to learn about dinosaurs, space, animals, nature & more. Fun learning activities for kids with videos, quizzes & galleries at DKFindOut.com

# data[].attributes.attachment_link.description
As a contribution to the building of training to support the communication of tsunami risk to the public, the IOC of UNESCO has developed the TsunamiTeacher Information and Resource Toolkit. The Toolkit brings together a wealth of new and existing information on tsunamis into a single, reliable, and verified global resource that is widely accessible to people, groups and governments around the world.

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Developing Grade-Band Specific Phenomena and Explanations Across K-12 

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Developing Grade-Band Specific Phenomena and Explanations Across K-12 

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Yale Program on Climate Change Communication

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These maps show how Americans' climate change beliefs, risk perceptions, and policy support vary at the state, congressional district, metro area, and county levels. NOTE: The Congressional District data are based on the 116th Congress. Recent and ongoing redistricting means that some of these data are not current (e.g. North Carolina) for the Congressional District level.

About Downscaling Climate Opinions

This version of the Yale Climate Opinion Maps is based on data through fall 2021. Public opinion about global warming is an important influence on decision making about policies to reduce global warming or prepare for the impacts, but American opinions vary widely depending on where people live. So why would we rely on just one national number to understand public responses to climate change at the state and local levels? Public opinion polling is generally done at the national level, because local level polling is very costly and time intensive. Our team of scientists, however, has developed a geographic and statistical model to downscale national public opinion results to the state, congressional district, and county levels. We can now estimate public opinion across the country and a rich picture of the diversity of Americans' beliefs, attitudes, and policy support is revealed.

Our national surveys show that 72% of Americans think global warming is happening. Our new YCOM model estimates, however, show that only 48% of people in Emery County, Utah agree. Meanwhile 71% in neighboring Grand County, Utah believe global warming is happening.

Explore the maps by clicking on your state, congressional district, or county and compare the results across questions and with other geographic areas. Beneath each map are bar charts displaying the results for every question at whichever geographic scale is currently selected. Note that the congressional districts for North Carolina are based on the 2019 (116th Congress) district boundaries and so do not reflect the latest redistricting there.

See the methods page for more information about uncertainty estimates. In some cases, numbers that should sum to 100% or differences from the national average that should sum to zero are off by one percentage point; these effects are due to rounding errors.

This research and website are funded by the Skoll Global Threats Fund, the Energy Foundation, the 11th Hour Project, the Grantham Foundation for the Protection of the Environment, the MacArthur Foundation, the Overlook Foundation and the Endeavor Foundation. We are very grateful to Connie Roser-Renouf, Ed Maibach, Lisa Fernandez, Eric Fine, Bessie Schwarz, Mike Slattery, and Seth Rosenthal for their assistance with and support of the project. For further questions about these maps or what they mean, please see our Frequently Asked Questions tab (above).

Methodology

This site provides estimates of U.S. climate change beliefs, risk perceptions, and policy preferences at the state and local levels - a new source of high-resolution data on public opinion that can inform national, state and local decision-making, policy, and education initiatives. The estimates are derived from a statistical model using multilevel regression with post-stratification (MRP) on a large national survey dataset (n>28,000), along with demographic and geographic population characteristics.

Validating models is essential for producing accurate results. Our estimates were validated using three different methods. First, cross-validation analyses were conducted within the dataset. The dataset was divided into two sets of respondents, with one part used to run the model and the other kept aside for validation. The model estimates were then compared to the results of the set aside respondents to directly quantify the percentage of correct answers the model predicted. These cross-validation tests were repeated multiple times using different sample sizes and dividing the data in different ways. Second, the model estimates derived from the full dataset were compared to the results of independent, representative state- and city-level surveys conducted in California, Colorado, Ohio, Texas, San Francisco, and Columbus, Ohio in 2013. The mean absolute difference between model estimates and validation survey results was 2.9 percentage points (SD = 1.5) among the four states (CA, TX, OH, CO) and 3.6 percentage points (SD = 2.9) among the two metropolitan areas (Columbus, OH, and San Francisco, CA), well within the margins of error for the survey results alone (at a 95% confidence level). Estimates have also been validated internally through a series of technical simulations. Third, some model estimates were compared with third-party survey data collected by other researchers in previous years.

For the 2021 model estimates, uncertainty ranges are based on 95% confidence intervals using 999 bootstrap simulations. These confidence intervals indicate that the 2020 model is accurate to approximately ±7 percentage points at the state and congressional district levels, and ±8 percentage points at the metro and county levels. Such error ranges include the error inherent in the original national surveys themselves, which is typically ±3 percentage points.

The availability of 2014, 2016, 2018, 2019, and 2020 estimates prompts questions about what has changed between these years in particular places. From our surveys, we know that public opinion on some questions has shifted over this period at the national level. However, our statistical model, as well as the census and election data that go into our model, have also all been updated for accuracy. Thus, estimated shifts in public opinion at the subnational level since 2014 may be due not only to opinion changes, but also changes in demographics and other variables that we are using to model opinions. In addition, our model tends to be conservative when making estimates, especially for places with smaller populations (where there are likely to be fewer survey respondents), so variations over time between states, counties, etc. may be underestimated. As a result, we recommend caution when interpreting any changes over time using different versions of our model, because we can't distinguish between what is a true shift in actual opinion between 2014, 2016, 2018, 2019, 2020, and 2021, and what reflects the model's improved ability to map climate opinions at the local scale. We are currently developing a new model explicitly for changes in opinions over time at the state level, so please join our mailing list if you would like to be alerted about these developments.

For more details on the model or methods, please see the peer-reviewed paper: Howe, P., Mildenberger, M., Marlon, J.R., and Leiserowitz, A., "Geographic variation in opinions on climate change at state and local scales in the USA," Nature Climate Change. DOI: 10.1038/nclimate2583.

Climate Change in the American Mind Survey Question Wording

Model estimates in the maps were derived from public responses to the following survey questions. The response categories for many questions were collapsed into a single variable for mapping. For example, for the question measuring how worried respondents are about global warming, "very worried" and "somewhat worried" were combined into a single measure of "worried." Likewise "Not very worried" and "Not at all worried" were combined into a single measure of "not worried." The responses below are color coded to indicate how they were grouped into the variables shown on the maps. Individuals who responded "Don't know" or who did not answer the question were not modeled separately and appear as gray segments within the bar charts.

BELIEFS

Global warming is happening
Recently, you may have noticed that global warming has been getting some attention in the news. Global warming refers to the idea that the world's average temperature has been increasing over the past 150 years, may be increasing more in the future, and that the world's climate may change as a result. What do you think: Do you think that global warming is happening?

Global warming is caused mostly by human activities
Assuming global warming is happening, do you think it is... ?

Caused mostly by human activities

Caused mostly by natural changes in the environment

None of the above because global warming isn't happening

Other

Don't know

Most scientists think global warming is happening
Which comes closest to your own view?

Most scientists think global warming is happening

There is a lot of disagreement among scientists about whether or not global warming is happening

Most scientists think global warming is not happening

Don't know enough to say

Global warming is affecting the weather in the United States
How strongly do you agree or disagree with the statement below?

RISK PERCEPTIONS

Worried about global warming
How worried are you about global warming?

Global warming will harm plants and animals
How much do you think global warming will harm plants and animal species?

Global warming will harm future generations
How much do you think global warming will harm future generations of people?

Global warming will harm people in developing countries
How much do you think global warming will harm people in developing countries?

Global warming will harm people in the US
How much do you think global warming will harm people in the United States?

Global warming will harm me personally
How much do you think global warming will harm you personally?

Global warming is already harming people in the US
When do you think global warming will start to harm people in the United States?

