Context

Have you taken a flight in the U.S. in the past 15 years? If so, then you are a part of monthly data that the U.S. Department of Transportation's TranStats service makes available on various metrics for 15 U.S. airlines and 30 major U.S airports. Their website unfortunately does not include a method for easily downloading and sharing files. Furthermore, the source is built in ASP.NET, so extracting the data is rather cumbersome. To allow easier community access to this rich source of information, I scraped the metrics for every airline / airport combination and stored them in separate CSV files.

Occasionally, an airline doesn't serve a certain airport, or it didn't serve it for the entire duration that the data collection period covers*. In those cases, the data either doesn't exist or is typically too sparse to be of much use. As such, I've only uploaded complete files for airports that an airline served for the entire uninterrupted duration of the collection period. For these files, there should be 174 time series points for one or more of the nine columns below. I recommend any of the files for American, Delta, or United Airlines for outstanding examples of complete and robust airline data.

* No data for Atlas Air exists, and Virgin America commenced service in 2007, so no folders for either airline are included.

Content

There are 13 airlines that have at least one complete dataset. Each airline's folder includes CSV file(s) for each airport that are complete as defined by the above criteria. I've double-checked the files, but if you find one that violates the criteria, please point it out. The file names have the format "AIRLINE-AIRPORT.csv", where both AIRLINE and AIRPORT are IATA codes. For a full listing of the airlines and airports that the codes correspond to, check out the airline_codes.csv or airport_codes.csv files that are included, or perform a lookup here. Note that the data in each airport file represents metrics for flights that originated at the airport.

Among the 13 airlines in data.zip, there are a total of 161 individual datasets. There are also two special folders included - airlines_all_airports.csv and airports_all_airlines.csv. The first contains datasets for each airline aggregated over all airports, while the second contains datasets for each airport aggregated over all airlines. To preview a sample dataset, check out all_airlines_all_airports.csv, which contains industry-wide data.

Each file includes the following metrics for each month from October 2002 to March 2017:

1. Date (YYYY-MM-DD): All dates are set to the first of the month. The day value is just a placeholder and has no significance.
2. ASM_Domestic: Available Seat-Miles in thousands (000s). Number of domestic flights * Number of seats on each flight
3. ASM_International*: Available Seat-Miles in thousands (000s). Number of international flights * Number of seats on each flight
4. Flights_Domestic
5. Flights_International*
6. Passengers_Domestic
7. Passengers_International*
8. RPM_Domestic: Revenue Passenger-Miles in thousands (000s). Number of domestic flights * Number of paying passengers
9. RPM_International*: Revenue Passenger-Miles in thousands (000s). Number of international flights * Number of paying passengers

* Frequently contains missing values

Acknowledgements

Thanks to the U.S. Department of Transportation for collecting this data every month and making it publicly available to us all.

Source: https://www.transtats.bts.gov/Data_Elements.aspx

Inspiration

The airline / airport datasets are perfect for practicing and/or testing time series forecasting with classic statistical models such as autoregressive integrated moving average (ARIMA), or modern deep learning techniques such as long short-term memory (LSTM) networks. The datasets typically show evidence of trends, seasonality, and noise, so modeling and accurate forecasting can be challenging, but still more tractable than time series problems possessing more stochastic elements, e.g. stocks, currencies, commodities, etc. The source releases new data each month, so feel free to check your models' performances against new data as it comes out. I will update the files here every 3 to 6 months depending on how things go.

A future plan is to build a SQLite database so a vast array of queries can be run against the data. The data in it its current time series format is not conducive for this, so coming up with a workable structure for the tables is the first step towards this goal. If you have any suggestions for how I can improve the data presentation, or anything that you would like me to add, please let me know. Looking forward to seeing the questions that we can answer together!

In 2023, the estimated number of scheduled passengers boarded by the global airline industry amounted to approximately *** billion people. This represents a significant increase compared to the previous year since the pandemic started and the positive trend was forecast to continue in 2024, with the scheduled passenger volume reaching just below **** billion travelers. Airline passenger traffic The number of scheduled passengers handled by the global airline industry has increased in all but one of the last decade. Scheduled passengers refer to the number of passengers who have booked a flight with a commercial airline. Excluded are passengers on charter flights, whereby an entire plane is booked by a private group. In 2023, the Asia Pacific region had the highest share of airline passenger traffic, accounting for ********* of the global total.

