The Daily Fine Particulate Matter data available on CDC WONDER are geographically aggregated daily measures of fine particulate matter in the outdoor air, spanning the years 2003-2008. PM2.5 particles are air pollutants with an aerodynamic diameter less than 2.5 micrometers. Reported measures are the daily measure of fine particulate matter in micrograms per cubic meter (PM2.5) (''µg/m''³), the number of observations, minimum and maximum range value, and standard deviation. Data are available by place (combined 48 contiguous states plus the District of Columbia, region, division, state, county), time (year, month, day) and specified fine particulate matter (''µg/m''³)value. County-level and higher data are aggregated from 10 kilometer square spatial resolution grids. In a study funded by the NASA Applied Sciences Program / Public Health Program, scientists at NASA Marshall Space Flight Center / Universities Space Research Association modified the regional surfacing algorithm of Al-Hamdan et al. (2009) and used it to generate continuous spatial surfaces (grids) of daily PM2.5 for the whole conterminous U.S. for 2003-2008. Two sources of environmental data were used as input to the surfacing algorithm, US Environmental Protection Agency (EPA) Air Quality System (AQS) PM2.5 in-situ data and National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth remotely sensed data. They also identified in a Geographic Information System (GIS) the associated geographic locations of the centroids of the gridded PM2.5 dataset in terms of the counties and states they fall into to enable aggregation to different geographic levels in CDC WONDER.

Abstract

Dataset quality ***: High quality dataset that was quality-checked by the EIDC team

These are the standard time aggregations EPA calculates and stores (we do not have monthly data). All have data files grouped by parameter: Criteria Gases, and Particulates Each group has data listed by year, in reverse order, back to 1990.

Each table entry has the file name, linked to the file, the size of the (zipped) file, the number of data rows in the file, and the date the file was last modified. EPA will update these files twice per year; in the spring and fall (late May and November). Keep in mind, data collection agencies have up to 6 months to report their data.

The files are all comma separated text with a header. Each aggregate level has a different format.

Methodology

For site description data, each unique geographic location that contains monitors is called a "site" in AQS. Information about the geographic setting is store in the site record, which are presented here. A unique site is identified by the combination of state code, county code, and site number (within county). It can also be identified by the latitude and longitude.

For monitor description data, each parameter that is measured at a site is considered a "monitor" in AQS. (So a "monitor" does not necessarily correspond to a physical instrument/sampler.) AQS tracks administrative information about monitors including who operates them, the methods being used, the networks they belong to, etc. That information is available in this file. A unique monitor is identified by the combination of state code, county code, site number (within county), parameter code, and parameter occurrence code ("POC", used to differentiate when a parameter is measured more than once at a site).

For daily summary data, each daily summary file contains data for every monitor (sampled parameter) in our database for each day. These files are separated by parameter (or parameter group) to make the sizes more manageable.

This file will contain a daily summary record that is:

1) The aggregate of all sub-daily measurements taken at the monitor.

2) The single sample value if the monitor takes a single, daily sample (e.g., there is only one sample with a 24-hour duration). In this case, the mean and max daily sample will have the same value.

The daily summary files contain (at least) one record for each monitor that reported data for the given day. There may be multiple records for the monitor if:

• There are calculated sample durations for the pollutant. For example, PM2.5 is sometimes reported as 1-hour samples and EPA calculates 24-hour averages.

• There are multiple standards for the pollutant (q.v. pollutant standards).

• There were exceptional events associated with some measurements that the monitoring agency has or may request be excluded from comparison to the standard.

%3C!-- --%3E

US Air Quality Metrics Dataset (2000-2023)

Overview and Data Compilation

This dataset spans from the year 2000 to 2023, comprising around 665,414 observations across 21 columns. It provides an analysis of air quality in the United States, with an emphasis on pollutants like Nitrogen Dioxide (NO2), Sulphur Dioxide (SO2), Carbon Monoxide (CO), and Ozone (O3). The data has been continuously updated, and most recently extended to include 2023 data, enhancing its research value.

Data Sources and Acknowledgments

The foundation of this dataset lies in the data provided by the U.S. Environmental Protection Agency (EPA). Significant contributions have also been made by Kagglers BrendaSo and ANGELA KIM, enriching its scope and utility.