Has personally experienced the effects of global warming
How much do you agree or disagree with the following statement: "I have personally experienced the effects of global warming"?

POLICY SUPPORT

Fund research into renewable energy sources

How much do you support or oppose the following policies?
Fund more research into renewable energy sources, such as solar and wind power

Regulate CO2 as a pollutant
How much do you support or oppose the following policies?
Regulate carbon dioxide (the primary greenhouse gas) as a pollutant

Set strict CO2 limits on existing coal-fired power plants
How much do you support or oppose the following policy?
Set strict carbon dioxide emission limits on existing coal-fired power plants to reduce global warming and improve public health. Power plants would have to reduce their emissions and/or invest in renewable energy and energy efficiency. The cost of electricity to consumers and companies would likely increase.

Require fossil fuel companies to pay a carbon tax
How much do you support or oppose the following policies?
Require fossil fuel companies to pay a carbon tax and use the money to reduce other taxes (such as income tax) by an equal amount.

Require utilities to produce 20% electricity from renewable sources
How much do you support or oppose the following policies?
Require electric utilities to produce at least 20% of their electricity from wind, solar, or other renewable energy sources, even if it costs the average household an extra $100 a year.

Provide tax rebates
Provide tax rebates for people who purchase energy-efficient vehicles or solar panels.
How much do you support or oppose the following policies?

The YPCCC is pleased to offer our downscaled climate change opinion estimates to the public. These data are distributed under the following terms of use. This is a legal agreement between you, the end-user ("User") and Yale University on behalf of the Yale Program on Climate Change Communication (the "YPCCC"). By downloading the survey data made available on this web site ("Data") you are agreeing to be bound by the terms and conditions of this agreement. If you do not agree to be bound by these terms, do not download or use the Data. The YPCCC hereby grants to the User a non-exclusive, revocable, limited, non-transferable license to use the Data solely for (1) research, scholarly or academic purposes, (2) the internal use of your business, or (3) your own personal non-commercial use. You may not reproduce, sell, rent, lease, loan, distribute or sublicense or otherwise transfer any Data, in whole or in part, to any other party, or use the Data to create any derived product for resale, lease or license. Notwithstanding the foregoing, you may incorporate limited portions of the Data in scholarly, research or academic publications or for the purposes of news reporting, provided you acknowledge the source of the Data (with express references to the YPCCC, as well as the complete title of the report) and include the following legend: The YPCCC bears no responsibility for the analyses or interpretations of the data presented here. THE DATA IS PROVIDED "AS IS" WITHOUT ANY WARRANTY OF ANY KIND, EITHER EXPRESS OR IMPLIED, ARISING BY LAW OR OTHERWISE, INCLUDING BUT NOT LIMITED TO WARRANTIES OF COMPLETENESS, NON-INFRINGEMENT, ACCURACY, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. THE USER ASSUMES ALL RISK ASSOCIATED WITH USE OF THE DATA AND AGREES THAT IN NO EVENT SHALL YALE BE LIABLE TO YOU OR ANY THIRD PARTY FOR ANY INDIRECT, SPECIAL, INCIDENTAL, PUNITIVE OR CONSEQUENTIAL DAMAGES INCLUDING, BUT NOT LIMITED TO, DAMAGES FOR THE INABILITY TO USE EQUIPMENT OR ACCESS DATA, LOSS OF BUSINESS, LOSS OF REVENUE OR PROFITS, BUSINESS INTERRUPTIONS, LOSS OF INFORMATION OR DATA, OR OTHER FINANCIAL LOSS, ARISING OUT OF THE USE OF, OR INABILITY TO USE, THE DATA BASED ON ANY THEORY OF LIABILITY INCLUDING, BUT NOT LIMITED TO, BREACH OF CONTRACT, BREACH OF WARRANTY, TORT (INCLUDING NEGLIGENCE), OR OTHERWISE, EVEN IF USER HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. The YPCCC has taken measures to ensure that the Data is devoid of information that could be used to identify individuals (e.g., names, telephone numbers, email addresses, social security numbers) who participated in or who were the subject of any research surveys or studies used to collect the Data ("Personally Identifying Information"). However, in the event that you discover any such Personally Identifying Information in the Data, you shall immediately notify the YPCCC and refrain from using any such Personally Identifying Information. This license will terminate (1) automatically without notice from the YPCCC if you fail to comply with the provisions of this agreement, or (2) upon written notice (by e-mail, U.S. or otherwise) from the YPCCC. Upon termination of this agreement, you agree to destroy all copies of any Data, in whole or in part and in any and all media, in your custody and control. This agreement shall be governed by, construed and interpreted in accordance with the laws of the State of Connecticut. You further agree to submit to the jurisdiction and venue of the courts of the State of Connecticut for any dispute relating to this Agreement. Please use the following citation in any work that makes use of the data and documentation as follows: Howe, Peter D., Matto Mildenberger, Jennifer R. Marlon, and Anthony Leiserowitz (2015). "Geographic variation in opinions on climate change at state and local scales in the USA." Nature Climate Change, doi:10.1038/nclimate2583. Direct any questions to the YPCCC at climatechange@yale.edu.

Frequently Asked Questions

What do these maps depict?

The maps depict estimates of the percentage of American adults (age 18 and over) who hold particular beliefs, attitudes, and policy preferences about global warming. The estimates were generated from a statistical model that incorporates actual survey responses, but combines these responses with demographic data from the U.S. Census to estimate opinions for different groups of people based on information such as their gender, race and ethnicity, and educational attainment; they also take into account changes in public opinion over time.

Where do the survey data underlying the estimates come from?

The data underlying the maps come from a large national survey dataset ( >28,000 respondents) collected between 2008 through 2021 as part of the Climate Change in the American Mind project led by the Yale Program on Climate Change Communication and the George Mason University Center for Climate Change Communication. Reports from the individual surveys are available here: CCAM Reports.

How many people did you survey in my state/county? / How can you say anything about opinions in my geography if you didn't survey anyone there?

The number of respondents surveyed varies for each geographic unit but is very large across the US as a whole. Since our surveys are nationally representative, the number of respondents in each state and county is proportional to its share of the national population. In other words, larger states and counties will have more respondents than smaller states and counties. In some smaller counties, very few, if any, respondents may have been surveyed. In those cases, our model uses information about the population of the county, information from other counties in the same state and region, and information about the characteristics of the county (such as how the county voted in previous elections) to make its estimates. While this may sound surprising, it makes sense when we consider the strongest influences on a person's individual opinions about climate change in the US - political views. Knowing the political leanings of a county's (or other unit's) residents, along with the proportion of different demographic groups within that county, allows us to predict resident's climate opinions very well. Adding additional information (e.g., about rural/urban context, carbon-intensive activities, etc.), further improves predictions. Moreover, for any given geographic unit, information is pooled from nearby units and from higher-level geographies (e.g. state level information is included for all counties within that state). All of this information together allows us to produce reliable estimates (see next question) for all counties even if the number of individuals sampled within a specific county is small.

How accurate are the estimates?

No model is perfect and there are uncertainties in the model estimates. To validate the model, we conducted independent surveys in four states (CA, TX, OH, CO) and two metropolitan areas (Columbus, OH and San Francisco, CA) and compared the survey results to our model estimates. On average, the model estimates differed from the survey results by 2.9 percentage points among the four states and 3.6 percentage points among the two metropolitan areas, within the survey margins of error. A series of technical simulations estimate that the model has an average margin of error of ±7 percentage points at the state and congressional district levels, ±8 percentage points at the metro and county levels. Such error ranges include the error inherent in the original national surveys themselves, which is typically ±3 percentage points. The model uncertainties are smaller at broad geographic scales (e.g., the state level), and are larger at finer geographic scales (e.g., at the county and city levels). The model estimates also tend to be conservative, so geographic areas with extremely high or low measures are not estimated as well as areas with values closer to the national average for each survey question.

