Quality Assurance for Air Sensors
On this page:
Making a Plan
Working with Data
- Quality Assurance Basics
- Common Quality Control Checks
- 2023 Air Sensor Quality Assurance Workshop
- Additional Resources
Making a Plan
Quality Assurance Project Plans (QAPPs)
What is a Quality Assurance Project Plan (QAPP)? A QAPP is a written document that explains how organizations ensure – using quality assurance (QA) and quality control (QC) activities – that the data collected can be used for its intended purpose. A QAPP gives more confidence that the data collected will meet the project objectives and help others understand the data quality.
A QAPP is for everyone! Whether mounting a single sensor on the side of a house to monitor air pollutants in wildfire smoke or deploying an air sensor network to assess air quality across a community (e.g., neighborhood, town, city, region) a detailed plan will help ensure that all tasks are completed and all sensors and supporting instruments (e.g., reference monitors, weather instruments) are collecting useful data for the desired application.
Some advantages to writing a QAPP are:
- All team members know their roles and responsibilities
- All team members agree to the plan for collecting quality data
- The project design can be shared with others (e.g., local air monitoring agency, university professor, environmental consultant) to solicit advice before investing time and money
- Potential problems can be identified at an early stage
- Processes are documented to assist with future data interpretation and presentation
The table below covers many topics that should be considered and documented in your project QAPP. The information in parenthesis corresponds to additional information on this topic within EPA’s Enhanced Air Sensor Guidebook.
Common Topics and Information to Include in a QAPP
Purpose and Organization Topics
| Topic | Information to Include |
|---|---|
| Purpose for monitoring | State the specific environmental topic/problem that is to be investigated, the decision to be made, or the outcome to be achieved using the sensor data. (See Section 3.2) |
| Project/task organization | Determine the roles and responsibilities of all key players in the project. |
| Engagement with local partners | Solicit insights from Tribal/state/local air quality or health agencies, universities, research organizations, or others. Engage them early and discuss the project and desired outcomes. (See Appendix B) |
| Project/task description | Summarize the work, objectives, schedule (timeline), and expected outcomes. |
| Data quality objectives and criteria | Define: 1) Why data are needed? 2) Does this data already exist? 3) What measurements are needed and what do they need to represent? 4) Is there a certain level of accuracy needed? (See Section 3.2) |
| Contingency planning | Determine backup plans if something changes during the project (e.g., What happens if staff depart? How to deal with sensors that fail? What happens if my site or equipment are vandalized?). Prepare for various potential outcomes and plan troubleshooting plans. |
| Training and experience | Identify any training and/or certification requirements (e.g., sensor operation, programming courses). |
| Documentation and records | Determine how air monitoring activities will be documented. This could include standard operating procedures (SOPs), quality assurance/quality control (QA/QC) forms, site logbooks, etc. |
| Topic | Information to Include |
|---|---|
| Measurement methods | Describe equipment and measurement methods used in the monitoring network. (See Section 2.3) |
| Siting criteria | Discuss the criteria for placing air sensors also considering site security/safety. (See Section 3.5.1) |
| Monitoring location(s) | Discuss the monitoring location or locations (for a network) selected and rationale. (See Section 3.5) |
| Instrument/equipment testing and inspection | Identify and describe how you will select the air sensor(s) and test and inspect them to determine that they are working properly. (See Section 3.4) |
| Instrument/equipment calibration and/or correction | Determine collocation locations and establish the calibration and/or collocation and data correction methods. (See Section 3.6) |
| Other data needed | Identify types of data that originate from other sources that may be used in the analysis. These data could include nearby reference monitor data, weather data, and/or traffic counts. |
Collection Topics
| Topic | Information to Include |
|---|---|
| Quality control (QC) | Describe the types of QC checks performed. (See Section 3.7.2) |
| Data processing and access | Understand how the data are processed, stored, and adjusted. Decide how you will access the data and who will own the data. (See Section 3.7.3 and Appendix C) |
| Verification and validation methods | Describe the methods or procedures that will be used to verify and validate data during the collection period. (See Section 3.7.2) |
| Data management | Determine how the air monitoring data will be managed, tracing the path of data generation in the field to the final data use and end storage. (See Section 3.7.3 and Appendix C) |