This dataset serves as a dynamic resource for researchers, policy-makers, and those interested in the trends and implications of air quality in the United States.

• Orignal Source: US EPA AQS Data

United States AQI: Kentucky-Indiana: Louisville/Jefferson County: Ozone data was reported at 38.000 Index in 16 May 2025. This records a decrease from the previous number of 48.000 Index for 15 May 2025. United States AQI: Kentucky-Indiana: Louisville/Jefferson County: Ozone data is updated daily, averaging 38.000 Index from Jan 1980 (Median) to 16 May 2025, with 14085 observations. The data reached an all-time high of 177.000 Index in 28 Jun 2023 and a record low of 3.000 Index in 06 Dec 2022. United States AQI: Kentucky-Indiana: Louisville/Jefferson County: Ozone data remains active status in CEIC and is reported by United States Environmental Protection Agency. The data is categorized under Global Database’s United States – Table US.ESG.E: Air Quality Index and Air Pollutants. [COVID-19-IMPACT]

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

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

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

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

Querying BigQuery tables

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

Acknowledgements

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

MOP03N_9 is the Measurements Of Pollution In The Troposphere (MOPITT) Carbon Monoxide (CO) gridded daily means (Near Infrared Radiances) version 9 data product. It contains daily mean-gridded versions of the daily Level 2 CO profile and total column retrievals. For this data product, the averaging kernels associated with each retrieval are also gridded and included in the Level 3 files. For a description of the file contents, refer to the File Spec Document. The MOPITT Level 2 Data Quality Statement contains additional information about the retrievals' quality and limitations. MOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft, on December 18, 1999. The MOPITT instrument was constructed by a consortium of Canadian companies and funded by the Space Science Division of the Canadian Space Agency. Data collection for this product is ongoing.

The daily meteorological data (temperature, relative humidity, wind direction, wind speed, rainfall), air pollution data (PM10, PM2.5, O3, CO, NO, NO2, SO2), and hospital visits for accidents (outpatient visits, emergency room visits) for each year of the 2007–2011 study period are included in the uploaded data file. There is also a README file, containing the detailed file structures for the data set, and the description for each data file. Interested readers can contact Kuo-Ying Wang at kuoying@mail.atm.ncu.edu.tw to access data as described above. Alternatively, interested readers can use following methods to obtain data (meteorology and air pollution) from Taiwan Environmental Protection Administration (EPA): 1) Click on http://taqm.epa.gov.tw/taqm/en/default.aspx; 2) Look on a list of items from the left column shown from the pop-up window. Search and click on an item called “Data Service”, which will lead to “Air Quality”. Then click on it to see “Hourly Value”. Then click on “Hourly Value” to download hourly data for all ambient air monitoring sites in Taiwan. (TAR)

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

United States AQI: Kentucky-Indiana: Louisville/Jefferson County: SO2 data was reported at 1.000 Index in 31 Aug 2023. This records an increase from the previous number of 0.000 Index for 30 Aug 2023. United States AQI: Kentucky-Indiana: Louisville/Jefferson County: SO2 data is updated daily, averaging 4.000 Index from Jan 1980 (Median) to 31 Aug 2023, with 15945 observations. The data reached an all-time high of 149.000 Index in 01 Oct 2014 and a record low of 0.000 Index in 30 Aug 2023. United States AQI: Kentucky-Indiana: Louisville/Jefferson County: SO2 data remains active status in CEIC and is reported by United States Environmental Protection Agency. The data is categorized under Global Database’s United States – Table US.ESG.E001: Air Quality Index and Air Pollutants. [COVID-19-IMPACT]

We created daily concentration estimates for fine particulate matter (PM2.5) at the centroids of each county, ZIP code, and census tract across the western US, from 2008-2018. These estimates are predictions from ensemble machine learning models trained on 24-hour PM2.5 measurements from monitoring station data across 11 states in the western US. Predictor variables were derived from satellite, land cover, chemical transport model (just for the 2008-2016 model), and meteorological data. Ten-fold spatial and random CV R2 were 0.66 and 0.73, respectively, for the 2008-2016 model and 0.58 and 0.72, respectively for the 2008-2018 model. Comparing areal predictions to nearby monitored observations demonstrated overall R2 of 0.68 for the 2008-2016 model and 0.58 for the 2008-2018 model, but we observed higher R2 (> 0.80) in many urban areas. These data can be used to understand spatiotemporal patterns of, exposures to and health impacts of PM2.5 in the western US where PM2.5 levels have been heavily impacted by wildfire smoke over this time period.