What does the gray color mean on some of the bars beneath the maps?

The gray area reflects people who refused to answer the question or said "don't know". We do not provide specific values for the gray areas because we did not develop estimates for these particular responses.

Do the maps account for differences in population density across the country?

No, the maps depict the estimated proportion of people within each geographic area who would answer each question as indicated. We have not adjusted the maps based on population density differences. It is important to keep in mind that some geographic areas may be large, but have few residents (e.g., Wyoming), while other geographic areas may be small, but have many residents (e.g., New Jersey). For reference, Wikipedia has a population density map here. The type of map used in this tool is called a choropleth map, which means the colors on the maps reflect the percentage of the population in a given geographic unit. These kinds of maps are used to represent everything from election results (e.g.,the red state / blue state maps common during presidential elections) to census and economic data (e.g., per capita income or unemployment rates).

Do these maps reflect changes in opinions due to recent extreme weather events like Superstorm Sandy?

Perhaps. The maps may reflect the impacts that specific extreme weather events had on public opinion in a given geographic unit. If public opinion in a particular area has been influenced by local events it is possible that the model would detect such an influence. However, data from specific events or types of events are not explicitly built into the model as predictor variables.

Can I use the data?

Yes. We encourage you to explore the maps and use the results in your own work. The data are available on our Data Download tab at the top of this page so that you can do your own analyses and create your own visualizations. If you publish an academic paper using these data please acknowledge the source by using the following citation:

Howe, P., Mildenberger, M., Marlon, J., & Leiserowitz, A. (2015) "Geographic variation in opinions on climate change at state and local scales in the USA," Nature Climate Change. DOI: 10.1038/nclimate2583.

If you publish a news article, visualization, blog post, or other publication using these data or maps, please include the link to the Yale Climate Opinion Maps website and attribution to the Yale Program on Climate Change Communication.

How do the YCOM maps compare from year to year?

We originally created YCOM in 2014 and then updated it in 2016, 2018, 2019, 2020, and 2021. Public concern about global warming has generally increased since 2014. However, improvements to our model, including new data that increase our ability to resolve differences between jurisdictions, and data for adults 18+ (prior estimates only included data for adults 25+) may have contributed to shifts in our current estimates as compared with those of 2014. As a result, we recommend caution interpreting any changes over time because we can't easily disaggregate what is a true shift between any particular year and what reflects the model's improved ability to map climate beliefs at the local scale. We are currently working on a new tool which will show changes in opinions over time for each state. Join our mailing list via the "Subscribe" button below on the right to be alerted when we release this new tool.

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Yale Climate Opinion Maps 2021 - Yale Program on Climate Change Communication

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Use the Yale Climate Opinion Maps to explore climate change beliefs, risk perceptions, and policy support at every geographic level in the United States.

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Question Formulation Technique (QFT) What do I do with the questions once students ask them??

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Question Formulation Technique (QFT) What do I do with the questions once students ask them??

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Share the Strategy and Source RQI Our materials are available under a Creative Commons Sharealike license. To comply with this license, you must include the following language on all materials you use, share, adapt, or create: "Source: The Question Formulation Technique (QFT) was created by the Right Question Institute (rightquestion.org)." As long as you include this source language, you are welcome to use, adapt, and share our strategies and materials for noncommercial use. You may also create new materials that reference the Question Formulation Technique (QFT) and/or other Right Question Institute strategies as long as you include the source language above. If you are interested in sharing RQI's resources for commercial purposes you must contact us by selecting the "Permission for Use" option on the contact form.

# data[].attributes.attachment_link.description
Share the Strategy and Source RQI Our materials are available under a Creative Commons Sharealike license. To comply with this license, you must include the following language on all materials you use, share, adapt, or create: "Source: The Question Formulation Technique (QFT) was created by the Right Question Institute (rightquestion.org)."

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Student Example for Cat Shelter Engineering Project

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Student Example for Cat Shelter Engineering Project

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Selecting Natural Phenomena to Drive Science Instruction and Assessment

# data[].attributes.subject
Selecting Natural Phenomena to Drive Science Instruction and Assessment

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HO 1 - Selecting Natural Phenomena to Drive Science Instruction and Assessment

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Developing Grade-Band Specific Phenomena and Explanations Across K-12 

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Developing Grade-Band Specific Phenomena and Explanations Across K-12 

# data[].attributes.headline
Phenomena bank by NGSS standard and grade

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Phenomena bank by NGSS standard and grade

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The Wonder of Science website aggregates the work of Paul Andersen around the NGSS (Next Generation Science Standards)

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Yale Program on Climate Change Communication

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These maps show how Americans' climate change beliefs, risk perceptions, and policy support vary at the state, congressional district, metro area, and county levels. NOTE: The Congressional District data are based on the 116th Congress. Recent and ongoing redistricting means that some of these data are not current (e.g. North Carolina) for the Congressional District level.

About Downscaling Climate Opinions

This version of the Yale Climate Opinion Maps is based on data through fall 2021. Public opinion about global warming is an important influence on decision making about policies to reduce global warming or prepare for the impacts, but American opinions vary widely depending on where people live. So why would we rely on just one national number to understand public responses to climate change at the state and local levels? Public opinion polling is generally done at the national level, because local level polling is very costly and time intensive. Our team of scientists, however, has developed a geographic and statistical model to downscale national public opinion results to the state, congressional district, and county levels. We can now estimate public opinion across the country and a rich picture of the diversity of Americans' beliefs, attitudes, and policy support is revealed.

Our national surveys show that 72% of Americans think global warming is happening. Our new YCOM model estimates, however, show that only 48% of people in Emery County, Utah agree. Meanwhile 71% in neighboring Grand County, Utah believe global warming is happening.

Explore the maps by clicking on your state, congressional district, or county and compare the results across questions and with other geographic areas. Beneath each map are bar charts displaying the results for every question at whichever geographic scale is currently selected. Note that the congressional districts for North Carolina are based on the 2019 (116th Congress) district boundaries and so do not reflect the latest redistricting there.

See the methods page for more information about uncertainty estimates. In some cases, numbers that should sum to 100% or differences from the national average that should sum to zero are off by one percentage point; these effects are due to rounding errors.

This research and website are funded by the Skoll Global Threats Fund, the Energy Foundation, the 11th Hour Project, the Grantham Foundation for the Protection of the Environment, the MacArthur Foundation, the Overlook Foundation and the Endeavor Foundation. We are very grateful to Connie Roser-Renouf, Ed Maibach, Lisa Fernandez, Eric Fine, Bessie Schwarz, Mike Slattery, and Seth Rosenthal for their assistance with and support of the project. For further questions about these maps or what they mean, please see our Frequently Asked Questions tab (above).

Methodology

This site provides estimates of U.S. climate change beliefs, risk perceptions, and policy preferences at the state and local levels - a new source of high-resolution data on public opinion that can inform national, state and local decision-making, policy, and education initiatives. The estimates are derived from a statistical model using multilevel regression with post-stratification (MRP) on a large national survey dataset (n>28,000), along with demographic and geographic population characteristics.