The database consists of a panel of domestic routes in Brazil from January 2018 to December 2019, comprised of passenger flights in the domestic civil aviation market. The flights operated between 30 airports (870 routes) serving Brazil’s capitals. Airfare price data from the "domestic passenger air transport fares" section of ANAC has been grouped into averages by route, month, and airline. Such data are part of the ANAC database of Marketable Airfare Microdata containing airfare information for all domestic and international flights (monetary value and number of seats sold) disaggregated by company, airport pair, and month. Regarding the control variables, data were extracted from the websites of the IBGE , the Ministry of Tourism, and the ANP .

To assist our country's aviation manufacturers in understanding the historical output value of the domestic aviation industry, a survey has been conducted on our country's aviation industry manufacturers since 1991, and the annual output value of the industry has been compiled to assist relevant manufacturers in upgrading their technical capabilities, enhancing their position in the international supply chain, and promoting the development of our country's aviation industry. In line with the government's promotion of open data measures, the Ministry of Economic Affairs, Industrial Development Bureau, has opened up the historical output value data of our country's aviation industry from today onwards, and welcomes all sectors to download and use it.

The number of flights performed globally by the airline industry has increased steadily since the early 2000s and reached **** million in 2019. However, due to the coronavirus pandemic, the number of flights dropped to **** million in 2020. The flight volume increased again in the following years and was forecasted to reach ** million in 2025.

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Our Flight Events data feed combines Spire Global satellite/terrestrial ADS-B flight event data with ch-aviation’s fleet, operator, and airport data providing an overview of all flights operated by airlines, business and general aviation players on a daily basis.

The value of our Flight Events data feed lies in its high-resolution integration of ADS-B flight tracking with ch-aviation’s comprehensive aircraft and operator data, delivering unmatched visibility into global aircraft movements. By identifying the aircraft type and registration for approximately 98% of all ADS-B-tracked flights, we offer an industry-leading solution for lessors, insurers, airports, OEMs, and analysts seeking precise, reliable, and actionable aviation intelligence.

• High-Resolution ADS-B Integration - Satellite and terrestrial ADS-B flight tracking combined with enriched aircraft and operator data for maximum accuracy and visibility • Comprehensive Aircraft Identification - Aircraft type and registration identified for approximately 98% of all ADS-B-tracked flights, using proprietary matching with ch-aviation data and supplementary publicly available authority data sources. • Global Flight Coverage - Tracks approximately 160,000–190,000 flights per day across commercial aviation, business jet, and general aviation sectors worldwide. • ACMI (Wet-Lease) and Cargo Customer Tracking - Detailed monitoring of ACMI operations, including identification of wet-lease activity between different operators as well as cargo customers identifying flights operated for integrators like DHL Express or FedEx as well as cargo customers such as Amazon. • Aircraft Utilisation Tracking - Tracking of flight hours and cycles at both the operator and individual tail number (aircraft) level • Matched Operator and Aircraft Data - Every flight is linked to comprehensive ch-aviation datasets, including aircraft ID, history, operator, variant, callsign, and airport details allowing customers to leverage the industry’s most comprehensive integration between ADS-B flight event and fleet/operator/airport data. • Fallback Data Enrichment - Where ch-aviation data is unavailable, civil aviation authority and ANSP sources are used to ensure continuity in aircraft identification and data accuracy. • Use Case-Driven Insights - Tailored for industry stakeholders like lessors, insurers, OEMs, airports, and analysts seeking operational, commercial, and technical flight data intelligence.

ch-aviation integrates its Commercial Aviation Aircraft Data and Business Jet Aircraft Data with Spire Global’s satellite-based ADS-B data that is fused by Spire with terrestrial feeds from AirNav and Wingbits.

This data is enriched with mapped callsigns, corrected hexcodes, regional partnership decoding, and identification of wet-leases and cargo customers, enabling detailed insight into each individual flight.

Where ch-aviation data is unavailable, public data from civil aviation authorities and ANSPs is used to ensure broad and reliable aircraft identification and coverage.

The data set is available historically going back to January 1, 2018.