This dataset contains key characteristics about the data described in the Data Descriptor Daily PM2.5 concentration estimates by county, ZIP code, and census tract in the western US 2008-2018. Contents:

    1. human readable metadata summary table in CSV format


    2. machine readable metadata file in JSON format

Individuals’ sensitivity to climate hazards is a central component of their vulnerability to climate change. In this paper, we introduce and outline the utility of a new intraindividual variability construct, affective sensitivity to air pollution (ASAP)–defined as the extent to which an individual’s affective states fluctuate in accordance with daily changes in air quality. As such, ASAP pushes beyond examination of differences in individuals’ exposures to air pollution to examination of differences in individuals’ sensitivities to air pollution. Building on known associations between air pollution exposure and adverse mental health outcomes, we empirically illustrate how application of Bayesian multilevel models to intensive repeated measures data obtained in an experience sampling study (N = 150) over one year can be used to examine whether and how individuals’ daily affective states fluctuate with the daily concentrations of outdoor air pollution in their county. Results indicate construct viability, as we found substantial interindividual differences in ASAP for both affect arousal and affect valence. This suggests that repeated measures of individuals’ day-to-day affect provides a new way of measuring their sensitivity to climate change. In addition to contributing to discourse around climate vulnerability, the intraindividual variability construct and methodology proposed here can help better integrate affect and mental health in climate adaptation policies, plans, and programs.

MOP03J_109 is the Measurements Of Pollution In The Troposphere (MOPITT) Beta CO gridded daily means (Near and Thermal Infrared Radiances) version 109 product is an unvalidated beta product subject to recalibration, contains daily mean gridded versions of the daily Level 2 CO profile and total column retrievals. The averaging kernels associated with each retrieval are also gridded and included in the Level 3 files. Data collection for this product is ongoing.For a description of the file contents, refer to the File Spec Document. The MOPITT Level 2 Data Quality Statement contains additional information about the retrievals' quality and limitations. [From the MOPITT version 3 Level 3 Data Quality Summary] MOPITT was successfully launched into sun-synchronous polar orbit aboard Terra, NASA's first Earth Observing System spacecraft, on December 18, 1999. The MOPITT instrument was constructed by a consortium of Canadian companies and funded by the Space Science Division of the Canadian Space Agency.

United States AQI: Georgia-South Carolina: Augusta-Richmond County data was reported at 53.000 Index in 25 Nov 2025. This records a decrease from the previous number of 67.000 Index for 24 Nov 2025. United States AQI: Georgia-South Carolina: Augusta-Richmond County data is updated daily, averaging 42.000 Index from Jan 1980 (Median) to 25 Nov 2025, with 16585 observations. The data reached an all-time high of 364.000 Index in 25 Jan 2017 and a record low of 12.000 Index in 16 Sep 2018. United States AQI: Georgia-South Carolina: Augusta-Richmond County data remains active status in CEIC and is reported by United States Environmental Protection Agency. The data is categorized under Global Database’s United States – Table US.ESG.E: Air Quality Index and Air Pollutants. [COVID-19-IMPACT]

United States AQI: Georgia-South Carolina: Augusta-Richmond County: NO2 data was reported at 8.000 Index in 13 Jan 2008. This stayed constant from the previous number of 8.000 Index for 12 Jan 2008. United States AQI: Georgia-South Carolina: Augusta-Richmond County: NO2 data is updated daily, averaging 8.000 Index from Jan 1986 (Median) to 13 Jan 2008, with 5233 observations. The data reached an all-time high of 40.000 Index in 11 Sep 2002 and a record low of 0.000 Index in 31 Aug 2006. United States AQI: Georgia-South Carolina: Augusta-Richmond County: NO2 data remains active status in CEIC and is reported by United States Environmental Protection Agency. The data is categorized under Global Database’s United States – Table US.ESG.E001: Air Quality Index and Air Pollutants.