Validating models is essential for producing accurate results. Our estimates were validated using three different methods. First, cross-validation analyses were conducted within the dataset. The dataset was divided into two sets of respondents, with one part used to run the model and the other kept aside for validation. The model estimates were then compared to the results of the set aside respondents to directly quantify the percentage of correct answers the model predicted. These cross-validation tests were repeated multiple times using different sample sizes and dividing the data in different ways. Second, the model estimates derived from the full dataset were compared to the results of independent, representative state- and city-level surveys conducted in California, Colorado, Ohio, Texas, San Francisco, and Columbus, Ohio in 2013. The mean absolute difference between model estimates and validation survey results was 2.9 percentage points (SD = 1.5) among the four states (CA, TX, OH, CO) and 3.6 percentage points (SD = 2.9) among the two metropolitan areas (Columbus, OH, and San Francisco, CA), well within the margins of error for the survey results alone (at a 95% confidence level). Estimates have also been validated internally through a series of technical simulations. Third, some model estimates were compared with third-party survey data collected by other researchers in previous years.

For the 2021 model estimates, uncertainty ranges are based on 95% confidence intervals using 999 bootstrap simulations. These confidence intervals indicate that the 2020 model is accurate to approximately ±7 percentage points at the state and congressional district levels, and ±8 percentage points at the metro and county levels. Such error ranges include the error inherent in the original national surveys themselves, which is typically ±3 percentage points.

The availability of 2014, 2016, 2018, 2019, and 2020 estimates prompts questions about what has changed between these years in particular places. From our surveys, we know that public opinion on some questions has shifted over this period at the national level. However, our statistical model, as well as the census and election data that go into our model, have also all been updated for accuracy. Thus, estimated shifts in public opinion at the subnational level since 2014 may be due not only to opinion changes, but also changes in demographics and other variables that we are using to model opinions. In addition, our model tends to be conservative when making estimates, especially for places with smaller populations (where there are likely to be fewer survey respondents), so variations over time between states, counties, etc. may be underestimated. As a result, we recommend caution when interpreting any changes over time using different versions of our model, because we can't distinguish between what is a true shift in actual opinion between 2014, 2016, 2018, 2019, 2020, and 2021, and what reflects the model's improved ability to map climate opinions at the local scale. We are currently developing a new model explicitly for changes in opinions over time at the state level, so please join our mailing list if you would like to be alerted about these developments.

For more details on the model or methods, please see the peer-reviewed paper: Howe, P., Mildenberger, M., Marlon, J.R., and Leiserowitz, A., "Geographic variation in opinions on climate change at state and local scales in the USA," Nature Climate Change. DOI: 10.1038/nclimate2583.

Climate Change in the American Mind Survey Question Wording

Model estimates in the maps were derived from public responses to the following survey questions. The response categories for many questions were collapsed into a single variable for mapping. For example, for the question measuring how worried respondents are about global warming, "very worried" and "somewhat worried" were combined into a single measure of "worried." Likewise "Not very worried" and "Not at all worried" were combined into a single measure of "not worried." The responses below are color coded to indicate how they were grouped into the variables shown on the maps. Individuals who responded "Don't know" or who did not answer the question were not modeled separately and appear as gray segments within the bar charts.

BELIEFS

Global warming is happening
Recently, you may have noticed that global warming has been getting some attention in the news. Global warming refers to the idea that the world's average temperature has been increasing over the past 150 years, may be increasing more in the future, and that the world's climate may change as a result. What do you think: Do you think that global warming is happening?

Global warming is caused mostly by human activities
Assuming global warming is happening, do you think it is... ?

Caused mostly by human activities

Caused mostly by natural changes in the environment

None of the above because global warming isn't happening

Other

Don't know

Most scientists think global warming is happening
Which comes closest to your own view?

Most scientists think global warming is happening

There is a lot of disagreement among scientists about whether or not global warming is happening

Most scientists think global warming is not happening

Don't know enough to say

Global warming is affecting the weather in the United States
How strongly do you agree or disagree with the statement below?

RISK PERCEPTIONS

Worried about global warming
How worried are you about global warming?

Global warming will harm plants and animals
How much do you think global warming will harm plants and animal species?

Global warming will harm future generations
How much do you think global warming will harm future generations of people?

Global warming will harm people in developing countries
How much do you think global warming will harm people in developing countries?

Global warming will harm people in the US
How much do you think global warming will harm people in the United States?

Global warming will harm me personally
How much do you think global warming will harm you personally?

Global warming is already harming people in the US
When do you think global warming will start to harm people in the United States?

Has personally experienced the effects of global warming
How much do you agree or disagree with the following statement: "I have personally experienced the effects of global warming"?

POLICY SUPPORT

Fund research into renewable energy sources

How much do you support or oppose the following policies?
Fund more research into renewable energy sources, such as solar and wind power

Regulate CO2 as a pollutant
How much do you support or oppose the following policies?
Regulate carbon dioxide (the primary greenhouse gas) as a pollutant

Set strict CO2 limits on existing coal-fired power plants
How much do you support or oppose the following policy?
Set strict carbon dioxide emission limits on existing coal-fired power plants to reduce global warming and improve public health. Power plants would have to reduce their emissions and/or invest in renewable energy and energy efficiency. The cost of electricity to consumers and companies would likely increase.

Require fossil fuel companies to pay a carbon tax
How much do you support or oppose the following policies?
Require fossil fuel companies to pay a carbon tax and use the money to reduce other taxes (such as income tax) by an equal amount.

Require utilities to produce 20% electricity from renewable sources
How much do you support or oppose the following policies?
Require electric utilities to produce at least 20% of their electricity from wind, solar, or other renewable energy sources, even if it costs the average household an extra $100 a year.

Provide tax rebates
Provide tax rebates for people who purchase energy-efficient vehicles or solar panels.
How much do you support or oppose the following policies?

The YPCCC is pleased to offer our downscaled climate change opinion estimates to the public. These data are distributed under the following terms of use. This is a legal agreement between you, the end-user ("User") and Yale University on behalf of the Yale Program on Climate Change Communication (the "YPCCC"). By downloading the survey data made available on this web site ("Data") you are agreeing to be bound by the terms and conditions of this agreement. If you do not agree to be bound by these terms, do not download or use the Data. The YPCCC hereby grants to the User a non-exclusive, revocable, limited, non-transferable license to use the Data solely for (1) research, scholarly or academic purposes, (2) the internal use of your business, or (3) your own personal non-commercial use. You may not reproduce, sell, rent, lease, loan, distribute or sublicense or otherwise transfer any Data, in whole or in part, to any other party, or use the Data to create any derived product for resale, lease or license. Notwithstanding the foregoing, you may incorporate limited portions of the Data in scholarly, research or academic publications or for the purposes of news reporting, provided you acknowledge the source of the Data (with express references to the YPCCC, as well as the complete title of the report) and include the following legend: The YPCCC bears no responsibility for the analyses or interpretations of the data presented here. THE DATA IS PROVIDED "AS IS" WITHOUT ANY WARRANTY OF ANY KIND, EITHER EXPRESS OR IMPLIED, ARISING BY LAW OR OTHERWISE, INCLUDING BUT NOT LIMITED TO WARRANTIES OF COMPLETENESS, NON-INFRINGEMENT, ACCURACY, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. THE USER ASSUMES ALL RISK ASSOCIATED WITH USE OF THE DATA AND AGREES THAT IN NO EVENT SHALL YALE BE LIABLE TO YOU OR ANY THIRD PARTY FOR ANY INDIRECT, SPECIAL, INCIDENTAL, PUNITIVE OR CONSEQUENTIAL DAMAGES INCLUDING, BUT NOT LIMITED TO, DAMAGES FOR THE INABILITY TO USE EQUIPMENT OR ACCESS DATA, LOSS OF BUSINESS, LOSS OF REVENUE OR PROFITS, BUSINESS INTERRUPTIONS, LOSS OF INFORMATION OR DATA, OR OTHER FINANCIAL LOSS, ARISING OUT OF THE USE OF, OR INABILITY TO USE, THE DATA BASED ON ANY THEORY OF LIABILITY INCLUDING, BUT NOT LIMITED TO, BREACH OF CONTRACT, BREACH OF WARRANTY, TORT (INCLUDING NEGLIGENCE), OR OTHERWISE, EVEN IF USER HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. The YPCCC has taken measures to ensure that the Data is devoid of information that could be used to identify individuals (e.g., names, telephone numbers, email addresses, social security numbers) who participated in or who were the subject of any research surveys or studies used to collect the Data ("Personally Identifying Information"). However, in the event that you discover any such Personally Identifying Information in the Data, you shall immediately notify the YPCCC and refrain from using any such Personally Identifying Information. This license will terminate (1) automatically without notice from the YPCCC if you fail to comply with the provisions of this agreement, or (2) upon written notice (by e-mail, U.S. or otherwise) from the YPCCC. Upon termination of this agreement, you agree to destroy all copies of any Data, in whole or in part and in any and all media, in your custody and control. This agreement shall be governed by, construed and interpreted in accordance with the laws of the State of Connecticut. You further agree to submit to the jurisdiction and venue of the courts of the State of Connecticut for any dispute relating to this Agreement. Please use the following citation in any work that makes use of the data and documentation as follows: Howe, Peter D., Matto Mildenberger, Jennifer R. Marlon, and Anthony Leiserowitz (2015). "Geographic variation in opinions on climate change at state and local scales in the USA." Nature Climate Change, doi:10.1038/nclimate2583. Direct any questions to the YPCCC at climatechange@yale.edu.

