  In recent years, smart city-based development has gained momentum due to its
versatile nature in architecture and planning for the systematic habitation of
human beings. According to World Health Organization (WHO) report, air
pollution causes serious respiratory diseases. Hence, it becomes necessary to
real-time monitoring of air quality to minimize effect by taking time-bound
decisions by the stakeholders. The air pollution comprises various compositions
such as NH3, O3, SO2, NO2, etc., and their concentrations vary from location to
location.The research work proposes an integrated framework for monitoring air
quality using rule-based Complex Event Processing (CEP) and SPARQL queries. CEP
works with the data stream based on predefined rules to detect the complex
pattern, which helps in decision support for stakeholders. Initially, the
dataset was collected from the Central Pollution Control Board (CPCB) of India
and this data was then preprocessed and passed through Apache Kafka. Then a
knowledge graph developed based on the air quality paradigm. Consequently,
convert preprocessed data into Resource Description Framework (RDF) data, and
integrate with Knowledge graph which is ingested to CEP engine using Apache
Jena for enhancing the decision support . Simultaneously, rules are extracted
using a decision tree, and some ground truth parameters of CPCB are added and
ingested to the CEP engine to determine the complex patterns. Consequently, the
SPARQL query is used on real-time RDF dataset for fetching the condition of air
quality as good, poor, severe, hazardous etc based on complex events detection.
For validating the proposed approach various chunks of RDF are used for the
deployment of events to the CEP engine, and its performance is examined over
time while performing simple and complex queries.