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Transportation and communication are crucial areas within analytics, especially in tackling safety and environmental challenges associated with the rapid expansion of urban centers and rising air traffic. One of the major hazards in aviation is bird strikes—collisions between aircraft and birds or other wildlife—which present a significant risk. These incidents can inflict severe damage on aircraft, particularly jet engines, and have even led to fatal accidents. Bird strikes are most common during critical flight stages such as takeoff, ascent, approach, and landing, when aircraft operate at lower altitudes where bird activity is more frequent.

The dataset provided by the FAA, covering incidents from 2019 to 2024, offers a comprehensive overview of bird strikes in the U.S. It includes detailed visualizations and analyses across several key areas:

• Trends Over Time: Yearly distribution of bird strike incidents.
• Airline Impact: Analysis of the top 10 U.S. airlines affected by bird strikes.
• Airport Incidents: Identification of the 50 U.S. airports with the highest frequency of bird strike incidents.
• Economic Impact: Yearly costs incurred by airlines and the aviation industry due to bird strikes.
• Timing and Altitude: When and at what altitude most bird strikes occur.
• Flight Phase: The phase of flight during which strikes are most likely to happen.
• Impact Analysis: How bird strikes affect flight operations, including aircraft damage.
• Pilot Awareness: Correlation between pilot knowledge of potential bird strike risks and the severity of the incidents.

This dataset offers valuable insights into bird strike patterns, focusing on factors such as aircraft type, location, flight phase, and the specific species involved. By analyzing these variables, it helps identify risk factors and trends, supporting the development of strategies to reduce the frequency and impact of bird strikes, ultimately enhancing aviation safety and risk mitigation.

Features: - AircraftType: The type of aircraft involved in the bird strike incident (e.g., "Airplane"). - AirportName: The name of the airport where the bird strike occurred (e.g., "LAGUARDIA NY", "DALLAS/FORT WORTH INTL ARPT"). - AltitudeBin: The altitude range (in feet) at which the bird strike occurred, divided into bins (e.g., "(1000, 2000]", "(30, 50]"). - MakeModel: The specific make and model of the aircraft involved (e.g., "B-737-400", "MD-80", "A-300"). - NumberStruck: The number of birds that were struck during the incident (e.g., "Over 100", "1", "26"). - NumberStruckActual: The actual number of birds that were struck during the incident (e.g., 859, 424, 261). - Effect: The effect of the bird strike on the aircraft, indicating whether it caused any damage or not (e.g., "Engine Shut Down", "No damage", "Caused damage"). - FlightDate: The date of the bird strike incident (e.g., "11/23/00 0:00"). - Damage: A description of the damage caused by the bird strike (e.g., "Caused damage", "No damage"). - Engines: The number of engines on the aircraft involved in the bird strike (e.g., 2 engines). - Operator: The airline or operator of the aircraft involved in the bird strike (e.g., "US AIRWAYS", "AMERICAN AIRLINES", "ALASKA AIRLINES"). - OriginState: The U.S. state where the aircraft originated (e.g., "New York", "Texas", "Washington"). - FlightPhase: The phase of flight during which the bird strike occurred (e.g., "Climb", "Landing Roll", "Approach", "Take-off run") - ConditionsPrecipitation: The weather condition related to precipitation at the time of the bird strike (e.g., "None", "Some Cloud"). - RemainsCollected?: Indicates whether bird remains were collected after the strike (e.g., "True" or "False"). - RemainsSentToSmithsonian: Indicates whether the bird remains were sent to the Smithsonian Institution for study (e.g., "True" or "False"). - Remarks: Additional comments or notes related to the incident, including specific details like the number of birds involved, actions taken, or other observations (e.g., "FLYING UNDER A VERY LARGE FLOCK OF BIRDS", "BIRD REMAINS ON F/O WINDSCREEN"). - WildlifeSize: The size of the bird or wildlife involved in the strike (e.g., "Small", "Medium"). - ConditionsSky: The sky condition at the time of the bird strike (e.g., "No Cloud", "Some Cloud"). - WildlifeSpecies: The species of the bird or wildlife involved in the strike (e.g., "European starling", "Rock pigeon", "Unknown bird - medium"). - PilotWarned: Indicates whether the pilot was warned about the potential for a bird strike (e.g., "Y" for Yes, "N" for No). - Cost: The cost incurred as a result of the bird strike (e.g., financial cost to repair damage or related expenses, usually in monetary value like 30,736). - Altitude: The specific altitude at which the bird strike occurred, typically in feet (e.g., 1500 feet, 50 feet). - PeopleInjured: The number of people injure...