Frequently Asked Questions

What do these maps depict?

The maps depict estimates of the percentage of American adults (age 18 and over) who hold particular beliefs, attitudes, and policy preferences about global warming. The estimates were generated from a statistical model that incorporates actual survey responses, but combines these responses with demographic data from the U.S. Census to estimate opinions for different groups of people based on information such as their gender, race and ethnicity, and educational attainment; they also take into account changes in public opinion over time.

Where do the survey data underlying the estimates come from?

The data underlying the maps come from a large national survey dataset ( >28,000 respondents) collected between 2008 through 2021 as part of the Climate Change in the American Mind project led by the Yale Program on Climate Change Communication and the George Mason University Center for Climate Change Communication. Reports from the individual surveys are available here: CCAM Reports.

How many people did you survey in my state/county? / How can you say anything about opinions in my geography if you didn't survey anyone there?

The number of respondents surveyed varies for each geographic unit but is very large across the US as a whole. Since our surveys are nationally representative, the number of respondents in each state and county is proportional to its share of the national population. In other words, larger states and counties will have more respondents than smaller states and counties. In some smaller counties, very few, if any, respondents may have been surveyed. In those cases, our model uses information about the population of the county, information from other counties in the same state and region, and information about the characteristics of the county (such as how the county voted in previous elections) to make its estimates. While this may sound surprising, it makes sense when we consider the strongest influences on a person's individual opinions about climate change in the US - political views. Knowing the political leanings of a county's (or other unit's) residents, along with the proportion of different demographic groups within that county, allows us to predict resident's climate opinions very well. Adding additional information (e.g., about rural/urban context, carbon-intensive activities, etc.), further improves predictions. Moreover, for any given geographic unit, information is pooled from nearby units and from higher-level geographies (e.g. state level information is included for all counties within that state). All of this information together allows us to produce reliable estimates (see next question) for all counties even if the number of individuals sampled within a specific county is small.

How accurate are the estimates?

No model is perfect and there are uncertainties in the model estimates. To validate the model, we conducted independent surveys in four states (CA, TX, OH, CO) and two metropolitan areas (Columbus, OH and San Francisco, CA) and compared the survey results to our model estimates. On average, the model estimates differed from the survey results by 2.9 percentage points among the four states and 3.6 percentage points among the two metropolitan areas, within the survey margins of error. A series of technical simulations estimate that the model has an average margin of error of ±7 percentage points at the state and congressional district levels, ±8 percentage points at the metro and county levels. Such error ranges include the error inherent in the original national surveys themselves, which is typically ±3 percentage points. The model uncertainties are smaller at broad geographic scales (e.g., the state level), and are larger at finer geographic scales (e.g., at the county and city levels). The model estimates also tend to be conservative, so geographic areas with extremely high or low measures are not estimated as well as areas with values closer to the national average for each survey question.

What does the gray color mean on some of the bars beneath the maps?

The gray area reflects people who refused to answer the question or said "don't know". We do not provide specific values for the gray areas because we did not develop estimates for these particular responses.

Do the maps account for differences in population density across the country?

No, the maps depict the estimated proportion of people within each geographic area who would answer each question as indicated. We have not adjusted the maps based on population density differences. It is important to keep in mind that some geographic areas may be large, but have few residents (e.g., Wyoming), while other geographic areas may be small, but have many residents (e.g., New Jersey). For reference, Wikipedia has a population density map here. The type of map used in this tool is called a choropleth map, which means the colors on the maps reflect the percentage of the population in a given geographic unit. These kinds of maps are used to represent everything from election results (e.g.,the red state / blue state maps common during presidential elections) to census and economic data (e.g., per capita income or unemployment rates).

Do these maps reflect changes in opinions due to recent extreme weather events like Superstorm Sandy?

Perhaps. The maps may reflect the impacts that specific extreme weather events had on public opinion in a given geographic unit. If public opinion in a particular area has been influenced by local events it is possible that the model would detect such an influence. However, data from specific events or types of events are not explicitly built into the model as predictor variables.

Can I use the data?

Yes. We encourage you to explore the maps and use the results in your own work. The data are available on our Data Download tab at the top of this page so that you can do your own analyses and create your own visualizations. If you publish an academic paper using these data please acknowledge the source by using the following citation:

Howe, P., Mildenberger, M., Marlon, J., & Leiserowitz, A. (2015) "Geographic variation in opinions on climate change at state and local scales in the USA," Nature Climate Change. DOI: 10.1038/nclimate2583.

If you publish a news article, visualization, blog post, or other publication using these data or maps, please include the link to the Yale Climate Opinion Maps website and attribution to the Yale Program on Climate Change Communication.

How do the YCOM maps compare from year to year?

We originally created YCOM in 2014 and then updated it in 2016, 2018, 2019, 2020, and 2021. Public concern about global warming has generally increased since 2014. However, improvements to our model, including new data that increase our ability to resolve differences between jurisdictions, and data for adults 18+ (prior estimates only included data for adults 25+) may have contributed to shifts in our current estimates as compared with those of 2014. As a result, we recommend caution interpreting any changes over time because we can't easily disaggregate what is a true shift between any particular year and what reflects the model's improved ability to map climate beliefs at the local scale. We are currently working on a new tool which will show changes in opinions over time for each state. Join our mailing list via the "Subscribe" button below on the right to be alerted when we release this new tool.

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Yale Climate Opinion Maps 2021 - Yale Program on Climate Change Communication

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Use the Yale Climate Opinion Maps to explore climate change beliefs, risk perceptions, and policy support at every geographic level in the United States.

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Interactive live map of the Earth's wind patterns, temperature, pollutant levels, etc.

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Interactive live map of the Earth's wind patterns, temperature, pollutant levels, etc.

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See current wind, weather, ocean, and pollution conditions, as forecast by supercomputers, on an interactive animated map. Updated every three hours.

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Student Example for Cat Shelter Engineering Project

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Student Example for Cat Shelter Engineering Project

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In this activity, students will examine line plots of NASA data and see that the sun heats up land, air, and water. Students will practice drawing conclusions based on graphed data of cloudy vs. clear sky observations.

