An Ontology Design Pattern for Modeling Pollution?

Abstract

Pollution has been identi ed as a signi cant risk to global ecosystems and living beings. However, the information about pollution is fragmented and there is no meaningful organization of information despite several ongoing e orts to monitor it. Organizing the information about the pollution, such as the pollutants, their observations at di erent spatio-temporal points and the carriers in the form of an ontology will be very helpful to the applications that work with the different heterogeneous pollution data sources. We propose an ontology design pattern (ODP) for pollution that captures its general characteristics and can be used as a building block for modeling speci c categories of pollution such as air, water and soil. The Pollution ODP is available on the ODP portal at http://ontologydesignpatterns.org/wiki/ Submissions:Pollution. It is also available for public comments at https://github.com/kracr/aq-structured-platform/blob/main/On tology/PollutionODP/PollutionODP.owl with an Apache License 2.0.

Keywords

• Pollution
• Ontology Design Pattern
• Pollutants
• Air Pollution

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The introduction of substances into the environment that are harmful to the living organisms is de ned as pollution [ 11 ]. These harmful substances can be solids, liquids, or gases produced in higher than usual concentrations. These substances are referred to as pollutants. Pollution could a ect many natural resources, such as the air, water sources and soil. The need to study these pollution types is of utmost importance. Air pollution is regarded as a global health emergency and the e ect of bad air quality is deadly. It leads to asthma, other respiratory illnesses and heart disease. Air pollution is responsible for more deaths than many other risk factors, including malnutrition, alcohol use and physical inactivity [ 6 ]. Similarly, there is a pressing need to study water and soil pollution [ 1 ]. Di erent ? Copyright © 2021 for this paper by its authors. Use permitted under Creative

Commons License Attribution 4.0 International (CC BY 4.0). heterogeneous data sources are made use of to build pollution monitoring applications [ 2,3 ]. An ontology design pattern(ODP) [ 8 ] that captures the abstract details of the pollution will be bene cial to these applications. 2

Related Work

Attempts to model pollution have focused on air pollution, probably because of the readily available data of pollutant concentrations. Claudine et al. [ 12 ] built an air quality ontology and used that along with the 3D models of a city. The ontology proposed in [ 13 ] links air pollutants with meteorological factors. EnvO1 is a very broad ontology that describes the environment by focusing on biomes. Dalia et al. [ 4 ] built an air pollution ontology by focusing on species, sensors, pollutants and meteorological factors. They have designed one ontology for each of these factors. We studied these ontologies along with several data sources such as pollutant concentration2, weather data3 and wind trajectory4 to design the Pollution ODP. We also capture the spatio-temporal aspect of pollution in the ODP, which has not been considered in some of the pollution ontologies. The Pollution ODP has been annotated to indicate the ODPs that were used and submitted for a review on the ODP community portal5. 3

Pollution ODP Description

The pollution ODP makes use of the trajectory ODP [ 9 ], observation ODP [ 5 ] and the stub meta-pattern [ 10 ]. We use cpannotationschema6 to describe the intent, scenarios, consequences and components of the ODP. The schema of the pollution ODP is given in Figure 1. We describe the concepts and the properties of the pollution ODP in the subsequent sections. 3.1

Concepts

{ Pollution. It is the core concept in the ODP to represent pollution and is linked to the contributors of pollution. Some of the instances of Pollution could be air pollution, water pollution, soil pollution, space pollution, and sound pollution. { Observation. The Observation concept is modeled from the Observation ODP. It represents a spatio-temporal observation. We use it to capture the concentration and prescribed standards for a particular pollutant. With respect to the Observation ODP, Pollutant and PrescribedStandardForPollutant is the situation and TimeEntity, PlaceEntity are the parameters of the observation.

1 http://environmentontology.org/

2 https://cpcb.nic.in/ 3 https://weatherstack.com/ 4 https://www.ready.noaa.gov/HYSPLIT disp.php 5 http://ontologydesignpatterns.org/wiki/Main Page 6 http://www.ontologydesignpatterns.org/schemas/cpannotationschema.owl { DirectContributor. The DirectContributor concept represents the contributors that directly a ect the pollution. Pollutant is a subclass of DirectContributor. Some examples of pollutants include biological pollutants (viruses, pathogens, bacteria, etc.), chemical pollutants (toxic metal, radionuclides, organophosphorus compounds, gases, etc.) and physical pollutants (sound, thermal energy, space debris, etc.) present in a particular environment. { IndirectContributor. The IndirectContributor concept represents concepts that indirectly contribute to the pollution at a particular spatio-temporal point. These include environmental factors like temperature, the air or water streams owing into or out of a particular place, the socio-economic factors such as policies and demographics. We have modeled only the Carrier concept as the subclass of IndirectContributor since other indirect contributors are speci c to certain kinds of pollution. { PlaceEntity and TimeEntity. These concepts denote the place and time for a spatio-temporal observation. They are linked to the Observation and TrajectoryPoint concepts by the atPlace and atTime properties. { PrescribedStandardForPollutants. This is a stub meta-pattern [ 10 ] that can be used to describe the prescribed ranges for pollutants. For a particular location, pollutants have a de ned range of permissible concentrations that are speci ed by the various global authorities7. A standard for a pollutant may change with time and place. Hence the PrescribedStandardForPollutants concept is linked to the Observation concept by the hasObservation property. 7 2005 WHO guidelines prescribe the range for air pollutants such as particulate matter (PM), ozone (O3), nitrogen dioxide (NO2), etc., available at http://whqlibdo c.who.int/hq/2006/WHO SDE PHE OEH 06.02 eng.pdf?ua=1. { Carrier. This concept is a subclass of the IndirectContributor concept and represents the air, water, or other kinds of streams owing into or out of a particular place. It is linked to the Trajectory concept through the hasTrajectory property. Carriers are generally observed to a ect the concentration of pollutants at a particular place and are important in modeling pollutants. To represent the pollutants that might be carried through a trajectory, the Carrier concept is linked to the Pollutant concept by the carriesPollutant property. To specify the location of the source of pollutants in a carrier trajectory, nearby property can be used. This links the pollutant sources such as factories in the case of wind stream carrier or drains in the case of water stream carrier to the TrajectoryPoint. The applications that have such a requirement can make use of the nearby property, but we excluded it from the ODP because it is not su ciently general. { Trajectory. The Trajectory concept represents a set of ordered spatiotemporal points and has been directly adopted from [ 9 ]. It is linked to the TrajectoryPoint and TrajectorySegment through the hasPoint and hasSegment properties. The nextPoint property links trajectory points in the appropriate order within a trajectory. The segments in the trajectory are de ned by a starting trajectory point fxi; yi; tig and an ending trajectory point fxj; yj; tjg where ti, tj denote time points such that ti < tj. The TrajectorySegment concept represents this notion of a segment which is connected to two xes through startsFrom and endsAt properties. 3.2