This dataset provides air quality index (AQI) data from major cities worldwide, covering the years 2015 to 2025. It is compiled from various sources, including government monitoring stations, environmental agencies, and open APIs.

The dataset includes daily AQI values along with major pollutants such as PM2.5, PM10, NO2, SO2, CO, and O3. Additional meteorological data such as temperature, humidity, and wind speed are also included to support deeper analysis.

Dataset Features: Date: The date of AQI measurement (YYYY-MM-DD). City: Name of the city where the AQI is recorded. Country: Country of the city. AQI: The daily air quality index value. PM2.5 (µg/m³): Fine particulate matter concentration. PM10 (µg/m³): Larger particulate matter concentration. NO2 (ppb): Nitrogen dioxide concentration. SO2 (ppb): Sulfur dioxide concentration. CO (ppm): Carbon monoxide concentration. O3 (ppb): Ozone concentration. Temperature (°C): Daily average temperature. Humidity (%): Daily average humidity. Wind Speed (m/s): Daily average wind speed. Potential Use Cases: ✅ Data Science & Machine Learning: Predict air quality trends, create AQI forecasting models, and build environmental monitoring applications. ✅ Health & Epidemiology: Analyze correlations between air pollution and respiratory diseases, cardiovascular conditions, and general health. ✅ Climate & Environmental Research: Study pollution patterns, seasonal variations, and their relation to climate change. ✅ Urban Planning & Policy Making: Help city planners implement better pollution control strategies.

Why This Dataset? 📌 10-year coverage (2015-2025) for long-term trend analysis. 📌 Global scope with diverse geographical representation. 📌 Multiple pollutants & weather data for comprehensive insights. 📌 Ready-to-use for ML models, EDA, and research.

United States AQI: North Carolina: Greensboro-High Point: CO data was reported at 8.000 Index in 01 Apr 2008. This records a decrease from the previous number of 10.000 Index for 31 Mar 2008. United States AQI: North Carolina: Greensboro-High Point: CO data is updated daily, averaging 15.000 Index from Dec 1989 (Median) to 01 Apr 2008, with 4648 observations. The data reached an all-time high of 57.000 Index in 16 Dec 1995 and a record low of 0.000 Index in 23 Oct 2007. United States AQI: North Carolina: Greensboro-High Point: CO data remains active status in CEIC and is reported by United States Environmental Protection Agency. The data is categorized under Global Database’s United States – Table US.ESG.E001: Air Quality Index and Air Pollutants.

United States AQI: Pennsylvania-New Jersey-Delaware-Maryland: Philadelphia-Camden-Wilmington: CO data was reported at 10.000 Index in 31 Dec 2024. This records an increase from the previous number of 6.000 Index for 30 Dec 2024. United States AQI: Pennsylvania-New Jersey-Delaware-Maryland: Philadelphia-Camden-Wilmington: CO data is updated daily, averaging 7.000 Index from Jan 1980 (Median) to 31 Dec 2024, with 16346 observations. The data reached an all-time high of 36.000 Index in 09 Mar 2017 and a record low of 2.000 Index in 10 Jan 2019. United States AQI: Pennsylvania-New Jersey-Delaware-Maryland: Philadelphia-Camden-Wilmington: CO data remains active status in CEIC and is reported by United States Environmental Protection Agency. The data is categorized under Global Database’s United States – Table US.ESG.E001: Air Quality Index and Air Pollutants. [COVID-19-IMPACT]

Context

This dataset deals with pollution in the U.S. Pollution in the U.S. has been well documented by the U.S. EPA.

Includes four major pollutants (Nitrogen Dioxide, Sulphur Dioxide, Carbon Monoxide and Ozone).