Airline Loyalty Program Promotion Dataset

This dataset contains information about customer activity and demographics related to an airline's loyalty program, including a promotional campaign aimed at enhancing program enrollment.

Files

1. Customer Flight Activity.csv

2. Customer Loyalty History.csv

Context

The airline implemented a promotional campaign (2018 Promotion) aimed at enhancing program enrollment. The dataset encompasses information regarding: - Customer flight activity and loyalty points - Program signups and enrollment details - Cancellations within the loyalty program - Comprehensive customer demographics

Potential Use Cases

• Analyze the effectiveness of the promotional campaign
• Predict customer churn/cancellations
• Identify high-value customer segments
• Understand factors influencing loyalty program engagement
• Optimize loyalty point redemption strategies

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The turnover of the air transport industry in Germany increased by 9.7 billion euros (+46.5 percent) in 2022 in comparison to the previous year. With 30.51 billion euros, the turnover thereby reached its highest value in the observed period. For the purpose of Eurstat Dataset NACE Rev.2 Section K turnover comprises the totals invoiced by the observation unit during the reference period, which corresponds to market sales of goods or services supplied to third parties.Find more statistics on the air transport industry in Germany with key insights such as number of enterprises, production value, and personnel costs.

The Airport Database and Resource Management (ADRM) market is experiencing robust growth, driven by the increasing need for efficient airport operations and enhanced passenger experience. The global market, estimated at $2 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 10% from 2025 to 2033, reaching approximately $5 billion by 2033. This expansion is fueled by several key factors. Firstly, the surge in air travel globally necessitates sophisticated systems to manage resources effectively, reducing delays and optimizing workflows. Secondly, the adoption of advanced technologies like Artificial Intelligence (AI) and Machine Learning (ML) within ADRM solutions is improving data analysis and predictive capabilities, leading to better resource allocation and improved decision-making. Furthermore, rising security concerns are driving demand for robust database management systems that can efficiently handle vast amounts of passenger and operational data, while ensuring compliance with stringent regulations. The increasing focus on sustainability within airports also contributes to market growth, as ADRM solutions facilitate optimized energy consumption and reduced waste. Segmentation within the ADRM market reveals strong growth in both software and service components. Software solutions, offering data analytics and predictive modeling capabilities, are experiencing higher demand due to their ability to provide actionable insights. Civil airports represent a larger market segment compared to military airports, primarily due to the higher volume of passenger traffic and operational complexities. Key players like SITA, Rockwell Collins, Amadeus, Sabre, and Thales are strategically investing in research and development to enhance their product offerings, fostering competition and driving innovation within the market. Regional analysis indicates North America and Europe are currently the dominant markets, driven by technological advancements and high adoption rates. However, rapid infrastructure development in Asia-Pacific and the Middle East & Africa is expected to create lucrative growth opportunities in these regions over the forecast period. Challenges remain, including the high initial investment costs associated with implementing ADRM systems and the need for skilled personnel to operate and maintain these complex technologies.

In 2021, the turnover of the air transport industry in the Netherlands decreased by 4.5 billion euros (-39.07 percent) since 2019. As a result, the turnover in the Netherlands saw its lowest number in 2021 with 6.9 billion euros. For the purpose of Eurstat Dataset NACE Rev.2 Section K turnover comprises the totals invoiced by the observation unit during the reference period, which corresponds to market sales of goods or services supplied to third parties.Find more statistics on the air transport industry in the Netherlands with key insights such as number of enterprises, production value, personnel costs, and number of employees.