Evaluation Topics
| Topics | Information to Include |
|---|---|
| Compare results with original objective(s) | Describe how the results obtained from this project will be reconciled with the project’s data quality objective(s). (See Section 3.2) |
| Evaluation, communication, and action | Describe how the results of the air monitoring project will be used. (See Section 3.8) |
QAPP Guidance and Templates
Quality Assurance Handbook and Toolkit for Participatory Science Projects
- Website providing a handbook, examples, templates, videos, an orientation guide and factsheets designed to help participatory science project document data quality and improve the usefulness of the data collected
Examples for Citizen Science Quality Assurance and Documentation, U.S. Environmental Protection Agency, EPA 206-B-18-001, March 2019
- Collection of examples that provide tools and procedures to help community science organizations properly document the quality of data
Templates for Citizen Science Quality Assurance and Documentation, U.S. Environmental Protection Agency, EPA 206-B-18-001, March 2019
- Templates that provide tools and procedures to help properly document the quality of data
- Editable templates
Community Science Air Monitoring Quality Assurance Plan Templates
- Guidance, provided by the New Jersey Department of Environmental Protection Division of Air Quality – Air Monitoring, on using air sensors for community projects; includes approaches to using sensors, types of sensors available, interpreting sensor data, four types of sensor projects and data quality assurance plan templates for each, and other helpful links
Quality Assurance Project Plan Standard, U.S. Environmental Protection Agency, Directive No. CIO 2105-S-02.0, July 2023
- Defines the minimum requirements for Quality Assurance Project Plans (QAPPs) for all EPA and non-EPA organizations performing environmental information gathering; QAPPs are an important part of the planning process for air quality monitoring projects
- QAPP Standard
- QAPP Standard Fact Sheet
- QAPP Standard FAQs
Quality Assurance Requirements for Organizations receiving EPA Financial Assistance
- Provides information about the requirements and documentation needed as well as key references available to assist applicants and recipients of financial assistance from EPA
Working with Data
Quality Assurance Basics
There are many activities involved in data collection beyond simply turning on the sensor and collecting measurements. Users will need additional preparation before and during data collection activities to ensure that useful data are collected. These tasks include:
Frequent data review. Reviewing data frequently (e.g., daily, weekly) lets you detect problems early, notice trends in the data, ensure that maintenance activities are completed, and become familiar with recurring patterns. For instance, creating a time series plot (i.e., a plot with the pollutant concentrations on the y-axis and the date and time on the x-axis) can be a good place to start. You might see typical patterns and develop a general sense of air quality in an area under different conditions. When typical conditions are known, it becomes easier to identify times when sensor readings are atypical and why these atypical readings are occurring.
Maintenance. Air sensors require preventive maintenance to ensure proper functionality and reliable data collection. Air sensor maintenance can include regularly scheduled cleaning of surfaces or inlets to prevent the buildup of bugs or dust, replacing filters, or replacing sensor detector components as they age. Maintenance can also include examining site conditions for any changes (e.g., vandalism, overgrown trees).
Troubleshooting. Problems with air sensors will likely occur and may require troubleshooting to resolve the problem and to continue collecting data. Troubleshooting might include visiting the sensor, contacting the manufacturer, seeking guidance from other air sensor users, or other activities.
Quality control (QC) checks. It is important to frequently review the data for problems such as outliers, drift, etc. Some sensor manufacturers may offer a software package or online user interface that offers some automated checks of the data to assist in this process. Note that automated checks may not catch subtle problems or may flag a real-life event as bad data. Do not solely rely on automatic QC checks to identify issues with the data.
Periodic collocation. Collocation can help quantify the accuracy of a sensor while periodic checks can help ensure that accuracy is not changing over time or in different conditions. Users should develop a periodic collocation approach to check the quality of the air sensor’s measurements.
Common Quality Control Checks
Quality Control (QC) procedures are activities that include collocation, correction of data, maintenance, automatic data checks, data review, and any other steps taken to reduce error from the sensor or instruments during a project.