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In this activity, learners explore the size and scale of the universe by shrinking cosmic scale in 4 steps, zooming out from the realm of the Earth and Moon to the realm of the galaxies. This informational brochure was designed as a follow-up take-home activity for teen and adult audiences.

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NASA scientist, Neil Gehrels, serves as your guide to this online lesson on gamma ray tools, which focuses on advances in detector technologies since the 1980s that have enabled us to capture and image high-energy phenomena. Dr.

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This video is narrated by NASA scientist Peter Griffith who explains fast and slow carbon cycling on Earth. A banana is an example of fast, young carbon. A chunk of coal is an example of old, slow carbon.

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In this activity, students will examine line plots of NASA data and see that the sun heats up land, air, and water. Students will practice drawing conclusions based on graphed data of cloudy vs. clear sky observations. This is a lesson about the size and scale of planets in the solar system.

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Selecting Natural Phenomena to Drive Science Instruction and Assessment

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Selecting Natural Phenomena to Drive Science Instruction and Assessment

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HO 1 - Selecting Natural Phenomena to Drive Science Instruction and Assessment

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Engineering for Empathy:  Wallet Design Project

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Climate Kids is produced by the Earth Science Communications Team at

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As humans we are part of the environment. With over 7.5 billion of us on Earth, our combined actions also have a big impact on the environment. As long as we are aware of the impact, we can do things as individuals, and working together as groups, to lessen the detrimental impact of billions of people. Explore important topics like air quality, water quality, the effects of climate change, and many others to make informed decisions about caring for our planet.

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Around 7 in the morning, Monu, 13, lifts his mosquito netting and crawls out of bed onto a dirt floor. Outside, his mother cooks breakfast over an open fire.

A few miles across New Delhi, the world's most polluted capital, 11-year-old Aamya finally gives in to her mom's coaxing. She climbs out of bed and treads down the hall, past an air purifier that shows the pollution levels in glowing numbers.

The air is relatively clean in Aamya's apartment in Greater Kailash II, one of Delhi's upper-middle-class neighborhoods. Well-fitted doors and windows make the home more airtight, and its rooms purr with the sound of three purifiers that scrub dangerous particles from the air.

Monu breathes fouler air. He lives in a hut in a slum near the Yamuna River, which itself is seriously polluted. This morning, he sits in the open entryway to his house, drinking milky tea. He is the seventh of nine children and watches as one of his brothers coughs and huddles for warmth near the family's wood-burning clay stove.

Air pollution killed more Indians last year than any other risk factor, and Delhi is among the most polluted cities in the country. But the burden is unequally shared.

Children from poor families in Delhi spend more of their lives outdoors. Their families are more likely to use wood-burning stoves, which create soot. They can't afford the air filters that have become ubiquitous in middle-class homes. And often, they don't even think much about air pollution, because they face more pressing threats, like running out of food.

Money can buy a family less exposure to Delhi's deadly pollution - but only to a point. Air purifiers and well-sealed rooms can do only so much. Though precise estimates are impossible, even well-off kids like Aamya could lose roughly a year of life because of the amount of toxic air they breathe. And Aamya has asthma, so her parents are especially concerned.

Still, over the course of one day, Monu was exposed to about four times as much pollution as Aamya. A long-term, consistent disparity like that could steal around five years more life from someone in Monu's position, compared with an upper-middle-class child like Aamya.

We know Monu was exposed to more pollution, because we measured it.

Working with researchers from ILK Labs, on Dec. 3 of last year, journalists with The New York Times tracked how much air pollution the two children were exposed to over the course of a single day.

As Monu and Aamya went about an otherwise ordinary school day, we followed them with cameras and air-quality monitors that measured how much fine particulate matter was in the air they breathed at any given moment. Known as PM2.5, these are tiny toxic particles, especially dangerous because they can infiltrate the bloodstream.

Monu and Aamya have never met, but their families know about each other. Their parents agreed to participate in this report after we explained what we could learn by measuring the pollution exposure of children from different backgrounds. Aamya's mother said she hoped it would help raise awareness about the greater health risks faced by families with fewer resources.

We could see the difference in the quality of the air they breathed, just from the filters in their pollution monitors.

Photos of the small filters that were inside the children's pollution monitors.

The pollution in Delhi has an almost physical presence.

You can see it, a haze just up the street. You can smell it, like an acrid campfire, and you can taste it on your tongue. It can make your eyes burn, your throat itch and your head pound. The tiny particles floating in the air increase the risk of blood clots that can cause heart attacks. They can damage your liver and brain.

Some of the particles are composed of very poisonous materials like arsenic and lead. Other components may be less toxic in and of themselves, but their cumulative effect is another matter. With alarming regularity, researchers release new findings on the many ways air pollution harms the human body.

Soon after our reporting, the coronavirus pandemic struck.

Pollution levels plummeted this spring during India's strict lockdown, producing a rare sight in the city: pure blue skies. Sadly, this was short lived. Once the lockdown lifted in summer, the pollution came back. And now, as winter bears down, the air pollution across India is once again hitting hazardous levels.

Doctors worry that the toxic air is making the virus even deadlier. Exposure to high levels of pollution causes inflammation of the airways, which makes people more prone to all kinds of infections.

"We're seeing it happen in front of us," said Dr. Arvind Kumar, a chest surgeon and founder of the Lung Care Foundation in New Delhi. "A lot of people are coming to hospitals from the periphery areas where population density and pollution levels are very high."

It is a reminder, if one were needed, that not everyone breathes the same air.

"The rich can have the best quality air purifiers," Dr. Kumar said. "The poor can't."

The pollution hung gray in the air the morning we drove to Monu and Aamya's homes. Government measurements put fine particulate matter at 130 micrograms per cubic meter - five times worse than the World Health Organization says is safe. But locals said it was a good day for air quality. Understandable, since the most polluted day there last year measured four times worse.

Aamya and Monu started their morning commute through the smog.

Monu rode his bike to a free open-air school under a bridge, about five minutes from his house down a dusty road. He likes physical activity, and he wants to be an officer in the Indian Army when he grows up.

Aamya likes sports, too, but she wants to be a musician. She rode to school with her mom in the air-conditioned cabin of the family Hyundai.

Aamya attends a private school, the Ardee School, known for its efforts to insulate its students from air pollution. The school costs about $6,000 per year.

The Ardee School posts pollution readings on its website and on a board in the building that uses colored flags to signal the air quality. When it gets too bad, students are required to wear masks. Very few wore one while we were there, because it was not considered a bad day.

A large air filter in the basement of Aamya's school

Monu's school is free - but it has neither walls nor doors. For these students, the outside air was the inside air. Volunteer teachers struggled to be heard as metro trains thundered overhead every five minutes.

All morning, while Monu was in class, cars and motorbikes whizzed past on the street next to his school, kicking up dust and clogging the air with exhaust fumes. Aamya's school had air purifiers in every room, linked together through a phone app that administrators monitored constantly.

Both Monu and Aamya sound fatalistic.

"It will keep increasing," Monu says. "If we have 10 sick kids today, it'll be 20 tomorrow. Lots of people will get sick, and their parents and doctors will say that it's because of the pollution."

Aamya thinks that the government is to blame, and that one person can't make much of a dent in the problem.

"There are a lot of trees, which are not helping that much," she says. "What my teacher says is that we can make a difference. But I don't believe in that, because we have tried a lot."

In the afternoon, after lunch at home, Monu went to another school, which he does every weekday. The outdoor pollution levels began to fall, as they do on most days when the morning traffic clears up and the winds shift.

There is no single cause of India's pollution problem - and no single solution.