The Airport Operational Database (AODB) market is experiencing robust growth, driven by the increasing need for efficient airport operations and enhanced passenger experience. The market, estimated at $2 billion in 2025, is projected to expand significantly over the forecast period (2025-2033), fueled by a Compound Annual Growth Rate (CAGR) of approximately 12%. This growth is primarily attributed to several factors. Firstly, the rising passenger traffic globally necessitates streamlined processes for flight scheduling, resource allocation, and passenger services. Secondly, advancements in data analytics and the integration of AODB with other airport systems are optimizing operational efficiency and reducing delays. The increasing adoption of cloud-based AODB solutions and the development of innovative features, such as real-time data visualization and predictive analytics, are further propelling market expansion. Finally, stringent regulatory compliance requirements and the focus on improving security are contributing to increased AODB adoption across various airport segments. The market segmentation reveals a significant demand across both military and civil applications, with the civil aviation sector dominating the market share. Within civil applications, Flight Information Management and Passenger Information Management systems hold the largest market segments, followed by Security Management. Geographic analysis shows strong market penetration in North America and Europe, driven by technologically advanced infrastructure and high passenger volumes. However, significant growth potential exists in the Asia-Pacific region due to rapid infrastructure development and increasing air travel demand in countries like China and India. While the market faces challenges such as high initial investment costs and integration complexities, the long-term benefits of improved operational efficiency, reduced costs, and enhanced passenger satisfaction are expected to overcome these restraints, fueling consistent market growth throughout the forecast period.

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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.

https://www.hackerearth.com/challenges/hackathon/enter-the-travel-verse/

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The goal is to identify a trend that leads to a new prediction using ARC’s data to incorporate it into a marketable data product within the B2B or B2B2C space.

Your task is to find creative ways to apply the vast data store from historical trends mapped into predictive analytics to specific recommendations for consumers and suppliers of air travel — the potential has no limit.

The Challenge - Review the provided airline ticketing dataset below Identify a problem in the travel and tourism industry where advanced awareness of current and future trends using airline ticketing data will solve. - Identify the audience in the B2B or B2B2C space that would find value in the solution. - Using Machine learning, data science technologies and/or advanced analytics to develop a solution that solves the problem that you have identified and defined. (For example, recommender systems, predictive analytics, etc.) - Create an application prototype (program, website, API etc.) and/or visual aid (such as a dashboard or video presentation) to demonstrate the business value of the proposed solution.

Airport Synthetic Data Generation Market Outlook

According to the latest research, the global airport synthetic data generation market size in 2024 is valued at USD 1.42 billion. The market is experiencing robust growth, driven by the increasing adoption of artificial intelligence and machine learning in airport operations. The market is projected to reach USD 6.81 billion by 2033, expanding at a remarkable CAGR of 18.9% from 2025 to 2033. One of the primary growth factors is the escalating need for high-quality, diverse datasets to train AI models for security, passenger management, and operational efficiency within airport environments.

Growth in the airport synthetic data generation market is primarily fueled by the aviation industry’s rapid digital transformation. Airports worldwide are increasingly leveraging synthetic data to overcome the limitations of real-world data, such as privacy concerns, data scarcity, and high labeling costs. The ability to generate vast amounts of representative, bias-free, and customizable data is empowering airports to develop and test AI-driven solutions for security, baggage handling, and passenger flow management. As airports strive to enhance operational efficiency and passenger experience, the demand for synthetic data generation solutions is expected to surge further, especially as regulatory frameworks around data privacy become more stringent.

Another significant driver is the growing sophistication of cyber threats and the need for advanced security and surveillance systems in airport environments. Synthetic data generation technologies enable the creation of diverse and complex scenarios that are difficult to capture in real-world datasets. This capability is crucial for training robust AI models for facial recognition, anomaly detection, and predictive maintenance, without compromising passenger privacy. The integration of synthetic data with real-time sensor and video feeds is also facilitating more accurate and adaptive security protocols, which is a top priority for airport authorities and government agencies worldwide.

Moreover, the increasing adoption of cloud-based solutions and the evolution of AI-as-a-Service (AIaaS) platforms are accelerating the deployment of synthetic data generation tools across airports of all sizes. Cloud deployment offers scalability, flexibility, and cost-effectiveness, enabling airports to access advanced synthetic data capabilities without significant upfront investments in infrastructure. Additionally, the collaboration between technology providers, airlines, and regulatory bodies is fostering innovation and standardization in synthetic data generation practices. This collaborative ecosystem is expected to drive further market growth by enabling seamless integration of synthetic data into existing airport management systems.