Table below details recommended QC checks that can be performed on an air sensor and its data. The checks are designed to catch problems early, correct them, and produce a useful, high-quality data set.
| Quality Control Check | Description |
|---|---|
| Units | Check that the sensor reports data in the correct units of measure. |
| Time | Check that the sensor reports data at the correct time and in the right time zone. Check times after any seasonal time changes (e.g., daylight savings time). |
| Timestamp | Determine the timestamp, which is the time when data are tagged by an instrument. Measurements and data averages will have times that either represent the beginning of the time period (time beginning) or the end of the period (time ending). |
| Matching Timestamps | Check the time zones and timestamps for each dataset to make sure they are similar when comparing measurements made by different instruments. |
| Data Review | Check data frequently (e.g., daily, weekly) to detect problems early, identify trends in the data, ensure that maintenance activities were completed, and become familiar with recurring patterns. |
| Data Completeness | Completeness measures the amount of data a sensor collects compared to the amount of data that was possible to collect if the sensor operated continuously, without data outages, during a period (e.g., 1-hour, 1-day). |
| Automatic Data Checks* | Software can check data for problems and outliers. Note that some data checks may not catch subtle problems or may flag an infrequent but real event. Do not solely rely on automatic QC to check data quality. |
| Manual Data Validation | Evaluate the data quality during the collection phase of the project to identify and correct potential problems that may arise. To accomplish this, analyze data to identify seasonal, day/night, and weekday/weekend patterns and weather changes. An absence of expected patterns may indicate a problem with the sensor or with the measurement approach. |
*Common automatic data checks include:
- Range. Check the minimum and maximum concentrations expected and recognize some air sensors may report slightly negative values.
- Rate of Change. Check the difference in data values from an air sensor between two consecutive time periods (e.g., hours). Flag the data if the difference, or rate of change, exceeds the value set by the user.
- Sticking. Check if data values are “stuck” at the same value for a specified number of hours. Establish criteria for the number of consecutive hours for which data can be reported at the same value.
- Duplicate sensor comparison. Some sensors incorporate two identical sensing components inside which provide two separate pollutant concentration measurements. Check the agreement between the readings and flag data if the difference exceeds an acceptable threshold.
- Buddy System. Check the difference between data values obtained from a single location and the average data values obtained from other nearby locations.
- Parameter-to-Parameter. Check two or more pollutants for known or expected physical or chemical relationships.
2023 Air Sensor Quality Assurance Workshop
The amount of data collected using air sensors can be overwhelming and, despite our best efforts, air sensor data can be messy. Potential sensor data issues may include:
- Poorly formatted data
- Inaccuracy
- Outliers
- Drift
- Interferents or influences
- Repeated values
- Missing data
Methods for identifying and addressing issues within sensor data are still under development. In July 2023, the EPA hosted the Air Sensors Quality Assurance (QA) Workshop to gather air sensor technical experts to discuss established and emerging QA methods to help support the user community in collecting accurate and actionable air sensor data.
Additional details and presentations can be found on the Air Sensors Quality Assurance Workshop Presentations website.
Additional Resources
EPA Air Sensor Collocation Instruction Guide, U.S. Environmental Protection Agency, Office of Research and Development
- Resource provides background information, links to web-based supporting materials, and instructions for evaluating the performance of air sensors by comparing the measurements made by collocated sensors and reference instruments.
EPA Air Sensor Collocation Macro Analysis Tool
- Excel-based tool that helps users compare data from air sensors to data from reference instruments.
Community in Action: A Comprehensive Guidebook on Air Quality Sensors, South Coast Air Quality Management District (South Coast AQMD), Air Quality Sensor Performance Evaluation Center (AQ-SPEC), September 2021
- Guidebook for community organizations that covers planning for monitoring using sensors; sensor deployment, use, and maintenance; and data handling, interpretation, and communication.
South Coast AQMD Low-Cost Sensor Data Analysis Guide
- Guide that provides some brief instructions to help community scientists interact with the data they are collecting as well as some questions to help guide their analysis.
- Specifies the regulatory requirements for the U.S. ambient air quality monitoring network including quality assurance procedures for operating air quality monitors and handling data; methodology and operating schedules for monitoring instruments; criteria for siting monitoring instruments; and air quality data reporting requirements.
Quality Assurance Handbook for Air Pollution Measurement Systems, Volume II, Ambient Air Quality Monitoring Program, U.S. Environmental Protection Agency, EPA-454/B-17-001, January 2017
- Handbook provides additional information and guidance (including pollutant-specific spatial scale characteristics) to assist tribal, state, and local monitoring organizations in developing and implementing a quality management system for the Ambient Air Quality Surveillance Program described in 40 CFR Part 58.