But Indians have learned to count on one thing: Fall and winter are pollution seasons. As air temperatures dip and wind speeds drop, pollutants concentrate over India's cities, especially in the north, which lies in the shadow of the Himalayas. The mountain range forms a barrier that cuts down air movement even further.

The pollutants themselves come from multiple sources.

By some estimates, vehicle exhaust accounts for around 20 to 40 percent of the PM2.5 in New Delhi, which is notorious for its traffic. Household fires and industrial emissions also play a role. And as the weather cools in the fall, farmers in rural areas burn remains from their crops, sending up huge clouds of black smoke that drift for miles and settle over the city.

The end result is that the city's smog is some of the thickest in the world.

India's government has not made battling pollution a priority. Many officials see it as a price they are willing to pay for rapid economic growth, which has lifted hundreds of millions of people out of poverty.

Outrage is not always easy to find on the street, either, no matter how smog-shrouded. Environmental activists say most people have no idea about how bad it really is.

"We are talking about people who grew up in rural areas and they come to the city with no preparation," said Ravina Kohli, a member of My Right to Breathe, a nationwide clean air group. "When they see polluted air, they don't even think it is polluted."

There is also little data on how socioeconomic disparities may worsen pollution exposure in New Delhi, according to Pallavi Pant, a staff scientist at the Health Effects Institute. "We aren't putting a careful enough lens on people's occupation, or where they live, or what their socioeconomic status is," she said.

Clearly, money helps.

Aamya's parents, for example, have managed to shield her from some of the pollution. But it isn't nearly enough.

In fact, researchers say, there is no amount of personal spending that can fix the problem. Much broader action needs to be taken, they say, to make India's cities healthy for everybody - rich or poor.

At day's end, an invisible enemy seeps through the doors and windows of rich and poor alike.

With school over, Aamya and Monu are back at their houses, settled in to do their homework.

When he is done, Monu watches his mom cook over an open fire, just like he did in the morning. At Aamya's home, a servant does the cooking in a separate room.

As Aamya's family takes their seats at the dinner table, the air purifiers continue their reassuring hum. But there is only so much the machines can do. In Delhi, the air is the air, and like most buildings in the city, Aamya's house is outmatched.

Monu's mother, Ranju, never thinks about air pollution, she says. When asked about it, she laughs, waving her hand dismissively and saying, "It's the least of my worries." Her day, after all, is long and hard, beginning at 4 a.m. and going until 10 at night.

Monu's family has no running water, so it's up to Ranju to fetch the water every day from a hand pump. She has nine children, and cooks and cleans for the family. She never went to school.

Aamya's mother, Bhavna, holds an M.B.A., worked for years as a marketing executive and, as a younger woman, lived in Paris. Air pollution is a major worry for her, especially because of Aamya's asthma.

"As children, our parents used to tell us 'You need to be more active, you need to be outside, play more sports,'" she recalls. "But we've reached a stage where we're telling our kids, 'No you can't go to the park, the air is very bad.'"

"The flexibility to just walk outside and go to a park has completely died," she said.

But even a warm bed is not entirely safe.

As Aamya sleeps, she is breathing in more pollutants than most children in the world.

It is far worse for Monu.

As he arranges the mosquito netting around his bed and lies down, his exposure is over twice as high as Aamya's. There is no escape for him. The most polluted part of his day happens at home, as he sinks into his dreams.

How we collected the data

There are many ways to measure air pollution, and many different pollutants that affect health. We focused on PM2.5 because it is especially dangerous and there are several low-cost sensors that can measure it. In the field, we worked with two pollution researchers from ILK Labs, Meenakshi Kushwaha and Adithi Upadhya. We also consulted with Joshua Apte, a pollution scientist at the University of California, Berkeley about research design.

On the ground, we used four different tools to collect PM2.5 data:

* AirBeam2, a small, low-cost device intended for personal use.

* PurpleAir PA-II, another low-cost device that has been extensively tested in academic research.

* DustTrak II 8530, a larger, professional PM2.5 monitor that can take measurements once every second.

* UPAS, which very accurately measures of exposure over a longer span of time.

The first three devices work by measuring how the particles in a sample of polluted air scatter a beam of laser light. The UPAS device takes a more accurate measurement by using a small air filter that has to be processed later by a lab. This device was the source of the numbers at the beginning of this story.

The AirBeam was our main tool for data collection, because it was small enough to keep with the kids for the entire day. Its internal sensor is made by the same company as the one in the PurpleAir PA-II, and studies have shown that measurements from the two sensors correspond well.

We were with the kids from early morning until late at night, and the AirBeam's battery couldn't last that long, so we connected it to a larger one. The AirBeam reports data through a phone app, but we wanted a fallback, so we connected the AirBeam to a tiny Raspberry Pi computer. We modified open-source software to control the AirBeam and download measurements to the Pi every few seconds. We also had to add a clock module to the Pi to keep more precise track of time.

We also collected data on humidity and black carbon concentrations.

How we processed the data

Measuring PM2.5 can be tricky, especially with lower-cost sensors. Humidity can cause particles to swell. Particle composition can throw off sensor optics. And there is always some amount of variation between individual sensors. Ms. Upadhya and Ms. Kushwaha used scientific techniques to adjust our data to correct for these factors.

A long-term research project by Dr. Apte had previously established how data from PurpleAir sensors matched data from a very accurate reference monitor in New Delhi. We used this relationship to adjust our PurpleAir data. We put our AirBeam devices near the reference sensor for about 8 days, and used linear regression to determine how to adjust that data as well. We additionally corrected our data for humidity levels using equations published by Chakrabarti et al.

The line charts in this story show a mix of AirBeam and PurpleAir data, depending on the sensor that was with each child during each part of the day. The corrections we applied and the similarities of the sensors in these two devices allowed us to smoothly patch holes in one dataset with the other.

The bar charts that accompany the videos in this story show that same data, augmented by more frequent, once-every-second readings by the DustTrak device. We did this to convey how Monu and Aamya's pollution exposure is constantly changing, but in scientific terms some of these fluctuations are likely to be only noise.

How we estimated how pollution might affect the kids' lifespans

It is impossible to use one day's worth of data to predict the actual long-term health effects of pollution for two children. We know enough about how pollution exposure reduces lifespans in general, though, to roughly estimate how many years could be lost if someone were exposed for their entire life to the amounts of pollution we observed for Monu and Aamya.

We estimated long-term PM2.5 exposure from one day of data in two steps.

First, we compared Monu and Aamya's 24-hour readings to the average ambient PM2.5 level from nearby monitoring stations in Delhi during the same time period, which was 128 μg/m ³. Monu was exposed to 148.9 μg/m ³ of PM2.5, or about 116% of the ambient level, and Aamya was exposed to 36.6 μg/m ³, about 29% of the ambient.

Then, we compared those percentages to the average ambient levels in 2018 and 2019, which was 108.9 μg/m ³. This gave us an annual exposure estimate of about 127 μg/m ³ for Monu and about 32 μg/m ³ for Aamya. Although useful as a rough estimate, this conversion assumes that every day for the kids tends to be similar to the day we were with them, which may not be true. It could also over- or underestimate household effects like Monu's exposure to cooking fires.

Our next task was to translate our rough estimate of annual pollution exposure into years of life lost.

There are few studies on how long-term exposure to fine particles reduces life expectancy in very high-pollution areas. The Air Quality Life Index produced by the University of Chicago is one of the few studies to focus on India. But their assessment uses an approach from a study in China to estimate that every 10 μg/m ³ of long-term PM2.5 exposure reduces a person's lifespan by one year. Results from the U.S., put that number lower: closer to half a year of life lost per 10 μg/m ³, sometimes even less. But results from studies in China or the U.S. don't necessarily apply to Indian cities.