• State Code : The code allocated by US EPA to each state
• County code : The code of counties in a specific state allocated by US EPA
• Site Num : The site number in a specific county allocated by US EPA
• Address: Address of the monitoring site
• State : State of monitoring site
• County : County of monitoring site
• City : City of the monitoring site
• Date Local : Date of monitoring

The four pollutants (NO2, O3, SO2 and O3) each has 5 specific columns. For instance, for NO2:

• NO2 Units : The units measured for NO2
• NO2 Mean : The arithmetic mean of concentration of NO2 within a given day
• NO2 AQI : The calculated air quality index of NO2 within a given day
• NO2 1st Max Value : The maximum value obtained for NO2 concentration in a given day
• NO2 1st Max Hour : The hour when the maximum NO2 concentration was recorded in a given day

Original Data

Acknowlegement

Foto von Chris LeBoutillier auf Unsplash

Context:

Carbon Monoxide (CO) is a colorless, odorless gas that can be harmful when inhaled in large amounts. CO is released when something is burned. The greatest sources of CO to outdoor air are cars, trucks and other vehicles or machinery that burn fossil fuels. A variety of items in your home such as unvented kerosene and gas space heaters, leaking chimneys and furnaces, and gas stoves also release CO and can affect air quality indoors.

Content:

The daily summary file contains data for every monitor (sampled parameter) in the Environmental Protection Agency (EPA) database for each day. This file will contain a daily summary record that is:

1. The aggregate of all sub-daily measurements taken at the monitor.
2. The single sample value if the monitor takes a single, daily sample (e.g., there is only one sample with a 24-hour duration). In this case, the mean and max daily sample will have the same value.

Within the data file you will find these fields: 1. State Code: The Federal Information Processing Standards (FIPS) code of the state in which the monitor resides.

1. County Code: The FIPS code of the county in which the monitor resides.

2. Site Num: A unique number within the county identifying the site.

3. Parameter Code: The AQS code corresponding to the parameter measured by the monitor.

4. POC: This is the “Parameter Occurrence Code” used to distinguish different instruments that measure the same parameter at the same site.

5. Latitude: The monitoring site’s angular distance north of the equator measured in decimal degrees.

6. Longitude: The monitoring site’s angular distance east of the prime meridian measured in decimal degrees.

7. Datum: The Datum associated with the Latitude and Longitude measures.

8. Parameter Name: The name or description assigned in AQS to the parameter measured by the monitor. Parameters may be pollutants or non-pollutants.

9. Sample Duration: The length of time that air passes through the monitoring device before it is analyzed (measured). So, it represents an averaging period in the atmosphere (for example, a 24-hour sample duration draws ambient air over a collection filter for 24 straight hours). For continuous monitors, it can represent an averaging time of many samples (for example, a 1-hour value may be the average of four one-minute samples collected during each quarter of the hour).

10. Pollutant Standard: A description of the ambient air quality standard rules used to aggregate statistics. (See description at beginning of document.)

11. Date Local: The calendar date for the summary. All daily summaries are for the local standard day (midnight to midnight) at the monitor.

12. Units of Measure: The unit of measure for the parameter. QAD always returns data in the standard units for the parameter. Submitters are allowed to report data in any unit and EPA converts to a standard unit so that we may use the data in calculations.

13. Event Type: Indicates whether data measured during exceptional events are included in the summary. A wildfire is an example of an exceptional event; it is something that affects air quality, but the local agency has no control over. No Events means no events occurred. Events Included means events occurred and the data from them is included in the summary. Events Excluded means that events occurred but data form them is excluded from the summary. Concurred Events Excluded means that events occurred but only EPA concurred exclusions are removed from the summary. If an event occurred for the parameter in question, the data will have multiple records for each monitor.

14. Observation Count: The number of observations (samples) taken during the day.

15. Observation Percent: The percent representing the number of observations taken with respect to the number scheduled to be taken during the day. This is only calculated for monitors where measurements are required (e.g., only certain parameters).

16. Arithmetic Mean: The average (arithmetic mean) value for the day.

17. 1st Max Value: The highest value for the day.

18. 1st Max Hour: The hour (on a 24-hour clock) when the highest value for the day (the previous field) was taken.

19. AQI: The Air Quality Index for the day for the pollutant, if applicable.

20. Method Code: An internal system code indicating the method (processes, equipment, and protocols) used in gathering and measuring the sample. The method name is in the next column.

21. Method Name: A short description of the processes, equipment, and protocols used in gathering and measuring the sample.

22. Local Site Name: The name of the site (if any) given by the State, local, or tribal air pollution control agency that operates it.

23. Address: The approximate street address of the monitoring site.

24. State Name: The name of the state where the monitoring site is located.

25. County Name: The name of the cou...