From a regional perspective, North America currently leads the airport synthetic data generation market, accounting for the largest share in 2024. This dominance is attributed to the presence of major technology vendors, high airport traffic, and early adoption of AI-driven solutions. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, fueled by rapid infrastructure development, increased air travel demand, and government initiatives to modernize airport operations. Europe, Latin America, and the Middle East & Africa are also exhibiting steady growth, supported by investments in smart airport projects and digital transformation strategies.

Component Analysis

The airport synthetic data generation market by component is segmented into software and services. Software solutions dominate the market, as they form the backbone of synthetic data generation, offering customizable platforms for data simulation, annotation, and validation. These solutions are crucial for generating large-scale, high-fidelity datasets tailored to specific airport applications, such as security, baggage handling, and passenger analytics. Leading software providers are continuously enh

The turnover of the air transport industry in Austria increased by 1.7 billion euros (+71.39 percent) in 2022. Therefore, the turnover in Austria reached a peak in 2022 with 4 billion euros. For the purpose of Eurstat Dataset NACE Rev.2 Section K turnover comprises the totals invoiced by the observation unit during the reference period, which corresponds to market sales of goods or services supplied to third parties.Find more statistics on the air transport industry in Austria with key insights such as number of enterprises, production value, personnel costs, and number of employees.

The NARSTO_EPA_SS_ST_LOUIS_AIR_CHEM_PM_MET_DATA were obtained between April 11, 2001 and July 21, 2003 during the St. Louis - Midwest Supersite program.The overall goal of the St. Louis - Midwest Supersite was to conduct aerosol physical and chemical measurements needed by the health effects community, the atmospheric science community and the regulatory community to properly assess the impact of particulate matter exposure on human health and to develop control strategies to mitigate these effects. Metropolitan St. Louis is a major population center well isolated from other urban centers of even moderate size, and is impacted by both distant and local sources. Local industry includes manufacturing,refining, and chemical plants. St. Louis is climatologically representative of the country's eastern interior, affected by a wide range of synoptic weather patterns and free of localized influences from the Great Lakes, Ocean, Gulf, and mountains. It accordingly provides an ideal environment for studying the sources, transport, and properties of ambient particles.The initial data types included:1) 5-minute PM 2.5 black carbon (880 nm) and uv-absorbing carbon (370 nm) measured by a Magee Scientific Aethalometer (Model AE-21).2) 1-hour PM 2.5 elemental carbon and blank-corrected organic carbon from semicontinuous thermo-optical analysis by the ACE-ASIA method.3) 24-hour PM 2.5 elemental carbon and organic carbon (both blank-corrected) from integrated filter with offline thermo-optical analysis by the ACE-ASIA method.4) 30-minute PM 2.5 metal composition from samples collected with a Semicontinuous Elements in Aerosol Sampler (SEAS) II.5) 5-minute meteorological data (wind, temperature, RH, solar radiation, atmospheric pressure, and precipitation) measured with a Climatronics anemometer, wind vane, thermocouple, lithium chloride sensor, pyranometer, barometer, and tipping bucket.6) 24-hour PM 1.0 filter mass concentration measured by sharp cut cyclone and gravimetric analysis.7) 1-hour PM 2.5 mass measured by an Andersen Continuous Ambient Mass Monitoring System (CAMMS).8) 24-hour PM 2.5 and PM 10 filter mass by Harvard Impactors and laboratory gravimetric analysis.The U.S. EPA Particulate Matter (PM) Supersites Program was an ambient air monitoring research program designed to provide information of value to the atmospheric sciences, and human health and exposure research communities. Eight geographically diverse projects were chosen to specifically address these EPA research priorities: (1) to characterize PM, its constituents, precursors, co-pollutants, atmospheric transport, and its source categories that affect the PM in any region; (2) to address the research questions and scientific uncertainties about PM source-receptor and exposure-health effects relationships; and (3) to compare and evaluate different methods of characterizing PM including testing new and emerging measurement methods. NARSTO (formerly North American Research Strategy for Tropospheric Ozone) is a public/private partnership, whose membership spans government, the utilities, industry, and academe throughout Mexico, the United States, and Canada. The primary mission is to coordinate and enhance policy-relevant scientific research and assessment of tropospheric pollution behavior; activities provide input for science-based decision-making and determination of workable, efficient, and effective strategies for local and regional air-pollution management. Data products from local, regional, and international monitoring and research programs are available.