Which estimate should we use? Scientists aren't sure, because there isn't enough data to know whether the relationship between exposure and years of life lost remains linear even at the extremely high levels of pollution seen in places like Delhi. We've used a relatively conservative estimate of half a year of life lost per 10 μg/m ³. According to Arden Pope, a professor of economics at Brigham Young University and a prominent expert on how pollution affects life expectancy, this is a reasonable estimate given what we know from other studies.

Applying these numbers to our estimates for long-term exposure gives us our estimate for life lost: if PM2.5 exposure were reduced to meet the World Health Organization guideline (10 μg/m ³), someone in Aamya's position could see about a year of added life, and someone in Monu's could gain more than six years.

There are many assumptions involved in making these estimates, among them: that Monu and Aamya will stay in similar situations for most of their lives; that the air pollution in Delhi won't get better; and that the air pollution in Delhi won't get worse.

There are few studies with age-specific findings, and long-term exposure to PM2.5 may affect children in ways we don't understand. The composition of pollution could have an effect, too: Monu inhales much more wood smoke than Aamya does.

Even so, these numbers give a rough sketch of how pollution can shave years off a child's life.

* Joshua Apte, assistant professor of environmental health sciences and environmental engineering at the University of California, Berkeley

* Kalpana Balakrishnan, professor of environmental health engineering at the Sri Ramachandra Institute of Higher Education and Research

* Anumita Roy Chowdhury, executive director of research and advocacy at the Centre for Science and Environment

* Douglas Dockery, former professor of environmental epidemiology at Harvard University

* Dr. Randeep Guleria, director of the All India Institute of Medical Sciences

* Arden Pope, professor of economics at Brigham Young University

* Dr. Harshal Ramesh Salve, assistant professor at the Centre for Community Medicine, All India Institute of Medical Sciences

* Anant Sudarshan, executive director for South Asia at the Energy Policy Institute, University of Chicago

By Jin Wu, Derek Watkins, Josh Williams, Shalini Venugopal Bhagat, Hari Kumar and Jeffrey Gettleman

Cinematography by Karan Deep Singh and Omar Adam Khan

Field production by Sidrah Fatma Ahmed

Meenakshi Kushwaha and Adithi Upadhya from ILK Labs helped collect and analyze data

Produced by Rumsey Taylor, Leslye Davis and Josh Keller

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Who Gets to Breathe Clean Air in New Delhi? (Published 2020)

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Around 7 in the morning, Monu, 13, lifts his mosquito netting and crawls out of bed onto a dirt floor. Outside, his mother cooks breakfast over an open fire. A few miles across New Delhi, the world's most polluted capital, 11-year-old Aamya finally gives in to her mom's coaxing.

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Interactive live map of the Earth's wind patterns, temperature, pollutant levels, etc.

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Interactive live map of the Earth's wind patterns, temperature, pollutant levels, etc.

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earth :: a global map of wind, weather, and ocean conditions

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See current wind, weather, ocean, and pollution conditions, as forecast by supercomputers, on an interactive animated map. Updated every three hours.

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NASA earth and space science resource collection

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NASA earth and space science resource collection

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Cloudy vs. Clear

In this activity, students will examine line plots of NASA data and see that the sun heats up land, air, and water. Students will practice drawing conclusions based on graphed data of cloudy vs. clear sky observations.

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In this activity, students will examine line plots of NASA data and see that the sun heats up land, air, and water. Students will practice drawing conclusions based on graphed data of cloudy vs. clear sky observations. This is a lesson about the size and scale of planets in the solar system.

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Sea Level Rise Viewer: View projected sea level rise at any coastal location up to 6 feet of inundation from flooding, hurricane storm surge, high tides.

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Engineering for Empathy:  Wallet Design Project

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Engineering for Empathy:  Wallet Design Project

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Potential bank of elementary science experiments

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Potential bank of elementary science experiments

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As humans we are part of the environment. With over 7.5 billion of us on Earth, our combined actions also have a big impact on the environment. As long as we are aware of the impact, we can do things as individuals, and working together as groups, to lessen the detrimental impact of billions of people. Explore important topics like air quality, water quality, the effects of climate change, and many others to make informed decisions about caring for our planet.

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Elementary School, Environmental Science Science Projects

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Please ensure you have JavaScript enabled in your browser. If you leave JavaScript disabled, you will only access a portion of the content we are providing. Here's how. As humans we are part of the environment. With over 7.5 billion of us on Earth, our combined actions also have a big impact on the environment.

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Would love ideas to incorporate more hands-on pieces in the investigations.

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GREAT site for students to learn through simulations and games! Free and Google classroom friendly!

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Last year I had a 90% pass rate and this year we improved to 92%. What makes this data even more amazing is we have a large population of special ed students on my team this...

Last year I had a 90% pass rate and this year we improved to 92%. What makes this data even more amazing is we have a large population of special ed students on my team this year and last year we had none! The biggest jump was in my pass advanced rate - it increased from 38% to 41% and 12 students had perfect scores! 15 of my students have IEPs and all of them passed the test. Our building went up from 87% pass to 91% in fifth grade. I think the numbers are amazing with our large special ed population!

I believe a lot of it is due to the work students did in LOL throughout the year to help review and reinforce 4th and 5th grade material.

I am happy to say the least.

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Legends of Learning: Math and Science Games

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Last year I had a 90% pass rate and this year we improved to 92%. What makes this data even more amazing is we have a large population of special ed students on my team this... Last year I had a 90% pass rate and this year we improved to 92%.

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Paige Keely Probes for Elementary School

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Paige Keely Probes for Elementary School

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Elicit student ideas through these probes

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Get access to files anywhere through secure cloud storage and file backup for your photos, videos, files and more with Google Drive.

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More Paige Keeley probes for physical science and others

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Interactives

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Share the Strategy and Source RQI Our materials are available under a Creative Commons Sharealike license. To comply with this license, you must include the following language on all materials you use, share, adapt, or create: "Source: The Question Formulation Technique (QFT) was created by the Right Question Institute (rightquestion.org)." As long as you include this source language, you are welcome to use, adapt, and share our strategies and materials for noncommercial use. You may also create new materials that reference the Question Formulation Technique (QFT) and/or other Right Question Institute strategies as long as you include the source language above. If you are interested in sharing RQI's resources for commercial purposes you must contact us by selecting the "Permission for Use" option on the contact form.

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Share the Strategy and Source RQI Our materials are available under a Creative Commons Sharealike license. To comply with this license, you must include the following language on all materials you use, share, adapt, or create: "Source: The Question Formulation Technique (QFT) was created by the Right Question Institute (rightquestion.org)."

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Share the Strategy and Source RQI Our materials are available under a Creative Commons Sharealike license. To comply with this license, you must include the following language on all materials you use, share, adapt, or create: "Source: The Question Formulation Technique (QFT) was created by the Right Question Institute (rightquestion.org)." As long as you include this source language, you are welcome to use, adapt, and share our strategies and materials for noncommercial use. You may also create new materials that reference the Question Formulation Technique (QFT) and/or other Right Question Institute strategies as long as you include the source language above. If you are interested in sharing RQI's resources for commercial purposes you must contact us by selecting the "Permission for Use" option on the contact form.

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Share the Strategy and Source RQI Our materials are available under a Creative Commons Sharealike license. To comply with this license, you must include the following language on all materials you use, share, adapt, or create: "Source: The Question Formulation Technique (QFT) was created by the Right Question Institute (rightquestion.org)."

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Wind and Ocean Currents Virtual Lab for MS

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Wind and Ocean Currents Virtual Lab for MS

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Wind and Ocean Currents Virtual Lab Assignment

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QFT Example

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Example of QFT template

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