Glossary of terms

List not in alphabetical order

Pollutant: Any substance, as certain chemicals or waste products, that renders the air, soil, water, or other natural resource harmful or unsuitable for a specific purpose. Specifically in our context, pollutant is the primary substance being measured by the sensors.

Meteorological Parameter: Non-pollutant parameters like temperature, humidity, atmospheric pressure, wind speed, wind direction, etc., that are typically measured alongside pollutant concentrations in the air sensors; they may or may not affect the pollutant concentration and may be used while analyzing pollutant measurement data.

Sensor: The most basic piece of hardware in a system. A chemical cell or physical device that produces an analytically useful signal by detecting or measuring the analyte (pollutant or meteorological parameter in our space).

Sensor System: An integrated set of hardware that uses one or more sensors to detect and/or measure the analyte, process signals, output parameters. May or may not include visual displays, battery for power, and ethernet or wireless internet.
Note: “Instrument” has a very similar definition, but many US-based researchers typically refer to a reference grade device when using the word “Instrument”

Sensor Network: A deliberate arrangement of connected sensor nodes measuring pollutant(s) concentration

(Sensor) Node: A point in a network of sensor devices that is capable of performing some processing, gathering sensory information and communicating with the gateway and/or other connected nodes in the network

Reference Method: Federal reference method, Federal equivalent method or test method, typically used by Governmental (e.g., State) agencies to measure pollutants

Reference Monitor: Instrument operated and maintained according to initial and ongoing Federal equivalent method (FEM)/Federal reference method (FRM) and Quality assurance (QA) protocols, often by a local, state, or federal government agency

Data Model: Software design technique for databases intended to support transaction capture and end-user queries. A data model organises data in tables, describes the relationships across data elements and tables, eliminates ambiguity and conflict, and provides efficient ways to access data according to user requirements.

Data Architecture: Defines the structure for data collection and storage along with data standards, metadata management, master data management (MDM), data quality rules and policies. Data architecture also includes data models and a high-level data flow of the system.

Data Management: Covers the end-to-end lifecycle of data, from acquisition/collection to archival and purging, and the flow of data through the various stages in the data system. Data Management includes data architecture, data governance, overall policies for handling, storing and sharing data. Data management minimizes the risks and costs of regulatory non-compliance, legal complications and security breaches.

Metadata: Metadata is data about data; it describes the data. Metadata provides context for data but metadata is not considered the principal data itself. A data system without metadata has very low chance of being highly useful. Metadata is the information and documentation which makes data understandable and shareable for users over time. Data remain usable, shareable, and understandable as long as the metadata remain accessible. Data + Context (metadata) = Information