Properties

{ atPlace, atTime. They connect Observation and TrajectoryPoint concepts to the PlaceEntity and TimeEntity respectively. Since an instance of Observation or TrajectoryPoint can be associated with at most one timestamp, the atPlace and atTime properties are functional. { startsFrom, endsAt. These functional properties connect a TrajectorySegment to starting and ending TrajectoryPoint representing the starting and ending point of a segment. { carriesPollutant. This property represents the pollutants that a Carrier can carry. It connects Carrier to the Pollutant concept and represents the pollutants being carried away by a carrier. The trajectory of the carrier dictates the displacement of pollutants. { hasTrajectory. This property connects the Carrier concept to the Trajectory. { hasContributor. This property connects the Pollution concept to the

Contributor concept. { hasPoint. This property connects the Trajectory with the TrajectoryPoint. { hasPrescribedStandards. This property connects the Pollutant concept to the stub PrescribedStandardForPollutant concept. { hasObservation. This property connects Pollutant and PrescribedStandardForPollutant concept to the Observation concept. It can be used to capture the concentration of pollutants or a prescribed standard for a particular pollutant with the TimeEntity and PlaceEntity as the parameters of the observation. { hasSegment. This property connects the Trajectory to the Trajectory

Segment. { nextPoint. This property links each point represented by the TrajectoryPoint to the next point forming a chain of ordered points. 3.3

Axioms of the Pollution ODP

The axioms that are part of the Pollution ODP are discussed here.

Carrier v 8carriesPollutant:Pollutant

Carrier v 9hasTrajectory:Trajectory Pollution v 9hasContributor:Contributor

Observation v 9atPlace:PlaceEntity

Observation v 9atTime:TimeEntity

TrajectoryPoint v 9hasPoint :Trajectory Trajectory v 9hasSegment:TrajectorySegment hasSegment startsFrom v hasPoint hasSegment endsAt v hasPoint (1) (2) (3) (4) (5) (6) (7) (8) (9)

Axioms 6, 7, 8 and 9 capture the relation between the Trajectory, TrajectoryPoint and the TrajectorySegment concepts. 3.4

Competency Questions

The Pollution ODP answers the following competency questions. 1. What are the contributors of the pollution? 2. What is the pollutant concentration at a particular time and place? 3. What are the carriers that contributed to the pollution? 4. What are the pollutants carried by a carrier? 5. What are the prescribed standards for a particular pollutant? 6. What is the trajectory of a carrier for a pollutant? 4

Use Cases

The Pollution ODP can be used as a building block to model various pollution types such as air, water and soil. They can be added as subclasses of the Pollution concept. An example use case of air pollution can describe the concentration of pollutants in the air at a particular spatio-temporal point through the Observation and Pollutant concepts. Similarly, the pollutants carried by the air stream can be captured by the Carrier concept. The Weather concept can be added as a subclass of IndirectContributor concept in the concrete implementation of this ODP to represent the weather related factors that contribute to air pollution. These concepts can be extrapolated to water, soil and other types of pollution as well.

Conclusion and Future Work

Pollution exists in many forms in the environment and a ects individuals as well as ecosystems. An ontology design pattern for modeling various sources and characteristics of pollution can be used e ectively as a building block by multiple applications that work with pollution data. We introduce an ODP for modeling pollution and discuss its competency questions, concepts and properties. We also describe some use cases of the ODP. The ODP and its documentation are publicly available at http://ontologydesignpatterns.org/wiki/Submissions: Pollution and at https://github.com/kracr/aq-structured-platform/bl ob/main/Ontology/PollutionODP/PollutionODP.owl with an Apache License 2.0.

We plan to use the Pollution ODP to model air pollution by considering the pollutant concentration, weather and wind trajectory data sources. The air pollution ontology will be populated using these data sources by converting the semi-structured data (csv, json, etc.) into an RDF8 graph using YARRRML [ 7 ].

Acknowledgements. This work is partially supported by the Infosys Center for Arti cial Intelligence (CAI) at IIIT-Delhi, India.

8 https://www.w3.org/TR/rdf11-primer/

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